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Dimensional accuracy of selective laser melting (SLM) parts is one of manufacturers' major concerns. The additive manufacturing (AM) process is characterized by high-temperature gradients, consolidation, and thermal expansion, which induce residual stress on the part. These stresses are released by separating the part from the baseplate, leading to plastic deformation. Thermo-mechanical finite elements (FE) simulation can be adopted to determine the effect of process parameters on final geometrical accuracy and minimize non-compliant parts. In this research, a geometry for process parameter calibration is presented. The part has been manufactured and then analyzed with industrial computed tomography (iCT). An FE process simulation has been performed considering material removal during base plate separation, and the computed distortions have been compared with the results of the iCT, revealing good accordance between the final product and its digital twin. [ABSTRACT FROM AUTHOR]

This research presents the application of a beam finite element, specifically derived for simulating bending–torsion coupling in equivalent box-beam structures with curvilinear stiffeners. The stiffener path was simulated and optimized to obtain an expected coupling effect with respect to four typical static load cases, including geometric constraints related to the additive manufacturing production method. The selected load condition was applied to the centroid of the beam section, and the structure performance was consequently determined. A variation in load position up to one-fourth of the beam width was considered for investigating the stiffener path variation corresponding to a minimum bending–torsion coupling effect. The results demonstrated the capability of such a beam finite element to correctly represent the static behavior of beam structures with curvilinear stiffeners and show the possibility to uncouple its bending–torsion behavior using a specific stiffener orientation. The simulation of a laser powder bed fusion process showed new opportunities for the application of this technology to stiffened panel manufacturing. [ABSTRACT FROM AUTHOR]

To achieve sustainable development worldwide, the United Nations set 17 Sustainable Development Goals (SDGs) for humanity to reach by 2030. Society is involved in the challenge, with firms playing a crucial role. Thus, a key question is to what extent firms engage with the SDGs. Efforts to map firms' contributions have mainly focused on analysing companies' reports based on limited samples and non-real-time data. We present a novel interdisciplinary approach based on analysing big data from an online social network (Twitter) with complex network methods from statistical physics. By doing so, we provide a comprehensive and nearly real-time picture of firms' engagement with SDGs. Results show that: (1) SDGs themes tie conversations among major UK firms together; (2) the social dimension is predominant; (3) the attention to different SDGs themes varies depending on the community and sector firms belong to; (4) stakeholder engagement is higher on posts related to global challenges compared to general ones; (5) large UK companies and stakeholders generally behave differently from Italian ones. This paper provides theoretical contributions and practical implications relevant to firms, policymakers and management education. Most importantly, it provides a novel tool and a set of keywords to monitor the influence of the private sector on the implementation of the 2030 Agenda. [ABSTRACT FROM AUTHOR]

The article investigates the economic contribution of tourism to the GDP. We review background methodologies, systematically collect data for EU countries, and develop a sound and ready-to-use procedure for computing indirect and total economic impacts. The routine is then applied to selected destinations for which a minimum standard in the quality of data from Tourism Satellite Accounts and Input–Output tables is met. Methodologically, the article provides a tool for estimating the total contribution of tourism to output, gross value added, and employment. Empirically, the comparison of results across EU economies shows a high degree of heterogeneity in the tourism share to GDP, which is critically discussed. Our procedure delivers a key tool to researchers, industry leaders, and policy makers willing to investigate income and employment consequences of scenarios differing in the evolution of tourism demand, something of high relevance in the COVID-19 era. [ABSTRACT FROM AUTHOR]

