Machine learning-driven sensor array based on luminescent metal–organic frameworks for simultaneous discrimination of multiple anions

Due to the high correlation of anions in waters to environmental quality and human health, thus there is urgent need for developing simple and effective sensors to discriminate multiple anions. Herein, a machine learning-assisted fluorescent sensor array based on two luminescent metal–organic frameworks (LMOFs, UiO-66-NH2 and UiO-66-OH) was developed for simultaneous discrimination of five anions (F−, PO43−, ClO44−, NO3−, and SO42−). Wherein, UiO-66-NH2 and UiO-66-OH were designed by anchoring 2,5-diaminoterephthalic acid and 2,5-dihydroxyterephthalic acid on UiO-66, respectively, which exhibited blue and green fluorescence emission, possessing good fluorescence property. Interestingly, the anions could effectively enhance the fluorescence intensity of UiO-66-NH2 and UiO-66-OH to generate diverse fluorescence responses and unique fingerprints, which could be utilized to develop a fluorescence sensor array for the rapid identification of five anions. Under the optimized conditions, the proposed sensor array showed good performance for identifying multiple anions and their mixtures with satisfactory sensitivity. More importantly, the integration of machine learning algorithm and sensor array has successfully achieved accurate identification and prediction of five anions in real water samples, affirming its practicability in actual samples. Our findings provided a promising tool for detecting multiple anions, and inspired potentials of the combination of sensor arrays and machine learning algorithm for pollution control in real waters.This work was supported by the National Natural Science Foundation of China (Grants No. 22176075, 22406068), Natural Science Foundation of Jiangsu Province (BK20240884).Chemical Engineering Journa

Self-healing mechanism in polymer composite materials

The current self-healing mechanisms are still a long way from being fully implemented, and most published studies have only shown successful damage repair at the laboratory level. The complex nature of these mechanisms makes it difficult to implement them in real-life situations where the component or structure must continue to function. For complete healing, a molecular-level chemical reaction is required with the aid of external stimuli such as heating, light, and temperature change. Existing self-healing mechanisms are almost impossible to implement in critical applications such as 3D-printed products due to the requirements of external stimulations and reactions. The objective of this research is to investigate the strain release behaviour during crack growth of polymeric beams under elastic loads for self-healing. The mechanical behaviour of polymer components has been studied for many years, and their basic features are well understood. In this study, the elastic and plastic responses of 3D-printed beams made of Acrylonitrile butadiene styrene (ABS), thermoplastic polyurethane (TPU), and thermoplastic elastomers (TPE) were investigated under different bending loads. Two types of 3D-printed beams were designed to test their elastic and plastic responses under different bending loads. These responses were used to develop an innovative self-healing mechanism based on origami capsules that can be triggered by crack propagation due to strain release in a structure. The origami capsules, made of TPU in the form of a cross with four small beams either folded or elastically deformed, were embedded in a simple ABS beam. When crack propagation occurred in the ABS beam, the strain was released, causing the TPU capsule to unfold with the arms of the cross in the direction of the crack path. This increased the crack resistance of the ABS beam, which was validated in a delamination test of a double cantilever specimen under quasi-static load conditions. The results showed the potential of the proposed self-healing mechanism as a novel contribution to existing practises primarily based on external healing agents. The self-healing mechanism of TPU and TPE origami capsules has been demonstrated and reported for the first time. These materials achieved a good balance of mechanical strength and self-healing ability. A thicker beam structure tends to yield higher strain energy than do low thickness values for the beam. Since the strain energy release is dependent on how much cracking has propagated, so the higher strain release from the DCB TPU star and roll contributes to the rate at which crack propagation extends.PhD in Manufacturin

Integrating corporate identity, social responsibility, and reputation: a triadic framework for sustainable branding in hospitality & tourism

Previous studies have explored the impact of corporate identity (CI), corporate social responsibility (CSR), and corporate reputation (CR), but they have largely overlooked the effects of inconsistent CSR strategies on unexpected outcomes among hospitality employees. To address this gap, this study examines the interplay among CI, CSR, and CR within the hospitality industry. Adopting a multidisciplinary approach, the research reviews the literature from marketing, design, organizational studies, and management. It then employs qualitative methods, including interviews with managers and focus groups with employees, supplemented by a survey conducted among hospitality and tourism employees in the UK, Malaysia, and Iran. The findings reveal 20 critical CI factors across corporate communication, visual identity, and management behavior, demonstrating that CI influences CSR and CR. This study introduces a triadic framework that integrates CI, CSR, and CR, offering a holistic perspective essential for sustainable branding in hospitality.International Journal of Hospitality Managemen

