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BACKGROUND: Cytarabine based therapy has been the standard consolidation regimen for AML (acute myeloid leukemia) for decades. However, the optimal dose, regimen and schedule is not known. HIDAC (high dose cytarabine at 18 g/m(2)) has been the conventional standard, however, recent studies have shown that intermediate doses of cytarabine (IDAC) have equal efficacy and lesser toxicities. METHODS: We retrospectively analysed 75 AML patients who entered consolidation out of 167 patients who underwent induction therapy between 2014 and 2018. HIDAC (at 18 g/m(2)) was given to 39 patients and 36 patients received IDAC at 9 g/m(2). RESULTS: Median age was 28 years (range 2–60). Male: female ratio was 1.02. More courses were administered in out-patient setting in IDAC group 61% (n = 58/95 courses) than in HIDAC 29% (n = 29/101 courses); p

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.

Multiple cardiovascular disease classification from Electrocardiogram (ECG) signal is necessary for efficient and fast remedial treatment of the patient. This paper presents a method to classify multiple heart diseases using one dimensional deep convolutional neural network (CNN) where a modified ECG signal is given as an input signal to the network. Each ECG signal is first decomposed through Empirical Mode Decomposition (EMD) and higher order Intrinsic Mode Functions (IMFs) are combined to form a modified ECG signal. It is believed that the use of EMD would provide a broader range of information and can provide denoising performance. This processed signal is fed into the CNN architecture that classifies the record according to cardiovascular diseases using softmax regressor at the end of the network. It is observed that the CNN architecture learns the inherent features of the modified ECG signal better in comparison with the raw ECG signal. The method is applied on three publicly available ECG databases and it is found to be superior to other approaches in terms of classification accuracy. In MIT-BIH, St.-Petersberg, PTB databases the proposed method achieves maximum accuracy of 97.70%, 99.71%, and 98.24%, respectively.

Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K-nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.

Sleep apnea, a serious sleep disorder affecting a large population, causes disruptions in breathing during sleep. In this paper, an automatic apnea detection scheme is proposed using single lead electroencephalography (EEG) signal to discriminate apnea patients and healthy subjects as well as to deal with the difficult task of classifying apnea and nonapnea events of an apnea patient. A unique multiband subframe based feature extraction scheme is developed to capture the feature variation pattern within a frame of EEG data, which is shown to exhibit significantly different characteristics in apnea and nonapnea frames. Such within-frame feature variation can be better represented by some statistical measures and characteristic probability density functions. It is found that use of Rician model parameters along with some statistical measures can offer very robust feature qualities in terms of standard performance criteria, such as Bhattacharyya distance and geometric separability index. For the purpose of classification, proposed features are used in K Nearest Neighbor classifier. From extensive experimentations and analysis on three different publicly available databases it is found that the proposed method offers superior classification performance in terms of sensitivity, specificity, and accuracy.

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.

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

Assuming that it is not possible to detach a dwelling from its location, this article highlights the relevance of space in the context of housing market analysis and the challenge of capturing the key elements of spatial structure in an automated valuation model: location attributes, heterogeneity, dependence and scale. Thus, the aim is to present a spatial automated valuation model (sAVM) prototype, which uses spatial econometric models to determine the value of a residential property, based on identification of eight housing characteristics (seven are physical attributes of a dwelling, and one is its location; once this spatial data is known, dozens of new variables are automatically associated with the model, producing new and valuable information to estimate the price of a housing unit). This prototype was developed in a successful cooperation between an academic institution (University of Aveiro) and a business company (PrimeYield SA), resulting the Prime AVM & Analytics product/service. This collaboration has provided an opportunity to materialize some of fundamental knowledge and research produced in the field of spatial econometric models over the last 15 years into decision support tools.

