Sustainable inventory management with trapezoidal demand and amelioration under carbon regulations

This study develops a sustainable inventory model that addresses product quality variations, demand fluctuations, and shortages over a specific planning horizon. The model captures the dual dynamics of product utility evolving through concurrent growth (amelioration) and decay (deterioration). Though inspired by poultry farming, the approach applies to other quality-sensitive sectors such as aquaculture, dairy, beverages, and pharmaceuticals, emphasizing its relevance for diverse perishable supply chains. The model integrates a trapezoidal demand function, Weibull amelioration and deterioration, and a carbon cap and tax policy to optimize replenishment decisions while promoting environmental sustainability. A nonlinear continuous costing optimization method is developed, incorporating Weibull’s instantaneous deterioration and amelioration effects. The Black Hole Algorithm and Quasi-Newton method are employed to solve for optimal order quantities and replenishment cycles. Numerical simulations and sensitivity analysis evaluate the impact of crucial parameters on inventory performance. Results indicate moderate improvement reduces holding costs, while uncontrolled growth leads to overstock and elevated carbon penalties. The carbon cap-and-tax policy also efficiently abates emissions while maintaining cost efficiency, highlighting the necessity for a balanced replenishment strategy for sustainable operations. This research contributes a unified framework integrating biological dynamics, demand variability, environmental regulations, and hybrid optimization for sustainable, cost-effective inventory management

Operator-Lipschitz functions on symmetrically normed ideals with non-trivial Boyd indices

We show that if the Boyd indices of a symmetrically normed ideal J of B(H) are non-trivial (differ from 1 and oo) then for any Lipschitz function f on C, the map N --> f(N) is Lipschitz on the set of normal operators with respect to the norm ||.||_J . In particular, we study ideals for which some enhanced forms of the Fuglede Theorem hold; this is necessary for the work with functions of normal operators. We also consider functions that have the property of J-stability with respect to an ideal J: if a normal operator N is perturbed by an operator X in J in such a way that the operator N +X is normal, then f(N +X) - f(N) belongs to J. As applications we present various results on Gateaux and Frechet J-differentiability of functions, and on the action of Lipschitz functions on the domains of derivations

Precision targeted cancer-associated fibroblast nano-regulator enhanced chemo-immunotherapy for triple-negative breast cancer

Cancer-associated fibroblasts (CAFs), major components of the triple-negative breast cancer (TNBC) tumor microenvironment, affect tumor growth and limit the therapeutic efficacy of various clinical approaches by forming a dense stromal physical barrier, inducing chemo-resistance and promoting a tumor-immunosuppressive microenvironment. Therefore, therapeutic strategies based on targeted modulation of CAFs are promising in the treatment of TNBC. However, precise targeting of CAFs faces difficulties due to the lack of specific markers for CAFs. Here, we designed a nanoparticle co-modified with anisamide and CAF cell membrane (CAFm) to load tetrandrine as a CAF nano-regulator (TET@ACNP), which is able to precisely target and modulate CAFs. Precise targeting of CAFs was achieved by the combination of the CAFm homing effect as well as the high affinity effect between anisamide and sigma receptors overexpressed on CAFs. TET@ACNP was able to inhibit CAFs activation and reduce collagen secretion, thereby breaking the physical barrier to facilitate the penetration of the first-line chemotherapeutic agent docetaxel (DTX) and the infiltration of cytotoxic T lymphocytes. In addition, TET@ACNP alleviates chemoresistance by inhibiting the Wnt/β-catenin pathway and inhibits IGF 2 expression to release the immunosuppressive microenvironment, ultimately enhancing chemotherapy effects and anti-tumor immunity. Our study proposes a comprehensive therapeutic strategy based on the precise targeting and regulation of CAFs in combination with chemotherapy to achieve multifaceted inhibition of TNBC. This is expected to be a universal platform to improve the therapeutic efficacy of different chemotherapeutic agents in various types of stroma-rich tumors