This paper offers insights on the major issues and challenges firms face in the Covid-19 pandemic and their concerns for Corporate Social Responsibility (CSR) themes. To do so, we investigate large Italian firms' discussions on Twitter in the first nine months of the pandemic. Specifically, we ask: How is firms' Twitter discussion developing during the Covid-19 pandemic? Which CSR dimensions and topics do firms discuss? To what extent do they resonate with the public? We downloaded Twitter posts by the accounts of large Italian firms, and we built the bipartite network of accounts and hashtags. Using an entropy-based null model as a benchmark, we projected the information contained in the network into the accounts layers, identifying a network of accounts. We find that the network is composed of 13 communities and accounts at the core of the network focus on environmental sustainability, digital innovation, and safety. Firms' ownership type does not seem to influence the conversation. While the relevance of CSR hashtags and stakeholder engagement is relatively small, peculiarities arise in some communities. Overall, our paper highlights the contribution of online social networks and complex networks methods for management and strategy research, showing the role of online social media in understanding firms' issues, challenges, and responsibilities, with common narratives naturally emerging from data. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) is an aggressive hematologic neoplasia with a complex polyclonal architecture. Among driver lesions, those involving the FLT3 gene represent the most frequent mutations identified at diagnosis. The development of tyrosine kinase inhibitors (TKIs) has improved the clinical outcomes of FLT3-mutated patients (Pt). However, overcoming resistance to these drugs remains a challenge. To unravel the molecular mechanisms underlying therapy resistance and clonal selection, we conducted a longitudinal analysis using a single-cell DNA sequencing approach (MissionBioTapestri® platform, San Francisco, CA, USA) in two patients with FLT3-mutated AML. To this end, samples were collected at the time of diagnosis, during TKI therapy, and at relapse or complete remission. For Pt #1, disease resistance was associated with clonal expansion of minor clones, and 2nd line TKI therapy with gilteritinib provided a proliferative advantage to the clones carrying NRAS and KIT mutations, thereby responsible for relapse. In Pt #2, clonal architecture was less complex, and 1st line TKI therapy with midostaurin was able to eradicate the leukemic clones. Our results corroborate previous findings about clonal selection driven by TKIs, highlighting the importance of a deeper characterization of individual clonal architectures for choosing the best treatment plan for personalized approaches aimed at optimizing outcomes. [ABSTRACT FROM AUTHOR]

In 2015, the United Nations established 17 Sustainable Development Goals (SDGs) to be reached by 2030. They are aimed at all members of society, including businesses, which could integrate SDGs within their Corporate Social Responsibility (CSR) strategies. As family firms constitute the most widespread form of business, understanding the factors that drive the decisions to pursue SDGs is essential. Thus, this research aims to understand why a family firm complies with SDGs. Additionally, it examines how SDGs enter, at the strategic level, activities and management practices. Following the case study method, the paper focuses on a family firm based in Italy, by means of documentary analysis, semi-structured interviews, and online data. In line with the Socioemotional Wealth (SEW) theory, family and personal values are crucial. While SDGs are only now entering the strategic level, their rationale has always been part of the family values, and the firm's culture. The selected firm is pursuing ten Goals. SDGs serve as a further motivator for family firms to behave more responsibly, meaning that they help to align the firm's strategy with the global challenges, building on existing family and firm's values. Our paper brings several contributions to the family business, CSR and SDGs literature, providing a representation of the reasons why a family firm pursues SDGs and the first reaction phases to SDGs. It brings practical implications for policymakers, highlighting the role of family values as the main drivers of the choice to invest in SDGs. By providing unique evidence, it is the first to connect the family business literature, CSR and SDGs. • Family values, personal beliefs and governance aspects are crucial drivers of the decision to pursue the SDGs. • SDGs helped the family firm to align its existing CSR activities to the global challenges. • SDGs serve as a motivator for family firms to increase their responsible behaviors. • The decision to adopt SDGs is consistent with SEW theory. [ABSTRACT FROM AUTHOR]

Demographic ageing is expected to affect labour markets in very different ways on a regional scale. Contributing to this debate, we explore the spatiotemporal patterns of recent distributional changes in the worker age structure and innovation output for German regions by conducting an exploratory space-time data analysis. Besides commonly used tools, we apply newly developed approaches which allow the investigation of joint dynamics of the spatial distributions. Overall, we find that innovation hubs tend to be located in areas with high skill concentrations, but also seem to coincide with favourable demographic age structures. We show that these concentrations are persistent over time due to clusterwise path dependence and spatial contagion forces. The spatiotemporal patterns speak in favour of a demographic polarization process of German regions where the post-reunification East-West divide is increasingly turning into a rural-urban divide. [ABSTRACT FROM AUTHOR]

