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Projects related to Geotechnical Engineering require thorough ground study to know the soil behaviour thus making it necessary to determine the strength and stability parameters of soil. The soils of Assam are primarily of four types viz., Alluvial (old and new), Piedmont, Hill and Lateritic soils with the alluvium abundantly available in the state. The most important soil parameters are the shear strength parameters, namely, cohesion (c) and angle of internal friction (Φ) which can be obtained using triaxial tests with different drainage conditions. But one triaxial test may take weeks to complete and are very expensive. Moreover, for large projects it is practically preposterous to conduct large number of triaxial tests. Hence, an alternate way is to develop a relation between different parameters which enable us to determine others. The main objective of this study is establishing prediction models that allow the shear parameters to be predicted from soil index properties. For this, three prediction models are developed by combining various soil parameters viz., Liquid Limit (LL), Plastic Limit (PL), Specific Gravity (G) and Optimum Moisture Content (OMC) using Multivariate Regression Analysis (MRA) and Artificial Neural Network (ANN). The results show better performance for ANN than MRA. [ABSTRACT FROM AUTHOR]

Background: This research aims to compare the differences in kinematic parameters associated with cross and uppercut punches between Sanda athletes (SA) and Boxing athletes (BA) to analyze their impacts on peak punching speed. Methods: The punches of BA (n = 20) and SA (n = 20) were compared utilizing a three-dimensional (3D) framework and high-speed cameras in terms of 13 key parameters. An independent samples t-test (α = 0.05) was employed to analyze the differences in punching between BA and SA. Meanwhile, a stepwise multiple linear regression equation was developed to analyze the influence of selected parameters on peak punching speed. Results: The results reveal that, among the 13 kinematic parameters, the six cross-related parameters and four uppercut-related parameters are significantly different (both p ≤ 0.05). The results of multivariate regression analysis unveils that the peak punching speed for the cross are influenced by the anteroposterior position of the center of gravity (in BA) and the maximum angular velocity of the shoulder (in SA). In contrast, for both BA and SA, the maximum angular velocity of the shoulder plays a critical impact on uppercut. Conclusions: These findings indicate that trunk and upper limbs significantly influence the peak punching speed, which provides suggestions for daily training regimen of SA and BA as well as their coaches. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive optimization of algal oil extraction and transesterification for sustainable biodiesel production. Freshwater Spirogyra algae underwent Soxhlet extraction using n-hexane. response surface methodology (RSM), principal component analysis (PCA), and multivariate regression analysis (MRA) were employed to investigate the effects of biomass–solvent ratio (BSR), algae particle size (APS), and extraction-contact time (E-CT) on algal oil yield (AOY). The extracted oil was then converted to biodiesel via transesterification, and the impacts of the methanol–oil ratio (MOR) and transesterification-contact time (T-CT) on biodiesel conversion efficiency (BCE) were analyzed. Results demonstrate that optimal BSR, APS, and E-CT for maximal AOY are 1:7, 400 µm, and 3–4 h, respectively. For transesterification, a MOR of 12:1 and a T-CT of 4 h yielded the highest BCE. Predictive models exhibited exceptional accuracy, with R2 values of 0.916 and 0.950 for AOY and BCE, respectively. The produced biodiesel complied with ASTM D6751 and EN 14214, showcasing its potential for renewable energy applications. [ABSTRACT FROM AUTHOR]

The aim is to propose an approach to predicting the risk of progression of chronic tonsillitis in patients based on multivariate regression analysis for timely, convenient, and accurate diagnosis with subsequent selection of effective treatment and preventive measures in ENT departments (otolaryngology departments). A number of 100 patients with various forms of chronic tonsillitis, whose average age was 37 years, and 51 women and 49 men, were examined according to a specially developed questionnaire to predict the risk of ChT progression. The most probable indicators of chronic tonsillitis progression were selected to build a multivariate regression model for predicting the risk of ChT progression, 13 probable factors of the ChT progression were analyzed, and 9 factors were used to build a multivariate regression model. Histogram of the residual deviations is distributed symmetrically, approaching the curve of the normal distribution of the residuals and the absence of systematic deviations from the normal-probability straight line, so we conclude that the statistical hypothesis that their distribution conforms to the normal distribution law is not rejected. The residuals relative to the predicted values are scattered chaotically, which indicates the absence of dependence on the predicted values for the risk of chronic tonsillitis progression. The coefficient of determination was calculated, which is 0.8258, which claims that 82.58% of the factors are considered in the prediction model of the risk of chronic tonsillitis progression. The coefficient of determination indicates how well the obtained observations confirm the mathematical model. The proposed multivariate regression model that considers the risk factors for the ChT progression makes it possible to predict potential meta tonsillar complications and the possibility of timely disease prevention. [ABSTRACT FROM AUTHOR]

The main objective of the present study was to reveal the significance of R&D in order to ensure sustainable economic growth. The present study consists of four main parts. In the first part of the study, the term R&D is defined in detail. The Solow model, which is the theoretical basis of the study, is discussed in the introduction. In the first part of the study, the term 'economic growth' is explained, as well. Different definitions of economic growth are given and then the basic resources of the economy, people, nature, funds, technological change, and innovation are examined. Ensuring economic growth and improving the quality of life is desired by all nations and peoples. Therefore, the policies and measures to be determined for this purpose are socrucial. In this sense, innovation-oriented economic models are examined. In addition, in the second part of the study, the academic literature is examined in terms of the relationship between loans provided by financial institutions to the private sector, R&D investments, and economic growth. In the third and final part of the study, the relationship between R&D, domestic credit provided to private sector, and economic growth are examined using multivariate regression, cointegration, FMOLS, DOLS, CCR, and ARDL analyzes for China, South Korea, and Japan fort he period between 1991 and 2020. In the conclusion part, all the results obtained are interpreted and the relationship between domestic credit provided to private sector, R&D, and economic growth are presented in a concrete way in terms of empirical findings. [ABSTRACT FROM AUTHOR]

