Suitable Analysis Techniques Research Articles

Adaptation of trend analysis over a small-scale region for assessing climate change parameters

ABSTRACT Several natural and anthropogenic activities mainly escalate the demand for water and food and develop implications for their availability. Hence, it is necessary to analyse the natural and man-made changes for managing resources precisely. This study focuses on assessing the trends of climatic factors such as rainfall and temperature using the Mann–Kendall (MK) test and innovative trend analysis (ITA) for the agricultural cropping seasons. The findings revealed that a statistically insignificant increasing trend in rainfall is observed for all three cropping seasons (i.e.) Kuruvai/Samba (June–September), Thaladi/Navarai (October–January), and Sornavari (February–May), while the mean temperature shows a significantly increasing trend for all three cropping seasons. The results obtained using the MK test are compared with those of the ITA test to identify the reliable trend technique for climatic factor analysis of small regions. To manage the impacts of climate change and develop sustainable conditions in agriculture, this micro-level study assists the decision makers to prefer suitable trend analysis techniques and helps in identifying the climate change adaptation strategies for small regions. Also, this study is helpful for rural farmers to increase their adaptability conditions with regard to the climate change impacts as they are the frontline victims of climate change.

Modular rewritable Petri nets: An efficient model for dynamic distributed systems

Modern distributed systems are becoming pervasive and increasingly provided with adaptation, (self-)reconfiguration and mobility capability. On one side, to face the challenges of the highly dynamic environments where they are deployed. On the other side, to keep production/maintenance costs down. Therein lies the increasing demand for formal models encompassing all of these aspects (besides concurrency). Hardly any of the classical formalisms like Petri Nets, Automata, and Process Algebra, even though powerful, permits designers to easily specify dynamic structural changes to systems and evaluate their impact on system behaviour. That has led to several extensions of classical formal models (e.g., the Pi calculus or the Nets-within-Nets paradigm) rarely accompanied by suitable analysis techniques. A recent formalization of a class of Rewritable Place-Transition Nets (RwPT) in Maude has proved potentially convenient to specify dynamically reconfigurable systems. Concerning analogous proposals, the RwPT formalism provides more abstraction/flexibility in modelling and efficiency in analysis. Nevertheless, its ability to scale the size of distributed systems built of several similar (nested) components is limited.This paper presents a compositional approach to define large RwPT models in a typical algebraic way and to exploit the modular structure of models during the analysis: Symmetries are implicitly captured by composite node-labelling (reflecting the model's hierarchical structure) that is preserved by net rewrites. A distributed, gracefully degrading production system is used as a case study. Experimental evidence points out the dramatic impact of the approach against a non-modular one and the advantages over alternative techniques. Even if the emphasis is on state-space-based verification, the paper shows the convenience of combining it with structural analysis, which is typical of Petri nets as well. For that purpose, rewrite-rule abstractions are given in the form of guidelines.

Healthcare analytics: A techno-functional perspective

The use of data analytics, particularly Artificial Intelligence (AI), has significantly transformed decision-making in healthcare. However, academic scholarship has expressed concerns over the misalignment of research priorities and their impact on data usage and choice of analytics. On the one hand, an inadequate understanding of healthcare themes and algorithmic nuances has posed difficulty for healthcare professionals to evaluate AI solutions and understand their generalizability and potential biases. On the other hand, the abundance of analytical tools, their complexity, and a misalignment between data type and analysis methods have presented major challenges for analysts in selecting a suitable method. As a result, healthcare research has mostly developed in silos. We propose a techno-functional framework that combines the perspectives of analysts and healthcare professionals, identifies key research themes (i.e., healthcare delivery, patient engagement, data management, market design, and policy and governance), and presents critical research questions that can guide future studies. We also propose a schema for segmenting analysis techniques based on data type and analytical complexity. This schema may help analysts select a suitable analysis technique and data to solve a specific problem. Our study aims to improve the understanding of healthcare analytics among researchers, analysts, and healthcare professionals and present a roadmap for future research.

PLS for designed experiments

In many emerging new areas of research, for example in -omics sciences, complex data sets are generated implementing well-designed experiments with the aim to study the effects of factors on responses measured by high-through analytical platforms. The data sets are multivariate in their nature and require suitable data analysis techniques to be investigated. Partial Least Squares (PLS) regression has been developed and successfully applied to solve multivariate regression problems where data are correlated, redundant and noisy, but it is not able to take explicitly into account the experimental design used to generate the data. In this study, we adapt PLS to model data generated by crossed factorial designs. A new method based on constrained PLS2 regression and a type III sums of squares-like approach based on PLS2 are developed to treat orthogonal and non-orthogonal designs, respectively. Post-transformation and Procrustes analysis are applied to discover the 'latent’ structure useful to investigate the relationships between factors and responses. The mathematical properties of the data decomposition, the inference process to assess the reliability of the model and some basic tools for model interpretation are presented and discussed. One simulated and two experimental data sets, one generated implementing an orthogonal design and one obtained by a non-orthogonal design, are investigated to show how the methodology works in practice.

