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Over the last decade there has been significant interest in international expansion of emerging market firms (EMFs) due to their distinctive patterns compared with developed market firms (DMFs). In this article, the authors develop an analytical model to generate prescriptive insights for pricing strategies when EMFs enter new foreign markets. Grounded in the international marketing and organizational learning literatures, the model accounts for local and multinational competition, the influence of country-of-origin effects, and the role of organizational learning in foreign markets. The results suggest that when an EMF enters a host market with a local competitor, it could generate higher profits even when charging a lower price than the local competitor. Additionally, the authors find that DMFs enjoy greater profitability than EMFs in foreign markets as a result of positive country-of-origin effects. Finally, the authors propose and validate the use of organizational learning as a process EMFs can use to surmount the negative impact of country-of-origin effects and achieve greater profitability than DMFs. [ABSTRACT FROM AUTHOR]

We considered two periodic review perishable inventory systems. The first system is controlled by a periodic review (T , S) policy, where orders are triggered at each review period T and when the stock is less than S. The second one is controlled by a periodic review (s , S) policy, where orders are triggered only if the stock level is below s. For both models, the lifetime of each product was assumed to follow a general distribution, whereas the lead time was constant. The demands followed a Poisson process, and all unsatisfied demand was lost. Using an approximate solution of the steady-state probabilities of the inventory level, we obtained a closed form of cost expression. Next, we conducted a simulation study to identify our solution accuracy. Finally, we performed a sensitivity analysis to evaluate the effect of the lifetime variation and cost parameters on the best policy and its cost. The obtained results were close to those of the optimal policy with an average accuracy of 3.7% for the periodic review (T , S) policy and 3.8% for the periodic review (s , S) policy. For the worst cases, where the accuracy is higher than 7%, we provided an analytical approach to improve the system accuracy. [ABSTRACT FROM AUTHOR]

The increasing demand for sustainable materials in high-value applications, particularly in the automotive industry, has prompted the development of biocomposites based on renewable or recyclable matrices and natural fibers as reinforcements. In this context, this paper aimed to produce composites with improved mechanical and thermal properties (tensile, flexural, and heat deflection temperature) through an optimized process pathway using a biobased polyamide reinforced with short basalt fibers. This study emphasizes the critical impact of fiber length, matrix adhesion, and the variation in matrix properties with increasing fiber content. These factors influence the properties of short-fiber composites produced via primary processing using extrusion and shaped through injection molding. The aim of this work was to optimize extrusion conditions using a 1D simulation software to minimize excessive fiber fragmentation during the extrusion process. The predictive model's capacity to forecast fiber degradation and the extent of additional fiber breakage during extrusion was evaluated. Furthermore, the impact of injection molding on these conditions was investigated. Moreover, a comprehensive thermomechanical characterization of the composites, comprising 10%, 20%, and 30% fiber content, was carried out, focusing on the correlation with morphology and processing using SEM and micro-CT analyses. In particular, how the extrusion process parameters adopted can influence fiber breakage and how injection molding can influence the fiber orientation were investigated, highlighting their influence in determining the final mechanical properties of short fiber composites. By optimizing the process parameters, an increment with respect to bio-PA11 in the tensile strength of 38%, stiffness of 140%, and HDT of 77% compared to the matrix were obtained. [ABSTRACT FROM AUTHOR]

Nowadays, retrofitting and rehabilitation of deteriorated reinforced concrete structures are becoming a growing need of the construction industry instead of demolishing aged structures. The application of fabric‐reinforced cementitious matrix (FRCM) on the existing concrete structures is one of the sustainable solutions to retrofit the concrete structures. This study used machine learning (ML) models such as linear regression (LR), support vector machines (SVM), and adaptive neuro‐fuzzy inference systems (ANFIS) to estimate the compressive strength (CS) of columns wrapped with FRCM. The experimental dataset of 301 column specimens was collected including input parameters such as cross‐sectional properties, mechanical properties of concrete and steel, and characteristics of FRCM material. Apart from ML models, seven analytical models were also used to compare the accuracy and precision of ML models. The results illustrate that the ANFIS model outperformed other ML models and established itself as a dependable and precise model. The R‐value of the ANFIS model was 0.9816, whereas R‐values of 0.9269 and 0.9572 were achieved by LR and SVM models, respectively. In addition, the MAPE value acquired by the ANFIS model was 1.52% which was lower than those of the LR model by 73.24%, and the SVM model by 60.60%, respectively. As the precision of the ANFIS model was higher as compared with SVM and LR models, so, the developed ANFIS‐based mathematical model can be easily used to predict the CS of FRCM‐strengthened concrete columns. The developed model is accurate, economical, and fast; and can be utilized by FRCM applicators and structural designers. [ABSTRACT FROM AUTHOR]

This paper investigates durability related properties of concretes containing copper heap leach residue (CHLR) as partial replacement of natural fine and coarse aggregates. The use of CHLR as aggregates in concretes promotes the reduction of the dependance on natural aggregates, upcycling of the waste as aggregates and the reduction of carbon footprint associated with natural aggregate productions. In this study, CHLR was washed, dried, and sieved to separate fine aggregate and coarse aggregate, and concretes were prepared with a cement content of 400 kg/m3 and water-cement ratio of 0.435 by replacing 25-75% natural fine and coarse aggregates. The concrete containing 50% CHLR as a partial replacement of natural CA and FA gained compressive strength of 52.9 and 54.0 MPa; drying shrinkage of 662 and 538 με; volume of permeable voids of 6.2 and 5.7%; 1485 and 2640 coulomb of charge passed in chloride permeability; and primary sorptivity coefficients of 4.0 × 10−3 and 4.3 × 10−3 mm/sec0.5 at 180 days, respectively. In contrast, these properties for the control specimens at the same age were 59.1 MPa, 394 με, 5.19%, 1280 coulomb, and 2.5 × 10−3 mm/sec0.5, respectively. The compressive strength and durability aspects declined in concretes using 75% CHLR coarse and fine aggregates. Existing analytical models for durability related properties of concrete containing natural aggregates are compared to that of the concretes using CHLR. Finally, backscattered electron images coupled with energy dispersive x-ray spectroscopy of the CHLR concretes were analyzed to understand the pore refinement, interfacial transition zones, microcracks, and hydration products influencing the durability aspects. [ABSTRACT FROM AUTHOR]

