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Mixed-Integer Linear Programming Model for Collision Avoidance Planning in Commercial Aircraft Formations
No full textWith advancements in technology, commercial aircraft formation flying is becoming increasingly feasible as an efficient and environmentally friendly flight method. However, gaps remain in practical implementation, particularly in collision avoidance for aircraft formations. Existing avoidance algorithms mainly focus on single aircraft or unmanned aerial vehicle swarms, lacking comprehensive studies on the complex interactions within commercial aircraft formations. To address this, this paper proposes an optimization model designed to generate safe and effective collision avoidance solutions for commercial aircraft formations. This model demonstrates avoidance paths for formations facing intruders and offers insights for developing formation flight strategies. This study explores response strategies for commercial aircraft formations encountering intruders, considering the difficulty of pilot maneuvers. The findings provide theoretical support for the practical implementation of commercial formation flying and may advance the adoption of this technology
Interfacial polymerization of dopamine with diamines for ultrastable Janus nanofiltration membranes and adhesives
No full textThe polymerization of dopamine can yield nature-inspired materials for diverse applications. Polydopamine (PDA) is used for modifying hydrophilic surfaces via deposition techniques; however, it lacks film-formation ability. In this study, we demonstrate the interfacial polymerization of dopamine with diamines via aza-Michael addition, which results in the formation of films possessing thicknesses in the range of nano- to micrometers with Janus properties for the latter. Experimental and in silico mechanistic investigations were conducted to determine the effects of the diamine chain length, monomer concentration, heteroatom, and solvent medium on film formation. The nanofilms exhibited exceptional stability in harsh organic solvents, ionic liquids, and strong acids. A 5-nm thick nanofilm composed of PDA and biomass-derived priamine exhibited controllable nanofiltration performance, demonstrating a high N,N-dimethylformamide solvent permeance of 26 L m−2 h−1 bar−1 and molecular weight cutoff of 540–704 g mol−1. Furthermore, the membrane was successfully applied in high-temperature nanofiltration. Additionally, the nanofilms demonstrated single- and double-sided adhesion on diverse surfaces, including wood and glass surfaces, which possess different wettability and roughness characteristics. This study provides a comprehensive understanding of the structure–property relationship, polymerization mechanism, membrane separation, and adhesive applications of PDA–diamine freestanding films.The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). We thank Amr Elsakran from KAUST for his assistance with the adhesion strength measurements. Fig. 1 and the graphical abstract were created by Ana Bigio, scientific illustrator at KAUST. The data is available in the supplementary information file as well as upon request from the corresponding author
Middlebox Assessment and Network Gaps: Observing Enforced Security
No full textNetwork middleboxes often manipulate traffic in ways that are invisible to users—silently filtering, blocking, or redirecting connections. For the network administrators installing them, they often come as blackboxes whose internal mechanisms are unknown. In this paper, we present a systematic approach to discover and better understand thesehidden enforcement mechanisms through a distributed testing framework. By running a series of reproducible test scenarios, we analyze how filtering is applied across the DNS and HTTP(S) layers. In total, we conducted 4,485 tests within a network, which revealed surprising differences in the blacklisting criteria applied across protocols. Additionally, we discovered firewall misconfigurations—including exploitable UDP holes. All findings were responsibly disclosed to the network and security teams that had provided support for these experiments, in order to let them address them in due time.We express our gratitude to the KAUST networking and security teams, and in particular to Luis Barreiro and Mohamed Ujaimi for facilitating and enabling the measurements, as well as for reviewing and providing feedback on this manuscript
Intrinsically conductive and highly stretchable liquid metal/carbon nanotube/elastomer composites for strain sensing and electromagnetic wave absorption
No full textWearable strain sensors translate mechanical deformations into electrical signals for healthcare monitoring. However, the limited ductility of conductive metals and polymers to maintain its conductivity under high degree of mechanical deformation is a crucial problem in existing fabrication methods for strain sensors. In this study, an intrinsically conductive and highly stretchable liquid metal (LM)/carbon nanotube (CNT)/polydimethylsiloxane (PDMS) composite-based wearable strain sensor with electromagnetic wave (EMW)-absorbing properties was explored. The effect of CNT inclusion in the LM/PDMS composites was investigated, revealing enhanced electrical conductivity and EMW-absorbing characteristics without additional mechanical sintering. Through a comprehensive evaluation considering electrical resistance, stretchability, and EMW-absorbing properties, LM/CNT/PDMS composites with 1.5 wt% CNT were determined to exhibit optimal performance as EMW absorbable strain