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Resonant defect states in atomic diamond lattices
No full textDespite years of effort, no compelling experimental evidence of Anderson localization of light in three-dimensional systems has yet been found. Recent theoretical studies suggest that the existence of longitudinal modes open a new channel for diffusion, preventing light localization in ensembles of resonant scatterers [1-3]. However, as we know from tight binding models, localization effects can be induced by tunneling from one defect state to the other inside a spectral gap [3]. In this work, we establish the foundation for future studies of transport phenomena within the spectral band gap of a diamond lattice, the simplest known atomic lattice to exhibit a spectral band gap for electromagnetic waves [4]. We begin by investigating the collective resonances of an atomic diamond lattice using the coupled dipole method [5], focusing on the density of states to characterize gap formation. We then analyze the impact of introducing substitutional disorder in the lattice. Finally, we employ a Bloch analysis of the perfect photonic crystal to compute the lattice’s Green’s functions, and investigate how states can be created inside the spectral band gap and, once formed, how they hybridize with the surrounding lattice modes.<br/
Integrating IoT and blockchain for smart urban energy management:Enhancing sustainability through real-time monitoring and optimization
Get PDFThis paper addresses the critical challenges of scalability, interoperability, and user adoption in IoT-blockchain integration for urban energy systems. Existing frameworks often rely on energy-intensive consensus mechanisms (e.g., Proof of Work) or centralized architectures, limiting their applicability to large-scale, sustainable smart cities. To bridge these gaps, we propose a novel IoT blockchain framework that uniquely combines hybrid consensus mechanisms (Proof of Stake + Practical Byzantine Fault Tolerance), K-means clustering for demand-response optimization, and lightweight IoT protocols (MQTT/CoAP) to ensure energy efficiency, scalability, and user-centric design. Our approach leverages real-world datasets (UK-DALE, PECAN Street) to train predictive models, cluster energy consumption patterns, and automate decentralized energy trading via blockchain smart contracts. Simulations demonstrate a 15% reduction in energy costs for high-consumption clusters, 80% lower energy use (50 kWh/tx vs. 500 kWh/tx for PoW), and near-linear scalability for 500+ IoT devices. A secure dashboard with AI-driven recommendations (e.g., peak-load alerts) further enhances stakeholder engagement. By addressing technical limitations of previous works, such as computational bottlenecks, lack of user interfaces, and poor interoperability, our framework provides actionable insights for policymakers to advance sustainable urban energy systems. These results position the proposed architecture as a transformative solution for scalable, eco-friendly smart cities.</p
Dual-site beta transcranial alternating current stimulation during a bimanual coordination task modulates functional connectivity between motor areas
Get PDFBackground: Communication within brain networks depends on functional connectivity. One promising approach to modulate such connectivity between cortical areas is dual-site transcranial alternating current stimulation (tACS), which non-invasively applies weak alternating currents to two brain areas.Objectives: In the current study, we aimed to modulate inter-regional functional connectivity with dual-site tACS to bilateral primary motor cortices (M1s) during bimanual coordination and, in turn, alter behaviour.Methods: Using functional magnetic resonance imaging (fMRI), we recorded participants’ brain responses during a bimanual coordination task in a concurrent tACS-fMRI design. While performing a slow and fast version of the task, participants received one of three types of beta (20 Hz) dual-site tACS over both M1s: zero-phase, jittered-phase or sham, in a within-participant, repeated measures design. Results: While we did not observe any significant tACS effects on behaviour, the study revealed an attenuation effect of zero-phase tACS on interhemispheric connectivity. Additionally, the two active types of tACS (zero-phase and jittered-phase) differed in the task-related M1 connectivity with other motor cortical regions, such as premotor cortex and supplementary motor area. Furthermore, individual E-field strengths were related to functional connectivity in the zero-phase condition.Conclusions: Dual-site beta tACS over both M1s altered functional connectivity between motor areas. However, this effect did not translate significantly to the behavioural level in the presence of a restricted sample size. Future studies may thus integrate mechanistic measures, such as measures of interhemispheric inhibition, to strengthen causal interpretations.</p
An experimental method for modelling the off-state thermodynamics of a cryocooler