Electroencephalogram (EEG) is getting special attention of late in the detection of sleep apnea as it is directly related to the neural activity. But apnea detection through visual monitoring of EEG signal by an expert is expensive, difficult, and susceptible to human error. To counter this problem, an automatic apnea detection scheme is proposed in this paper using a single lead EEG signal, which can differentiate apnea patients and healthy subjects and also classify apnea and non-apnea frames in the data of an apnea patient. Each sub-frame of a given frame of EEG data is first decomposed into band-limited intrinsic mode functions (BLIMFs) by using the variational mode decomposition (VMD). The advantage of using VMD is to obtain compact BLIMFs with adaptive center frequencies, which give an opportunity to capture the local information corresponding to varying neural activity. Furthermore, by extracting features from each BLIMF, a temporal within-frame feature variation pattern is obtained for each mode. We propose to fit the resulting pattern with the Rician model (RiM) and utilize the fitted model parameters as features. The use of such VMD-RiM features not only offers better feature quality but also ensures very low feature dimension. In order to evaluate the performance of the proposed method, K nearest neighbor classifier is used and various cross-validation schemes are carried out. Detailed experimentation is carried out on several apnea and healthy subjects of various apnea-hypopnea indices from three publicly available datasets and it is found that the proposed method achieves superior classification performances in comparison to those obtained by the existing methods, in terms of sensitivity, specificity, and accuracy.

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.

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.

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

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.

Braille is the ubiquitous method used to consume text material by the visually impaired and blind but braille forms of most books and educational material are not available especially in third world countries. For displaying digital text documents in braille form, electronic refreshable displays, have been available for quite some time. However since inception, they have been very expensive and out of reach financially for many in third world countries. This paper presents a solution to these problems – an open source text to braille scanner along with a unique low cost refreshable braille display. The proposed display generates all Braille characters by simple sliding arrangement of two eleven-slot pre-protruded sliders utilizing permutations of the slots. Unlike conventional piezo-electric based displays that consumes a large amount of power to generate each character by dynamically actuating various slots, the proposed display requires extremely low power just to carry out linear sliding of the sliders without the need of individual slot excitation. The text scanner is presented in the form of a mobile app, built using Google's open source optical character recognition (OCR) engine. The scanner app is currently capable of converting English text printed in a multitude of fonts into braille in real time without any internet connectivity. The app also features assistive technologies such as voice guidance and tactile feedback to assist the visually impaired user in scanning documents. The proposed device is implemented in a modular fashion such that one can easily expand to have more characters to be displayed. Finally the performance of the proposed display device has been tested under various conditions and a very satisfactory performance is achieved, in terms of cost, comfort of use and speed.

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.

During conflicts such as climatic catastrophe, war etc. in various countries, including underdeveloped countries, there has been a lack of experienced medical personnel leading to a significant rise in the death toll. To overcome this crisis, a real time automatic injecting device (AID) with robust vein detection is presented in this paper. This device is capable of automatically injecting medicines as well as withdrawing blood reducing the need for skilled nurses or doctors in these conflicted areas. Vein detection is a basic step for blood withdrawal. It has been a major problem for medical services such that even experienced doctors often have difficulty determining the best possible vein for intravenous drug delivery especially for children obese, patriarch. Improper detection of veins may lead to severe problems, starting off with blood clot, rashes, bruises etc. to amputation in worst case scenario. The procedure used for vein detection in this paper involves the use of a modified near IR camera to capture the image, from which, via image processing, the vein position for injecting can be determined with high precision. Live video feedback is included to ensure the perfect alignment of the needle and the determined vein position for injecting. A mechanical structure is developed to perform all the necessary hardware setup. Detected vein position for injection is sent to the mechanical system via Bluetooth for injecting medicines or withdrawing blood. With this system, experiments have been carried out and it resulted 92% accuracy in vein detection and very high precision in aligning needle with the targeted position, verified by Bangladesh University of Engineering and Technology Medical Center, which is comparable to the accuracy of the experienced physicians. It is believed that with the cost effective design presented in this paper, AID can bring significant improvement in the modernization of the medical sector.