In a multi-reservoir system, the stochastic nature of basin data resulting from rainfall introduces risk into water management operations. Effective management that accounts for these risks can obtain maximum benefits from the system. This study presents a description of a multi-reservoir water resources system with hydroelectric power plants, utilizing the energy optimization model developed by OPAN in 2007. The model was applied to reservoirs located successively on the Lower Kızılırmak River in the Kızılırmak Basin, with the objective function being the maximization of firm power by using drought period inflows and total energy by using monthly average inflows. The study considered three scenarios: deterministic, probabilistic, and risky (critical cases), with probabilities of inflows from the basin being determined for the latter. Monthly inflows with determined probabilities were used to obtain data for the risky case. Optimum operating levels were determined based on this data to maximize firm power and total energy. According to the operating levels, the reservoir with the largest useful volume manages the operation. The values obtained from the optimization were then used in multivariate regression analysis using the Statistical Package for Social Scientists (SPSS), a statistical analysis program. The analysis explored the effects of monthly operating levels in the reservoirs, the amount of inflow released from the spillway, the amount of inflow released for energy production, the monthly average inflow to the reservoirs, and firm power values on energy production. [ABSTRACT FROM AUTHOR]

Special concentrically braced frames (SCBFs) with in‐plane buckling (IPB) braces are often designed using the provisions applicable for out‐of‐plane buckling (OOPB) brace systems. As a result, the IPB brace systems may fail in the undesirable modes as observed in past experimental studies. In this study, a novel simplified analytical formulation based on the rigid beam spring model (RBSM) has been proposed to prevent the out‐of‐plane buckling of IPB braces. A high‐fidelity finite element model (FEM) is generated and validated with the experimental studies incorporating the influence of the residual stress, imperfection, and strain hardening. The variability, sensitivity, and uncertainties involved in predicting the direction of buckling of braces are examined. A parametric study has been carried out to investigate the influence of thickness and clearance of the gusset and knife plates, geometric imperfections, slenderness ratios, compactness ratios, residual stresses, and frame actions considering the most destabilizing effect in the OOPB mode. The third mode of buckling, termed as the mixed‐mode of buckling, has been observed in this study. A design criterion is proposed to get the desired IPB mode of buckling of braces in SCBFs. Multivariate regression analysis is carried out to incorporate the impact of various parameters as well as uncertainties in the formulation of the design criteria. The validity of the proposed equation is verified by comparing the findings of past experimental studies for various loading scenarios. Finally, a resistance factor of 0.8 in the LRFD approach has been proposed for the IPB brace systems. [ABSTRACT FROM AUTHOR]

Water-supply network systems are an indispensable social infrastructure in modern society. Therefore, water supply utilities require high system robustness towards both accidents and natural disasters. In this study, we define a work performance indicator (WPI) to quantitatively measure the robustness of water supply utilities (WSU) in Japan. The WPI was defined from three perspectives: reliability, safety, and sustainability. Based on actual data expressing the work performance of Japanese WSU from 1980 to 2018, we illustrate the numerical results of WPIs to determine their regional characteristics. We applied statistical data analysis techniques such as multivariate regression analysis, cluster analysis, and principal component analysis to calculate the WPIs and investigate the historical and regional characteristics and trends of WSU in Japan during the investigated period. These approaches can be applied to the design and improvement of natural disaster mitigation policies, such as those focused on earthquakes in Japan. [ABSTRACT FROM AUTHOR]

Background: The current German S3 guideline for polytrauma lists five criteria for prehospital intubation: apnea, severe traumatic brain injury (GCS ≤8), severe chest trauma with respiratory failure, hypoxia, and persistent hemodynamic instability. These guideline criteria, used in adults in daily practice, have not been previously studied in a collection of severely injured children. The aim of this study was to assess the extent to which the criteria are implemented in clinical practice using a multivariate risk analysis of severely injured children. Methods: Data of 289,698 patients from the TraumaRegister DGU® were analyzed. Children meeting the following criteria were included: Maximum Abbreviated Injury Scale 3+, primary admission, German-speaking countries, years 2008–2017, and declaration of intubation. Since children show age-dependent deviating physiology, four age groups were defined (years old: 0–2; 3–6; 7–11; 12–15). An adult collective served as a control group (age: 20–50). After a descriptive analysis in the first step, factors leading to prehospital intubation in severely injured children were analyzed with a multivariate regression analysis. Results: A total of 4489 children met the inclusion criteria. In this cohort, young children up to 2 years old had the significantly highest injury severity (Injury Severity Score: 21; p ≤ 0.001). Falls from both high (> 3 m) and low heights (< 3 m) were more common in children than in adults. The same finding applied to the occurrence of severe traumatic brain injury. When at least one intubation criterion was formally present, the group up to 6 years old was least likely to actually be intubated (61.4%; p ≤ 0.001). Multivariate regression analysis showed that Glasgow Coma Scale score ≤ 8 in particular had the greatest influence on intubation (odds ratio: 26.9; p ≤ 0.001). Conclusions: The data presented here show for the first time that the existing criteria in the guideline for prehospital intubation are applied in clinical practice (approximately 70% of cases), compared to adults, in the vast majority of injured children. Although severely injured children still represent a minority of all injured patients, future guidelines should focus more on them and address them in a specialized manner. [ABSTRACT FROM AUTHOR]

Study Design: Retrospective multicenter.Objective: The aim was to investigate the factors involved in, and their relative contributions to, the overall accuracy of robot-assisted pedicle screw placement.Summary Of Background Data: Robot-assisted surgery has reportedly resulted in greater accuracy for placement of pedicle screws than conventional methods. There are many potential factors affecting the accuracy of pedicle screws placed with a robot. No study has investigated these factors in a robust way.Materials and Methods: Radiographic and clinical data of three centers were pooled. Preoperative and postoperative computerized tomographies were obtained by all three centers to assess the accuracy of the placed screws. The primary outcome measured was accuracy of pedicle screws placed with the robot. The authors performed a multivariate regression analysis to determine the significant patient-related and screw-related variables and their relative contribution to the overall accuracy. In addition, an ordinal regression analysis was conducted to investigate the effects of different variables on accuracy of robot-placed screws graded by Gertzbein-Robbins grading system (GRS).Results: The total contribution of all studied variables to overall accuracy variation as measured by offsets between the placed and planned screws was only 18%. Obesity, long constructs, female gender, surgeon, and vertebral levels were among the factors that had small contributions to the different screw offsets. For GRS grades, significant variables were gender (Log odds: 0.62, 95% CI: 0.38-0.85), age (Log odds: 0.02, 95% CI: 0.01-0.03), length of constructs (Log odds: 0.07, 95% CI: 0.02-0.11), screw diameter (Log odds: 0.55, 95% CI: 0.39-0.71), and length of the screws (Log odds: 0.03, 95% CI: 0.01-0.05). However, these variables too, regardless of their significant association with the accuracy of placed screws, had little contribution to overall variability of accuracy itself (only about 7%).Conclusion: The accuracy of screws placed with robotic assistance, as graded by GRS or measured offsets between planned and placed screw trajectories, is minimally affected by different patient-related or screw-related variables due to the robustness of the robotic navigation system used in this study.Level Of Evidence: Level III. [ABSTRACT FROM AUTHOR]