Nanosized natural products for immune dysregulation: a systematic review of mechanisms, therapeutic applications, and translational challenges

Background: Natural products possess remarkable bidirectional immunomodulatory properties, offering great promise for treating diseases characterized by immune dysregulation, such as cancer and inflammation. However, poor solubility, accelerated metabolism, and the lack of tissue specificity hinder their clinical translation. Nanotechnology has emerged as a powerful strategy to overcome these intrinsic limitations, enhancing the bioavailability and therapeutic potential of natural products. Purpose: To evaluate how nanosized natural products overcome these barriers to enhance the immunoregulatory efficacy of natural products. Also to summarise their mechanisms and therapeutic advantages with a view to elaborating on future translational challenges. Methods: All the relevant clinical and research studies conducted on the application and efficacy of nanosized natural products in immune dysregulation diseases were included, as retrieved from PubMed, Web of Science, ScienceDirect, and Google Scholar, following the use of specific search terms. Results: This review systematically evaluates important advancements in nanosized natural products-based immunomodulators, including nano drugs and vaccine adjuvants. By integrating evidence from in vitro, in vivo, and clinical studies, we highlight how nanosized natural products protect biologically active substance, prolong immune responses, enable targeted delivery, and alleviate toxic side effects. We have discussed the mechanisms by which these nanosized natural products modulate key immunosuppressive components, including T lymphocytes, dendritic cells, macrophages, natural killer cells, and neutrophils, to restore equilibrium to homeostasis. The use of nanosized natural products in the enhancement of cancer immunotherapy and their ameloriaton of concomitant inflammatory diseases is also discussed extensively. Conclusion: Nanosized natural products represent a transformative approach for precise immunomodulation. They bridge the gap between the therapeutic potential of natural compounds and clinical application. Future success hinges on addressing challenges in scalable manufacturing, long-term toxicology, and designing smarter stimuli-responsive delivery systems

Conflict of Interest: justifying international cooperation in populist discourse

Contrary to the stereotypical assumption that the foreign policy of populists is geared towards conflict, much of the literature in recent years has converged on the understanding that populism results in a complex and often seemingly incoherent mix of cooperation and confrontation. Populist leaders often adopt a confrontational stance towards other states and international institutions, yet they are also capable of striking deals, defusing tensions and reconciling with multilateral settings. This inconsistency is due to a variety of factors like geopolitical and economic pressures or the thick-ideological proclivities of populists. But in this article, we are interested in how populists reconcile the contradictory trend to antagonize internationally but end up striking deals. Drawing on the literature on populist discourses and a view of foreign policy as political management of state-society relations, we argue that this reconciliation takes place primarily at a discursive level, as populists deploy a discourse of cooperation that remains consistent with the binary and Manichean logic of populism. We identify three populist discursive strategies of justifying cooperation after conflict: elite-splitting; issue-bundling and audience-hopping. We demonstrate our argument by comparing two cases of populist compromising with the EU following a protracted period of confrontation: Greece’s acceptance of a third bailout from the Eurozone under Alexis Tsipras in 2015; and Britain’s signing of a final Brexit deal under Boris Johnson in 2020

Framework for analysis of the logical vulnerability of authentication procedures

While AI has made strides in knowledge and action modelling, challenges remain in addressing security concerns like logical vulnerabilities in authentication policies. These vulnerabilities arise from flawed or missing authentication mechanisms, making operations unintentionally accessible. Our objective is to model the domain and find such vulnerabilities. Our approach is based on a novel three-level framework, specifically focusing on identifying logical vulnerabilities in authentication policies. Each level is built on top of the previous one. The first is the ontological level, where we model the static domain using Description Logics serialised as Ontology Web Language, providing a foundational representation of classes and relationships. The second is the logical level, where action rules, capturing system dynamics, are formalised using Horn Clause and First-Order Logic, serialised as Semantic Web Rule Language. We address the frame problem through efficient parameter utilisation as a side effect. The third is the analytical level, where we transform action rules into a policies graph to validate and visualise them and transform assertions into an instance graph to visualise the specific instance of the world to facilitate the analysis. We leverage the reasoner and control constant in an algorithmic approach, which detects vulnerabilities in the policies by finding vulnerable situations. We demonstrate the framework’s effectiveness and practicality through experimental evaluation with two real-world applications. Results highlight its scalability, explainability, and accuracy in detecting vulnerabilities, showcasing its potential to enhance security policy analysis

