Load Factor Research Articles

Accelerating the penetration of clean electricity to promote the low carbonization of high-speed railways: A probabilistic framework

Transitioning from carbon-intensive travel modes to high-speed railways (HSR) is widely recognized as a crucial pathway to achieving the ‘carbon peak and carbon neutrality’ goals in the transport sector. However, it remains unclear whether HSR can meet carbon peak goals on time, under the high uncertainty of future electricity development. This study integrates scenario analysis and Monte Carlo simulation into life cycle assessment, proposing a probabilistic framework for dynamically simulating the carbon dioxide (CO2) emissions over the entire life cycle of HSR. Monte Carlo simulation-Latin hypercube sampling method is introduced to model the uncertainty in the development of electricity. The results show that with the gradual increase in the proportion of clean electricity, the time when the CO2 emission intensity of HSR is lower than that of electric cars and electric coaches is most probable to occur in the 2nd–3rd years and the 5th–14th years of operation, respectively. The emission trend of HSR is primarily influenced by the passenger growth rate and the depth of transition to clean electricity, with minimal impact from the loading factor and initial passenger volume. When the passenger growth rate reaches 5 % by 2030, transitioning solely to clean electricity can peak CO2 emissions from HSR by 2030. However, with passenger growth rates of 6 %, 7 %, 8 %, and 9 %, the peak time is most probable to be delayed by 6–7 years, 13–14 years, 16 years, and 19 years, respectively. These findings suggest that achieving CO2 emission peak goals in the transport system requires collaborative efforts across multiple sectors, including transport, energy, and industry sectors. The results contribute to a deeper understanding of the pathways for achieving carbon peak in the transport sector.

Load forecasting method based on CNN and extended LSTM

With the continuous development of power systems and the growing demand, accurate power load prediction has become essential to ensure stable system operation and optimize energy allocation. The collection and analysis of historical load data have become increasingly important, and the factors affecting load changes have grown more complex. To address this challenge, this paper proposes a short-term load prediction method based on convolutional neural networks (CNN) and multilayer extended long short-term memory (LSTM) neural networks. The method first extracts local features from historical data using 1D convolution operations in the CNN. Then, a multilayer extended LSTM network captures long-term dependencies in the data from multiple dimensions for more accurate load prediction. The prediction experiments use real load and related factor data from a city in eastern Mongolia. Results demonstrate that the proposed method outperforms other deep learning-based load prediction methods in direct single-step, four-step, and 32-step load predictions in terms of MAPE, RMSE, and MAE indexes. Additionally, the proposed model can be extended to more prediction application scenarios, including intelligent transportation and financial analysis.

Study on stress concentration and fatigue life of tubing with slip indentation

The indentation caused by slip on the outer wall of tubing is a significant contributor to stress concentration and, consequently, fatigue failure in the tubing string. Through an analysis of the interaction between slips and tubing, a mathematical model to predict slip-tubing interaction and slip crushing load is formulated, accounting for external pressure, internal pressure, and axial forces. On this basis, the influence factors and influence rules of slip crushing load are studied, and three-dimensional yield surface and tri-axial stress ellipse of tubing are investigated, considering different transverse load factors and design factors. To accurately forecast the fatigue life of tubing subjected to slip indentation, two models are established: one for fatigue life prediction and the other for tubing string vibration. In order to quantitatively assess the stress concentration arising from slip indentation, a finite element model of tubing featuring slip indentation is established. Finite element analysis reveals that stress concentration and non-uniformity are evident in the vicinity of slip indentation, with their severity intensifying as the indentation depth grows. The initial step in assessing fatigue life involves fatigue life tests on tubing material subjected to varying stress levels. Subsequently, a case study is conducted and the variations of wellhead pressure and axial stress are evaluated. Fatigue life analysis reveals that the fatigue life is notably sensitive to variations in stress amplitude and slip indentation depth. An increase in the magnitude of alternating stress and the depth of slip indentation will result in a significant reduction in the fatigue lifespan. The methodologies employed in this research, along with the resulting findings, offer a robust theoretical framework and a solid practical basis for forecasting and managing stress concentration and fatigue durability in tubing affected by slip indentation.