This study presents a fast and simple estimation method for dosimetry assessment of a clothed human body exposed to electromagnetic fields in millimeter-wave bands. A multivariate regression algorithm was given instead of conventional numerical methods for the first time, which utilized the exact value of skin surface temperature rise that was computed by the Monte-Carlo method by changing the oblique incidence wave angle, the cross-polarization power ratio, and the air gap spacing from the cloth to the body. The detailed formula for bioheat transfer analysis considering the abovementioned key parameters is first introduced. Then, a polynomial function is derived by multivariate regression analysis using the simulation result to estimate the skin temperature rises under various exposure conditions and scenarios. In comparison with those results obtained by the traditional methods, the proposed regression algorithm significantly reduced the expense of computational resources. The results estimated using the developed function show a maximum error of less than 0.013 °C for the skin tissues of different body parts and different cloth materials, demonstrating that the proposed method is easy and efficient for the estimation of a high degree of skin temperature rise in a relatively complicated radio-wave exposure environment in millimeter-wave bands. [ABSTRACT FROM AUTHOR]

In the present study, experiments were carried out on circular model bridge piers individually as well as with two-piers-in-tandem arrangement under clear water scour conditions predominantly caused by formation of horseshoe vortex. Independent collar plates around bridge piers and a continuous collar plate covering both the piers were installed as countermeasures against scouring. In case of two piers, efficacy of scour-protection devices for front and rear piers was checked for the overall stability. Performance potential was noted to be about 67% and 100% for continuous collar plates of sizes 2.5D and 3.0D around two piers at average bed level with spacing of 2.0D; where D is the diameter of the pier. During degradation of general bed level, a group of three continuous collar plates was employed around both the piers to ensure considerable reduction of scour. A group of three collar plates of sizes 2.5D and 3.0D having an inter-plate spacing Cs equal to D/4 with the middle plate placed at the average bed level reduced scour by 79 and 82%, respectively, in comparison to the scour depth for an unprotected pier. A mathematical model was developed by multivariate regression analysis to prediction the scour depth for both the pier arrangements. [ABSTRACT FROM AUTHOR]

• A novel QSPR model for predicting the DCN of hydrocarbon species and mixtures is reported. • The QSPR model used a combination of molecular descriptors with an overall low degeneracy. • DCNs of aviation fuels were predicted using the QSPR model and GCxGC analyses. • DCN prediction for jet fuels were in good agreement with IQT measurements. This study presents a computational methodology for determining the Derived Cetane Number (DCN) of practical aviation fuels. The proposed approach integrates a novel Quantitative Structure-Property Relationship (QSPR) model designed to predict DCN for hydrocarbon species and mixtures with fuel composition analysis obtained through Two-Dimensional Gas Chromatography (GCxGC). The QSPR model used 20 independent variables computed based on selected hydrocarbon molecular descriptors, including functional groups and distance-based topological indexes. The multivariate regression analysis was used to train the QSPR model based on a dataset consisting of 48 individual hydrocarbon species and 157 surrogate mixtures. The model demonstrated robust predictive capabilities with high coefficients of determination (R2) of 0.96 on the training dataset and 0.94 on the independent testing dataset. The latter consisted of 43 surrogate mixtures formulated both in-house and sourced from archived literature. The application of the QSPR model for practical jet fuels involves specifying the detailed jet fuel compositions using GCxGC analysis and a randomization algorithm based on a database featuring over 17,000 distinct hydrocarbon species. The overall model's performance on practical jet fuels aligns closely with its performance on the training and testing datasets, affirming its practical utility. To enhance prediction accuracy of the proposed computational approach for practical jet fuels, density was explored as a potential constraining property to narrow randomization results with those detailed composition having a density similar to the actual fuel. In this regard, a novel relationship was established to predict fuel densities based on compositional characteristics. Despite the promising results in density prediction, this study indicates that density alone is insufficient to effectively constrain randomized compositions for significantly improved DCN predictions, and thus, further development is required. [ABSTRACT FROM AUTHOR]

In bench blasting, backbreak is the unwanted result that causes instability to the highwall and can lead to safety hazards. Hence, it is utmost necessary to minimize the generation of backbreak to improve mine's safety. It has always been difficult to predict the backbreak because of various parameters involved, i.e. blast design, explosive properties, rock mass, etc. In this study, multivariate regression analysis (MVRA) and genetic programming (GP) techniques were performed on 70 blast data sets of previously published papers. Both the models have been developed and tested with the same mine data set. For validation of the models, a total of 14 trial blasts have been conducted in Indian coal mines with different geological strata and other parameters. The values of R2, RMSE, MAPE and prediction level at 25% and 90% were computed for GP and MVRA techniques. Also, the GP model is compared with the other state-of-the-art techniques. It has been found that the level of prediction for validation data set at 25% using GP is 78.57% and for MVRA is 21.42%. The mean magnitude of relative error (MMRE) value for GP and MVRA is 0.18 and 0.42, respectively. The results show that the GP is a more efficient tool for prediction of backbreak in comparison with MVRA. On performing sensitivity analysis, it has been found that stemming length and powder factor are the most influencing parameters to backbreak. [ABSTRACT FROM AUTHOR]

Concrete highway bridge remaining service life has been at the forefront of bridge owners' concerns for several years in terms of growing bridge maintenance needs. Distresses in concrete bridge components occur due to physical, chemical, and mechanical factors that manifest as observable symptoms of deterioration. Deterioration models realistically link the observable defects to the several explanatory variables affecting the deterioration that aid in the planning of appropriate corrective actions. Only a limited number of explanatory variables have been considered in the published literature in the development of bridge component deterioration models. This study investigates the effect of multiple relevant explanatory variables affecting the bridge component condition including aging, loads and environmental conditions, and design characteristics using a multivariate regression analysis approach. Deterioration models for both superstructure and substructure of reinforced and prestressed concrete bridges are developed and validated in the present study. The proposed models will aid the bridge engineers to predict the concrete bridge component conditions based on bridge characteristics for planning preventive maintenance. [ABSTRACT FROM AUTHOR]

