Robust Statistical Methods Research Articles

Analysis of the Current Account Balance in the Turkish Economy with NARX: A Non-linear Approach

This paper reveals that the current account balance is a crucial macroeconomic performance indicator in the Turkish economy. For this purpose, this paper employs multiple independent variables that penetrate the macroeconomy significantly for the empirical analysis of Turkey’s current account balance. NARX Artificial Neural Network, a robust and non-linear statistical method, is used for the empirical analysis in this study. The outcome of the analysis demonstrates that over 90% of the current account balance can be explained by multiple independent variables, which have a significant impact on the macroeconomy. This empirical finding denotes that the current account balance is strongly related to a complex and multiple set of macroeconomic variables. Consequently, considering the current account balance as a critical macroeconomic performance indicator is crucial for the Turkish economy. In this context, the performance evaluation of the political-economic system in Turkey should be based on the current account balance.

Cultural and demographic energy system awareness and preference: Implications for future energy system design in the United States

This research investigates the differences in awareness and preference of racial groups toward the types of energy that they would like to see incorporated into the future energy system. Based on a national survey of 3000 respondents conducted in 2020, we analyze the differences in awareness, preferences and attitudes among racial groups in the US, and how they will influence future energy system design, cognizant of a rapidly changing demographic. Utilizing robust statistical analysis methods, we ascertain cultural and demographic based differences in energy technology awareness, opinions and deployment preferences, and, building on this evidence base, develop an energy system model which allocates these preferences toward future energy system design. The findings are contrasted with current national energy system goals, highlighting the need for both bottom-up policy input which is culturally and demographically aware, alongside top-down energy system goals and guidelines.

Moving on from Concussion?

Close to a million children are evaluated emergently each year in the United States for traumatic brain injury, and most of these injuries are mild traumatic brain injury, or concussion. Symptoms, such as headache, fatigue, and dizziness, lasting more than four weeks are termed persistent post-concussive symptoms (PPCS) and can be quite disabling for children and adolescents. What proportion of children develop PPCS? In this issue of The Journal Chadwick et al present a systematic review to address this question. This particular meta-analysis was conducted rigorously with robust statistical methods, adhered to PRISMA and MOOSE guidelines, and deployed the Newcastle-Ottawa scales to address study quality. The estimated proportion of children with PPCS was 35.1% (95% CI 26.3% to 45.0%). This finding is striking. While this meta-analysis has limitations and is subject to underlying biases from studies often derived from specialty concussion clinics, we must acknowledge that perhaps more than 300 000 children each year experience PPCS, particularly teens and females. This finding highlights the burden that PPCS is placing on our health care and educational systems. While preventing concussion is a major children's health priority, such traumatic brain injury and PPCS have likely been highly prevalent for decades, long before the recent explosion of attention to and research on concussion in children. Interventions for PPCS are clearly needed for the reintegration of affected children back to school and daily activities. No matter how we consider the data, this meta-analysis makes clear that PPCS is common, and we need to develop ways for children to move on effectively from concussion. Article page 131

Quantifying the Natural Variation of ‘Data Signatures’ from Aerosols Using Statistical Control Bands