The present review focuses on the theoretical model developments made in floating flexible net fish cages and the floating bodies application to offshore aquaculture. A brief discussion of the essential mathematical equations related to various theoretical models of flexible net cages in the frequency domain is presented. The single and array of floating or submerged flexible net cages connected with or without mooring lines are discussed. Further, as the combined effect of the hydroelastic behaviour of floaters and the flexible behaviour of fish cages are necessary to assess their efficiency and survivability from structural damages, the issues and the knowledge gap between the recent and future models are also discussed. In conclusion, the practical suggestions concerning advancements in future research and directions within floating flexible net cages and the hydroelastic response of elastic floaters are highlighted. [ABSTRACT FROM AUTHOR]

This study focuses on the essential task of monitoring the strength of structural concrete in construction and rehabilitation projects. The conventional non-destructive testing (NDT) approach offers an indirect estimation of concrete compressive strength. However, the existing models lack the ability to address the estimation of compressive strength in concrete mixtures containing bottom ash (BA) and recycled coarse aggregate (RCA). This study aims to bridge this gap by accurately estimating the compressive strength of such concrete using the ultrasonic pulse velocity (UPV) technique. To achieve this objective, various concrete specimens with different cement content, water–cement ratios (w/c), recycled coarse aggregate, and bottom ash contents through weight batching, were prepared. UPV and compressive strength measurements were taken on cylindrical concrete specimens after 28 and 90 days of curing. The analysis revealed an exponential relationship between the compressive strength and UPV, with a high correlation coefficient across the evaluated concrete mixes, independent of the curing time. Ultimately, two robust models were developed to predict the compressive strength of concrete mixes containing different percentages of RCA and BA, respectively, at 28 and 90 days of curing. These models offer valuable insights and practical tools for ensuring the structural integrity of concrete in construction and rehabilitation projects involving bottom ash and recycled coarse aggregate. [ABSTRACT FROM AUTHOR]

Flexible facing systems, the main element of which is a pinned net, are widely used to stabilise ground layers on slopes. Today, this technique benefits from decades of successful experience. The optimisation of their design has motivated a great deal of research, based on experiments and numerical modelling, particularly in recent years. This literature review first gives a synthetic overview of the available analytical design methods, before presenting the various research studies that have been carried out on flexible facing systems. The response of flexible facing systems is then discussed in a didactic manner, focusing on the membrane and paying particular attention to the mechanisms and parameters that influence this response. The relevance of current practices in terms of component characterisation tests and analytical approaches to design is then discussed. Finally, the latest research is presented and the best design criteria are discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the mechanics behavior of eccentric rectangular hollow section (ERHS) X-joints subjected to axial compression in the brace, based on which analytical formulas are established to predict the strength of the ERHS X-joints. Static testing is conducted on two ERHS X-joint and one RHS X-joint specimens under brace compression, and the corresponding finite element (FE) analysis is performed. It is found that the ultimate strength of the ERHS X-joints with a medium brace/chord width ratio (β) is 39% higher than that of the RHS X-joints. The strength and initial stiffness of the ERHS X-joints increase when β increases. Based on the load transferring mechanism, two analytical models and the corresponding equations are proposed to predict the strength of the ERHS X-joints with β≤0.925 and β=1.0. The proposed models are validated through experimental data and FE results. [ABSTRACT FROM AUTHOR]

Water table configuration gives rise to hierarchically nested groundwater flow systems. However, there remains a lack of comprehensive understanding regarding the controlling factors of water table and its impact on flow systems. Moreover, it remains challenging to identify characteristics of water table space variation through limited groundwater observations at the regional scale. Based on two ideal two-dimensional cross-section analytical models, this study presents a simplified approach to preliminarily assess the nonlinear interactions between water table variation and three driving factors: Topography, geology and climate. Two criteria, C1 and C2, are utilized to address issues at different scales ranging from basin to local: (i) the influence of various factors on water table configuration; and (ii) the influence of water table on groundwater flow pattern. Then, the Ordos Plateau is taken as an example to explore the role of the water table in nested groundwater systems using the provided approach and criterion. The application of this approach in the Ordos Plateau demonstrates its appropriateness as a practical method for preliminarily determining the characteristics of water table configuration and its impact on flow systems. The study explores the mechanism influencing spatial variation in the water table and improves understanding of the interaction between topography, geology, and climate on groundwater flow patterns.

Recently, a new proposed technique named; Externally Bonded Reinforcement on Grooves (EBROG) has been presented as a strengthening method to postpone or eliminate the undesirable debonding failure mode that is accompanying to Externally Bonded Reinforcement (EBR) method. This paper introduces an experimental and analytical study to evaluate the validity and accuracy of the formulas that were introduced by the standard codes and researchers, in estimating the ultimate load of RC continuous beams strengthened with carbon fiber reinforced polymer (CFRP) sheets in flexure. The experimental program includes testing of ten RC beams with cross section of (200×130) mm and two equal spans, each span has a 1050 mm length. The comparison between the ultimate load collected from the test results and that predicted using ten different analytical models is conducted. It is found that the ACI 440.2R-17 and most of other analytical models well predicted the ultimate load of RC beams strengthening with one layer of CFRP sheets using EBR, but most of these models significantly underestimate the ultimate load of tested beams that were strengthened using EBROG method. ACI, Shehata el. al., and Teng et al. models give close predicted load to the experimental results in case of EBR beams strengthened with two layers.