Hydrodynamic modeling of unstretched length variations in nonlinear catenary mooring systems for floating PV installations in small Indonesian Islands

Floating photovoltaic (FPV) systems offer a promising renewable energy solution, particularly for coastal waters. This preliminary numerical study proposes a single-array pentamaran configuration designed to maximize panel installation and enhance stability by reducing rolling motion. The study investigates the effect of mooring length on the motion behavior of FPV systems and actual line tension using the Boundary Element Method (BEM) in both frequency and time domains under irregular wave conditions. The results demonstrate that the mooring system significantly reduces all horizontal motion displacements, with reductions exceeding 90%. Even with a reduction of up to 51% in the unstretched mooring length, from the original design (304.53 m) to the shortest alternative (154.53 m), the motion response shows minimal change. This is supported by RMSE values of only 0.01 m/m for surge, 0.02 m/m for sway, and 0.09 deg/m for yaw. In the time-domain response, the shortened mooring line demonstrates improved motion performance. This improvement comes with the consequence of stronger nonlinearity in restoring forces and stiffness, resulting in higher peak tensions of up to 15.79 kN. Despite this increase, all configurations remain within the allowable tension limit of 30.69 kN, indicating that the FPV’s system satisfies safety criteria.This research was funded by Institut Teknologi Sepuluh Nopember (ITS) for providing financial support for the study project through the “ITS Center Collaboration Research Scheme” with grant number 1322/PKS/ITS/2024.Modellin

From raw data to monotonic and trendable features reflecting degradation trends in turbofan engines

The performance of prognostic models relies heavily on the form and trend of the extracted features. However, the raw data collected from physical systems are inherently noisy, large in volume, and exhibit significant variability, which makes them unsuitable for direct use in prognostics. These characteristics poorly reflect the degradation behavior of physical systems and contribute to the uncertainty of prognostic outcome. Hence, transforming this data into relevant features and carefully selecting them is crucial for meeting the specific needs of prognostic models. This paper aims to address data processing challenges by focusing on extraction and selection of high-quality monotonic features which clearly reflect the degradation and can reduce prognostics uncertainty. The proposed framework comprises three main stages: Data pre-processing, feature extraction, and feature selection. It includes a fitness analysis to evaluate the monotonicity and trendability of features supplemented by visual inspections to identify relevant features. Applied to the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset from the NASA Ames Prognostics Data Repository, the framework reduces noise, improves feature monotonicity and trendability, and facilitates the selection of useful features - essential aspects for effective prognostic methods.This research was supported by the Centre for Digital Engineering and Manufacturing, Cranfield University, United Kingdom, 10.13039/5011000008592024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference (ONCON

Evaluating the scope of peer review in digital Forensics: insights from Norway and the U.K.

This paper investigates the implementation and utilisation of peer review practices in digital forensics (DF) within Norway and the U.K. Through a comprehensive survey of 113 DF practitioners and managers, we explore the extent to which peer review is integrated into DF investigations and the variations in practices between these two countries. Our findings reveal that while both Norway and the U.K. recognize the importance of peer review in ensuring the integrity and accuracy of DF work, there is a tendency to limit peer reviews to the examination of reports, rather than extending them to more thorough verification of results and methodologies. Utilising the Peer Review Hierarchy for DF as an analytical framework, our study highlights a significant gap in the depth of peer review practices, with both countries primarily focusing on lower-level reviews that are less likely to detect critical errors. The paper discusses the implications of these findings in the field of DF, emphasising the need for more robust and comprehensive peer review mechanisms to enhance the quality and reliability of digital evidence. Furthermore, we discuss the systemic and resource-related challenges that may hinder the implementation of more extensive peer review practices.Science & Justic

High performance rechargeable aluminium ion batteries enabled by strategy of covalent organic frame material

