20505 research outputs found
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Methodological advances in diatom research for aquatic biomonitoring and water quality evaluation
No full textDiatoms, a unique class of microalgae, play a pivotal role in global ecosystems due to their photosynthetic capabilities and adaptability to diverse aquatic environments. Composed of opaline silica, they serve as sensitive indicators for water quality monitoring. This review covers various diatom research techniques, including sampling and isolation. It highlights methods for different substrata, such as epiphyton, epipelon, and epipsammon, as well as traditional isolation approaches like agar plate methods and serial dilution. Automation techniques, such as flow cytometry (FACS), are also discussed. The review emphasizes the potential of diatoms for pollutant removal, particularly heavy metals, and presents various diatom-based indices for water quality assessment. Future research is encouraged to refine isolation techniques, enhance the effectiveness of indices, and explore biochemical pathways that could improve diatoms’ bioremediation capabilities, thereby fully leveraging their potential for environmental monitoring and remediation.Financial support for this study had been provided by ASEAN-India Science, Technology & Innovation Cooperation, AISTDF Secretariat, Department of Science & Technology Government of India, India [Grant number CRD/2022/000595].Archives of Microbiolog
Defect depth estimation using through-transmission pulsed thermography: a numerical and experimental investigation
No full textThrough-transmission pulsed thermography is widely recognised for offering higher defect resolution than reflection mode, yet its development has been hindered by challenges such as quantifying defect depth. This study addresses the depth quantification gap by introducing a novel depth estimation technique based on the relationship between defect depth and the Fourier number. The method is validated through both finite element modelling and laboratory experiments using calibration samples with embedded air-gap defects at known depths. Results show that depth estimation accuracy improves as defects approach the backwall, consistently across both simulation and experimental environments. Finite element analysis also demonstrates that the proposed technique outperforms the log second derivative method typically used in reflection mode. These findings advance the capability of through-transmission thermography for precise subsurface defect characterisation.This research was performed with the help of the EPSRC platform grant (grant number EP/P027121/1)The authors of this paper would also like to thank the Cranfield Industrial Partnership PhD Scholarships Scheme (CIPPS), Cranfield University and Sun resources for co-funding this research.International Journal of Thermal Science
Improving ADM1 predictions via Bayesian analysis for continuous anaerobic digestion
No full textThe Anaerobic Digestion Model No.1 (ADM1) application for continuous anaerobic digestion is often constrained by challenges in reliably calibrating model parameters, especially when long-term data are unavailable. This study presents a Bayesian inference-based framework that enables ADM1 calibration using only initial-stage digester performance data. A custom Python implementation was developed, integrating modules for global sensitivity analysis, Bayesian calibration and parameter identifiability evaluation. Key microbial and ionic parameters were refined through Random Balance Designs–Fourier Amplitude Sensitivity Test (RBD-FAST), identifying sugar/acetate degraders and cation/anion levels in the inoculum as critical drivers of steady-state performance.
With informative priors derived from similar ADM1 studies, the model was calibrated with less than two hydraulic retention times of data and validated against steady-state performance data. It predicted pH and total chemical oxygen demand (tCOD) with mean percentage errors of 1.10 % and 5.38 % respectively. Biogas production trends were captured within the 95 % credible interval for 63.14 % of observations. Compared to uniform priors, the Bayesian approach with informative priors improved predictive accuracy. Jensen-Shannon divergence revealed that hydrolysis rates is the most identifiable for thermally hydrolysed sludge.