Quantifying the impact of subgrade stiffness on the railway dynamic load factor using instrumented wheelset and MRail measurements

Quantifying the impact of subgrade stiffness on the railway dynamic load factor using instrumented wheelset and MRail measurements

Any way the wind blows: quantifying superbubbles and their outflows in simulated galaxies across z ≈ 0 − 3

Abstract We present an investigation of clustered stellar feedback in the form of superbubbles identified within eleven galaxies from the FIRE-2 (Feedback in Realistic Environments) cosmological zoom-in simulation suite, at both cosmic noon (1 < z < 3) and in the local Universe. We study the spatially-resolved multiphase outflows that these supernovae drive, comparing our findings with recent theory and observations. These simulations consist of five LMC-mass galaxies and six Milky Way-mass progenitors (with a minimum baryonic particle mass of mb.min = 7, 100M⊙). For all galaxies, we calculate the local and galaxy-averaged mass and energy loading factors from the identified outflows. We also characterize the multiphase morphology and properties of the identified superbubbles, including the ‘shell’ of cool (T < 105 K) gas and break out of energetic hot (T > 105 K) gas when the shell bursts. We find that these simulations, regardless of redshift, have mass-loading factors and momentum fluxes in the cool gas that largely agree with recent observations. Lastly, we also investigate how methodological choices in measuring outflows can affect loading factors for galactic winds.

Safety of Oral Food Challenges for Individuals with Low Levels of Cow's Milk-Specific Immunoglobulin E Antibodies.

Cow's milk (CM) is one of the most common food allergens in Japan. The oral food challenge (OFC) of CM is important for the definite diagnosis of children with CM allergy and it is recommended to be actively and safely performed in individuals with low CM-sIgE levels. This study aimed to investigate the safety of low-dose CM-OFC in individuals with low CM-sIgE levels and discuss the prognostic factors and appropriate approaches for assessing the starting doses of CM-OFC in these individuals. We retrospectively analyzed 6,929 OFC tests conducted between January 1, 2017 and December 31, 2021; of which, 1,390 were CM-OFC tests. The characteristics, OFC-positive rates, CM loading, and related factors were analyzed in 138 cases involving low CM-sIgE levels. Stepwise OFC tests were conducted according to the FA guidelines in Japan using an open and unblinded method. Among 138 individuals with low CM-sIgE levels, 110 (79.7%) passed the OFC test without any symptoms. Among the cases with OFC-positive status, 50.0% (14/28) cases showed symptoms with low-dose OFC (30-105 mg CM protein). Moreover, complete CM elimination was associated with a significantly high OFC-positive rate, and 60.0% (12/20) of the cases involving complete CM elimination showed symptoms with low-dose OFC. Eighty percent of the patients with low CM-sIgE levels safely completed the OFC test. Nevertheless, careful observation is essential during low-dose OFC test in cases with low CM-sIgE levels, especially in the cases with complete elimination. The starting dose of the OFC test should be re-evaluated and modified using baked milk or a lower dose of CM to ensure safety and early outgrowth of CM allergy.

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies.

Development of the Indonesian sexual quality of life scale for women (ISQOL-W)

Sexual quality of life is greatly influenced by sexual function, yet thorough measures remain limited and vary across regions. In Indonesia, diverse cultural and religious contexts notably shape sexual quality of life. Thus, it is important to have a tool that considers both internal and external aspects. The aim of this study was to develop a valid and reliable scale that specifically measures Indonesian sexual quality of life for women (ISQOL-W), considering cultural and social factors. A total of 228 participants were recruited from April 30, 2023, to September 30, 2023. The ISQOL-W was developed through two phases: item generation and psychometric evaluation. In the first phase, 68 items were drafted based on definitions and dimensions of sexual quality of life. In the second phase, exploratory factor analysis resulted in 34 items with a loading factor greater than 0.4 (ranging from 0.41 to 0.84) which were categorized into four domains/factors: sexual-related emotion, sexual fantasy, sexual arousal, and sexual relationship. The Cronbach’s α coefficient for the overall questionnaire was 0.915, with subscale reliability ranging from 0.826 to 0.903. Further analysis revealed significant differences in ISQOL-W scores across relationship status, educational level, history of last sexual activity, and type of sexual activity. In conclusion, the ISQOL-W demonstrated robust psychometric properties, supporting its use in future studies. This study represents the first effort to develop and validate a sexual quality of life scale specifically for Indonesian women.

