Luigi Bregant

Recent studies have shown that comfort can be influenced more by psychological processes than from the characteristics of environmental stimulation. This is relevant for different industrial sectors, where comfort is defined only as a function of the intensity of external stimuli. In the present study, we measured physiological and psychological comfort during the exposure to four levels of acoustic noise [from 45 to 55 dB(A)] corresponding to different comfort classes inside a full-scale mock-up of a cruise ship cabin. We found an increase of psychological and physiological discomfort for higher noise intensities, but not for all the intensities defining the comfort classes. Furthermore, we found that negative psychological states determine a lower physiological sensitivity to acoustic noise variations compared to positive states. Our results show that, at normal/low intensities, psychological processes have a greater role in determining acoustic comfort when compared to the stimulus intensity. Practitioner Summary: This study shows that psychological factors can be more relevant in determining acoustic comfort inside a ship cabin than the intensity of acoustic stimulus itself. This finding suggests that the cruise industry should consider not only the engineering measurements when evaluating comfort on board, but also the passenger’ psychological state. Abbreviations: AIC: akaike information criterion; CCT: colour correlated temperature; cd/m²: candela/square meters; df: degrees of freedom; F-test: Fisher's test; HF: high frequency; HR: heart rate; HRV: heart rate variability; HSV: hue saturation value; K: kelvin; LF: low frequency; LF/HF: low frequency to high frequency ratio; lme: linear mixed effects; ms: milliseconds; nu: normalized unit; p: p value; pNN50: percentage of adjacent pairs of normal to normal RR intervals differing by more than 50 milliseconds; r²: coefficient of determination; r_c: concordance correlation coefficient; RMSSD: square root of the mean normal to normal RR interval; SD: standard deviation; SDNN: standard deviation of normal to normal RR intervals; SEM: standard error of the mean; t-test: student's tests; χ²: chi-square test

Biogas is a versatile carrier of renewable energy produced by means of the digestion of crops, residues, or other wastes, and storage of the gas in dedicated plants. Safety regulations impose operational and technical restrictions to areas interested by the presence of the biogas in order to limit the explosion risk. Conversely, other areas of the plant are not subject to strict safety requirements. Nevertheless, explosions can be triggered in these areas as well, as the present investigation confirms. In the specific case, the production of biogas by anaerobic digestion in a storm‐water tank and its infiltration and accumulation into a technical building through a pipe connection caused the explosion of the building itself. The authors describe the procedures adopted for the enquiry and suggest possible improvements to the safety regulations for biogas plants.

The article presents a study on the measures adopted in an existing coke plant in order to reduce levels of benzo(a)pyrene dispersed in the environment surrounding the facility. The area has been affected by significant levels of pollution from polycyclic aromatic compounds due to the fugitive emissions from the coke oven, which did not comply with the most recent regulation. A monitoring programme was therefore launched in order to support a framework for intervention. Several problems with the battery made it necessary to implement a novel system to collect and abate fugitive emissions during the operations. The monitoring of the fugitive emissions from the coke oven, before, during and after the implementation of the system, has shown a drop of the visible emission time at charging below 30 s, matched by the return of pollution from benzo(a)pyrene in the area below the legal limits (1 ng/Nm³). When compared to the installation costs, these results suggest that the proposed solution can be replicated in other existing by-product coke plants.

In order to perform the accurate tuning of a machine and improve its performance to the requested tasks, the knowledge of the reciprocal influence among the system's parameters is of paramount importance to achieve the sought result with minimum effort and time. Numerical simulations are an invaluable tool to carry out the system optimization, but modeling limitations restrict the capabilities of this approach. On the other side, real tests and measurements are lengthy, expensive, and not always feasible. This is the reason why a mixed approach is presented in this work. The combination, through recursive cokriging, of low-fidelity, yet extensive, numerical model results, together with a limited number of highly accurate experimental measurements, allows to understand the dynamics of the machine in an extended and accurate way. The results of a controllable experiment are presented and the advantages and drawbacks of the proposed approach are also discussed.