The impact of market orientation on university spin-off business performance

Extant literature on the antecedents of university spin-off (USO) business performance has developed with the aim of highlighting those drivers that could foster the performance of such firms, focusing on a variety of factors. Less interest has been devoted to the market orientation\u2013performance relationship, despite the positive link frequently found in the marketing literature. The aim of the present paper is therefore to fill this gap and investigate the relationship between market orientation (MO) and USO performance using the Netval database of Italian research spin-offs. To measure MO, we adopted an ad hoc questionnaire, and after testing its validity with a factor analysis, we performed a regression model. The results show that MO, particularly some of its components (customer intelligence generation, intelligence dissemination, integration and inter-functional coordination), has an impact on business performance. This contribution presents some valuable research implications useful for academics, but professionals from new high-tech ventures and technology transfer offices may also benefit from this knowledge.Une importante bibliographie sur les ant\ue9c\ue9dents de la performance des spin-off universitaires a d\ue9j\ue0 \ue9t\ue9 d\ue9velopp\ue9e, dans le but de mettre en \ue9vidence les \ue9l\ue9ments qui pourraient favoriser la performance de ces entreprises, et en mettant l'accent sur divers facteurs. Malgr\ue9 le lien positif qui est g\ue9n\ue9ralement observ\ue9 dans la litt\ue9rature marketing, la relation entre l'Orientation March\ue9 (OM) et la performance a \ue9t\ue9 moins approfondie. L'objectif de cet article est donc de combler cette lacune en examinant la relation entre l'orientation march\ue9 et les performances des SOU (Spin-Off Universitaires) de la base de donn\ue9es Netval, d\ue9di\ue9e aux spin-off de la recherche italienne. Afin de mesurer l\u2019OM, nous avons adopt\ue9 un questionnaire ad hoc et, apr\ue8s avoir test\ue9 sa validit\ue9 avec une analyse factorielle, nous avons r\ue9alis\ue9 un mod\ue8le de r\ue9gression. Les r\ue9sultats montrent que l\u2019OM a effectivement un impact sur la performance de l'entreprise, en particulier sur certaines de ses dimensions (production de renseignements sur les clients, diffusion de renseignements, int\ue9gration et coordination inter-fonctionnelle). Cette \ue9tude pr\ue9sente des perspectives de recherche utiles pour les universitaires, mais aussi pour les professionnels des nouvelles entreprises haute technologie et les bureaux de transfert de technologie, qui pourraient b\ue9n\ue9ficier de ces connaissances

Valutazione sperimentale di metodologie di rettificazione e impatto su algoritmi di visione stereo

Ricavare informazioni dalla realtà circostante è un obiettivo molto importante dell'informatica moderna, in modo da poter progettare robot, veicoli a guida autonoma, sistemi di riconoscimento e tanto altro. La computer vision è la parte dell'informatica che se ne occupa e sta sempre più prendendo piede. Per raggiungere tale obiettivo si utilizza una pipeline di visione stereo i cui passi di rettificazione e generazione di mappa di disparità sono oggetto di questa tesi. In particolare visto che questi passi sono spesso affidati a dispositivi hardware dedicati (come le FPGA) allora si ha la necessità di utilizzare algoritmi che siano portabili su questo tipo di tecnologia, dove le risorse sono molto minori. Questa tesi mostra come sia possibile utilizzare tecniche di approssimazione di questi algoritmi in modo da risparmiare risorse ma che che garantiscano comunque ottimi risultati

On the optimum number of cooperating nodes in interfered cluster-based sensor networks

This paper presents a cooperative multiple-input multiple-output (MIMO) scheme for a wireless sensor network consisting of inexpensive nodes, organised in clusters and transmitting data towards sinks. The transmission is affected by hardware imperfections, imperfect synchronisation, data correlation among nodes of the same cluster, channel estimation errors and interference among nodes of different clusters. Within this setting, we are interested in determining the number of nodes per cluster that maximises the energy efficiency of the network. The analysis is conducted in the asymptotic regime in which the number N of sensor nodes per cluster grows large without bound. Numerical results are used to validate the asymptotic analysis in the finite system regime and to investigate different configurations. It turns out that the optimum number of sensor nodes per cluster increases with the inter-cluster interference and with the number of sinks

