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Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications – device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution.

There is substantial interest in the current literature – spanning finance, economics, engineering and medical imaging – on the relationship structure between several nodes in a complex system. In this paper, we extend the literature by developing model and inference for complex networks in terms of latent factors, by extracting the hidden factors that plays significant role in the configuration of inter node relationships. Together, we extend inferences to applications where the underlying network structure is also latent; that is, the adjacency matrix is unobserved. Here, we consider a Bayesian variant of the matrix factorization technique to develop a structural model of the latent adjacency matrix. There are many potential applications. For illustration, we consider a latent network of firms in the in the US automotive sector, where the central object is to understand the impact of an economic shock on firms (or nodes of the network). An important question centers around the factors that affect the stability and resilience of inter node relationships in a network. In the automotive sector application, we would like to know whether these relationship structures are driven by collaborative or competitive environments? What are the effects of a collaborative or competitive role played by a specific firm on the configuration of the relationship network? Using accounting data on firm sales and costs, we use our proposed object oriented factorization methodology to provide explanation of the estimated network links between 3 major US auto manufacturers and their intermediate suppliers.

This editorial summarises the papers published in issue 14(4) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper analyses the impact of re-exports on bilateral trade data. The second paper proposes a new measure for the popular smart specialisation index (S3). The third paper proposes a new solution for the so-called reflection problem in spatial or social interaction models. The fourth paper analyses bankruptcy spillovers in the state of Maryland. The final paper estimates a system of equations to analyse the mutual relationship between the domestic migration of highly-skilled graduates and innovation and productivity in U.S. states.

In the present work, nanoconjugates of shRNA-plasmid and a non-viral nanoceramic vector, e.g., Mg–Al layered double hydroxide (Mg-Al LDH), were synthesized and intercalated. Subsequently, these particles with an average size of 40–60 nm, were transfected into mammalian neuroblastoma cells (SH-SY5Y). The as prepared Mg–Al LDH was able to protect the incorporated shRNA-plasmid against a range of pH values, DNaseI, endonucleases, and serum components. To test the applicability of the nanoconjugate for future in-vivo studies, serum from three different model experimental animals viz, mouse, rat and guinea pig was used for the serum protection study. Additionally, we showed that prolonged storage at different temperatures does not affect the quality of the nanoconjugate. Using this nanoconjugate to transform cells, a maximum internalization of ∼26% at 24h was achieved. Lastly, we demonstrated effective and safe delivery of the plasmid by measuring GFP production and shRNA-induced knockdown of TNF alpha.

The possibility of the use of small non-coding RNAs as therapeutics in targeted treatment of damaged hard tissue are presently being explored largely and for the same, a good understanding of the underlying molecular mechanisms of osteoblast and osteoblast differentiation is necessary, which would lead to better fixation of the implant. In the present communication, we made an attempt to incorporate a model non coding RNA viz. short hairpin RNA (shRNA), in the form of shRNA-plasmid, in a porous bioactive coating of phosphate free bioactive glass (PFBG) on surgical grade SS316L implant material, used as reservoir/carrier of the biomolecule and evaluated its time dependent inhibitory action on the osteoclastogenic cytokine, tumor necrosis factor alpha (TNFα), that in turn aids in growth and differentiation of osteoblast cells, for faster remodelling, repair and healing of the damaged hard tissue at the implanted site. Upon treatment with the shRNA-plasmid, results exhibited significant downregulation (approximately 12-fold) of the TNFα, as evidenced from the sandwich ELISA assay and confirmed by quantitative reverse transcription–polymerase chain reaction (qRT–PCR) analysis, followed by upregulation of alkaline phosphatase (approximately 4-fold), corresponding to osteoblast formation, on the contrary, confirming a pivotal role of the same in osteoblast formation. The flow cytometry data showed significant cellular uptake (16.04%) of the shRNA-plasmid into osteoclast cells at a time period of 24h, that was in turn corroborated by confocal imaging.

In continuation with our previous report w.r.t the synthesis of phase pure and mixed phase calcium aluminum layered double hydroxide (CaAl-LDH) and its subsequent evaluation with regard to anticancer property, the present communication demonstrates the intercalation of plasmid encoded short hairpin RNA (shRNA) into the interlayer space of CaAl-LDH nanoparticle (precipitated both at pHs 8.5 and 12.5, designated as sample A and B respectively) by a simple technique to obtain the requisite loading, followed by evaluation of the cellular uptake and localization. The presence of interlayer carbonate anion (CO32−) as an impurity, were quantified both in the samples A and B, using ion chromatography, of which, the later exhibited a higher value (6.10 ppm) which in turn had hindered the intercalation of the shRNA, largely. Further, the cellular uptake using colon cancer cell line (HCT 116) could be correlated, based on the above data, as, for sample A, a threefold higher value (9.34%) was found in a period of 72 h, compared to sample B (3.54%), without using any transfection reagent. This observation was corroborated using confocal imaging to understand the internalization at the cellular level.

