Crowd Logistics: A Survey of Successful Applications and Implementation Potential in Northern Italy

Nowadays, last-mile logistics represents the least efficient stage of supply chains, covering up to 28% of the total delivery cost and causing significant environmental emissions. In the last few years, a wide range of collaborative economy business models has emerged across the globe, rapidly changing the way services were traditionally provided and consumed. Crowd logistics (CL) is a new strategy for supporting fast shipping services, entrusting the management of the last-mile delivery to the crowd, i.e., normal people, who agree to deliver goods to customers located along the route they have to travel, using their own transport means, in exchange for a small reward. Most existing studies have focused on evaluating the opportunities and challenges provided by CL through theoretical analysis and literature reviews, while others have proposed models for designing such emerging distribution networks. However, papers analyzing real successful applications of CL worldwide are lacking, despite being in high demand. This study attempted to fill this gap by providing, at first, an overview of real CL applications around the globe to set the stage for future successful implementations. Then, the implementation potential of CL in northern Italy was assessed through a structured questionnaire delivered to a panel of 214 people from the Alma Mater Studiorum University of Bologna (Italy) to map the feasibility of a crowd-based system in this area. The results revealed that about 91% of the interviewees were interested in using this emerging delivery system, while the remaining respondents showed some concern about the protection of their privacy and the safeguarding of the goods during transport. A relevant percentage of the interviewees were available to join the system as occasional drivers (ODs), with a compensation policy preference for a fixed fee per delivery rather than a variable reward based on the extra distance traveled to deliver the goods

Exergetic analysis and exergy loss reduction in the milk pasteurization for Italian cheese production

The cheese industry has high energy consumption, and improvements to plant efficiency may lead to a reduction of its environmental impact. A survey on a sample of small-medium Italian cheese factories was carried out in order to assess the efficiency of heat recovery of the milk pasteurization equipment for the cheese production. Then, an exergetic analysis to calculate the related exergy loss was carried out together with a cost-benefit analysis to identify the optimized value of the heat efficiency. The exergy loss reduction was determined throughout an exergy analysis that takes into account this last value and the comparison with the previous exergy losses. Finally, the feasibility and the consequent additional reduction of exergy losses were verified, if a cogeneration heat and power (CHP) combined to the pasteurization equipment is assumed. Results show a current heat recovery efficiency of 93.2% in the Italian cheese factories; a close connection between the exergetic losses and the efficiency of the heat recovery exchanger; the optimized recovery efficiency equal to 97.3% obtained from the cost-benefit analysis; a related important exergetic loss reduction of 1245% in the heat exchangers, as a second result of the exergetic analysis; a similar reduction of the exergy loss ( 1242%) of the whole system, as a third result of the exergetic analysis; a total exergy loss reduction of 22.9 kJ kg 121milk, which corresponds to a lower environmental impact due to CO2 reduction; a further reduction of the exergy loss of 1210% when the cogeneration heat and power CHP are used

Forrageiras de verão.

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A two-step methodology for product platform design and assessment in high-variety manufacturing

The delayed product differentiation (DPD) recently rose as a hybrid production strategy able to overcome the main limits of make to stock (MTS) and make to order (MTO), guaranteeing the management of high variety and keeping low storage cost and quick response time by using the so-called product platforms. These platforms are a set of sub-systems forming a common structure from which a set of derivative variants can be efficiently produced. Platforms are manufactured and stocked following an MTS strategy. Then, they are customized into different variants, following an MTO strategy. Current literature proposes methods for platform design mainly using optimization techniques, which usually have a high computational complexity for efficiently managing real-size industrial instances in the modern mass customization era. Hence, efficient algorithms need to be developed to manage the product platforms design for such instances. To fill this gap, this paper proposes a two-step methodology for product platforms design and assessment in high-variety manufacturing. The design step involves the use of a novel modified algorithm for solving the longest common subsequence (LCS) problem and of the k-medoids clustering for the identification of the platform structure and the assignment of the variants to the platforms. The platforms are then assessed against a set of industrial and market metrics, i.e. the MTS cost, the variety, the customer responsiveness, and the variants production cost. The evaluation of the platform set against such a combined set of drivers enhancing both company and market perspectives is missing in the literature. A real case study dealing with the manufacturing of a family of valves exemplifies the efficiency of the methodology in supporting companies in managing high-variety to best balance the proposed metrics

Flapless Cone Beam Computed Tomography-Guided Implant Surgery with Contextual Transcrestal Sinus Lift Augmentation Using New Bone Compactor Tools

In the present paper, the authors present a case report of premolar edentulism in the upper jaw treated through a guided flapless oral implant surgery with contextual crestal sinus lift, performed with a system of manual screw-tapered bone expanders (B&B Dental, San Benedetto, BO, Italy). The surgery was planned by means of dedicated software, through which the data obtained from the CBCT and from intraoral scanner impression were matched, with consequent production of a surgical template. The proposed surgical procedure is minimally invasive, very simple, and fast and ensures good comfort for the patient by avoiding the elevation of mucoperiosteal flaps and uncomfortable malleting maneuvers. In addition, the presented method shows a good degree of correspondence between the ideal position of the implant in the planning phase and the actual one detectable after the surgery

Experimental evaluation on the applicability of necrobiome analysis in forensic veterinary science

Despite the wide usage of animals as models in forensic studies, the investigations of fundamental legal questions involving domesticated and nondomesticated animals were always given marginal attention compared to "human forensic," and only recently the interest in the discipline is increasing. Our research focuses on the effect of the fur coat on the activity and development of microbial decomposers. In order to test this variable never assessed before, rabbit carcasses were used and results show that: (i) distinct and significant temporal changes in terms of metabolic activity and taxa distribution can be tracked over the decomposition process; (ii) the richness and the diversity of the bacterial communities does not significantly vary over time, but it does not mean that the species Operational Taxonomic Units (OTUs) do not change; (iii) the presence/absence of the fur on the carcasses does not significantly affect either the bacterial communities' functional activity or the diversity intra- and intercommunity, neither at phylum nor at family resolution; (iv) the functional activity and the ecological diversity of the bacterial communities are significantly affected by the body region, while the relative abundance is not. Obtained data confirm previous observations and provide new insight in the Forensic Veterinary field in terms of equally using them in order to derive a statistical model for the PMI estimation. As a future perspective, a contribution to the Forensic Entomology approach will be given in legal investigations when domestic or wild animals are involved, regardless of the presence of a hair layer

Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals

BRS Resteveiro: nova cultivar de inverno para solos hidromórficos.

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