Standard Load Research Articles

Design methodology for heavy-haul railway track foundations under 40-tonne axle loads

ABSTRACT Heavy haul railway, recognized for high capacity and efficiency, is gaining traction worldwide. However, heavier axle loads can accelerate track and foundation deterioration without robust structural design. This study proposes a practical design methodology for heavy-haul track foundations under 40-tonne axle loads. Short axle spacing was considered in analyzing train load distribution, revealing significant stress superposition on the subgrade. Based on general strength, deformation, and long-term stability, a standardized multi-axle load pattern (4Z1800/2400) is introduced to capture these effects. The recommended design ensures cumulative deformation remains within the subgrade “working zone” by integrating induced stress levels and fill materials, surpassing single-factor approaches. Long-term stability, indicated by cumulative deformation approaching slow convergence, serves as the control requirement. Proposed design solutions include: 1) roadbed thickness of 3.5 m, with subgrade reaction modulus (K30) ≥110 MPa/m below the roadbed; 2) lower roadbed of 2.3 m, K30 ≥130 MPa/m, with thickness adjustments as needed; 3) upper roadbed of 0.7 m using high-quality graded gravel. These measures effectively control deformation, thus ensuring the performance and longevity of heavy-haul track foundations under extreme axle loads. The outcomes support global heavy-haul development.

Construction and validation of an overconfidence scale in investment decisions

Existing studies that directly measure the three types of overconfidence—overprecision, overplacement, and overestimation—are largely exploratory, highlighting the need for further confirmatory research to establish robust overconfidence measures. Moreover, there is no comprehensive instrument for analyzing these types of overconfidence in the context of investment decisions. To address these gaps, the present study aims to develop and validate a scale for assessing overconfidence in investment decisions, considering its three forms. The research involved 380 Brazilian participants and employed confirmatory factor analysis and standard multivariate validation methods. The scale, based on a theoretical framework of the three types of overconfidence, consisted of 30 questions (10 for each type) designed to measure the degree of overconfidence in investment decision-making. The scale demonstrated acceptable factor loadings and internal and external validity, indicating its suitability for evaluating overconfidence in investment decisions. The standardized factor loadings showed p values < 0.01 for all factors. The average variance extracted was above 0.5, and the composite reliability exceeded 0.7 for all three constructs analyzed. These results support the convergent validity of the proposed measurement model. In this way, the developed scale fills a gap in the literature by providing a valid tool to assess overconfidence in investment decision-making, which can be practically applied by financial market participants, such as investors, fund managers, and financial professionals. This instrument enables more informed and personalized investment decisions, mitigates the risks of bias in decision-making, and contributes to a more efficient and resilient financial market.

A Multi-Year Study of Forchlorfenuron’s Effects on Physical Fruit Quality Parameters in A. chinensis var. chinensis

CPPU, N-(2-Chloro-4-pyridyl)-N-phenylurea, is a synthetic cytokinin extensively used to enhance fruit size and overall quality in several crops, including kiwifruit. This study aimed to investigate the effects of three different CPPU application strategies (2.3, 3.0, and 4.6 ppm) and two crop load levels on key fruit quality parameters at harvest, as well as on post-harvest storage performance. Our results demonstrate that two applications of CPPU (4.6 ppm) significantly increased fruit weight, especially under standard crop-load conditions, likely due to more efficient resource allocation. Additionally, fruit firmness improved with two or three CPPU applications, probably because of enhanced cell wall development. Crop load consistently influenced fruit firmness, with lower loads resulting in softer fruits. The soluble solids content was not significantly affected by the two CPPU applications; however, it was notably influenced by crop load, with fruits from the standard crop load showing higher sugar accumulation. A similar trend was observed in fruit dry weight, where CPPU had a greater impact under standard crop loads. Regarding post-harvest performance, CPPU applications showed a limited effect on maintaining fruit firmness during the first five months of storage. Overall, CPPU can be a potential strategy to enhance fruit quality, but its effectiveness depends heavily on field management practices. Therefore, controlling field variables is essential to fully realize the benefits of CPPU and to avoid interference with the plant’s physiological responses.

