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Use of Bayesian Networks to understand sustainability requirements
Get PDFSustainability is a key requirement in contemporary engineering design, but it is difficult to quantify due to its multidimensionality. We propose the application of Bayesian Networks for modeling the cause and effect of engineering systems and their environment. Emphasis is placed on capturing the impact on sustainability indicators of design decisions. These include the performance of the system, its economic viability in terms of cost, and its environmental and societal impacts. The method leverages data from simulation models, enabling the designer to perform assumption-free inferences on the variables at play.This research was funded by Aerospace Technology Institute grant number 10003388.14th EASN International ConferenceEngineering Proceeding
Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization
Get PDFThis paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.King Saud University; JCYJ20190806160218174This work was supported in part by the National Natural Science Foundation of China under Grants 62271399 and 62206221, in part by National Key Research and Development Program of China under Grant 2020YFB1807003, in part by Foundation of the Science, Technology, and Innovation Commission of Shenzhen Municipality under Grant JCYJ20190806160218174, in part by Zhejiang Provincial Natural Science Foundation of China under Grant LQ24F010003, in part by the Distinguished Scientist Fellowship Program (DSFP) at King Saud University, Riyadh, Saudi Arabia, and in part by the Bournemouth University Qualiy research funding: Flying ad-hoc networking and its applications.GLOBECOM 2024 - 2024 IEEE Global Communications Conferenc
Multisensory design in memory research: the £1 coin case in the digital era
Get PDFThis study explores the effects of multisensory memory on memory for everyday objects, with a particular focus on memory for £1 coins. The study delves into the intersection of sensory anthropology, sensory history, and sensory sociology to examine how multisensory experiences affect memory persistence. The study used a dual-task paradigm and cross-modal stimuli to investigate the effectiveness of different sensory combinations in enhancing memory. Post-epidemic era, unlike offline experiences, this experiment utilised an online survey and a variety of media formats including text, images, video, audio and physical objects. The results showed that multisensory interactions significantly improved short-term memory recall over single-sensory modalities, while visual elements such as colours and shapes had a lasting effect on long-term memory. The study also highlights the potential of multisensory engagement in educational environments and museum experiences, gathering reliable data for future projects in which computers simulate human behaviour.2024 International Symposium on Design Studies and Intelligence Engineering (DSIE-2024)Design Studies and Intelligence Engineerin
A comparative analysis of circular economy practices in Saudi Arabia
No full textThe rise in urbanisation and resource consumption has highlighted the urgent need for sustainable economic models. The traditional linear economy, which relies heavily on non-renewable resources, exceeds the Earth’s capacity and poses significant sustainability challenges. As a result, there is an increasing necessity to transition towards a circular economy (CE) as a more sustainable alternative. Saudi Arabia, one of the world’s largest economies, is striving to implement this shift due to considerable environmental and economic challenges. However, the country currently lacks a dedicated circular economy strategy, which hinders its efforts to address issues such as waste management and excessive consumption. To bridge this gap, a comprehensive framework was developed to assess and compare Saudi Arabia’s circular economy initiatives, strategies, and policies with those of China, Japan, and Europe. Data were collected and analysed using thematic analysis, allowing for the identification of key similarities and differences between these regions. The study revealed notable variations in policies and practices, highlighting best practices that Saudi Arabia could adopt to strengthen its sustainability efforts. The findings underscore the importance of incorporating global best practices while tailoring strategies to the Kingdom’s specific needs. Policymakers and researchers in Saudi Arabia can utilise these insights to support a more effective transition towards a circular economy. Future research could adopt a quantitative approach, using indicators and metrics to enhance the impact of these findings.Sustainabilit
Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: an advanced approach toward sustainable and carbon-neutral wastewater treatment
Get PDFIntegrating carbon capture and utilization (CCU) technologies into wastewater treatment plants (WWTPs) is essential for mitigating greenhouse gas (GHG) emissions and enhancing environmental sustainability, but further advancements in process monitoring and control are critical to optimizing treatment performance. This study investigates the application of artificial intelligence (AI) modeling to enhance process monitoring and control in a novel integrated CCU biotechnology with a moving bed biofilm reactor (MBBR) sequenced with an algal photobioreactor (aPBR). This system reduces GHG and odour emissions simultaneously. Several machine learning (ML) models, including artificial neural networks (ANNs), support vector machines (SVM), random forest (RF), and least-squares boosting (LSBoost), were tested. The LSBoost was the most suitable for modeling the MBBR + aPBR system, exhibiting the highest accuracy in