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Cytokines and inflammatory biomarkers and their association with post-operative delirium: a meta-analysis and systematic review
Get PDFDelirium is a prevalent cognitive disorder among older patients and a common phenomenon following major surgical procedures. This study aimed to identify the significant proteomic biomarkers and examine their association with postoperative delirium (POD). Four electronic databases were used to identify the published articles between 1st January 2000 and 31st December 2023. Among the included 40 studies, the meta-analysis investigated 13 potential cytokines and inflammatory biomarker proteins linked with postoperative delirium. The Hedge’s g standardized mean difference (SMD) was applied to calculate the effect size, with 95% confidence intervals (CIs), under the fixed effect or random effect model based on the heterogeneity index of I2. Patients with POD exhibited significantly higher elevated levels of inflammatory biomarkers IL-6 (SMD = 1.45), CRP (SMD = 1.26), GFAP (SMD = 1.15), IL-1B (SMD = 0.95), IL-10 (SMD = 0.57), IL-8 (SMD = 0.56), MCP-1 (SMD = 0.39), and NFL (SMD = 0.44), suggesting that these proteins may play an inevitable role in delirium-associated cytokines and inflammatory response, development and progression of delirium. Conversely, a reduction in IGF-1 protein level (SMD = − 0.24) was also significantly associated with POD, suggesting a potential vulnerability to delirium. This study paves the way for future research aimed at early diagnosis, personalized treatment, and the development of novel therapeutic strategies to manage delirium effectively
Examining the Influence of ICT on Carbon Emissions in Emerging Economies
No full textThis study explores the influence of digitalization of the economy on carbon emissions in emerging economies (BRICS) by taking in account the impact of green technology. The empirical results are obtained by using the sophisticated econometric techniques of Feasible Generalized Least Squares (FGLS) and Panel Corrected Standard Errors (PCSE). The findings reveal that adoption of digital technology in the economy improves the environment by lowering carbon emissions. The intuition is that digital innovations encourage a move towards more environmentally friendly production methods and increase energy efficiency across a range of industries. Green technology enhances this effect by serving as a mediator. The findings also show that high income level and globalization lowers emissions while urbanization tends to raise carbon emissions. Based on the empirical results, the study recommends that BRICS countries should emphasize investments in the digitalization of the economy and green technology incentives since clean technology and resource management may drastically cut emissions
Heterointerface engineering of polymer-based electromagnetic wave absorbing materials
Get PDFHeterointerface engineering has drawn considerable interest in tuning interfacial polarization and promoting impedance matching. Therefore, it has become a key strategy for optimizing electromagnetic wave (EMW) absorption. This comprehensive review primarily focused on the EMW absorbing strategies of polymer-based materials, emphasizing the critical developments of heterointerface engineering. A possible EMW absorbing mechanism of polymer-based materials was proposed, emphasizing the synergism of multi-components, microstructure design, and heterointerface engineering. Key innovations in structural design such as porous structure, multilayered structure, and segregated structure are explored, highlighting their contributions to enhancing EMW absorption. Also, the review highlights the latest research progress of advanced conductive polymer-based and insulating polymer-based materials with desirable EMW absorption performance; their fabrication methods, structures, properties, and EMW absorption mechanisms were elucidated in detail. Key challenges on polymer-based EMW absorbing materials are presented followed by some future perspectives
Advances in Soft Strain and Pressure Sensors
No full textSoft strain and pressure sensors represent a breakthrough in material engineering and nanotechnology, providing accurate and reliable signal detection for applications in health monitoring, sports management, human-machine interface, or soft robotics, when compared to traditional rigid sensors. However, their performance is often compromised by environmental interference and off-axis mechanical deformations, which lead to nonspecific responses, as well as unstable and inaccurate measurements. These challenges can be effectively addressed by enhancing the sensors’ specificity, making them responsive only to the desired stimulus while remaining insensitive to unwanted stimuli. This review systematically examines various materials and design strategies for developing strain and pressure sensors with high specificity for target physical signals, such as tactility, pressure distribution, body motions, or artery pulse. This review highlights approaches in materials engineering that impart special properties to the sensors to suppress interference from factors such as temperature, humidity, and liquid contact. Additionally, it details structural designs that improve sensor performance under different types of off-axis mechanical deformations. This review concludes by discussing the ongoing challenges and opportunities for inspiring the future development of highly specific electromechanical sensors
Performance of the corrugated metal pipe sliplining method using FRP pipes
Get PDFCorrugated Metal Pipes (CMP) have been extensively used in culverts for decades. Currently, there is a significant growing demand for CMPs to be either replaced or rehabilitated due to their deterioration caused by corrosion.