In regression analyses, correlations between independent variables (e.g. process variables) and dependent variables (e.g. product quality) are of major interest. However, only statistically significant correlations ensure a reliable interpretation of how process variables affect product qualities. In this respect, accurate time alignment of independent variables is crucial to obtain regression models with acceptable validation that are influenced by temporal phenomena (e.g. industrial processes) only. In this study, the commonly used static form of time alignment, where only the distances between consecutive process parameters and the average production speed are considered, is compared to a newly developed dynamic calculation of time lags. The dynamic calculation of time lags was achieved by modelling the continuous bulk material flow. The two different methods of calculation were then applied on an industrial production of particleboards to predict final board strength properties. Results of regression models showed that the use of dynamically calculated time lags improved the predictability of the internal bond strength of boards by 67% compared to statically calculated time lags. Consequently, final product strength properties could be predicted more accurately, which should lead to lower costs of rejects and a higher efficiency of material inputs. [ABSTRACT FROM PUBLISHER]

This paper introduces a new julia package, LRMoE, a statistical software tailor-made for actuarial applications, which allows actuarial researchers and practitioners to model and analyse insurance loss frequencies and severities using the Logit-weighted Reduced Mixture-of-Experts (LRMoE) model. LRMoE offers several new distinctive features which are motivated by various actuarial applications and mostly cannot be achieved using existing packages for mixture models. Key features include a wider coverage on frequency and severity distributions and their zero inflation, the flexibility to vary classes of distributions across components, parameter estimation under data censoring and truncation and a collection of insurance ratemaking and reserving functions. The package also provides several model evaluation and visualisation functions to help users easily analyse the performance of the fitted model and interpret the model in insurance contexts. [ABSTRACT FROM AUTHOR]

Fabric-based microfluidic analytical devices (μADs) have emerged as a promising material for replacing paper μADs thanks to their superior properties in terms of stretchability, mechanical strength, and their wide scope of applicability in wearable devices or embedded in garments. The major obstacle in their widespread use is the lack of a technique enabling their massive fabrication at a negligible-to-nil cost. In response, we report the development of a wax ink with proper thixotropic and hydrophobic properties, fully compatible with automatic screen-printing that allows the one step massive fabrication of microfluidics on a cotton/elastane fabric, with a printing resolution 400 μm (hydrophilic channel) and 1000 μm (hydrophobic barrier), without being necessary any post curing. The cost of the ink (50 g) and of each microfluidic device is ca. 2.3 and 0.007 €, respectively. The active component of the ink was a refined beeswax in a matrix based on ethyl cellulose in 2-butoxy ethyl acetate. Screen-printed fabric μADs were used for the simultaneous colorimetric determination of pH and urea in untreated human sweat by using multivariate regression analysis. This method enabled the direct measurement of urea using urease, regardless of the sweat's pH, and shows strong agreement with a reference method. [Display omitted] • Wax screen-printable ink for massive fabrication of negligible-to-nil cost μFADs. • Printing resolution: 400 μm (hydrophilic channel) and 1000 μm (hydrophobic barrier). • Thermal or UV post curing is not necessary. • Simultaneously determination of pH and urea in untreated human sweat. • Determination of urea, despite the sweat acidity, by multivariate regression analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a method to detect air pollution by applying a hyperspectral imaging algorithm for visible light, near infrared, and far infrared. By assigning hyperspectral information to images from monocular, near infrared, and thermal imaging, principal component analysis is performed on hyperspectral images taken at different times to obtain the solar radiation intensity. The Beer–Lambert law and multivariate regression analysis are used to calculate the PM2.5 and PM10 concentrations during the period, which are compared with the corresponding PM2.5 and PM10 concentrations from the Taiwan Environmental Protection Agency to evaluate the accuracy of this method. This study reveals that the accuracy in the visible light band is higher than the near-infrared and far-infrared bands, and it is also the most convenient band for data acquisition. Therefore, in the future, mobile phone cameras will be able to analyze the PM2.5 and PM10 concentrations at any given time using this algorithm by capturing images to increase the convenience and immediacy of detection. [ABSTRACT FROM AUTHOR]

Accurate prediction of the height of the water-conducting fracture zone (WCFZ) is very important for safe coal mining, water resource storage, and protecting the environment. The proportion coefficient of hard rock and the actual height of the top caving coal were defined to predict the height of the WCFZ more accurately. A multivariate regression analysis method was adopted to establish a nonlinear model for predicting the height of the WCFZ based on various factors, including mining height, panel width, proportion of hard rock, mining depth, and extraction rate. Investigations conducted using data from western China show that the ratio of the height of the WCFZ to the mining height obtained using the actual height of the top caving coal is ≈ 20, which is consistent with the distribution of the longwall full-seam mining technology. The values predicted by the multiple regression model in this study are closer to the measured values than those derived using traditional empirical equations, with a relative error < 10%. The ratio of the height of the WCFZ to mining height was generally higher in the western mining area than in the eastern mining area, due to the increase in panel dimensions and harder lithology. However, the influences of extraction rate and mining depth on the development of the WCFZ were found to be relatively weak. Finally, the model accurately predicted the height of the WCFZ in panel 12,511 of the Bulianta coal mine. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer has eventually become a popular choice for retrofitting and strengthening of structural entities. Hence, a need arises to know the quantitative as well as qualitative effect of these techniques on the original integrity of the concerned structural entity. In the present study, the effect of change in width to depth ratio, percentage of steel and loading intensity in a simply supported reinforced concrete (RC) beam on the maximum deflection, stress, strain energy, natural frequencies and deflections of various modes of free vibration is obtained with the help of ANSYS Workbench 19.0 software till the failure of RC beam and a comparison is sought between the original beam and a failed beam retrofitted with one, two and three layers of GFRP sheets (Epoxy S-Glass UD). A standard beam of fixed dimensions and a two point loading was first simulated in the software while comparing the results with the manual calculations of maximum deflections to ascertain the most appropriate settings of the simulation in the three dimensional analysis environment of the software. Various beams were then analyzed altering the width to depth ratio, the percentage of tension steel and the loading intensity over the beam and the results of the static as well as modal (frequency) analysis were noted including the deflections, stresses, strains, strain energies, natural frequencies of first 5 modes of free vibrations and their respective displacements. The same beams were then retrofitted with layers of GFRP sheets with their principal axis inclined at an angle of 45° with the span of the beam. The same analysis was done on the beams and the results were noted. These results are compared, and a multivariate regression analysis is performed over the results to obtain equations to testify the observations and the trends. This whole process gives a conclusion that the effect of retrofitting is insignificant for single layer GFRP while the most effective results were obtained for the case of triple layer GFRP. Deflections, stress and strain energy with their relative change across various cases are accurately related to the varying parameters through regression analysis. Frequency is also accurately related but the relative change in frequency is not well established. [ABSTRACT FROM AUTHOR]