Misfolded, pathological tau protein propagates from cell to cell causing neuronal degeneration in Alzheimer’s disease and other tauopathies. The molecular mechanisms of this process have remained elusive. Unconventional secretion of tau takes place via several different routes, including direct penetration through the plasma membrane. Here, we show that tau secretion requires membrane interaction via disulphide bridge formation. Mutating residues that reduce tau interaction with membranes or formation of disulphide bridges decrease both tau secretion from cells, and penetration through artificial lipid membranes. Our results demonstrate that tau is indeed able to penetrate protein-free membranes in a process independent of active cellular processes and that both membrane interaction and disulphide bridge formation are needed for this process. QUARK-based de novo modelling of the second and third microtubule-binding repeat domains (MTBDs), in which the two cysteine residues of 4R isoforms of tau are located, supports the concept that this region of tau could form transient amphipathic helices for membrane interaction.

Friction can be reduced or eliminated when the contact interface is subjected to an external vibration; we refer to this phenomenon here as vibration-activated lubricity. According to prior literature, vibration-activated lubricity is limited to oscillation amplitudes and frequencies that depend strongly on case-specific experimental variables such as the instrument resonance frequency, sliding speed, and slip length of the tribo-pair. This paper aims to overcome these limitations and clarify their origins. Specifically, we used a quartz crystal microbalance (QCM) to directly oscillate the sample at a fixed frequency and at oscillation speeds that exceeded the sliding speed by orders of magnitude. Under these direct oscillation conditions, vibration-activated lubricity persisted for alumina probes ranging from 50-1500 µm in diameter, loads from 20 µN − 5 mN, speeds from 5 µm/s − 1 mm/s, gold and single crystal molybdenum disulfide samples, and two instruments – a custom microtribometer and a commercial atomic force microscope. Under all conditions, vibration-activated lubricity was characterized by very small but non-vanishing friction coefficients (~ 0.01–0.05). Our findings suggest that the following criteria satisfy the conditions for robust vibration-activated lubricity: (1) direct coupling between the oscillator and the sample; (2) probe inertial or spring forces > > available friction; (3) oscillation speed > > sliding speed; (4) oscillation amplitude ≅ or > the slip length.

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS. Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

Hematopoietic stem-cell transplantation (HSCT) is a life-saving procedure often performed to cure relapsed and difficult-to-treat malignancies. Only a handful of centers in India were initially involved in the delivery of these services. However, in the last decade, more than 100 centers in the private and public domain have started offering transplant services in the country. Moreover, there are funding options, which has opened up this expensive treatment options for economically backward patients. Costs apart, there are multiple social, familial, and emotional challenges faced by these patients. A multidisciplinary support team involving social workers, psychologists, and transplant nurses, besides the treating hematologist/oncologist, is required for the optimum care of these patients. These challenges, in the Indian context, are often unique. Unfortunately, there is limited information and resource available to guide counseling of patients planned for HSCT in India. We conducted a workshop at our center where a panel of experts with experience in dealing with patients undergoing HSCT discussed issues faced by them. These discussions constitute a valuable resource for counseling patients planned for HSCT. They were transcribed by a postgraduate doctor and are summarised here in a case-based format.

