Leveraging ensemble clustering for privacy-preserving data fusion:Analysis of big social-media data in tourism

Discovering knowledge from social media becomes a trend in many domains such as tourism, where users' feedback and rating are the basis of recommendation systems. In this context, cluster analysis has been a major tool to disclose user groups by which the process of collaborative filtering can better determine a personalised suggestion. Matching this to the curse of big data is a challenge with previous studies either implementing conventional techniques on a distributed system or making use of data sampling. Specific to ensemble clustering, only a few aim to obtain both scalability and privacy preserving that are significant to handling social data. This paper presents a new bi-level framework of ensemble clustering in which an instance-segment based analysis is adopted to ensure data privacy and reduce the complexity of clustering the whole dataset. Unlike existing studies, instead of drawing a single clustering from each segment, multiple clusterings are selected to better represent instances therein. Based on published tourism datasets and different experimental settings, the new approach usually outperforms its baselines whilst being competitive to related methods found in the literature. Additional case studies on simulated big datasets and noisy variations are reported and discussed in addition to the analysis of algorithmic parameters

Mechanical properties and texture profile analysis of beef burgers and plant-based analogues

Cultivated meat, or cultured meat, is lab-grown from animal stem cells, differentiated into muscle and/or fat, to yield meat products. The process is more sustainable and more ethical than traditional farming, allowing to meet growing consumer demand. However, there remains a challenge in replicating the organoleptic properties of commercially available meat products for cultivated meat applications. Consequently, this study employs single-cycle uniaxial testing (flexion, tension, compression, cutting) governed by ISO standards, and texture profiling analysis, to ascertain the modulus, yield strain, hardness, adhesiveness, cohesiveness, springiness, resilience and chewiness of seven commercially available burgers. These were tested both raw and cooked, and comprise beef (including a range of beef contents, fat percentages and price points) and plant-based analogues. Here, we show that (i) both mechanical (flexural, compressive and cutting yield strains) and textural (cohesiveness, springiness and resilience) properties reveal clear and statistically significant divides between the cooked properties of beef compared to plant-based burgers; (ii) moreover, hardness and chewiness yield statistically significant results able to distinguish between high beef content burgers (over 95%), low beef content burgers (below 81%) and plant-based alternatives, and thus, are best suited to characterise burger properties; and (iii) there exists key target values for cultivated meat products to replicate the mechanical and textural characteristics of farmed beef burgers, identified for the first time. These findings provide novel insights into the mechanical and textural characterisation of beef and plant-based burgers, and may contribute to future developments in cultivated meat to ensure consumer acceptance

Dynamic risk assessment approach for analysing cyber security events in medical IoT networks

Advancements in Medical Internet of Things (MIoT) technology ease remote health monitoring and effective management of medical devices. However, these developments also expose systems to novel cyber security risks as sophisticated threat actors exploit infrastructure vulnerabilities to access sensitive data or deploy malicious software, threatening patient safety, device reliability, and trust. This paper introduces a lightweight dynamic risk assessment approach using scenario-based simulations to analyse cyber security events in MIoT infrastructures and supplement cyber security activities within organisations. The approach includes synthetic data and threat models to enrich discrete-event simulations, offering a comprehensive understanding of emerging threats and their potential impact on healthcare settings. Our simulation scenario illustrates the model’s behaviour in processing data flows and capturing the characteristics of healthcare settings. Our findings demonstrate its validity by highlighting potential threats and mitigation strategies. The insights from these simulations highlight the model’s flexibility, enabling adaptation to various healthcare settings and supporting continuous risk assessment to enhance MIoT system security and resilience

Solitons and coherent structures in optics: 50th anniversary of the prediction of optical solitons in fiber

Nonlinear optics is a continuously expanding field due to its wide range of applications covering temporal, spectral, polarization or spatial features of the light signals. 2023 was the celebration of 50 years since the numerical prediction of the existence of solitons in optical fiber by Hasegawa and Tappert and, to celebrate this milestone, Optics Communications invited submissions related to nonlinear coherent structures in optical waveguides involving the Kerr nonlinearity. The purpose of this Special Issue was to provide an overview of recent ongoing progress and trends in advancing the knowledge, understanding, and novel applications of optical solitons and other related nonlinear structures. Both theoretical and experimental reports or discussions were welcomed