The natural variation of the data signatures of airborne aerosols from calibrated cigarette particles were quantified using enhanced Bonferroni methods. The significance of the problem of improving analytical methods for understanding the natural variation of airborne particles cannot be understated given the positive impact for mitigating harmful airborne particles. The data presented in this paper were obtained using experiments to examine the effect of a carbon-brush-based bipolar ionization on filtration efficiency of a MERV 10 filter in a recirculating HVAC system. Ionization technology is deployed throughout the world as a multilayered approach with filtration for improving indoor air quality. Despite its wide use, ionization is still considered an emerging technology due to a dearth of peer-reviewed literature. Poorly designed test protocols and a lack of robust statistical methods for analyzing experimental data are the primary reasons. Presented herein is a statistical groundwork for analyzing ionization-efficacy data from highly controlled and properly designed particulate-matter test trials. Results are presented for three experimental groups where bipolar ionization was used to study the behaviors of data signatures from cigarette-smoke aerosol particles ranging in size from 49.6 to 201.7 nm. Statistical control bands of the data from these experimental groups revealed that bipolar ionization had significant changes to the pdfs and reductions in the natural variation of the data signatures for the particle count (number of particles) across all particle sizes. Statistical control bands may provide enhanced quantitative knowledge of variation and provide expanded inference that goes beyond examination of percentiles only. The implications from this research are profound, as it lays the groundwork for the development of highly effective ionization-filtration layered strategies to mitigate the hazards of airborne particulates and is the first step towards creating robust efficacy test standards for the industry.

Abstract 2146: A quantitative in vivo pharmacogenomics platform uncovers biomarkers of therapy response

Abstract Cancer is characterized by heterogeneous genetic alterations that drive changes in cell state that often impact responses to therapy. Recent successes in the development of therapies targeting oncogenes has dramatically improved cancer care for subsets of patients and generated a wave of interest in precision cancer medicines. However, efforts to broadly discover effective targeted therapies have been stymied by a limited understanding of how response to drugs is modulated by tumor genotype. Here, we describe an in vivo functional cancer pharmacogenomics (PGx) platform optimized for the discovery of genetic drivers of response to therapies. By ​​coupling somatic genome editing, DNA barcoding, next generation sequencing, and robust statistical methods with genetically engineered mouse models of human lung cancer, the effects of different candidate therapies can be precisely quantified across thousands of tumors of diverse tumor suppressor genotypes. These tumors are initiated de novo in the native microenvironmental context in mice with an intact adaptive immune system. To illustrate the translational value of our platform, we tested a standard-of-care chemotherapy combination of carboplatin and pemetrexed (carbo/pem) in mice with oncogenic KrasG12D-driven lung tumors with inactivation of 22 putative tumor suppressors. Treatment after 12 weeks of tumor growth for 3 weeks led to greater than 75% reduction in tumor sizes relative to vehicle controls. Surprisingly, although chemotherapy affects cell proliferation pathways rather than a defined genetic component, we identified a diverse spectrum of genotype-specific sensitivity and resistance to carbo/pem treatment in our model. We next determined the extent to which our empirical PGx data could predict human clinical outcomes by analyzing clinicogenomic data from >200 patients with KRAS mutant lung cancer who received combination chemotherapy treatment. Training a multivariate Cox proportional hazards (PH) model on overall survival from treatment initiation using our 22 cancer-associated genes as binary features in combination with baseline clinical data revealed a remarkable agreement between the Cox-PH coefficients and our empirical PGx data. Further analysis of the human survival data using multiple machine learning (ML) methods identified Random Survival Forests (RSF) as the top performing model. Importantly, using mouse empirical PGx data to rank genes for feature selection significantly improved the predictive power of the ML model. Together, these results demonstrate how leveraging our PGx platform together with human data within a machine learning framework may improve the prediction of patient clinical outcomes, and thus profoundly impact our ability to realize the promise of precision medicine. Citation Format: Michael Rosen, David Amar, Ian Winters, Hira Rizvi, Wensheng Nie, Gregory Wall, Dmitri Petrov, Monte Winslow, Charles Rudin, Joseph Juan. A quantitative in vivo pharmacogenomics platform uncovers biomarkers of therapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2146.