OBJECTIVE: To study genetic variants and their function within genes coding for complement receptors in pre-eclampsia. DESIGN: A case-control study. SETTING: Pre-eclampsia is a common vascular disease of pregnancy. The clearance of placenta-derived material is one of the functions of the complement system in pregnancy. POPULATION: We genotyped 500 women with pre-eclamptic pregnancies and 190 pregnant women without pre-eclampsia, as controls, from the FINNPEC cohort, and 122 women with pre-eclamptic pregnancies and 1905 controls from the national FINRISK cohort. METHODS: The functional consequences of genotypes discovered by targeted exomic sequencing were explored by analysing the binding of the main ligand iC3b to mutated CR3 or CR4, which were transiently expressed on the surface of COS-1 cells. MAIN OUTCOME MEASURES: Allele frequencies were compared between pre-eclamptic pregnancies and controls in genetic studies. The functional consequences of selected variants were measured by binding assays. RESULTS: The most significantly pre-eclampsia-linked CR3 variant M441K (P = 4.27E-4, OR = 1.401, 95% CI = 1.167-1.682) displayed a trend of increased adhesion to iC3b (P = 0.051). The CR4 variant A251T was found to enhance the adhesion of CR4 to iC3b, whereas W48R resulted in a decrease of the binding of CR4 to iC3b. CONCLUSIONS: Results suggest that changes in complement-facilitated phagocytosis are associated with pre-eclampsia. Further studies are needed to ascertain whether aberrant CR3 and CR4 activity leads to altered pro- and anti-inflammatory cytokine responses in individuals carrying the associated variants, and the role of these receptors in pre-eclampsia pathogenesis. TWEETABLE ABSTRACT: Genetic variants of complement receptors CR3 and CR4 have functional consequences that are associated with pre-eclampsia. publishedVersion

In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability., 13 pages

Efforts to reliably measure AFM lateral forces have been impeded by the difficulties in obtaining appropriate calibration standards, applying those force standards to the apex of the tip, and quantifying calibration uncertainty. Here we propose a new method, Traceable Lateral Force Calibration (TLFC), which combines the reliability of direct methods with the convenience of indirect/semi-direct methods. Like other direct methods, ours comprise three essential steps: (1) fabrication of a spring (the Traceable Reference Lever or TRL); (2) calibration of the TRL spring constant; (3) conversion of measurable TRL deflections into absolute lateral force measurements based on its pre-calibrated spring constant (TLFC method). The TRL device, a simple two-axis cantilever, is easy to design, fabricate, and directly pre-calibrate with a standard laboratory microbalance. Following pre-calibration, the TRL device becomes a convenient absolute standard for AFM lateral force measurements. This paper describes the complete method and demonstrates its primary merits, which include (1) traceability to measurement standards; (2) ease of use by outside user groups; (3) absolute measurement errors 1 N/m normal stiffness); (4) robustness over a wide range of common loads, instruments, probes, and environments. While the method and proof-of-concept devices described in this paper were designed primarily for moderate to high load cantilevers (> 1 N/m), we discuss how a next generation of compliant TRL devices can be used with the TLFC method to reliably calibrate arbitrary AFM cantilevers (

We use microsimulation combined with a model of the COVID-19 impacts on individuals and households to obtain projections of households in destitution in the United Kingdom. The projections are estimated at two levels: aggregate quarterly for the UK, for all quarters of 2020; and annual for 2020 differentiated by region, sector and household demographics. At the aggregate level, destitution is projected to be about three times higher than the non-COVID counterfactual level in 2020Q2, as well as substantially higher than the non-COVID case for the remainder of the year. This increased destitution is initially largely due to the effect on the self-employed, and as the Furlough scheme is drawn down, also on the unemployed. Impacts upon different regions and sectors vary widely, and so do variations across different household types. The sectors particularly affected are construction and manufacturing, while London and its closely connected regions (South East and the Midlands) are most severely affected. Single adult households suffer the most, and the adverse effects increase with number of children in the household. That the effects upon youth remain high is a particularly worrying sign, and very high increases in destitution are also projected for 25–54 year olds and the elderly (75 years and older). Further, severe adverse effects are projected for sections of society and the economy where multiple impacts are coincident. Robust and sustained mitigation measures are therefore required.

In continuation of our previous work, which proposed a new direction of application of the bare, phase pure CaAl LDH as an active anticancer agent, the efficacy of the system was assessed by determination of half maximal inhibitory concentration (IC50) of the same on human colon cancer (HCT 116) cell line and human breast cancer (MCF 7) cell line. This was in turn established by tracing the involvement of Ca2+ ion with the CAMKIIα protein- a key player in the Ca2+/Calmodulin pathway which remains overexpressed in human colon cancer cell. CAMKIIα regulates the reactive oxygen species (ROS) production by decreasing the activity of superoxide dismutase (SOD) enzyme and hence SOD activity was also evaluated in our study. The results exhibit significant involvement of the bare, phase pure CaAl LDH at cellular level to down regulate CAMKIIα and SOD gene and its active involvement in apoptosis could also be traced.