Unlike conventional ADM1 calibration approaches that require long-term steady-state data, this Bayesian framework achieves reliable predictions using only early-stage observations. By enabling accurate simulation of organic contaminant degradation and system stability from limited data, the framework supports risk-informed design and operation of anaerobic digesters and offers a solution for data-scarce industrial settings to improve safety, sustainability, and optimisation.Journal of Environmental Managemen
Warehousing 5.0 for the future of the logistics industry
No full textSpecial edition: Warehousing 5.0 for the Future of the Logistics IndustryThis editorial introduces and contextualises the International Journal of Production Research Special Issue on ‘Warehousing 5.0 for the Future of the Logistics Industry’. Building on the principles of Industry 5.0, the concept of Warehousing 5.0 redefines warehouse operations as human-centric, intelligent, sustainable, and resilient systems. It emphasises the integration of advanced automation and analytics with human well-being, environmental stewardship, and data responsibility–shifting the focus from efficiency alone to a balanced socio-technical paradigm. The Special Issue received 45 submissions, from which 12 papers were accepted after rigorous peer review. Together, these studies advance understanding across four interconnected themes: (T1) Human Factors and Human-Centric Design, (T2) Optimisation and Efficiency in Robotics and Automation, (T3) Energy Efficiency and Sustainable Operations, and (T4) Data-Driven and AI-Enabled Warehousing. The contributions highlight innovations in ergonomic design, collaborative robotics, energy-aware scheduling, stochastic and multi-objective optimisation, wearable sensing, and AI-enabled vision systems, demonstrating how operational efficiency can coexist with human welfare and environmental responsibility. Synthesising across these themes, the editorial identifies key insights on human–technology symbiosis, sustainable digitalisation, and cyber-physical-social integration in warehouses. It also outlines future research directions on adaptive human–robot collaboration, circular logistics, responsible AI, and integrative modelling. The practical and policy implications discussed provide a framework for managers and decision-makers to implement Warehousing 5.0 principles effectively. Collectively, the Special Issue contributes to shaping a new generation of resilient, sustainable, and human-aware warehouses, reinforcing IJPR's leadership in advancing innovative and responsible production and logistics systems.International Journal of Production Researc
Guardrailing LLM and agentic decisions for 6G AI-RAN
No full textLarge language model (LLM)-based agents are envisioned as cornerstones for autonomous, zero-touch 6G AI-RAN operations. Numerous frameworks adopt LLM-based agents as decision-makers to optimize network configurations, orchestrate resources, and interact with users and connected use cases. However, intrinsic limitations (hallucinations, misaligned human values) and extrinsic adversarial threats (jail-breaks, prompt injections) pose critical risks to network safety, reliability, and privacy—challenges largely overlooked in existing literature. This paper addresses this gap by reviewing state-of-the-art guardrail techniques for 6G AI-RAN. We categorize guardrails across model-level and agent-level layers and map them to common agent application patterns in 6G networks, providing practical foundations for designing trustworthy agentic decision-making frameworks in future 6G AI-RAN systems.This work is supported by EPSRC CHEDDAR: Communications Hub For Empowering Distributed ClouD Computing Applications And Research (EP/X040518/1) (EP/Y037421/1)2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC
Data "Melt pool geometry control of Ti-6Al-4V utilizing multi-energy source laser-arc + wire directed energy deposition."
No full textExcel .xml providing deposition parameters and geometry measurements accompanied with .tif and .jpeg imagesThis study investigates the applicability of a novel laser-arc multi-energy deposition of Ti-6Al-4V with independent control of bead geometry and thermal input. A plasma transferred arc is used to generate an initial melt pool and melt wire feedstock, before controlled lateral elongation of the melt pool via a fiber laser and galvo scanner. Using previously identified published processing parameters for mild steel deposition, the applicability to Ti-6Al-4V was first investigated. Once successful bead geometry control was achieved, process parameters more conducive to additive manufacturing were investigated. This included investigation of the energy per unit area required to achieve accurate deposition of Ti-6Al-4V with minimal penetration and investigation into scanning strategy. In each case, optical microscopy was conducted and analysis of the bead geometry, penetration and heat-affected zone considered to determine the effect of each parameter change. The results demonstrated that independent control of bead geometry and thermal input could be achieved, allowing deposition of Ti-6Al-4V at a desired scan width and layer height and providing a framework for future multi-energy source directed energy deposition of Ti-6Al-4V.Engineering and Physical Sciences Research Council (EPSRC
Industrial case study of aerial systems using ray-tracing and antenna optimisation
No full textAn industrial case study of Unmanned Aerial System (UAS) operation and communications for integrated satellite-terrestrial networks in remote regions is considered. The objective is to evaluate and optimize the Quality of Service (QoS) of communication networks that combine satellite backhaul and ground-based transmission infrastructure for UAS operations. To address this challenge, this paper proposes a new algorithm for antenna tilt optimization procedure aiming to enhance terrestrial coverage, and maximise average Received Signal Strength Indicator (RSSI) along predefined UAS paths using terrain-aware ray-tracing models. A preliminary analysis of the satellite link is performed using simulation-based model of phased array terminal, assessing its suitability for Beyond Visual Line of Sight (BVLOS) operations through latency and RSSI profiling under variable link conditions. In addition, the study quantifies the QoS of the terrestrial network by analyzing RSSI, Signal-to-Interference-plus-Noise Ratio (SINR), latency, and throughput across UAS routes between key islands. The findings highlight the effectiveness of hybrid satellite-terrestrial architectures in extending coverage and reliability for critical UAS operations in geographically challenging environments. This work informs future network planning strategies for remote UAS deployments.This work is supported by Connectivity for Remote Orkney Future Transport (CROFT) project, which is funded by the European Space Agency (ESA) under the Advanced Research in Telecommunications Systems (ARTES) program2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC
AI-driven 5G networks for autonomous positioning system platform
No full textUnmanned Aerial Vehicles (UAVs) are becoming essential for various urban applications, such as surveillance, delivery, logistics, disaster management, and traffic
monitoring. However, their positioning performance in urban environments can be
limited due to challenges such as non-line-of-sight (NLOS) propagation, multipath
interference, and signal blockage caused by tall buildings, trees, and other obstacles.