Label-free integrated microfluidic plasmonic biosensor from vertical-cavity surface-emitting lasers for SARS-CoV-2 receptor binding domain protein detection.

The nanoplasmonic sensor of the nanograting array has a remarkable ability in label-free and rapid biological detection. The integration of the nanograting array with the standard vertical-cavity surface-emitting lasers (VCSEL) platform can achieve a compact and powerful solution to provide on-chip light sources for biosensing applications. Here, a high sensitivity and label-free integrated VCSELs sensor was developed as a suitable analysis technique for COVID-19 specific receptor binding domain (RBD) protein. The gold nanograting array is integrated on VCSELs to realize the integrated microfluidic plasmonic biosensor of on-chip biosensing. The 850 nm VCSELs are used as a light source to excite the localized surface plasmon resonance (LSPR) effect of the gold nanograting array to detect the concentration of attachments. The refractive index sensitivity of the sensor is 2.99 × 106 nW/RIU. The aptamer of RBD was modified on the surface of the gold nanograting to detect the RBD protein successfully. The biosensor has high sensitivity and a wide detection range of 0.50 ng/mL - 50 µg/mL. This VCSELs biosensor provides an integrated, portable, and miniaturized idea for biomarker detection.

Meshed neuronal mitochondrial networks empowered by AI-powered classifiers and immersive VR reconstruction.

Mitochondrial networks are defined as a continuous matrix lumen, but the morphological feature of neuronal mitochondrial networks is not clear due to the lack of suitable analysis techniques. The aim of the present study is to develop a framework to capture and analyze the neuronal mitochondrial networks by using 4-step process composed of 2D and 3D observation, primary and secondary virtual reality (VR) analysis, with the help of artificial intelligence (AI)-powered Aivia segmentation an classifiers. In order to fulfill this purpose, we first generated the PCs-Mito-GFP mice, in which green fluorescence protein (GFP) could be expressed on the outer mitochondrial membrane specifically on the cerebellar Purkinje cells (PCs), thus all mitochondria in the giant neuronal soma, complex dendritic arborization trees and long projection axons of Purkinje cells could be easily detected under a laser scanning confocal microscope. The 4-step process resolved the complicated neuronal mitochondrial networks into discrete neuronal mitochondrial meshes. Second, we measured the two parameters of the neuronal mitochondrial meshes, and the results showed that the surface area (μm2) of mitochondrial meshes was the biggest in dendritic trees (45.30 ± 53.21), the smallest in granular-like axons (3.99 ± 1.82), and moderate in soma (27.81 ± 22.22) and silk-like axons (17.50 ± 15.19). These values showed statistically different among different subcellular locations. The volume (μm3) of mitochondrial meshes was the biggest in dendritic trees (9.97 ± 12.34), the smallest in granular-like axons (0.43 ± 0.25), and moderate in soma (6.26 ± 6.46) and silk-like axons (3.52 ± 4.29). These values showed significantly different among different subcellular locations. Finally, we found both the surface area and the volume of mitochondrial meshes in dendritic trees and soma within the Purkinje cells in PCs-Mito-GFP mice after receiving the training with the simulating long-term pilot flight concentrating increased significantly. The precise reconstruction of neuronal mitochondrial networks is extremely laborious, the present 4-step workflow powered by artificial intelligence and virtual reality reconstruction could successfully address these challenges.

Relationship Between the Periodontal Status of Pregnant Women and the Incidence and Severity of Pre-term and/or Low Birth Weight Deliveries: A Retrospective Observational Case-Control Study.