This editorial summarizes the papers published in issue 14(1) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper applies the Shapley-based decomposition approach to determine the impact of firm-, linkage- and location-specific factors to the survival probability of enterprises. The second paper applies Bayesian comparison methods to identify simultaneously the most likely spatial econometric model and spatial weight matrix explaining new business creation. The third paper compares the performance of continuous and discrete approaches to explain subjective well-being across space. The fourth paper applies a multiple imputation approach to determine regional purchasing power parities at the NUTS-3 level using data available at the NUTS-2 level. Finally, the last paper constructs a regional input-output table for Japan from its national counterpart using and comparing the performance of four non-survey techniques.

The reflexive property for rings was introduced by Mason and play roles in noncommutative ring theory. A ring R is called reflexive if for $$a, b \in R$$, $$aRb = 0$$ implies $$bRa = 0$$. Recently, Kheradmand et al. introduced the notion of RNP (reflexive-nilpotents-property) rings by restricting the reflexive property to nilpotent elements. In this article, we study reflexive-nilpotents-property skewed by a ring endomorphism $$\alpha $$ and introduce the notion of $$\alpha $$-skew RNP rings. We investigate various properties and extensions of these rings and also determine the structure of minimal noncommutative $$\alpha $$-skew RNP rings.

We propose generalized moments LASSO estimator, combining LASSO with GMM, for penalized variable selection and estimation under the spatial error model with spatially autoregressive errors. We establish parameter consistency and selection sign consistency of the proposed estimator in the low dimensional setting when the parameter dimension p < sample size n , as well as the high dimensional setting with p greater than and growing with n. Finite sample performance of the method is examined by simulation, compared against the LASSO for IID data. The methods are applied to estimation of a spatial Durbin model for the Aveiro housing market (Portugal).

This editorial summarizes the papers published in issue 13(3) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper challenges the home market hypothesis that large countries host more firms relative to their size than small countries by considering the lobbying activities of multinational firms. The second paper analyzes the implications of a spatial weight matrix used to estimate a spatial econometric model that depends on an endogenous economic variable. By adding a spatial context, the third paper provides a novel contribution to the literature on international norms in de facto measures of human rights performance. The fourth paper examines the determinants of accepting informal work in Poland. The fifth paper deals with non-stationarity and cointegration in a dynamic spatial econometric panel data model when the number of observations in the time - rather than in the cross-sectional- domain tends to infinity.

Purpose Literature, spanning industrial organization and strategic management disciplines, uses variance decomposition to understand the relative importance of firm, industry and business group effects in shaping profitability variations. Some literature analyzes firm profitability under transition to liberalization. Previous research has taken a static before-and-after view on institutional change. This paper aims to focus on the dynamic process of liberalization in India, analyzing how different institutional regime changes alter firm behavior leading to changes in profitability patterns. Design/methodology/approach Based on a panel data set of several thousand Indian firms, spanning the 26-year period between 1980-1981 and 2005-2006, the authors determine the relative importance of firm, industry and business group effects in explaining manufacturing firms’ profitability variances across different institutional phases. The authors evaluate three propositions that help assess transition dynamics between phases. They determine the quantum of catch-up or falling behind by firms. Findings Different industries emerge as profitability leaders, as the economy progresses through different liberalization phases. Business groups that have been more effective in resource appropriation, rent-seeking, politician management and non-market activities in a controlled regime are replaced as profit leaders by those that, in a free-market economy, can be capable of intra-business resource allocation tasks and leveraging corporate capabilities. Originality/value The approach demonstrates how to analyze the underlying detailed structure of firm-level data, and performance outcomes, to derive nuanced interpretation of factors giving rise to the effects that explain profitability variances, and how to assess the way these effects behave over time. The dynamic evidence-based approach highlights what factors matter, where, when and why, in influencing profitability variances, which are a key dimension of industrial and economic performance.

This paper develops a new identification result for the causal ordering of observation units in a recursive network or directed acyclic graph. Inferences are developed for an unknown spatial weights matrix in a spatial lag model under the assumption of recursive ordering. The performance of the methods in finite sample settings is very good. Application to data on portfolio returns produces interesting new evidences on the contemporaneous lead–lag relationships between the portfolios and generates superior predictions.

The papers in this special issue cover a wide range of areas in the methodology and application of spatial econometrics. The first develops a generalized method of moments (GMM) estimator for the spatial regression model from a second-order approximation to the maximum likelihood (ML). The second develops Bayesian estimation in a stochastic frontier model with network dependence in efficiencies, with application to industry dynamics. The third studies cross-country convergence under the Lotka–Volterra model and obtains new insights into spatial spillovers. The penultimate paper develops robust specification tests for the social interactions model under both ML and GMM frameworks. The final paper proposes identification and GMM estimation in a high-order spatial autoregressive model with heterogeneity, common factors and spatial error dependence.

