Background Quantum algorithms exploit superposition and parallelism to address complex combinatorial problems, many of which fall into the non-polynomial (NP) class. Sudoku, a widely known logic-based puzzle, is proven to be NP-complete and thus presents a suitable testbed for exploring quantum optimization approaches. The Ising model-originally introduced for NP-hard Ising spin glass problems-provides a natural mathematical framework for expressing constraints in a form amenable to quantum computation.Objective This work aims to develop a quantum Sudoku solver inspired by the Ising model that minimizes the number of logical qubits required, making it suitable for today's resource-limited quantum hardware. The broader goal is to demonstrate a modeling strategy that may generalize to other NP optimization problems.Methods The solver construction begins by translating Sudoku constraints into mathematical expressions represented through couplings of atomic spins. These constraints are formulated into observable operators using Pauli operators, enabling the calculation of expectation values over candidate quantum states. Individual quantum algorithmic components are then integrated into a global optimization pipeline. The performance and correctness of this solver are evaluated through the Quantum Approximate Optimization Algorithm (QAOA) combined with the COBYLA classical optimizer within the IBM Qiskit SDK. A code example illustrates the implementation of multiple puzzle constraints and the verification of the resulting quantum circuits.Results The modeling approach successfully encodes Sudoku rules into an Ising Hamiltonian with reduced qubit requirements. Preliminary evaluations using QAOA and COBYLA demonstrate that the solver can identify puzzle-consistent solutions while maintaining low resource consumption. The quantum circuit construction matches theoretical expectations, and the code snippet confirms successful constraint enforcement within the Qiskit environment.Conclusions The proposed quantum Sudoku solver highlights the potential of Ising-based formulations to address NP optimization problems on near-term quantum devices. By reducing logical qubit usage without sacrificing algorithmic integrity, this strategy may support broader applications in constrained optimization. The implementation serves as both a proof of concept and a practical guide for extending Ising-inspired quantum modeling to other NP-hard domains.
While AI ethics ensures fairness, accountability, and protection of user rights, dark patterns manipulate users to take unintended actions on digital interfaces. Related studies uncover limited insights into how reliably; human experts and AI models can detect dark patterns within a specific taxonomy. Our research fills this gap by asymmetrically examining cross-origin detection performance of human and AI/LLM evaluators (each evaluator’s ability to detect dark patterns generated by the opposite source) to understand their limitations and future potentials. Using GPT-4.1, we generated 200 UI images (with matched dark and non-dark pattern pairs) and selected 200 UI images collected 200 human-created UI screenshots from the ContextDP/AidUI dataset, based on computational, methodological, and statistical considerations. We calculated inter-rater reliability, recall, and error distribution. The results show that UX experts achieved substantial agreement (k = 0.75) and significantly higher recall (r = 0.99) over AI/LLMs. We present a novel study which explore the performance of AI/LLMs and UX experts in detecting dark patterns in UI images, and provide a benchmark dataset that could be useful to future research, while discussing empirical insights into the role, limitations, and promise of AI/LLMs in UI/UX design ethics and auditing, in realistic deployment scenarios.
Surface fouling remains a critical challenge for medical devices and chemosensor systems operating in biological environments, where nonspecific adsorption of proteins, cells, and microorganisms can lead to signal drift, reduced sensitivity, and shortened device lifetime. Conventional antifouling strategies rely primarily on synthetic hydrophilic polymer coatings, such as polyethylene glycol and polyvinylpyrrolidone, which are effective but face limitations related to long-term stability, thickness, and compatibility with surface-sensitive sensing modalities. In this review, we focus on hydrophobins derived from mushroom-forming and filamentous fungi as a bio-based alternative for antifouling and anti-wetting surface modification. Mushroom-derived hydrophobins are small amphiphilic proteins capable of spontaneous self-assembly into nanometer-scale films that modulate surface energy, wettability, and interfacial friction without requiring covalent functionalization. The current state of research on hydrophobin structure, classification, and self-assembly is reviewed, followed by a synthesis of reported antifouling and tribological behaviors relevant to medical and sensor-adjacent surfaces. Representative experimental observations are discussed to illustrate trends consistent with the literature, without establishing new performance benchmarks. The implications of mushroom-derived hydrophobin coatings for chemosensors and biosensors are examined, particularly with respect to signal stability, surface accessibility, and durability. Limitations and future research directions are outlined to support translation into practical sensing technologies.
