Co-digestion of macroalgae for biogas production: an LCA-based environmental evaluation

Algae represent a favourable and potentially sustainable source of biomass for bioenergy-based industrial pathways in the future. The study, performed on a real pilot plant implemented in Augusta (Italy) within the frame of the BioWALK4Biofuels project, aims to figure out whether seaweed (macroalgae) cultivated in near-shore open ponds could be considered a beneficial aspect as a source of biomass for biogas production within the co-digestion with local agricultural biological waste. The LCA results confirm that the analysed A and B scenarios (namely the algae-based co-digestion scenario and agricultural mix feedstock scenario) present an environmental performance more favourable than that achieved with conventional non-renewable-based technologies (specifically natural gas - Scenario C). Results show that the use of seaweed (Scenario A) represent a feasible solution in order to replace classical biomass used for biofuel production from a land-based feedstock. The improvement of the environmental performances is quantifiable on 10% respect to Scenario B, and 38 times higher than Scenario

Surrogate-based Real-time Curbside Management for Ride-hailing and Delivery Operations

The present work investigates surrogate model-based optimization for real-time curbside traffic management operations. An optimization problem is formulated to minimize the congestion on roadway segments caused by vehicles stopping on the segment (e.g., ride-hailing or delivery operations) and implemented in a model predictive control framework. A hybrid simulation approach where main traffic flows interact with individually modeled stopping vehicles is adopted. Due to its non-linearity, the optimization problem is coupled with a meta-heuristic. However, because simulations are time expensive and hence unsuitable for real-time control, a trained surrogate model that takes the decision variables as inputs and approximates the objective function is employed to replace the simulation within the meta-heuristic algorithm. Several modeling techniques (i.e., linear regression, polynomial regression, neural network, radial basis network, regression tree ensemble, and Gaussian process regression) are compared based on their accuracy in reproducing solutions to the problem and computational tractability for real-time control under different configurations of simulation parameters. It is found that Gaussian process regression is the most suited for use as a surrogate model for the given problem. Finally, a realistic application with multiple ride-hailing vehicle operations is presented. The proposed approach for controlling the stop positions of vehicles is able to achieve an improvement of 20.65% over the uncontrolled case. The example shows the potential of the proposed approach in reducing the negative impacts of stopping vehicles and favorable computational properties

Surface-acoustic-wave driven planar light-emitting device

Electroluminescence emission controlled by means of surface acoustic waves (SAWs) in planar light-emitting diodes (pLEDs) is demonstrated. Interdigital transducers for SAW generation were integrated onto pLEDs fabricated following the scheme which we have recently developed. Current-voltage, light-voltage and photoluminescence characteristics are presented at cryogenic temperatures. We argue that this scheme represents a valuable building block for advanced optoelectronic architectures

Model-based traffic state estimation for link traffic using moving cameras

Traffic State Estimation (TSE) is the process of inferring traffic conditions based on partially observed data using prior knowledge of traffic patterns. The type of input data used has a significant impact on the accuracy and methodology of TSE. Traditional TSE methods have relied on data from either stationary sensors like loop detectors or mobile sensors such as GPS-equipped floating cars. However, both approaches have their limitations. This paper proposes a method for estimating traffic states on a road link using vehicle trajectories obtained from cameras mounted on moving vehicles. It involves combining data from multiple moving cameras to construct time-space diagrams and using them to estimate parameters for the link's fundamental diagram (FD) and densities in unobserved regions of space-time. The Cell Transmission Model (CTM) is utilized in conjunction with a Genetic Algorithm (GA) to optimize the FD parameters and boundary conditions necessary for accurate estimation. To evaluate the effectiveness of the proposed methodology, simulated traffic data generated by the SUMO traffic simulator was employed incorporating 140 different space-time diagrams with varying lane density and speed. The evaluation of the simulated data demonstrates the effectiveness of the proposed approach, as it achieves a low root mean square error (RMSE) value of 0.0079 veh/m and is comparable to other CTM-based methods. In conclusion, the proposed TSE method opens new avenues for the estimation of traffic state using an innovative data collection method that uses vehicle trajectories collected from on-board cameras.Comment: Under review for journal submissio

Atmospheric circulation patterns, cloud-to-ground lightning, and locally intense convective rainfall associated with debris flow initiation in the Dolomite Alps of northeastern Italy

