A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis

An extensive sample (2%) of private vehicles in Italy are equipped with a GPS device that periodically measures their position and dynamical state for insurance purposes. Having access to this type of data allows to develop theoretical and practical applications of great interest: the real-time reconstruction of traffic state in a certain region, the development of accurate models of vehicle dynamics, the study of the cognitive dynamics of drivers. In order for these applications to be possible, we first need to develop the ability to reconstruct the paths taken by vehicles on the road network from the raw GPS data. In fact, these data are affected by positioning errors and they are often very distanced from each other (~2 Km). For these reasons, the task of path identification is not straightforward. This thesis describes the approach we followed to reliably identify vehicle paths from this kind of low-sampling data. The problem of matching data with roads is solved with a bayesian approach of maximum likelihood. While the identification of the path taken between two consecutive GPS measures is performed with a specifically developed optimal routing algorithm, based on A* algorithm. The procedure was applied on an off-line urban data sample and proved to be robust and accurate. Future developments will extend the procedure to real-time execution and nation-wide coverage.

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications

Ill-conditioned inverse problems frequently arise in life sciences, particularly in the context of image deblurring and medical image reconstruction. These problems have been addressed through iterative variational algorithms, which regularize the reconstruction by adding prior knowledge about the problem's solution. Despite the theoretical reliability of these methods, their practical utility is constrained by the time required to converge. Recently, the advent of neural networks allowed the development of reconstruction algorithms that can compute highly accurate solutions with minimal time demands. Regrettably, it is well-known that neural networks are sensitive to unexpected noise, and the quality of their reconstructions quickly deteriorates when the input is slightly perturbed. Modern efforts to address this challenge have led to the creation of massive neural network architectures, but this approach is unsustainable from both ecological and economic standpoints. The recently introduced GreenAI paradigm argues that developing sustainable neural network models is essential for practical applications. In this thesis, we aim to bridge the gap between theory and practice by introducing a novel framework that combines the reliability of model-based iterative algorithms with the speed and accuracy of end-to-end neural networks. Additionally, we demonstrate that our framework yields results comparable to state-of-the-art methods while using relatively small, sustainable models. In the first part of this thesis, we discuss the proposed framework from a theoretical perspective. We provide an extension of classical regularization theory, applicable in scenarios where neural networks are employed to solve inverse problems, and we show there exists a trade-off between accuracy and stability. Furthermore, we demonstrate the effectiveness of our methods in common life science-related scenarios. In the second part of the thesis, we initiate an exploration extending the proposed method into the probabilistic domain. We analyze some properties of deep generative models, revealing their potential applicability in addressing ill-posed inverse problems.

A new approach for the dynamic modelling of the human knee

Mathematical models of the knee joint are important tools which have both theoretical and practical applications. They are used by researchers to fully understand the stabilizing role of the components of the joint, by engineers as an aid for prosthetic design, by surgeons during the planning of an operation or during the operation itself, and by orthopedists for diagnosis and rehabilitation purposes. The principal aims of knee models are to reproduce the restraining function of each structure of the joint and to replicate the relative motion of the bones which constitute the joint itself. It is clear that the first point is functional to the second one. However, the standard procedures for the dynamic modelling of the knee tend to be more focused on the second aspect: the motion of the joint is correctly replicated, but the stabilizing role of the articular components is somehow lost. A first contribution of this dissertation is the definition of a novel approach — called sequential approach — for the dynamic modelling of the knee. The procedure makes it possible to develop more and more sophisticated models of the joint by a succession of steps, starting from a first simple model of its passive motion. The fundamental characteristic of the proposed procedure is that the results obtained at each step do not worsen those already obtained at previous steps, thus preserving the restraining function of the knee structures. The models which stem from the first two steps of the sequential approach are then presented. The result of the first step is a model of the passive motion of the knee, comprehensive of the patello-femoral joint. Kinematical and anatomical considerations lead to define a one degree of freedom rigid link mechanism, whose members represent determinate components of the joint. The result of the second step is a stiffness model of the knee. This model is obtained from the first one, by following the rules of the proposed procedure. Both models have been identified from experimental data by means of an optimization procedure. The simulated motions of the models then have been compared to the experimental ones. Both models accurately reproduce the motion of the joint under the corresponding loading conditions. Moreover, the sequential approach makes sure the results obtained at the first step are not worsened at the second step: the stiffness model can also reproduce the passive motion of the knee with the same accuracy than the previous simpler model. The procedure proved to be successful and thus promising for the definition of more complex models which could also involve the effect of muscular forces.

