Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling

Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurse’s assignment. Based on the idea of using probabilistic models, the BOA builds a Bayesian network for the set of promising solutions and samples these networks to generate new candidate solutions. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed algorithm may be suitable for other scheduling problems.

Geological realism in hydrogeological and geophysical inverse modeling: A review

Scientific curiosity, exploration of georesources and environmental concerns are pushing the geoscientific research community toward subsurface investigations of ever-increasing complexity. This review explores various approaches to formulate and solve inverse problems in ways that effectively integrate geological concepts with geophysical and hydrogeological data. Modern geostatistical simulation algorithms can produce multiple subsurface realizations that are in agreement with conceptual geological models and statistical rock physics can be used to map these realizations into physical properties that are sensed by the geophysical or hydrogeological data. The inverse problem consists of finding one or an ensemble of such subsurface realizations that are in agreement with the data. The most general inversion frameworks are presently often computationally intractable when applied to large-scale problems and it is necessary to better understand the implications of simplifying (1) the conceptual geological model (e.g., using model compression); (2) the physical forward problem (e.g., using proxy models); and (3) the algorithm used to solve the inverse problem (e.g., Markov chain Monte Carlo or local optimization methods) to reach practical and robust solutions given today's computer resources and knowledge. We also highlight the need to not only use geophysical and hydrogeological data for parameter estimation purposes, but also to use them to falsify or corroborate alternative geological scenarios.

Modeling, Simulation, and Characterization of Space Debris in low-Earth Orbit

Every space launch increases the overall amount of space debris. Satellites have limited awareness of nearby objects that might pose a collision hazard. Astrometric, radiometric, and thermal models for the study of space debris in low-Earth orbit have been developed. This modeled approach proposes analysis methods that provide increased Local Area Awareness for satellites in low-Earth and geostationary orbit. Local Area Awareness is defined as the ability to detect, characterize, and extract useful information regarding resident space objects as they move through the space environment surrounding a spacecraft. The study of space debris is of critical importance to all space-faring nations. Characterization efforts are proposed using long-wave infrared sensors for space-based observations of debris objects in low-Earth orbit. Long-wave infrared sensors are commercially available and do not require solar illumination to be observed, as their received signal is temperature dependent. The characterization of debris objects through means of passive imaging techniques allows for further studies into the origination, specifications, and future trajectory of debris objects. Conclusions are made regarding the aforementioned thermal analysis as a function of debris orbit, geometry, orientation with respect to time, and material properties. Development of a thermal model permits the characterization of debris objects based upon their received long-wave infrared signals. Information regarding the material type, size, and tumble-rate of the observed debris objects are extracted. This investigation proposes the utilization of long-wave infrared radiometric models of typical debris to develop techniques for the detection and characterization of debris objects via signal analysis of unresolved imagery. Knowledge regarding the orbital type and semi-major axis of the observed debris object are extracted via astrometric analysis. This knowledge may aid in the constraint of the admissible region for the initial orbit determination process. The resultant orbital information is then fused with the radiometric characterization analysis enabling further characterization efforts of the observed debris object. This fused analysis, yielding orbital, material, and thermal properties, significantly increases a satellite’s Local Area Awareness via an intimate understanding of the debris environment surrounding the spacecraft.