Ape jam 2025: exploring cognitive enrichment

The workshop will explore how digital enrichment, including touchscreen technology, may be used to conduct research into the cognitive capabilities of great and smaller apes, facilitate welfare and enrichment opportunities and support a range of multimodal inputs and outputs, associated with the screen and the environment

Vile sovereignty: the carnival of power

In this paper we seek to extend Bakhtin’s reading of the folk carnival and apply it to help understand the carnivalesque, performative aspects of state power. Drawing on the work of Agamben, Foucault, Lacan and Žižek and recent scholarship on the role of laughter in the Stalinist totalitarian culture, we argue that the state can also laugh and that it can have its own carnival tradition as well. To explore what we propose to call the carnival of power, we examine three iterations of this tradition: the festive exercise of state violence, state carnivalisers, and the carnivalesque style in governance

AI-powered system for an efficient and effective cyber incidents detection and response in cloud environments

The growing complexity and frequency of cyber threats in cloud environments call for innovative and automated solutions to maintain effective and efficient incident response. This study tackles this urgent issue by introducing a cutting-edge AI-driven cyber incident response system specifically designed for cloud platforms. Unlike conventional methods, our system employs advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to provide accurate, scalable, and seamless integration with platforms like Google Cloud and Microsoft Azure. Key features include an automated pipeline that integrates Network Traffic Classification, Web Intrusion Detection, and Post-Incident Malware Analysis into a cohesive framework implemented via a Flask application. To validate the effectiveness of the system, we tested it using three prominent datasets: NSL-KDD, UNSW-NB15, and CIC-IDS-2017. The Random Forest model achieved accuracies of 90%, 75%, and 99%, respectively, for the classification of network traffic, while it attained 96% precision for malware analysis. Furthermore, a neural network-based malware analysis model set a new benchmark with an impressive accuracy rate of 99%. By incorporating deep learning models with cloud-based GPUs and TPUs, we demonstrate how to meet high computational demands without compromising efficiency. Furthermore, containerisation ensures that the system is both scalable and portable across a wide range of cloud environments. By reducing incident response times, lowering operational risks, and offering cost-effective deployment, our system equips organizations with a robust tool to proactively safeguard their cloud infrastructure. This innovative integration of AI and containerised architecture not only sets a new benchmark in threat detection but also significantly advances the state-of-the-art in cybersecurity, promising transformative benefits for critical industries. This research makes a significant contribution to the field of AI-powered cybersecurity by showcasing the powerful combination of AI models and cloud infrastructure to fill critical gaps in cyber incident response. Our findings emphasise the superior performance of Random Forest and deep learning models in accurately identifying and classifying cyber threats, setting a new standard for real-world deployment in cloud environments

AI surveillance in education: unpacking ethical dilemmas and the snake-oil promises of AI-infused technosolutionism

This chapter examines the ethical implications of the increasing integration of AI surveillance technologies within educational settings, including proctoring software, classroom monitoring tools, and behavioral analytics. The swarm approach employed reflects diverse perspectives on this complex issue. It unpacks how AI surveillance reduces multifaceted students and faculty to data points, disproportionately targeting marginalized learners and misreading neurodivergent behaviors. The chapter critiques the snake's oil sales pitch of AI-infused technosolutionist discourse and foregrounds concerns regarding the erosion of student and lecturer autonomy, the alteration of student-lecturer relationships and the erosion of trust, the normalization of surveillance, and the amplification of existing inequalities and biases through algorithmic processes. The chapter concludes by emphasizing the need for educators to foster a learning environment that amplifies human potential