The increase in emissions of toxic gasses such as hydrogen sulfide (H2S) and carbon dioxide (CO2), resulting from growing urbanization and industrialization, has caused environmental and public health problems, making the implementation of air purification techniques through adsorption important. Thus, modeling the gas adsorption process is fundamental for good agreement with experimental data, employing mathematical models that enable the prediction of adsorption capacity. In this way, the present work aimed to compare different analytical breakthrough curve models (Thomas, Yoon–Nelson, Adams–Bohart, and Yan) for the adsorption of H2S and CO2 in fixed-bed columns, using experimental data from the literature, estimating the curve parameters through the Markov Chain Monte Carlo (MCMC) method with the Metropolis–Hastings algorithm, and ranking using the determination coefficients (R2 and R2Adjusted) and the Bayesian Information Criterion (BIC). The models showed better agreement using the estimation of maximum adsorption capacity (qs, N0) and the constants of each model (kth, kyn, and kba). In the adsorption of H2S, the Yan model stood out for its precision in estimating qs. For the adsorption of CO2, the Adams–Bohart model achieved better results with the estimation of N0, along with the Yoon–Nelson model. Furthermore, the use of this method allows for a reduction in computational effort compared to models based on complex differential equations. [ABSTRACT FROM AUTHOR]

This study investigated the effects of intercalation and exfoliation of the graphite flake particles on the mechanical properties of elastomeric composites using analytical, computational, and experimental techniques. Relying on the modified Eshelby and incremental Mori–Tanaka approach, a new micromechanics model was proposed to estimate Young's modulus and develop a constitutive model for expanded graphite (EG)-filled elastomer in finite strain deformation. It was found that when the initial graphite flakes expanded, the actual volume fraction and ultimate aspect ratio of the dispersed particles significantly affected the mechanical properties of the EG-reinforced composites. The present model and subsequent predictions reflect the initial flake diameter, actual volume fraction, ultimate aspect ratio of the particles, and other convenient physical properties of the composite constituents. In addition, molecular dynamics simulations were performed to evaluate the elastic properties of the EG-Matrix interphase region using the COMPASS force field function. The MD results showed that Young's modulus of the interphase region increased significantly as the quantity of the graphene layer increased. The micromechanics-constitutive model results were validated using the experimentally determined stress–strain relation of the composites. EG- and CB-elastomer nanocomposite specimens were fabricated and tested in the experimental program. Field emission scanning electron microscopy (FESEM) analysis of the cryo-fractured surfaces of the elastomer compounds showed good interactions between the constituents. Numerical computations indicated that the proposed model incorporating the new parameters could accurately predict the experiment at a small particle concentration. [ABSTRACT FROM AUTHOR]

Cross-laminated timber (CLT) buildings are constructed by connecting CLT panels with dowel-type fasteners and mechanical anchors, which govern the lateral behavior of these structures. Although these connections are mostly distributed along the perimeter of the CLT panels, creating highly redundant structures, previous research on the lateral behavior of CLT structures has mainly focused on the behavior of the connections placed at the base of the CLT shear walls, such as hold downs and angle brackets, while limited attention has been paid to the connections between perpendicular walls and their effect on the lateral response of CLT shear walls. This paper presents a comprehensive experimental study aimed at investigating the effects of the interaction between perpendicular walls on the lateral response of CLT shear walls. CLT shear walls with three different aspect ratios were analyzed by means of monotonic and cyclic tests, both in single wall configuration and connected to perpendicular walls. The experimental findings demonstrate that the structural interaction due to the perpendicular walls significantly affects the lateral response of a CLT shear wall, resulting in increased lateral stiffness, lateral capacity, and deformation capacity. In addition, the study presents two analytical models for the prediction of the lateral stiffness and the lateral capacity of CLT shear walls connected to perpendicular walls. The analytical models were validated based on the experimental results, showing reasonable agreement. [ABSTRACT FROM AUTHOR]

The regional seismic risk assessment of reinforced concrete (RC) building portfolios is a critical issue in earthquake engineering due to their high vulnerability and widespread distribution in seismic prone areas. A pertinent aspect in regional seismic risk applications is the ability to accurately quantify the exceedance of any damage state, generally via fragility functions. To this end, this study derives analytical fragility functions for large-scale seismic risk applications of non-ductile RC buildings with masonry infills characteristic of the Italian peninsula and Southern Europe in general. These were derived using a large database of archetype buildings developed to represent the temporal evolution in construction practice in Italy based on an extensive literature review and interviews with practising engineers and architects. Fragility functions for several infilled RC taxonomy classes were derived for multiple damage states using state-of-the-art analysis on detailed numerical models. Average spectral acceleration was adopted as the intensity measure throughout, since it has been shown to notably reduce dispersion and bias in quantifying the response, and subsequently refine the seismic risk estimates, for these typologies. The fragility functions are compared against empirical data collected following past earthquakes in Italy, namely L'Aquila 2009 and Umbria-Marche 1997. The development of empirical fragility functions was carried out using a novel derivation of average spectral acceleration-based ground-motion fields considering spatial and cross-period correlation models, which is a key component and development in this study. This paper shows how recent advances in analytical fragility function development can be integrated with past empirical observations to give more accurate and representative damage estimates for regional assessment. [ABSTRACT FROM AUTHOR]

