Search Results (1,746)

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness. Full article

Our research focuses on evaluating the preliminary stability of solid lipid nanoparticles (SLNs) in order to identify an optimal formulation for studying the skin penetration of SLNs loaded with sesamol, with a view to developing potential cosmetic applications. For this study, SLNs were prepared with varying lipid and surfactant compositions and produced through homogenization and ultrasonication. The particle size (PS), polydispersity index (PDI), zeta potential (ZP), and encapsulation efficiency (EE) were analyzed for the different formulations. We identified OP2Se as the optimal formulation for skin penetration assessment due to its stable PS, PDI, ZP, and EE over time, with a Turbiscan Stability Index (TSI) below 1 after a month, indicating favorable stability conditions. The in vitro skin permeation study compared sesamol-loaded SLNs with a control sesamol hydrogel, revealing controlled release characteristics ideal for localized skin effects without significant bloodstream penetration, attributed to the SLNs’ 200 nm particle size. Further exploration could enhance skin retention and targeting, potentially extending penetration studies and reducing particle size to improve accumulation in hair follicles. Exploring SLN applications beyond sesamol, such as incorporating mineral filters for suncare, offers promising avenues, underscoring SLNs’ versatility in cosmetic formulations and skincare applications. Full article

Sub-Saharan Africa (SSA) is undergoing rapid urbanization, yet research comparing urban expansion and agricultural land loss in peri-urban areas is scarce. This study utilizes multi-temporal Landsat imagery to examine the impact of urban growth on agricultural land and fragile ecosystems in Kampala (a mega city) and Mbarara (a regional urban center) in Uganda. We distinguish between random and systematic land-use and land-cover (LULC) transitions in the landscape. The results reveal substantial urban expansion. Kampala’s urban area surged from 7.14% in 1989 to 55.10% in 2015, while Mbarara increased from 6.37% in 2002 to 30.95% in 2016. Correspondingly, agricultural land decreased, from 48.02% to 16.69% in Kampala, and from 39.92% to 32.08% in Mbarara. Notably, a significant proportion of urban growth in both cities encroached upon agricultural land (66.7% in Kampala and 57.8% in Mbarara). The transition from agricultural to built-up areas accounted for 14.72% to 28.45% of the landscapes. Additionally, unsustainable practices led to the conversion of wetlands and forests to agricultural land, with approximately 13% of wetlands and 23% of Savannah and forests being converted between 2001 and 2015. These findings underscore the necessity of monitoring LULC changes for sustainable urban growth management, emphasizing the importance of preserving agricultural land and ecosystems to ensure present and future food security. This research contributes to the understanding of urbanization’s impact on peri-urban agricultural land and ecosystems in SSA, providing insights that are crucial for informed urban planning and policy formulation aimed at sustainable development in the region. Full article

The Ro60/SSA2 autoantigen is an RNA-binding protein and a core component of nucleocytoplasmic ribonucleoprotein (RNP) complexes. Ro60 is essential in RNA metabolism, cell stress response pathways, and cellular homeostasis. It stabilises and mediates the quality control and cellular distribution of small RNAs, including YRNAs (for the ‘y’ in ‘cytoplasmic’), retroelement transcripts, and misfolded RNAs. Ro60 transcriptional dysregulation or loss of function can result in the generation and release of RNA fragments from YRNAs and other small RNAs. Small RNA fragments can instigate an inflammatory cascade through endosomal toll-like receptors (TLRs) and cytoplasmic RNA sensors, which typically sense pathogen-associated molecular patterns, and mount the first line of defence against invading pathogens. However, the recognition of host-originating RNA moieties from Ro60 RNP complexes can activate inflammatory response pathways and compromise self-tolerance. Autoreactive B cells may produce antibodies targeting extracellular Ro60 RNP complexes. Ro60 autoantibodies serve as diagnostic markers for various autoimmune diseases, including Sjögren’s disease (SjD) and systemic lupus erythematosus (SLE), and they may also act as predictive markers for anti-drug antibody responses among rheumatic patients. Understanding Ro60’s structure, function, and role in self-tolerance can enhance our understanding of the underlying molecular mechanisms of autoimmune conditions. Full article