Socioeconomic characteristics, health behaviours, and the utilisation and quality of healthcare are prime examples of socioeconomic, cultural and demographic phenomena that are inherently spatial in nature. Understanding the spatial structure of these factors is particularly relevant in order to efficiently allocate resources. This paper explores the general equilibrium spatial structure of health outcomes and health behaviours across Scottish health boards using a variant of the spatial Durbin model which allows for an a priori unknown spatial weights matrix. The results suggest that there is substantial spatial dynamics in behaviours across health boards and that these spillovers are, as expected, asymmetric. We then demonstrate how the model can be used to estimate the behavioural and health impact of a targeted education policy within each health board taking into account both the direct effect on the particular health board itself and the indirect effect in terms of spillovers. The results illustrate how the dynamic effects should play a large role in designing place based policies that maximise the overall effectiveness of health interventions. Taking into account the spatial dynamics allows policy makers to better target resources and interventions on particular clusters where the direct and indirect spillover benefits are likely to be the greatest in terms of improving health.

It is widely believed that setting monetary policy through a majority voting committee has major benefits. A monetary policy committee can take personalities out of monetary policy decisions. Critical to understanding these claims is an assessment of how such a committee functions. In this paper we examine interactions and influences between committee members when there is heterogeneity among members. We are able to identify significant interactions and directions of influence among Monetary Policy Committee members at the Bank of England.

Sacrococcygealteratoma (SCT) is known as a disease of infants but is rare in adults.. This is an uncommon location of teratoma in adults and malignant transformation has been rarely described. The clinical, radiological and histopathological characteristics of this case are described.Although complete surgical resection is the standard of care, with malignant transformation SCTs havea poor outcome.We report here the case of a 53 yr old female with sacrococcygeal teratoma

Wireless capsule endoscopy (WCE) is one of the most recent and effective video technologies to diagnose gastrointestinal (GI) diseases, such as ulcer in GI tract. Because of long duration of WCE videos, it is a burden for the physicians to analyze large number of image frames. This time consuming process often leads to human error. Hence, an automatic scheme for ulcer image detection in WCE video has great demand. In this paper, an automatic scheme based on image histogram is proposed to detect ulcer frames in WCE video. In order to compute the histograms from pixel values, instead of using conventional RGB color plane, gray scale is used, which offers the scope of utilizing brightness of color. Based on extensive experimentation, it is found that histogram patterns, obtained from ulcer and non-ulcer images in gray scale, exhibit significant differences. Cumulative pixel number of the pixel values in gray scale over an optimum threshold is chosen as feature through histogram analysis. Moreover, this 1-D feature offers computational advantage and ease of implementation. For the purpose of classification, the support vector machine (SVM) supervised classifier is used. Also, performance of the proposed method obtained from several other classifiers is compared. The performance of the proposed method is tested on several WCE images taken from publicly available WCE video database and it is found that, the proposed method offers superior classification performance, in comparison to that obtained by some existing methods, in terms of accuracy, specificity, and sensitivity.

We present the design and implementation of a prototype of Bangla voice controlled robotic motion based on an efficient and unique algorithm for accurately detecting Bangla voice commands. For the detection of Bangla voice commands to control the movement, our developed speech detection system incorporates Mel-Frequency Cepstrum Coefficients based speech feature extraction mechanism and a feature matching technique using Vector Quantization. Our proposed Bangla voice controlled robotic motion can be very effective in rescue operation and disaster management system where a rescuer can dynamically give the rescue robot voiced instructions instead of being stagnant in a place for its controlling. Additional features including dynamic path generating algorithm for the traversed path, capability to accurately follow a specific map to reach a certain destination, detection of different gases and dynamic obstacle avoidance as well as precise position and movement control system add to the flexibility and dimension of the robotic motion. Apart from its possible application in industrial automation, Bangla voice controlled robotic motion can also encourage the view of a robotic machine as a partner rather than a tool by providing a smooth, easy and flexible mean of self-reliant movement for the physically challenged people speaking Bangla language throughout the world.