Modeling chemistry in massive star forming regions with internal PDRs

Over the past decades star formation has been a very attractive field because knowledge of star formation leads to a better understanding of the formation of planets and thus of our solar system but also of the evolution of galaxies. Conditions leading to the formation of high-mass stars are still under investigation but an evolutionary scenario has been proposed: As a cold pre-stellar core collapses under gravitational force, the medium warms up until it reaches a temperature of 100 K and enters the hot molecular core (HMC) phase. The forming central proto-star accretes materials, increasing its mass and luminosity and eventually it becomes sufficiently evolved to emit UV photons which irradiate the surrounding environment forming a hyper compact (HC) and then a ultracompact (UC) HII region. At this stage, a very dense and very thin internal photon-dominated region (PDR) forms between the HII region and the molecular core. Information on the chemistry allows to trace the physical processes occurring in these different phases of star formation. Formation and destruction routes of molecules are influenced by the environment as reaction rates depend on the temperature and radiation field. Therefore, chemistry also allows the determination of the evolutionary stage of astrophysical objects through the use of chemical models including the time evolution of the temperature and radiation field. Because HMCs host a very rich chemistry with high abundances of complex organic molecules (COMs), several astrochemical models have been developed to study the gas phase chemistry as well as grain chemistry in these regions. In addition to HMCs models, models of PDRs have also been developed to study in particular photo-chemistry. So far, few studies have investigated internal PDRs and only in the presence of outflows cavities. Thus, these unique regions around HC/UCHII regions remain to be examined thoroughly. My PhD thesis focuses on the spatio-temporal chemical evolution in HC/UC HII regions with internal PDRs as well as in HMCs. The purpose of this study is first to understand the impact and effects of the radiation field, usually very strong in these regions, on the chemistry. Secondly, the goal is to study the emission of various tracers of HC/UCHII regions and compare it with HMCs models, where the UV radiation field does not impact the region as it is immediately attenuated by the medium. Ultimately we want to determine the age of a given region using chemistry in combination with radiative transfer.

Experimental and modeling studies of forced convection storage and drying systems for sweet potatoes

Sweet potato is an important strategic agricultural crop grown in many countries around the world. The roots and aerial vine components of the crop are used for both human consumption and, to some extent as a cheap source of animal feed. In spite of its economic value and growing contribution to health and nutrition, harvested sweet potato roots and aerial vine components has limited shelf-life and is easily susceptible to post-harvest losses. Although post-harvest losses of both sweet potato roots and aerial vine components is significant, there is no information available that will support the design and development of appropriate storage and preservation systems. In this context, the present study was initiated to improve scientific knowledge about sweet potato post-harvest handling. Additionally, the study also seeks to develop a PV ventilated mud storehouse for storage of sweet potato roots under tropical conditions. In study one, airflow resistance of sweet potato aerial vine components was investigated. The influence of different operating parameters such as airflow rate, moisture content and bulk depth at different levels on airflow resistance was analyzed. All the operating parameters were observed to have significant (P < 0.01) effect on airflow resistance. Prediction models were developed and were found to adequately describe the experimental pressure drop data. In study two, the resistance of airflow through unwashed and clean sweet potato roots was investigated. The effect of sweet potato roots shape factor, surface roughness, orientation to airflow, and presence of soil fraction on airflow resistance was also assessed. The pressure drop through unwashed and clean sweet potato roots was observed to increase with higher airflow, bed depth, root grade composition, and presence of soil fraction. The physical properties of the roots were incorporated into a modified Ergun model and compared with a modified Shedd’s model. The modified Ergun model provided the best fit to the experimental data when compared with the modified Shedd’s model. In study three, the effect of sweet potato root size (medium and large), different air velocity and temperature on the cooling/or heating rate and time of individual sweet potato roots were investigated. Also, a simulation model which is based on the fundamental solution of the transient equations was proposed for estimating the cooling and heating time at the centre of sweet potato roots. The results showed that increasing air velocity during cooling and heating significantly (P < 0.05) affects the cooling and heating times. Furthermore, the cooling and heating times were significantly different (P < 0.05) among medium and large size sweet potato roots. Comparison of the simulation results with experimental data confirmed that the transient simulation model can be used to accurately estimate the cooling and heating times of whole sweet potato roots under forced convection conditions. In study four, the performance of charcoal evaporative cooling pad configurations for integration into sweet potato roots storage systems was investigated. The experiments were carried out at different levels of air velocity, water flow rates, and three pad configurations: single layer pad (SLP), double layers pad (DLP) and triple layers pad (TLP) made out of small and large size charcoal particles. The results showed that higher air velocity has tremendous effect on pressure drop. Increasing the water flow rate above the range tested had no practical benefits in terms of cooling. It was observed that DLP and TLD configurations with larger wet surface area for both types of pads provided high cooling efficiencies. In study five, CFD technique in the ANSYS Fluent software was used to simulate airflow distribution in a low-cost mud storehouse. By theoretically investigating different geometries of air inlet, plenum chamber, and outlet as well as its placement using ANSYS Fluent software, an acceptable geometry with uniform air distribution was selected and constructed. Experimental measurements validated the selected design. In study six, the performance of the developed PV ventilated system was investigated. Field measurements showed satisfactory results of the directly coupled PV ventilated system. Furthermore, the option of integrating a low-cost evaporative cooling system into the mud storage structure was also investigated. The results showed a reduction of ambient temperature inside the mud storehouse while relative humidity was enhanced. The ability of the developed storage system to provide and maintain airflow, temperature and relative humidity which are the key parameters for shelf-life extension of sweet potato roots highlight its ability to reduce post-harvest losses at the farmer level, particularly under tropical climate conditions.