Study of materials and technology of ancient floor mosaics' substrate

Ancient pavements are composed of a variety of preparatory or foundation layers constituting the substrate, and of a layer of tesserae, pebbles or marble slabs forming the surface of the floor. In other cases, the surface consists of a mortar layer beaten and polished. The term mosaic is associated with the presence of tesserae or pebbles, while the more general term pavement is used in all the cases. As past and modern excavations of ancient pavements demonstrated, all pavements do not necessarily display the stratigraphy of the substrate described in the ancient literary sources. In fact, the number and thickness of the preparatory layers, as well as the nature and the properties of their constituent materials, are often varying in pavements which are placed either in different sites or in different buildings within a same site or even in a same building. For such a reason, an investigation that takes account of the whole structure of the pavement is important when studying the archaeological context of the site where it is placed, when designing materials to be used for its maintenance and restoration, when documenting it and when presenting it to public. Five case studies represented by archaeological sites containing floor mosaics and other kind of pavements, dated to the Hellenistic and the Roman period, have been investigated by means of in situ and laboratory analyses. The results indicated that the characteristics of the studied pavements, namely the number and the thickness of the preparatory layers, and the properties of the mortars constituting them, vary according to the ancient use of the room where the pavements are placed and to the type of surface upon which they were built. The study contributed to the understanding of the function and the technology of the pavements’ substrate and to the characterization of its constituent materials. Furthermore, the research underlined the importance of the investigation of the whole structure of the pavement, included the foundation surface, in the interpretation of the archaeological context where it is located. A series of practical applications of the results of the research, in the designing of repair mortars for pavements, in the documentation of ancient pavements in the conservation practice, and in the presentation to public in situ and in museums of ancient pavements, have been suggested.

A candidate gene approach to identify DNA markers associated with meat quality and production traits: investigation of several cathepsin genes in Italian heavy pigs.

