Bayesian logistic regression for presence-only data

Presence-only data are referred to situations in which a censoring mechanism acts on a binary response which can be partially observed only with respect to one outcome, usually denoting the \textit{presence} of an attribute of interest. A typical example is the recording of species presence in ecological surveys. In this work a Bayesian approach to the analysis of presence-only data based on a two levels scheme is presented. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to censoring and the other one due to sampling. In the paper, through the use of a stratified sampling design with non-overlapping strata, a new formulation of the logistic model for presence-only data is proposed. In particular, the logistic regression with linear predictor is considered. Estimation is carried out with a new Markov Chain Monte Carlo algorithm with data augmentation, which does not require the a priori knowledge of the population prevalence. The performance of the new algorithm is validated by means of extensive simulation experiments using three scenarios and comparison with optimal benchmarks. An application to data existing in literature is reported in order to discuss the model behaviour in real world situations together with the results of an original study on termites occurrences data

Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data

Presence-only data are referred to situations in which, given a censoring mechanism, a binary response can be observed only with respect to on outcome, usually called \textit{presence}. In this work we present a Bayesian approach to the problem of presence-only data based on a two levels scheme. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to the censoring and one due to the sampling. We propose a new formalization for the logistic model with presence-only data that allows further insight into inferential issues related to the model. We concentrate on the case of the linear logistic regression and, in order to make inference on the parameters of interest, we present a Markov Chain Monte Carlo algorithm with data augmentation that does not require the a priori knowledge of the population prevalence. A simulation study concerning 24,000 simulated datasets related to different scenarios is presented comparing our proposal to optimal benchmarks.Comment: Affiliations: Fabio Divino - Division of Physics, Computer Science and Mathematics, University of Molise Giovanna jona Lasinio and Natalia Golini - Department of Statistical Sciences, University of Rome "La Sapienza" Antti Penttinen - Department of Mathematics and Statistics, University of Jyv\"{a}skyl\"{a} CONTACT: [email protected], [email protected]

The impact of a massive migration flow on the regional population structure: The case of Italy

Low economic growth rates are a common problem in many developed countries in Europe. This paper aims to highlight the possible role of demographic factors. Problems of low growth may be exacerbated by an increase in dependency ratios. However, large-scale migrations have been shown to positively affect the age composition of a population. Focusing on Italy, we estimate the impact of migration on the working age population ratio, population size and gross domestic product. We also show that migration may affect the economic gap between the North and South, posing a new potential problem to policymakers.

Gas Exchange and Injection Modeling of an Advanced Natural Gas Engine for Heavy Duty Applications

The scope of the work presented in this paper was to apply the latest open source CFD achievements to design a state of the art, direct-injection (DI), heavy-duty, natural gas-fueled engine. Within this context, an initial steady-state analysis of the in-cylinder flow was performed by simulating three different intake ducts geometries, each one with seven different valve lift values, chosen according to an estabilished methodology proposed by AVL. The discharge coefficient (Cd) and the Tumble Ratio (TR) were calculated in each case, and an optimal intake ports geometry configuration was assessed in terms of a compromise between the desired intensity of tumble in the chamber and the satisfaction of an adequate value of Cd. Subsequently, full-cycle, cold-flow simulations were performed for three different engine operating points, in order to evaluate the in-cylinder development of TR and turbulent kinetic energy (TKE) under transient conditions. The latest achievements in open source mesh generation and motions were applied, along with time-varying and case-fitted inizialization values for the fields of intake pressure and temperature. Finally, direct-injection of natural gas in the cylinder was incorporated in full-cycle simulations, to evaluate the effects of injection on charge motions and charge homogeneity at the estimated spark timing. Three specific engine operating points were simulated and different combinations of turbochargers and valve lift laws were tested. Results consistency was verified by means of validations with data from 1D simulations and literature

Manufacturing in the world: where next?

