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The overwhelming increase of parcel transports has prompted the need for effective and scalable intelligent logistics systems. In parallel, with the advent of Industry 4.0, a tight integration of Internet of Things technologies and Big Data analytics solution has become necessary to effectively manage industrial processes and to early predict product faults or service disruptions. In the context of good transports, the development of smart monitoring tools is particularly useful for couriers to ensure effective and efficient parcel deliveries. However, the existing predictive maintenance frameworks are not tailored to parcel delivery services. We present REDTag Service, an integrated framework to track and monitor the shipped packages. It relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of the package at each delivery step. The framework provides back-end functionalities for smart data transmission, management, storage, and analytics. A machine-learning process is included to promptly analyze the features describing event-related data to predict potential breaks of the goods in the packages. The framework provides also a dynamic view on the integrated data tailored to the different stakeholders, as well as on the prediction outcomes, enabling immediate feedback and model improvements. We analyze a realworld dataset including event-related data about parcel transports. To validate the hypothesis that the acquired data contains information relevant to predict the package status (i.e., broken or safe), we empirically analyze the performance of different, scalable classifiers. The experimental results confirm, in good approximation, the predictive power of the models extracted from the event-related features. To the best of the authors' knowledge, this work is the first attempt to address predictive maintenance in smart good transport logistics to predict package breaks from real-world data. INDEX TERMS Big data analytics, Industry 4.0, intelligent transports and logistics, Internet of Things, machine learning, predictive maintenance.

En route to madangamines: A unified strategy has been developed for the enantioselective assembly of functionalized diazatricyclic synthetic precursors of madangamines (see scheme; Bn=benzyl, Boc=tert‐butoxycarbonyl, Mbs=para‐methoxybenzenesulfonyl).

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Using simplified model derivatives, the assembly of the macrocyclic rings of madangamines, including the 13-and 14-membered D rings of madangamines C-E, the all-cis-triunsaturated 15-membered D ring of madangamine A, and the (Z,Z)-unsaturated 11-membered E ring common to madangamines A-E, has been studied.The marine sponges belonging to the order Haplosclerida are a source of numerous polycyclic alkaloids with a variety of skeletal structures (manzamines, sarains, nakadomarin A, ingenamines, madangamines, among others), which share a common biogenetic origin from oligomeric macrocycles bearing a partially reduced 3-alkylpyridine moiety. 1 In particular, madangamines (Figure 1) possess an unprecedented diazatricyclic core (rings A-C) and two macrocyclic rings connecting N-7 with C-9 (ring D) and N-1 with C-3 (ring E). 2 Although significant in vitro cytotoxicity against a number of cancer cell lines has been

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The energy performance certificate (EPC) is a document that certifies the average annual energy consumption of a building in standard conditions and allows it to be classified within a so-called energy class. In a period such as this, when greenhouse gas emissions are of considerable importance and where the objective is to improve energy security and reduce energy costs in our cities, energy certification has a key role to play. The proposed work aims to model and characterize residential buildings’ energy efficiency by exploring heterogeneous, geo-referenced data with different spatial and temporal granularity. The paper presents TUCANA (TUrin Certificates ANAlysis), an innovative data mining engine able to cover the whole analytics workflow for the analysis of the energy performance certificates, including cluster analysis and a model generalization step based on a novel spatial constrained K-NN, able to automatically characterize a broad set of buildings distributed across a major city and predict different energy-related features for new unseen buildings. The energy certificates analyzed in this work have been issued by the Piedmont Region (a northwest region of Italy) through open data. The results obtained on a large dataset are displayed in novel, dynamic, and interactive geospatial maps that can be consulted on a web application integrated into the system. The visualization tool provides transparent and human-readable knowledge to various stakeholders, thus supporting the decision-making process.

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