Get PDFWe present a method for modelling the thermodynamic behaviour of a cryocooler in the case of intermittent operation of the cooler. Such intermittent operation can, for instance, be applied to prevent cooler interference when cooling ultra-sensitive devices. In this case, the cooler is switched off during actual operation of these devices. Since these devices usually are sensitive to temperature variations, it is important to know the thermal response of the cooler when switching it on and off. Our approach in predicting this response is based on simple RC modelling of the separate cooler stages, in which the thermal resistance R and the heat capacity C are considered temperature dependent. In order to determine these dependencies, the cooler is characterized in warm-up experiments where the cold-stage temperatures are recorded as functions of time. In the paper, we present our modelling approach and the method to derive the model parameters from the warm-up experiments. The presented methodology is illustrated by experiments performed with a commercial two-stage cryocooler.</p
A Quantitative Printability Framework for Programmable Assembly of Pre‐Vascular Patterns via Laser‐Induced Forward Transfer
Get PDFThe defined vascularization of complex and intricate tissue constructs remains an unmet need in tissue engineering and regenerative medicine. While large constructs require vasculature for oxygen, nutrient supply, and waste clearance, their incorporation within biofabricated tissues is essential for developmental and disease modeling studies. There is, therefore, a critical demand to establish reproducible and organized vascular networks within in vitro models to ensure experimental robustness and quantitative interpretability. Current micropatterning and biofabrication strategies are limited in emulating native geometrical complexity, throughput, and resolution, while self-assembly approaches rely on inherently random network formation. Here, laser-induced forward transfer (LIFT) is utilized, offering high spatial resolution for deterministic micropatterning of cells with high viability. A unique droplet quality assessment framework is established through a multiparametric study to objectively identify a printability window, assigning a single-indexed score per printing condition. Within the optimal transfer regime, control over droplet concentration is demonstrated. The impact of pattern density on early vascular morphogenesis is explored, highlighting the effect of geometrical design on network formation. Finally, these findings are leveraged for the spatially controlled assembly of multicellular vascular patterns, offering a reproducible strategy for high-resolution micropatterning and addressing a key limitation in the biofabrication of physiologically relevant tissue models
Advances in AI-Based Solutions for the Multi-Depot Vehicle Routing Problem:A Review of Recent Trends and Future Directions
No full textThe Multi-Depot Vehicle Routing Problem (MDVRP) is a complex combinatorial optimization challenge that involves determining the most efficient routes for a fleet of vehicles operating from multiple depots while considering constraints such as capacity, time windows, and customer demands. Due to its NP-hard nature, MDVRP presents significant difficulties in finding optimal solutions. In recent years, artificial intelligence (AI) techniques, particularly metaheuristic algorithms inspired by natural processes and deep learning (DL) and reinforcement learning (RL) methods, have gained prominence for their ability to offer scalable and efficient solutions to MDVRP. Integrating hybrid approaches combining metaheuristics with DL has been especially noteworthy, providing enhanced solution quality and computational efficiency. Studies have shown that hybrid optimization strategies, such as clustering techniques with genetic algorithms (GAs), optimize customer assignments and overall route planning. This paper systematically reviews recent literature from the past ten years to identify common AI-based approaches for addressing MDVRP. It discusses potential directions for future research to advance the field further. Future research should focus on real-time data integration through IoT, which can create more dynamic and adaptive solutions. Additionally, AI methodologies, such as RL algorithms paired with metaheuristics, hold the potential for addressing larger, real-world MDVRP instances. Applying AI to practical domains like e-commerce logistics, health logistics, and emergency response has demonstrated significant operational improvements. Continued exploration of multi-objective optimization that balances cost, route balance, and energy efficiency is crucial for handling real-world complexities, including demand and capacity uncertainties.</p
Beyond Value Iteration for Parity Games:Strategy Iteration with Universal Trees
Get PDFParity games have witnessed several new quasi-polynomial algorithms since the breakthrough result of Calude et al. (STOC 2017). The combinatorial object underlying these approaches is a universal tree, as identified by Czerwiński et al. (SODA 2019). By proving a quasi-polynomial lower bound on the size of a universal tree, they have highlighted a barrier that must be overcome by all existing approaches to attain polynomial running time. This is due to the existence of worst case instances which force these algorithms to explore a large portion of the tree. As an attempt to overcome this barrier, we propose a strategy iteration framework which can be applied on any universal tree. It is at least as fast as its value iteration counterparts, while allowing one to take bigger leaps in the universal tree. Our main technical contribution is an efficient method for computing the least fixed point of 1-player games. This is achieved via a careful adaptation of shortest path algorithms to the setting of ordered trees. By plugging in the universal tree of Jurdziński and Lazić (LICS 2017), or the Strahler universal tree of Daviaud et al. (ICALP 2020), we obtain instantiations of the general framework that take time O(mn2 log n log d) and O(mn2 log3 n log d) respectively per iteration.</p