Full‐Scale Beam‐To‐Column Subassemblage Testing for Seismic Evaluation of Deep Columns

ABSTRACTSteel moment frames composed of wide‐flange steel members are commonly used in seismic regions, with deep and slender column sections often selected to economically satisfy drift limit requirements. The section slenderness makes the columns more susceptible to local and global buckling and subsequent axial shortening when subjected to combined high axial forces and lateral deformations. Numerous tests have been conducted on individual column members under a wide range of axial loads and loading patterns. Experimental data of subassembly or complete frame configurations providing insight into system level interaction of the column with the frame are more limited. To address this concern, a full‐scale testing program was conducted on four cruciform beam‐to‐column subassemblage subjected to loading patterns based on cyclic quasi‐static and slow hybrid simulation. Quasi‐static tests followed standard AISC loading protocol with constant column axial load ranging from 20% to 40% of the yield capacity. Advanced hybrid simulations subjected the specimens to realistic earthquake loading patterns to levels consistent with design basis earthquake (DBE) and maximum considered earthquake (MCE) ground motions. A full nonlinear model of a complete 6‐story frame was developed for the numerical substructure in the hybrid simulation. The observed local and global responses of two quasi‐static and two hybrid tests are presented, providing valuable data towards improving the understanding of progress of damage for these systems through advanced testing techniques.

Investigation of Teicoplanin Trough Concentrations and Safety Following High-Dose Loading in a Pediatric Population.

Therapeutic drug monitoring-informed teicoplanin dosage adjustments are recommended for safe and effective use. The authors' group previously reported that only half of children reached the recommended blood concentration range at the standard teicoplanin loading dose. It has been suggested that higher loading doses are necessary; however, the usefulness and safety of high-dose loading in pediatric patients in clinical practice are unknown. This retrospective cohort study was conducted between January 2018 and June 2021 using electronic medical records. The analysis included 2- to 16-year-old patients treated with teicoplanin who met the eligibility criteria. We assessed the trough concentration of teicoplanin and its safety after high-dose loading in pediatric patients. Overall, 86 patients received a high-dose loading regimen (15 mg/kg every 12 hours for 3 doses, followed by 10 mg/kg once daily). Notably, 55 of the 86 patients (64%) achieved the target trough concentration (>15 mg/L) at significantly higher rates without increasing the incidence of organ damage compared with the standard loading regimen. Multivariate analysis revealed significant differences in age and renal function as factors that inhibited the attainment of the target trough concentration. Simulation analysis using a nomogram stratified by age and renal function revealed that the predicted teicoplanin trough levels were within the target trough values in 73% of patients. High-dose teicoplanin loading safely increases trough blood concentrations in the pediatric population. For further optimization, the dose selection should be stratified according to age and renal function.

Fault Arc Detection Method Based on Improved ShuffleNet V2 Network

Fault arcs exhibit randomness, with current waveforms closely mirroring those of standard nonlinear load operations, posing challenges for traditional series fault arc detection methods. This study presents an improved detection approach using a lightweight convolutional neural network model, ShuffleNet V2. Current data from household loads were collected and preprocessed to establish a comprehensive database, leveraging one-dimensional convolution and channel attention mechanisms for precise analysis. Experimental results demonstrate a high fault arc detection accuracy of 97.8%, supporting real-time detection on the Jetson Nano embedded platform, with an efficient detection cycle time of 15.65 ms per sample. The proposed approach outperforms existing methods in both accuracy and speed, providing a robust foundation for developing advanced fault arc circuit breakers.

Evaluation of 3D-Printed Connectors in Chair Construction: A Comparative Study with Traditional Mortise-and-Tenon Joints.

The present paper investigates the possibility of replacing the traditional L-type corner joint used in chair construction with a 3D printed connector, manufactured using the Fused Filament Fabrication (FFF) method and black PLA as filament. The connector was designed to assemble the legs with seat rails and stretchers, and it was tested under diagonal tensile and compression loads. Its performance was compared to that of the traditional mortise-and-tenon joint. Stresses and displacements of the jointed members with connector were analyzed using non-linear Finite Element Method (FEM) analysis. Both connector and mortise-and-tenon joint were employed to build chair prototypes made from beech wood (Fagus sylvatica L.). Digital Image Correlation (DIC) method was used to analyze the displacements in the vicinity of the jointed members of the chairs. Seat and backrest static load tests were carried out in order to verify if the chairs withstand standard loading requirements. Results indicated that the 3D printed connector exhibited equivalent mechanical performance as the traditional joint. The recorded displacement values of the chair with 3D-printed connectors were higher than those of the traditional chair reaching 0.6 mm on the X-axis and 1.1 mm on the Y-axis, without any failures under a maximum vertical load of approximately 15 kN applied to the seat. However, it successfully withstood the loads for seating and backrest standard tests, in accordance with EN 1728:2012, without any structural failure. This paper presents a new approach for the chair manufacturing sector, with potential applicability to other types of furniture.

