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2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Maria Oliva

Purpose According to the INTERPOL definition, money laundering is: “any act or attempted act to conceal or disguise the identity of illegally obtained proceeds so that they appear to have originated from legitimate sources”. Along this line, the purpose of this paper is to investigate the link amongst money laundering, mafia and food activities, in the Italian provinces. Design/methodology/approach By using annual data over the period 2010 to 2018, the author estimates balanced panel data using the instrumental variables approach. The analysis includes both fixed and random effects, as well as robustness checks. Findings The main findings of this paper reveal that, in most Italian provinces, money launderers are deterred by the probability of being identified. In particular, the deterrent action of police and investigative forces seems to be very effective. Moreover, the results of the empirical analysis show that mafia-type organisations and food activities are positively correlated with money laundering. Originality/value This paper aims to provide a specific study on the link between apparently legal activities (food and beverage) and money laundering; a link that has so far been analysed mainly on a theoretical level. Moreover, it provides several insights in terms of policy implications.

Annika Fjelkner-Pihl

AbstractThis article adds to a growing body of literature on how various types of social relations can work synergistically to promote students' academic success. Students’ study-related social networks affect academic outcome in higher education. The network literature in education generally explores students’ various relations separately, rather than their multiplex relations or when individuals share several relations. This approach risks missing the full complexity of the student experience. The aim of the present study is to add to the discussion on student social networks and attainment in higher education by further exploring multiplex relations maintained in a specific study program, in which a large share of students in the cohort commute. A survey was distributed to students in one cohort (n = 146). The findings revealed that, in this cohort, students’ friendship, working and learning networks overlap substantially, and that centrality in the friendship and in the student multiplex networks was positively and significantly related to academic outcome, whereas centrality in the working and learning networks was not. Points for future research are suggested, and practical implications for those supporting student learning in higher education are discussed.

2021 ◽  
Mehmet Emin Cihangir

Abstract This study aims to determine how to choose the correct parameter for a specific study area in landslide susceptibility and how it gives results in vector or raster-based models. In the literature, factor parameters of landslide preparing and triggering conditions are used deliberately or randomly in raster or vector-based models. In this study, the landslide inventory was analyzed together with geological, topographic-morphological, environmental, and triggering parameters, and the parameters specific to the study area and its scale were decided. In order to obtain high efficiency from the models, the parameter data were taken from the landslide depletion zone. Raster-based models and vector-based models were created according to qualitative and quantitative approaches. Model outputs resulted in close Roc Curve results ranging from 0.79 to 0.92. The study area was divided into slope units and then the model output data were transferred to these units. In order to make the result easier to use, the units obtained according to the result of each model were combined, thus a single map output was obtained from 5 different raster and vector-based models. Overall, this study presents 1) the importance of the use of landslide inventory and how to use the inventory. 2) Parameters should be selected according to field analysis and field-scale rather than randomly. 3) By combining raster and vector-based on landslide susceptibility studies, make it easier to use as a base map in hazard and risk studies with a single output.

2021 ◽  
Vol 13 (8) ◽  
pp. 4219-4240
Anna Špačková ◽  
Vojtěch Bareš ◽  
Martin Fencl ◽  
Marc Schleiss ◽  
Joël Jaffrain ◽  

Abstract. Commercial microwave links (CMLs) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation, or humidity. The CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) mainly focused on the rainfall observations by monitoring a 38 GHz dual-polarized CML of 1.85 km path length at a high temporal resolution (4 s), as well as a co-located array of five disdrometers and three rain gauges over 1 year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4923125 (Špačková et al., 2021). The data quality is generally satisfactory for further analysis, and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.

Andreas Ziegler ◽  
Kristin Forßmann ◽  
Sabine Konopka ◽  
Katja Krockenberger

Abstract Background The European Medical Device Regulation 2017/745 (MDR) has its date of application in May 2021. This new legislation has refined and expanded the need of manufacturers to have a postmarket surveillance (PMS) system. According to this legislation, a postmarket clinical follow-up (PMCF) plan is also required. Manufacturers of high-risk medical devices are obliged to conduct both PMCF and PMS studies. There is thus the need to generate evidence from clinical data. Objectives The conduct of several studies for PMS and PMCF can be cumbersome. We therefore aim to present a modular approach to combine PMS and PMCF studies into a single study. Materials and Methods We extracted the topics listed in the MDR, especially Annex XV, Section 3, the Good Clinical Practice for medical devices (EN 14155:2020, Annex A). In addition, we added topics according to the SPIRIT and the SPIRIT-PRO statement and created a draft clinical investigation plan (CIP). Results The CIP template is provided as part of the manuscript. The modular concept has passed the required regulatory and legal requirements for one specific study. Conclusion A modular approach for combining PMCF and PMS studies in a single CIP has been developed and implemented, and it is ready for use. The provided CIP template should enable other researchers and groups to adopt this concept according to their needs.