Damage detection on the blade of an operating wind turbine via a single vibration sensor and statistical time series methods: Exploring the performance limits of robust methods

The study aims at damage detection on the blade of an operating Vestas V27 wind turbine via a single vibration response sensor under varying Environmental and Operating Conditions (EOCs), and through this, at exploring the performance limits of robust vibration-based Statistical Time Series type methods. Three trailing edge progressive, 15 cm, 30 cm, and 45 cm long, damage scenarios are examined using signals from a 104-day-long measurement campaign. The study is based on three robust methods: an Unsupervised Principal Component Analysis AutoRegressive model–based (U-PCA-AR) method, an Unsupervised Multiple Model AutoRegressive model–based (U-MM-AR) method, and an Unsupervised Principal Component Analysis Multiple Model AutoRegressive model–based (U-PCA-MM-AR) method. Comparisons with a nominal Unsupervised AutoRegressive model–based (U-AR) method and an 8-sensor–based method are also reported. The results of the study are based on a systematic and thorough assessment procedure using 7000 inspection experiments and confirm that single-sensor-based detection is indeed feasible via the aforementioned robust methods, with the best performance achieved by the U-PCA-MM-AR method which reaches up to 100% True Positive Rate at 4%, 1%, and 0% False Positive Rate for the 15, 30, and 45 cm damage scenarios, respectively. This performance is on par with that of the 8-sensor–based method and indicative of the high capabilities offered by the robust vibration-based Statistical Time Series type methods. The sensitivity of the methods with respect to the sensor location and the AutoRegressive model order is also examined.

Network-Based Analysis Reveals Gene Signature in Tip Cells and Stalk Cells.

Angiogenesis occurs during various physiological or pathological processes such as wound healing and tumor growth. Differentiation of vascular endothelial cells into tip cells and stalk cells initiates the formation of new blood vessels. Tip cells and stalk cells are endothelial cells with different biological characteristics and functions. The aim of this study was to determine the mechanisms of angiogenesis by exploring differences in gene expression of tip cells and stalk cells. Raw data were retrieved from NCBI Gene Expression Omnibus (GSE19284). Data were reanalyzed using bioinformatics methods that employ robust statistical methods, including identification of differentially expressed genes (DEGs) between the stalk and tip cells, Weighted Gene Correlation Network Analysis (WGCNA), gene ontology and pathway enrichment analysis using DAVID tools, integration of Protein-Protein Interaction (PPI) networks and screening of hub genes. DEGs of stalk and tip cells were grouped as dataset A. Gene modules associated with differentiation of stalk and tip cells screened by WGCNA were named dataset B. Further, we retrieved existing markers of angiogenesis from previous experimental studies on tip and stalk cells which we called dataset C. Intersection of datasets A, B and C was used as a candidate gene. Subsequently, we verified the results applying Quantitative Polymerase Chain Reaction (Q-PCR) to our clinical specimen. In general, the Q-PCR results coincide with the majority of the expression profile. We identified five candidate genes, including ESM1, CXCR4, JAG1, FLT1 and PTK2, and two pathways, including Rap1 signaling pathway and PI3K-Akt signaling pathway in vascular endothelial cells that differentiate into tip cells and stalk cells using bioinformatics analysis. Bioinformatics approaches provide new avenues for basic research in different fields such as angiogenesis. The findings of this study provide new perspectives and a basis for the study of molecular mechanisms of vascular endothelial cell differentiation into stalk and tip cells. Genes and pathways identified in this study are potential biomarkers and therapeutic targets for angiogenesis in the tumor.

Effects of biological and environmental factors on the variability of Paralonchurus brasiliensis (Sciaenidae) density: An GAMLSS application

The hypothesis of this study was based on the premise that complex interactions between biological and environmental factors may explain variability in fish population dynamics. To test this, Paralonchurus brasiliensis (Sciaenidae), a keystone species associated with coastal waters, was used as a model fish species. Generalized Additive Models for Location, Scale and Shape were performed to evaluate relationships among biological and environmental variables. Fish biomass, repletion index (RI), hepatosomatic index (HSI), condition factor (K) and sex ratio was in models as biological factors whereas water temperature, salinity, organic matter, grain size and depth were included as environmental variables. The results revealed significant effects of biological and abiotic factors on population density of P. brasiliensis. The selected model explained 94% of the variability in the explanatory variables. Fish biomass, HSI and K were the most important biological factors, whereas temperature and organic matter were the most important abiotic factors. Variability in fish density suggests that HSI and K both could be used as an additional ecosystem indicator of environmental status (food availability) and fish population health. We also highlight the importance of using robust and flexible statistical methods to model fish population dynamics and the need to incorporate biological and environmental factors into fishery resource management.

