Abstract:

Bovine Respiratory Disease (BRD) remains one of the most consequential health and economic challenges in U.S. beef production, particularly within integrated systems where microbial, environmental, and management factors intersect. This review synthesizes contemporary epidemiological insights, emphasizing BRD’s multifactorial pathogenesis driven by dynamic host-pathogen-environment interactions involving agents such as Mannheimia haemolytica, Pasteurella multocida, and Mycoplasma bovis, alongside stressors from transportation, weaning, and commingling. BRD imposes annual losses exceeding two billion dollars through diminished feed efficiency, reduced carcass yield, increased treatment costs, and mortality. Despite progress in vaccination, biosecurity, and therapeutic interventions, BRD persists due to diagnostic subjectivity and limitations of traditional control measures. The review underscores emerging innovations, including precision livestock technologies, AI-enabled surveillance, and metabolomic biomarkers as transformative tools for early detection and targeted mitigation, while noting barriers related to cost, data harmonization, and scalability. The rising threat of antimicrobial resistance further highlights the need for stewardship frameworks that balance therapeutic effectiveness and public health priorities. Additionally, the paper analyzes policy and economic considerations, arguing for coordinated efforts among producers, veterinarians, researchers, and regulators. BRD is reframed as a systems-level challenge requiring integrated scientific, operational, and regulatory strategies to enhance resilience and sustainability across U.S. beef production.

Abstract: Bioidentical hormone replacement therapy (BHRT) traditionally operates within a triad consisting of sex hormones, thyroid hormones, and adrenal glucocorticoids. Despite widespread adoption, a substantial proportion of patients experience persistent dysglycemia, adrenal instability, fluctuations in symptom control, and inconsistent responses to therapy even when laboratory values appear biochemically normalized. These clinical patterns suggest that an essential regulatory element is missing from the current BHRT conceptual model.This narrative review proposes the Insulin–Cortisol–Vitamin C (ICV) Axis as a previously unrecognized hormonal network central to metabolic and endocrine homeostasis. Insulin profoundly influences sex-hormone binding globulin (SHBG), estradiol and testosterone bioavailability, progesterone responsiveness, thyroid hormone conversion, mitochondrial ATP production, and cortisol reactivity—yet insulin is rarely evaluated in BHRT. Cortisol, in turn, directly modulates insulin sensitivity and metabolic function, while vitamin C is required for cortisol synthesis, adrenal recovery, endothelial nitric oxide signaling, mitochondrial redox regulation, and antioxidant defense. Together, disturbances in these three components can generate characteristic clinical presentations frequently encountered in BHRT practice.In parallel, emerging evidence—including metabolic insights from GLP-1 receptor agonist therapy—indicates that vitamin C status and oxidative stress modulation play broader roles in insulin sensitivity and hormonal signaling than previously recognized. Integrating these findings, the ICV Axis provides a systems-level framework capable of explaining BHRT treatment failures, variable patient responses, and persistent symptomatology despite standard hormone optimization.The purpose of this review is to synthesize biochemical, endocrine, and nutritional evidence supporting this new axis, and to outline a clinically actionable update to BHRT incorporating insulin dynamics and vitamin C sufficiency. Recognition of the ICV Axis represents a conceptual advancement that can improve therapeutic outcomes across metabolic, endocrine, and integrative medical practice.

Abstract: This study seeks to establish a consistent theoretical foundation to address enduring open questions in physics—such as the origins of life, free will, the subjective experience of time, consciousness, and biological intelligence—by exploring the connection between the stochastic quantum hydrodynamic model (SQHM) and the order generation in systems far from thermodynamic equilibrium, all converging in the manifestation of physical reality. It offers novel insights into the emergence of order, biological systems, and associated functions such as biological intelligence, free will, consciousness, and the behavior of social structures. These insights are grounded in the assumption of a discrete spacetime structure, enabling an analogy of the universe as a running discrete computation, where emergent physical laws arise from the computation’s intrinsic problem-solving goals. This perspective carries profound implications for physical evolution, suggesting that everyday reality, the origin of life, social interactions, and consciousness itself are intrinsic features of the universal physics. It introduces the idea that free will may emerge as a functional mechanism guiding the universe’s progression toward increasingly efficient and organized states, states in which order is preserved to the greatest extent possible. This view embodies a form of bounded probabilism, standing in sharp contrast to the concept of total, unconstrained randomness, which reduces the universe to a mere cosmic game of dice. It also offers a novel perspective through which artificial intelligence can be framed and its limitations, as well as its differences from biological intelligence, can be better understood. This view highlights how the quantum foundations of the universe contribute to the expression of consciousness, outlining potential avenues for advancing AI toward a more faithful emulation of conscious experience.