Literature worldwide has linked periodontitis to preterm births and/or low-birth-weight infants. However, to our knowledge, research on this topic is scarce in India. The United Nations Children's Fund (UNICEF) reports that South Asian nations, particularly India, have the highest rates of preterm births and low-birth-weight infants as well as periodontitis due to poor socioeconomic conditions. Overall, 70% of perinatal fatalities result from prematurity and/or low birth weight, which also increases the incidence of morbidity and drives up the cost of postpartum care by a factor of ten. The frequency and severity of the illness may be greater among the Indian population due to their poor socioeconomic status. To lower the mortality rate and the cost of postnatal care, it is necessary to investigate the impact and severity of the effect of periodontal conditions on pregnancy outcomes in India. Following the collection of obstetric and prenatal records from the hospital in compliance with inclusion and exclusion criteria, a sample of 150 pregnant women were chosen from public healthcare clinics for the research. Within three days of delivery following enrolment in the trial, a single physician recorded each subject's periodontal condition using the University of North Carolina-15 (UNC-15) probe under artificial lighting and the Russell periodontal index. The gestational age was calculated based on the latest menstrual cycle, and a medical professional would order an ultrasound if they felt it was essential. The doctor weighed the newborns soon after delivery and in accordance with this prenatal record. The acquired data was analyzed using a suitable statistical analysis technique. The severity of a pregnant woman's periodontal disease was significantly correlated with the infant's birth weight and gestational age. Preterm births and low-birth-weight infants became more prevalent as the severity of the periodontal disease rose. The results demonstrated that periodontal disease in pregnant women may increase the risk of preterm delivery and low birth weight in infants.

AES lithium chemical mapping in buried interface of all‐solid‐state battery materials

All‐solid‐state batteries (ASSBs) have been attracting attention as the next generation batteries. Despite the growing interest, there are still many challenges faced in the commercial use of ASSBs. One of the biggest issues is the internal resistance generated at the interface between solid electrolyte (SE) and electrode. Understanding the solid–solid interactions requires chemical analysis near the interface. Auger electron spectroscopy (AES) is a suitable surface analysis technique to determine the lithium chemical state with high spatial resolution. However, SEs are often sensitive to electron beam irradiation, and the chemical state of SEs is easily changed during measurement. Therefore, in this study, scanning Auger microprobe (SAM) incorporated into a multitechnique XPS system was used to evaluate the electron beam damage on a LiPON (lithium phosphorus oxynitride) surface, and then the optimal conditions for AES mapping were determined. Using the optimal conditions, lithium chemical maps from LiPON/LiCoO2 cross‐section were obtained. In this paper, the results of quantitative examination of electron beam damage on LiPON surface and lithium chemical mapping will be discussed.

Efficient recognition of dynamic user emotions based on deep neural networks

The key issue at this stage is how to mine the large amount of valuable user sentiment information from the massive amount of web text and create a suitable dynamic user text sentiment analysis technique. Hence, this study offers a writing feature abstraction process based on ON-LSTM and attention mechanism to address the problem that syntactic information is ignored in emotional text feature extraction. The study found that the Att-ON-LSTM improved the micro-average F1 value by 2.27% and the macro-average F value by 1.7% compared to the Bi-LSTM model with the added attentivity mechanisms. It is demonstrated that it can perform better extraction of semantic information and hierarchical structure information in emotional text and obtain more comprehensive emotional text features. In addition, the ON-LSTM-LS, a sentiment analysis model based on ON-LSTM and tag semantics, is planned to address the problem that tag semantics is ignored in the process of text sentiment analysis. The experimental consequences exposed that the accuracy of the ON-LSTM and labeled semantic sentiment analysis model on the test set is improved by 0.78% with the addition of labeled word directions compared to the model Att-ON-LSTM without the addition of labeled semantic information. The macro-averaged F1 value improved by 1.04%, which indicates that the sentiment analysis process based on ON-LSTM and tag semantics can effectively perform the text sentiment analysis task and improve the sentiment classification effect to some extent. In conclusion, deep learning models for dynamic user sentiment analysis possess high application capabilities.

Interactive annotation of geometric ornamentation on painted pottery assisted by deep learning

Abstract In Greek art, the phase from 900 to 700 BCE is referred to as the Geometric period due to the characteristically simple geometry-like ornamentations appearing on painted pottery surfaces during this era. Distinctive geometric patterns are typical for specific periods, regions, workshops as well as painters and are an important cue for archaeological tasks, such as dating and attribution. To date, these analyses are mostly conducted with the support of information technology. The primitives of an artefact’s ornamentation can be generally classified into a set of distinguishable pattern classes, which also appear in a similar fashion on other objects. Although a taxonomy of known pattern classes is given in subject-specific publications, the automatic detection and classification of surface patterns from object depictions poses a non-trivial challenge. Our long-term goal is to provide this classification functionality using a specifically designed and trained neural network. This, however, requires a large amount of labelled training data, which at this point does not exist for this domain context. In this work, we propose an effective annotation system, which allows a domain expert to interactively segment and label parts of digitized vessel surfaces. These user inputs are constantly fed back to a Convolutional Neural Network (CNN), enabling the prediction of pattern classes for a given surface area with ever increasing precision. Our work paves the way for a fully automatic classification and analysis of large surface pattern collections, which, with the help of suitable visual analysis techniques, can answer research questions like pattern variability or change over time. While the capability of our proposed annotation pipeline is demonstrated at the example of two characteristic Greek pottery artefacts from the Geometric period, the proposed methods can be readily adopted for the patternation in any other chronological periods as well as for stamped motifs.