Analysis of Sustainability Disclosure and Performance Indicators in Pakistan to Determine the Value Relevance of Sustainability Reporting

The aim of the research is twofold: first, to measure the corporate sustainability disclosure determination rate (CSDR) and examine its relationship with sustainability performance indicators (SPI). Second, to analyze the value relevance of sustainability investment (SI) with corporate financial performance (FP) from a sample of the top 85 companies listed on the Pakistan Stock Exchange (PSX). Using content analysis, we developed a sustainability reporting index based on 20 indicators, and robust statistical methods were also applied to analyze the linkage between CSDR - SPI and SI- FP using a regression analysis for panel data. The content analysis of data shows that to some extent,companies disclose non-financial information on community development, social, and environmental progression. The findings of the study indicate the absence of a significant relationship between CSDR -SPI and SI - FP, and Pakistani companies do not enjoy the financial benefits of sustainability investments.

Information and volatility linkages between real estate, equity, bond and money markets in Australia

PurposeThe purpose of this paper is to examine information and volatility linkages among real estate, equity, bond and money markets in Australia.Design/methodology/approachA novel rational expectations framework of financial contagion (Kodres and Pritsker, 2002), along with a combination of robust statistical methods including simple and dynamic correlations and generalized impulse response (Fereidouni et al., 2014) have been employed using data covering three dynamic pre-pandemic economic cycles, namely, global financial crisis (GFC) period, pre-pandemic housing boom and pre-pandemic housing downturn from 2008 (February) to 2019 (December).FindingsResults reveal information linkages across real estate, equity, bond and money markets through correlations in return and volatilities of these series. Finding indicates that the three financial markets (equity, bond and money markets) are interdependent and integrated through information and volatility linkages during the GFC period and pre-pandemic housing downturn period. Financial markets have stronger associations with real estate market during pre-pandemic housing boom. The findings contribute to the general notion that the performances of three financial markets are closely related to the “boom” phase of the real estate cycle.Originality/valueThis research provides an extension of existing literature regarding the information and volatility contagion of the expanded set of core investment markets in Australia. The findings could assist household buyers and investors in designing strategic investment portfolios/hedging strategies and minimizing asset specific risks through diversification over short-term and long-term. In addition, results could support the maintenance, growth and development of a combination of competitive balanced investment markets including real estate, equity, bond and money markets in post-pandemic economy.

A robust biostatistical method leverages informative but uncertainly determined qPCR data for biomarker detection, early diagnosis, and treatment.

As a common medium-throughput technique, qPCR (quantitative real-time polymerase chain reaction) is widely used to measure levels of nucleic acids. In addition to accurate and complete data, experimenters have unavoidably observed some incomplete and uncertainly determined qPCR data because of intrinsically low overall amounts of biological materials, such as nucleic acids present in biofluids. When there are samples with uncertainly determined qPCR data, some investigators apply the statistical complete-case method by excluding the subset of samples with uncertainly determined data from analysis (CO), while others simply choose not to analyze (CNA) these datasets altogether. To include as many observations as possible in analysis for interesting differential changes between groups, some investigators set incomplete observations equal to the maximum quality qPCR cycle (MC), such as 32 and 40. Although straightforward, these methods may decrease the sample size, skew the data distribution, and compromise statistical power and research reproducibility across replicate qPCR studies. To overcome the shortcomings of the existing, commonly-used qPCR data analysis methods and to join the efforts in advancing statistical analysis in rigorous preclinical research, we propose a robust nonparametric statistical cycle-to-threshold method (CTOT) to analyze incomplete qPCR data for two-group comparisons. CTOT incorporates important characteristics of qPCR data and time-to-event statistical methodology, resulting in a novel analytical method for qPCR data that is built around good quality data from all subjects, certainly determined or not. Considering the benchmark full data (BFD), we compared the abilities of CTOT, CO, MC, and CNA statistical methods to detect interesting differential changes between groups with informative but uncertainly determined qPCR data. Our simulations and applications show that CTOT improves the power of detecting and confirming differential changes in many situations over the three commonly used methods without excess type I errors. The robust nonparametric statistical method of CTOT helps leverage qPCR technology and increase the power to detect differential changes that may assist decision making with respect to biomarker detection and early diagnosis, with the goal of improving the management of patient healthcare.