Abstract: The complexity of modern cyber-physical systems is steadily increasing as their functional scope expands and as regulations become more demanding. To cope with this complexity, organizations are adopting methodologies such as Model-based Systems Engineering (MBSE). By creating system models MBSE promises significant advantages such as improved traceability, consistency, and collaboration. On the other hand, the adoption of MBSE faces challenges in both the introduction and the operational use. In the introduction phase, challenges include high initial effort and steep learning curves. In the operational use phase, challenges arise from the difficulty of retrieving and reusing information stored in system models. Research on the support of MBSE through Artificial Intelligence (AI), especially Generative AI, has so far focused mainly on easing the introduction phase, for example by using Large Language Models (LLM) to assist in creating system models. However, Generative AI could also support the operational use phase by helping stakeholders access the information embedded in existing system models. This study introduces an LLM-based multi-agent system that applies a Graph-Retrieval-Augmented-Generation (GraphRAG) strategy to access and utilize information stored in MBSE system models. The system’s capabilities are demonstrated through a chatbot that answers questions about the underlying system model. This solution reduces the complexity and effort involved in retrieving system model information and improves accessibility for stakeholders who lack advanced knowledge in MBSE methodologies. The chatbot was evaluated using the architecture of a battery electric vehicle as a reference model and a set of 100 curated questions and answers. When tested across four large language models, the best-performing model achieved an accuracy of 93 percent in providing correct answers.

Abstract: Spectral line broadening is a central diagnostic in atomic physics and astrophysics, yet residual linewidths remain even after accounting for conventional mechanisms such as natural, Doppler, collisional, and Stark or Zeeman effects. This study introduces the concept of coherence restructuring work as defined in the First Law of Coherence Thermodynamics, proposing that residual broadening represents the dissipative footprint of non Markovian field engagement. The approach extends thermodynamic formalism to include a memory dependent functional derived from generalized Langevin dynamics, and applies it to atomic spectra. We explain why hydrogen spectra exhibit minimal restructuring, while multi electron atoms and astrophysical systems reveal broadened lines consistent with history dependent coherence demands. Conclusions indicate that residual linewidths encode structural learning processes, reframing quantum collapse as a thermodynamic phenomenon driven by coherence restructuring rather than observer dependent measurement. This interpretation unifies atomic, stellar, and gravitational systems under a single coherence principle, offering a measurable pathway to probe non Markovian dynamics in both laboratory and astrophysical contexts.

Abstract: Trichloroethylene (TCE) and tetrachloroethylene (PCE) are chlorinated organic liquids widely employed in various industrial processes. However, due to their high toxicity and cancerogenic proprieties, these compounds are recognized as environmental pollutants. Therefore, the removal of TCE and PCE from wastewater is a crucial objective for environmental protection. This work investigated the adsorption capacity of syndiotactic polystyrene (sPS) fibers, activated in the nanoporous crystalline δ form, to remove volatile organic compounds from aqueous solutions. TCE can be adsorbed in the nanoporous crystalline δ form of sPS, leading to the formation of a clathrate structure, in which it acts as the guest molecule. This adsorption mechanism allows for high process selectivity, as well as the capture of even trace amounts (in the ppb range) of the pollutants under consideration, in relatively short times (e.g., 67 hours). Also, a process with two successive adsorption tests was performed replacing the solid used for the first contact with the contaminated solution with fresh δ-sPS fibers. This approach allowed the reduction of TCE concentration down to 8 ppb. In conclusion, δ-sPS nanoporous fibers demonstrated a great potential for the efficient removal of chlorinated organic compounds from wastewater, providing a promising alternative to conventional adsorption processes.

