Reverse-Safe Data Structures for Text Indexing

We introduce the notion of reverse-safe data structures. These are data structures that prevent the reconstruction of the data they encode (i.e., they cannot be easily reversed). A data structure D is called z-reverse-safe when there exist at least z datasets with the same set of answers as the ones stored by D. The main challenge is to ensure that D stores as many answers to useful queries as possible, is constructed efficiently, and has size close to the size of the original dataset it encodes. Given a text of length n and an integer z, we propose an algorithm which constructs a z-reverse-safe data structure that has size O(n) and answers pattern matching queries of length at most d optimally, where d is maximal for any such z-reverse-safe data structure. The construction algorithm takes O(n ω log d) time, where ω is the matrix multiplication exponent. We show that, despite the n ω factor, our engineered implementation takes only a few minutes to finish for million-letter texts. We further show that plugging our method in data analysis applications gives insignificant or no data utility loss. Finally, we show how our technique can be extended to support applications under a realistic adversary model

How to restart? An agent-based simulation model towards the definition of strategies for COVID-19 "second phase" in public buildings

Restarting public buildings activities in the "second phase" of COVID-19 emergency should be supported by operational measures to avoid a second virus spreading. Buildings hosting the continuous presence of the same users and significant overcrowd conditions over space/time (e.g. large offices, universities) are critical scenarios due to the prolonged contact with infectors. Beside individual's risk-mitigation strategies performed (facial masks), stakeholders should promote additional strategies, i.e. occupants' load limitation (towards "social distancing") and access control. Simulators could support the measures effectiveness evaluation. This work provides an Agent-Based Model to estimate the virus spreading in the closed built environment. The model adopts a probabilistic approach to jointly simulate occupants' movement and virus transmission according to proximity-based and exposure-time-based rules proposed by international health organizations. Scenarios can be defined in terms of building occupancy, mitigation strategies and virus-related aspects. The model is calibrated on experimental data ("Diamond Princess" cruise) and then applied to a relevant case-study (a part of a university campus). Results demonstrate the model capabilities. Concerning the case-study, adopting facial masks seems to be a paramount strategy to reduce virus spreading in each initial condition, by maintaining an acceptable infected people's number. The building capacity limitation could support such measure by potentially moving from FFPk masks to surgical masks use by occupants (thus improving users' comfort issues). A preliminary model to combine acceptable mask filters-occupants' density combination is proposed. The model could be modified to consider other recurring scenarios in other public buildings (e.g. tourist facilities, cultural buildings).Comment: 21 pages, 16 figures; submitted to Building and Environmen

Hydro-mechanical Modelling of the Airbus A380 Nose Landing Gear Extension/Retraction Systems

Modelling and Simulation is a branch of engineering in continuous development across the industry, and follows the evolution of computer technology and simulation tools. By means of simulation, it is possible to explore and test several design solutions in a virtual environment, and to obtain performance predictions before the physical devices are actually produced. This capability is used throughout the aerospace industry, where the development of a project requires huge investments, and the need of accurate predictions is part of the design process from its early stage, to minimise risks and wastes. The work described in this dissertation was developed within the Simulation and Modelling Group of the Airbus UK Landing Gear Department. It is focused on the description of the approach, the techniques and the tools used to perform a hydro-mechanical simulation of the Airbus A380 Nose Landing Gear Extension/Retraction Systems. The creation of the hydraulic model using the AMESim modelling tool is described, as well as the development of a mechanical model of the Nose Landing Gear with the ADAMS modelling tool. The mechanical model already existed, but major rework was necessary in order to couple it with the hydraulic model by means of co-simulation. The setup of the co-simulation platform is explained, and the results of the validation process for the integrated models are presented, showing the process followed to tune the hydro-mechanical model, to match its dynamic behaviour with reference data. Finally, the method adopted to extract the Pressure-Flow characteristics of the hydraulic model is described

COVID-19 impact on end-user's maintenance requests. A text mining approach

COVID-19 pandemic changed our way of working, limiting the usual physical attendance of working spaces. Despite the drastic reduction in the number of daily users due to the pandemic restrictions, working buildings were often kept open to provide services to internal and external users. Pandemic obliged to change operation and maintenance (O&M) plans, due to the increase of ventilation requirements and the reduction of other types of services, with a strong impact on cost and management. Now the pandemic is reducing its effects and is time to question the future asset of buildings’ O&M plans, based on the pandemic lesson. Data collected by Computerized Maintenance Management Systems (CMMS) during COVID-19 then become an important source of understanding the future management of working places. End-users’ maintenance requests are usually expressed by natural language, then a text mining approach can be a useful tool to discover hidden knowledge from unstructured data stored in CMMS. This study applies text mining methods, including sentiment analysis, to the field of building maintenance, with the scope to evaluate how COVID-19 changed some aspects of the facility management process, including users’ perception

flooding risk in existing urban environment from human behavioral patterns to a microscopic simulation model

Abstract Climate changes-related floods will seriously strike population in existing urban environment. Despite Current assessment methods seem to underestimate the human behaviors influence on individuals' safety, especially during outdoor evacuation. Representing pedestrians' evacuation would allow considering the "human" factor in risk analysis. This work proposes a flood-induced pedestrians' evacuation simulation model, based on a combined microscopic approach. Behavioral rules, obtained by real events videotapes analyses, are organized in an agent-based model. Motion criteria proposals are based on the Social Force Model. Experimental motion quantities values are offered. The model will be implemented in a risk assessment simulation tool