sensors. The characterization of the electromechanical properties demonstrated the high stretchability and mechanical reliability of the resulting material with a gauge factor of 5.35 within the strain range of 50–100% and a low hysteresis under a stain of 80%, confirming the reliability of the fabricated sensor. The practical performance of the LM/CNT/PDMS composites was analyzed by adhering them to various parts of the body with joints and measuring the changes in their relative resistance, affirming their potential for use in healthcare monitoring devices for point-of-care testing (POCT) that require electromechanical reliability under repeated tensile deformation.This work was supported by the Global Research Development Center (GRDC) Cooperative Hub Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) (RS-2023-00257595).This work was fnancially supported by the Global Research Development Center (GRDC) Cooperative Hub Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) (RS-2023–00257595)
Inducing electronic rearrangement through Co3B-Mo2B5 catalysts: Efficient dual-function catalysis for NaBH4 hydrolysis and 4-nitrophenol reduction
No full textAmorphous bimetallic borides, as a new generation of catalytic nanomaterials with modifiable electronic properties, are of great importance in the design of high-efficiency catalysts for NaBH4 hydrolysis. This study synthesizes an amorphous Co3B-Mo2B5 catalyst using a self-sacrificial template strategy and NaBH4 reduction for both NaBH4 hydrolysis and the reduction of 4-nitrophenol. The catalyst delivers an impressive hydrogen generation rate of 7690.5 mL min−1 g−1 at 25 °C, coupled with a rapid reaction rate of 0.701 min−1 in the reduction of 4-nitrophenol. The enhanced catalytic performance is attributed to the unique amorphous structure and the electron rearrangement between Co3B and Mo2B5. Experimental and theoretical analyses suggest electron transfer from Co3B to the Mo2B5, with the electron-deficient Co3B site favoring BH4− adsorption, while the electron-rich Mo2B5 site favoring H2O adsorption. Furthermore, Co3B-Mo2B5 demonstrated potential for energy applications, delivering a power output of 0.3 V in a hydrogen-air fuel cell.This work has been supported by the National Natural Science Foundation of China (Nos. 52363028, 21965005), Natural Science Foundation of Guangxi Province (Nos. 2021GXNSFAA076001, 2018GXNSFAA294077), Guangxi Technology Base and Talent Subject (Nos. GUIKE AD23023004, GUIKE AD20297039)
Sustainable Production of Bio-Based Geraniol: Heterologous Expression of Early Terpenoid Pathway Enzymes in <i>Chlamydomonas reinhardtii</i>
No full textGeraniol is a monoterpene alcohol with a rose-like aroma, used in food and cosmetics and for its anti-inflammatory, antibacterial, and insect-repellent properties. Geraniol is commonly chemically synthesized from petroleum-based sources in a highly energy-demanding process with a large carbon footprint. Alternatively, geraniol can be derived from plant-based essential oils but with relatively low yields and limitations from seasonal cultivation. Here, a sustainable geraniol biosynthesis alternative was established in the photosynthetic green microalga Chlamydomonas reinhardtii. Three enzymes─geraniol synthase from Catharanthus roseus (CrGES), geranyl diphosphate synthase from Lithospermum erythrorhizon (LeGPPS), and a modified 1-deoxy-d-xylulose-5-phosphate synthase from Salvia pomifera (SpDXS)─were strategically redesigned for high expression from the algal nuclear genome. Various enzyme combinations and subcellular localizations were tested, resulting after 48 h in up to 1 mg geraniol/L (corresponding to 1.8 mg/g of dry weight) secreted into the culture medium. This work demonstrates a promising route for sustainable, CO2-based production of geraniol in microalgae and provides a foundation for further optimization.The authors would like to acknowledge the support of the technology platform and infrastructure at the Center for Biotechnology (CeBiTec) of Bielefeld University and the technology platform and infrastructure of the University of Verona (Centro Piattaforme Tecnologiche).This research was supported by the University of Verona grant no. JRVR2021BALLOTTARI and by the EUROPEAN Innovation Council (HORIZON-EIC-2022-TRANSITION-01 – AS-TEASIER - grant number 101099476) to M.B
Charge carrier transport in two-dimensional benzimidazole-based perovskites
No full textTwo-dimensional (2D) perovskites have emerged as promising candidates for field-effect transistors (FETs) due to their pronounced stability in the presence of insulating bulky organic spacer cations. However, the underlying mechanism of the charge carrier transport in these 2D perovskite semiconductors remains elusive. In this study, the temperature dependence of the charge carrier properties of benzimidazolium tin iodide perovskite ((Bn)2SnI4) is studied to evaluate the corresponding transport mechanism on nanoscopic and macroscopic dimensions. By combination of solvent engineering to optimize the morphology of perovskite thin films and choice of the organic imidazole-based spacer inducing hydrogen bonding with the inorganic [SnI6]4− octahedron layer, less ionic defects are generated resulting in suppressed ion movement. It was possible to separate the influence of mobile ions and temperature on the charge carrier transport in transistors. The decline of the charge carrier mobility with temperature decrease in the device indicates a hopping mechanism for macroscopic transport. On the other hand, the local charge transport was determined by ultrafast terahertz