Electrical machines that can run at high speeds are more and more studied as they can respond to the increasing need of power onboard of aircrafts. However, to allow high-speed operability mechanical handling of the rotating parts need to be insured. In this paper an analytic design process of a novel high-speed induction machine is presented. The analytical magnetic and mechanical models developed are presented and validated with finite element simulations. The magnetic model is based on a classic equivalent electrical diagram of induction machine with a specific adaptation for the rotor leakage inductance as the squirrel cage is buried. The mechanical model is based on a field displacement approach leading to the stress tensor in all the rotating part. A four degrees of freedom vibration analysis model considering gyroscopic effects based on Euler–Lagrange equation allows to identify the critical speeds of the system. It is shown that some geometrical parameters will have opposed effects on the two physics. Thus, an optimization-based coupling between the different physics allows to design rapidly the desired machine regarding any technical specifications as analytical models are being used. [ABSTRACT FROM AUTHOR]

Mechanical properties of short fiber reinforced composites manufactured with fused deposition modeling is affected by variables such as printing properties and internal structure of the composite. In this study, the relation between the tensile properties of 3D-printed short fiber reinforced composites, printing orientation and internal structure is inspected. Samples of different printing orientations are manufactured. These samples are inspected under X-ray micro computed tomography (micro-CT) and their fiber orientation distributions and fiber properties are determined. Then, these internal structure properties are applied to analytical material models to estimate the tensile properties of composites. Estimated material properties are compared with tensile test results. It turns out that analytical models that consider internal structure properties provide more accurate estimations than other analytical models that does not take these into account. [ABSTRACT FROM AUTHOR]

The advent of Artificial Intelligence (AI) technologies has precipitated the rise of asynchronous video interviews (AVIs) as an alternative to conventional job interviews. These one-way video interviews are conducted online and can be analyzed using AI algorithms to automate and speed up the selection procedure. In particular, the swift advancement of Large Language Models (LLMs) has significantly decreased the cost and technical barrier to developing AI systems for automatic personality and interview performance evaluation. However, the generative and task-unspecific nature of LLMs might pose potential risks and biases when evaluating humans based on their AVI responses. In this study, we conducted a comprehensive evaluation of the validity, reliability, fairness, and rating patterns of two widely-used LLMs, GPT-3.5 and GPT-4, in assessing personality and interview performance from an AVI. We compared the personality and interview performance ratings of the LLMs with the ratings from a task-specific AI model and human annotators using simulated AVI responses of 685 participants. The results show that LLMs can achieve similar or even better zero-shot validity compared with the task-specific AI model when predicting personality traits. The verbal explanations for predicting personality traits generated by LLMs are interpretable by the personality items that are designed according to psychological theories. However, LLMs also suffered from uneven performance across different traits, insufficient test-retest reliability, and the emergence of certain biases. Thus, it is necessary to exercise caution when applying LLMs for human-related application scenarios, especially for significant decisions such as employment. [ABSTRACT FROM AUTHOR]

Multimodal aspect-based sentiment analysis (MABSA) aims to determine the sentiment polarity of each aspect mentioned in the text based on multimodal content. Various approaches have been proposed to model multimodal sentiment features for each aspect via modal interactions. However, most existing approaches have two shortcomings: (1) The representation gap between textual and visual modalities may increase the risk of misalignment in modal interactions; (2) In some examples where the image is not related to the text, the visual information may not enrich the textual modality when learning aspect-based sentiment features. In such cases, blindly leveraging visual information may introduce noises in reasoning the aspect-based sentiment expressions. To tackle these shortcomings, we propose an end-to-end MABSA framework with image conversion and noise filtration. Specifically, to bridge the representation gap in different modalities, we attempt to translate images into the input space of a pre-trained language model (PLM). To this end, we develop an image-to-text conversion module that can convert an image to an implicit sequence of token embedding. Moreover, an aspect-oriented filtration module is devised to alleviate the noise in the implicit token embeddings, which consists of two attention operations. After filtering the noise, we leverage a PLM to encode the text, aspect, and image prompt derived from filtered implicit token embeddings as sentiment features to perform aspect-based sentiment prediction. Experimental results on two MABSA datasets show that our framework achieves state-of-the-art performance. Furthermore, extensive experimental analysis demonstrates the proposed framework has superior robustness and efficiency. [ABSTRACT FROM AUTHOR]

Sentiment analysis can be used to derive knowledge that is connected to emotions and opinions from textual data generated by people. As computer power has grown, and the availability of benchmark datasets has increased, deep learning models based on deep neural networks have emerged as the dominant approach for sentiment analysis. While these models offer significant advantages, their lack of interpretability poses a major challenge in comprehending the rationale behind their reasoning and prediction processes, leading to complications in the models' explainability. Further, only limited research has been carried out into developing deep learning models that describe their internal functionality and behaviors. In this timely study, we carry out a first of its kind overview of key sentiment analysis techniques and eXplainable artificial intelligence (XAI) methodologies that are currently in use. Furthermore, we provide a comprehensive review of sentiment analysis explainability. [ABSTRACT FROM AUTHOR]

