Showing papers by "Northumbria University published in 2021"

Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions

Abstract: Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs) are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/ or energy efficiency of wireless systems. These devices are capable of reconfiguring the wireless propagation environment by carefully tuning the phase shifts of a large number of low-cost passive reflecting elements. In this article, we aim to answer four fundmental questions: 1) Why do we need RISs? 2) What is an RIS? 3) What are RIS's applications? 4) What are the relevant challenges and future research directions? In response, eight promising research directions are pointed out.

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya

Abstract: On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis.

Abstract: BACKGROUND Sarcopenia is defined as the loss of muscle mass and strength. Despite the seriousness of this disease, a single diagnostic criterion has not yet been established. Few studies have reported the prevalence of sarcopenia globally, and there is a high level of heterogeneity between studies, stemmed from the diagnostic criteria of sarcopenia and the target population. The aims of this systematic review and meta-analysis were (i) to identify and summarize the diagnostic criteria used to define sarcopenia and severe sarcopenia and (ii) to estimate the global and region-specific prevalence of sarcopenia and severe sarcopenia by sociodemographic factors. METHODS Embase, MEDLINE, and Web of Science Core Collections were searched using relevant MeSH terms. The inclusion criteria were cross-sectional or cohort studies in individuals aged ≥18 years, published in English, and with muscle mass measured using dual-energy x-ray absorptiometry, bioelectrical impedance, or computed tomography (CT) scan. For the meta-analysis, studies were stratified by diagnostic criteria (classifications), cut-off points, and instruments to assess muscle mass. If at least three studies reported the same classification, cut-off points, and instrument to measure muscle mass, they were considered suitable for meta-analysis. Following this approach, 6 classifications and 23 subgroups were created. Overall pooled estimates with inverse-variance weights obtained from a random-effects model were estimated using the metaprop command in Stata. RESULTS Out of 19 320 studies, 263 were eligible for the narrative synthesis and 151 for meta-analysis (total n = 692 056, mean age: 68.5 years). Using different classifications and cut-off points, the prevalence of sarcopenia varied between 10% and 27% in the studies included for meta-analysis. The highest and lowest prevalence were observed in Oceania and Europe using the European Working Group on Sarcopenia in Older People (EWGSOP) and EWGSOP2, respectively. The prevalence ranged from 8% to 36% in individuals <60 years and from 10% to 27% in ≥60 years. Men had a higher prevalence of sarcopenia using the EWGSOP2 (11% vs. 2%) while it was higher in women using the International Working Group on Sarcopenia (17% vs. 12%). Finally, the prevalence of severe sarcopenia ranged from 2% to 9%. CONCLUSIONS The prevalence of sarcopenia and severe sarcopenia varied considerably according to the classification and cut-off point used. Considering the lack of a single diagnostic for sarcopenia, future studies should adhere to current guidelines, which would facilitate the comparison of results between studies and populations across the globe.

The Miocene: The Future of the Past

Abstract: The Miocene epoch (23.03–5.33 Ma) was a time interval of global warmth, relative to today. Continental configurations and mountain topography transitioned towards modern conditions, and many flora and fauna evolved into the same taxa that exist today. Miocene climate was dynamic: long periods of early and late glaciation bracketed a ∼2 Myr greenhouse interval – the Miocene Climatic Optimum (MCO). Floras, faunas, ice sheets, precipitation, pCO2, and ocean and atmospheric circulation mostly (but not ubiquitously) covaried with these large changes in climate. With higher temperatures and moderately higher pCO2 (∼400–600 ppm), the MCO has been suggested as a particularly appropriate analogue for future climate scenarios, and for assessing the predictive accuracy of numerical climate models – the same models that are used to simulate future climate. Yet, Miocene conditions have proved difficult to reconcile with models. This implies either missing positive feedbacks in the models, a lack of knowledge of past climate forcings, or the need for re‐interpretation of proxies, which might mitigate the model‐data discrepancy. Our understanding of Miocene climatic, biogeochemical, and oceanic changes on broad spatial and temporal scales is still developing. New records documenting the physical, chemical, and biotic aspects of the Earth system are emerging, and together provide a more comprehensive understanding of this important time interval. Here we review the state‐of‐the‐art in Miocene climate, ocean circulation, biogeochemical cycling, ice sheet dynamics, and biotic adaptation research as inferred through proxy observations and modelling studies.

Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

Abstract: An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV’ UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs’ trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.

A Multitier Deep Learning Model for Arrhythmia Detection

Abstract: An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient–doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected.