The cathepsin enzymes represent an important family of lysosomal proteinases with a broad spectrum of functions in many, if not in all, tissues and cell types. In addition to their primary role during the normal protein turnover, they possess highly specific proteolytic activities, including antigen processing in the immune response and a direct role in the development of obesity and tumours. In pigs, the involvement of cathepsin enzymes in proteolytic processes have important effects during the conversion of muscle to meat, due to their influence on meat texture and sensory characteristics, mainly in seasoned products. Their contribution is fundamental in flavour development of dry-curing hams. However, several authors have demonstrated that high cathepsin activity, in particular of cathepsin B, is correlated to defects of these products, such as an excessive meat softness together with abnormal free tyrosine content, astringent or metallic aftertastes and formation of a white film on the cut surface. Thus, investigation of their genetic variability could be useful to identify DNA markers associated with these dry cured hams parameters, but also with meat quality, production and carcass traits in Italian heavy pigs. Unfortunately, no association has been found between cathepsin markers and meat quality traits so far, in particular with cathepsin B activity, suggesting that other genes, besides these, affect meat quality parameters. Nevertheless, significant associations were observed with several carcass and production traits in pigs. A recent study has demonstrated that different single nucleotide polymorphisms (SNPs) localized in cathepsin D (CTSD), F (CTSF), H and Z genes were highly associated with growth, fat deposition and production traits in an Italian Large White pig population. The aim of this thesis was to confirm some of these results in other pig populations and identify new cathepsin markers in order to evaluate their effects on cathepsin activity and other production traits. Furthermore, starting from the data obtained in previous studies on CTSD gene, we also analyzed the known polymorphism located in the insulin-like growth factor 2 gene (IGF2 intron3-g.3072G>A). This marker is considered the causative mutation for the quantitative trait loci (QTL) affecting muscle mass and fat deposition in pigs. Since IGF2 maps very close to CTSD on porcine chromosome (SSC) 2, we wanted to clarify if the effects of the CTSD marker were due to linkage disequilibrium with the IGF2 intron3-g.3072G>A mutation or not. In the first chapter, we reported the results from these two SSC2 gene markers. First of all, we evaluated the effects of the IGF2 intron3-g.3072G>A polymorphism in the Italian Large White breed, for which no previous studies have analysed this marker. Highly significant associations were identified with all estimated breeding values for production and carcass traits (P<0.00001), while no effects were observed for meat quality traits. Instead, the IGF2 intron3-g.3072G>A mutation did not show any associations with the analyzed traits in the Italian Duroc pigs, probably due to the low level of variability at this polymorphic site for this breed. In the same Duroc pig population, significant associations were obtained for the CTSD marker for all production and carcass traits (P < 0.001), after excluding possible confounding effects of the IGF2 mutation. The effects of the CTSD g.70G>A polymorphism were also confirmed in a group of Italian Large White pigs homozygous for the IGF2 intron3-g.3072G allele G (IGF2 intron3-g.3072GG) and by haplotype analysis between the markers of the two considered genes. Taken together, all these data indicated that the IGF2 intron3-g.3072G>A mutation is not the only polymorphism affecting fatness and muscle deposition in pigs. In the second chapter, we reported the analysis of two new SNPs identified in cathepsin L (CTSL) and cathepsin S (CTSS) genes and the association results with meat quality parameters (including cathepsin B activity) and several production traits in an Italian Large White pig population. Allele frequencies of these two markers were evaluated in 7 different pig breeds. Furthermore, we mapped using a radiation hybrid panel the CTSS gene on SSC4. Association studies with several production traits, carried out in 268 Italian Large White pigs, indicated positive effects of the CTSL polymorphism on average daily gain, weight of lean cuts and backfat thickness (P<0.05). The results for these latter traits were also confirmed using a selective genotype approach in other Italian Large White pigs (P<0.01). In the 268 pig group, the CTSS polymorphism was associated with feed:gain ratio and average daily gain (P<0.05). Instead, no association was observed between the analysed markers and meat quality parameters. Finally, we wanted to verify if the positive results obtained for the cathepsin L and S markers and for other previous identified SNPs (cathepsin F, cathepsin Z and their inhibitor cystatin B) were confirmed in the Italian Duroc pig breed (third chapter). We analysed them in two groups of Duroc pigs: the first group was made of 218 performance-tested pigs not selected by any phenotypic criteria, the second group was made of 100 Italian Duroc pigs extreme and divergent for visible intermuscular fat trait. In the first group, the CTSL polymorphism was associated with weight of lean cuts (P<0.05), while suggestive associations were obtained for average daily gain and backfat thickness (P<0.10). Allele frequencies of the CTSL gene marker also differed positively among the visible intermuscular extreme tails. Instead, no positive effects were observed for the other DNA markers on the analysed traits. In conclusion, in agreement with the present data and for the biological role of these enzymes, the porcine CTSD and CTSL markers: a) may have a direct effect in the biological mechanisms involved in determining fat and lean meat content in pigs, or b) these markers could be very close to the putative functional mutation(s) present in other genes. These findings have important practical applications, in particular the CTSD and CTSL mutations could be applied in a marker assisted selection (MAS) both in the Italian Large White and Italian Duroc breeds. Marker assisted selection could also increase in efficiency by adding information from the cathepsin S genotype, but only in the Italian Large White breed.

Novel techniques for the remote sensing of photosynthetic processes

Remote sensing (RS) techniques have evolved into an important instrument to investigate forest function. New methods based on the remote detection of leaf biochemistry and photosynthesis are being developed and applied in pilot studies from airborne and satellite platforms (PRI, solar-induced fluorescence; N and chlorophyll content). Non-destructive monitoring methods, a direct application of RS studies, are also proving increasingly attractive for the determination of stress conditions or nutrient deficiencies not only in research but also in agronomy, horticulture and urban forestry (proximal RS). In this work I will focus on some novel techniques recently developed for the estimation of photochemistry and photosynthetic rates based (i) on the proximal measurement of steady-state chlorophyll fluorescence yield, or (ii) the remote sensing of changes in hyperspectral leaf reflectance, associated to xanthophyll de-epoxydation and energy partitioning, which is closely coupled to leaf photochemistry and photosynthesis. I will also present and describe a mathematical model of leaf steady-state fluorescence and photosynthesis recently developed in our group. Two different species were used in the experiments: Arbutus unedo, a schlerophyllous Mediterranean species, and Populus euroamericana, a broad leaf deciduous tree widely used in plantation forestry. Results show that ambient fluorescence could provide a useful tool for testing photosynthetic processes from a distance. These results confirm also the photosynthetic reflectance index (PRI) as an efficient remote sensing reflectance index estimating short-term changes in photochemical efficiency as well as long-term changes in leaf biochemistry. The study also demonstrated that RS techniques could provide a fast and reliable method to estimate photosynthetic pigment content and total nitrogen, beside assessing the state of photochemical process in our plants’ leaves in the field. This could have important practical applications for the management of plant cultivation systems, for the estimation of the nutrient requirements of our plants for optimal growth.