Abstract: Staphylococcus aureus is an important microorganism that has the ability to form biofilm on a various range of surfaces. Factors contributing to the reduction of the effectiveness of the treatment are the development of resistance to antimicrobial drugs. Essential oils (EO) are effective and economical alternatives, however with the disadvantage of rapid oxidation, nanoencapsulation is an alternative that improves stability, reduces toxicity and controls the release of oil. Nanoprecipitation with Poly-lactide was used to obtain nanoparticles (NP) with EO. The antibiofilm effect was observed by the broth microdilution method. A cytotoxic assay was performed using a VERO cell line. Nanoparticles were found to be nanometric, round with regular structures. EO and NP show antibacterial and antibiofilm activity against S. aureus. NP was less cytotoxic than EO.aure Nanoparticle prevented rapid EO evaporation and degradation and enhanced its stability. NP stability was studied using zeta potential. Its value was determined to be around -23.1 mV, which indicates that NP are in fact stable. Melting temperature and melting enthalpy for Blank NP were 54.29 °C and 429.63 J/g. The decreasing in melting enthalpy from 429.63 to 115.83 J/g in NP containing EO makes this system favorable to controlled release of essential oils. NP has a smaller area under the peak, indicating that the EO may modify the crystalline organization, facilitating melting and thus the release of EO. EO and NP presented a growth inhibition of planktonic and biofilm formation against S. aureus. NP were less cytotoxic than free EO. Thus, these findings may contribute to the development of new strategies against infections caused by S. aureus.

Abstract: Each of the studied manifolds has a pair of B-metrics, interrelated by an almost contact structure. The case where each of these metrics gives rise to an η-Ricci–Bourguignon almost soliton, where η is the contact form, is studied. In addition, the geometry-rich case where the soliton potential is torse-forming and is pointwise collinear on the Reeb vector field with respect to each of the two metrics is considered. Ricci tensors and scalar curvatures are expressed as functions of the parameters of the pair of almost solitons. Particular attention is paid to the special case when the manifold belongs to the only possible basic class of the corresponding classification. A necessary and sufficient condition has been found for these almost solitons to be η-Einstein for both metrics.

Abstract: Background and objectives: The purpose of this study was to investigate whether high ethmoid sinus volume (ESV) constitutes a risk factor for the formation of orbital blowout fractures (OBF) after craniofacial trauma and whether it affects the fracture pattern. Materials and Methods: This is a retrospective case-control study involving subjects over 15 years of age who presented to the emergency department with craniofacial trauma. The case group included subjects with OBF, while the control group included subjects without any facial fractures. The case group was divided into subgroups according to the fracture location. We performed volumetric measurements on computed tomography images of the ethmoid sinuses of subjects in the case and control groups using the fully automated 3D Slicer application. The mean ESV values of the groups were compared using the necessary statistical methods. P-values below 0.05 were considered significant. Results: The case group consisted of 108 (median age: 41.5 years; 76 males, 69%), and the control group consisted of 122 (median age: 38 years; 84 males, 69%) subjects. OBFs were more frequent in males (69%), most commonly detected in the orbital floor (68.2%), and were bilateral in two (1.8%) subjects. The mean ESV in the case group (3.91 ± 1.39 cm³) was significantly higher than that in the control group (2.82 ± 0.94 cm³) (p< 0.001). Unlike the cases with medial wall fractures and those with orbital floor fractures, there was no significant difference in mean ESV between the cases with medial wall and orbital floor fractures and the control group (p= 0.562). Conclusions: A large ethmoid sinus not only increases the risk of orbital blowout fracture but also has an impact on the fracture pattern. Based on the data obtained from our study, we identified a large ethmoid sinus as a predictive risk factor for orbital blowout fracture.

