Multivariate Analysis Techniques Research Articles

Multivariate versus univariate spectrum analysis of dentine sialophosphoprotein (DSPP) for root resorption prediction: a clinical trial

BackgroundA force applied during orthodontic treatment induces inflammation to root area and lead to root resorption known as orthodontically induced inflammatory root resorption (OIIRR). Dentine sialophosphoprotein (DSPP) is one of the most abundant non-collagenous proteins in dentine that was released into gingival crevicular fluid (GCF) during OIIRR. The aim of this research is to compare DSPP detection using the univariate and multivariate analysis in predicting classification level of root resorption.MethodsThe subjects for this study consisted of 30 patients in 3 group classified as normal, mild, and severe groups of OIIRR. The GCF samples were taken from upper permanent central incisors in the normal and mild group while the upper primary second molars in the severe group. The DSPP qualitative detection limit was determined by analyzing the whole absorption spectrum utilizing multivariate analysis embedded with different preprocessing method. The multivariate analysis represents the multi-wavelength spectrum while univariate analyzes the absorption of a single wavelength.ResultsThe results showed that the multivariate analysis technique using partial least square-discriminate analysis (PLS-DA) with the preprocess method has successfully improved in classification prediction for the normal and mild group at 0.88 percent accuracy. The multivariate using PLS-DA algorithm with Mean Center preprocess method was able to predict normal and mild tooth resorption classes better than the univariate analysis. The classification parameters have improved in term of the specificity, precision and accuracy.ConclusionTherefore, the multivariate analysis helps to predict an early detection of tooth resorption complimenting the sensitivity of the univariate analysis.Trial registration NCT 05077878 (14/10/2021).

Socio-Economic and Cultural Context in the Development of Early Mathematical Competencies: A Comparative Study of Specific Educational Contexts in Chile and Spain.

This research presents the findings of a comparative study of mathematical competence among 130 students (M = 54.08months; SD = 2.57) from vulnerable school contexts in Chile and the Spanish public school system. The study analyses a set of general and specific domain precursors for which evidence of socioeconomic background exists. Using multivariate regression and discriminant analysis techniques, we calculated similarities and differences between groups by comparing these precursors. Significant differences were found between the Spanish and Chilean groups (p < .05); however, no differences were observed in non-symbolic comparison and receptive vocabulary. Possible reasons for the existence and extent of these differences are discussed in terms of socio-cultural and educational contexts.

One-of-a-kind production (OKP) planning and control: a comprehensive review and future research directions

PurposeThe aim of this paper is to identify some specificities of production planning and control (PPC) activities in the one-of-a-kind-production (OKP) process through an extensive literature review. Relevant aspects related to systems and PPC activities in the context of OKP environment are discussed, and six opportunities for future research are highlighted.Design/methodology/approachThe following research is based on a review of 53 articles published in peer-reviewed journals over the past three decades. After an initial descriptive analysis based on bibliometric indicators, a cluster analysis of 15 most cited articles was carried out using multivariate data analysis techniques and in-depth analysis.FindingsThe results reveal some specificities inherent to the clusters featured in the research, including aspects of planning, control and systems for OKP process. This cluster addresses information regarding next-generation manufacturing systems, scheduling and design science, computer simulation and project approach. On the other hand, the authors point out six topics for future research regarding contemporary issues associated with PPC in the context of OKP.Originality/valueThis paper fills an important gap regarding OKP production planning and control practices. The results provide a theoretical overview of different PPC practices suitable for the OKP environment. Furthermore, it can provide insights for scientific developments in order to manage the complexity inherent in the OKP process.