Optimal Location Identification of Solar PV Systems in Distributed Generators Based on Prediction of Load Flow and Factor Using Rule Based Deep Learning Algorithm

Optimal Location Identification of Solar PV Systems in Distributed Generators Based on Prediction of Load Flow and Factor Using Rule Based Deep Learning Algorithm

Allowable stress rating compared to load and resistance factor rating for timber bridges with and without steel-beam retrofit

This paper presents the relevancy of the Allowable Stress Rating (ASR) and the Load and Resistance Factor Rating (LRFR) methods for timber bridges. Benchmark bridges constructed in the 1930′s are upgraded with hollow structural steel (HSS) beams and three-dimensional finite element models provide technical information that is necessary for examining their behavior and rating evaluations. Complying with published manuals, a total of 17 rating vehicles are considered across three categories (Design, Legal, and Permit) so as to generate the maximum responses of the bridges. The position of the vehicles dominates the deflection profiles of the unrepaired bridges. After installing the HSS beams, the live loads are redistributed and stiffness enhancement is noticed in the transverse direction of the bridges. The rating factors calculated with ASR exceed those with LRFR for the unrepaired bridges, regardless of vehicle configurations, and the level of disparity between these rating approaches increases when the bridges are repaired owing to differences in load factors. In terms of sensitivity to average daily truck traffic, the LRFR factors of the bridges under the Legal vehicles are more responsive than the factors subjected to other vehicle types. From a probability perspective, the compatibility of these rating methodologies varies contingent upon vehicle categories and the presence of the HSS beams. Practice guidelines are proposed to facilitate the conversion of rating factors between ASR and LRFR as a function of the present condition of constructed timber bridges.

Lithography-Based Metal Manufacturing of Copper: Influence of Exposure Parameters on Green Part Strength

Copper’s high thermal and electrical conductivity enables its application in heat exchangers and high-frequency components. For those applications, additive manufacturing has advantages with respect to functional integration, miniaturization, and reduced waste. However, the processing of copper is a challenge for established laser-based processes since copper’s high reflectivity impedes energy input. Sinter-based additive manufacturing processes do not exhibit this limitation since the energy for the fusion of material is applied by thermal energy during sintering. This makes them an ideal candidate for copper manufacturing. In the following work, Lithography-based Metal Manufacturing (LMM) of copper is demonstrated. Curing behavior is investigated by single-layer exposure (SLE) tests measuring curing thickness for different loading factors, particle sizes, and exposure times. Bending strength is investigated as a function of exposure time, loading factor, and orientation in the building space. A higher exposure time and lower loading factors increase bending strength. Furthermore, samples with different loading factors are produced to measure the impact of the loading factor on sintered density. For these parameters, no clear trend is demonstrated.

Assessing the Carbon Intensity of e-fuels Production in European Countries: A Temporal Analysis

The transport sector heavily relies on the use of fossil fuels, which are causing major environmental concerns. Solutions relying on the direct or indirect use of electricity through e-fuel production are emerging to power the transport sector. To ensure environmental benefits are achieved over this transition, an accurate estimation of the impact of the use of electricity is needed. This requires a high temporal resolution to capture the high variability of electricity. This paper presents a previously unseen temporal analysis of the carbon intensity of e-fuels using grid electricity in countries that are members of the European Network of Transmission System Operators (ENTSO-E). It also provides an estimation of the potential load factor for producing low-carbon e-fuels according to the European Union legislative framework. This was achieved by building on top of the existing EcoDynElec tool to develop EcoDynElec_xr, a python tool enabling—with an hourly time resolution—the calculation, visualisation, and analysis of the historical time-series of electricity mixing from the ENTSO-E. The results highlight that, in 2023, very few European countries were reaching low carbon intensity for electricity that enables the use of grid electricity for the production of green electrolytic hydrogen. The methodological assumptions consider the consumption of the electricity mix instead of the production mix, and the considered time step is of paramount importance and drastically impacts the potential load factor of green hydrogen production. The developed tools are released under an open-source license to ensure transparency, result reproducibility, and reuse regarding newer data for other territories or for other purposes.