Abstract: Background/Objectives: Caring for a child with Autism Spectrum Disorder (ASD) is often associated with elevated psychological distress and reduced life satisfaction. Mindfulness-based interventions may offer substantial benefits by enhancing emotional regulation, reducing maladaptive cognitive patterns, and strengthening mindful parenting. This randomized controlled trial (RCT) examined the effectiveness of an eight-week Mindfulness-Based Cognitive Therapy (MBCT) program, enriched with mindful parenting practices, on parental mental health and parent-reported child behavior outcomes. Methods: Fifty-six parents of children with ASD were randomly assigned to an MBCT intervention group (n = 30) or a waitlist-control group (n = 26). Participants completed assessments at baseline (T0), post-intervention (T1), and one-month follow-up (T2), including the DASS-21, PANAS, and SWLS. Parents rated the overall severity of their child’s behavior problems to explore indirect treatment effects. Results: All participants receiving MBCT (100%) completed the program successfully and reported high acceptability. At baseline, no significant differences were observed between groups. Compared to controls, the MBCT group demonstrated significant reductions in depression, anxiety, and stress, alongside increases in positive affect and life satisfaction at T1. These improvements were further strengthened or maintained at T2. However, the control group showed no significant changes across time. Additionally, parents in the MBCT group reported indirect improvements in their children’s behavioral adjustment at T1 and T2. Conclusions: Findings demonstrate that MBCT constitutes an effective intervention for reducing parental psychopathology and indirectly enhancing child positive behavior, emphasizing the importance of incorporating mindfulness and mindful parenting components into family-centered interventions for parents of children with ASD.

Abstract: Sentiment analysis of Arabic text, particularly on social media platforms, presents a for-midable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of emojis to convey emotional context. This paper presents SiAraSent, a hybrid framework that integrates tra-ditional text representations, emoji-aware features, and deep contextual embeddings based on Arabic transformers. Starting from a strong and fully interpretable baseline built on TF–IDF-weighted character and word N-grams combined with emoji embeddings, we progressively incorporate SinaTools for linguistically informed preprocessing and Ara-BERT for contextualized encodings. The framework is evaluated on a large-scale dataset of 58,751 Arabic tweets labeled for sentiment polarity. Our design works within four experi-mental configurations: (1) a baseline traditional machine learning architecture that em-ploys TF-IDF, N-grams, and emoji features with an SVM classifier; (2) an LLM feature ex-traction approach that leverages deep contextual embeddings from the pre-trained Ara-BERT model; (3) a novel hybrid fusion model that concatenates traditional morphological features, AraBERT embeddings, and emoji-based features into a high-dimensional vector; and (4) a fully fine-tuned AraBERT model specifically adapted for the sentiment classifi-cation task. Our experiments demonstrate the remarkable efficacy of our proposed framework, with the fine-tuned AraBERT architecture achieving an accuracy of 93.45%, a significant 10.89% improvement over the best traditional baseline.

Abstract:

According to the World Health Organisation (WHO), conditions linked to the brain account for 28% of the social burden of all diseases, the largest sector, surpassing cancer and cardiovascular disease (CVD). Our incomplete understanding of human neurodegeneration biology is at the center of the devastating impacts it brings on our societies. A fundamental translational effect in those therapies is evident in that none have succeeded in registration-sized clinical trials. The outcome are coexisting therapies that remain largely palliative, managing symptoms or slowing decline but not providing hope for a reversal or cure. Increasing evidence has positioned the gut-brain axis (GBA) as a key modulator of neurodegeneration hallmarks, often inducing or progressing disorders such as Alzheimer’s, Parkinson’s and Multiple Sclerosis. Traditional research tools fail to recapitulate the accurate physiology of organ systems in humans, leading to the development of organoid technologies and organ-on-a-chip platforms. This literature review comprehensively analyses efforts to model neurodegenerative disorders through in vitro models, evaluating advancements in intestinal, cerebral, GBA, blood-brain barrier and other multi-organ systems. Further, the paper ties back to the known pathophysiology of such diseases and the GBA’s influence to evaluate limitations of current disease modelling approaches, offering future directions that enable applications in drug discovery. These technologies mark a transformative shift in methods to understand both the mechanistic causation and therapeutic strategies for previously incurable diseases, expanding the possibilities to improve the lives of millions of diagnosed patients.

