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Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

In recent years, there has been growing interest in the use of stainless steel (SS) in reinforced concrete (RC) structures due to its distinctive corrosion resistance and excellent mechanical properties. To ensure effective synergy between SS and concrete, it is necessary to develop a time-saving approach to accurately determine the ultimate bond strength τ_u between the two materials in RC structures.

Three robust machine learning (ML) models, including support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost), are employed to predict τ_u between ribbed SS and concrete. Model hyperparameters are fine-tuned using Bayesian optimization (BO) with 10-fold cross-validation. The interpretable techniques including partial dependence plots (PDPs) and Shapley additive explanation (SHAP) are also utilized to figure out the relationship between input features and output for the best model.

Among the three ML models, BO-XGBoost exhibits the strongest generalization and highest accuracy in estimating τ_u. According to SHAP value-based feature importance, compressive strength of concrete f_c emerges as the most prominent feature, followed by concrete cover thickness c, while the embedment length to diameter ratio l/d, and the diameter d for SS are deemed less important features. Properly increasing c and f_c can enhance τ_u between ribbed SS and concrete.

An online graphical user interface (GUI) has been developed based on BO-XGBoost to estimate τ_u. This tool can be utilized in structural design of RC structures with ribbed SS as reinforcement.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

Based on the mentoring literature, this study aims to explore the mentoring functions developed by the travel industry and develop a multiple mentoring function scale (MMFS) for tour leaders.

Study One of this research involved in-depth interviews with 23 tour leaders for establishing the initial question bank and pilot test scale. In Study Two, the initial scale was pilot tested by 225 tour leaders for identifying the factor structure through exploratory factor analysis. In Study Three, the model was verified through confirmatory factor analyses of 251 calibration samples. In Study Four, cross-validation was verified by 227 validation samples.

In Study One, through the content analyses, an initial question bank of 51 questions was established. The question bank was reviewed by 15 experts, resulting in the development of a pilot test scale of 36 questions. In Study Two, 16 questions were retained after the initial scale was pilot tested, and four dimensions were identified, including career competency, reverse mentoring, career development and psychosocial functions. In Study Three, the model was verified through confirmatory factor analyses and confirmed to have good reliability and construct validity. Finally, a first-order, four-factor scale that consisted of 16 questions was established. In Study Four, cross-validation was verified.

MMFS establishment underwent a rigorous development process, and the scale’s reliability and validity are supported. It is suggested that future research should develop and construct a multifunctional model for tour-leading professionals based on the MMFS to evaluate the long-term impact of the MMFS.

Previous studies have mainly focused on typical mentoring functions. Multiple mentoring functions derived from the characteristics of tour leaders and guides have rarely been discussed. This research can fill the gap in the application of mentoring system to human resource management research in the tourism industry. As a result, this four-dimensional multiple mentoring functions scale provides a progressive perspective and be regarded as the first version of the scale development in the tourism industry.

基于师徒文献, 本研究探讨了旅游业的师徒功能, 并为旅游领队发展多元师徒功能量表（MMFS）。

研究一, 涉及与 23 位领队的深度访谈, 以建立初始题库和预试量表。研究二, 初始量表由225名领队进行试测, 通过探索性因素分析确定因素结构。研究三, 通过验证性因素分析251个校准样本以验证模型。研究四, 由 227 个验证样本进行复合效化。

研究一, 经过内容分析, 建立了51道题的初始题库。初始题库由 15 位专家审阅, 最终形成36 个问题的预试量表。研究二, 预试量表进行预试后, 保留了16个问题, 确定了四个构面, 包括职业能力、反向师徒、职业发展和社会心理功能。研究三, 通过验证性因素分析对模型进行验证, 证实具有良好的信度和建构效度。建立了一个由 16 个题项所组成的一阶四因素量表。研究四, 复合效化获得验证。