photoconductivity measurements revealing an increasing mobility to 17 cm2 V−1 s−1 with temperature decrease implying a band mechanism on the nanoscopic scale. The local charge carrier mobility is associated with the particularly regular structure of the octahedral [SnI6]4− sheets induced by symmetric hydrogen bonding with the benzimidazolium cation. Our results provide key insights on the charge transport properties of perovskite semiconductors, which have important implications for realizing high-performance electronic devices.S. L. Wang and Z. Ling thank the China Scholarship Council (CSC, 201906890035 and 202006890007) for financial support. M. Mandal acknowledges postdoctoral support from the Alexander von Humboldt Foundation. D. Andrienko acknowledges funding of the Deutsche Forschungsgemeinschaft (DFG) Priority Program SPP2196, project 424708673 and the KAUST Office of Sponsored Research, grant OSR-CRG2020-4350. Open Access funding provided by the Max Planck Society
ALARB: An Arabic Legal Argument Reasoning Benchmark
No full textWe introduce ALARB, a dataset and suite of tasks designed to evaluate the reasoning capabilities of large language models (LLMs) within the Arabic legal domain. While existing Arabic benchmarks cover some knowledge-intensive tasks such as retrieval and understanding, substantial datasets focusing specifically on multistep reasoning for Arabic LLMs, especially in open-ended contexts, are lacking. The dataset comprises over 13K commercial court cases from Saudi Arabia, with each case including the facts presented, the reasoning of the court, the verdict, as well as the cited clauses extracted from the regulatory documents. We define a set of challenging tasks leveraging this dataset and reflecting the complexity of real-world legal reasoning, including verdict prediction, completion of reasoning chains in multistep legal arguments, and identification of relevant regulations based on case facts. We benchmark a representative selection of current open and closed Arabic LLMs on these tasks and demonstrate the dataset's utility for instruction tuning. Notably, we show that instruction-tuning a modest 12B parameter model using ALARB significantly enhances its performance in verdict prediction and Arabic verdict generation, reaching a level comparable to that of GPT-4o
Evaluating the performance of seasonal pumped hydro storage coordinated operation with cascade hydropower station integrating variable renewable energy
No full textSeasonal pumped hydro storage (SPHS) presents a promising solution for China's evolving power systems dominated by variable renewable energy (VRE) sources with pronounced seasonal variations. Unlike conventional pumped storage addressing short-term fluctuations, SPHS integrates new upstream reservoirs with existing cascade hydropower through reversible units and pipelines to mitigate seasonal generation disparities in river-based hydropower systems. This study develops a refined nested long-short term production simulation model to evaluate SPHS's potential in facilitating VRE integration. The model examines three operational modes (non-regulation, short-term regulation, seasonal regulation) across different hydrological conditions and seasonal periods. Case results demonstrate that SPHS implementation enhances system VRE accommodation capacity by 20 % despite having only 8.6 % (1:11.6 ratio) of the downstream reservoir's storage capacity. Annual generation increases by 1.60 %, 1.02 %, and 0.84 % in wet, normal, and dry years respectively, while maintaining stable power supply. The analysis reveals significant sensitivity of system performance to SPHS's water storage redistribution strategies, suggesting seasonal flexibility optimization could yield additional benefits. The findings demonstrate SPHS's capacity to address seasonal hydropower variability while establishing a scalable framework for advancing pumped hydro storage's operational flexibility and evaluation methodologies in long-term renewable energy integration scenarios.This research is supported by the National Natural Science Foundation of China (51909222), Technology Project of State Grid Shaanxi Electric Power Company Limited (5226KY23001M).
Reference
Spatial data fusion adjusting for preferential sampling using integrated nested Laplace approximation and stochastic partial differential equation
No full textSpatially misaligned data can be fused by using a Bayesian melding model that assumes that underlying all observations there is a spatially continuous Gaussian random field. This model can be employed, for instance, to forecast air pollution levels through the integration of point data from monitoring stations and areal data derived from satellite imagery. However, if the data present preferential sampling, that is, if the observed point locations are not independent of the underlying spatial process, the inference obtained from models that ignore such a dependence structure may not be valid. In this paper, we present a Bayesian spatial model for the fusion of point and areal data that takes into account preferential sampling. Fast Bayesian inference is performed using the integrated nested Laplace approximation and the stochastic partial differential equation approaches. The performance of the model is assessed using simulated data in a range of scenarios and sampling strategies that can appear in real settings. The model is also applied to predict air pollution in the USA.The research conducted in this study was supported by GeoHealth research group, King Abdullah University of Science and Technology (KAUST)