Emerging rechargeable aluminium-ion batteries (RAIBs) are a sustainable option for the next generation of low-cost, high-safety and large-scale energy storage technologies. While the unsatisfying availability of traditional inorganic materials has limited the development of RAIBs, the advance of organic materials is expected to be a breakthrough towards high-performance cathode. However, the existing extensive research often focuses on the selection of appropriate organic monomers or stay in the tentative stage of preliminary polymerization. It is difficult to break through the inherent characteristics of the instability of small organic ones and the easy aggregation and accumulation of macromolecular polymers, which is no doubt ignoring the huge potential of organic compounds for structural design at the molecular level. In this connection, our study demonstrates a material design strategy that introduces active functional groups to small molecular monomers and polymerizes them into REDOX active covalent organic framework (COF) with multiple N-containing groups. Theoretical simulations and ex-situ analysis revealed the key function of C-N and C=N as active sites for reversible storage of AlCl2 + ions. In addition, the macro-ring frame brings enhanced structural stability and environmental tolerance for COF in complex electrolyte, resulting in significantly improved electrochemical performance. At 1 A g−1, it exhibits a high specific capacity of 161.2 mAh g−1 and an excellent cycle life of approximately 100 % coulombic efficiency after more than 3,000 cycles. This work fully demonstrates the operability of the design strategy to synthesize COF from small molecular organics by introducing reactive functional groups and its great potential in the role of cathode materials in RAIBs. The success meanwhile provides an inspiration for the development of COF-based organic battery system in large-scale energy storage.This work was supported by Opening Project of Guangxi Key Laboratory of Calcium Carbonate Resources Comprehensive Utilization (Grant No. HZXYKFKT202206), Guangxi Natural Science Foundation (Grant No. 2024GXNSFFA010003), Project entrusted by enterprise (Grant No. HX20210521 and Grant No. HX20230264) and Hezhou University Research Project (Grant No. 2023ZDPY01).Chemical Engineering Journa

Securing UAV flying ad hoc wireless networks: authentication development for robust communications

Unmanned Aerial Vehicles (UAVs) have revolutionized numerous domains by introducing exceptional capabilities and efficiencies. As UAVs become increasingly integrated into critical operations, ensuring the security of their communication channels emerges as a paramount concern. This paper investigates the importance of safeguarding UAV communication against cyber threats, considering both intra-UAV and UAV–ground station interactions in the scope of the Flying Ad Hoc Networks (FANETs). To leverage the advancements in security methodologies, particularly focusing on Physical Unclonable Functions (PUFs), this paper proposes a novel authentication framework tailored for UAV networking systems. Investigating the existing literature, we categorize related studies into authentication strategies, illuminating the evolving landscape of UAV security. The proposed framework demonstrated a high level of security with lower communication and computation costs in comparison with selected studies with similar types of attacks. This paper highlights the urgent need for strong security measures to mitigate the increasing threats that UAVs encounter and ensure their sustained effectiveness in a variety of applications. The results indicate that the proposed protocol is sufficiently secure and, in terms of communication cost, achieves an 18% improvement compared to the best protocol in the referenced studies.Sensor

Supersonic flow field reconstruction using CNNs

The accurate prediction of a projectile’s aerodynamic coefficients is crucial in high-precision external ballistic calculations. The aerodynamic forces and moments exerted on a projectile in flight influence key performance parameters such as range, accuracy, time of flight and stability. A large body of work has therefore been dedicated to understanding the flow dynamics around projectile bodies and obtaining the critical force and moment coefficients. This has been traditionally achieved in aeroballistic range experiments, wind tunnel set-ups and through the use of numerical models. Nevertheless, a widespread still exists between different techniques, revealing the fluid physics is not yet fully understood. A better understanding of the aerodynamics at play is accessible through a combination of the three techniques. However, reliable wind tunnel results will require matching a series of similarity parameters imposed by the firing conditions, which will inevitably relate to the physical scale of the models used. The size of small calibre projectiles may prove challenging for measurement in wind tunnel set-ups, however upscaling the models inappropriately will result in unrepresentative flow fields due to wall interactions and blockage effects. On the other hand, sting supports for wind tunnel models disturb a smaller portion of the flow with increasing projectile scale, particularly in terms of wake perturbation - a key contributor to aerodynamic coefficients. Clearly, scale effects have important consequences, however they have not been explicitly treated in the supersonic projectile literature. This study aims to explore the effects and limits of projectile scaling in supersonic wind tunnels, through a series of experimental techniques (Schlieren visualization, pressure measurements, force balance measurements...) and numerical modelling. Additionally, we aim to develop the Background-Oriented-Schlieren technique a step further through the use of machine learning models to reconstruct complete flow fields from optical data.Royal Higher Institute for Defense, BelgiumDefence and Security Doctoral Symposia 2024 (DSDS24

Guest editorial: transforming food supply chains: harnessing the potential of the digital era

The International Journal of Logistics Managemen