Sliplining is one of the simplest and cost-effective methods of CMP culvert rehabilitation. This paper examined
the structural performance of CMP sliplined by Fibre Reinforced Polymer (FRP) composites considering the effects
of host pipe and the grout infill. In this research, CMP, FRP pipe and FRP sliplined CMP were subjected to parallel plate load test (PPLT) and the load deflection behaviour and failure modes were examined. A low strength flowable mortar mix was used as the Controlled Low Strength Material (CLSM) to fill the annular space between pipes. It was observed that the core failure of the FRP pipe governs the failure of the sliplined CMP. A change in load deflection behaviour of the host pipe of sliplined CMP system was also observed with reference to the PPLT results of the FRP pipe sample itself. Further, the failure modes of the FEM and the failure modes
observed in the experiments were in good agreement
Two way gateways for bettongs
No full textRaw data of behavioural interactions of bettongs and a variety of gateways for safe haven
A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting
Get PDFThe precise monitoring and timely alerting of river water levels represent critical measures aimed at safeguarding the well-being and assets of residents in river basins. Achieving this objective necessitates the development of highly accurate river water level forecasts. Hence, a novel hybrid model is provided, incorporating singular value decomposition (SVD) in conjunction with kernel-based ridge regression (SKRidge), multivariate variational mode decomposition (MVMD), and the light gradient boosting machine (LGBM) as a feature selection method, along with the Runge–Kutta optimization (RUN) algorithm for parameter optimization. The L-SKRidge model combines the advantages of both the SKRidge and ridge regression techniques, resulting in a more robust and accurate forecasting tool. By incorporating the linear relationship and regularization techniques of ridge regression with the flexibility and adaptability of the SKRidge algorithm, the L-SKRidge model is able to capture complex patterns in the data while also preventing overfitting. The L-SKRidge method is applied to forecast water levels in the Brook and Dunk Rivers in Canada for two distinct time horizons, specifically one- and three days ahead. Statistical criteria and data visualization tools indicates that the L-SKRidge model has superior efficiency in both the Brook (achieving R = 0.970 and RMSE = 0.051) and Dunk (with R = 0.958 and RMSE = 0.039) Rivers, surpassing the performance of other hybrid and standalone frameworks. The results show that the L-SKRidge method has an acceptable ability to provide accurate water level predictions. This capability can be of significant use to academics and policymakers as they develop innovative approaches for hydraulic control and advance sustainable water resource management
Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading: A comprehensive FELA analysis using three stability factors and machine learning
Get PDFThis study examines the stability of three-dimensional rectangular tunnel headings in drained c-ϕ soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on the upper and lower bound solutions for three stability factors: cohesion, surcharge, and soil unit weight (Nc, Ns, and Nγ). Based on Terzaghi's principle of superposition, the analysis evaluates tunnel stability under varying parameters, such as cover-depth ratio (H/D), width-depth ratio (B/D), and friction angle (ϕ). The results align closely with previous studies, and practical design charts are provided for calculating minimum support pressures. Additionally, machine learning models (ANN and XGBoost) are used to develop accurate correlations between input parameters and stability results. A relative importance index analysis is conducted to assess the impact of these parameters. This research enhances understanding of tunnel stability and offers practical insights for tunnel design
Secured Multi-Objective Optimisation-Based Protocol for Reliable Data Transmission in Underwater Wireless Sensor Networks
Get PDFUnderwater wireless sensor network (UWSN) requirements have increased beyond applications in environmental monitoring and underwater exploration to military surveillance. The complex underwater environment raises many challenges due to high propagation delays, limited bandwidth, high error rates, and dynamic underwater currents. Most traditional clustering algorithms do not consider the multifaceted requirements of UWSNs. In most cases, a single objective is optimised at the cost of other essential factors, such as energy consumption, network robustness, and data transmission reliability. This paper proposes a new UWSN protocol based on the tiger beetle optimisation (TBO) algorithm for multiobjective K-means clustering (TBO-MOK). The protocol comprises adaptive search procedures motivated by tiger beetle hunting behaviors and lightweight AES-based encryption for data security. TBO-MOK is excellent in multiobjective optimisation since it simultaneously considers performance metrics of more than one aspect. Many problems are resolved by TBO-MOK, which optimises all the involved performance metrics to provide balanced energy usage and robust communication links. Comprehensive simulations demonstrate that TBO-MOK outperforms the traditional LEACH, PSO, and GA approaches in grossly enhancing network lifetime, energy efficiency, load balancing, and data transmission reliability. These results show the potential of TBO-MOK to provide a more effective and resilient solution for UWSNs
Exploring Governance for accreditation in the education sector using blockchain technology: a systematic literature review
Get PDFThe current education accreditation process poses a significant risk globally to the quality of education due to the increased falsification of academic certificates. Although previous studies have highlighted the potential benefits of blockchain technology in this context, there remains an opportunity towards a thorough investigation into the governance factors that influence the implementation of blockchain technology within the education sector. The accreditation system becomes increasingly important as a result of the emergence of a new learning ecosystem that enables the propagation of academic credits. It fosters an integrative learning approach by facilitating the accumulation of academic credits from a variety of higher education institutions, thereby promoting a learning ecosystem. The fundamental concept is to recognize the existence of a variety of learning pathways and to democratize educationTo this end, we conducted a comprehensive review of existing studies on the governance mechanisms for accreditation in the education sector using Blockchain technology. We identified 63 journal articles using four academic databases (EBScohost, Emerald, insight, Sage Journals, Scopus, Science direct) from 2018 to 2023. The literature appears devoid of proposals for a governance framework even though in the conventional paradigm such a framework is crucial in ensuring authenticity of credentials