The Notion of the “Concept Instance”: Problems in Modeling Concept Change in SKOS

The U.S. National Science Foundation metadata registry under development for the National Science Digital Library (NSDL) is a repertory intended to manage both metadata schemes and schemas. The focus of this draft discussion paper is on the scheme side of the development work. In particular, the concern of the discussion paper is with issues around the creation of historical snapshots of concept changes and their encoding in SKOS. Through framing the problem as we see it, we hope to find an optimal solution to our need for a SKOS encoding of these snapshots. Since what we are seeking to model is concept change, it is necessary at the outset to make it clear that we are not talking about changes to a concept of such a nature that would require the declaration a new concept with its own URI.In the project, we avoid the use of the terms “version” and “versioning” with regard to changes in concepts and reserve their use to the significant changes of schemes as a whole. Significant changes triggering a new scheme version might include changes in scheme documentation that express a significant shift in the purpose, use or architecture of the scheme. We use the term “snapshot” to denote the state of a scheme at identifiable points in time. Thus, snapshots are identifiable views of a scheme that record the incremental changes that have occurred to concepts, relationships among concepts, and scheme documentation since the last snapshot. Aspects of concept change occur that we need to capture and make available both through the registry and through potentially in transmission of a scheme to other registries. We call these capturings “concept instances.”

Extending the Simple Knowledge Organization System for Concept Management in Vocabulary Development Applications

In this article, we describe the development of an exten- sion to the Simple Knowledge Organization System (SKOS) to accommodate the needs of vocabulary devel- opment applications (VDA) managing metadata schemes and requiring close tracking of change to both those schemes and their member concepts. We take a neo- pragmatic epistemic stance in asserting the need for an entity in SKOS modeling to mediate between the abstract concept and the concrete scheme. While the SKOS model sufficiently describes entities for modeling the current state of a scheme in support of indexing and search on the Semantic Web, it lacks the expressive power to serve the needs of VDA needing to maintain scheme historical continuity. We demonstrate prelimi- narily that conceptualizations drawn from empirical work in modeling entities in the bibliographic universe, such as works, texts, and exemplars, can provide the basis for SKOS extension in ways that support more rig- orous demands of capturing concept evolution in VDA.

Integrated soil fertility management in eastern and western Africa: The role of knowledge and innovation systems, its adoption and impact