We develop a model of labor productivity as a combination of capital-labour ratio, vintage of capital stock, regional externalities, and total factor productivity (TFP). The skewness of TFP distribution is related to different growth theories. While negative skewness is consistent with the neo-Schumpeterian idea of catching up with leaders, zero skewness supports the neoclassical view that deviations from the frontier reflect only idiosyncratic productivity shocks. We argue that positive skewness is consistent with an economy where exogenous technology is combined with non-transferable knowledge accumulated in specific sectors and regions. This argument provides the framework for an empirical model based on stochastic frontier analysis. The model is used to analyse regional and sectoral inequalities in Denmark.

This editorial summarizes and comments on the papers published in issue 11(3) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper proposes spatial and a-spatial indicators to describe the networks of airline companies around the world. The second paper sets forth a two-regime gravity-type model with an endogenous threshold parameter to assess the effect of labour market conditions on interregional migration flows. The third paper utilizes micro-data to explain student migration flows to higher education institutions. The fourth paper is among the first to make use of simulation-based location quotients in a multiregional input-output model. Finally, the last paper provides a purely economic-theoretical model on cooperative limit pricing in the context of spatial competition. ResumeCet editorial resume et commente les articles parus dans le numero 11(3) afin de placer la barre de la recherche economique spatiale appliquee plus haut et de souligner les nouvelles tendances. Le premier article propose des indicateurs spatiaux et non-spatiaux pour presenter les reseaux de compagnies aeriennes partout dans le monde. Le deuxieme article expose un modele de gravite type a deux regimes dote d'un parametre de seuil endogene pour evaluer l'impact des conditions du marche du travail sur les flux migratoires interregionaux. Le troisieme article emploie des donnees microeconomiques afin d'expliquer les flux migratoires des etudiants a destination des etablissements d'enseignement superieur. Le quatrieme article compte parmi les premiers a se servir des quotients de localisation sur base de la simulation dans le contexte d'un modele des entrees-sorties multiregional. Pour terminer, le dernier article fournit un modele purement economico-theorique sur les prix limites des cooperatives dans le cadre de la concurrence spatiale. ResumenEste editorial es un resumen y una observacion acerca de los articulos publicados en el numero 11(3) con la finalidad de elevar el liston en la investigacion economica espacial aplicada y resaltar las nuevas tendencias. En el primer articulo se proponen indicadores espaciales y no espaciales para describir las redes de las companias aereas de todo el mundo. En el segundo articulo se presenta un modelo de tipo gravitacional de dos regimenes con un parametro de umbral endogeno para evaluar el efecto de las condiciones del mercado laboral en los flujos de migracion interregional. En el tercer articulo se utilizan datos micro para explicar los flujos de migracion estudiantil a centros de educacion superior. El cuarto articulo es uno de los primeros en utilizar cocientes de localizacion basados en una simulacion en un modelo plurirregional de entradas y salidas. Finalmente, el ultimo articulo ofrece un modelo puramente economico-teorico sobre el precio del limite de las cooperativas en el contexto de la competencia espacial. ????????????????????????, ????????????, ???????????????????????????????????????????????????, ??????????????????????????????????????????, ????????????????????????????????????????????????????????????, ???????????????, ???????????

We present a model for studying regional and sectoral variation in total factor productivity (TFP) and develop an empirical test, based on the skewness of TFP distribution, to empirically distinguish between different growth theories. While negative skewness is consistent with the neo-Schumpeterian idea of catching up with leaders, zero skewness supports the neoclassical view that deviations from the frontier reflect only idiosyncratic productivity shocks. We argue that positive skewness corresponds to a model where the combination of exogenous technology with non-transferable knowledge accumulated in specific sectors and regions explains TFP. This argument provides the framework for an empirical model based on stochastic frontier analysis. The model is used to analyse regional and sectoral inequalities in productive efficiency across Danish sectors and regions.

The proliferation of new information technologies throughout the world has raised some important questions for policymakers as to how developing countries can benefit from their diffusion. This important volume compares the advantages and disadvantages of the IT revolution through detailed studies of a variety of developed and developing nations and regions: Argentina, Estonia, the EU, India, Japan, Korea, Mexico, South Africa, Thailand and the USA.