Solubilisation & purification of membrane proteins using benzylamine-modified SMA polymers

Extraction of proteins from the membrane using styrene maleic acid co-polymers (SMA), forming SMA lipid particles (SMALPs), has allowed for the first time the purification of membrane proteins with their lipid bilayer environment. To date, SMA2000 has been the most effective polymer used for this purpose, with a 2:1 ratio of styrene:maleic acid, and styrene and maleic acid moieties spread statistically throughout the chain. However, SMA2000 is a highly polydisperse polymer that contains an array of different polymer lengths and sequences. RAFT polymerisation offers much better control over the polymer length; however, homogeneous distribution of styrene and maleic acid throughout the polymer is difficult to achieve. Instead, here RAFT polymerisation was used to produce a 1:1 styrene:maleic anhydride polymer, which was then modified with benzylamine. This mimics the 2:1 hydrophobic:hydrophilic nature of SMA2000, while controlling the length and obtaining a homogeneous distribution of the hydrophobic moieties (styrene and N-benzylmaleimide). SMA-benzylamine (SMA-BA) polymers of three different lengths (2, 4, and 7 kDa) were all able to solubilise purified lipids, cellular membranes, and a range of specific proteins. However, the larger 7 kDa polymer solubilised membranes more slowly and less efficiently than the shorter polymers. This also affected the yield of purified protein obtained by affinity purification with this polymer. The smallest 2 kDa polymer solubilised membranes the fastest but appeared to offer less stability to the extracted proteins. The SMA-BA polymers were more sensitive to Mg2+ ions than SMA2000. SMA-BA 4 kDa was otherwise comparable to SMA2000 and even gave a higher degree of purity

Net zero fuel (mixed hydrogen and biofuels) cement clinker: Characterisation, microstructure, and performance.

Over 35% of the CO2 associated with cement production comes from operational energy. The cement industry needs alternative fuels to meet its net zero emissions target. This study investigated the influence of hydrogen mixed with biofuels, herein designated net zero fuel as an alternative to coal, on the clinker quality and performance of cement produced in an industrial cement plant. Scanning electron microscopy, X-ray diffraction and nuclear magnetic resonance were coupled to study the clinker mineralogy and polymorphs. Hydration and microstructure development in plain and slag blended cements based on the clinker were compared to commercial cement equivalent. The results revealed a lower alite/belite ratio, but a significant proportion of the belite was of the α’H-C2S polymorph. These reacted faster and compensated for the alite/belite ratio. Gel and micro-capillary pores were densified, which reduced total porosity and attained comparable strength to the reference plain and blended cement. This study demonstrates that the investigated net zero fuel-produced clinker meets compositional and strength requirements for plain and blended cement, providing a feasible pathway for the cement industry to lower its operational carbon significantly

Catalytic micro-structured ceramic beads and efficacy evaluation through SMX degradation in PMS-activated systems

This study tackles the challenge of applying fine and nano-catalyst particles in Advanced Oxidation Processes (AOPs), primarily due to the complexities of nanoparticle removal from treated water to prevent secondary nano-hazards. We propose an innovative solution: micro-structured ceramic beads (MSCBs, approximately 3 mm in diameter) with a unique anisotropic pore structure. For the first time, we prepared MSCBs impregnated with cobalt oxide (Co/MSCBs) using a phase-inversion and sintering-assisted process. The Co/MSCBs were investigated for the degradation of sulfamethoxazole (SMX) in the peroxymonosulfate (PMS) induced AOPs system under mild reaction conditions. The effects of operating parameters (e.g., SMX concentration, reaction temperature, and catalyst dosage) in the Co/MSCBs|PMS system were studied on three different types of catalytic ceramic beads: 2Co/MSCB0 (beads with a common isotropic pore structure), 2Co/MSCB1 (beads with radial finger-like microstructures and a denser outer skin-layer), and 2Co/MSCB2 (beads with finger-like microstructures and no outer skin layer). At 20 °C, 2Co/MSCB2 (59.1 %) demonstrated a higher degradation efficiency for 40 mg/L SMX in comparison to 2Co/MSCB1 (54.9 %) and 2Co/MSCB0 (49.6 %). Additionally, it is noteworthy that the sample 2Co/MSCB2, after being used and regenerated, exhibited significantly a higher catalytic performance (70.83 % removal in 20 min during the 16th run) than the fresh one (70.47 % removal in 120 min). After reactions, Co/MSCBs can be readily separated from the bulk solution and used for the next run, making them ideal for practical applications. Furthermore, a radical quenching experiment was conducted, and a plausible catalytic mechanism was proposed. This research presents a new approach for the fabrication of micro-structured ceramic beads that are capable of effectively overcoming the diffusion limitations encountered in both heterogeneous reactions and adsorption processes