The rise in population growth, as well as nutrient mining, has contributed to low agricultural productivity in Sub-Saharan Africa (SSA). A plethora of technologies to boost agricultural production have been developed but the dissemination of these agricultural innovations and subsequent uptake by smallholder farmers has remained a challenge. Scientists and philanthropists have adopted the Integrated Soil Fertility Management (ISFM) paradigm as a means to promote sustainable intensification of African farming systems. This comparative study aimed: 1) To assess the efficacy of Agricultural Knowledge and Innovation Systems (AKIS) in East (Kenya) and West (Ghana) Africa in the communication and dissemination of ISFM (Study I); 2) To investigate how specifically soil quality, and more broadly socio-economic status and institutional factors, influence farmer adoption of ISFM (Study II); and 3) To assess the effect of ISFM on maize yield and total household income of smallholder farmers (Study III). To address these aims, a mixed methodology approach was employed for study I. AKIS actors were subjected to social network analysis methods and in-depth interviews. Structured questionnaires were administered to 285 farming households in Tamale and 300 households in Kakamega selected using a stratified random sampling approach. There was a positive relationship between complete ISFM awareness among farmers and weak knowledge ties to both formal and informal actors at both research locations. The Kakamega AKIS revealed a relationship between complete ISFM awareness among farmers and them having strong knowledge ties to formal actors implying that further integration of formal actors with farmers’ local knowledge is crucial for the agricultural development progress. The structured questionnaire was also utilized to answer the query pertaining to study II. Soil samples (0-20 cm depth) were drawn from 322 (Tamale, Ghana) and 459 (Kakamega, Kenya) maize plots and analysed non-destructively for various soil fertility indicators. Ordinal regression modeling was applied to assess the cumulative adoption of ISFM. According to model estimates, soil carbon seemed to preclude farmers from intensifying input use in Tamale, whereas in Kakamega it spurred complete adoption. This varied response by farmers to soil quality conditions is multifaceted. From the Tamale perspective, it is consistent with farmers’ tendency to judiciously allocate scarce resources. Viewed from the Kakamega perspective, it points to a need for farmers here to intensify agricultural production in order to foster food security. In Kakamega, farmers with more acidic soils were more likely to adopt ISFM. Other household and farm-level factors necessary for ISFM adoption included off-farm income, livestock ownership, farmer associations, and market inter-linkages. Finally, in study III a counterfactual model was used to calculate the difference in outcomes (yield and household income) of the treatment (ISFM adoption) in order to estimate causal effects of ISFM adoption. Adoption of ISFM contributed to a yield increase of 16% in both Tamale and Kakamega. The innovation affected total household income only in Tamale, where ISFM adopters had an income gain of 20%. This may be attributable to the different policy contexts under which the two sets of farmers operate. The main recommendations underscored the need to: (1) improve the functioning of AKIS, (2) enhance farmer access to hybrid maize seed and credit, (3) and conduct additional multi-locational studies as farmers operate under varying contexts.

Strategic Orientations, Dynamic Capabilities, and Firm Performance: an Analysis for Knowledge Intensive Business Services

The strategic orientations of a firm are considered crucial for enhancing firm performance and their impact can be even greater when associated with dynamic capabilities, particularly in complex and dynamic environments. This study empirically analyzes the relationship between market, entrepreneurial and learning orientations, dynamic capabilities, and performance using an integrative approach hitherto little explored. Using a sample of 209 knowledge intensive business service firms, this paper applies structural equation modeling to explore both direct effects of strategic orientations and the mediating role of dynamic capabilities on performance. The study demonstrates that learning orientation and one of the dimensions of entrepreneurial orientation have a direct positive effect on performance. On the other hand, dynamic capabilities mediate the relationships between some of the strategic orientations and firm performance. Overall, when dynamic capabilities are combined with the appropriate strategic orientations, they enhance firm performance. This paper contributes to a better understanding of the knowledge economy, given the important role knowledge intensive business services play in such a dynamic and pivotal sector.

3D Surveying and Data Management towards the Realization of a Knowledge System for Cultural Heritage

The research activities involved the application of the Geomatic techniques in the Cultural Heritage field, following the development of two themes: Firstly, the application of high precision surveying techniques for the restoration and interpretation of relevant monuments and archaeological finds. The main case regards the activities for the generation of a high-fidelity 3D model of the Fountain of Neptune in Bologna. In this work, aimed to the restoration of the manufacture, both the geometrical and radiometrical aspects were crucial. The final product was the base of a 3D information system representing a shared tool where the different figures involved in the restoration activities shared their contribution in a multidisciplinary approach. Secondly, the arrangement of 3D databases for a Building Information Modeling (BIM) approach, in a process which involves the generation and management of digital representations of physical and functional characteristics of historical buildings, towards a so-called Historical Building Information Model (HBIM). A first application was conducted for the San Michele in Acerboli’s church in Santarcangelo di Romagna. The survey was performed by the integration of the classical and modern Geomatic techniques and the point cloud representing the church was used for the development of a HBIM model, where the relevant information connected to the building could be stored and georeferenced. A second application regards the domus of Obellio Firmo in Pompeii, surveyed by the integration of the classical and modern Geomatic techniques. An historical analysis permitted the definitions of phases and the organization of a database of materials and constructive elements. The goal is the obtaining of a federate model able to manage the different aspects: documental, analytic and reconstructive ones.