Metabolomic Profile of Citrus limon Leaves (‘Verna’ Variety) by 1H-NMR and Multivariate Analysis Technique

The elaboration and definition of “metabolic fingerprints” can subsidize both the identification and determination of plant varieties, as well as the increase in knowledge about the responses and adaptations of plants to external and/or internal factors. The lemon tree (Citrus limon Burm.) is one of the most important crops in the Spanish southeast and is often consumed around the world. Although the study and characterization of its fruits are common due to its economic interest, its leaves are limited to specific functionalized studies related to the objective of the work (extraction of essential oils, stabilizing agent, aromatic extracts, etc.). So, this study aimed to identify the primary and secondary metabolites of Citrus limon Burm. (‘Verna’ variety) leaf samples cultivated under different conditions (three rootstocks and three culture media). In total, 19 metabolites were identified for all samples, of which 9 were amino acids, 5 organic acids, 3 sugars and 2 intermediate metabolites. The results pointed to a limited influence, both of the substrate and of the crop rootstock, on the metabolomic differentiation of lemon leaves. Knowledge and foliar metabolomic differentiation can offer important information that supports the application of crop foliar treatments but also helps in the management of diseases and pests.

18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy.

ObjectiveReliable markers to predict the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) are lacking. We aimed to assess the ability of 18F-FDG PET/MRI to predict response to nCRT among patients undergoing curative-intent surgery.MethodsPatients with histological-confirmed LARC who underwent curative-intent surgery following nCRT and restaging with 18F-FDG PET/MRI were included. Statistical correlation between radiomic features extracted in PET, apparent diffusion coefficient (ADC) and T2w images and patients’ histopathologic response to chemoradiotherapy using a multivariable logistic regression model ROC-analysis.ResultsOverall, 50 patients were included in the study. A pathological complete response was achieved in 28.0% of patients. Considering second-order textural features, nine parameters showed a statistically significant difference between the two groups in ADC images, six parameters in PET images and four parameters in T2w images. Combining all the features selected for the three techniques in the same multivariate ROC curve analysis, we obtained an area under ROC curve of 0.863 (95% CI, 0.760–0.966), showing a sensitivity, specificity and accuracy at the Youden’s index of 100% (14/14), 64% (23/36) and 74% (37/50), respectively.ConclusionPET/MRI texture analysis seems to represent a valuable tool in the identification of rectal cancer patients with a complete pathological response to nCRT.

How Does Starch Structure Impact Amylolysis? Review of Current Strategies for Starch Digestibility Study.

In vitro digestibility of starch is a common analysis in human nutrition research, and generally consists of performing the hydrolysis of starch by α-amylase in specific conditions. Similar in vitro assays are also used in other research fields, where different methods can be used. Overall, the in vitro hydrolysis of native starch is a bridge between all of these methods. In this literature review, we examine the use of amylolysis assays in recent publications investigating the complex starch structure-amylolysis relation. This review is divided in two parts: (1) a brief review of the factors influencing the hydrolysis of starch and (2) a systematic review of the experimental designs and methods used in publications for the period 2016–2020. The latter reports on starch materials, factors investigated, characterization of the starch hydrolysis kinetics and data analysis techniques. This review shows that the dominant research strategy favors the comparison between a few starch samples most frequently described through crystallinity, granule type, amylose and chain length distribution with marked characteristics. This strategy aims at circumventing the multifactorial aspect of the starch digestion mechanism by focusing on specific features. An alternative strategy relies on computational approaches such as multivariate statistical analysis and machine learning techniques to decipher the role of each factor on amylolysis. While promising to address complexity, the limited use of a computational approach can be explained by the small size of the experimental datasets in most publications. This review shows that key steps towards the production of larger datasets are already available, in particular the generalization of rapid hydrolysis assays and the development of quantification approaches for most analytical results.

A new prediction model for left ventricular systolic function recovery after catheter ablation of atrial fibrillation in patients with heart failure: The ANTWOORD Study