Purpose – Theoretical and empirical research suggests a connection between the presence of role models and the emergence of entrepreneurs. Existing entrepreneurs may act as role models for self-employment candidates by providing successful examples. The purpose of this paper is to show that role models matter in aggregate decision outcomes regarding self-employment. Design/methodology/approach – By explicitly considering the self-employment rates of the natives, which may influence locally the decisions of immigrants towards entrepreneurship, the authors develop a simple model that explains immigrant self-employment rates for a sample of 2,490 Swiss municipalities. In addition, the authors accommodate for the presence of spatial spillovers in the distribution of rates, and test a spatial autoregressive model which takes into account the average self-employment rates of immigrants living in nearby municipalities. Findings – The evidence shows a significant (positive) effect of such spatial network effects, which are characterized by a quick distance decay, suggesting spatial spillovers at the household and social network level. Additionally, the paper shows that local conditions and immigrant pool characteristics differ, with respect to self-employment choices, when examining separately urban and rural contexts. Finally, a spatial sensitivity analysis shows that the findings are consistent over different assumptions on the nature of spatial interaction. Originality/value – By using highly geographically disaggregated data, the authors are able to test the extent to which the influence of role models can be traced, and to measure these effects both within local units as well as in neighbouring units. Understanding the relationship between the native and immigrant attitudes towards entrepreneurship, the local characteristics of the immigrant community and geography is crucial in a country with a relevant share of the working population made up by immigrants. [ABSTRACT FROM AUTHOR]

Culture is gaining increasing importance in the modern tourism industry and represents a significant force of attraction for tourists (both domestic and international). Cultural tourism allows destinations and regions to expand their customer base, diversify their offer, extend the stay of the tourist, and reduce seasonality. Great efforts are made, by national governments and regions, in order to obtain official designation regarding the relevance of their historical/cultural attractions, for example through UNESCO's World Heritage Sites (WHS) list. Such an aspect seems particularly relevant for a country like Italy, which has a high number of entries in the WHS list and where regions take an active role in promoting tourism. Using an 12-year panel of domestic tourism flows, we investigate the importance of the regional endowment in terms of WHS from two perspectives: (a) by separately estimating the effects, on tourism flows, of WHS located in the residence region of tourists and in the destination region; and (b) by taking into account potential spatial substitution/complementarity effects between regions due to their WHS endowment. Finally, a sensitivity analysis is offered to evaluate the spatial extent of the latter. [ABSTRACT FROM AUTHOR]

ABSTRACT The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analyzing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region-specific information (e.g., the endowment of natural resources, or the size of the 'home market') that is usually incorporated in the fixed effects coefficients. The advantages of our proposed procedure are that the spatial filter, by incorporating region-specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time-stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive coefficients estimated for unemployment data for German NUTS-3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates. [ABSTRACT FROM AUTHOR]

Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding, and accounting, of spatial liaisons is needed in order to effectively forecast regional economic variables. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation (SAC) in geographically referenced data. The experiments carried out in this article are concerned with the analysis of the SAC observed for unemployment rates in 439 NUTS-3 German districts. The authors employ a semiparametric approach—spatial filtering—in order to uncover spatial patterns that are consistently significant over time. The authors first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, they describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, the authors exploit the resulting spatial filter as an explanatory variable in a panel modeling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Their experiments show that the computed spatial filters account for most of the residual SAC in the data. [ABSTRACT FROM AUTHOR]

In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models-in terms of explanatory variables and NN structures-we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models. [ABSTRACT FROM AUTHOR]

Research and development (R&D), in the field of nanomaterials is expected to be a major driver of innovation and economic growth. Consequently, it is of great interest to understand which factors facilitate the creation of new technological knowledge. The existing literature has typically addressed this question by employing a knowledge production function based on firm-, regional- or even country-level data. Estimating the effects for the entire national system of innovation, however, assumes poolability of regional data. We apply our reasoning to Germany, which has well-known regional disparities, in particular between the East and the West. Based on analyses at the NUTS 3 regional level, we find different knowledge production functions for the East and the West. Moreover, we investigate how our results are affected by the adoption of alternative aggregation levels. Overall, our findings suggest that a careful evaluation of poolability and aggregation is required before estimating knowledge production functions at the regional level. [ABSTRACT FROM AUTHOR]