Fenomeni di transizione degli organismi edilizi, criteri operativi di inserimento ambientale e procedure di intervento

La presente tesi indaga le potenzialità, in termini di criteri operativi di inserimento ambientale e procedure di intervento, che possono derivare dall’analisi dei fenomeni insediativi facendo riferimento ai concetti di ‘transizione’ e ‘resilienza’. L’attuale periodo di crisi sembra scaturire dal disequilibrio di due fattori: le esigenze umane e l’ambiente. La contestualizzazione degli interventi, il graduale adattamento alle risorse ambientali locali e la valorizzazione dei processi “dal basso” sembrano consentire di riappropriarsi sia del valore identitario dei luoghi, dando risposta ai problemi di natura sociale evidenziati, sia della eco-compatibilità delle trasformazioni, del corretto utilizzo delle risorse energetiche e della gestione delle dinamiche economiche, in risposta ai problemi ambientali analizzati. Il prefigurare applicazioni pratiche del modello di trasformazione indagato alla scala edilizia, utilizzando tavole parametriche di analisi del costruito, viste d’insieme planivolumetriche ed elaborazioni di dati e immagini, può consentire la gestione di eventuali fasi di programmazione e di pianificazione da parte delle amministrazioni finalizzate a favorire e non ostacolare i presenti e futuri fenomeni di transizione. Particolarmente significativa appare l’analisi delle diverse tendenze di ricerca progettuale in atto, con riferimento a contributi caratterizzati da un’impostazione fenomenologica e tipo-morfologica, che dimostra l’attualità degli argomenti affrontati. La ricerca di Dottorato si conclude con l’applicazione dei criteri operativi di inserimento ambientale e delle procedure di intervento individuati ad uno specifico caso di studio.

Advanced aspects of radiation protection in the use of particle accelerators in the medical field

In this work, the well-known MC code FLUKA was used to simulate the GE PETrace cyclotron (16.5 MeV) installed at “S. Orsola-Malpighi” University Hospital (Bologna, IT) and routinely used in the production of positron emitting radionuclides. Simulations yielded estimates of various quantities of interest, including: the effective dose distribution around the equipment; the effective number of neutron produced per incident proton and their spectral distribution; the activation of the structure of the cyclotron and the vault walls; the activation of the ambient air, in particular the production of 41Ar, the assessment of the saturation yield of radionuclides used in nuclear medicine. The simulations were validated against experimental measurements in terms of physical and transport parameters to be used at the energy range of interest in the medical field. The validated model was also extensively used in several practical applications uncluding the direct cyclotron production of non-standard radionuclides such as 99mTc, the production of medical radionuclides at TRIUMF (Vancouver, CA) TR13 cyclotron (13 MeV), the complete design of the new PET facility of “Sacro Cuore – Don Calabria” Hospital (Negrar, IT), including the ACSI TR19 (19 MeV) cyclotron, the dose field around the energy selection system (degrader) of a proton therapy cyclotron, the design of plug-doors for a new cyclotron facility, in which a 70 MeV cyclotron will be installed, and the partial decommissioning of a PET facility, including the replacement of a Scanditronix MC17 cyclotron with a new TR19 cyclotron.

Studi per una operante storia del territorio. Il libro incompiuto di Saverio Muratori

Muratori concepisce Studi per una operante storia del territorio come una lettura del territorio, una sua interpretazione. E’ una raccolta di disegni (255 disegni a mano e 18 prove di stampa), in cui sono riportati soltanto i tratti rilevanti dei territori considerati al fine dello studio. La tesi ha come obiettivo quello di compiere un’analisi filologica dei documenti e cercare di cogliere le finalità, valutando la sua attendibilità ai giorni nostri. Nella scelta del titolo, Studi per una operante storia del territorio. Il libro incompiuto di Saverio Muratori,inserendo nel titolo quello originale dell'opera, si rende esplicita l’intenzione filologica di base da cui si sviluppano le considerazioni fatte nello svolgimento di questa ricerca, costantemente attenta alla coerenza con i disegni analizzati. Questa coerenza è dettata dalla volontà di perseguire le logiche di confronto che hanno caratterizzato il lavoro di Muratori. Il titolo che descrive il corpus dei disegni mette in primo piano il sistema uomo-natura, che il territorio rappresenta nella sua totalità di unicum. La tesi è suddivisa in due parti. La prima verte sullo studio dei materiali d’archivio relativi agli Studi sul territorio, in cui è spiegato il metodo di catalogazione utilizzato. Fondamentale, ai fini di un primo approccio alla ricerca, è il contributo dato dall’architetto Alessandro Giannini attraverso la preliminare schedatura del materiale ed dell'analisi riportata in Leggendo le minute dell’Atlante del territorio di Saverio Muratori. La seconda parte della tesi verte su un percorso dialettico tra la filosofia, la teoria, la didattica e la pratica progettuale di Muratori, ambiti da tenere in considerazione per una interpretazione critica alla sua metodologia, che ha come fine ultimo il reale, quindi il territorio, in cui trova, prima attraverso la trattazione teorica esposta in Civiltà e territorio, la concreta conclusione nei disegni degli Studi per una operante storia del territorio.