predicting CO2 (R2 = 0.97) and H2S (R2 = 0.95) emissions from the MBBR. LSBoost also achieved the highest accuracy for predicting CO2 (R2 = 0.85) and H2S (R2 = 0.97) outlet concentrations from the aPBR. These findings underscore the importance of aligning AI algorithms to the characteristics of the treatment technology. The proposed AI models outperformed conventional statistical methods, demonstrating their ability to capture the complex, nonlinear dynamics typical of processes in environmental technologies. This study highlights the potential of AI-driven monitoring and control systems to significantly improve the efficiency of CCU biotechnologies in WWTPs for climate change mitigation and sustainable wastewater management.This work was supported by the University of Salerno through FARB projects (300393FRB22OLIVA, 300393FRB22NADDE, 300393FRB23NADDE, 300393FRB22ZARRA). Additionally, the outcomes of this study have benefited from insights and developments within the SPORE-MED project, part of the PRIMA program funded by the European Union (Agreement 2322).Chemospher
Phytochemical profiling and anti‐bacterial activity of Red Delicious apple pomace with integrated hydrolysis to obtain fermentable sugars: a biorefinery approach
Get PDFApple pomace (AP) is a byproduct of juice processing, rich in nutritionally important compounds like carbohydrates, phenolic compounds, dietary fiber, and minerals. It is a potential feedstock for sugar‐based biorefineries. This study explored AP physicochemical and bioactive compounds and their hydrolysis to extract fermentable sugar. Results showed that AP had a below‐neutral pH, moisture, acidity, and ash. The AP contained crude fiber (27.22%), total sugar (36.75%), and reduced sugar (12.22%). The extracts contained minerals like potassium, calcium, magnesium, aluminum, iron, boron, and zinc. A GC–MS study analyzed the phytochemicals present in AP extracts, revealing prominent antibacterial activity. The optimal conditions for enzymatic hydrolysis were found to be 1 mg/g substrate of cellulase and pectinase at 50°C and pH 5.0 at 24 h of incubation. Enzymatically hydrolyzed AP showed a high yield of reducing sugar (38.33%) compared to non‐hydrolyzed AP (12.22%). The study suggests that AP, currently discarded as industrial bio‐waste, is still a source of phytochemicals with significant antioxidant and antibacterial activities.The work was supported by the institutions in EthiopiaFood Science & Nutritio
Facile supersaturation control strategies for regulating nucleation and crystal growth in membrane crystallisation
No full textThe integration of crystallisation into membrane distillation has been proposed for brine mining as a method to effectively control primary nucleation which uses the excess supersaturation to create a new crystal phase. However, primary nucleation desaturates the solvent, subsequently introducing competition between crystal growth and nucleation mechanisms, which must be regulated to achieve a high yield and good product quality. This study therefore investigated supersaturation control strategies that can regulate nucleation and crystal growth mechanisms in membrane distillation crystallisation (MDC) following induction. Membrane area was used to adjust supersaturation, as this can modify kinetics without introducing changes to mass and heat transfer within the boundary layer. An increase in concentration rate shortened induction time and raised supersaturation at induction. This broadened the metastable zone width, and reduced scaling, due to the increased supersaturation driving force which favours a homogeneous primary nucleation pathway. Modulating supersaturation also repositioned the system within specific regions of the metastable zone that can favour crystal growth versus primary nucleation. Scaling was further mitigated using in-line filtration to ensure crystal retention within the crystalliser to reduce deposition. This permitted a consistent supersaturation rate to be sustained, enabling a longer hold-up time following induction. Population balance confirmed a reduction in nucleation rate with longer hold-up times, due to the desaturation of the solvent caused by crystal growth which resulted in larger crystal sizes. Through segregating the crystal phase into the bulk solution, growth can be more closely controlled to improve habit, shape and purity, independent of nucleation, due to the development of two discrete regions of supersaturation. The supersaturation control strategies described herein are unique to MDC and address the acknowledged challenge of supersaturation control within existing industrial evaporative crystalliser design.This research was financially supported by the European Research Council Starting Grant, ‘Sustainable chemical alternatives for reuse in the circular economy’ (StG, SCARCE, 714080)Separation and Purification Technolog
Development and optimisation of ex situ portable X-ray fluorescence spectroscopy for heterogenous post-metallurgical sites