The corporate strategic planning of businesses in sub-Saharan Africa (SSA) largely focuses on immediate financial performance with minimal credence to social sustainability. Thus, studies on the linkage between corporate governance (CG) and sustainability reporting have focused on developed economies. This study therefore investigated the role of institutional ownership in the impact of board committee characteristics on social sustainability reporting. This study involved strongly balanced panel data with 1969 observations of 275 publicly listed non-financial firms in SSA within the timeframe of 2012 to 2021. Data were analyzed using STATA 14.1. The hypotheses were tested using the two-step system of the generalized method of moment (GMM) using the Arellano–Bond dynamic panel data estimation method. The rate of social sustainability reporting was 39.4%. Relatively, Mauritian and South African firms had the most effective board committee characteristics and higher levels of social sustainability reporting. Although institutional ownership had no significant effect on social sustainability reporting, it moderated the effect of sustainability committee independence and sustainability committee gender diversity on social sustainability reporting. This paper presents a new perspective on the corporate governance and social sustainability literature by examining the effect of institutional ownership on board committee characteristics and social sustainability reporting in SSA. In terms of policy implication, there is the need for mandatory regulatory and legal CG framework that is regularly updated at national and regional levels in SSA to motivate listed firms to establish sustainability committees with efficient characteristics to promote social sustainability reporting. Full article

We employed a non-parametric causality test based on Singular Spectrum Analysis (SSA) and used the Vector Error Correction Model (VECM) and Information Share Model (IS) to measure the relationship between the futures and spot prices for seven major agricultural commodities in China from 2009 to 2017. We found that the agricultural futures market has potential leading information in price discovery. The results of an Impulse Response Function (IRF) analysis also showed that the spot prices react to shocks from the future market and have a lasting impact. This confirms our findings reported for the causality test and information share analysis. Full article

The surge in disordered EV charging demand, driven by the rapid growth in the ownership of electric vehicles (EVs), has highlighted the potential for significant disruptions in photovoltaic (PV)-connected distribution networks (DNs). This escalating demand not only presents challenges in meeting charging requirements to satisfy EV owners and grid fast-charging stations (GFCSs) but also jeopardizes the stable operation of the distribution network. To address these challenges, this study introduces a novel model called SOR&KANO for charging decisions, which focuses on addressing the dual-sided demand of GFCSs and EVs. The proposed model utilizes the salp swarm algorithm-convolutional neural network (SSA-CNN) to predict the PV output and employs Monte Carlo simulation to estimate the charging load of EVs, ensuring accurate PV output prediction and efficient EV distribution. To optimize charging decisions for reserved EVs (REVs) and non-reserved EVs (NREVs), this study applies the multi-verse optimizer (MVO) in conjunction with time-of-use (TOU) tariff guidance. By integrating the SOR&KANO model with the MVO algorithm, this approach enhances satisfaction levels for GFCSs by balancing the charging demand, increasing utilization rates, and improving voltage quality within the DN. Simultaneously, for EVs, the optimized scheduling strategy reduces charging time and costs while addressing concerns related to range anxiety and driver fatigue. The efficacy of the proposed approach is validated through a simulation on a modified IEEE-33 system, confirming the effectiveness of the optimal scheduling methods proposed in this study. Full article

This study addresses speed sensor aging and electrical parameter variations caused by prolonged operation and environmental factors in flywheel energy storage systems (FESSs). A model reference adaptive system (MRAS) flywheel speed observer with parameter identification capabilities is proposed to replace traditional speed sensors. The proposed method uses reference and adjustable models to identify the stator resistance and permanent magnet flux (PM Flux) to mitigate the adverse effects of electrical parameter changes on control performance. The Tent chaotic mapping-improved Sparrow Search Algorithm (SSA) optimizes the Proportional-Integral (PI) controller parameters for the dual closed-loop and MRAS speed adaptation laws of the flywheel motor. Moreover, a self-switching parameter identification (SSPI) scheme, which constructs a cost function based on the current, parameter identification, and speed errors, is proposed to prevent inaccuracies in parameter identification. The MRAS observer selects the appropriate PI adaptive mechanism based on the error values, thereby enhancing identification accuracy. Simulink simulations show significant improvements in the rapidity and accuracy of the Tent-SSA optimized MRAS flywheel speed observer, enhancing the stability and robustness of the flywheel rotor. Experimental validation on a constructed FESS platform confirms the feasibility of this method. Full article