Hospital Customer Competition Issues are being addressed through Confirmation Factor Analysis on Experiential Marketing, Brand Trust, and Loyalty

Introduction The post-COVID-19 pandemic has caused an epidemiological transition that has pushed the pace of development of the health service system even further. Issues and revolutions in the health service industry have made all health service industry providers compete with each other to get service users with competitive advantages in the potential global health market. The hospital has improved revenue sustainability while promoting quality assurance practices. These healthcare industry managers have become adept at introducing business practices into business operations to increase the market for hospital customers. Exploratory factor analysis on important marketing dimensions such as experiential marketing, brand trust, and patient loyalty is very much needed in the issue of competition in the health service industry in hospitals. Materials and Methods This research was quantitative research with a survey approach to outpatients with a sample size of n=255 patients according to Lemeshow (1997). Identification of loading factor analysis includes experiential marketing variables, brand trust, and loyalty. Data analysis used IBM SPSS Statistics 26 Results The research results showed 1) an important loading factor on the experiential marketing variable is the Think dimension indicator (0.845), 2) an important loading factor on the brand trust variable is the brand characteristic dimension (0.945) and 3) an important loading factor on variable loyalty is Repeat Purchase (0.925) Conclusion Dimension of brand trust is the most important dimension in the issue of competition in the health service industry in hospitals. Strengthening and expanding hospital branding as a promotion strategy to add positive value to the reputation of patient and community services at the hospital.

Diseño y análisis dinámico de un edificio multifamiliar de 4 niveles sin sótano en la Provincia de Chanchamayo

In the present investigation, the design of a 4-story building without a basement located in the Province of Chanchamayo of Peru was carried out, intended for a multi-family home made up of structural elements such as columns, beams, and plates, which each one was designed and modeled under the Peruvian Load Regulations E. 020, Earthquake-resistant Design E. 030, Reinforced Concrete E. 060, Masonry E. 070 and General Design Conditions A. 010. The use of these Standards is established in the National Building Regulations that allowed compliance with the objective of designing and carrying out the Dynamic Analysis of the Building under the Earthquake methodology, which is based on the Load factor and resistance design method. Modeling was carried out with the help of ETABS software to obtain results necessary for the design and analysis of the structure. Finally, it was obtained that the building will use the structural system for structural walls, which has a basic reduction coefficient equal to 6 and it is concluded that the methodology used allows guaranteeing the safety, functionality and quality service of a building.

A data-driven approach for analyzing contributions of individual loading factors to GNSS-measured bridge displacements

A bridge may displace due to various loadings (e.g., thermal (Xia et al. in Struct Control Health Monit 28(7):e2738, 2013), winds (Owen et al. in J Wind Eng Ind Aerodyn 206:104389, 2020), and vehicles (Xu et al. in J Struct Eng 133(1):3–11, 2007)) acting upon the bridge. Identifying the contributions of individual loading factors to the measured bridge displacements is important for understanding the structural health conditions of the bridge. There is however no effective method to quantify the contributions when multiple loadings act simultaneously on a bridge. We propose a new data-driven method, termed random forest (RF)-assisted variational mode decomposition (RF-AVMD), for more effective identification of dominant loading factors and for quantifying the contributions of individual loading factors to the measured bridge displacements. The proposed method is applicable to studying the displacements of any bridge structures and allows for the first time to separate the contributions of individual loadings. The effectiveness of the proposed method is validated using data from Tsing Ma Bridge (TMB), a large suspension bridge in Hong Kong recorded during two consecutive strong typhoons. The results reveal that the transverse displacements of TMB mid-span were controlled by the crosswinds, the longitudinal displacements were dominated by the temperature and winds along the bridge centerline, and the vertical displacements were mainly due to the winds along the bridge centerline, temperature, and traffic flows. Displacement time series that responded to each loading factor was derived. The proposed method provides important new insights into the impacts of individual loadings on the displacements of long-span bridges.