This editorial summarizes the papers published in issue 13(4) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper develops an economic geography model with trade costs in all sectors and different shares of unskilled labour in all locations. The second paper translates an economic geography model into a dynamic spatial econometric model and then estimates the unknown parameters to test for congestion spillover effects among Chinese cities. The following paper also investigates spillover effects, but of sovereign and banking risks across countries. The fourth paper empirically examines if a higher market potential results in higher average productivity and lower productivity dispersion of Italian retailers. The fifth paper demonstrates that modelling more than one spatial lag in the independent variables, using different specifications of the spatial weight matrix, can be used as a tool to correct for an omitted variable bias. The final paper develops a test for the existence of non-parametric non-linearities in a linear spatial econometric model.

To test the intracameral safety of nepafenac and its efficacy in inhibiting prostaglandin synthesis during phacoemulsification surgery.The safety evaluation was conducted in normal eyes of rabbits, 0.1ml of 0.3% and 1% nepafenac was injected intracamerally. Extensive studies to detect adverse response ranged from a gross examination of eyes under slit lamp biomicroscope, fluorescein dye test, Schirmer tear test, test for corneal sensitivity, intraocular pressure measurement (IOP), specular microscopy, electroretinography(ERG), and histopathological examination of intraocular tissues. Efficacy of nepafenac was studied by intracameral injection of 0.1%, 0.3% nepafenac, nepafenac 0.3%+1% lignocaine, and 1% lignocaine alone, before phacoemulsification surgery and intraoperative mydriasis along with PGE2(ProstaglandinE2) secretion were recorded.Single 0.1ml of 0.3% or 1% nepafenac did not significantly (p 0.05) alter physiological parameters and histology of cornea, iris, and retina. Nepafenac 0.3% effectively inhibited PGE2 secretion. No significant (p 0.05) prevention of miosis was recorded with 0.1% or 0.3% nepafenac. However, a combination of 0.3% nepafenac + 1% lignocaine and 1% lignocaine alone significantly (p 0.05) arrested miosis during the intraoperative period.An intracameral concentration of up to 1% nepafenac does not adversely affect the rabbit eye. Nepafenac fails to prevent miosis but inhibits prostaglandin release during phacoemulsification surgery.

Raising the bar (6). Spatial Economic Analysis. This editorial summarizes and comments on the papers published in issue 12(4) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper addresses the question of whether ‘jobs follow people’ or ‘people follow jobs’. The second paper develops a new methodology to determine functional regions. The third paper is a major contribution to the growing literature on new modelling approaches and applications of disaster impact models. The fourth paper focuses on the costs and benefits of higher education. The fifth paper develops a two-step procedure to identify endogenously spatial regimes in the first step using geographically weighted regression, and to account for spatial dependence in the second step. Finally, the sixth paper estimates a dynamic spatial panel data model to explain house prices and to show that restricted housing supply in the city of Cambridge, UK, has some undesirable labour market effects.

A ring R is called reversible if for $$a, b \in R$$ , $$ab=0$$ implies $$ba=0$$ . These rings play an important role in the study of noncommutative ring theory. Kafkas et al. (Algebra Discrete Math. 12 (2011) 72–84) generalized the notion of reversible rings to central reversible rings. In this paper, we extend the notion of central reversibility of rings to ring endomorphisms. We investigate various properties of these rings and answer relevant questions that arise naturally in the process of development of these rings, and as a consequence many new results related to central reversible rings are also obtained as corollaries to our results.

This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normal-inverse-gamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense data-generating-processes. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects., Comment: Previously: Developments on the Bayesian Structural Time Series Model: Trending Growth

Housing is more than a living space; it represents a social status capable of promoting several forms and levels of socialization and segregation. Housing is characterized by a set of attributes and functions that are valued differently, in which the consumption of “good housing” is made considering the preferences of their attributes, perceived tangibly/intangibly by the consumers. The reciprocal relation between housing and space, and the lack of structured information capable of apprising the key drivers of such dimensions increase the complexity of understanding the rational mechanisms of evaluating the real housing value. Despite the challenges associated with the modelling of housing markets, urban studies and spatial econometric literature provide a broad (but unfinished) theoretical framework and practical tools that are able to describe, understand, and predict households' housing consumption. Thus, the aim of this chapter is to present concepts and techniques to rationally capture individual and collective housing preferences, and the way in which they interact.