The knowledge of thermally modified strength properties is crucial in processes such as underground coal gasification, disposal of radioactive wastes in deep geological reservoirs and restoration of fire-damaged structures. However, due to the unavailability of dedicated laboratory equipment, scientists and engineers need to go across various laboratory facilities for testing their desired sample. Further, it delays the process and increases the cost of the project. Since there is a lack of empirical equations that can provide the strength of a thermally modified rock from the strength at room temperature, new predictive models have been developed using multivariate regression analysis (MVRA), artificial neural network and adaptive neuro-fuzzy inference system (ANFIS), to predict the uniaxial compressive strength (UCS) and tensile strength (TS) of both uncooled (NC) and heat-treated (WC) samples of a fine-grained Indian sandstone that has been widely used as a major building material in most of the Indian monuments. The UCS and TS of NC and WC samples have been predicted from the physical properties, viz. temperature (T), density (D), porosity (P), thermal expansion coefficients (EL and EV) and ultrasonic wave velocities (VP and VS). The performance and prediction efficiency of the models have been compared on the basis of performance indices, namely the correlation coefficient (R2), the mean absolute percentage error, the root mean square error and the variance account for. Based on the comparative study, it was concluded that ANFIS provides a relatively closer estimation as compared to MVRA. [ABSTRACT FROM AUTHOR]

Highlights • The research adopted multivariate regression analysis for FRP-confined concrete. • A total of 23 statistical models were proposed. • The highest R2 value achieved 99%. • Mixed models could perform superior than the linear regression approach. • Effects of individual independent variables in concrete performance were evaluated. Abstract This study applied newly developed multivariate statistical models to estimating the mechanical properties of recycled aggregate concrete cylinder encased by fiber reinforced polymer (FRP). Two different types of RFPs were applied, namely flax FRP and polyester FRP. Ten independent variables were predefined including the FRP type and cylinder size. It was found that several mixed models outperformed the traditional linear regression approach, based on the accuracy and residual value distribution. Individual factor analysis indicated that the fiber thickness and layer number had more significant impacts on the strength and strain of FRP-encased concrete's transitional point, compared to their impacts at the ultimate state. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption in buildings, more and more energy efficient technologies for the building-HVAC system have been developed. Several studies showed that occupant behaviour has a much larger influence on the energy performance of a building than the thermal process within the building façade. This underlines the need to investigate the influence of occupant behaviour on building performance. This paper presents the results of a questionnaire survey related to occupant behaviours and preferences in residential buildings. Multivariate regression analyses are conducted to outline main patterns and correlations between energy use, building characteristics and occupant behaviours. Results show a strong relationship between occupants’ actions and characteristics of the built environment – in particular the construction year of the building. Furthermore, occupants have a notable impact on building performance: the chosen set point temperature and the total hours of heating system utilization, which both increase with the age of the building, lead to an increase of the fuel consumption. In conclusion, occupant behaviour is affected by the characteristics of the building, which requires additional studies to increase our understanding of occupant behavioural patterns in existing and modern buildings. [ABSTRACT FROM AUTHOR]

Study Design: Prospective single-center study.Objective: Study investigates how dynamic balance performance complements 2D static radiographic measurements and demographics in terms of understanding health-related quality of life in adult spinal deformity (ASD) patients.Summary Of Background Data: Recent insights suggest that demographic variables have a stronger impact on health-related quality of life than 2D radiographic spinopelvic parameters in ASD patients.Methods: Nine healthy volunteers and 36 ASD patients following inclusion criteria were recruited. Demographics, Scoliosis Research Society Score-22r (SRS-22r), Oswestry Disability Index (ODI), Core Outcome Measures Index (COMI), 2D radiographic spinopelvic measurements, and performance on Balance Evaluation Systems Test (BESTest), and Trunk Control Measurement Scale (TCMS) were determined for each subject. Nonparametric tests, Spearman correlations, univariate, and stepwise-like linear multivariate regression analysis were performed.Results: BESTest and TCMS had significant lower values in the ASD group versus the control group (P = 0.000). In the ASD group, Cumulative Illness Rating Scale (CIRS) correlated fair to ODI, COMI (0.441 ≥ r ≥ 0.383, P < 0.021) and to SRS-22-r (r = -0.335, P = 0.046), Mini Mental State Examination correlated fair to COMI (r = -0.352, P = 0.035), "Pelvic Incidence minus Lumbar Lordosis" correlated fair to ODI (r = 0.361, P = 0.031), BESTest correlated moderate to ODI and COMI (r ≤ -0.505; P ≤ 0.002), TCMS correlated fair to ODI (r = -0.356; P = 0.033). CIRS and BESTest were significant predictive variables for COMI based on univariate analysis in ASD patients. Multivariate regression analysis including demographics, 2D static radiographic parameters, and dynamic balance scales identified BESTest as single independent variable (P = 0.000) to predict COMI (adjusted R = 0.285) in ASD patients.Conclusion: BESTest has a higher potential than demographic and 2D radiographic spinopelvic parameters to predict quality of life in ASD patients. Further research is necessary to identify the impact of ASD on quality of life.Level Of Evidence: 3. [ABSTRACT FROM AUTHOR]

Blast-induced ground vibration (BIGV) is an undesirable environmental issue in and around mines. Usage of a high amount of explosive causes ground vibrations that are harmful to the nearby habitats and dwellings. In this paper, an attempt has been made to monitor the BIGV with low-cost wireless sensor network (WSN) and prediction of peak particle velocity (PPV) using an artificial neural network (ANN) technique at ACC Dungri limestone mine, Bargarh, Odisha, India. Eleven blasts PPV were recorded at different locations using wireless sensor network prototype system. The data has been transmitted by ZigBee (IEEE 802.15.4) protocol. The results are very promising and the recorded PPV varies from 0.191 mm/s to 8.60 mm/s. A three-layer, feed-forward back propagation neural network consists of 6 input parameters, 5 hidden neurons, and one output parameters were trained. Obtained results were compared based on correlation of determination (R²) and standard error between recorded and predicted values of PPV. [ABSTRACT FROM AUTHOR]

