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Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts’ performance and the result shows significant improvements by using  the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice.

Examining the influence of land use/land cover transformation on meteorological variables has become imperative for maintaining long-term climate sustainability. Rapid growth and haphazard expansion have caused the conversion of prime agricultural land into a built-up area. This study used multitemporal Landsat data to analyze land use/land cover (LULC) changes, and Terra Climate monthly data to examine the impact of land transformation on precipitation, minimum and maximum temperature, wind speed, and soil moisture in the Aurangabad district of Maharashtra state in India during 1999–2019. Multiple linear regression and correlation analysis were performed to determine the association among LULC classes and climatic variables. This study revealed rapid urbanization in the study area over the years. The built-up area, water bodies, and barren lands have recorded a steep rise, while the agricultural area has decreased in the district. Drastic changes were observed in the climatic variables over the years. The precipitation and wind speed have shown decreasing trends during the study period. A positive relationship between soil moisture and agricultural land was found through a correlation analysis. Conspicuous findings about the positive relationship between the agricultural land and maximum temperature need further investigation. A multiple linear regression analysis demonstrated a negative relationship between the built-up area and precipitation. The intensity of the precipitation has reduced as a consequence of the developmental activities in the study area. Moreover, a positive relationship was observed between the built-up area and maximum temperature. Thus, this study calls for policy implications to formulate a futuristic land-use plan considering climate change projection in the district.

Non-uniform temperature distributions in air-conditioned areas can reduce the energy efficiency of air conditioners and cause uncomfortable thermal sensations for occupants. Furthermore, it is impractical to use physical sensors to measure the local temperature at every position. This study developed a soft-sensing model that integrates the fundamentals of thermodynamics and transport phenomena to predict the temperature at the target position in space. Water experiments were conducted to simulate indoor conditions in an air-conditioning cooling mode. The transient temperatures of various positions were measured for model training and validation. The velocity vectors of water flow were acquired using the particle image velocimetry method. Correlation analysis of various positions was conducted to select the input variable. The soft-sensing model was developed using the multiple linear regression method. The model for the top layer was modified by the correction of dead time. The experimental results showed the temperature inhomogeneity between different layers. The temperature at each target position under two initial temperatures and two flow rates was accurately predicted with a mean absolute error within 0.69 K. Moreover, the temperature under different flow rates can be predicted with one model. Therefore, this soft-sensing model has the potential to be integrated into air-conditioning systems.

Background The size of the plantar intrinsic and extrinsic foot muscles has been shown to be associated with toe flexor strength (TFS). Previous studies adopted the size of limited plantar intrinsic foot muscles or a compartment containing several muscles as an independent variable for TFS. Among the plantar intrinsic and extrinsic foot muscles, therefore, it is unclear which muscle(s) primarily contributes to TFS production. The present study aimed to clarify this subject. Methods In 17 young adult men, a series of anatomical cross-sectional area of individual plantar intrinsic and extrinsic foot muscles was obtained along the foot length and the lower leg length, respectively, using magnetic resonance imaging. Maximal anatomical cross-sectional area (ACSA max ) and muscle volume (MV) for each constituent muscle of the plantar intrinsic foot muscles (flexor hallucis brevis; flexor digitorum brevis, FDB; abductor hallucis; adductor hallucis oblique head, ADDH-OH; adductor hallucis transverse head, ADDH-TH; abductor digiti minimi; quadratus plantae) and extrinsic foot muscles (flexor hallucis longus; flexor digitorum longus) were measured. TFS was measured with a toe grip dynamometry. Results TFS was significantly associated with the ACSA max for each of the ADDH-OH (r = 0.674, p  = 0.003), ADDH-TH (r = 0.523, p  = 0.031), and FDB (r = 0.492, p  = 0.045), and the MV of the ADDH-OH (r = 0.582, p  = 0.014). As for the ADDH-OH, the correlation coefficient with TFS was not statistically different between ACSA max and MV ( p  = 0.189). Stepwise multiple linear regression analysis indicated that ACSA max and MV of the ADDH-OH alone explained 42 and 29%, respectively, of the variance in TFS. Conclusion The ADDH-OH is the primary contributor to TFS production among the plantar intrinsic and extrinsic foot muscles as the result of the stepwise multiple linear regression analysis.

Sugarcane is one of the leading commercial crops grown in India. The prevailing weather during the various crop-growth stages significantly impacts sugarcane productivity and the quality of its juice. The objective of this study was to predict the yield of sugarcane during different growth periods using machine learning techniques viz., random forest (RF), support vector machine (SVM), stepwise multiple linear regression (SMLR) and artificial neural networks (ANN). The performance of different yield forecasting models was assessed based on the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE) and model efficiency (EF). Among the models, ANN model was able to predict the yield at different growth stages with higher R2 and lower nRMSE during both calibration and validation. The performance of models across the forecasts was ranked based on the model efficiency as ANN > RF > SVM > SMLR. This study demonstrated that the ANN model can be used for reliable yield forecasting of sugarcane at different growth stages.