The primary focus in designing medical diagnosis and instrumentation devices apart from improving accuracy is to enhance their ease of usage and making them more accessible and inexpensive. Blood sugar level is a significant parameter to consider during any medical check-up. High or inconsistent levels of glucose in blood is an alarming sign and may imply the presence of diabetes. Hence, regular monitoring the concentration of sugar in blood thus becomes imperative. Conventionally this is done using devices which invade the skin barrier and require using needles and strips. The invasive process not only constitutes health hazards but also requires spending on replacements of the strips, the cost of which is not negligible. Using infra-red spectroscopy a quantitative correlation between the concentration of glucose and of near IR radiation can be established. The work presented here introduces a device capable of acquiring a measurement in a non-invasive manner. The device which is to be worn on a fingertip calculates the concentration of glucose present by first measuring the amount of radiation passing through the blood vessels in the subject's finger and then using a calibration curve to find a value in mmol/L. To compensate for variations in finger depth, the distance between the IR emitter and receiver was measured using an IR sensor and the IR absorbance readings were normalized to eliminate any aberrant effect this could potentially pose.

For all practical purposes, it is necessary to build a simple small e-commerce startup using Internet of Things (IoT) to use machines instead of human labor. It would also help human employment by making it easier to start a small e-commerce startup with an affordable budget and limited manpower. The main purpose of this research was to implement an instant IoT-based automated packaging system that can be easily applicable in a small e-commerce system. Initially a demo website is developed where customers can place an order. After submission of the order, the products will be packaged by an automated control system. After the implementation of the prototype, different relevant parameters were measured with the change of load. The cost and feasibility of the method are analysed after that. Lastly various improvements of the prototype are proposed to make it compatible with large scale E-commerce system.

Wear tests are inherently destructive and wear surfaces are often consumed before the worn volume can be detected with traditional methods. Elucidating the subtler features of incipient wear requires improvements in our ability to quantify, locate, and follow wear at the asperity scale. The topographic difference method provides a possible solution, but its use is limited by the difficulty in perfectly repositioning samples and the uncertain effect of imperfect repositioning on the wear measurement. This paper quantified the detection limits of the topographic difference method under the conditions of typical repositioning errors, surface topographies, and measurement approaches. With repositioning errors on the order of 2 μm, the raw/uncorrected topographic difference method reliably detected worn volumes as small as 10 μm3, which rivals the most sensitive macroscale wear measurements reported in the literature. Following virtual posttest realignment, wear volumes as small as 0.1 μm3 were detected. Using standard topographic difference methods in an interrupted wear test allowed us to map asperity-scale wear onto features of the unworn surface profile, observe a complex distribution of real contact areas, and follow the gradual removal of individual asperities with a resolution of 0.1 μm3. To our knowledge, these are the first direct observations of the asperity-scale wear process within a multiasperity macroscale contact.

This editorial summarizes the papers published in issue 14(2) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper deals with past and current challenges for regional science research. The second paper investigates whether people living in deprived neighbourhoods have less chance of succeeding in a job application. The third paper finds evidence that real estate firms can avoid price competition when market shares of their allies increase in the vicinity. The fourth paper is methodological: it considers a spatial autoregressive (SAR) model with heterogeneous coefficients and extensively analyzes the impact of this extension on the direct and indirect effects estimates. The fifth paper proposes an innovative method to estimate the elements of the spatial weight matrix in a spatial econometric model. The final paper is econometric-theoretical: it proposes a new generalized method of moments (GMM) estimator of the coefficients of a SAR model if the error terms are heteroskedastic of an unknown form.

Functional regression over irregular domains: variation in the shadow price of living space. Spatial Economic Analysis. Hedonic house price models need to account for spatial heterogeneity – the variation in the functional surface of shadow prices. In this context, the complexity of spatial domains raises issues for traditional spatial econometric methods. Specifically, discontinuities in the spatial surface need to be accounted for, including irregular boundaries, peninsulas and interior holes. Motivated by an application to housing markets, we develop a method for estimating the functional surface of a regression coefficient that varies over such an irregular spatial domain. Spatially varying coefficients for a specific regressor are estimated by a combination of three smoothing problems using splines based on finite element analysis. The effect of additional regressors is also allowed. We verify finite sample performance using a simulation study, and develop an application to the Aveiro–Ilhavo ...