Two-Dimensional Coupled Wave Theory for Triangular Lattice TM-polarised Photonic Crystal Surface Emitting Lasers

This paper presents a coupled-wave analysis of triangular-lattice photonic crystal surface emitting lasers (PCSELs) with transverse magnetic polarization. Six plane waves coupled by Bragg diffraction describe the two-dimensional optical coupling. Resonant mode frequencies are calculated for a lattice of circular holes at various fill factors and compared to the plane-wave expansion method. Analytical equations for coupling constants and mode frequencies are derived, and mode degeneracy as a function of fill factor is examined. Comparision to a square lattice TM mode PCSEL shows improved in-plane 2D coupling. The general equations for arbitrary unit cell dielectric functions are discussed, with predictions of the lasing mode supported by finite device calculations

Short-term gain, long-term loss: Exploring the effects of Covid-19 survival strategies on rural livelihoods and the agrarian economy

In this paper, we explore how the practices of agricultural chain actors within the contingencies of the Covid-19 crisis, may have contributed to precarious rural livelihoods and the agrarian economy. Developing our contribution in the context of Ghana’s agricultural sector, which is grappling with socio-economic and sustainability challenges such as land degradation, climate change, and biodiversity loss, we identified salient survival practices in the actions adopted during the Covid-19 pandemic which resulted in short-term gain, but also accounted for the long-term intractable decline in production and for producers’ wellbeing. Explicating a fine analysis of how individual practices induced by the pandemic may have contributed to foster a decline in the agrarian economy, our study goes on to shed light on the devastating outcomes of the pandemic on rural livelihoods and the agrarian economies often marked by weak institutions and underdeveloped markets

Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series

Background: Anomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decision-making, helping to identify issues like operational inefficiencies, fraud and emerging health complications. Objectives: This study presents a novel method for detecting both collective and individual anomalies in healthcare data through time series analysis using unsupervised machine learning. The dual-strategy approach leverages two methodologies: a ’practice centre-based approach’ which monitors changes across different practice centres and a ’process-based approach’ which focuses on identifying anomalies within individual centres. The former allows for early detection of systemic issues, while the latter highlights specific irregularities within a centre's operations. Methods: The study utilised a dataset over 500,000 medical records from multiple GP practice centres in the UK collected between 2018–2023. Data are clustered using DBSCAN to identify collective anomalies from deviations from linear trends in consecutive two-month scatterplots. Individual anomalies are identified by examining the SOM-clustered time series of various medical processes within a specific practice centre, where graphs show deviation from the typical pattern. Findings: Our approach addresses some challenges posed by the complexity and sensitivity of healthcare data by not requiring personal information. The method offers accurate visual representations making the data accessible and interpretable for non-technical users. Unlike traditional methods focusing solely on subsequence anomalies, our technique analyses the collective behaviour across multiple time series providing a more comprehensive perspective. Conclusion: This study underscores the importance of integrating unsupervised anomaly detection with clinical expertise to ensure that statistically anomalous patterns align with clinical relevance. The dual-strategy clustering method holds significant potential for enabling timely interventions, proactively identifying potential crises, and ultimately contributing to better decision-making and operational efficiency within the healthcare sector