Body motion is an important channel for human communication and plays a crucial role in automatic emotion recognition. This work proposes a multiscale spatio-temporal network, which captures the coarse-grained and fine-grained affective information conveyed by full-body motion and decodes the complex mapping between emotion and body movement. The proposed method consists of three main components. First, a scale selection algorithm based on the pseudo-energy model is presented, which guides our network to focus not only on long-term macroscopic body expressions, but also on short-term subtle posture changes. Second, we propose a hierarchical spatio-temporal network that can jointly process posture covariance matrices and 3D posture images with different time scales, and then hierarchically fuse them in a coarse-to-fine manner. Finally, a spatio-temporal iterative (ST-ITE) fusion algorithm is developed to jointly optimize the proposed network. The proposed approach is evaluated on five public datasets. The experimental results show that the introduction of the energy-based scale selection algorithm significantly enhances the learning capability of the network. The proposed ST-ITE fusion algorithm improves the generalization and convergence of our model. The average classification results of the proposed method exceed 86% on all datasets and outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The use of alkali‐activated concrete (AAC) as an alternative construction material to Portland cement‐based concrete (PCC) has been widely encouraged by its enhanced mechanical and durability performance and environmental benefits. However, AAC exhibits low flexural and tensile strength, limiting its application in areas where high post‐cracking flexural and tensile load‐bearing capacity are needed. Steel fibers can be added to improve the composite ductility and toughness. Steel fiber‐reinforced alkali‐activated concrete (SFRAAC) is a new emerging technology with research studies evaluating the effect of fiber addition on its mechanical properties still in the early stages. To promote the application of SFRAAC, analytical models predicting their mechanical performance are needed. This study evaluates the applicability to SFRAAC of previously published analytical models developed for steel fiber‐reinforced cement‐based concrete (SFRPCC). Experimental data available in the literature have been collected to create an extensive database to validate and then calibrate these currently available correlations between mechanical properties for SFRAAC. The prediction models considered in this study correlate the mechanical performance of SFRAAC, that is, compressive strength, modulus of elasticity, splitting tensile strength, flexural and residual flexural strength, to the compressive strength of the reference concrete without fibers, the fiber volume fraction and the fiber reinforcing index. Thus, by knowing the performance of the AAC matrix and the fiber properties and dosage, it is possible to predict the overall mechanical behavior of the steel fiber‐reinforced composite. [ABSTRACT FROM AUTHOR]

The prospects are substantiated of using the air data system with one fixed flush-mounted receiver, built-in sensors of incoming flow parameters, and calculator on ultralight and small aircraft. Based on the previously developed analytical models for determining the output signals of this promising system, the study of the instrumental and methodic errors of its measurement channels was carried out. Analytical models are obtained for the instrumental and methodic errors of measurement channels of the air data system with one fixed flush-mounted receiver of incoming air flow parameters. [ABSTRACT FROM AUTHOR]

Determining the magnetocaloric effect (MCE) in dense nanoparticle systems (DNSs) poses a challenge due to the increased complexity of matter at the nanoscale. Given the interparticle magnetic interactions, diverse particle size and shape distributions, and the presence of inhomogeneous magnetic phases, selecting a suitable phenomenological model is essential to describe the temperature dependence of magnetic behavior in DNSs. Herein, we chose a cost‐effective Ni100‐xCrx DNS with adjustable magnetic transitions to showcase the resilience of the MCE across a broad temperature range (147–614 K). While the hyperbolic tangent model appears more fitting for materials with a single Curie temperature (TC), such as its parent bulk alloys, in the presence of a TC distribution a Gaussian distribution model proves to be better suited for DNSs. The latter model yields a magnetic entropy change, ΔSmax = 0.09–0.15 J kg‐K−1 in the DNS at a tiny field of 0.1T. The correlations between the broadening of the MCE peak and TC distribution are attributed to the particle size distribution and chemical inhomogeneity present in the DNS, paving the way for fine‐tuning MCE‐related properties such as the relative cooling power (13.17–33.45 J kg−1) and adiabatic temperature change (0.03–0.17 K). Our methodology not only enhances the potential for designing innovative MCE materials with broader operating ranges but also validates the universality of our phenomenological model for other families of nanocrystalline/nanogranular oxides/alloys thin films. [ABSTRACT FROM AUTHOR]

Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge devices necessitate latency-targeted neural network compression. However, measuring latency on real devices is challenging and expensive. Therefore, this letter presents a novel and efficient framework, named EvoLP, to accurately predict the inference latency of models on edge devices. This predictor can evolve to achieve higher latency prediction precision during the network compression process. Experimental results demonstrate that EvoLP outperforms previous state-of-the-art approaches by being evaluated on three edge devices and four model variants. Moreover, when incorporated into a model compression framework, it effectively guides the compression process for higher model accuracy while satisfying strict latency constraints. We open-source EvoLP at https://github.com/ntuliuteam/EvoLP. [ABSTRACT FROM AUTHOR]

Machine learning models play a crucial role in sports monitoring by effectively identifying various activities and tracking the number of repetitions during repetitive movements. However, creating models that accurately detect different types of exercises and provide feedback on movement adjustments for wearable devices remains a challenge. In this letter, we propose an alternative approach that addresses this issue by using the restricted Boltzmann machine (RBM) algorithm to learn, evaluate, and provide adjustment feedback based on inertial sensor data in real-time. Our experimental results show that by evaluating body segments individually, highly specialized models can be generated from a small set of movement repetitions. Moreover, these models have the capability to offer users precise recommendations on how to fine-tune the intensity, acceleration, and amplitude of the monitored segment. By using our proposed method, there is a great potential to enhance the accuracy and effectiveness of wearable devices used for sports monitoring. [ABSTRACT FROM AUTHOR]

In this paper, the authors present an extension of the scope of the previously conducted research to the full three-dimensional computer simulation (using the finite element method), which takes into account the interaction between: heavy caterpillar tracks system – working platform – weak subsoil. The article presents a computer model considering two caterpillars, resting on elastic-plastic sub-soil, with standard Mohr-Coulomb yield conditions, allowing for computer simulation of the behavior of the system up to achievement of ultimate limit state. The results of the above model are treated as the reference for a simplified Analytical Models of estimating the limit state, which might be used in design procedures. In turn, these Analytical Models are enhancements of previously presented one. The most important results concluding form the Analytical Model are simple interaction formulas, in the space of moments acting on the machine-subsoil system, limiting a domain of safety in given soil conditions.