Get PDFPortable X-ray fluorescence spectroscopy (pXRF) is widely used for rapid measurement of metals in soils, however, thorough evaluation of common pre-processing methods and their effectiveness is limited. This study addresses processing methods using samples collected at a highly heterogeneous post-metallurgical site containing, basic oxygen steelmaking (BOS) slag and soil; the former being an important source of potentially toxic and valuable elements. Impact of pre-treatment processes, (sieving, drying, grinding, sample vessel, and ignition) on the accuracy of pXRF measurements was compared against reference ICP-MS measurements.Of the twelve elements detected, four showed qualitative (Cr and Fe r2 ≥ 0.60, RSD ≤ 30%) or quantitative (Mn and Ca r2 ≥ 0.70, RSD ≤ 20%) measurements for raw samples. Improving to six elements after pre-processing (Sr qualitative, and Pb, Cr, Mn, Ca, Fe quantitative). Sieving and grinding improved precision (average RSD fell by 7.17 and 8.37% respectively), while drying and grinding enhanced accuracy (average r2 increased by 0.03 and 0.10 respectively). This study provides the first evidence that organic matter does not significantly impact pXRF accuracy or precision (average r2 and RSD changed by zero and − 0.32%, respectively). The two distinct matrices (BOS slag and soil) on-site resulted in a bimodal concentration distribution and a negative correlation for Ti. Importantly, this research proposes that not all common pre-processing steps are necessary to generate high-quality data due to their negligible impact on accuracy or precision (such as incineration to remove organic matter), thereby increasing the speed and reducing the cost of data collection.This research was funded by the European Regional Development Fund as part of the Interreg Northwest Europe project “Regeneration of past metallurgical sites and deposits through innovative circularity for raw materials” (REGENERATIS) (NWE918).Environmental Geochemistry and Healt
Digital transformation framework for the egyptian manufacturing industry
Get PDFDigital transformation is of crucial importance in the manufacturing industry, especially
after the COVID-19 pandemic because of the increasing need for remote working and
socially distanced workplaces. However, there is a lack of a clear and well-defined
process to implement digital transformation in manufacturing. This research aims to
develop an innovative framework to support digital transformation in the manufacturing
sector and define its key assessment criteria for its successful implementation.
This research contributes to the body of literature by creating a prioritised DT
implementation method that is specifically customised for the manufacturing industry in
Egypt. Additionally, this study considers the majority of DT process implementation
factors, including leadership support, human factors, and digital technologies. A mixed
research methodology approach has been employed in this research. Firstly, an
extensive literature review related to digital transformation in the manufacturing industry
was conducted. Secondly, a mixed method techniques were implemented to gather the
required data from experts who are involved in digital transformation projects in their
companies. Validation of the results was carried out using real life industrial case
studies and experts judgment.
The digital transformation process comprises eight stages covering technology,
management, communications, and customer elements. The main contribution of this
research work is the balance between the different elements of digital transformation –
digital technologies, leadership and strategy, people and business processes – to
create an integrated 8-step process of digital transformation in the manufacturing sector
of developing economies such as the Egyptian economy.
A new readiness assessment model was developed to measure the degree of
readiness of manufacturing organisations to begin and develop their digital
transformation processes. The model uses two dimensions as assessment criteria
namely, Business Competencies Dimension and Technology Competencies Dimension.
In addition, a proposed model of four main attributes and 19 sub-attributes was
developed to determine the most important attributes regarding digital leadership given
the special nature of developing economies and cultural and environmental changes in
the Egyptian economy. The results indicated that an innovation mindset is the most
important attribute to becoming a successful digital leader, followed by a digital
business mindset, then the transformational mindset, and finally the inspirational
mindset.
Digital Transformation processes are unique and different from one case to another.
Helping manufacturing organisations assess their readiness for Digital Transformation
processes from a strategic level helps them better understand their capabilities and
draw their vision based on this assessment.PhD in Manufacturin
A contactless human vital sign monitoring system using a Doppler radar
Get PDFTraditional vital signs monitoring methods are associated with certain limitations, such as requiring direct skin contact, causing potential distractions from sensors, and being susceptibility to electromagnetic interferences. Non-contact measurement methods have attracted growing interest but existing solutions face challenges in terms of working distance, accuracy, lack of temporal resolution, and applications in real-time heartbeat detection. This study introduces a novel non-contact vital sign monitoring system based on a K-band radar with dedicated algorithms for demodulation, parametric filter design, and self-adapting real-time heartbeat detection and extraction. This system provides precise and reliable per-beat heartrate detection and measurement, under a wide range of distances and working conditions, achieving superior performances and functionalities compared to existing contact-based implementations. By comparing with photoplethysmography and phonocardiogram sensors, the proposed system achieved real-time accurate per-beat heart rate measurement, with a relative root mean squared error of < 5 % for working distance of up to 3.2 m. Additionally, it can detect apneas and abnormal breathing activities, while also generating phonocardiogram and vibrography signals as a contactless virtual stethoscope. We anticipate that the solution will enable a wide range of applications including critical personnel monitoring, home and healthcare surveillance, self-assisted public health devices, and rescue operations.Biomedical Signal Processing and Contro