Background and aims: Large metropolitan areas often exhibit multiple morbidity hotspots. However, the identification of specific health hazards, associated with the observed morbidity patterns, is not always straightforward. In this study, we suggest an empirical approach to the identification of specific health hazards, which have the highest probability of association with the observed morbidity patterns. Methods: The morbidity effect of a particular health hazard is expected to weaken with distance. To account for this effect, we estimate distance decay gradients for alternative locations and then rank these locations based on the strength of association between the observed morbidity and wind-direction weighted proximities to these locations. To validate this approach, we use both theoretical examples and a case study of the Greater Haifa Metropolitan Area (GHMA) in Israel, which is characterized by multiple health hazards. Results: In our theoretical examples, the proposed approach helped to identify correctly the predefined locations of health hazards, while in the real-world case study, the main health hazard was identified as a spot in the industrial zone, which hosts several petrochemical facilities. Conclusion: The proposed approach does not require extensive input information and can be used as a preliminary risk assessment tool in a wide range of environmental settings, helping to identify potential environmental risk factors behind the observed population morbidity patterns. [ABSTRACT FROM AUTHOR]

Modern spectroscopic and sensor technologies combined with multivariate modelling are increasingly used for the quantitative analysis of complex mixtures. Their performance depends directly on the data design chosen for model training and validation. A well-balanced calibration experiment with the fewest samples possible presents additional challenges when several mixture components (factors) need to be calibrated on the same dataset and subsequently quantified from the same multivariate measurement. This practically important problem stays poorly addressed by the theory of experimental design. This theoretical work systematically formulates the requirements to an optimal calibration/validation dataset and introduces a new family of calibration designs, where the samples are placed along the diagonals of an experimental space that is a hypercube. Such placement is appropriate due to reasonable assumptions about the linear nature of analytical response. Suggested filling schemes allow economical diagonal designs with intrinsic validation to be built for multiple factors presented in as many levels as the number of samples. The most important practical cases of two and three factors are considered in detail, and generalization to higher dimensions is outlined. Diagonal designs of any complexity can be generated using a simple geometrical scheme or with a supplied script. [ABSTRACT FROM AUTHOR]

Surface texture and friction are two primary characteristics for pavement safety evaluation. Understanding their relationship is critical to reduce potential traffic crashes especially at wet conditions. Texture data obtained from existing systems are restricted on either a small portion on pavement surface or one line-of-sight profile, and the currently used texture indicators, such as Mean Profile Depth (MPD), and Mean Texture Depth (MTD) only reveal partial aspects of texture property. With the emerging 3D laser imaging technology, acquiring full-lane 3D pavement surface data at sub-millimeter resolution and at highway speeds has been made possible via the newly developed PaveVision3D Ultra data collection system. In this study using 1 mm 3D data collected from PaveVision3D Ultra, four types of texture indicators (amplitude, spacing, hybrid, and functional parameters) are calculated to represent various texture properties for pavement friction estimation. The relationships among those texture indicators and pavement friction are examined. MPD and Skewness - two height texture parameters, Texture Aspect Ratio (TAR) -- a spatial parameter, and Surface Bearing Index (SBI) -- a functional parameter are found to be the four most contributing parameters for pavement friction prediction. Finally a multivariate regression model is developed based on residual plot analysis methods to estimate pavement friction with the R-squared value of 0.95. This study would be beneficial in the continuous measurement and evaluation of pavement safety for project- and network-level pavement surveys. [ABSTRACT FROM AUTHOR]

Objective Factors affecting postoperative hearing results of patients with otosclerosis were analyzed. Methods Included were 191 patients with otosclerosis in whom 234 primary stapes surgeries were performed from August 1991 to December 2011 by one surgeon in three tertiary hospitals. Evaluation of factors affecting postoperative hearing thresholds at individual frequencies were performed by logistic regression analysis. Results Closure of the air-bone gap (ABG) after surgery was good at 2 kHz, but poor at 4 kHz and frequencies under 1 kHz. In addition, improvement at 8 kHz was worse than that at any other frequency. Multivariate logistic regression analysis by the stepwise method showed that under the mean preoperative ABG (odds ratio [OR] = 2.42), unilaterality (OR = 2.53) and male sex (OR = 2.65) were significantly better prognostic factors at 250 Hz. At 500 Hz, under mean preoperative ABG (OR = 2.56) was the significantly better factor. No significant factors were found at 1 kHz. Cochlear otosclerosis (OR = 3.57) was a significantly worse prognostic factor at 2 kHz. Under mean preoperative ABG (OR = 2.82) and younger age (OR = 1.03) were significantly better prognostic factors at 4 kHz. At 8 kHz, worse preoperative air conduction threshold (OR = 1.96) was a significantly better prognostic factor. Conclusion Preoperative ABG, preoperative air conduction threshold, cochlear otosclerosis, male sex, laterality and age were significant prognostic factors for postoperative hearing outcome in otosclerosis patients. Especially, preoperative ABG was a significant prognostic factor at multiple frequencies. We suppose that patients with larger preoperative ABG have some other otosclerotic lesions outside the oval window niche. [ABSTRACT FROM AUTHOR]

Abstract: Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. [Copyright &y& Elsevier]

Despite federal legislation to equalize healthcare for children with limited English language proficiency, some state healthcare agencies and programs fall short in providing children's linguistic services for mental healthcare. While some states have been aggressive in passing cultural and linguistic laws aimed at providing protection for children, other states have not, limiting children of all ages to potential substandard care. This research uses state-level data and multivariate regression analysis to explore why some states are adopting these laws, whereas others are not. We find two dissimilar forces with unrelated goals must work in tandem to bring about policy change-the desire of civil rights and liberty groups to ensure equality in the delivery of healthcare services, and the desire of state legislature to reduce healthcare costs. [ABSTRACT FROM AUTHOR]

This paper deals with the application of a support vector machine (SVM) optimization technique to predict the blast-induced ground vibration. Peak particle velocity (PPV) is an important parameter to be kept under control to minimize the damage caused due to the ground vibration. A number of previous researchers have tried to use different empirical methods to predict PPV, but these empirical equations have their limitations due to its less versatile application and acceptability from field conditions. Therefore, it is difficult to apply these empirical equations to predict PPV because they are based on limited parameters which does not really reflect and connect with real influencing parameters. In this paper, SVM technique is used for the prediction of PPV by incorporating blast design and explosive parameters, and the suitability of one technique over other has been tested based on the results. To avoid the biasness in man-made choice of parameters of SVM, we have used the chaos optimization algorithm to find the optimal parameters which can help the model to enhance the learning efficiency and capability of prediction. Datasets have been obtained from one of the large opencast mine from southeastern coalfield limited, Chhattisgarh, India. One hundred and twenty-seven datasets were used to establish SVM architecture, and 10 datasets have been randomly chosen for validation of SVM model to see its prediction potential. The results obtained have been compared with different vibration predictors, multivariate regression analysis, artificial neural network and the superiority of application on SVM over previous methodology. The mean absolute percentage error using SVM is very low (0.001) as compared to other predictors indicate its better prediction capability. [ABSTRACT FROM AUTHOR]

