Assessing the benefits of supply chain trust: NK simulation-based methodology and application

Previous literature has emphasized that developing trust among supply chain (SC) firms is a critical element in achieving SC effectiveness. Since developing trust is an expensive task, however, making an informed decision whether to invest or not in trust requires careful assessment of trust benefits. Therefore, we advance a simulation-based methodology to quantify performance improvements associated with trust in SCs. We develop an NK simulation model of a generic SC that captures the SC dynamics under two alternative scenarios, characterized by the presence and absence of trust respectively. A procedure is then illustrated to quantify the benefits of trust in the SC. We also apply our proposed methodology to a real-world SC. Results show that, when trust is pervasive across the SC, performance increases at both the levels of the overall SC and its leading firm

Ecosystem indicators for measuring industrial symbiosis

Industrial symbiosis (IS) is a collaborative approach among firms involving physical exchanges of materials, energy, and wastes, which creates economic advantages for firms and environmental benefits for the society. In this paper, we adopt an ecosystem approach to conceptualize the network of firms involved in IS relationships (ISN), in terms of organisms (firms), functions (waste exchange), and services (environmental benefits), and provide new insight on how to assess and compute IS performance indicators. In particular, we designed five classes of indicators aimed at assessing 1) the impact of services provided by ISNs on the environment, 2) the performance of the ISN services, 3) how the single functions contribute to ISN services, 4) the performance of the ISN functions, and 5) how the single firms contribute to ISN functions. A numerical example is also discussed showing how to compute them and the information they provide. The proposed indicators are useful to develop proper strategies to increase the efficiency of the system in exploiting the IS synergies, to improve the symbiotic exchanges carried out in ISNs, and to identify firms contributing most to IS benefits. Hence, they may assist managers of ISNs and policymakers in decision-making aspects, an urgent need of the literature

Features of the Higher Education for the Circular Economy: The Case of Italy

The higher education system plays a critical role in supporting the transition towards a circular economy (CE). It helps create business leaders and policymakers having appropriate skills, competences, and consciousness referring to the CE challenges. Nevertheless, few studies have specifically investigated how the higher education system is addressing the CE, how the current academic offering is integrating the CE principles, and which skills and competences are currently provided. This paper overcomes these limitations by investigating the current offering of the higher education for the CE in Italy. We analyze the academic programs, courses, and modules at different levels of 49 Italian universities and, by means of a detailed classification of the learning outcomes, provide a clear picture of the knowledge, skills, and competences offered by the CE education. We finally discuss implications of our findings concerning the development of CE education and CE jobs

Unevenness Point Descriptor for Terrain Analysis in Mobile Robot Applications

In recent years, the use of imaging sensors that produce a three-dimensional representation of the environment has become an efficient solution to increase the degree of perception of autonomous mobile robots. Accurate and dense 3D point clouds can be generated from traditional stereo systems and laser scanners or from the new generation of RGB-D cameras, representing a versatile, reliable and cost-effective solution that is rapidly gaining interest within the robotics community. For autonomous mobile robots, it is critical to assess the traversability of the surrounding environment, especially when driving across natural terrain. In this paper, a novel approach to detect traversable and non-traversable regions of the environment from a depth image is presented that could enhance mobility and safety through integration with localization, control and planning methods. The proposed algorithm is based on the analysis of the normal vector of a surface obtained through Principal Component Analysis and it leads to the definition of a novel, so defined, Unevenness Point Descriptor. Experimental results, obtained with vehicles operating in indoor and outdoor environments, are presented to validate this approach

A New Reconfigurable Agricultural Vehicle Controlled by a User Graphical Interface: Mechanical and Electronic Aspects

The use of innovative mobile vehicles with increasingly advanced mechatronic aspects in the agricultural sector is becoming, in recent years, a stimulating field of research and comparison. In particular, the problem addressed in the present work refers to improving the locomotion of mobile vehicles on agricultural terrain by reducing the soil damage and improve the overall performance. Agricultural vehicles generally use tracks and wheels for locomotion; the main difference between the two systems is the contact area with the ground and, consequently, the pressure distribution. The present work presents a new reconfigurable agricultural vehicle that can switch from one locomotion system to another, choosing the suitable configuration according to the terrain conditions. All the mechanical and electronic aspects of the prototype developed are analyzed together with an in-depth analysis of the management of the innovative functions through a user-friendly graphical interface able to control the vehicle