Seismic risk of Open Spaces in Historic Built Environments: A matrix-based approach for emergency management and disaster response

Abstract Earthquakes affect the safety of the users hosted in both indoor and outdoor urban built environments, especially in Historic Built Environments (HBEs). Many full HBE-scale risk-assessment methods are defined, while methodologies oriented to local analysis of meso-scale elements, such as Open Spaces (OSs), are still limited. Nevertheless, OSs play a crucial role in the first emergency phases, like in the evacuation process, since they host emergency paths and gathering areas. The seismic risk of an OS mainly depends on the combination of the damage suffered from facing buildings and the exposure, which mainly refers to the quantification of human lives. Damage levels result from the combination of vulnerability and hazard-related issues, while exposure is essentially affected by the number of OS users, whose spatial distribution is strongly time-dependent. Methods to quickly combine these issues are needed, especially in view of the deeper insights for the implementation of risk-reduction strategies (i.e. according to simulation-based approaches). This work offers a novel methodology to quickly perform Seismic Risk Assessment and Management of an OS by correlating damage levels to exposure-related issues. The method is composed of two specific matrices, which are developed according to quick literature-based approaches prone to rapid meso-scale applications in HBEs, also by non-expert technicians. The "damage matrix" links the site hazard to the building vulnerability. The assessed damage levels are combined with the users' exposure into the "consequences matrix", to estimate the risk in emergency conditions for the OS users, thus supporting decision-makers in promoting robustness/preparedness strategies

Substring Complexity in Sublinear Space

Shannon's entropy is a definitive lower bound for statistical compression. Unfortunately, no such clear measure exists for the compressibility of repetitive strings. Thus, ad-hoc measures are employed to estimate the repetitiveness of strings, e.g., the size $z$ of the Lempel-Ziv parse or the number $r$ of equal-letter runs of the Burrows-Wheeler transform. A more recent one is the size $\gamma$ of a smallest string attractor. Unfortunately, Kempa and Prezza [STOC 2018] showed that computing $\gamma$ is NP-hard. Kociumaka et al. [LATIN 2020] considered a new measure that is based on the function $S_T$ counting the cardinalities of the sets of substrings of each length of $T$, also known as the substring complexity. This new measure is defined as $\delta= \sup\{S_T(k)/k, k\geq 1\}$ and lower bounds all the measures previously considered. In particular, $\delta\leq \gamma$ always holds and $\delta$ can be computed in $\mathcal{O}(n)$ time using $\Omega(n)$ working space. Kociumaka et al. showed that if $\delta$ is given, one can construct an $\mathcal{O}(\delta \log \frac{n}{\delta})$-sized representation of $T$ supporting efficient direct access and efficient pattern matching queries on $T$. Given that for highly compressible strings, $\delta$ is significantly smaller than $n$, it is natural to pose the following question: Can we compute $\delta$ efficiently using sublinear working space? It is straightforward to show that any algorithm computing $\delta$ using $\mathcal{O}(b)$ space requires $\Omega(n^{2-o(1)}/b)$ time through a reduction from the element distinctness problem [Yao, SIAM J. Comput. 1994]. We present the following results: an $\mathcal{O}(n^3/b^2)$-time and $\mathcal{O}(b)$-space algorithm to compute $\delta$, for any $b\in[1,n]$; and an $\tilde{\mathcal{O}}(n^2/b)$-time and $\mathcal{O}(b)$-space algorithm to compute $\delta$, for any $b\in[n^{2/3},n]$

design of a smart system for indoor climate control in historic underground built environment

Abstract The application of sensors-actuators networks in Building Heritage can lead to significant improvement in indoor climate control, with the aim to both reduce energy consumption, and improve conditions for occupants and hosted Heritage. This study proposes the preliminary design of a smart indoor climate control system, based on low-impact application criteria, which can be applied to visited underground built environment. The system is based on the balance of hygrothermal loads. Sensors and actuators requirements are defined, and control algorithm are based on the comparison between real-time monitored and "natural" temperature and hygrometric values (for stationary and transitory conditions)

Determining behavioural-based risk to SLODs of urban public open spaces: Key performance indicators definition and application on established built environment typological scenarios

A behavioural-based approach can be used to assess how users’ reactions to surrounding environmental conditions can alter the urban Built Environment (BE) risk to Slow Onset Disasters (SLODs). Public Open Spaces (POSs) in the BE are relevant scenarios, due to micro-climate-related stress, users’ vulnerabilities (e.g., age, health frailty) and exposure time. Simulation methods can support behavioural-based risk-assessment, but results are generally site-specific. Performing analysis on BE Typologies (BETs) can improve robustness, since BETs represent archetypes from real-world scenarios. This work adopts a behavioural-based approach to evaluate time-dependant users’ risks of POSs in different BETs due to SLODs-related stress (i.e., heat, air pollution). UTCI and AQI values are mapped within each BET. Users’ distributions are then calculated depending on thermal acceptability correlations. Key Performance Indicators are developed associating users’ distribution to SLODs effects on health (i.e., sweat rate, water loss; health affection rate probability). The approach is applied to Italian BETs, under one relevant climate, rating their heat and air pollution risks. Results suggest critical conditions for toddlers. In detail, about 2-hour high heat exposure could result in dehydration, while 1-hour exposure to low NO2 concentration could result in +1% mortality probability. This approach could potentially support decision-makers on BE risk-assessment