Engagement and Self-Regulation Scale for Higher Education Students (ES-R): Development and Psychometric Properties

In the current university education scenario, various aspects are evident that negatively affect students' academic performance and increase dropout levels during the first year. In this context, one of the relevant factors is to analyze the engagement and self-regulation of learning as input for designing strategies with a clear intention of facilitating learning. Hence the importance to develop appropriate instruments to investigate how engaged the students are with their training process, a situation that affects self-regulation of learning, which is a crucial skill in a student's success. For this reason, this work aims to develop and analyze the scale's psychometric properties that allow knowing about the engagement and self-regulation of student learning in the face of schoolwork and their role in learning. The measures are based on a multidimensional perspective of the engagement-self-regulation construct through structural equations. The sample was recruited from a population of first-year college students. The results confirmed that students' engagement and self-regulation comprise multiple related but distinct dimensions. This is evidenced by the standardized factor loadings, which presented values above 0.6, and the calculated Average Variance Extracted, which presented values equal to or greater than 0.5. There was also empirical evidence supporting measurement invariance and predictive validity. Specifically, the differences in CFI and RMSEA were determined to be equal to or below 0.01 and 0.015, respectively. These findings demonstrate the robustness of the scale's psychometric properties.

A Greedy Tabu Dual Heuristic algorithm for the cyclic pickup and delivery problem with 3D loading constraints

The optimization of auto parts supply chain logistics plays a decisive role in the development of the automotive industry. To reduce logistics costs and improve transportation efficiency, this paper addresses the joint optimization problem of multi-vehicle pickup and delivery transportation paths under time window constraints, coupled with the three-dimensional loading of goods. The model considers mixed time windows, three-dimensional loading constraints, cyclic pickup and delivery paths, varying vehicle loads and volumes, flow balance, and time window constraints. Evaluation rules for the three-dimensional loading test of goods are also set, resulting in constructing a comprehensive optimization model for the inbound logistics of auto parts and components. In this study, a Greedy-Tabu Dual-Heuristic algorithm is proposed, which integrates an Improved Greedy Algorithm with an Enhanced Tabu Search Algorithm based on the -sampling strategy. The overall problem-solving process for the Improved Greedy Algorithm and the Tabu Search Algorithm is outlined. The superiority, efficiency, and stability of the improved algorithm are verified by solving cases of various sizes and analyzing the algorithm’s results before and after improvement. A case study involving the third-party logistics company R Enterprise compares the pickup and delivery-separated Milk-Run mode with the simultaneous delivery and pickup Milk-Run mode. The proposed method shows a 26.67% reduction in total distance traveled and a 46.60% decrease in waiting time compared to the traditional Milk-Run approach. Additionally, when evaluated against the standard 3D loading inspection method, the proposed approach improves average vehicle load utilization by 17% and vehicle volume utilization by 15%. These findings verify the applicability and superiority of the proposed algorithms and models in practical scenarios.

Local seismic load on the column of industrial building

When determining the standard seismic load on a single-stored industrial building, the calculation model may vary depending on the task and the structural and technological characteristics of the building. In most cases, a single-mass cantilever model is accepted, providing sufficient accuracy in determining the system's operation. This paper proposes a method for determining the local seismic load on the column of a one-stored industrial building from a top running overhead crane. The method takes into account the location of the crane relative to the column during an earthquake and, accordingly, the way this load is applied to the column.