Backgroundin patients with heart failure (HF) and atrial fibrillation (AF), AF ablation improves left ventricular ejection fraction (LVEF), along with prognosis, in a variable percentage of patients. We aimed to investigate the predictors of LVEF recovery after AF ablation and to develop a prediction model for individualized assessment. Methodswe conducted an observational, retrospective, single-centre study on 111 consecutive patients with AF and HF with impaired LVEF (<50%) undergoing ablation. Patients were divided into Responder vs. Non-Responder according to the “Universal definition of HF”. Clinical predictors were evaluated by multivariate logistic regression analysis and cross-validation technique. Independent predictors were used to build an internally validated prediction model. ResultsResponders (54%) had significantly shorter QRS duration and less dilated left atrium. Persistent AF and absence of a known etiology were more frequent among Responders. AF recurrence was similar between the two groups (p = 0.2), but the percentage of patient with persistent AF after ablation was significantly lower among Responders (p < 0.001). Absence of known etiology, presence of persistent AF, left atrial volume index<50 mL/m2, and QRS < 120 msec were independent predictors of LVEF recovery and composed the Score (AUC 0.93;95%CI 0.88–0.98–p < 0.001). Patients with Score ≤ 1 had 90% likelihood of LVEF recovery, compared to 5% in patients with 3–6. ConclusionsPatients with wide QRS, known HF etiology, dilated left atrium, and paroxysmal AF were less likely to recover LVEF after AF ablation. A new score system based on the above-mentioned parameters adequately predicts LVEF recovery after AF ablation. These results warrant confirmation and prospective validation.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products.

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

DIVIS: a semantic DIstance to improve the VISualisation of heterogeneous phenotypic datasets

BackgroundThanks to the wider spread of high-throughput experimental techniques, biologists are accumulating large amounts of datasets which often mix quantitative and qualitative variables and are not always complete, in particular when they regard phenotypic traits. In order to get a first insight into these datasets and reduce the data matrices size scientists often rely on multivariate analysis techniques. However such approaches are not always easily practicable in particular when faced with mixed datasets. Moreover displaying large numbers of individuals leads to cluttered visualisations which are difficult to interpret.ResultsWe introduced a new methodology to overcome these limits. Its main feature is a new semantic distance tailored for both quantitative and qualitative variables which allows for a realistic representation of the relationships between individuals (phenotypic descriptions in our case). This semantic distance is based on ontologies which are engineered to represent real-life knowledge regarding the underlying variables. For easier handling by biologists, we incorporated its use into a complete tool, from raw data file to visualisation. Following the distance calculation, the next steps performed by the tool consist in (i) grouping similar individuals, (ii) representing each group by emblematic individuals we call archetypes and (iii) building sparse visualisations based on these archetypes. Our approach was implemented as a Python pipeline and applied to a rosebush dataset including passport and phenotypic data.ConclusionsThe introduction of our new semantic distance and of the archetype concept allowed us to build a comprehensive representation of an incomplete dataset characterised by a large proportion of qualitative data. The methodology described here could have wider use beyond information characterizing organisms or species and beyond plant science. Indeed we could apply the same approach to any mixed dataset.

Assessment of mass bathing event (Kumbh- 2019) impact on the river water quality by using multivariate analysis and water quality index (WQI) techniques at Sangam (Prayagraj), India.

This study evaluates the changes in the water quality of the River Ganga and the Yamuna due to the mass bathing events on the occasion of Kumbh-2019 at Sangam in Prayagraj district, Uttar Pradesh, India, by using multivariate statistical techniques and Water Quality Index (WQI). Water samples were collected from 15 locations and analyzed for 17 water quality parameters during the Kumbh-2019 period. The concentration of water quality parameters was used to estimate the WQI. A change in the value of WQI during the studied period was used to interpret the effect of mass bathing. The result of the Analysis of variance (ANOVA) analysis indicates significant variation in the concentration of Electrical conductivity (EC), Total Dissolved Solids (TDS), Chemical Oxygen Demand (COD), anions (HCO3−, Cl−, SO42−, PO43−, NO3−), H4SiO4 and Most Probable Number (MPN) count during Kumbh-2019 event. The concentration of water quality parameters COD, NH4+, NO3− and H4SiO4 showed an increase of 31%, 55%, 40%, and 16%, respectively, during the Kumbh-2019 period. The high MPN count (920 - >1800/100 ml) observed during the Kumbh period makes it unfit for drinking and bathing. The pH, COD, NH4, and MPN counts are primary factors contributing to the water quality index (WQI) during the Kumbh period. The WQI value observed during the Kumbh period was relatively higher and ranged between 59 and 132, indicating the degradation of water quality from very poor to unsuitable for drinking purposes during the Kumbh-2019.