Abstract Traditional survey methods can provide noisy data arising from recall, memory and other biases. Technological advances (particularly in neuroscience) are opening new ways of monitoring physiological processes through non-intrusive means. Such dense continuous data provide new and fruitful avenues for complementing self-reported data with a better understanding of human dynamics and human interactions. In this study, we use a survey to collect positive affect (feelings) data from more than 300 individuals over a period of 24 h, and at the same time, map their core activities (5000 recorded activities in total) with measurements of their heart rate variability (HRV). Our results indicate a robust correlation between the HRV measurements and self-reported affect. By drawing on the neuroscience and wellbeing literature we show that dynamic HRV results are what we expect for positive affect, particularly when performing activities like sleep, travel, work, exercise and eating. This research provides new insights into how to collect HRV data, model and interpret it.

Membranotronic devices are artificial neural membranes mimicing the functionality of biological neural networks. These devices rely on the emergence of negative differential resistance (NDR). A minimalistic physical model for membranotronic devices capable of generating NDR is presented. The model features a deformable membrane with holes that facilitate ion currents. The deformation of the membrane, induced by electrostatic pressure from an applied voltage, modulates these currents. The model comprises a well‐established mechanical framework for describing deformable membranes with holes, alongside a model for ionic current that considers temperature‐dependent ion mobilities. It is demonstrated that the model can faithfully reproduce NDR across a wide and physically realistic range of parameter combinations. Furthermore, the simulations reveal that the temperature of the electrolyte can exceed its boiling point, resulting in bubble formation. To mitigate this issue, materials with high heat transfer coefficients and low conductivity are recommended. In essence, the work bridges the gap between artificial membranotronic devices and biological neural networks by providing a robust physical model capable of emulating NDR, a key feature in the operation of such systems. This advancement in membranotronics holds great promise for the development of bioinspired soft artificial neuromimetic systems that closely mimic their biological counterparts. [ABSTRACT FROM AUTHOR]

Urbanization places greater demand on the link between downtown areas and suburbs, due to commuters’ long-distance and diverse trips. As an emerging form of park-and-ride (PNR) services, remote PNR (RPR) facilities have proved to be more economical and environmentally friendly, allowing travelers to park in a suburban area and travel to a rail station via bus. In this regard, a generalized simulation-based bilevel model for optimizing the locations and capacities of RPR facilities is developed in this article. A hybrid algorithm integrating Bayesian optimization, branch and bound, and trust region sequential quadratic programming is proposed to achieve an optimal solution. The proposed integrated method balances the desired efficiency and accuracy through the combination of machine learning-based technology and mathematical optimization methodology. The validity of the proposed model is tested on a large-scale real-world transportation network in Halle, Germany. Modeling and analyzing RPR schemes using the proposed framework may provide new insights into improving social welfare. [ABSTRACT FROM AUTHOR]

This paper presents a parametric study of linear induction motor for design purpose. The chosen mathematical model uses a 2D formulation with magnetic vector potential A. The implementation of the model is carried out with the finite element method on the free platform Gmsh-GetDP. Circuit model is coupled to FE model so that constant voltage supply mode can be considered. This work aims to highlight the effect of the pole pair number on the characteristics and the performances of the linear induction machine through two numerical models associated to an analytical one. Furthermore, the study shows the effect of the pole pair number on the phase imbalance and the spatial harmonic spectrum of the machine. Reducing this imbalance and higher order harmonics presence will increase machine performances. [ABSTRACT FROM AUTHOR]

Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of reviews corresponding to a target. Existing methods equally treat and aggregate individual reviews’ sentiment to judge the overall sentiment polarity. However, the confidence of each review is not equal in sentiment quantification where sentiment perturbation arising from high- and low-confidence reviews may degrade the accuracy of Sentiment Quantification. Specifically, fake reviews with deceptive sentiments are low confidence, which perturbs the overall sentiment prediction. Whereas, some reviews generated by responsible users are high confidence. They contain authoritative suggestions so they should be emphasized in Sentiment Quantification. In this paper, we design and build COSE, a confidence-aware sentiment quantification framework, which can measure the confidence of individual reviews to eliminate sentiment perturbation and facilitate sentiment quantification. We design a Review Graph that achieves review confidence modeling in an unsupervised manner and obtains review confidence representations. Moreover, we develop a dynamic fusion attention mechanism, which produces sentiment “de-perturbation” vectors to eliminate the sentiment perturbation based on the confidence representations. Extensive experiments on large-scale review datasets validate the significant superiority of COSE over the state-of-the-art. [ABSTRACT FROM AUTHOR]

For text sentiment analysis, state-of-the-art neural language models have demonstrated promising performance. However, they lack interpretability, require vast volumes of annotated data, and are typically specialized for tasks. In this article, we explore a connection between fine-tuned Transformer models and unsupervised LDA approach to cope with text sentiment analysis tasks, inspired by the concept of Neuro-symbolic AI. The Transformer and LDA models are combined as a feature extractor to extract the hidden representations of the input text sequences. Subsequently, we employ a feedforward network to forecast various sentiment analysis tasks, such as multi-label emotion prediction, dialogue quality prediction, and nugget detection. Our proposed method obtains the best results in the NTCIR-16 dialogue evaluation (DialEval-2) task, as well as cutting-edge results in emotional intensity prediction using the Ren_CECps corpus. Extensive experiments show that our proposed method is highly explainable, cost-effective in training, and superior in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Traditional survey methods can provide noisy data arising from recall, memory and other biases. Technological advances (particularly in neuroscience) are opening new ways of monitoring physiological processes through non-intrusive means. Such dense continuous data provide new and fruitful avenues for complementing self-reported data with a better understanding of human dynamics and human interactions. In this study, we use a survey to collect positive affect (feelings) data from more than 300 individuals over a period of 24 h, and at the same time, map their core activities (5000 recorded activities in total) with measurements of their heart rate variability (HRV). Our results indicate a robust correlation between the HRV measurements and self-reported affect. By drawing on the neuroscience and wellbeing literature we show that dynamic HRV results are what we expect for positive affect, particularly when performing activities like sleep, travel, work, exercise and eating. This research provides new insights into how to collect HRV data, model and interpret it. [ABSTRACT FROM AUTHOR]