过往的研究主要集中在典型的师徒功能上。很少有人讨论基于领队和导游的特点而衍生出的多元师徒功能。本研究填补了师徒制在旅游行业人力资源管理研究中的应用空白。因此, 这四个构面的多元师徒功能量表提供了一个渐进的视角, 被视为量表发展的旅游业第一版。

MMFS的建立经历了严格的开发过程, 量表的信度和效度得到支持。建议未来的研究应基于 MMFS开发和建构旅游领队专业人士的多元功能模型, 以评估MMFS 的后续影响。

Basándose en la bibliografía sobre la mentoría, este estudio exploró las funciones de mentoría desarrolladas por el sector de los viajes y elaboró una escala de funciones de mentoría múltiple (MMFS) para guías y jefes de grupo turísticos.

En el Estudio Uno de esta investigación se realizaron entrevistas en profundidad a 23 guías turísticos para establecer el banco de preguntas inicial y la escala de prueba piloto. Estudio Dos, la escala inicial fue sometida a una prueba piloto por 225 guías de tours para identificar la estructura factorial mediante un análisis factorial exploratorio. Estudio Tres, el modelo se verificó mediante análisis factoriales confirmatorios 251 muestras de calibración. En el Estudio Cuatro, la validación cruzada se verificó mediante 227 muestras de validación.

Estudio Uno: a través de los análisis de contenido, se estableció un banco de preguntas inicial de 51 preguntas. El banco de preguntas fue revisado por 15 expertos, lo que dio lugar a la elaboración de una escala de prueba piloto de 36 preguntas. Estudio Dos, se retuvieron 16 preguntas, tras la prueba piloto de la escala inicial, y se identificaron cuatro dimensiones, que incluían la competencia profesional, la mentoría inversa, el desarrollo profesional y las funciones psicosociales. Estudio Tres, el modelo se verificó mediante análisis factoriales confirmatorios y se confirmó que tenía una buena fiabilidad y validez de constructo. Por último, se estableció una escala de primer orden y cuatro factores que constaba de 16 preguntas. En el Estudio Cuatro, se verificó la validación cruzada.

Los estudios anteriores se han centrado principalmente en las funciones típicas de la mentoría. Rara vez se han tratado las funciones múltiples de mentoría derivadas de las características de los guías y jefes de grupo turísticos. Esta investigación puede llenar el vacío existente en la aplicación del sistema de mentoría a la investigación de la gestión de recursos humanos en la industria turística. Como resultado, esta escala de funciones de mentoría múltiple de cuatro dimensiones proporciona una perspectiva progresista y puede considerarse como la primera versión del desarrollo de la escala en la industria turística.

El establecimiento de la MMFS se sometió a un riguroso proceso de desarrollo, y la fiabilidad y validez de la escala están respaldadas. Se sugiere que en futuras investigaciones se desarrolle y construya un modelo multifuncional para guías profesionales de turismo basado en la MMFS para evaluar el impacto a largo plazo de la MMFS.

Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report.

The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

This study focuses on the classification of targets with varying shapes using radar cross section (RCS), which is influenced by the target’s shape. This study aims to develop a robust classification method by considering an incident angle with minor random fluctuations and using a physical optics simulation to generate data sets.

The approach involves several supervised machine learning and classification methods, including traditional algorithms and a deep neural network classifier. It uses histogram-based definitions of the RCS for feature extraction, with an emphasis on resilience against noise in the RCS data. Data enrichment techniques are incorporated, including the use of noise-impacted histogram data sets.

The classification algorithms are extensively evaluated, highlighting their efficacy in feature extraction from RCS histograms. Among the studied algorithms, the K-nearest neighbour is found to be the most accurate of the traditional methods, but it is surpassed in accuracy by a deep learning network classifier. The results demonstrate the robustness of the feature extraction from the RCS histograms, motivated by mm-wave radar applications.

This study presents a novel approach to target classification that extends beyond traditional methods by integrating deep neural networks and focusing on histogram-based methodologies. It also incorporates data enrichment techniques to enhance the analysis, providing a comprehensive perspective for target detection using RCS.

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

This research has not been published anywhere else.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.