Effect of emotional labor on customer satisfaction: The study of multiple moderators in hospitality industry of Pakistan

Emotional labor is the foremost focus for both academicians and practitioners of human resource management from the last two decades. It has become mandatory job for service workers to create, sustain and construct progressive emotional state with customer throughout service encounter process. Different emotional labor strategies execute by service worker capitulate diverse outcomes, since customer may perceive them as fake, robotic or sincere. This study is discussing the relationship between emotional labor and customer satisfaction in the context of hospitality industry in Pakistan. The contemporary research portrays the effect of emotional labor on customer satisfaction by the role of three key moderators which are self-monitoring, co-worker support and customer satisfaction. The selected context is hotel industry of Pakistan which incorporates all five star hotels representing different provinces of country. Accumulated sample size comprises 185 respondents exemplifying front office department of all five star hotels of country. Multivariate data analysis techniques like exploratory factor analysis (EFA), cronbach's alpha and linear multiple regressions for moderators through Hayes process have been used to analyze the data. Data analysis postulates constructive set of findings. Results demonstrates that role of emotional labor is substantial in order to achieve customer satisfaction. Emotional labor is more effective due to deployment of three moderators which are self-monitoring, co-worker support and customer participation. Another noteworthy outcome of research is that, deep acting is more prone towards customer satisfaction in comparison with surface acting. Lastly, this study is dynamic for practitioners in hotel industry and provides valuable insight in this context. In the end limitations and future directions are made by researcher.

Caracterización físico-química de maíces locales de Chiapas, México

The physical-chemical quality of maize has become important for the food industry, so it is a condition for the selection of any local cultivar or improved varieties. The physical-chemical quality of local maize from La Sepultura Biosphere Reserve (REBISE), Chiapas, was characterized, to demonstrate that the maize diversity in the reserve presents potential for the flour and tortilla industry in Mexico. Collections of eighteen local maize populations associated with different races and two commercial hybrid materials were evaluated. Nineteen variables were characterized using multivariate techniques of Factor Analysis and Clusters. By reduction of data, six components were extracted. Typology of maize varieties merged into four groups: Group I) Of high projection in the components of breaking strength (Fu Rup RE, 300.25 gf) and pericarp dissolution (pericarp, 5.39 %; retained pericarp, 47.91 %); Group II) also high scoring in breaking strength and its persistence (Fu Rup RE, 277.25 gf), but low scoring for grain consistence and protein yield (flotation index 24.50); weight of 100 grains 33.31 g, (grain proteins 9.94 %); Group III) presented greater projection in the component of grain density, and moisture of the nixtamal and the dough (hectoliter weight 73.5 kg-hl, elongation 13.60 mm, dough moisture 57.95 %, Fu Rup RE, 220.83 gf); Group IV) showed lower projection in nixtamal moisture (44.48 %) and dough, so as elasticity of the tortillas (12.94 mm). The physical-chemical variables contributed to identify cultivar groups on the quality of the grain.

Investigating Contributing Factors on Aggressive Driving Based on a Structural Equation Model

Tailored countermeasures that may significantly improve road traffic safety can be proposed and implemented if the relationship between various associated factors and aggressive driving is well understood. However, this relationship remains unknown, as driving behavior is complex, and the interrelationships among variables are not easy to identify. Considering this situation, this paper constructed a model based on a structural equation model (SEM) and factor analysis (FA), which is a multivariate statistical analysis technique used to analyze structural relationships. The model is applied in a case study using data from the Shanghai Naturalistic Driving Study. In the case study, 16 variables were grouped into five latent factors in the SEM, and the model fits the data well. Compared with other variables, the results show that age had the most significant positive impact on aggressive driving behavior (older drivers exhibited high aggressive driving frequency). Adverse weather negatively impacted driver behavior (lower speed and high longitude acceleration), which in turn negatively affected aggressive driving behavior. In addition, the results show that driver factors (such as age and sex) were the main factors influencing vehicle use (such as hard acceleration), and the environment was the main factor determining risky scenarios, where safety-critical situations increase. This paper provides a reference for defining and determining aggressive driving and a model for exploring the relationship between driving safety factors and aggressive driving, which can be used in real-world applications for improving driving safety with applications in advanced driver-assistance (ADAS) and traffic enforcement safety control systems.