Cancer outcomes in women without upfront surgery for ductal carcinoma in situ:Observational cohort study
Get PDFObjective: To determine the risk of subsequent ipsilateral invasive breast cancer in women who do not receive upfront surgery on diagnosis of ductal carcinoma in situ (DCIS).Design: Observational cohort study using data abstracted directly from patients' medical records and from a national cancer registry in patients with primary DCIS diagnosed between 2008 and 2015.Setting: Commission on Cancer accredited facilities (n=1330) in the US.Participants: 1780 women with diagnosis of primary DCIS on needle biopsy who were alive and free of invasive breast cancer at 6 months after diagnosis.Interventions: No surgery within 6 months of diagnosis.Main outcome measures: Primary outcome: ipsilateral invasive breast cancer; secondary outcome: death due to breast cancer. Subgroup analysis by risk status, based on eligibility criteria of ongoing active monitoring trials: low risk if aged ≥40 years at diagnosis of an imaging detected, nuclear grade I/II, and hormone receptor positive DCIS; high risk otherwise.Results: Median age at diagnosis was 63 years, and median follow-up was 53.3 months. Among all 1780 women, the number of ipsilateral invasive breast cancer events was 115 (6.5%) and the number of deaths from breast cancer was 29 (1.6%). The 8 year cumulative incidence of ipsilateral invasive breast cancer was 10.7% (95% confidence interval (CI) 8.4% to 12.8%). Incidence of invasive cancer differed by both disease and patient related factors, with 8 year cumulative incidences of ipsilateral invasive breast cancer ranging from 8.5% (95% CI 4.7% to 12.1%) among women at low risk (n=650) to 13.9% (10.5% to 17.2%) among those at high risk (n=833). The 8 year disease specific survival probability was 96.4% (95% CI 95.0% to 97.9%) overall and 98.1% (96.7% to 99.6%) among women at low risk.Conclusions: In a cohort of patients who did not receive initial surgery for DCIS, the 8 year cumulative incidence of invasive cancer in the same breast varied between 8% and 14%. Effective risk stratification tools and shared decision making are essential for this patient population.</p
Deciphering the transformation of sounds into meaning:Insights from disentangling intermediate representations in sound-to-event DNNs
Get PDFNeural representations estimated from functional MRI (fMRI) responses to natural sounds in non-primary auditory cortical areas resemble those in intermediate layers of deep neural networks (DNNs) trained to recognize sounds. However, the nature of these representations remains poorly understood. In the current study, a convolutional DNN (YAMNet), pre-trained to map sound spectrograms to semantic categories, is used as a computer simulation of the human brain's processing of natural sounds. A novel sound dataset is introduced and employed to test the hypothesis that sound-to-event DNNs represent basic mechanisms of sound generation (here, human actions) and physical properties of the sources (here, object materials) in their intermediate layers. Systematic changes to those latent representations are made with the help of a disentangling flow model. The manipulations are shown to cause a predictable effect on DNN's semantic output. By demonstrating this mechanism in silico, the current study paves the way for neuroscientific experiments aiming to verify it in vivo. Code available at https://github.com/TimHenry1995/LatentAudio.</p
Design and Evaluation of a Torque-Controlled Ankle Exoskeleton Using the Small-Scale Hydrostatic Actuator:miniHydrA
Get PDFA small-scale electro-hydrostatic actuator, termed miniHydrA, was developed based on biomechanical requirements for gait and integrated into an ankle exoskeleton. The key advantage of this actuator concept lies in its compact size and the low mass of its output stage, combined with the ability to deliver high support torques, sufficient for full human assistance. During development, hydraulic cylinder leakage and friction were identified as key challenges. To address control requirements, a dedicated control strategy was proposed and implemented. The prototype exoskeleton was evaluated for joint torque tracking performance across a range of torques (0–120 Nm), both in benchtop tests and during treadmill walking trials. In benchtop experiments, zero-torque tracking was achieved with a mean absolute error ranging from 0.03 to 2.26 Nm across frequencies from 0 to 5 Hz. During treadmill walking, torque tracking errors ranged from 0.70 to 0.95 Nm, with no observable deviations in ankle joint kinematics among the three test subjects. These results show the feasibility of the miniHydrA for remote actuation. Compared to Bowden cables, commonly used in exoskeletons and exosuits, the proposed actuator concept offers two key advantages: it is better suited for high-torque applications, and its friction characteristics can be more accurately predicted and modeled, enabling more effective feedforward control.</p