Abstract: This comprehensive analysis examines the American AI Exports Program through a multi-dimensional framework encompassing technical architecture, governance structures, market strategy, and policy implementation. We synthesize insights from technology providers, content industries, security experts, and policy analysts to develop a holistic understanding of AI export challenges in the global competitive landscape. The paper presents a multi-layer framework architecture with strategic, governance, technical, and market layers, supported by detailed visualizations including architectural diagrams, decision matrices, risk assessment frameworks, and implementation roadmaps. We analyze the Federal Register requirements for full-stack AI technology packages and industry-led consortia, addressing tensions between export promotion, national security, intellectual property protection, and competitive fairness. Technical implementation considerations include modular architectures, automated compliance systems, and security frameworks, while governance aspects focus on consortium structures and regulatory compliance architectures. Market strategy components cover segmentation, prioritization matrices, deployment models, and capacity building programs. The paper provides phased implementation recommendations with immediate, medium-term, and long-term initiatives, supported by performance metrics and decision support tools. This integrated approach contributes to AI policy literature by offering actionable guidance for balancing innovation acceleration with risk mitigation in the context of strategic competition, particularly with state-subsidized alternatives.

Abstract: In the context of “Kolmogorov consciousness”, the complexity of an object to be computed can be defined by the complexity of a computing system―typically a computer―represented as the number of bits in the minimal algorithm required to compute the object. In condensed matter systems under equilibrium and nonequilibrium conditions, macroscopic properties distinct from elementary ones can emerge from large numbers of particles. Such a large system size allows system properties to change as control parameters change, producing phase transitions. Inspired by this, it is natural to consider that a similar state transition could occur in computing systems at some critical point of Kolmogorov complexity when the system is optimized. In this paper, we propose a computational model that realizes a functional differentiation of random neural network into two subnetworks―one specialized for memory and the other for program execution―through an evolutionary change of the network. The model suggests a neural mechanism for a human-like thought process, described as inference based on extracting rules embedded in random input and storing those inputs in long- or short- term memory. Because the proposed evolutionary model with sufficiently large complexity is driven by constraints that lead to an optimized state―such as selecting minimal-complexity systems among many high-complexity alternatives―the resulting network can be regarded as an optimized “descriptor” of memory and program. The computational results indicate that the seeds of consciousness emerge when the system’s Kolmogorov complexity decreases from that of random neural networks to that of organized networks configured as descriptors of fluctuating environments. We refer to this stage as algorithmic consciousness.

Abstract: This report mainly focuses on researching, identifying and analysing popular cyber attacks affecting individuals and organisations worldwide. Our group is needed to research about theselected cyberattack, Distributed Denial-of-Service（DDoS) background, recent relatedcyberattack cases, and reasons for getting attacked and think about the series of countermeasures to enhance the security measurement and prevent it from happening again. After thorough research, our group found that DDoS attack is one of the most unpreventable because it exploits weaknesses of the network topologies and standard protocols which makes it very difficult to prevent, hackers just have to overwhelm the system server. Thus, we think of implementing a “dynamic network traffic analysis and adaptive filtering system” and “blockchain-based traffic authentication” to enhance the security measures. Both systems are effective in filtering flooded traffic packages sent by non-human devices from overwhelming the server’s resources.

Abstract: A spiral spin arrangement with a magnetic unit cell 28 times the size of nuclear one has been reported for the Fe spins below T_N = 80 K in bilayered van der Waals gapped FeOCl. In this work, we used neutron magnetic diffraction and ac magnetic susceptibility to reveal a much-reduced magnetic unit cell of 4 times the size of nuclear one for the Fe spins below T_N = 119 K, when 27% of non-magnetic Na were intercalated into the van der Waals gaps of FeOCl. X-ray emission spectra and X-ray absorption edge spectra reveal charge transfers from the intercalated Na into the Fe sites that reduce the Fe³⁺ into Fe²⁺ ions, giving a significantly larger Fe-O-Fe bond angle that largely strengthens the strength of antiferromagnetic superexchange (AFMSE) coupling over the competing ferromagnetic direct exchange (FMDE) coupling between the two neighboring Fe ions, driving to a higher degree of magnetic symmetry and a significantly higher Neel temperature for the Fe spins in Na_0.27FeOCl.