Dynamic identification of a strategic building of the sixties with a mixed structure

The present paper shows and discusses the results of the identification procedure applied to the building of the Municipality of Castellaneta, Taranto (Puglia, Italy). The case study has been chosen for its structural complexity; indeed, the building has been built in two successive phases, block A built between 1955 and 1957, and block B, for completion, presumably built between the years 1960 and 1961. Block A constitutes the main building and represents the original nucleus. The structure was subsequently subject to structural interventions to replace some load-bearing walls with steel beams. The building constituting block B, joined to the original body of the building, has a structure in load-bearing masonry, while the floors, more recently built, are also in brick-concrete, but with pre-cast prestressed joists. The dynamic response of the building was assessed through OMA (Operational Modal Analysis) identification technique which allows you to determine the properties of the structure in real operating conditions, without the need to interrupt the activities inside the structure itself. The structural excitement, therefore, is due only to the environmental condition. A 3D model was defined in PRO_SAP; the results obtained from the finite element model were subsequently compared with those obtained from the experimental model

A System for Optimizing Fertilizer Dosing in Innovative Smart Fertigation Pipelines: Modeling, Construction, Testing and Control

Smart fertigation is a topic of great interest in the effort to optimize different activities involved in local and extensive agriculture for assisting crops, optimizing production by using wireless technologies, data-processing electronic boards and sensors network. With the advent of Agriculture 4.0, similar to Industry 4.0, Information Communication Technology (ICT), associated with mechatronics, is giving an added value to this technique allowing optimization of water, fertilizers, control of water flow in pipes and period of irrigation. This paper intends to illustrate findings related to an innovative low cost system for assisting crops and achieving an accurate farming by investigating on the design, construction, testing and control of dosing system for liquid and granular fertilizers. Four different dosage systems have been designed, realized and tested with different granular and liquid fertilizers; the analysis of an extensive experimental campaign allows to define the characteristic and the mathematical expressions for each analyzed fertilizer and for each dosage system. The accurate modeling allows to control with extreme precision the realized dosing systems after estimating the quantity of fertilizer which the crop needs by means of the smart fertigation system. The obtained results permit the optimization of the fertilizer dosage in terms of quantity, which at the same time translates into lower production costs, greater environmental sustainability and optimization of production in terms of quantity and quality

MEMS-based Micro-scale Wind Turbines as Energy Harvesters of the Convective Airflows in Microelectronic Circuits

As an alternative to conventional batteries and other energy scavenging techniques, this paper introduces the idea of using micro-turbines to extract energy from wind forces at the microscale level and to supply power to battery-less microsystems. Fundamental research efforts on the design, fabrication, and test of micro-turbines with blade lengths of just 160 μm are presented in this paper along with analytical models and preliminary experimental results. The proof-of-concept prototypes presented herein were fabricated using a standard polysilicon surface micro-machining silicon technology (PolyMUMPs) and could effectively transform the kinetic energy of the available wind into a torque that might drive an electric generator or directly power supply a micro-mechanical system. Since conventional batteries do not scale-down well to the microscale, wind micro-turbines have the potential for becoming a practical alternative power source for microsystems, as well as for extending the operating range of devices running on batteries

A Compact Affordable Electronic Nose Device to Monitor Air Toxic Compounds: A Filter Diagonalization Method Approach

This paper introduces a compact, affordable electronic nose (e-nose) device which aim is to detect volatile compounds that could affect human health, such as carbon monoxide, combustible gas, hydrogen, methane, and smoke, among others. Such artificial olfaction device consists of an array of six metal oxide semiconductor (MOS) sensors and a computer-based system for data acquisition, processing, and visualization. This study further proposes the use of the Filter Diagonalization Method (FDM) to extract the spectral contents of the signals obtained from the sensors. Preliminary results show that the prototype is functional and that the FDM approach is suitable for a later classification stage. Example deployment scenarios of the proposed enose include indoor facilities (buildings and warehouses), compromised air quality places (mines and sanitary landfills), public transportation, mobile robots, and wireless sensor networks

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the dierent operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects oered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%