An innovative electron paramagnetic resonance and statistical analysis approach to investigate the geographical origin of multi-layered samples from a Renaissance painting

The present study aims at unravelling whether an innovative, integrated method based on electron paramagnetic resonance (EPR) spectroscopy and statistical analysis could shed light on the mineralogical attribution of calcium carbonate, CaCO3, microcrystalline powders found in an unusual preparation layer of a Renaissance painting on wood. Moreover, the undertaken investigation was performed to assess, if possible, the geographical provenance of the same carbonate powders. A specifically designed procedure was operated based on previous methods applied to the investigation of ancient white and colored marbles.Microscopic samples were taken from the painting with the aim of keeping both their size and mass at a minimum possible. All investigated samples consist of a highly complex, multi-layered, heterogeneous material, where the calcium carbonate aliquot is very limited. The investigation has been carried out in a non-destructive way, registering the EPR spectra of the fragments, identifying the diagnostic Mn(II) spectra, parameterizing them, and comparing them with the most extensive available database through a set of robust statistical methods. The main results include the attribution to calcite (marble rather than chalk), the mineralogical speciation of the carbonate layer and the reduction of the possible calcite provenance sites to few localities where marble was exploited. Among them, the marble from Apuan Alps is one of the likely provenances of this calcite.

Social media fatigue during the lockdown phase of the COVID-19 pandemic

Background: The coronavirus pandemic has led to deleterious effects on health, physical, and mental. The pandemic, onset since the end of 2019, has negatively impacted individuals across all sociodemographic variables. The widespread pandemic pushed global governments to enforce sanitizing, masking, and maintaining physical distancing. This lockdown meant that everyone was restricted at home, pushing academic, and work activities to take virtual means. Furthermore, this implicated excessive screen use due to increased virtual means of working. Methodology: The present study investigates social media fatigue (SMF) and the psychological effect of the same among participants of India and Nigeria. The study deploys 299 participants (males and females) for the same. Results: The findings from the study indicate that though SMF exists, it is not a prominent effect seen, considering that social media use helped people emotionally connect to their loved ones. Conclusion: This study provides data on SMF in participants from India and Nigeria during the lockdown period of the pandemic, an area of research that has not been looked into much. The study builds a scope for further research to delve into the impact of SMF at a global level, studying it with more robust statistical methods.

Precise distances from OGLE-IV member RR Lyrae stars in six bulge globular clusters

