Development of a Digital Twin Model for Real-Time Assessment of Collisione Hazards

The AEC industry is nowadays one of the most hazardous industries in the world. The construction sector employees about 7% of the world’s work force but is responsible for 30-40% of fatalities. As statistics demonstrate, interferences between workers-on-foot and moving vehicles have caused several injuries and fatalities over the years. Despite safety organizational measures, passive safety devices imposed by regulations and efforts from training procedures, scarce improvements have been recorded. Recent research studies propose technology driven approaches as the key solutions to integrate standard health and safety management practices. This is motivated by the evidence that the dynamics of complex systems can hardly be predicted; rather a proactive approach to health and safety is more effective. Current technologies installed on construction equipment can usually react according to a strict logic, such as sending proximity alerts when workers and equipment are too close. Nevertheless, these approaches barely do make informed decisions in real-time, e.g. including the level of reactiveness of the endangered worker. In similar circumstances a digital twin of the construction site, updated by real-time data from sensors and enriched by artificial intelligence, can pro-actively support activities, forecasting dangerous scenarios on the base of several factors. In this paper a laboratory mock-up has been assumed as the test case, supported by a game engine, which is able to replicates the job site for the execution of bored piles. In such a scenario populated by an avatar of a sensor-equipped worker and a virtual driller, a Bayesian network, implemented within the game engine and fed in runtime by sensor data, works out collision probability in real-time in order to send warnings and avoid fatal accidents

Penicillamine-related lichenoid dermatitis and utility of zinc acetate in a wilson disease patient with hepatic presentation, anxiety and spect abnormalities

Wilson disease is an autosomal recessive disorder of hepatic copper metabolism with consequent copper accumulation and toxicity in many tissues and consequent hepatic, neurologic and psychiatric disorders. We report a case of Wilson disease with chronic liver disease; moreover, in our patient, presenting also with high levels of state anxiety without depression, 99mTc-ECD- SPECT showed cortical hypoperfusion in frontal lobes, more marked on the left frontal lobe. During the follow-up of our patient, penicillamine was interrupted after the appearance of a lichenoid dermatitis, and zinc acetate permitted to continue the successful treatment of the patient without side-effects. In our case the therapy with zinc acetate represented an effective treatment for a Wilson disease patient in which penicillamine-related side effects appeared. The safety of the zinc acetate allowed us to avoid other potentially toxic chelating drugs; this observation is in line with the growing evidence on the efficacy of the drug in the treatment of Wilson disease. Since most of Wilson disease penicillamine-treated patients do not seem to develop this skin lesion, it could be conceivable that a specific genetic factor is involved in drug response. Further studies are needed for a better clarification of Wilson disease therapy, and in particular to differentiate specific therapies for different Wilson disease phenotypes

Biallelic SQSTM1 mutations in early-onset, variably progressive neurodegeneration.

OBJECTIVE: To characterize clinically and molecularly an early-onset, variably progressive neurodegenerative disorder characterized by a cerebellar syndrome with severe ataxia, gaze palsy, dyskinesia, dystonia, and cognitive decline affecting 11 individuals from 3 consanguineous families. METHODS: We used whole-exome sequencing (WES) (families 1 and 2) and a combined approach based on homozygosity mapping and WES (family 3). We performed in vitro studies to explore the effect of the nontruncating SQSTM1 mutation on protein function and the effect of impaired SQSTM1 function on autophagy. We analyzed the consequences of sqstm1 down-modulation on the structural integrity of the cerebellum in vivo using zebrafish as a model. RESULTS: We identified 3 homozygous inactivating variants, including a splice site substitution (c.301+2T>A) causing aberrant transcript processing and accelerated degradation of a resulting protein lacking exon 2, as well as 2 truncating changes (c.875_876insT and c.934_936delinsTGA). We show that loss of SQSTM1 causes impaired production of ubiquitin-positive protein aggregates in response to misfolded protein stress and decelerated autophagic flux. The consequences of sqstm1 down-modulation on the structural integrity of the cerebellum in zebrafish documented a variable but reproducible phenotype characterized by cerebellum anomalies ranging from depletion of axonal connections to complete atrophy. We provide a detailed clinical characterization of the disorder; the natural history is reported for 2 siblings who have been followed up for >20 years. CONCLUSIONS: This study offers an accurate clinical characterization of this recently recognized neurodegenerative disorder caused by biallelic inactivating mutations in SQSTM1 and links this phenotype to defective selective autophagy

Exploring BIM and NLP applications: a scientometric approach

In AECO (Architecture, Engineering, Construction and Owner-operated) industry information are mainly defined and exchanged in natural language through textual files and documents. On the contrary, a Building Information Modeling (BIM) approach requires machine processable data and information. Natural Language Processing (NLP) allows to process textual information into structured format. About BIM in AECO industry, and NLP in different sectors, several literature reviews have been conducted. However, none of them highlighted the possible connections between the two topics. This study provides a scientometric analysis aiming to investigate possible combined applications of BIM and NLP. A quantitative literature review approach is employed, using data visualization and science mapping applied on bibliometric meta- data. The performed analysis uncovered possible directions for further research on NLP and BIM combined applications in AECO sector. Most active authors, key research patterns, and institutional affiliations are identified. The main applications areas of a combined NLP and BIM approach in AECO are: Information Retrieval and Information Enrichment of BIM models, Automatic Compliance Checking, and Safety and Risk Management. The keywords pattern analysis highlighted the main tools which allows to link Semantic BIM and NLP methods and technologies, i.e. Ontology and Machine Learning algorithms. The scientometric analysis also reveals a gap related to the Preliminary design and Requirement definition phases, highlighting a possible research area not covered by the Academia as of now