Feasibility analysis and verification of standard load spectrum of wheel-coupled road simulation test rig

The wheel-coupled road simulation test rig has a wide range of applications in whole-vehicle test. Obtaining the drive spectrum of the wheel coupling road simulation rig quickly and accurately has always been a key issue in the industry. This paper reviewed the basic theory of test rig, and discussed the feasibility of the rig's standard spectrum. Then, this paper took a domestic car model as the test vehicle and collected actual road data from the Yancheng test site. By analyzing and processing these data, the paper verified the feasibility of the standard load spectrum for the wheel-coupled test rig. This research provides theoretical and empirical support for the development of a standard load spectrum for wheel-coupled road simulation rigs.

Physiological impact of oral carbohydrate preload in healthy volunteers.

Oral carbohydrate loading has become a key component of Enhanced Recovery After Major Surgery (ERAS) pathways, with potential improvements in patient comfort, nausea and vomiting, ileus and length of stay. The contribution of each component of ERAS, including carbohydrate beverages, remains unclear. We aimed to determine the impact of standardized oral carbohydrate fluid loading on haemodynamics, stroke volume and gastric state in a group of healthy volunteers. Twenty-three volunteer participants free of known cardiovascular or gastrointestinal disease, consumed 400 mL of a proprietary carbohydrate solution. Heart rate (HR), blood pressure (BP), stroke volume (SV) and gastric cross-sectional area (CSA) were measured with bedside ultrasound at baseline, 30/60/120 min post-ingestion. There were small decreases in HR, BP (<10%) that were statistically significant. There were small increases (<10%) in SV at 30 and 60 min post-ingestion, but SV had returned to baseline by 120 min. There were no changes in SV variation. Gastric CSA increased immediately post-ingestion, then decreased in a linear fashion before returning to baseline by 120 min. In a standard 400 mL dose of a carbohydrate beverage, only small increases in SV could be demonstrated in the first-hour post-ingestion. All patients had largely returned to their baseline SV and gastric CSA state within 2 h post-fluid ingestion. A mild decrease in HR and BP was noted that persisted at 120 min. A 400 mL carbohydrate beverage does not appear to have favourable cardiovascular effects in a healthy population.

Validation of the Modified Japanese 7C Readiness Scale for Parental Intentions of Child COVID-19 Vaccination

Parental willingness is a prerequisite for childhood immunization. This study evaluated whether parental vaccination readiness and willingness, measured using the modified Japanese 7C Vaccination Readiness Scale, predicted vaccination behavior. In December 2021, 2 months before the approval of COVID-19 vaccines for children aged 5–11 years in Japan, 1190 parents of children in this age group were surveyed regarding their intention to vaccinate their children against COVID-19. The survey utilized an online version of the modified 7C Vaccination Readiness Scale, comprising seven components: Confidence, Complacency, Constraints, Calculation, Collective Responsibility, Compliance, and Conspiracy. The modified 7C components were analyzed for model fit and their association with parental intention using structural equation modeling. The scale demonstrated good model fit (goodness-of-fit index: 0.98), with high standardized factor loadings for parental readiness (factor loadings: 0.84). In January 2024, the same parents were surveyed about their children’s COVID-19 vaccination status (valid responses: 536). The relationship between the components of the modified 7C scale and actual vaccination behavior was examined. Parents with low readiness to vaccinate their children against COVID-19 exhibited a significantly higher non-vaccination rate. These findings suggest that the modified 7C scale could support the development of targeted interventions to improve childhood vaccination uptake.

A coaxial solid state nonlinear pulse forming line with an exponentially tapered ferrite composite core.

The need to optimize size, weight, and power of high-power microwave (HPM) systems has motivated the development of solid-state HPM sources, such as nonlinear transmission lines (NLTLs), which utilize gyromagnetic precession or dispersion to generate RF. One recent development implemented the NLTL as a pulse forming line (PFL) to form a nonlinear pulse forming line (NPFL) system that substantially reduced the system's size by eliminating the need for a separate PFL; however, matching standard loads can be challenging. This paper describes the development of a tapered NPFL using an exponentially tapered composite based ferrite core containing 60% nickel zinc ferrite (by volume) encased in polydimethylsiloxane (PDMS) and encapsulated in a 5% barium strontium titanate shell. The tapers exponentially change the line's impedance from a 50Ω standard HN connection to 25Ω before tapering back to 50Ω. We characterized the core behavior by obtaining magnetization curves and ferromagnetic resonance measurements. The rise time (10%-90%) of the pulse decreased from ∼6ns for 5kV charging voltage to 1.8ns for 15kV charging voltage. Under unbiased conditions, the system generated HPM with a center frequency of ∼850MHz with a 3 dB bandwidth of 125MHz. Magnetic biases of 15 and 25 kA/m increased the modulation depth and decreased the center frequency to ∼500MHz for 15kV charging voltage.