GA-NIFS: JWST/NIRSpec IFS view of the z ∼ 3.5 galaxy GS5001 and its close environment at the core of a large-scale overdensity

We present JWST Near-Infrared Spectrograph (NIRSpec) observations in integral field spectroscopic (IFS) mode of the galaxy GS5001 at redshift z = 3.47, the central member of a candidate protocluster in the GOODS-S field. The data cover a field of view (FoV) of 4″ × 4″ (∼30 × 30 kpc2) and were obtained as part of the Galaxy Assembly with NIRSpec IFS (GA-NIFS) GTO programme. The observations include both high (R ∼ 2700) and low (R ∼ 100) spectral resolution data, spanning the rest-frame wavelength ranges 3700–6780 Å and 1300–11850 Å, respectively. These observations enable the detection and mapping of the main optical emission lines from [O II]λλ3726, 29 to [S III]λ9531. We analysed the spatially resolved ionised gas kinematics and interstellar medium properties, including obscuration, gas metallicity, excitation, ionisation parameter, and electron density. In addition to the main galaxy (GS5001), the NIRSpec FoV covers a close companion in the south, with three sub-structures with velocities blueshifted by ∼ − 150 km s−1 with respect to GS5001, and another source in the north redshifted by ∼200 km s−1. Optical line ratio diagnostics indicate star formation ionisation and electron densities of ∼500 cm−3 across all sources in the FoV. The gas-phase metallicity in the main galaxy is 12+log(O/H) = 8.45 ± 0.04, and slightly lower in the companions (12+log(O/H) = 8.34 − 8.42), consistent with the mass-metallicity relation at z ∼ 3. We find peculiar line ratios (high log[N II]/Hα = [−0.45, −0.3], low log[O III]/Hβ = [0.06, 0.10]) in the northern part of GS5001. These could be attributed to either higher metallicity, or to shocks resulting from the interaction of the main galaxy with the northern source. We identify a spatially resolved outflow in the main galaxy, traced by a broad symmetric component in Hα and [O III], with an extension of about 3 kpc. We find maximum outflow velocities of ∼400 km s−1, an outflow mass of (1.7 ± 0.4)×108 M⊙, a mass outflow rate of 23 ± 5 M⊙ yr−1, and a mass loading factor of 0.23. These properties are compatible with star formation being the driver of the outflow. Our analysis of these JWST NIRSpec IFS data therefore provides valuable, unprecedented insights into the interplay between star formation, galactic outflows, and interactions in the core of a z ∼ 3.5 candidate protocluster.

Reactive Extraction of Carboxylic Acids (Lactic, Propionic and Succinic Acids) using Tri-n-Butyl Amine Dissolved in Various Diluents

The current study intends to look into the extraction of lactic acid, propionic acid and succinic acids from aqueous solutions of these substances utilizing various solvent-extractant combinations to ascertain the most effective composition for the reactive extraction. The commercial value of succinic, propionic, and lactic acids in industrial processes led to their selection as the carboxylic acids. The process of recovering carboxylic acids from their aqueous solutions is accomplished by the Process Intensification method of reactive Tri-n-Butyl Amine was employed in the experiments because it has better stability, a lower water solubility, and is more eco-friendly than other extractants of the amine type. The extractants were dissolved in various diluents with various chemical structures, including 1-octanol, chloroform, and dimethylene chloride. Each experiment was conducted at 298.15 K. The distribution coefficient (KD), loading factor (Z), and extraction yield (%E) were used to compare the results in the range of 0.1 to 0.4 N acid concentration at various concentrations of Tri-n-Butyl Amine (10% to 40%). It was observed that at 0.1N and 40% amine concentration succinic acid attains a maximum distribution coefficient KD is 8.01 and equilibrium complexion KE1 is 7.5 for Tri-n-Butyl Amine, followed by propionic acid and lactic acid and a (1:1) type of acid amine complex was observed. The kinetic studies that were carried out using stirred cells to figure out the kinetic parameters such as order of the reaction with respect to acid and amine, mass transfer coefficient, and rate constant are also reported. The reaction with the highest Hatta number, MH=14, occurs in an instantaneous regime in 1-octanol when succinic acid and Tri-n-Butyl Amine interact.