Chemical Profiling and Characterization of Different Cultivars of Cannabis sativa L. Inflorescences by SPME-GC-MS and UPLC-MS

The chemical profile of the Cannabis sativa L. female inflorescences is rather complex being characterized by a large number of molecules belonging to different chemical classes. Considering the numerous applications in various fields, including the medical and pharmaceutical sectors, that have seen a large use of Cannabis genus in recent years, a precise characterization of the matrices is essential. In this regard, the application of adequate and suitable sampling and analysis techniques becomes important in order to provide an identification of the metabolites characterizing the profile of the sample under examination. The goal of this work is to provide additional information on the chemical composition of the inflorescences of five C. sativa different cultivars grown in Emilia Romagna (Italy) through the application of sophisticated analysis techniques such as Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry and Ultra-Performance Liquid Chromatography-Mass Spectrometry (SPME-GC-MS and UPLC-MS). The obtained data highlighted the presence of a high number of volatile and non-volatile compounds, thus allowing a comparative evaluation of the different samples. Furthermore, an in-depth statistical survey by Principal Components Analysis (PCA) and HeatMap, Hierarchical luster Analysis (HCA) and Partial Least Squares Discriminant Analysis (PLS-DA-VIP), was conducted to consider any correlations between the investigated cultivars. The findings of this study may help to provide more information on the C. sativa inflorescences useful for potential applications of their metabolites in scientific research.

Design and Optimization Formula SAE Drivetrain Components

Abstract: The overall objective of this paper is to refine the drivetrain system design, selection of suitable materials and analysis techniques for FSAE vehicles. The main objective of this work is to optimize the hub and Upright for 10-inch rim, in terms of reducing the overall weight of the system, additional accessories for variable camber and appropriate selection of bearings. To achieve this, the safety deformation factor, the fatigue resistance of the system and the fit of the entire wheel set were taken into account. Software used in design and validation are Solidworks, Ansys Keywords: FSAE, Wheel Hub, Upright, Shims, Brackets

Call for Papers: “Real-World Bio-Psycho-Social Assessments”

Call for Papers: “Real-World Bio-Psycho-Social Assessments”

Combined diffusion‐relaxometry microstructure imaging: Current status and future prospects

Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure—combined diffusion‐relaxometry imaging. Combined diffusion‐relaxometry acquisitions vary multiple MR contrast encodings—such as b‐value, gradient direction, inversion time, and echo time—in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters—such as diffusivity, T1, T2, and T2∗. This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single‐contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.

Highly selective and sensitive sandwich immunosensor platform modified with MUA-capped GNPs for detection of spike Receptor Binding Domain protein: A precious marker of COVID 19 infection

A label-free electrochemical biosensing system as a suitable analysis technique for COVID 19 specific spike receptor-binding domain protein (RBD) was developed with an aim to facilitate the diagnosis of coronavirus. A novel production procedure for the fabrication of gold nanoparticles (GNPs)-capped 11-mercaptoundecanoic acid (MUA) modified bioelectrode was presented and its application potential for RBD biosensing was examined. The bioelectrode fabrication protocol was based on covalent ester linking formation between hydroxylated ITO electrode and GNPs-capped MUA (GNPs@MUA) with carboxyl ends. For this aim, spherical GNPs were prepared and characterized with scanning-transmission electron microscopy (S-TEM), UV–vis, and Raman spectroscopy. The synthesized GNPs were functionalized with MUA yielding Au-S bonds. Then, covalent immobilization of anti-RBD antibodies on the GNPs@MUA was performed with the help of carbodiimide coupling chemistry. The assembly processes of GNPs@MUA, anti-RBD antibodies and RBD antigens were characterized electrochemical, chemical and morphological techniques. GNPs@MUA was used as immobilization environment and provided the most effective surface design for target immunosensor. The resulting immunosensor is further applied to the impedimetric detection of RBD and it displayed a linear response to RBD antigen in the linear range of 0.002−100 pg mL−1 with a limit of detection of 0.577 fg mL−1 and sensitivity of 0.238 kohmpgmL−1 cm−2. The fabricated immunosensor had a good repeatability, long storage, stability and a reusable property after simple regeneration process. Furthermore, it was successfully employed for selective determination of RBD in artificial nasal secretion samples.