Post-diagnostic support is a critical yet underdeveloped aspect of dementia care, especially for autistic adults who present with distinct cognitive, sensory, and communication needs. Although interventions such as medication management, psychosocial support, environmental modifications, and carer training are known to improve outcomes, their relevance and accessibility for autistic individuals remain poorly understood. As part of the Second International Summit on Intellectual Disability and Dementia, an international working group examined the intersection of autism and dementia with a focus on post-diagnostic care. Drawing on interdisciplinary expertise, the group identified key barriers and opportunities in clinical practice, caregiving, and service delivery. Recommendations are organized across seven areas, including models of post-diagnostic support, caregiving contexts, pharmacological and non-pharmacological interventions, environmental adaptations, and care planning. The discussion emphasizes the complex needs of autistic adults-many of whom have co-occurring intellectual disabilities, psychiatric conditions, or chronic health issues-and the need for individualized approaches that account for sensory sensitivities and communication differences. Existing dementia care frameworks often fail to address these complexities, resulting in significant service gaps. The report calls for urgent investment in research, workforce training, and policy reform to promote equitable, autism-informed post-diagnostic support and improve quality of life for this underserved population.Lay AbstractAutistic adults who develop dementia often experience challenges that are not well addressed by current dementia care systems. After a dementia diagnosis, people may need help with memory, communication, behavior changes, and daily living. For autistic adults, these supports must be adapted to their individual sensory sensitivities, communication styles, and social differences. This article reports on the work of an international group of researchers, clinicians, and advocates who met during the Second International Summit on Intellectual Disability and Dementia. The group examined how post-diagnostic support for autistic adults with dementia could be improved. They reviewed existing evidence, identified key barriers to care, and proposed strategies to strengthen services in areas such as medication use, environmental design, caregiver training, and personalized care planning. The report emphasizes that many autistic adults also have intellectual disabilities, mental health conditions, or long-term physical health issues, which can make care more complex. Current dementia care frameworks often overlook these overlapping needs, resulting in limited or unsuitable supports. The authors call for more research, workforce training, and autism-informed policy changes to ensure that post-diagnostic care is equitable, individualized, and responsive. Enhancing understanding and adapting support can help autistic adults with dementia maintain dignity, comfort, and quality of life.
The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network monitoring with real time carbon and energy tracking. Using the publicly available Carbon Aware Cybersecurity Traffic Dataset comprising 2300 flow level observations, we benchmark Logistic Regression, Random Forest, Support Vector Machine, Isolation Forest, and XGBoost models across energy, carbon, and performance dimensions. Each experiment is executed in a controlled Colab environment instrumented with the CodeCarbon toolkit to quantify power draw and equivalent CO2 output during both training and inference. We construct an Eco Efficiency Index that expresses F1 score per kilowatt hour to capture the trade off between detection quality and environmental impact. Results reveal that optimized Random Forest and lightweight Logistic Regression models achieve the highest eco efficiency, reducing energy consumption by more than forty percent compared to XGBoost while sustaining competitive detection accuracy. Principal Component Analysis further decreases computational load with negligible loss in recall. Collectively, these findings establish that integrating carbon and energy metrics into cybersecurity workflows enables environmentally responsible machine learning without compromising operational protection. The proposed framework offers a reproducible path toward sustainable carbon accountable cybersecurity aligned with emerging US green computing and federal energy efficiency initiatives.