Raising the bar (5). Spatial Economic Analysis. This editorial summarizes and comments on the papers published in this issue 12(1) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper examines the impact of the level of education on the decision to migrate and finds that it is approximately twice as large if both variables are modelled simultaneously. The second paper is one of the first papers to introduce a spatial component to models of international environmental agreements and to develop an exciting overlap with New Economic Geography. The third paper provides a tool, applied to Beijing, with which urban economic planners can investigate the role of variation and selection mechanisms in cluster development and identify possible paths of growth. The fourth paper contributes to the existing literature on retail geography by examining the role of consumption possibilities as an urban amenity. The fifth paper develops a Bayesian estimator of a linear regression model with spatial lags among the dependent variable, the explanatory variables and the disturbances. Finally, the sixth paper develops a semi-parametric generalized method of moments (GMM) estimator for a spatial autoregressive model with space-varying coefficients of the explanatory variables and a spatial autoregressive coefficient common to all units.

This editorial summarizes and comments on the papers published in issue 11(4) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper deals with common factors and spatial dependence in the error term specification of a production function model. The second paper sets forth a New Economic Geography (NEG) model with production activities that vary in their complexity, so as to analyse the impact on specialization patterns across different regions. The third paper measures the efficiency of local public investments using a relatively unknown econometric technique in which the time span over which the variables in the regression equation are measured is increased by one time period every run. The fourth paper adopts a conditional quantile regression approach to determine the impact of people employed in informal jobs on the wage distribution in Colombia and five of its regions. Finally, the last paper proposes and tests two new Bayesian variable selection approaches for spatial econometric models.

Peripheral primitive neuroectodermal tumours (pPNETs) are uncommon tumours constituting 1% of all soft tissue sarcomas. Duodeno jejunal junction is an uncommon location of pPNET. The clinical, radiological, histopathological and immunohistochemical characteristics of this case are described here. Multimodality treatments of such tumours include surgery, chemotherapy and radiotherapy. We present here an extremely rare case of peripheral primitive neuro-ectodermal tumour of the duodeno - jejunal junction in a 38

This paper proposes a novel stochastic optimization algorithm based on the biology of plant reproduction system. The plants are taken as solution variables and distributed in the solution space. The plant with highest fitness value is assumed to be in a fertile land with suitable climatic conditions and is made to disperse several seeds randomly in the solution space. The other lesser fit plants scatter fewer seeds around themselves, the number of seeds being made directly proportional to the plant’s fitness. The process is repeated till convergence is attained. The new method is tested on standard test functions and the results obtained are promising. A comparison of the new method with Particle swarm Optimization (PSO) and Genetic Algorithm (GA) shows relatively improved convergence characteristics.

The present research reports a facile synthesis of bare phase pure CaAl-LDH nanoparticle (sample A) and intercalation of anti-cancer drug etoposide (ETO) into the same via simple anion exchange technique to obtain ETO loaded CaAl-LDH (sample B). The basal spacing (d002) of sample A was increased from 8.5764 A to 17.18 A upon intercalation of ETO. Further ETO loading was found to be 27.72% as estimated by UV–Vis spectrophotometer. The release profile of ETO from sample B in phosphate buffer saline (PBS) at pH 7.4 follows quasi-Fickian diffusion phenomenon in Korsmeyer–Peppas kinetics model. In vitro cell viability was undertaken using A549 (human lung adenocarcinoma) cell line in a dose dependent manner to determine the synergistic anti-cancer potential of sample B. It was observed that at an equivalent dose, the cancer cell viability was substantially reduced, in case of sample B compared to sample A and bare etoposide drug, respectively, which was further corroborated by cell proliferation/migration assay. The cellular uptake using A549 cell line showed an increasing trend with increasing time period (11.67% and 19.30% at 24 h and 72 h) respectively, confirmed by flow cytometry, exhibiting a substantial reduction of CAMKIIα protein for sample A and sample B to 110.52 pg/ml and 95.14 pg/ml respectively. Results also showed significant down regulation of SOD gene activity. All the above results exhibited the synergistic anti-cancer potential of sample B for possible application in the management of non small cell lung carcinoma (NSCLC).

In addition to the three special issue papers, issue 15.1 contains two papers on input-output analysis. The first paper provides a thorough analysis of the cross entropy (CE) method to build input-output tables at sub-territorial levels or to update them in time. The second paper proposes a spatial input-output location quotient accounting for the co-location of related industries within the same area and for spatial spillovers of concentration into neighboring areas.

Object categorization and transportation holds a significant position in the repertoire of tasks performed in industries every day. Thus the utility of automating this process is manifold. Nowadays, in industrial environments robust communication and image acquisition setup is present for monitoring and security purposes. Our motivation was to use this sensor network and build a lightweight, real-time, simple yet reliable object carrying system. We hereby present a prototype of an automated system, that with some little prior knowledge about the assigned task can adapt itself in a variable environment and perform the task successfully. The complete software and hardware system had been developed and was experimented intensively.