Aim: In this study, the curative effect of valsartan on hypertension based on its effect on visfatin levels and lipid profiles was investigated. Materials and methods: Forty patients aged 38-60 with hypertension were included in the study. In order to determine initial visfatin levels and lipid profiles, blood samples were taken from the patients and all were started on 80-mg valsartan tablets. All patients were monitored for 12 weeks. Results: By using multivariate regression analysis, it was determined that visfatin levels affected systolic blood pressure (SBP) (P = 0.012, beta; -0.43, 95% CI: -0.092 to -0.012). After the patients received the valsartan treatment for 12 weeks, a significant increase in visfatin levels was observed (pre- and posttreatment, respectively: 0.42 ± 0.22 ng/mL and 0.71 ± 0.36 ng/mL, P < 0.001). When the lipid profile changes of patients were examined, only low-density lipoprotein (LDL) levels showed a statistically significant change (pre- and posttreatment, respectively: 150.32 ± 13.22 mg/dL and 143.82 ± 7.53 mg/dL, P = 0.008). Conclusion: In this study, it was found that visfatin levels affected SBP in newly diagnosed hypertensive patients and valsartan treatment increased visfatin levels. In addition, the use of valsartan reduced LDL levels. [ABSTRACT FROM AUTHOR]

This paper aims at assessing the seismic performance of Tuned Mass Dampers (TMDs) based on sets of recorded ground motions. For the simplest configuration of a structure-TMD assembly, in a comprehensive study characteristic response quantities are derived and statistically evaluated. Optimal tuning of TMD parameters is discussed and evaluated. The response reduction by application of a TMD is quantified depending on the structural period, inherent damping of the stand-alone structure, and ratio of TMD mass to structural mass. The effect of detuning on the stroke of the TMD and on the structural response is assessed and quantified. It is verified that a TMD damping coefficient larger than the optimal one reduces the peak deflection of the TMD spring significantly, whereas the response reduction of the main structure remains almost unaffected. Analytical relations for quantifying the effect of a TMD are derived and subsequently evaluated. These relations allow the engineer in practice a fast and yet accurate assessment of the TMD performance. [ABSTRACT FROM AUTHOR]

Due to the harsh conditions of space, the detectors within satellite-based multispectral imagers are always at risk of damage or failure. In particular, 15 out of the 20 detectors that produce the 1.6- μm band 6 of Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua are either dead or noisy. In this paper, we describe a quantitative image restoration (QIR) algorithm that is able to accurately estimate and restore the data lost due to multiple-detector failure. The small number of functioning detectors is used to train a restoration function that is based on a multivariate regression using the information in a spatial-spectral window around each restored pixel. The information from other spectral bands allows QIR to perform well even when standard image interpolation breaks down due to large contiguous sections of the image being missing, as is the case for MODIS band 6 on Aqua. We present a comprehensive evaluation of the QIR algorithm by simulating the Aqua damage using the working 1.6- μm band of MODIS on Terra and then comparing the QIR restoration to the original (unbroken) Terra image. We also compare our results with other researchers' prior work that has been based on the assumption that band 6 could be approximated well solely as a function of the related band 7. We present empirical evidence that there is information in the other 500- and 250-m bands, excluding bands 6 and 7, that can inform the estimation of missing band 6 data. We demonstrate superior performance of QIR over previous algorithms as reflected by a reduced root-mean-square-error evaluation. The QIR algorithm may also be adapted to other cases and provides a powerful and general algorithm to mitigate the risks of detector damage in multispectral remote sensing. [ABSTRACT FROM AUTHOR]

In the mid-1990s, the East Asian countries experienced severe financial crisis that were followed by deep economic downturns. A variety of methodologies have been used to understand the nature of the Asian financial crisis. However, the impact of the 1997 Asian financial crisis on the efficiency of the financial industry has yet to be studied. By employing the Data Envelopment Analysis (DEA) approach the present study attempts to examine for the first time the impact of the 1997 Asian financial crisis on the efficiency of the Malaysian and Thailand banking sectors, two of the East Asian countries that were severely affected by the crisis. The study focuses on three major approaches vis., intermediation, value added and operating approaches. The results clearly bring forth the high degree of inefficiency in the Malaysian and Thailand banking sectors, particularly a year after the crisis. We find that the Malaysian banking sector has exhibited a higher TE levels during the post crisis period under the intermediation and value added approaches, while TE seems to be lower under the operating approach. The empirical findings suggest that the Thailand banking sector has exhibited a lower TE level during the post crisis period under all approaches. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

1. Changes in cladoceran subfossils in the surface sediments of 54 shallow lakes were studied along a European latitude gradient (36–68°N). Multivariate methods, such as regression trees and ordination, were applied to explore the relationships between cladoceran taxa distribution and contemporary environmental variables, with special focus on the impact of climate. 2. Multivariate regression tree analysis showed distinct differences in cladoceran community structure and lake characteristics along the latitude gradient, identifying three groups: (i) northern lakes characterised by low annual mean temperature, conductivity, nutrient concentrations and fish abundance, (ii) southern, macrophyte rich, warm water lakes with high conductivity and high fish abundance and (iii) Mid-European lakes at intermediate latitudes with intermediate conductivities, trophic state and temperatures. 3. Large-sized, pelagic species dominated a group of seven northern lakes with low conductivity, where acid-tolerant species were also occasionally abundant. Small-sized, benthic-associated species dominated a group of five warm water lakes with high conductivity. Cladoceran communities generally showed low species-specific preferences for habitat and environmental conditions in the Mid-European group of lakes. Taxon richness was low in the southern-most, high-conductivity lakes as well as in the two northern-most sub-arctic lakes. 4. The proportion of cladoceran resting eggs relative to body shields was high in the northern lakes, and linearly (negatively) related to both temperature and Chl a, indicating that both cold climate (short growing season) and low food availability induce high ephippia production. 5. Latitude and, implicitly, temperature were strongly correlated with conductivity and nutrient concentrations, highlighting the difficulties of disentangling a direct climate signal from indirect effects of climate, such as changes in fish community structure and human-related impacts, when a latitude gradient is used as a climate proxy. Future studies should focus on the interrelationships between latitude and gradients in nutrient concentration and conductivity. [ABSTRACT FROM AUTHOR]