This paper develops and applies neural network (NN) models to forecast regional employment patterns in Germany. Computer-aided optimization tools that imitate natural biological evolution to find the solution that best fits the given case (namely, genetic algorithms, GAs) are also used to detect the best NN structure. GA techniques are compared with more `traditional' techniques which require the supervision of experienced analysts. We test the performance of these techniques on a panel of 439 districts in West and East Germany. Since the West and East datasets have different time spans, the models are estimated separately for West and East Germany. The results show that the West and East NN models perform with different degrees of precision, mainly because of the different time spans of the two datasets. Automatic techniques for the choice of the NN architecture do not seem to outperform selection procedures based on the supervision of expert analysts. [ABSTRACT FROM AUTHOR]

Using a panel of 439 German regions, we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. [ABSTRACT FROM AUTHOR]

Monoclinic celsian has been obtained by a non-conventional way, i.e. the thermal treatment of 4A-type commercial zeolite: a stoichiometric amount of barium ions was included in the zeolite by means of a cation-exchange treatment. A small concentration of alkali ions (either sodium or lithium) was also present in the exchanged zeolite, since previous studies had underlined the positive effect of these ions on the formation of monoclinic celsian. This particular synthesis proves to be economic, due to the low-cost precursors, the low temperatures and the short times required. However, the presence of even a small content of alkali ions and a residual amorphous phase can negatively affect the insulating properties of the material, thus hindering its use in electrical insulating systems. The results so far obtained show that the material keeps good insulating properties at low temperature, but its conductivity and dielectric losses strongly increase above 200 °C. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we offer a meta-analytical synthesis of recent (simulation) studies on environmental tax reform (ETR). The studies considered here look at both environmental effects (e.g., reduction in CO2 emission) and economic effects (e.g., employment gains) consequent upon such a tax reform. The existing statistical results from the literature mainly suggest that the tax type, the recycling policy, and the economic model used in the simulations significantly influence the chance that a ‘double dividend’ effect can be obtained. These empirical results are, however, not entirely conclusive regarding the question of which combination of policies and models will lead to a higher double dividend. This issue is investigated in our study by a quantitative meta-analytic approach. Our meta-analytic statistical experiment demonstrates that the total effect of a tax-and-recycle policy has a significant influence on the economic variables (second dividend), when employment is used. It is also shown that different definitions of the double dividend contribute in determining the success of ETR, in particular since the effects on GDP are less clear than for employment. These findings should be taken into consideration when deploying an ETR in a policy context, in order to prevent a situation where ETR is rejected or accepted solely due to characteristics of one simulation study rather than through a wide set of results from different studies. [Copyright &y& Elsevier]

Ropeginterferon-alfa2b (ropegIFNα2b) is a long-acting IFN formulation with broad FDA/EMA approval as a therapy of polycythemia vera (PV) with no symptomatic splenomegaly. There is currently lack of information on the real-world patient selection, including the impact of local reimbursement policies, and drug management, particularly: type/timing of screening and follow-up tests; absolute/relative contraindications to therapy; ropegIFNα2b dose and combinations with hydroxyurea. As a sub-analysis of the PV-ARC retrospective study (NCT06134102), we here report our monocenter experience with ropegIFNα2b in the period from January 2021, corresponding to drug availability outside clinical trial, and December 2023. Among the 149 patients with EMA/FDA indication, only 55 (36.9%) met the local reimbursement criteria and 18 (12.1%) received ropegIFNα2b. Thanks to appropriate screening, relative/absolute contraindications to ropegIFNα2b were detected and managed in a multidisciplinary manner. Efficacy and safety of ropegIFNα2b was confirmed, with 3 cases of early molecular response. General use of low ropegIFNα2b dose, with frequent need for hydroxyurea combinations, was noted. This real-world experience suggests a significant impact of local regulations on drug prescription and the need for greater real-world data collection on ropegIFNα2b in PV patients. Also, it describes appropriate multidisciplinary screening and monitoring procedures during ropegIFNα2b therapy. [ABSTRACT FROM AUTHOR]