Maternal health is a critical public health issue worldwide, with Sub-Saharan Africa (SSA) facing severe challenges in maternal mortality and morbidity. Despite global efforts, progress in SSA remains slow. The Sustainable Development Goals highlight the need for urgent action in this area. Value-based healthcare presents a promising approach to enhance maternal health in SSA by maximising health outcomes for mothers and newborns, enriching patient experiences and efficient resource use. However, the impact and implementation of value-based healthcare in Sub-Saharan Africa’s maternal health sector are not well studied. To evaluate the effectiveness and value of value-based healthcare interventions in improving maternal health outcomes in Sub-Saharan Africa. A systematic review was carried out drawing on articles from six databases published between 2000 and 2024. The results suggest that VBHC can significantly improve maternal health, evidenced by successful implementations like obstetric units in Sierra Leone and integrated care in South Africa, which improved outcomes and cost-effectiveness. The success of value-based healthcare interventions hinges on addressing access to quality care, infrastructure, and socioeconomic barriers. Further research is essential to confirm value-based healthcare efficacy in SSA and guide policy for better maternal health outcomes. Full article

This paper investigates the efficacy of various data-driven decomposition methods combined with Phase Permutation Entropy (PPE) to form a promising complexity metric for analyzing time series. PPE is a variant of classical permutation entropy (PE), while the examined data-driven decomposition methods include Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Empirical Wavelet Transform (EWT), Seasonal and Trend decomposition using Loess (STL), and Singular Spectrum Analysis-based decomposition (SSA). To our knowledge, this combination has not been explored yet. Our primary aim is to assess how these preprocessing methods affect PPE’s ability to capture temporal structural complexities within time series. This evaluation encompasses the analysis of both simulated and econometric time series. Our results reveal that combining SSA with PPE produces superior advantages for measuring the complexity of seasonal time series. Conversely, VMD combined with PPE proves to be the less advantageous strategy. Overall, our study illustrates that combining data-driven preprocessing methods with PPE offers greater benefits compared to combining them with traditional PE in quantifying time series complexity. Full article

The operation of the subway system necessitates a comprehensive understanding of passenger flow characteristics at station locations, as well as a keen awareness of the average travel distances at these stations. Moreover, the travel distances at the station level bear a direct relationship with the built environment composed of land use characteristics within the station’s catchment area. To this end, we selected the land use features within an 800 m radius of the station (land use area, distribution of points of interest, and the surrounding living environment) as the influencing factors, with the travel distances at peak hours on the subway network in Xi’an as the research subject. An improved SSA-XGBOOST-SHAP interpretable machine learning framework was established. The research findings demonstrate that the proposed enhanced model outperforms traditional machine learning or linear regression methods in terms of R-squared, MAE, and RMSE. Furthermore, the distance from the city center, road network density, the number of public transit routes, and the land use mix have a pronounced influence on travel distances, reflecting the significant impact that mature built environments can have on passenger attraction. Additionally, the analysis reveals a notable nonlinear relationship and threshold effect between the built environment variables comprising land use and the travel distances during peak hours. The research results provide data-driven support for operational strategy management and line capacity optimization, as well as theoretical underpinnings for enhancing the efficiency and sustainability of the entire subway system. Full article