This study seeks to examine the performance of 37 Bangladeshi commercial banks between 1997 and 2004. The empirical findings of this study suggest that bank specific characteristics, in particular loans intensity, credit risk, and cost have positive and significant impacts on bank performance, while non-interest income exhibits negative relationship with bank profitability. During the period under study the results suggest that the impact of size is not uniform across the various measures employed. The empirical findings suggest that size has a negative impact on return on average equity (ROAE), while the opposite is true for return on average assets (ROAA) and net interest margins (NIM). As for the impact of macroeconomic indicators, we conclude that the variables have no significant impact on bank profitability, except for inflation which has a negative relationship with Bangladeshi banks profitability. [ABSTRACT FROM AUTHOR]

This paper introduces the use of a multivariate regression analysis to explain factors that impact aggregate energy intensity. This kind of study is useful to evaluate the past and predicts the future trends for energy-policy evaluation. Historical aggregate fuel and electricity intensities of the entire U.S. manufacturing sector (Standard Industrial Classification, SIC, codes of 20–39) over the 1977–1998 period are used to develop the proposed multivariate regression model. The proposed model allows identifying the structural effect of aggregate energy intensity changes without relying on detailed disaggregated energy data. Its results are validated by comparison with those from conventional decomposition techniques based on economic index numbers. For illustration, the historical aggregate fuel intensity of the U.S. primary metal industry (SIC 33) is used as an example of a situation for which economic index numbers fail to decompose the historical aggregate energy intensity since the disaggregated energy data are unavailable, while the multivariate regression analysis can still be applied. Empirical results show that a structural shift contributes to decreases of about 28, 41 and 19% of total declines of U.S. manufacturing aggregate fuel, U.S. manufacturing aggregate electricity, and U.S. primary metal industry aggregate fuel intensities, respectively, for the 1977–1998 period. The method based on multivariate regression models estimates the time series structural effects within deviation averages of 8.5 and 7.0% of the time series structural effect estimates based on the economic index numbers for the U.S. manufacturing aggregate fuel and electricity intensities, respectively, even though the multivariate regression model does not require disaggregated energy data. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a systematic approach based on robust statistical techniques for development of a data-driven soft sensor, which is an important component of the process analytical technology (PAT) and is essential for effective quality control. The data quality is obviously of essential significance for a data-driven soft sensor. Therefore, preprocessing procedures for process measurements are described in detail. First, a template is defined based on one or more key process variables to handle missing data related to severe operation interruptions. Second, a univariate, followed by a multivariate principal component analysis (PCA) approach, is used to detect outlying observations. Then, robust regression techniques are employed to derive an inferential model. A dynamic partial least squares (DPLS) model is implemented to address the issue of auto-correlation in process data and thus to provide smoother estimation than using a static regression model. The proposed methodology is illustrated through applications to a cement kiln system for estimation of variables related to product quality, i.e., free lime, and to emission quality, i.e., nitrogen oxides (NOx) emission. The case studies reveal the effectiveness of the systematic framework in deriving data-driven soft sensors that provide reasonably reliable one-step-ahead predictions. [Copyright &y& Elsevier]

The problem of fitting a surge function to a set of data such as that for a drug response curve is considered. A variety of different techniques are applied, including using some fundamental ideas from calculus, the use of a CAS package, and the use of Excel's regression features for fitting a multivariate linear function to a set of transformed data. The results of the different approaches are contrasted and discussed. [ABSTRACT FROM AUTHOR]

In recent years, the use of chemometric tools together with analytical techniques has been increasing in forensics due to their useful role in extracting additional reliable and objective information, which can be of great help in finding and understanding the connections between the forensic evidence and the suspects involved in complex crimes. Although there are various reviews available that deal with discussing the most widely used chemometric methods and their applications in forensics, most of them focus on multivariate classification and discrimination methods, overlooking multivariate regression methods. The present review aims to prove the powerful applicability of multivariate regression methods in one of the most challenging fields of forensics – forensic dating. Based on the authors' own experience and recent research studies, the review has been structured in three sections, covering the benefits and drawbacks of using multivariate regression methods in forensic dating and how to overcome these drawbacks, including also a methodological proposal on how to make optimal use of these methods. This review intends to provide a solid ground for emphasising and encouraging the application of chemometric-based methodologies in the coming years, in order to provide solutions to those challenges posed by forensic dating which cannot be solve by conventional methodologies. • Emphasis on the potential of chemometric-based methodologies applied to forensic dating. • Strategies to overcome the drawbacks of data pre-processing and (O)PLSR modelling in forensic dating. • Methodological proposal for the optimal application of multivariate regression methods to forensic dating. [ABSTRACT FROM AUTHOR]

To have general validity, empirical results must converge. To be credible, an experimental science must understand the limitations and be able to explain the disagreements of empirical results. We describe an experiment to replicate previous studies which claim that estimation by analogy outperforms regression models, in the experiment, 68 experienced practitioners each estimated a project from a dataset of 48 industrial COTS projects. We applied two treatments, an analogy tool and a regression model, and we used the estimating performance when aided by the historical data as the control. We found that our results do not converge with previous results. The reason is that previous studies have used other datasets and partially different data analysis methods, and last but not least, the tools have been validated in isolation from the tool users. This implies that the results are sensitive to the experimental design: the characteristics of the dataset, the norms for removing outliers and other data points from the original dataset, the test metrics, significance levels, and the use of human subjects and their level of expertise. Thus, neither our results nor previous results are robust enough to claim any general validity. [ABSTRACT FROM AUTHOR]

Thirty-two patients with advanced gastric cancer underwent continuous hyperthermic peritoneal perfusion (CHPP) combined with surgery: to prevent peritoneal recurrence in 15 patients without peritoneal metastasis (prophylactic CHPP) and to treat 17 patients with peritoneal metastases (therapeutic CHPP). The postoperative outcome was compared with that of control patients treated with surgery alone. Peritoneal recurrence was less frequent (26%) and the 5-year survival rate was significantly higher (39%) in the patients with prophylactic CHPP than in 40 control patients (42 and 17%, respectively). The patients with therapeutic CHPP showed significantly better median survival than did 20 control patients (11 vs. 6 months). Cox multivariate regression analysis revealed that CHPP was an independent prognostic factor in the prophylactic study (hazard ratio = 0.3965), and that the independent prognostic factor in the therapeutic study was not CHPP but complete resection of the peritoneal metastasis. Thus, CHPP has no marked benefit for established peritoneal metastasis. CHPP for the prevention of peritoneal recurrence may have a beneficial effect on long-term survival, but a prospective randomized trial is needed to clarify its prognostic value. [ABSTRACT FROM AUTHOR]