During pregnancy, the vaginal microbiome plays an important role in both maternal and neonatal health outcomes. Throughout pregnancy, the vaginal microbial composition undergoes significant changes, including a decrease in overall diversity and enrichment with Lactobacillus spp. In turn, the modifications in the microbial profiles are associated with shifts in the composition of vaginal metabolites. In this study, we characterized the vaginal metabolic profiles throughout pregnancy at two different gestational ages, correlating them with a microscopic evaluation of the vaginal bacterial composition. A total of 67 Caucasian pregnant women presenting to the Family Advisory Health Centres of Ravenna (Italy) were enrolled and a vaginal swab was collected at gestational ages 9–13 weeks (first trimester) and 20–24 weeks (second trimester). The composition of the vaginal microbiome was assessed by Nugent score and women were divided in 'H' (normal lactobacilli-dominated microbiota), 'I' (intermediate microbiota), and 'BV' (bacterial vaginosis) groups. Starting from the cell-free supernatants of the vaginal swabs, a metabolomic analysis was performed by means of a 1H-NMR spectroscopy. From the first to the second trimester, a greater number of women showed a normal lactobacilli-dominated microbiota, with a reduction of cases of dysbiosis. These microbial shifts were associated with profound changes in the vaginal metabolic profiles. Over the weeks, a significant reduction in the levels of BV-associated metabolites (e.g. acetate, propionate, tyramine, methylamine, putrescine) was observed. At the same time, the vaginal metabolome was characterized by higher concentrations of lactate and of several amino acids (e.g. tryptophan, threonine, isoleucine, leucine), typically found in healthy vaginal conditions. Over time, the vaginal metabolome became less diverse and more homogeneous: in the second trimester, women with BV showed metabolic profiles more similar to the healthy/intermediate groups, compared to the first trimester. Our data could help unravel the role of vaginal metabolites in the pathophysiology of pregnancy. [ABSTRACT FROM AUTHOR]

Summary: NPM1‐mutated acute myeloid leukaemia (NPM1mut AML) represents a mostly favourable/intermediate risk disease that benefits from allogeneic haematopoietic stem cell transplantation (HSCT) in case of measurable residual disease (MRD) relapse or persistence after induction chemotherapy. Although the negative prognostic role of pre‐HSCT MRD is established, no recommendations are available for the management of peri‐transplant molecular failure (MF). Based on the efficacy data of venetoclax (VEN)‐based treatment in NPM1mut AML older patients, we retrospectively analysed the off‐label combination of VEN plus azacitidine (AZA) as bridge‐to‐transplant strategy in 11 NPM1mut MRD‐positive fit AML patients. Patients were in MRD‐positive complete remission (CRMRDpos) at the time of treatment: nine in molecular relapse and two in molecular persistence. After a median number of two cycles (range 1–4) of VEN–AZA, 9/11 (81.8%) achieved CRMRD‐negative (CRMRDneg). All 11 patients proceeded to HSCT. With a median follow‐up from treatment start of 26 months, and a median post‐HSCT follow‐up of 19 months, 10/11 patients are alive (1 died from non‐relapse mortality), and 9/10 patients are in MRDneg status. This patient series highlights the efficacy and safety of VEN–AZA to prevent overt relapse, achieve deep responses and preserve patient fitness before HSCT, in patients with NPM1mut AML in MF. [ABSTRACT FROM AUTHOR]

Few studies have focused on elderly (>80 years) amyotrophic lateral sclerosis (ALS) patients, who represent a fragile subgroup generally not included in clinical trials and often neglected because they are more difficult to diagnose and manage. We analyzed the clinical and genetic features of very late-onset ALS patients through a prospective, population-based study in the Emilia Romagna Region of Italy. From 2009 to 2019, 222 (13.76%) out of 1613 patients in incident cases were over 80 years old at diagnosis, with a female predominance (F:M = 1.18). Elderly ALS patients represented 12.02% of patients before 2015 and 15.91% from 2015 onwards (p = 0.024). This group presented with bulbar onset in 38.29% of cases and had worse clinical conditions at diagnosis compared to younger patients, with a lower average BMI (23.12 vs. 24.57 Kg/m2), a higher progression rate (1.43 vs. 0.95 points/month), and a shorter length of survival (a median of 20.77 vs. 36 months). For this subgroup, genetic analyses have seldom been carried out (25% vs. 39.11%) and are generally negative. Finally, elderly patients underwent less frequent nutritional- and respiratory-supporting procedures, and multidisciplinary teams were less involved at follow-up, except for specialist palliative care. The genotypic and phenotypic features of elderly ALS patients could help identify the different environmental and genetic risk factors that determine the age at which disease onset occurs. Since multidisciplinary management can improve a patient's prognosis, it should be more extensively applied to this fragile group of patients. [ABSTRACT FROM AUTHOR]