Cross-lingual Search Intent Understanding Framework Based on Multi-modal User Behavior

This paper proposes a novel cross-lingual search intent understanding framework leveraging multi-modal user behavior analysis. With the increasing complexity of network traffic and the diversity of user behaviors across languages, traditional approaches often struggle to capture and interpret user search intent in multilingual contexts accurately. Our framework integrates multiple behavioral signals, including query patterns, click sequences, and temporal dynamics, through a sophisticated neural tensor network architecture. The system employs a dual-encoder structure with shared parameters to maintain semantic consistency across languages while incorporating a dynamic behavior sequence learning mechanism to capture temporal dependencies. Experimental evaluation was conducted on a large-scale dataset comprising over 6 million user interactions across four language pairs (EN-ZH, EN-ES, EN-FR, EN-DE) collected over six months. The framework significantly improves over baseline methods, demonstrating an average cross-lingual accuracy of 0.923 and behavior prediction precision of 0.891. Ablation studies reveal the critical role of multi-head attention mechanisms and temporal modeling in maintaining system performance. The framework retains real-time processing capabilities with an average latency of 45ms per request under standard load conditions. Our research advances the field of cross-lingual information retrieval by introducing a practical approach to integrating behavioral signals with linguistic features, providing valuable insights for developing more sophisticated multilingual search systems.

Utilizing realistic loading histories for the calibration of nonlinear components in seismic analysis

Hysteretic models that simulate the hysteretic response of key structural components are generally employed in the nonlinear seismic analysis of structures. The calibration of hysteretic model parameters is crucial for achieving accurate analysis results in structural seismic assessment. The calibration process is commonly conducted by tuning hysteretic model parameters to align with the experimental results of a single component tested under standardized reverse-cyclic loading protocols. The underlying assumption of such a calibration method is that a structural model at the system level, using a well-tuned hysteretic model capable of accurately replicating the test results of a single component under a standardized incremental cyclic loading protocol, can predict the dynamic response of the structural system subjected to ground motion excitations with an acceptable level of accuracy. However, due to the simplified and often unrealistic loading protocols used for model calibrations, this assumption has been challenged recently by both numerical and experimental studies. In this paper, calibration methods utilizing more realistic loading histories are evaluated and compared to more conventional incremental cyclic loading-based protocols. The evaluation of calibration methods is carried out by quantifying the calibration relevance, utilizing a framework of virtual experiments that incorporates uncertainties in hysteretic model parameters. Analyses are conducted based on a case study of BRB components and BRBFs. Additionally, four calibration error quantification methods, considering characteristics in the transitional and plastic ranges of hysteresis curves of BRB, are proposed and compared. The results demonstrate that it is in fact advantageous to use realistic loading histories in component calibration of BRBs. An improved formulation of the calibration error is also proposed for the optimization of hysteretic parameters.

Validation and adaptation of the moral outrage scale in the Peruvian adult population

BackgroundMoral outrage (MO) is an emotional experience that arises in situations where a moral standard is violated. Despite its relevance, there are few psychometric studies in the Latin American context, characterized by inequality and social problems. This highlights the need for specific assessment instruments for this construct. The purpose of this study was to analyze the psychometric properties of an MO scale in the Peruvian population.MethodsThe sample consisted of 881 Peruvians (57.2% women, Mage = 24.5 years, SD = 9.07 years). The moral outrage scale was administered to participants. To analyze the data, two models were tested: a structural equation model (SEM) and an exploratory structural equation model (ESEM).ResultsThe CFA confirmed a two-factor model (emotional and cognitive), with satisfactory fit indices and standardized factor loadings greater than 0.70. The ESEM model also showed improved fit indices. Measurement invariance across sex was established, and convergent validity was demonstrated between the dimensions of MO and authoritarianism, as well as other sociodemographic variables such as sex, age, and socioeconomic level.ConclusionsThe MO scale tested in this study is a reliable, valid, and invariant instrument, suitable for assessing moral outrage in both men and women within the Peruvian context.