Perceiving the 3d world from single images

Depth represents a crucial piece of information in many practical applications, such as obstacle avoidance and environment mapping. This information can be provided either by active sensors, such as LiDARs, or by passive devices like cameras. A popular passive device is the binocular rig, which allows triangulating the depth of the scene through two synchronized and aligned cameras. However, many devices that are already available in several infrastructures are monocular passive sensors, such as most of the surveillance cameras. The intrinsic ambiguity of the problem makes monocular depth estimation a challenging task. Nevertheless, the recent progress of deep learning strategies is paving the way towards a new class of algorithms able to handle this complexity. This work addresses many relevant topics related to the monocular depth estimation problem. It presents networks capable of predicting accurate depth values even on embedded devices and without the need of expensive ground-truth labels at training time. Moreover, it introduces strategies to estimate the uncertainty of these models, and it shows that monocular networks can easily generate training labels for different tasks at scale. Finally, it evaluates off-the-shelf monocular depth predictors for the relevant use case of social distance monitoring, and shows how this technology allows to overcome already existing strategies limitations.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.

Wind loading predictions using computational fluid dynamics

Using Computational Wind Engineering, CWE, for solving wind-related problems is still a challenging task today, mainly due to the high computational cost required to obtain trustworthy simulations. In particular, the Large Eddy Simulation, LES, has been widely used for evaluating wind loads on buildings. The present thesis assesses the capability of LES as a design tool for wind loading predictions through three cases. The first case is using LES for simulating the wind field around a ground-mounted rectangular prism in Atmospheric Boundary Layer (ABL) flow. The numerical results are validated with experimental results for seven wind attack angles, giving a global understanding of the model performance. The case with the worst model behaviour is investigated, including the spatial distribution of the pressure coefficients and their discrepancies with respect to experimental results. The effects of some numerical parameters are investigated for this case to understand their effectiveness in modifying the obtained numerical results. The second case is using LES for investigating the wind effects on a real high-rise building, aiming at validating the performance of LES as a design tool in practical applications. The numerical results are validated with the experimental results in terms of the distribution of the pressure statistics and the global forces. The mesh sensitivity and the computational cost are discussed. The third case is using LES for studying the wind effects on the new large-span roof over the Bologna stadium. The dynamic responses are analyzed and design envelopes for the structure are obtained. Although it is a numerical simulation before the traditional wind tunnel tests, i.e. the validation of the numerical results are not performed, the preliminary evaluations can effectively inform later investigations and provide the final design processes with deeper confidence regarding the absence of potentially unexpected behaviours.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Practical Management of the Endangered African Wild Dog (Lycaon Pictus): Implementation of Research Methodologies, Management Tools and Validation of Non-Invasive Endocrinology Applications

The porpoise of this study was to implement research methodologies and assess the effectiveness and impact of management tools to promote best practices for the long term conservation of the endangered African wild dog (Lycaon pictus). Different methods were included in the project framework to investigate and expand the applicability of these methodologies to free-ranging African wild dogs in the southern African region: ethology, behavioural endocrinology and ecology field methodologies were tested and implemented. Additionally, research was performed to test the effectiveness and implication of a contraceptive implant (Suprenolin) as a management tool for the species of a subpopulation hosted in fenced areas. Attention was especially given to social structure and survival of treated packs. This research provides useful tools and advances the applicability of these methods for field studies, standardizing and improving research instruments in the field of conservation biology and behavioural endocrinology. Results reported here provide effective methodologies to expand the applicability of non-invasive endocrine assessment to previously prohibited fields, and validation of sampling methods for faecal hormone analysis. The final aim was to fill a knowledge gap on behaviours of the species and provide a common ground for future researchers to apply non-invasive methods to this species research and to test the effectiveness of the contraception on a managed metapopulation.