Context. RR Lyrae stars are useful standard candles allowing one to derive accurate distances for old star clusters. Based on the recent catalogues from OGLE-IV and Gaia Early Data Release 3 (EDR3), the distances can be improved for a few bulge globular clusters. Aims. The aim of this work is to derive an accurate distance for the following six moderately metal-poor, relatively high-reddening bulge globular clusters: NGC 6266, NGC 6441, NGC 6626, NGC 6638, NGC 6642, and NGC 6717. Methods. We combined newly available OGLE-IV catalogues of variable stars containing mean I magnitudes, with Clement’s previous catalogues containing mean V magnitudes, and with precise proper motions from Gaia EDR3. Astrometric membership probabilities were computed for each RR Lyrae, in order to select those compatible with the cluster proper motions. Applying luminosity–metallicity relations derived from BaSTI α-enhanced models (He-enhanced for NGC 6441 and canonical He for the other clusters), we updated the distances with relatively low uncertainties. Results. Distances were derived with the I and V bands, with a 5 − 8% precision. We obtained 6.6 kpc, 13.1 kpc, 5.6 kpc, 9.6 kpc, 8.2 kpc, and 7.3 kpc for NGC 6266, NGC 6441, NGC 6626, NGC 6638, NGC 6642, and NGC 6717, respectively. The results are in excellent agreement with the literature for all sample clusters, considering the uncertainties. Conclusions. The present method of distance derivation, based on recent data of member RR Lyrae stars, updated BaSTI models, and robust statistical methods, proved to be consistent. A larger sample of clusters will be investigated in a future work.

Pulsar_spectra: A pulsar flux density catalogue and spectrum fitting repository

Abstract We present the pulsar_spectra software repository, an open-source pulsar flux density catalogue and automated spectral fitting software that finds the best spectral model and produces publication-quality plots. The python-based software includes features that enable users in the astronomical community to add newly published spectral measurements to the catalogue as they become available. The spectral fitting software is an implementation of the method described in Jankowski et al. (2018, MNRAS, 473, 4436) which uses robust statistical methods to decide on the best-fitting model for individual pulsar spectra. pulsar_spectra is motivated by the need for a centralised repository for pulsar flux density measurements to make published measurements more accessible to the astronomical community and provide a suite of tools for measuring spectra.

A Systematic Literature Review of Multi-Criteria Analysis Model Methods on Sustainability Weighting for Ethanol Plant

The sustainability weighting is crucial as it is practically implemented into sustainability evaluation, especially in industrial development. Sustainability is about the interconnection between three aspects of sustainability impact such as economic impact, environmental impact, and social impact. Multi-Criteria Analysis (MCA) model play important roles to measure the weighting for each impact according to the scenario and criteria selected based on scientific rules and robust statistical methods. However, there were insufficient studies on the existing literature sustainability weighting model from MCA method for the ethanol plant. Hence, the present paper demonstrates a systematic literature review of MCA model methods on sustainability weighting for the ethanol plant. There are two steps involved in systematic literature reviews: formulation of the research question and systematic searching strategies consisting of identification, screening, eligibility, quality appraisal, data abstraction and analysis. The review is based on leading databases; Scopus – ScienceDirect, Springer, Taylor and Francis, and one supporting database – Google Scholar. From the review, the preferable MCA weighting model for sustainability evaluation of ethanol plants is ‘integrated Analytical Hierarchy Process (AHP)’ rather than ‘standalone AHP’. The paper offered a significant contribution to the body of knowledge and sustainability evaluation purposes.

Bridging implementation gaps to connect large ecological datasets and complex models.

Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree‐ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea mariana and Picea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced—UVAFME. Through this process, we provide preliminary ecological inference about the long‐term competitive dynamics between slow‐growing P. mariana and relatively faster‐growing P. glauca. Incorporating tree‐ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster‐growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.

Identifying user geolocation with Hierarchical Graph Neural Networks and explainable fusion