The Dolomite Alps of northeastern Italy experience debris flows with great frequency during the summer months. An ample supply of unconsolidated material on steep slopes and a summer season climate regime characterized by recurrent thunderstorms combine to produce an abundance of these destructive hydro-geologic events. In the past, debris flow events have been studied primarily in the context of their geologic and geomorphic characteristics. The atmospheric contribution to these mass-wasting events has been limited to recording rainfall and developing intensity thresholds for debris mobilization. This study aims to expand the examination of atmospheric processes that preceded both locally intense convective rainfall (LICR) and debris flows in the Dolomite region. 500 hPa pressure level plots of geopotential heights were constructed for a period of 3 days prior to debris flow events to gain insight into the synoptic-scale processes which provide an environment conducive to LICR in the Dolomites. Cloud-to-ground (CG) lightning flash data recorded at the meso-scale were incorporated to assess the convective environment proximal to debris flow source regions. Twelve events were analyzed and from this analysis three common synoptic-scale circulation patterns were identified. Evaluation of CG flashes at smaller spatial and temporal scales illustrated that convective processes vary in their production of CF flashes (total number) and the spatial distribution of flashes can also be quite different between events over longer periods. During the 60 min interval immediately preceding debris flow a majority of cases exhibited spatial and temporal colocation of LICR and CG flashes. Also a number of CG flash parameters were found to be significantly correlated to rainfall intensity prior to debris flow initiation

Restoring an eroded legitimacy: the adaptation of nonfinancial disclosure after a scandal and the risk of hypocrisy

Purpose \u2013 This study contributes to the literature on hypocrisy in corporate social responsibility by investigating how organizations adapt their nonfinancial disclosure after a social, environmental or governance scandal. Design/methodology/approach \u2013 The present research employs content analysis of nonfinancial disclosures by 11 organizations during a 3-year timespan to investigate how they responded to major scandals in terms of social, environmental and sustainability reporting and a content analysis of independent counter accounts to detect the presence of views that contrast with the corporate disclosure and suggest hypocritical behaviors. Findings \u2013 Four patterns in the adaptation of reporting \u2013 genuine, allusive, evasive, indifferent \u2013 emerge from information collected on scandals and socially responsible actions. The type of scandal and cultural factors can influence the response to a scandal, as environmental and social scandal can attract more scrutiny than financial scandals. Companies exposed to environmental and social scandals are more likely to disclose information about the scandal and receive more coverage by external parties in the form of counter accounts. Originality/value \u2013 Using a theoretical framework based on legitimacy theory and organizational hypocrisy, the present research contributes to the investigation of the adaptation of reporting when a scandal occurs and during its aftermath

Acoustic charge transport in n-i-n three terminal device

We present an unconventional approach to realize acoustic charge transport devices that takes advantage from an original input region geometry in place of standard Ohmic input contacts. Our scheme is based on a n-i-n lateral junction as electron injector, an etched intrinsic channel, a standard Ohmic output contact and a pair of in-plane gates. We show that surface acoustic waves are able to pick up electrons from a current flowing through the n-i-n junction and steer them toward the output contact. Acoustic charge transport was studied as a function of the injector current and bias, the SAW power and at various temperatures. The possibility to modulate the acoustoelectric current by means of lateral in-plane gates is also discussed. The main advantage of our approach relies on the possibility to drive the n-i-n injector by means of both voltage or current sources, thus allowing to sample and process voltage and current signals as well.Comment: 9 pages, 3 figures. Submitted to Applied Physics Letter

FURTHER DEVELOPMENT OF AN ALGEBRAIC INTERMITTENCY MODEL FOR SEPARATION-INDUCED TRANSITION UNDER ELEVATED FREE-STREAM TURBULENCE

A constitutive law for the Reynolds stresses during boundary layer laminar-to-turbulent transition, constructed in previous work by elastic-net regression on an experimental data base, has been incorporated in an algebraic intermittency model. The objective is prediction improvement of transition in a separated layer under an elevated free-stream turbulence level. The modelling for such cases functions through additional production terms in the transport equations of turbulent kinetic energy and specific dissipation rate of a k-ω turbulence model. A sensor detects the front part of a separated layer and activates the production terms. These express the effect of Klebanoff streaks generated upstream of separation on the Kelvin-Helmholtz instability rolls in the separated part of the layer. By the Klebanoff streaks, the breakdown is faster and the speed of breakdown increases by the combined effects of a large adverse pressure gradient and an elevated free-stream turbulence level

Moving Horizon Trend Identification Based on Switching Models for Data Driven Decomposition of Fluid Flows

Modal decomposition is pretty popular in fluid mechanics, especially for data-driven analysis. Dynamic mode decomposition (DMD) allows to identify the modes that describe complex phenomenona such as those physically modelled by the Navier-Stokes equation. The identified modes are associated with residuals, which can be used to detect a meaningful change of regime, e.g., the formation of a vortex. Toward this end, moving horizon estimation (MHE) is applied to identify the trend of the norm of the residuals that result from the application of DMD for the purpose to automatically classify the time evolution of fluid flows. The trend dynamics is modelled as a switching nonlinear system and hence an MHE problem is solved in such a way to monitor the time behavior of the fluid and quickly identify changes of regime. The stability of the estimation error given by MHE is proved. The combination of DMD and MHE provide successful results as shown by processing experimental datasets of the velocity field of fluid flows obtained by a particle image velocimetry