The excessive extraction of river sand has led to significant ecological issues. Moreover, the environmental impact and resource demand of cement production have increasingly turned the spotlight on sea sand as a viable alternative due to its abundance and ease of extraction. Concurrently, alkali-activated binders, a novel type of low-carbon cementitious material, have gained attention for their low energy consumption, high durability, and effective chloride ion fixation capabilities. However, they are susceptible to carbonation. Introducing a controlled sea sand amount can raise the materials’ carbonation resistance, although carbonation may raise the concentration of free Cl⁻ within the structure to levels that could risk the integrity of steel reinforcements by accelerating corrosion. In this context, the current study investigates sea sand alkali-activated slag (SSAS) concrete prepared with varying water–binder (W/B) ratios to evaluate its impact on flowability, mechanical strength, performances, and chloride ion distribution post-carbonation. The results demonstrate that the mechanical property of SSAS concrete diminishes as the water-to-binder ratio increases, with a more pronounced reduction observed. The depth of carbonation in mortar specimens also rises with the W/B ratio, whereas the compressive strength post-carbonation initially decreases before showing an increase as carbonation progresses. Furthermore, carbonation redistributes chloride ions in SSAS, leading to a peak Cl⁻ concentration near the carbonation front. However, this peak amplitude does not show a clear correlation with changes in the W/B ratio. This study provides a theoretical foundation for employing sea sand and alkali-activated concrete. Full article

In the construction of urban underground shield tunnels, uneven deformation can easily occur when the shield passes through soft soil and other poor strata. Such deformation has a significant impact on surface settlement and may cause potential safety hazards to the surrounding existing buildings, directly affecting the safety of urban operation. When simulating and predicting surface settlements, the small-strain soil hardening model can more accurately characterize the mechanical parameters of soil. Nevertheless, its parameters are numerous and complicated to determine accurately, so parameter inversion is needed to determine the accurate parameters of the soft soil layer in order to more accurately predict the surface settlement. This study uses the EFAST method to analyse the sensitivity of the HSS model parameters of soft soil strata. It is determined that the parameters that have the most significant impact on the surface settlement are the reference tangent modulus, rebound modulus, and effective cohesion. Then, XGBoost’s fast calculation speed and high precision of SSA inversion are used to inverse and optimize the parameters with high sensitivity. Finally, according to the parameters of the soft soil layer obtained from inversion and measured data, the settlement deformation and safety behaviour of existing buildings are analysed. Combined with the actual shield tunnel project in a city along a river, the inversion calculation shows that the overall average error of the transverse monitoring section is 1.04 mm, and the average maximum error of each monitoring point in the overall shield process is 2.87 mm. The prediction effect is significantly improved compared with the original parameters. The accuracy of the inversion of soil layer parameters is verified from the perspective of time and space. The average settlement of the river embankment foundation is 2.5 mm. Compared with the original parameter data, the prediction results have been greatly improved, and the settlement deformation results are more consistent with the measured data. Full article

Monodisperse Pt nanoparticles supported on carbon (Pt/C) were prepared via an impregnation method. By changing the concentration of the platinum precursor in the initial reagent mixture, the average particle size (d) could be controlled to within a narrow range of less than 2 nm. The specific activity (SA) of these materials, when applied to the oxygen reduction reaction (ORR), increased rapidly with d in the range below 1.8 nm, with a maximum SA at d = 1.3 nm. This value is approximately four times that of a commercial Pt/CB catalyst. The electrochemical active area, ECAA (electrochemical surface area (ECSA)/specific surface area (SSA) × 100), decreased drastically from 100% with decreases in d below 1.3 nm. In this study, we present a correlation between SA and ECAA as a means of determining the appropriate d for polymer electrolyte fuel cells (PEFCs) and propose an optimal size. Full article

With the continuous development of the power Internet of Things (PIoT), smart devices (SDs) have been widely used in electric power inspections. Due to the limited resources of intelligent inspection SDs and the distance of overhead transmission lines, many inspection tasks cannot be processed promptly. This paper proposes a multi-hop-based end-edge cooperative computing (MHCC) scheme to address inspection task processing in power IoT. We formulate a multi-hop task offloading problem that minimizes the energy consumption of inspection SDs with delay constraints. We develop a JDPSO algorithm to solve the multi-hop task offloading problem and evaluate the algorithm’s performance based on numerous simulation experiments. The experimental results show that JDPSO reduces the system’s energy consumption by 56.30%, 48.56%, 78.87%, 50.89%, 30.85%, and 68.31%, and also reduces delay by 50.69%, 42.78%, 58.67%, 44.84%, 10.22%, and 62.88% compared to GA, RSA, SSA, MFO, DOA, and ALC schemes. Full article