Purpose – The past three decades have seen the transformation of manufacturing involving its global dispersion and fragmentation. However, a number of recent developments appear to suggest that manufacturing may be entering a new era of flux that will impact the configuration of production around the globe. The purpose of this paper is to address the major emerging themes that may shape this configuration and concludes that most of them are still in their initial stages and are not likely to create a radical shift in the next few years in how manufacturing is configured around the world. These themes were presented in a special session on “Manufacturing in the World – Where Next?” at the 2013 EurOMA Conference in Dublin, Ireland. Design/methodology/approach – The paper provides a series of perspectives on some key considerations pertaining to the future of manufacturing. An evaluation of their likely impact is offered and insights for the future of manufacturing are presented. Findings – The importance of a focus on the extended manufacturing network is established. The need for customer engagement and a forward looking approach that extends to the immediate customer and beyond emerges as a consistent feature across the different perspectives presented in the paper. There is both the potential and need for the adoption of innovative business models on the part of manufacturers. Originality/value – The paper presents in-depth perspectives from scholars in the field of manufacturing on the changing landscape of manufacturing. These perspectives culminate in a series of insights on the future of global manufacturing that inform future research agendas and help practitioners in formulating their manufacturing strategies

Exploiting the potential of manufacturing network embeddedness: an OM perspective

Purpose The purpose of this paper is to provide guidance in setting the level of autonomy (i.e. parental control) of plants in a network to enhance operational performance. In particular, the effect of autonomy on performance is analysed directly and indirectly through internal manufacturing network integration (MNI) and external supply chain integration (SCI) as two dimensions of manufacturing network embeddedness. Design/methodology/approach The analysis is based on data from 441 manufacturing plants in 17 countries. Data were gathered during the Sixth International Manufacturing Strategy Survey. Five main constructs were obtained after carrying out a confirmatory factor analysis: plant autonomy, internal MNI, external SCI, efficiency and effectiveness. Direct and indirect relationships among the constructs are tested through a structural equation model. Findings Higher levels of autonomy correlate with higher effectiveness and similar efficiency. However, lower autonomy leads to higher levels of manufacturing network and SCI, which enhance performance. Although not statistically significant, the analysis of the total effects reveals a mildly positive effect of autonomy on effectiveness and negative effect on efficiency, which requires further investigation. Research limitations/implications Further research could include headquarters’ perspectives or additional determinants (e.g. business strategy objectives). Practical implications Managers should set autonomy levels strategically: higher for effectiveness and lower for efficiency. However, lower autonomy can also strengthen internal MNI and external SCI, thus improving operational performance. Originality/value The concept of manufacturing network embeddedness highlights the importance of considering external supply chain and internal MNI in the same framework, as both dimensions can affect operational performance

Global supply chain management in the manufacturing industry: Configurations, improvement, programs and performance

This book highlights the importance of supply chain management, namely exchanging information and coordinating efforts with suppliers and customers, in order to support globalization strategies determined by companies. Globalization does not only refer to the act of selling all around the world, but also to sourcing and manufacturing on the global scale, and all of this needs to be coordinated flawlessly. As reported in the book, in the manufacturing industry more than 32% of companies source, manufacture and/or sell for more than 50% outside the continent they belong to. Within this context, this book provides a conceptualization of the above-mentioned phenomena and ends up with the guidelines considered to be useful for companies in supporting their globalization strategies. First of all, four different global supply chain models are identified. Next, for each model, the best investments in the supply chain that aim to improve performance are defined. Finally, the effect of contextual variables is taken into account, and, in particular, it shows that the structure of the supply chain from one end to another (also called the value chain) holds a crucial role. The results are supported by empirical data from an international survey (IMSS) and case studies in the electric motors industry