Gardnerella vaginalis (GV) is an anaerobic bacterial species involved in the pathogenesis of bacterial vaginosis (BV), a condition of vaginal dysbiosis associated with adverse pregnancy outcomes. GV strains are categorized into four clades, characterized by a different ability to produce virulence factors, such as sialidase. We investigated the distribution of GV clades and sialidase genes in the vaginal ecosystem of a cohort of pregnant women, assessing the correlations between GV clades and the whole vaginal microbiome. A total of 61 Caucasian pregnant women were enrolled. Their vaginal swabs, collected both at the first and third trimester of pregnancy, were used for (i) evaluation of the vaginal status by Nugent score, (ii) vaginal microbiome profiling by 16S rRNA sequencing, (iii) detection and quantification of GV clades and sialidase A gene by qPCR assays. DNA of at least one GV clade was detected in most vaginal swabs, with clade 4 being the most common one. GV clade 2, together with the presence of multiple clades (>2 simultaneously), were significantly associated with a BV condition. Significantly higher GV loads and sialidase gene levels were found in BV cases, compared to the healthy status. Clade 2 was related to the major shifts in the vaginal microbial composition, with a decrease in Lactobacillus and an increase in several BV-related taxa. As the number of GV clades detected simultaneously increased, a group of BV-associated bacteria tended to increase as well, while Bifidobacterium tended to decrease. A negative correlation between sialidase gene levels and Lactobacillus, and a positive correlation with Gardnerella, Atopobium, Prevotella, Megasphaera, and Sneathia were observed. Our results added knowledge about the interactions of GV clades with the inhabitants of the vaginal microbiome, possibly helping to predict the severity of BV and opening new perspectives for the prevention of pregnancy-related complications. [ABSTRACT FROM AUTHOR]

This article evaluates the research and development (R&D) enhancing effects of two large public grant schemes for the German biotechnology industry (BioRegio and BioProfile). Both grant schemes are organized in the form of contents for cooperation and aimed at fostering the performance of innovative firms by their organization in research clusters. We apply a difference-in-differences estimation technique in a generalized linear model framework, which allows us to control for different initial regional conditions in R&D activity of the biotech sector. Our econometric findings support the view that winners generally outperform non-winning participants during the treatment period, thus indicating that exclusive funding as well as the stimulating effect of being a “winner” have positive effects on R&D activity in the short-term. Apart from this direct winner effect, for the non-winning participants no beneficial indirect effect due to a mobilization of local actors during the application phase could be detected. Finally, first attempts in estimating the long-term effects of the contests for cooperation approach on the winner regions' R&D activity in the post-treatment period show ambiguous results. [ABSTRACT FROM PUBLISHER]