Equilibria in Interdependent Natural-gas and Electric Power Markets: an Analytical Approach

Natural-gas and electric power systems and their corresponding markets have evolved over time independently. However, both systems are increasingly interdependent since combined cycle gas turbines that use natural gas to produce electricity increasingly couple them together. Therefore, suitable analysis techniques are most needed to comprehend the consequences on market outcomes of an increasing level of integration of both systems. There is a vast literature on integrated natural-gas and electric power markets assuming that the two markets are operated centrally by a single operator. This assumption is often untrue in the real world, which necessitates developing models for these interdependent yet independent markets. In this vein, this paper addresses the gap in the literature and provides analytical Nash-Cournot equilibrium models to represent the joint operation of natural-gas and electric power markets with the assumption that the market participants in each market make their own decisions independently seeking the maximum profits, as often is the case in the real world. We develop an analytical equilibrium model and apply the Karush-Kuhn-Tucker (KKT) approach to obtain Nash-Cournot equilibria for the interdependent natural-gas and electric power markets. We use a double-duopoly case to study the interaction of both markets and to derive insightful analytical results. Moreover, we derive closed-form analytical expressions for spot-market equilibria in both natural-gas and electric power markets, which are relevant and of practical significance for decision makers. We complement the double-duopoly study with a detailed sensitivity analysis.

A case-based reasoning system for recommendation of data cleaning algorithms in classification and regression tasks

Recently, advances in Information Technologies (social networks, mobile applications, Internet of Things, etc.) generate a deluge of digital data; but to convert these data into useful information for business decisions is a growing challenge. Exploiting the massive amount of data through knowledge discovery (KD) process includes identifying valid, novel, potentially useful and understandable patterns from a huge volume of data. However, to prepare the data is a non-trivial refinement task that requires technical expertise in methods and algorithms for data cleaning. Consequently, the use of a suitable data analysis technique is a headache for inexpert users. To address these problems, we propose a case-based reasoning system (CBR) to recommend data cleaning algorithms for classification and regression tasks. In our approach, we represent the problem space by the meta-features of the dataset, its attributes, and the target variable. The solution space contains the algorithms of data cleaning used for each dataset. We represent the cases through a Data Cleaning Ontology. The case retrieval mechanism is composed of a filter and similarity phases. In the first phase, we defined two filter approaches based on clustering and quartile analysis. These filters retrieve a reduced number of relevant cases. The second phase computes a ranking of the retrieved cases by filter approaches, and it scores a similarity between a new case and the retrieved cases. The retrieval mechanism proposed was evaluated through a set of judges. The panel of judges scores the similarity between a query case against all cases of the case-base (ground truth). The results of the retrieval mechanism reach an average precision on judges ranking of 94.5% in top 3 (P@3), for top 7 (P@7) 84.55%, while in top 10 (P@10) 78.35%.

Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model: The Art of “Reinventing Yourself” to Analysis the World in Which We Live

The spread of omnipresent sensing technology brings with it an increasing number of innovative models. The smart mobility initiatives offer new opportunities for Intelligent Systems to maximize the utilization of real-time data that are streaming out of different sensory resources. In recent years, the convergence trend of Big Data, Cloud and IoT has received considerable attention in industry and academia. A huge amount of data is generated every day from information systems and modern digital technologies such as the Internet of things (IoT) and cloud computing. The analysis of these massive data requires a lot of effort at multiple levels to extract knowledge to facilitate decision-making. Big data analysis is therefore a topical area of research and development. The main objective of this survey is to propose Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model. Additionally, this paper explores the big data characteristics, challenges, analysis techniques, and various tools associated with it. The recommendation of the suitable analysis techniques of big data that could reduce the time and increase efficiency is discussed.

Optimum life test plan for Type-I hybrid censored Weibull distributed products sold under general rebate warranty

The selection of an appropriate life test plan is extremely important for any product as it not just improves the quality of the product but also reduces testing costs. In this approach, however, the choice of suitable costs play an important role. In this paper, a decision model is developed to determine the optimal life testing plan (LTP) by minimising the relevant costs involved for non-repairable products sold under the general rebate warranty. LTP are developed in the presence of Type-I hybrid censoring for products having Weibull distributed lifetimes. A constrained optimisation approach is followed considering both producer's and consumer's risk and suitable analysis techniques are employed in obtaining the optimal solution. Monte Carlo simulation is conducted for numerical illustration. In order to study the sensitivity of the optimal solution due to mis-specification of parameter values and cost components, a well-designed sensitivity analysis is incorporated using a real-life Type-I hybrid censored data set.

Sensor Networks for Aerospace Human-Machine Systems.

Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator’s cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator’s states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator’s cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.