Real-time and high-quality video compression for telesurgery

LAUREA MAGISTRALEOggigiorno le soluzioni basate sul deep learning sono ampiamente diffuse in differenti contesti. Il loro utilizzo nel dominio chirurgico potrebbe risultare uno strumento utile per affrontare le sfide proposte dalle nuove frontiere della medicina. Infatti, le applicazioni di telemedicina sono divenute ormai realtà grazie ai progressi della tecnologia nel campo delle telecomunicazioni e del sistema di codifica video, e richiedono sistemi sofisticati per archiviare e trasmettere big-data, quali ad esempio video ad alta risoluzione. Nel caso specifico della trasmissione video, sono presenti vincoli in termini di latenza e larghezza di banda per garantire l'applicazione in tempo reale. La qualità deve essere comunque preservata. Per la chirurgia da remoto, bassa latenza e larghezza di banda sono essenziali per assicurare la stabilità del sistema impiegato. Anche se gli approcci tradizionali sono altamente performanti, un miglioramento ulteriore consentirebbe un aumento dell'efficienza con conseguente diffusione di questi servizi. Poiché gli standard correnti utilizzati per la compressione video, i.e., H.264/AVC e H.265/HEVC, hanno raggiunto altissimi livelli in termini di prestazioni, è necessario esplorare soluzioni alternative per la loro ottimizzazione, oppure sviluppare nuove tecniche di compressione. I metodi di Deep Learning (DL) possono considerarsi adatte allo scopo, poichè in grado di superare le limitazioni proprie dei codec tradizionali. In questa tesi si propone una rete neurale per migliorare le prestazioni di H.264/AVC in termini di qualità, larghezza di banda e latenza per la chirurgia mini-invasiva assistita da robot. Si propone un autoencoder binario per comprimere il residuo, ossia la differenza tra il frame originale e quello compresso. L'output prodotto dalla rete è sommato a quello di H.264/AVC al fine di ottenere una migliore ricostruzione dell'immagine, riducendo tempo di compressione. Lo schema proposto supera il codec tradizionale sia in termini di qualità che di velocità nello scenario dei bassi bitrate. Inoltre, è di facile implementazione e potrebbe essere ulteriormente ottimizzato, divenendo un potente strumento per la telemedicina.Nowadays deep learning-based solutions are widely spread among different fields. The employment in the surgical domain may result a useful tool to address the challenges proposed by the new frontiers of medicine. Indeed, telementoring, teleoperation and remote diagnosis, now realities thanks to advances in telecommunication technology and video coding system, require sophisticated system to storage and transmit big data, e.g., high-resolution videos. Focusing on video transmission, constrains are present in terms of latency and bandwidth to guarantee the real time application, without losing quality. In the specific case of remote surgery, low-latency and bandwidth are essential to ensure the stability of the system employed. Even though traditional approaches are highly performant, a further improvement would increase the efficiency, thus the employment, of these services. Since the leading standards for video compression, i.e., H.264/AVC and H.265/HEVC, have reached a turning point in terms of performance, alternative solutions for their optimizations and brand-new schemes needs to be explored. Deep Learning (DL) techniques may be well suited for the purpose, as they can overcome the limitations featured by the traditional video codecs. In this work, a deep learning-based method is proposed to enhance the performance of H.264/AVC in terms of quality, bandwidth and latency for Robot Assisted Minimally Invasive Surgery (RAMIS), namely for the Robotic Assisted Radical Prostatectomy (RARP). A binary autoencoder is proposed to compress the residual, thus the difference between the original and the compressed frame. The output of the network is summed to the one of H.264/AVC to obtain a better image reconstruction while saving compression time. The scheme proposed overcomes the traditional codec both in terms of quality and speed in a low bitrate scenario. Moreover, it is computational friendly and it could be further optimized to become a powerful tool for telemedicine applications

How Do Industry 4.0 Technologies Boost Collaborations in Buyer-Supplier Relationships?

Overview: Business leaders often consider digital technologies an enabler of new business models and market opportunities, but they often overlook their potential impact on the entire value chain. Considering three Industry 4.0 technologies—big data analytics and cloud computing, track and tracing, and simulation and modeling software—we identify the opportunities and challenges that emerge in the context of managing supply chain relationships. This study uses data from an international survey to test how these three Industry 4.0 technologies increase visibility and integration between buyers and suppliers and how they impact supply chain performance. Our results show mixed evidence: although all three technologies directly improve supply chain performance, big data analytics and cloud computing and simulation and modeling also fully support collaborative supply chain models, while track and tracing tools create more visible supply chains but are detrimental to obtaining higher process integration with suppliers. Surprisingly, buyer-supplier collaboration, in terms of visibility and integration, matters more than the technologies themselves