(Spatial) panel data are routinely modeled in discrete time ( DT). However, compelling arguments exist for continuous-time ( CT) modeling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical analysis in DT is an oversimplification, gives an incomplete representation of reality, and may lead to misinterpretation of estimation results. The most compelling reason for a CT approach is that, in contrast to DT modeling, it allows adequate modeling of dynamic adjustment processes. This article introduces spatial dependence in a CT modeling framework. We propose a nonlinear structural equation model ( SEM) with latent variables for estimation of the exact discrete model ( EDM), which links CT model parameters to DT observations. The use of a SEM with latent variables enables a specification that accounts for measurement errors in the variables, leading to a reduction of attenuation bias (i.e., disattenuation). The SEM-CT model with spatial dependence developed here is the first dynamic SEM with spatial dependence. A simple regional labor market model for Germany, comprising changes in unemployment and population as endogenous state variables, and changes in regional average wages and in the structure of the manufacturing sector as exogenous input variables, illustrates this spatial econometric SEM-CT framework. El modelamiento de datos panel espaciales se realiza habitualmente utilizando una conceptualización del tiempo discreto (TD). Sin embargo, existen argumentos de peso para conceptualizar el tiempo de manera continua (TC). En concreto, la mayoría de procesos sociales se desarrolla en TC, por lo que el análisis estadístico en DT trae como consecuencia una simplificación excesiva de los procesos, da una representación incompleta de la realidad, y puede conducir a una interpretación errónea de los resultados de la estimación. La razón más convincente para el uso de un enfoque CT es que a diferencia de modelos DT, una conceptualización CT permite el modelado adecuado de los procesos de ajuste dinámico ( dynamic adjustment). Este artículo incorpora la dependencia espacial en un marco de modelamiento con CT. Los autores proponen un modelo de ecuaciones estructurales no lineal ( nonlinear structural equation model -SEM) con variables latentes para la estimación del modelo discreto exacto ( exact discrete model-EDM), que vincula los parámetros del modelo CT a las observaciones de DT. El uso de un SEM con variables latentes permite una especificación que da cuenta de los errores de medición en las variables, dando lugar a una reducción del sesgo de atenuación (es decir, 'desatenuacion'). El modelo SEM-CT con dependencia espacial desarrollado en el presente estudio es el primer SEM dinámico con dependencia espacial. Para ilustrar el marco conceptual SEM-CT los autores presentan un modelo simple del mercado laboral regional de Alemania. El modelo está compuesto por los cambios en el desempleo y la población como variables endógenas de estado, y los cambios en los salarios regionales promedio y en la estructura del sector manufacturero como variables de entrada exógenas. （空间）面板数据通常基于离散时间（DT）进行建模。然而更令人信服的观点是基于连续时间（CT）进行（空间）面板数据建模。特别是多数社会过程均在连续时间中演化，基于离散时间的统计分析可能过度简化，使得对现实状况的表达不完备，并可能导致对估计结果的错误解释。相比于离散时间（DT）建模，连续时间（CT）建模最具说服力的原因在于在建模过程中允许足够多的动态调整。本文介绍了CT模型框架中的空间依赖性。把CT模型参数链接到DT观察值中，我们提出了用于估计精确离散模型（EDM）的包含潜变量的非线性结构方程模型（SEM）。包含潜在变量的SEM提供了变量测量误差的计算方案，使得衰减偏差（如反衰减性）减小。本文了提出的空间相关SEM-CT模型是第一个动态空间相关的SEM模型，并以德国一个简单的区域劳动力市场模型为例，以失业和人口构成变化为内生状态变量，以区域平均工资和制造业结构部门变化为外生输入变量，阐述了该空间计量SEM- CT模型的框架。 [ABSTRACT FROM AUTHOR]

The thermal endurance of nanocomposites based on isotactic poly(propylene) filled with nanosilicates, is evaluated by a short-term analytical procedure. The procedure is based on oxidative stability measurements by isothermal thermogravimetric analysis in a pure oxygen flow, over the temperature range form 120 to 150 °C, and provides the activation energy of the thermo-oxidative degradation process. The activation energy values thus obtained were compared with those derived by scanning thermogravimetric analysis at different heating rates. The thermal endurance lines for the investigated materials can be drawn with the aid of a conventional life test, based on weight loss measurement as a function of aging time, according to the IEC 60216 procedure. A noticeable increase of the thermal endurance of the isotactic poly(propylene) based nanocomposite with respect to the unmodified polymer is found out. [ABSTRACT FROM AUTHOR]

Abstract: Monoclinic celsian derived from an innovative route, i.e. cation exchanged zeolites heat-treated at low temperature, was added at different contents (10, 20, 30wt%) to a glass matrix, in order to improve its mechanical and electrical performances. The effect of the celsian reinforcement was evaluated by testing several properties of the composite materials, such as the elastic modulus, abrasion resistance, flexural strength and electrical insulation. The results so far obtained suggest that the addition of the monoclinic celsian to the glass matrix may produce low-cost particulate composites with interesting technological properties. [Copyright &y& Elsevier]

The direct‐current and alternating‐current electrical behavior of nanocomposites, formed by isotactic polypropylene partially modified with maleic anhydride and filled with different amounts of modified synthetic clay, has been studied; moreover, the conduction mechanisms and the relaxation processes that take place in the materials have been investigated. The nanocomposites containing small clay contents exhibit direct‐current insulating properties comparable to or even higher than those observed in the polymeric matrix. However, as the synthetic clay content increases, the ionic contribution to conductivity becomes considerable. The nanocomposites also show a slightly higher permittivity and loss factor than the host material because of the appearance of a thermally activated relaxation process in the frequency range of 10−2 to 102 Hz at the investigated temperatures. © 2007 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 45: 705–713, 2007 [ABSTRACT FROM AUTHOR]