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Abstract: The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electrical Conductivity (EC), Nitrate-Nitrogen ( NO 3 -N), Nitrite-Nitrogen ( NO 2 -N), Phosphate ( P O 4 3 - ), potential for Hydrogen (pH), Temperature (T) and Turbidity (TUR) are used for building the prediction models. Various input data combinations are investigated and assessed in terms of prediction performance, using numerical indices and graphical comparisons. The analysis shows that the ETR model generally produces more accurate WQI predictions for both training and testing phases. Although including all the ten input variables achieves the highest prediction performance ( R 2 t e s t = 0.98 , R M S E t e s t = 2.99 ), a combination of input parameters including only BOD, Turbidity and Phosphate concentration provides the second highest prediction accuracy ( R 2 t e s t = 0.97 , R M S E t e s t = 3.74 ). The uncertainty analysis relative to model structure and input parameters highlights a higher sensitivity of the prediction results to the former. In general, the ETR model represents an improvement on previous approaches for WQI prediction, in terms of prediction performance and reduction in the number of input parameters.

Short-Term Self-Scheduling of Virtual Energy Hub Plant Within Thermal Energy Market

Abstract: Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs’ scheduling becomes more prominent. In this article, a new approach to energy hubs’ scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach.

UAV-Assisted and Intelligent Reflecting Surfaces-Supported Terahertz Communications

Abstract: In this letter, unmanned aerial vehicles (UAVs) and intelligent reflective surface (IRS) are utilized to support terahertz (THz) communications. To this end, the joint optimization of UAV’s trajectory, the phase shift of IRS, the allocation of THz sub-bands, and the power control are investigated to maximize the minimum average achievable rate of all users. An iteration algorithm based on successive Convex Approximation with the Rate constraint penalty (CAR) is developed to obtain UAV’s trajectory, and the IRS phase shift is formulated as a closed-form expression with introduced pricing factors. Simulation results show that the proposed scheme significantly enhances the rate performance of the whole system.

Fused Deposition Modelling: Current Status, Methodology, Applications and Future Prospects

Abstract: This is an accepted manuscript of an article published by Elsevier in Additive Manufacturing on 02/10/2021, available online: https://doi.org/10.1016/j.addma.2021.102378 The accepted version of the publication may differ from the final published version.

Interface Engineering via Ti3C2Tx MXene Electrolyte Additive toward Dendrite-Free Zinc Deposition.

Abstract: Zinc metal batteries have been considered as a promising candidate for next-generation batteries due to their high safety and low cost. However, their practical applications are severely hampered by the poor cyclability that caused by the undesired dendrite growth of metallic Zn. Herein, Ti3C2Tx MXene was first used as electrolyte additive to facilitate the uniform Zn deposition by controlling the nucleation and growth process of Zn. Such MXene additives can not only be absorbed on Zn foil to induce uniform initial Zn deposition via providing abundant zincophilic-O groups and subsequently participate in the formation of robust solid-electrolyte interface film, but also accelerate ion transportation by reducing the Zn2+ concentration gradient at the electrode/electrolyte interface. Consequently, MXene-containing electrolyte realizes dendrite-free Zn plating/striping with high Coulombic efficiency (99.7%) and superior reversibility (stably up to 1180 cycles). When applied in full cell, the Zn-V2O5 cell also delivers significantly improved cycling performances. This work provides a facile yet effective method for developing reversible zinc metal batteries.

Entrepreneurial ecosystems: a dynamic lifecycle model

Abstract: The concept of entrepreneurial ecosystems has been used as a framework to explain entrepreneurial activities within regions and industrial sectors. Despite the usefulness of this approach, the concept is under-theorized, especially with regard to the evolution of entrepreneurial ecosystems. The current literature is lacking a theoretical foundation that addresses the development and change of entrepreneurial ecosystems over time and does not consider the inherent dynamics of entrepreneurial ecosystems that lead to their birth, growth, maturity, decline, and re-emergence. Taking an industry lifecycle perspective, this paper addresses this research gap by elaborating a dynamic entrepreneurial ecosystem lifecycle model. We propose that an ecosystem transitions from an entrepreneurial ecosystem, with a focus on new firm creation, towards a business ecosystem, with a core focus on the internal commercialization of knowledge, i.e., intrapreneurial activities, and vice versa. Our dynamic model thus captures the oscillation that occurs among entrepreneurs and intrapreneurs through the different phases of an ecosystem’s lifecycle. Our dynamic lifecycle model may thus serve as a starting point for future empirical studies focusing on ecosystems and provide the basis for a further understanding of the interrelatedness between and co-existence of new and incumbent firms.

Data Augmentation and Dense-LSTM for Human Activity Recognition Using WiFi Signal

Abstract: Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.

Hierarchical Honeycomb-Structured Electret/Triboelectric Nanogenerator for Biomechanical and Morphing Wing Energy Harvesting