Summary Housing submarkets have been defined by different criteria: (i) similarity in house attributes; (ii) similarity in hedonic prices; and (iii) substitutability of houses. We show that spatial clustering on (i) and (ii) also satisfies criterion (iii) and develop inferences based on functional linear regression of a hedonic house price model. Then, we delineate submarkets by clustering (jointly) on the surfaces of the estimated functional partial effects and housing features. The above model incorporates both spatial heterogeneity and endogenous spatial dependence. Application to an urban conglomeration in Portugal implies submarkets that emphasize the historical and endogenous evolution of urban spatial structure. Copyright © 2015 John Wiley & Sons, Ltd.

This editorial summarises the papers published in issue 13.1 so as to raise the bar in applied spatial economic research and highlight new trends. The first paper adopts a scale neutral approach to investigate the spatial mechanisms that cause regional innovation and growth. The second paper claims that population-weighting when calculating indices of regional inequality might lead to inconsistent outcomes. The third paper estimates the effect of distance between family residence and higher education institution on a student's academic performance, thereby accounting for endogenous regressors. The fourth paper shows an inverted U-shaped relationship between economic development at region of origin and the propensity to migrate using a multilevel approach. The fifth paper provides spatial econometric evidence of price competition between sellers of used books on Amazon.com. The last paper estimates a hedonic housing price equation and parameterizes the spatial weight matrix to determine how far back in time buyers, sellers and realtors are looking at the housing market.

Lack of experienced medical personnel has been a severe crisis in underdeveloped countries. Conflicts such as climatic catastrophe, war etc. leads to an increase in death toll. To combat this problem, this paper presents Auto-HRID (Automated Heart Rate Monitoring and Injecting Device) which can inject medicines as well as withdraw blood automatically reducing the need for skilled nurses or doctors. Vein detection is one of the most researched topics among bio-medical techniques. Even experienced doctors often struggle to find the best possible vein for intro-venous drug delivery especially for children, obese, patriarch. Improper detection of veins can lead to several problems such as blood clot, rashes, bruises etc. and in worst case scenario, amputation. To overcome the problem, in this paper, a novel algorithm is proposed using amplified pulse sensor (APS) to detect vein position. Auto-HRID can detect both vein positions accurately and precisely and measure the heart rate. Using the processed data from APS, a mechanical structure is developed to inject vaccines and medicines into the body and to withdraw blood from the user. With proper development, it is expected that the proposed system can bring significant improvement in health care.

Atrial fibrillation (AF) is a irregular heartbeat which can lead to many heart-related complications. The risk due to this complications can be greatly minimized if AF can be detected early. Therefore, computer aided AF detection technique from Electrocardiogram (ECG) signal is in great demand. This paper proposes an automatic efficient atrial fibrillation detection scheme from Differential Electrocardiogram (dECG) signals. At first, from the given ECG signal, baseline signal is removed with the help of median filter. Then, variation patterns in consecutive samples are analyzed and R-peaks are detected from dECG. After that, statistical features are extracted from the possible P-wave location from each ECG bit. Finally, to classify the Atrial Fibrillation frames with the help of extracted statistical features, the Linear Discriminant Analysis (LDA) classifier is employed. Experimentation on a publicly available database show that the proposed methodology shows noteworthy performance in detection of AF in terms of accuracy, sensitivity and specificity.

Being one of the most effective video technologies, wireless capsule endoscopy (WCE) offers the physicians to diagnose the gastrointestinal (GI) diseases like ulcer non-invasively. Physicians, while analyzing the WCE videos, find it tedious to detect ulcer because of the huge amount of image frames present in WCE videos. This tedious reviewing process at times leads to inaccuracy in diagnosing ulcer. This paper proposes an automatic technique to detect ulcer frames from WCE videos utilizing the histogram in Y plane of Y I Q color space which utilizes human color-response characteristics. Exhaustive experimentation on publicly available WCE video database validate that significant differences can be obtained between ulcer and non-ulcer images in histogram patterns of Y plane. Cumulative pixel number in Y plane over an optimum threshold is chosen as feature through histogram analysis. Moreover, advantage in computation and implementation is ensured through the proposed 1-D feature for ulcer detection. The supervised support vector machine (SVM) classifier with Gaussian radial basis function (RBF) kernel is used to evaluate the classification performance.