Decomposing urban-rural differences in multimorbidity among older adults in India: a study based on LASI data

BackgroundMultimorbidity is defined as the co-occurrence of two or more than two diseases in the same person. With rising longevity, multimorbidity has become a prominent concern among the older population. Evidence from both developed and developing countries shows that older people are at much higher risk of multimorbidity; however, urban-rural differential remained scarce. Therefore, this study examines urban-rural differential in multimorbidity among older adults by decomposing the risk factors of multimorbidity and identifying the covariates that contributed to the change in multimorbidity.MethodsThe study utilized information from 31,464 older adults (rural-20,725 and urban-10,739) aged 60 years and above from the recent release cross-sectional data of the Longitudinal Ageing Study in India (LASI). Descriptive, bivariate, and multivariate decomposition analysis techniques were used.ResultsOverall, significant urban-rural differences were found in the prevalence of multimorbidity among older adults (difference: 16.3; p < 0.001). The multivariate decomposition analysis revealed that about 51% of the overall differences (urban-rural) in the prevalence of multimorbidity among older adults was due to compositional characteristics (endowments). In contrast, the remaining 49% was due to the difference in the effect of characteristics (Coefficient). Moreover, obese/overweight and high-risk waist circumference were found to narrow the difference in the prevalence of multimorbidity among older adults between urban and rural areas by 8% and 9.1%, respectively. Work status and education were found to reduce the urban-rural gap in the prevalence of multimorbidity among older adults by 8% and 6%, respectively.ConclusionsThere is a need to substantially increase the public sector investment in healthcare to address the multimorbidity among older adults, more so in urban areas, without compromising the needs of older adults in rural areas.

Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecule identification. However, profiling complex samples remains a challenge because SERS peaks are likely to overlap, confounding features when multiple analytes are present in a single sample. In addition, SERS often suffers from high variability in signal enhancement due to nonuniform SERS substrate. The machine learning classification techniques widely used for facial recognition are excellent tools to overcome the complexity of SERS data interpretation. Herein, we reported a sensor for classifying coffee beverages by integrating SERS, feature extractions, and machine learning classifiers. A versatile and low-cost SERS substrate, called nanopaper, was used to enhance Raman signals of dilute compounds in coffee beverages. Two classic multivariate analysis techniques, Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC), were used to extract the significant spectral features, and the performance of various machine learning classifiers was evaluated. The combination of DAPC with Support Vector Machine (SVM) or K-Nearest Neighbor (KNN) shows the best performance for classifying coffee beverages. This user-friendly and versatile sensor has the potential to be a practical quality-control tool for the food industry.

Technology, human resource competencies and productivity in nascent petroleum industries: an empirical study

PurposeTechnology competencies (TC) and human resources (HR) play a vital role in enhancing productivity in any industry. Yet the significance and interplay of these two factors in developing economies such as Uganda that are kick-starting oil exploration is not clear. Using structural equation modeling (SEM), the underlying relationship between technology, human resources and productivity in the petroleum industry is established.Design/methodology/approachTo examine the interrelationship among the independent factors (TC and HR) and the dependent factor (productivity), a questionnaire was used to collect data from respondents in Uganda. All the targeted respondents come from the oil exploration side of the industry in Uganda. SEM, a multivariate statistical analysis technique, was applied to analyze the underlying relationships among variables.FindingsThe findings suggest that TC and HR positively and significantly influence the petroleum industry productivity (PI). Both TC and HR explain a 32% variation of the observed improvement in productivity. The relationship between the independent variables (TC and HR) and dependent variable (PI) is summarized using the equation ΔPI = 0.36 TC + 0.25 HR, with TC having a more significant effect on PI than HR.Practical implications The study thus proposes to governments and oil companies in resource constrained environments that adoption of advanced technologies in oil exploration plays a relatively much bigger role and has an overarching impact on productivity especially in countries with small scale production, or in hostile environments or with unconventional hydrocarbon reservoirs.Originality/valueFor developing economies with fewer resources that often face economic tradeoffs, the study examines the significance of TC and HR development in expanding the oil and gas sectors. This work, to the best of the authors’ knowledge, is among the few studies that have examined the impact and the interplay of TC and HR on the productivity of emerging oil industries in developing economies such as Uganda.