Michael C. Thrun

Although distance measures are used in many machine learning algorithms, the literature on the context-independent selection and evaluation of distance measures is limited in the sense that prior knowledge is used. In cluster analysis, current studies evaluate the choice of distance measure after applying unsupervised methods based on error probabilities, implicitly setting the goal of reproducing predefined partitions in data. Such studies use clusters of data that are often based on the context of the data as well as the custom goal of the specific study. Depending on the data context, different properties for distance distributions are judged to be relevant for appropriate distance selection. However, if cluster analysis is based on the task of finding similar partitions of data, then the intrapartition distances should be smaller than the interpartition distances. By systematically investigating this specification using distribution analysis through the mirrored-density (MD plot), it is shown that multimodal distance distributions are preferable in cluster analysis. As a consequence, it is advantageous to model distance distributions with Gaussian mixtures prior to the evaluation phase of unsupervised methods. Experiments are performed on several artificial datasets and natural datasets for the task of clustering.

2021 ◽  
Vol 81 (8) ◽  
Martin Hirsch ◽  
Rafał Masełek ◽  
Kazuki Sakurai

AbstractA certain class of neutrino mass models predicts long-lived particles whose electric charge is four or three times larger than that of protons. Such particles, if they are light enough, may be produced at the LHC and detected. We investigate the possibility of observing those long-lived multi-charged particles with the MoEDAL detector, which is sensitive to long-lived particles with low velocities ($$\beta $$ β ) and a large electric charge (Z) with $$\Theta \equiv \beta /Z \lesssim 0.15$$ Θ ≡ β / Z ≲ 0.15 . We demonstrate that multi-charged scalar particles with a large Z give three-fold advantage for MoEDAL; reduction of $$\Theta $$ Θ due to strong interactions with the detector, and enhancement of the photon-fusion process, which not only increases the production cross-section but also lowers the average production velocity, reducing $$\Theta $$ Θ further. To demonstrate the performance of MoEDAL on multi-charged long-lived particles, two concrete neutrino mass models are studied. In the first model, the new physics sector is non-coloured and contains long-lived particles with electric charges 2, 3 and 4. A model-independent study finds MoEDAL can expect more than 1 signal event at the HL-LHC ($$L = 300$$ L = 300 $$\hbox {fb}^{-1}$$ fb - 1 ) if these particles are lighter than 600, 1100 and 1430 GeV, respectively. These compare with the current ATLAS limits 650, 780 and 920 GeV for $$L = 36$$ L = 36 $$\hbox {fb}^{-1}$$ fb - 1 . The second model has a coloured new physics sector, which possesses long-lived particles with electric charges 4/3, 7/3 and 10/3. The corresponding MoEDAL’s mass reaches at the HL-LHC are 1400, 1650 and 1800 GeV, respectively, which compare with the current CMS limits 1450, 1480 and 1510 GeV for $$L = 36$$ L = 36 $$\hbox {fb}^{-1}$$ fb - 1 . In a model-specific study we explore the parameter space of neutrino mass generation models and identify the regions that can be probed with MoEDAL at the end of Run-3 and the High-Luminosity LHC.

2021 ◽  
Vol 9 (4) ◽  
pp. 1-22
Perambur Neelakanta ◽  
Dolores De Groff

The objective of this study is to deduce signal-to-noise ratio (SNR) based loglikelihood function involved in detecting low-observable targets (LoTs) including drones Illuminated by a low probability of intercept (LPI) radar operating in littoral regions. Detecting obscure targets and drones and tracking them in near-shore ambient require ascertaining signal-related track-scores determined as a function of radar cross section (RCS) of the target. The stochastic aspects of the RCS depend on non-kinetic features of radar echoes due to target-specific (geometry and material) characteristics; as well as, the associated radar signals signify randomly-implied, kinetic signatures inasmuch as, the spatial aspects of the targets fluctuate significantly as a result of random aspect-angle variations caused by self-maneuvering and/or by remote manipulations (as in drones).  Hence, the resulting mean RCS value would decide the SNR and loglikelihood ratio (LR) of radar signals gathered from the echoes and relevant track-scores decide the performance capabilities of the radar. A specific study proposed here thereof refers to developing computationally- tractable algorithm(s) towards detecting and tracking hostile LoTs and/or drones flying at low altitudes over the sea (at a given range, R) in littoral regions by an LPI radar. Estimation of relevant detection-theoretic parameters and decide track-scores in terms of maximum likelihood (ML) estimates are presented and discussed.

2021 ◽  
Vol 12 ◽  
Zhixia Chen ◽  
Mei Sun

Leadership ostracism widely exists in all types of organizations, yet specific study regarding this trend is limited. With this study, we explore the influencing mechanisms of leadership ostracism through case interview based on literature analysis and grounded theory. Results show that leadership ostracism is the integration of a triadic interaction process between subordinate performance, leadership characteristics, and organizational environment. Based on Padilla's destructive leadership toxic triangle model, we constructed a toxic triangle model of leadership ostracism. Through comparison, we found that these two triad models overlap in the areas of narcissism and power consciousness of supervisors, the self-concept of subordinates, and the management system of situational factors, indicating that leadership ostracism is itself a type of destructive leadership. In addition, the uniqueness, and differences in leadership ostracism are reflected in the model, including stereotypes, and results orientation of supervisors, political skills, job performance, and cognitive style of subordinates, the power distance, Chaxu climate, and organizational politics of the situational elements. Theoretical and practical implications are discussed in the research field that provides prospects for future orientation.

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