This study provides an in‐depth investigation of the use of analytical and numerical methods in analyzing electrical machines. Although numerical models such as the finite‐element method (FEM) can handle complex geometries and saturation effects, they have significant computational burdens, are time‐consuming, and are inflexible when it comes to changing machine geometries or input values. Analytical models based on magnetic equivalent circuits (MEC) or solving Maxwell's equations can be faster and more flexible, but less accurate for complex machine structures. The paper focuses on the recent development of analytical models for brushless permanent‐magnet (PM) machines that have become increasingly popular in low and medium‐power applications. The literature review covers the recent developments in analytical models for PM machines with respect to various machine quantities such as magnetic flux density components, induced voltage, inductances, electromagnetic force/torque, efficiency, or unbalanced magnetic force (UMF). It outlines the advantages and disadvantages of different analytical models such as the zero‐dimensional (0‐D), one‐dimensional (1‐D), two‐dimensional (2‐D), and three‐dimensional (3‐D) analytical methods, as well as the Maxwell and basic mathematical analysis. Although the MEC models are faster than the numerical model, they are not as accurate for various structures of electrical machines including a great magnetic air gap. They also note that the analytical models based on the Maxwell equations are faster than the numerical ones and have the potential to obtain acceptable accuracy similar to the numerical models in electrical machines. Overall, this literature review provides valuable insights for researchers and engineers in selecting appropriate analytical models for PM machines. It highlights the trade‐offs between accuracy and computational efficiency when choosing between numerical and analytical models, and the flexibility of analytical models to address changes in machine geometries or input values. Additionally, this helps researchers save time in determining appropriate references regarding the analytical models of brushless PM machines. [ABSTRACT FROM AUTHOR]

The mining method that is still often used in salt deposits is the room-and-pillar mining method, in which the dimensioning of the most requested element in the system is followed. The pillars are the elements subjected to the greatest loads. Knowing the size and distribution mode of the secondary state of stress—deformation—is a necessity that can lead to the design and realization of stable, reliable underground excavations. This paper proposes an analytical assessment model of the secondary stress state in the pillars between the operating rooms, as well as in the whole system room–pillar–floor, based on the results obtained from laboratory research through modeling and in situ research. For this purpose, the evaluation of the secondary stress state was carried out considering the following methods: (1) the dimensioning method based on the theory of limit equilibrium, taking into account the effective stress in the pillars; and (2) the mechanics of the continuous environment based on the design of some analytical models for evaluating the secondary stress-deformation state in the pillar and floor. The exploitation of one of the largest salt deposits in Romania is used as a case study, and the stability of the exploitation system with rooms and pillars is evaluated by analytical methods. The secondary state of tension was calculated at different points on the height of the pillar. Through the proposed algorithm, the value of the axial component of the secondary stress state at different points along the axis of a pillar located at a depth of 100 m varies between 1.498 and 1.657 MPa, compared to the value obtained by the finite element method and in situ measurements, which was 1.64 MPa. The comparison revealed a high degree of agreement between the results obtained for the depth of 100 m using both the FEM and laboratory and in situ measurements. This suggests that the proposed algorithm is a reliable method for predicting the secondary stress state. The presented algorithm can be extended in the field of mining deposits, where mining methods with rooms and pillars are used. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of advanced technologies in the Internet of Things era, higher requirements are needed for next‐generation electronic devices. Fortunately, organic thin film transistors (OTFTs) provide an effective solution for electronic skin and flexible wearable devices due to their intrinsic features of mechanical flexibility, lightweight, simple fabrication process, and good biocompatibility. So far considerable efforts have been devoted to this research field. This article reviews recent advances in various promising and state‐of‐the‐art OTFTs as well as related integrated circuits with the main focuses on: (I) material categories of high‐mobility organic semiconductors for both individual transistors and integrated circuits; (II) effective device architectures and processing techniques for large‐area fabrication; (III) important performance metrics of organic integrated circuits and realization of digital and analog devices for future smart life; (IV) applicable analytical models and design flow to accelerate the circuit design. In addition, the emerging challenges of OTFT‐based integrated circuits, such as transistor uniformity and stability are also discussed, and the possible methods to solve these problems at both transistor and circuit levels are summarized.

Abstract Determining the magnetocaloric effect (MCE) in dense nanoparticle systems (DNSs) poses a challenge due to the increased complexity of matter at the nanoscale. Given the interparticle magnetic interactions, diverse particle size and shape distributions, and the presence of inhomogeneous magnetic phases, selecting a suitable phenomenological model is essential to describe the temperature dependence of magnetic behavior in DNSs. Herein, we chose a cost‐effective Ni100‐xCrx DNS with adjustable magnetic transitions to showcase the resilience of the MCE across a broad temperature range (147–614 K). While the hyperbolic tangent model appears more fitting for materials with a single Curie temperature (TC), such as its parent bulk alloys, in the presence of a TC distribution a Gaussian distribution model proves to be better suited for DNSs. The latter model yields a magnetic entropy change, ΔSmax = 0.09–0.15 J kg‐K−1 in the DNS at a tiny field of 0.1T. The correlations between the broadening of the MCE peak and TC distribution are attributed to the particle size distribution and chemical inhomogeneity present in the DNS, paving the way for fine‐tuning MCE‐related properties such as the relative cooling power (13.17–33.45 J kg−1) and adiabatic temperature change (0.03–0.17 K). Our methodology not only enhances the potential for designing innovative MCE materials with broader operating ranges but also validates the universality of our phenomenological model for other families of nanocrystalline/nanogranular oxides/alloys thin films.