Learning Transfers over Several Programming Languages

Large language models (LLMs) have recently become remarkably good at improving developer productivity for high-resource programming languages. These models use two kinds of data: large amounts of unlabeled code samples for pretraining and relatively smaller amounts of labeled code samples for fine-tuning or in-context learning. Unfortunately, many programming languages are low-resource, lacking labeled samples for most tasks and often even lacking unlabeled samples. Therefore, users of low-resource languages (e.g., legacy or new languages) miss out on the benefits of LLMs. Cross-lingual transfer learning uses data from a source language to improve model performance on a target language. It has been well-studied for natural languages, but has received little attention for programming languages. This paper reports extensive experiments on four tasks using a transformer-based LLM and 11 to 41 programming languages to explore the following questions. First, how well cross-lingual transfer works for a given task across different language pairs. Second, given a task and target language, how to best choose a source language. Third, the characteristics of a language pair that are predictive of transfer performance, and fourth, how that depends on the given task.Comment: 16 pages, 5 figures, 5 table

Smart city pilot projects using LoRa and IEEE802.15.4 technologies

Information and Communication Technologies (ICTs), through wireless communications and the Internet of Things (IoT) paradigm, are the enabling keys for transforming traditional cities into smart cities, since they provide the core infrastructure behind public utilities and services. However, to be effective, IoT-based services could require different technologies and network topologies, even when addressing the same urban scenario. In this paper, we highlight this aspect and present two smart city testbeds developed in Italy. The first one concerns a smart infrastructure for public lighting and relies on a heterogeneous network using the IEEE 802.15.4 short-range communication technology, whereas the second one addresses smart-building applications and is based on the LoRa low-rate, long-range communication technology. The smart lighting scenario is discussed providing the technical details and the economic benefits of a large-scale (around 3000 light poles) flexible and modular implementation of a public lighting infrastructure, while the smart-building testbed is investigated, through measurement campaigns and simulations, assessing the coverage and the performance of the LoRa technology in a real urban scenario. Results show that a proper parameter setting is needed to cover large urban areas while maintaining the airtime sufficiently low to keep packet losses at satisfactory levels

Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain

Code Large Language Models (Code LLMs) are being increasingly employed in real-life applications, so evaluating them is critical. While the general accuracy of Code LLMs on individual tasks has been extensively evaluated, their self-consistency across different tasks is overlooked. Intuitively, a trustworthy model should be self-consistent when generating natural language specifications for its own code and generating code for its own specifications. Failure to preserve self-consistency reveals a lack of understanding of the shared semantics underlying natural language and programming language, and therefore undermines the trustworthiness of a model. In this paper, we first formally define the self-consistency of Code LLMs and then design a framework, IdentityChain, which effectively and efficiently evaluates the self-consistency and general accuracy of a model at the same time. We study eleven Code LLMs and show that they fail to preserve self-consistency, which is indeed a distinct aspect from general accuracy. Furthermore, we show that IdentityChain can be used as a model debugging tool to expose weaknesses of Code LLMs by demonstrating three major weaknesses that we identify in current models using IdentityChain. Our code is available at https://github.com/marcusm117/IdentityChain.Comment: Code available at https://github.com/marcusm117/IdentityChai

The Role of Adsorption and pH of the Mobile Phase on the Chromatographic Behavior of a Therapeutic Peptide

The impact of two different stationary phases and ion-pair reagents on the retention behavior of glucagon, a therapeutic peptide consisting of 29 amino acidic residues, has been investigated under reversed-phase elution conditions. Retention of glucagon was investigated under isocratic conditions by varying the fraction of the organic modifier in the range of 28–38% (v/v). The two stationary phases have been characterized in terms of excess adsorption isotherms to understand the preferential adsorption of eluent components on them. Results suggest that the ligand characteristics and the pH of the mobile phase play a pivotal role on retention

Mutation within TARDBP leads to Frontotemporal Dementia without motor neuron disease.

2009 AUG 4. [EPUB AHEAD OF PRINT

IoT protocols, architectures, and applications

The proliferation of embedded systems, wireless technologies, and Internet protocols have made it possible for the Internet-of-things (IoT) to bridge the gap between the physical and the virtual world and thereby enabling monitoring and control of the physical environment by data processing systems. IoT refers to the inter-networking of everyday objects that are equipped with sensing, computing, and communication capabilities. These networks can collaborate to autonomously solve a variety of tasks. Due to the very diverse set of applications and application requirements, there is no single communication technology that is able to provide cost-effective and close to optimal performance in all scenarios. In this chapter, we report on research carried out on a selected number of IoT topics: low-power wide-area networks, in particular, LoRa and narrow-band IoT (NB-IoT); IP version 6 over IEEE 802.15.4 time-slotted channel hopping (6TiSCH); vehicular antenna design, integration, and processing; security aspects for vehicular networks; energy efficiency and harvesting for IoT systems; and software-defined networking/network functions virtualization for (SDN/NFV) IoT