Evaporator Evaluation as a Heat Exchanger in Refrigeration Systems at Constant Thermal Load and Fouling Factor

Heat exchangers are widely used in various industrial installations, one of which is their use in air conditioning systems. In the design process of heat exchangers, the main focus is to determine the dimensions of the device. This is done to determine the performance of the heat exchanger, where the calculation is based on a constant thermal load and a constant fouling factor. When the heat exchanger is first operated, the mass flow rate of the cooling fluid must be lower than the design point to maintain a constant cooling load. However, the conditions for the formation of deposits on the surface of the heat exchanger cannot be known. Therefore, designing a heat exchanger with a constant fouling factor is less realistic.

Study on Mechanical Properties and Safety of Ultra-Thin Reactive Powder Concrete Prefabricated Slabs Applied to I-Beam Joints of Bridges

Conventional methods for constructing bridge I-beam joints face several challenges, including heavy precast slabs, complicated transportation and lifting procedures, strict accuracy requirements, lengthy construction timelines, and increased safety risks. The use of ultra-thin, high-performance reactive powder concrete (RPC) prefabricated slabs can effectively resolve these issues. However, research in this area is limited, leaving our understanding of the strength and feasibility of ultra-thin RPC slabs for I-beam joints incomplete. Therefore, this study conducts a thorough examination of the strength and safety aspects of these slabs to assess their practical suitability. First, 11 numerical models are generated to evaluate the bearing capacity of ultra-thin RPC slabs, determining key factors such as cracking load, ultimate load, and safety factor according to relevant codes and standards. This establishes a theoretical foundation for practical engineering applications. Next, several sets of ultra-thin RPC slabs that meet material performance criteria are prefabricated to study the mechanical properties under equivalent concentrated load. Finally, two types of in situ temporary construction loads are encountered in the safety calculations of the RPC slabs. This study aims to provide a robust theoretical framework and technical support for the application and advancement of ultra-thin RPC prefabricated slabs in bridge I-beam joints.

Oxidative degradation of chitosan by Fe-MCM-41 heterogeneous Fenton-like system

We herein disclosed an efficient multiphase Fenton-like catalytic system for oxidative degradation of chitosan. By utilizing Fe-MCM-41, featured with regular mesoporous structure and high specific surface area, the activation efficiency of H2O2 was significantly enhanced and the chitosan degradation efficiency proved by 28.1% higher than the conventional system using H2O2 alone. Under optimized conditions (5 g/L chitosan, 0.5 g/L Fe-MCM-41, 0.16 mol/L CH3COOH, 0.86 mol/L H2O2, 50 °C, 140 min), the viscosity reduction rate of chitosan reached an impressive 98.2%. Among the catalysts tested, Fe-MCM-41 with a loading factor of x = 0.12 demonstrated optimal degradation performance. After four recycles, the degradation efficiency maintained > 93.6%, demonstrating its excellent stability and recyclability for potential industrial applications. Kinetic studies provided further elucidation of the reaction mechanism, which indicated that the degradation process of chitosan followed a first-order kinetic model, with an apparent activation energy (Ea) of 48.91 kJ/mol. This novel and efficient strategy for chitosan degradation, addressed the challenges of catalyst recovery and secondary pollution typically associated with traditional Fenton systems, and posed broad application potential for polysaccharide material processing.