Piezoelectric elements (PEMs) are used in a variety of applications. In this paper, we developed a new simple sensorless method for a Piezoelectric Actuator (PEA), which includes piezostack elements and a three-stage amplification mechanism.This research focuses on a piezoelectric actuator that incorporates a three-stage amplification system, where the outcome of one stage serves as the input for the subsequent one. The actuator receives two types of inputs: the voltage applied to the piezoelectric elements and the mechanical load it carries. Its output is defined by the rotation angle observed at the end of the third amplification stage. To indirectly measure the actuator's displacement, a basic external circuit is utilized.The precise movement of these actuators is essential. To circumvent the high costs and limitations associated with highly accurate displacement sensors, there has been a growing interest in sensorless control methods. Certain electrical signals, when measured, can provide an estimation of displacement. However, induced voltage measurements are not effective for piezoelectric stacks. Two more promising measures are the voltage and current of the piezoelectric material.Given that the electrical charge on these actuators closely reflects their displacement with minimal hysteresis across a broad frequency spectrum, it's proposed that displacement can be effectively gauged through current measurements that assess charge.The core contribution of this paper is the introduction and validation, both theoretically and experimentally, of a hybrid algorithm that leverages these two electrical signals to enhance the accuracy of displacement estimates. This was confirmed using a laboratory setup.The primary benefit of this research is the presentation of a straightforward sensorless control algorithm, poised for further exploration within the realm of piezoelectric actuators. The simplicity of both the theoretical model and the sensorless technique facilitates their application across a diverse range of piezoelectric actuators and amplification systems, thereby streamlining the design, modeling, and control strategy development for various actuators.The innovation of this study stems from the application of an uncomplicated sensorless estimation algorithm, coupled with a system-level perspective on piezoelectric actuators. This approach utilizes a simple, adaptable model suitable for a wide array of applications and operational techniques.

Abstract This study explores the potential use of low-cost natural fiber reinforced rope polymers (FRRP) to improve the compressive behavior of circular, square, and rectangular reinforced concrete (RC) specimens. A total of 42 specimens were tested under monotonic axial compression in three groups. Groups were formed to differentiate specimens with different cross-sectional shapes such as circular, square, and rectangular. The findings demonstrate that FRRP can effectively boost the compressive behavior of RC columns. Circular specimens with three-layer hemp FRRP exhibited a 200% increase in compressive strength and a 270% improvement in corresponding strain. Cotton FRRP provided a 117% boost in compressive strength and a 233% enhancement in strain. In square specimens, three-layer hemp FRRP resulted in a 110% rise in compressive strength and a 186% increase in strain, while cotton confinement yielded improvements of 95% and 144%, respectively. For the square and rectangular specimens, the improvement in the compressive behavior was reduced compared to the circular specimens because of stress concentrations near corners. Moreover, the study showed that the hemp FRRP confinement outperformed the cotton confinement. The investigation also revealed that the existing analytical models were inadequate in predicting the mechanical properties of RC confined with natural FRRP. Therefore, the study introduces novel equations to predict the compressive strength and corresponding strain for both hemp and cotton confined concrete in various cross-sectional types. These proposed equations exhibit a good level of accuracy in predicting the compressive strength and corresponding strain.

The rapid development of the Internet of Things (IoT) has put great challenges on the next generation's communication technologies. Optical Camera Communication (OCC), with its advantages of the green principle, less electromagnetic interference, and harmonious integrations, can serve as a complementary, and sometimes viable alternative technology to mainstream Radio Frequency (RF)-based one. Designing efficient OCC platforms crucially involves accurate modeling of the OCC system and evaluating the efficiency of OCC-based techniques, which then stimulates related practical applications. This paper aims to provide a systematic perspective to understand OCC and to present design guidelines for OCC systems from theory to practice. First, the main parameters and analytical models for OCC basic components are presented to provide guidance on hardware parameter selection and system simulations. Next, typical implementation procedures and common performance evaluation criteria for an overall OCC system are provided. Then, detailed goal-oriented OCC enhancement solutions are summarized aiming to provide systematic guidelines for system performance enhancement. Finally, OCC designed examples are presented based on the results of our OCC experimental platform and some future research directions are discussed.

The open-slot stator topology, which allows for preformed coils and an excellent fill factor, has been regarded as a suitable candidate for low-speed operation such as direct-drive systems. This paper presents a rapid and accurate design optimization of surface-mounted permanent magnet (PM) machines equipped with the open-slot stator configuration. The analytical method, utilizing the coenergy-based subdomain model, is developed to predict the various electromagnetic performances. The proposed analytical model is decoupled into two components, relevant and irrelevant to the optimization process. Subsequently, the teaching-learning-based optimization (TLBO), a human-based algorithm for global solutions, is applied to the analytical model to search for the best compromise belonging to the Pareto-optimal front. The design methodology presented in this article is well-suited for the open-slot stator configuration, providing a computationally efficient and accurate approach to address multi-constrained minimization problems. In the end, the reliability of the proposed model is verified by finite element (FE) results.