Abstract: Flexible, compact, lightweight and sustainable power sources are indispensable for modern wearable and personal electronics and small-unmanned aerial vehicles (UAVs). Hierarchical honeycomb has the unique merits of compact mesostructures, excellent energy absorption properties and considerable weight to strength ratios. Herein, a honeycomb-inspired triboelectric nanogenerator (h-TENG) is proposed for biomechanical and UAV morphing wing energy harvesting based on contact triboelectrification wavy surface of cellular honeycomb structure. The wavy surface comprises a multilayered thin film structure (combining polyethylene terephthalate, silver nanowires and fluorinated ethylene propylene) fabricated through high-temperature thermoplastic molding and wafer-level bonding process. With superior synchronization of large amounts of energy generation units with honeycomb cells, the manufactured h-TENG prototype produces the maximum instantaneous open-circuit voltage, short-circuit current and output power of 1207 V, 68.5 μA and 12.4 mW, respectively, corresponding to a remarkable peak power density of 0.275 mW cm−3 (or 2.48 mW g−1) under hand pressing excitations. Attributed to the excellent elastic property of self-rebounding honeycomb structure, the flexible and transparent h-TENG can be easily pressed, bent and integrated into shoes for real-time insole plantar pressure mapping. The lightweight and compact h-TENG is further installed into a morphing wing of small UAVs for efficiently converting the flapping energy of ailerons into electricity for the first time. This research demonstrates this new conceptualizing single h-TENG device's versatility and viability for broad-range real-world application scenarios. Highlights: 1 Create a hierarchical honeycomb-inspired triboelectric nanogenerator (TENG) with excellent transparency, compactness, lightweight and deformability.2 Amplify capacitance variation by dividing large hollow space into numerous energy generation units with porous honeycomb architecture.3 Demonstrate self-powered insole plantar pressure mapping applications by the self-sustained elastic nature of the h-TENG device.4 Integrate the h-TENG into the morphing wing of small-unmanned aerial vehicles for converting flapping motions into electricity for the first time.

Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks

Abstract: Large stocks of soil organic carbon (SOC) have accumulated in the Northern Hemisphere permafrost region, but their current amounts and future fate remain uncertain. By analyzing dataset combining >2700 soil profiles with environmental variables in a geospatial framework, we generated spatially explicit estimates of permafrost-region SOC stocks, quantified spatial heterogeneity, and identified key environmental predictors. We estimated that Pg C are stored in the top 3 m of permafrost region soils. The greatest uncertainties occurred in circumpolar toe-slope positions and in flat areas of the Tibetan region. We found that soil wetness index and elevation are the dominant topographic controllers and surface air temperature (circumpolar region) and precipitation (Tibetan region) are significant climatic controllers of SOC stocks. Our results provide first high-resolution geospatial assessment of permafrost region SOC stocks and their relationships with environmental factors, which are crucial for modeling the response of permafrost affected soils to changing climate.

Human milk oligosaccharide DSLNT and gut microbiome in preterm infants predicts necrotising enterocolitis

Abstract: Objective Necrotising enterocolitis (NEC) is a devastating intestinal disease primarily affecting preterm infants. The underlying mechanisms are poorly understood: mother’s own breast milk (MOM) is protective, possibly relating to human milk oligosaccharide (HMO) and infant gut microbiome interplay. We investigated the interaction between HMO profiles and infant gut microbiome development and its association with NEC. Design We performed HMO profiling of MOM in a large cohort of infants with NEC (n=33) with matched controls (n=37). In a subset of 48 infants (14 with NEC), we also performed longitudinal metagenomic sequencing of infant stool (n=644). Results Concentration of a single HMO, disialyllacto-N-tetraose (DSLNT), was significantly lower in MOM received by infants with NEC compared with controls. A MOM threshold level of 241 nmol/mL had a sensitivity and specificity of 0.9 for NEC. Metagenomic sequencing before NEC onset showed significantly lower relative abundance of Bifidobacterium longum and higher relative abundance of Enterobacter cloacae in infants with NEC. Longitudinal development of the microbiome was also impacted by low MOM DSLNT associated with reduced transition into preterm gut community types dominated by Bifidobacterium spp and typically observed in older infants. Random forest analysis combining HMO and metagenome data before disease accurately classified 87.5% of infants as healthy or having NEC. Conclusion These results demonstrate the importance of HMOs and gut microbiome in preterm infant health and disease. The findings offer potential targets for biomarker development, disease risk stratification and novel avenues for supplements that may prevent life-threatening disease.

Efficient single-atom Ni for catalytic transfer hydrogenation of furfural to furfuryl alcohol

Abstract: The employment of single-atom catalysts in the catalytic transfer hydrogenation (CTH) of furfural (FF) to furfuryl alcohol (FAL) has never been effectively explored. Herein, a catalyst of Ni single-atoms supported on nitrogen doped carbon (Ni-SAs/NC) is synthesized and first ever utilized in the CTH of FF to FAL. Atomically dispersed Ni–N4 sites change the electron density at the metal center and exhibit specific adsorption and desorption to FF and FAL, promoting an outstanding catalytic performance with a turnover frequency (TOF) of 832 h−1 and selectivity as high as 97.1% at 130 °C for 2 h. Such performance is 9-fold higher than that of supported Ni nanocatalysts. The Ni-SAs/NC catalyst also exhibits superior stability for the CTH of FF and excellent catalytic activity for other α,β-unsaturated aldehydes. This work provides a new strategy of producing green chemical compounds using catalytic biomass conversion and suggests the future application of long-lasting single-atom catalysts for emerging sustainable technologies.