This study employs gray system theory to refine the multiple linear regression model, resulting in the development of the gray multiple linear regression method. Utilizing School A as the primary subject, this approach involves gray scaling the intercultural communication skills of college instructors (independent variable) and the effectiveness of Chinese culture dissemination (dependent variable). This transformation aids in resolving the sequence of whitened background values for each variable. This transformation aids in sequencing the whitened background values for each variable. The regression coefficients are then calculated using the Cholesky method to determine the linear correlation between the variables and assess how intercultural communication skills influence cultural dissemination outcomes. The analysis of the influence of college teachers’ intercultural communicative competence on the effect of cultural transmission found that the language competence of college foreign language teachers is related to “attitude toward learning Chinese excellent culture” (0.432) and “whether it is necessary to teach the idea of Chinese excellent culture in the classroom” (0.503) both have significant correlations. The analytical results of this paper provide a reference basis for promoting the wide dissemination of Chinese excellent culture, as well as a direction for improving the cross-cultural communicative competence of foreign language teachers in colleges and universities.

We estimated fine-mode black carbon (BC) concentrations at the surface using AERONET data from five AERONET sites in Korea, representing urban, rural, and background. We first obtained the columnar BC concentrations by separating the refractive index (RI) for fine-mode aerosols from AERONET data and minimizing the difference between separated RIs and calculated RIs using a mixing rule that can represent a real aerosol mixture (Maxwell Garnett for water-insoluble components and volume average for water-soluble components). Next, we acquired the surface BC concentrations by establishing a multiple linear regression (MLR) between in-situ BC concentrations from co-located or adjacent measurement sites, and columnar BC concentrations, by linearly adding meteorological parameters, month, and land-use type as the independent variables. The columnar BC concentrations estimated from AERONET data using a mixing rule well reproduced site-specific monthly variations of the in-situ measurement data, such as increases due to heating and/or biomass burning and long-range transport associated with prevailing westerlies in the spring and winter, and decreases due to wet scavenging in the summer. The MLR model exhibited a better correlation between measured and predicted BC concentrations than those based on columnar concentrations only, with a correlation coefficient of 0.64. The performance of our MLR model for BC was comparable to that reported in previous studies on the relationship between aerosol optical depth and particulate matter concentration in Korea. This study suggests that the MLR model with properly selected parameters is useful for estimating the surface BC concentration from AERONET data during the daytime, at sites where BC monitoring is not available.

This study aimed to investigate whether the morphology of the temporomandibular joint (TMJ) is associated with chewing patterns while considering skeletal morphology, sex, age, and symptoms of temporomandibular disorder (TMD). A cross-sectional observational study of 102 TMJs of 80 patients (age 16–40 years) was performed using pretreatment records of cone-beam computed tomography imaging of the TMJ, mandibular kinesiographic records of gum chewing, lateral and posteroanterior cephalometric radiographs, patient history, and pretreatment questionnaires. To select appropriate TMJ measurements, linear regression analyses were performed using TMJ measurements as dependent variables and chewing patterns as the independent variable with adjustment for other covariates, including Nasion-B plane (SNB) angle, Frankfort-mandibular plane angle (FMA), amount of lateral mandibular shift, sex, age, and symptoms of TMD. In multiple linear regression models adjusted for other covariates, the length of the horizontal short axis of the condyle and radius of the condyle at 135° from the medial pole were significantly (p < 0.05) associated with the chewing patterns in the frontal plane on the working side. “Non-bilateral grinding” displayed a more rounded shape of the mandibular condyle. Conversely, “bilateral grinding” exhibited a flatter shape in the anteroposterior aspect. These findings suggest that the mandibular condyle morphology might be related to skeletal and masticatory function, including chewing patterns.

There is a growing need for collaborative supply chain management. This collaboration has been attracting the researchers due to their critical impact to improve business value. The use of information technology (IT) which is presently widely used in almost areas of business and engineering is considered prerequisites of today's complex supply chain. Current study shows that a broad array of references in supply chain collaboration especially in identifying influencing factors on Information Technology are partial and limited. This study was aimed at reviewing a comprehensive understanding of dominant IT factors that influence successful supply chain management. Based on thorough analysis on the state of the art literature review, a proposed construct of influencing IT factors in supply chain collaboration is offered. Multiple linear regression technique is used to model the proposed construct. The result of analysis suggests that a further research on identifying factors and determining samples is required to model accuracy.

Unreasonable human activities may cause the accumulation of heavy metals (HMs) in the agricultural soil, which will ultimately threaten the quality of soil environment, the safety of agricultural products, and human health. Therefore, the accumulation characteristics, potential sources, and health risks of HMs in agricultural soils in China’s subtropical regions were investigated. The mean Hg, Cu, Zn, Pb, and Cd concentrations of agricultural soil in Jinhua City have exceeded the corresponding background values of Zhejiang Province, while the mean concentrations of determined 8 HMs were less than their corresponding risk-screening values for soil contamination of agricultural land in China. The spatial distribution of As, Cr, Ni, Cu, and Pb were generally distributed in large patches, and Hg, Zn, and Cd were generally sporadically distributed. A positive definite matrix factor analysis (PMF) model had better performance than an absolute principal component–multiple linear regression (APCS-MLR) model in the identification of major sources of soil HMs, as it revealed higher R2 value (0.81–0.99) and lower prediction error (−0.93–0.25%). The noncarcinogenic risks (HI) of the 8 HMs to adults and children were within the acceptable range, while the carcinogenic risk (RI) of children has exceeded the safety threshold, which needs to be addressed by relevant departments. The PMF based human health risk assessment model indicated that industrial sources contributed the highest risk to HI (32.92% and 30.47%) and RI (60.74% and 61.5%) for adults and children, followed by agricultural sources (21.34%, 29.31% and 32.94% 33.19%). Therefore, integrated environmental management should be implemented to control and reduce the accumulation of soil HMs from agricultural and industrial sources.