Abstract: Southern blight, a fungal disease favored by hot and humid conditions in southeastern United States, poses a serious challenge to hemp production in Tennessee. Black plastic mulch (BPM), commonly used for weed control, can exacerbate the disease. There is limited information on the effects of straw mulch (SM), known to moderate soil temperatures and moisture, or planting time in disease management. Field studies were conducted in 2022 and 2023 at Tennessee State University to evaluate the effects of planting time, mulch type, and bio-fungicide application on disease severity, weed suppression, plant growth, and cannabinoid production in floral hemp. SM significantly reduced southern blight incidence and moderated soil temperature, while BPM increased both. Early planting reduced disease severity by 28% in 2022 and by 53% and 34% in 2023 for first and second planting dates, respectively. SM lowered soil temperature by 6%, enhanced chlorophyll content by 30%, and increased plant height and biomass by 20% and 25%, respectively. Early planting increased cannabidiol (CBD) concentration by 0.4%, while late planting increased tetrahydrocannabinol (THC) by 0.25%. These findings demonstrate that integrating straw mulch with early planting can reduce disease severity, stabilize soil microclimate, and enhance hemp productivity under warm, humid conditions.

Abstract: This study discusses the effect of green finance on carbon emission reduction and the mechanism of technological innovation in China, especially analyzes the comprehensive effect of green finance in the process of low-carbon transformation of urban agglomerations in China. Based on the panel data of China city from 2000 to 2023, this study evaluates the impact of green finance on carbon emissions by using various models, such as interaction model and intermediary adjustment model. The results show that both green finance and technological innovation can significantly reduce carbon emissions, and the synergistic effect of them is obviously stronger than that of a single path. The mechanism analysis shows that green finance can achieve coordinated emission reduction by promoting green technology innovation, upgrading industrial structure and improving the level of regional digital economy. Heterogeneity analysis shows that the regional emission reduction effect of Yangtze River Delta urban agglomeration with developed digital economy, high degree of digital transformation and concentrated cities is more significant. This study not only expands the theoretical framework of green financial collaborative governance, but also provides a new empirical basis and policy reference for other international urban agglomerations to achieve high-quality and low-carbon development.

Abstract: Cognitive biases are evolutionarily adaptive mental shortcuts rooted in automatic processing, yet their expression varies widely across individuals due to differences in personality structure, cultural communication patterns, and generational socialization. Drawing on research in behavioral psychology, cognitive science, organizational behavior, and cross-cultural communication, this paper presents an integrated framework for predicting dominant cognitive biases by combining three complementary models: Kahler’s process communication model, Lewis’s cultural communication model, and Strauss and Howe’ generational cohort theory. The study outlines the design of an 11-item instrument grounded in these frameworks and evaluates its preliminary validity, reliability and perceived accuracy. By identifying how psychological, cultural, and temporal factors shape bias tendencies, the model offers insight into how individuals interpret organizational purpose, challenge assumptions, and adapt their decision-making in uncertain environments. This predictive approach also supports talent mapping, and the formation of cognitively diverse teams, which strengthen strategic adaptability, and contribute to more effective and inclusive organizational practices.

Abstract: The formation of interfacial charge transfer (ICT) complexes between phenolic ligands and metal oxide surfaces enables surface functionalization strategies with potential applications in catalysis and bioconjugation. In this study, magnetite (Fe₃O₄) nanoparticles were modified with two phenolic ligands, 5-aminosalicylic acid (5ASA) and caffeic acid (CA), to generate ICT complexes capable of covalent or non-covalent enzyme immobilization, respectively. The modified nanomaterials were structurally characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), and Fourier-transform infrared spectroscopy (FTIR). Horseradish peroxidase (HRP) was immobilized on these functionalized supports. Catalytic activity was evaluated using pyrogallol oxidation assays, with systematic variations in nanoparticle mass and enzyme concentration. The Fe₃O₄/5ASA–HRP system exhibited a maximum activity of 2.5 U per 20 mg of support (approximately 125 U/g), whereas Fe₃O₄/CA showed minimal activity under the same conditions. Data from enzyme loading studies confirmed that 5ASA-enabled covalent attachment resulted in significantly higher immobilization efficiency (up to 1068 mg/g) compared to the CA system. The magnetic properties of Fe₃O₄ allowed for rapid recovery of the biocatalysts using an external magnetic field. These results highlight the effectiveness of ICT-based functionalization for enzyme immobilization, positioning Fe₃O₄/5ASA as a promising platform for robust and reusable biocatalysts in environmental and industrial applications.