The Impact of Time Management Behaviors on Employee Performance in Humanitarian Service Organization: A Study of Selected NPOs in Ghana

ABSTRACT The purpose of this work is to analyze the effect of time management behaviors on work performance of humanitarian services of non-for-profit organizations (NPOs) in Ghana. Using a survey of NPOs in Ghana, different multivariate analytical techniques were employed to analyze the data. By this, we tested three symmetrical hypotheses of employees’ time behaviors leading to outcomes of employee performance in NPOs. Results indicated that short range time management behavior has a strong impact on employee work performance in NPOs. Results also indicated that long range time management behavior has a positive impact on employee work performance in NPOs. Similarly, our findings demonstrated that employee time management attitude is strongly linked to their performance in NPOs. This study concludes that time management behaviors or practices should be of great concern to organizational analysis in terms of time management and employee productivity which may go a long way to create competitive advantage for organizations.

Rainfall intensity and catchment size control storm runoff in a gullied blanket peatland

Upland blanket peat is widespread in the headwaters of UK catchments, but much of it has been degraded through atmospheric pollution, vegetation change and erosion. Runoff generation in these headwaters is an important element of downstream flood risk and these areas are increasingly the focus of interventions to restore the peat ecosystem and to potentially mitigate downstream flooding. Here we use a series of multivariate analysis techniques to examine controls on storm runoff behavior within and between ten blanket peat catchments all within 5 km of one another and ranging in size from 0.2 to 3.9 ha. We find that: 1) for all 10 catchments, rainfall intensity is the dominant driver for both magnitude and timing of peak discharge, and that total and antecedent rainfall is important for peak discharge only in small storms; 2) there is considerable inter-catchment variability in: runoff coefficient, lag time, peak runoff, and their predictability from rainfall; however, 3) a significant fraction of the inter-catchment variability can be explained by catchment characteristics, particularly catchment area; and 4) catchment controls on peak discharge and runoff coefficient for small storms highlight the importance of storage and connectivity while those for large events suggest that surface flow attenuation dominates. Together these results suggest a switching rainfall-runoff behavior where catchment storage, connectivity and antecedent conditions control small discharge peaks but become increasingly irrelevant for larger storms. Our results suggest that, in the context of Natural Flood Management potential, expanding depression storage (e.g. distributed shallow water pools) in addition to existing restoration methods could increase the range of storms within which connectivity and storage remain important and that for larger storms measures which target surface runoff velocities are likely to be important.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms-that is, algorithms that do not require ground truth labels-that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

Drivers of Employee Engagement in the Banking Industry

This study attempts to identify the factors that drive employee engagement in the banking industry. The empirical setting of the study was fourteen Indian banks. A survey with the help of a questionnaire was conducted to collect data (n=700). Data were analyzed using multivariate data analysis techniques. A model identifying the variables that drive employee engagement was developed and tested. Results of exploratory factor analysis revealed six factors that drive employee engagement: emotional satisfaction, perceived fairness, personal development, culture, clear communication, and compensation. Results of regression analysis showed that two of these factors, i.e. emotional satisfaction and compensation were significant in the overall model. Integration of the results with the reinforcement theory suggests that satisfaction of the emotional needs of the employees and system of compensation can be systematically, selectively and differentially used in banks to align employee behaviours with behaviours typically associated with high levels of engagement and organizational success.