Research on the multi-area cooperative control method for novel power systems

As new energy sources increasingly penetrate novel power systems, the coordinated control of frequency regulation units has become more challenging, leading to the degraded performance of power grid control. The automatic generation control methods based on traditional reinforcement learning overly depend on exploration, making it difficult quickly obtain optimal solutions for multi-area cooperative control and resulting in suboptimal performance. To address this issue, this paper proposes a multi-area coordinated control method using a greedy actor-critic algorithm enhanced with expert experience replay. This method decouple the role of entropy for exploration and policy collapse, utilizing a proposal policy to secure high-value actions for policy updates. Thus the exploration and exploitation are balanced and the local optima is avoided. Additionally, the expert experience replay collects expert demonstration data with high-value to assist the learning of multi-agent. Therefore, the meaningless exploration in early training is reduced and the rapid attainment of optimal solutions is facilitated. This paper validates the proposed method through simulations on the model of the improved IEEE standard two-area load frequency control and the model of the Sichuan, Chongqing, and Hubei three-area. Compared with other various reinforcement learning methods, the proposed method is demonstrated with superior control performance.

The Development and Validation of an Artificial Intelligence Chatbot Dependence Scale.

In recent years, a plethora of artificial intelligence (AI) chatbots have been developed and made available to the public. Consequently, an increasing number of individuals are integrating AI chatbots into their daily lives for various purposes. This trend has also raised concerns regarding AI chatbot dependence. However, a valid and reliable scale to assess AI chatbot dependence is yet to be developed. Therefore, this study was designed to develop and validate an AI chatbot dependence scale. We obtained initial items from previous publications and in-depth interviews. Subsequently, item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability, and validity analyses were performed to validate the AI chatbot dependence scale. Seventeen items underwent item analysis and EFA, resulting in a single-factor model with eight items explaining 58.42% of the total variance. The CFA indicated that our AI chatbot dependence scale had acceptable model fitting indices, with standardized loadings ranging between 0.50 and 0.76. In addition, this scale exhibited good reliability and validity. Thus, the current AI chatbot dependence scale can effectively evaluate individuals' dependence on AI chatbots in their daily lives.

Development and validation of the Out-of-Hospital Adherence Questionnaire for Stroke Patients (OHAQ-SP).

The extent of rehabilitation of stroke patients outside the hospital is contingent upon their dedication to rehabilitative exercises, adherence to a suitable diet, and medication protocols. Therefore, multiple aspects of compliance must be assessed. Questionnaires are indirect measurements that are economical and easy to use. However, questionnaires should meet specific criteria to minimize errors and ensure reprehensibility of the results. To develop a new questionnaire-based assessment tool for out-of-hospital treatment adherence in stroke patients and to evaluate the psychometric properties and feasibility of the questionnaire by conducting preliminary measurements in the stroke population. The questionnaire should have at least one validity measure and one reliability measure. A preliminary "OHAQ-SP" was developed based on a literature review and two rounds of Delphi expert consultation. Between April 1 and July 2022, 308 stroke patients revisited the neurology departments of four Grade A tertiary hospitals in Shanghai using the convenience sampling approach. Various statistical methods, such as item analysis, correlation analysis, exploratory factor analysis, confirmatory factor analysis, and the internal consistency measure Cronbach's α coefficient, were employed to assess the reliability and validity of the questionnaire. A total of 290 questionnaires were collected; the Cronbach's α coefficients were 0.936, 0.862, 0.916, and 0.928 for the different sections, and the overall Cronbach's α coefficient for the entire scale was 0.902. The results of the confirmatory factor analysis were CMIN/DF = 2.490, RMR = 0.05, GFI = 0.901, NFI = 0.927, IFI = 0.955, and RMSEA = 0.054. The standardized loading values varied between 0.717 and 0.916. The combined reliabilities of the four dimensions were 0.936, 0.864, 0.917, and 0.929, respectively. The average variance extraction values for the four dimensions are 0.676, 0.614, 0.611, and 0.652, respectively. The OHAQ-SP is a reliable and accurate instrument for evaluating medication use, nutritional status, and behavior throughout the out-of-hospital rehabilitation phase.