These factors lead to reduced positioning accuracy and unreliable communication. To
address these issues, this thesis introduces three key and novel contributions. First,
it presents one of the first real-world evaluations of the 5G network performance for
UAV operations at altitudes between 50 and 110 meters, using XCAL-based field trials. This provides new insights into the altitude-dependent Quality of Service (QoS)
parameters such as latency, throughput, and handover (HO) efficiency and provides
practical recommendations for UAV-specific connectivity protocols. Second, a novel
hybrid positioning framework is proposed that integrates the observed time difference of arrival (OTDOA) of the new 5G radio (NR) with the fusion of sensor and
barometric pressure sensor through an Extended Kalman Filter (EK). This combination significantly improves positioning accuracy (2.8–7 m) in GNSS GNSS-challenged
urban environment, which has not been demonstrated in prior UAV studies. Third,
the thesis introduces a lightweight feedforward neural network (FNN) for mitigating
NLOS errors in 5G-based UAV positioning. Trained on simulated MATLAB data, the
model corrects time-of-arrival (TOA) measurements in real time, reducing positioning error to 1.3 m in LOS and 1.7 m in NLOS, outperforming conventional methods.
Unlike existing solutions, this model is designed for real-time deployment on UAV
platforms with limited resources. Overall, this research strengthens UAV navigation
and connectivity in urban airspace by combining 5G advancements, sensor fusion,
and AI-powered error correction. The novelty lies in the integration of real-world 5G
performance analysis, a hybrid OTDOA sensor fusion framework, and an AI-based
NLOS correction model into a unified solution for reliable, accurate, and scalable Urban Air Mobility (UAM), opening the door to future improvements in AI-driven 5G
networks for autonomous system platforms.PhD in Aerospac
Dataset: Using model compounds to show how a change in thinking is required for regulation of brominated haloacetic acids in drinking water
No full textThis project looks at the Disinfection By-Products (DBPs) formed from model compounds during their chlorination with bromide present. This data set is the results obtained from the analysis for trihalomethanes (THMs) and haloacetic acids (HAAs).UK Water Industry Research (UKWIR)EP/5023666/
Dataset: Cladding fragment impact prediction using material properties
No full textTerrorist attacks involving improvised explosive devices (IEDs) have more recently included fragmentation such as nuts, bolts and ball bearings. Under theTerrorism (Protection of Premises) Act 2025, building owners must incorporate public protection procedures and to do so correctly they must understand how the building would perform against an attack. As more external cladding products come onto the market ranging from ceramics and timber products tofibre cement boards and high-pressure laminates, there is a risk that not all cladding will protect occupants from blast driven fragmentation. This research assesses the suitability of standard laboratory material tests to predict the V50 ballistic limit, a measure of penetration resistance. Plastic impact testing wasnot suitable for the full set of materials, flexural strength showed moderate correlation and two types of hardness testing Mohs and Shore D were the most accurate predicters when combined with material groupings (ceramic, fibrous and non-fibrous). A validation exercise with seven additional non-ceramic products demonstrated Shore D prediction errors of up to 26%. This supports the potential utility of the simple test for comparing and estimating penetration resistance of external cladding products. It is recommended that further testing on a wider variety of materials is completed to build the dataset and refine the regression equations.British Arm