Determining user geolocation from social media data is essential in various location-based applications — from improved transportation/supply management, through providing personalized services and targeted marketing, to better overall user experiences. Previous methods rely on the similarity of user posting content and neighboring nodes for user geolocation, which suffer the problems of: (1) position-agnostic of network representation learning, which impedes the performance of their prediction accuracy; and (2) noisy and unstable user relation fusion due to the flat graph embedding methods employed. This work presents Hierarchical Graph Neural Networks (HGNN) – a novel methodology for location-aware collaborative user-aspect data fusion and location prediction. It incorporates geographical location information of users and clustering effect of regions and can capture topological relations while preserving their relative positions. By encoding the structure and features of regions with hierarchical graph learning, HGNN can primarily alleviate the problem of noisy and unstable signal fusion. We further design a relation mechanism to bridge connections between individual users and clusters, which not only leverages the information of isolated nodes that are useless in previous methods but also captures the relations between unlabeled nodes and labeled subgraphs. Furthermore, we introduce a robust statistics method to interpret the behavior of our model by identifying the importance of data samples when predicting the locations of the users. It provides meaningful explanations on the model behaviors and outputs, overcoming the drawbacks of previous approaches that treat user geolocation as “black-box” modeling and lacking interpretability. Comprehensive evaluations on real-world Twitter datasets verify the proposed model’s superior performance and its ability to interpret the user geolocation results.

Study on Optimum IUPAC Adsorption Isotherm Models Employing Sensitivity of Parameters for Rigorous Adsorption System Performance Evaluation

Adsorption cooling technologies driven by low-grade thermal or solar power are used as an energy-efficient alternative to conventional refrigeration and air conditioning systems. Explicit understanding of the adsorption cycles requires precise determination of the performance parameters, replication of the experimental data, and the rigorous study of the adsorption heat transformation method. Hence, the optimum adsorption isotherms model must be identified. Scientists often face difficulties in selecting the suitable isotherm model as there are many models for a particular form of adsorption isotherm. The present study introduces a novel approach for choosing the optimal models for each type of International Union of Pure and Applied Chemistry (IUPAC) classified adsorption isotherm using robust statistical methods. First, the box-and-whisker plots of error identification are employed. Tóth for Type-I(a) and Type-I(b), modified BET for Type-II, GAB for Type-III, Universal for Type-IV(a), and Type-IV(b), Sun Chakrabarty for Type-V, and Yahia et al. for Type-VI were found lower than the other candidate models in box-and-whisker plot. The optimality of our selected models was further verified using analysis of variance (ANOVA), pairwise Tukey honest significant difference (HSD) test, Kruskal–Wallis rank-sum test, and pairwise Wilcoxon rank-sum test. In short, rigorous statistical analysis was performed to identify the best model for each type of isotherm by minimizing error. Moreover, specific cooling effect (SCE) of Maxsorb III/ethanol and silica gel/water pairs were determined. Results showed that Tóth is the optimal isotherm model for the studied pairs, and the SCE values obtained from the model agree well with experimental data. The optimum isotherm model is indispensable for the precise designing of the next generation adsorption cooling cycles.

An Unsupervised and Robust Line and Word Segmentation Method for Handwritten and Degraded Printed Document

Segmentation of text lines and words in an unconstrained handwritten or a machine-printed degraded document is a challenging document analysis problem due to the heterogeneity in the document structure. Often there is un-even skew between the lines and also broken words in a document. In this article, the contribution lies in segmentation of a document page image into lines and words. We have proposed an unsupervised, robust, and simple statistical method to segment a document image that is either handwritten or machine-printed (degraded or otherwise). In our proposed method, the segmentation is treated as a two-class classification problem. The classification is done by considering the distribution of gap size (between lines and between words) in a binary page image. Our method is very simple and easy to implement. Other than the binarization of the input image, no pre-processing is necessary. There is no need of high computational resources. The proposed method is unsupervised in the sense that no annotated document page images are necessary. Thus, the issue of a training database does not arise. In fact, given a document page image, the parameters that are needed for segmentation of text lines and words are learned in an unsupervised manner. We have applied our proposed method on several popular publicly available handwritten and machine-printed datasets (ISIDDI, IAM-Hist, IAM, PBOK) of different Indian and other languages containing different fonts. Several experimental results are presented to show the effectiveness and robustness of our method. We have experimented on ICDAR-2013 handwriting segmentation contest dataset and our method outperforms the winning method. In addition to this, we have suggested a quantitative measure to compute the level of degradation of a document page image.