Molecular simulations and modeling of pharmaceutically relevant RNA-processing enzymes

The two-metal-ion architecture is a structural feature found in a variety of RNA processing metalloenzymes or ribozymes (RNA-based enzymes), which control the biogenesis and the metabolism of vital RNAs, including non-coding RNAs (ncRNAs). Notably, such ncRNAs are emerging as key players for the regulation of cellular homeostasis, and their altered expression has been often linked to the development of severe human pathologies, from cancer to mental disorders. Accordingly, understanding the biological processing of ncRNAs is foundational for the development of novel therapeutic strategies and tools. Here, we use state-of the-art molecular simulations, complemented with X-ray crystallography and biochemical experiments, to characterize the RNA processing cycle as catalyzed by two two-metal-ion enzymes: the group II intron ribozymes and the RNase H1. We show that multiple and diverse cations are strategically recruited at and timely released from the enzymes’ active site during catalysis. Such a controlled cations’ trafficking leads to the recursive formation and disruption of an extended two-metal ion architecture that is functional for RNA-hydrolysis – from substrate recruitment to product release. Importantly, we found that these cations’ binding sites are conserved among other RNA-processing machineries, including the human spliceosome and CRISPR-Cas systems, suggesting that an evolutionarily-converged catalytic strategy is adopted by these enzymes to process RNA molecules. Thus, our findings corroborate and sensibly extend the current knowledge of two-metal-ion enzymes, and support the design of novel drugs targeting RNA-processing metalloenzymes or ribozymes as well as the rational engineering of novel programmable gene-therapy tools.

Big data analytics and modeling for Human Mobility

The fast development of Information Communication Technologies (ICT) offers new opportunities to realize future smart cities. To understand, manage and forecast the city's behavior, it is necessary the analysis of different kinds of data from the most varied dataset acquisition systems. The aim of this research activity in the framework of Data Science and Complex Systems Physics is to provide stakeholders with new knowledge tools to improve the sustainability of mobility demand in future cities. Under this perspective, the governance of mobility demand generated by large tourist flows is becoming a vital issue for the quality of life in Italian cities' historical centers, which will worsen in the next future due to the continuous globalization process. Another critical theme is sustainable mobility, which aims to reduce private transportation means in the cities and improve multimodal mobility. We analyze the statistical properties of urban mobility of Venice, Rimini, and Bologna by using different datasets provided by companies and local authorities. We develop algorithms and tools for cartography extraction, trips reconstruction, multimodality classification, and mobility simulation. We show the existence of characteristic mobility paths and statistical properties depending on transport means and user's kinds. Finally, we use our results to model and simulate the overall behavior of the cars moving in the Emilia Romagna Region and the pedestrians moving in Venice with software able to replicate in silico the demand for mobility and its dynamic.

Innovation in aquaculture. Modeling the adoption and implementation processes of innovations in the Italian aquaculture sector

Since the turn of the century, fisheries have maintained a steady growth rate, while aquaculture has experienced a more rapid expansion. Aquaculture can offer EU consumers more diverse, healthy, and sustainable food options, some of which are more popular elsewhere. To develop the sector, the EU is investing heavily. The EU supports innovative projects that promote the sustainable development of seafood sectors and food security. Priority 3 promotes sector development through innovation dissemination. This doctoral dissertation examined innovation transfer in the Italian aquaculture sector, specifically the adoption of innovative tools, using a theoretical model to better understand the complexity of these processes. The work focused on innovation adoption, emphasising that it is the end of a well-defined process. The Awareness Knowledge Adoption Implementation Effectiveness (AKAIE) model was created to better analyse post-adoption phases and evaluate technology adoption implementation and impact. To identify AKAIE drivers and barriers, aquaculture actors were consulted. "Perceived complexity"—barriers to adoption that are strongly influenced by contextual factors—has been used to examine their perspectives (i.e. socio-economic, institutional, cultural ones). The new model will contextualise the sequence based on technologies, entrepreneur traits, corporate and institutional contexts, and complexity perception, the sequence's central node. Technology adoption can also be studied by examining complexity perceptions along the AKAIE sequence. This study proposes a new model to evaluate the diffusion of a given technology, offering the policy maker the possibility to be able to act promptly across the process. The development of responsible policies for evaluating the effectiveness of innovation is more necessary than ever, especially to orient strategies and interventions in the face of major scenarios of change.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.

Kinetic modeling for the synthesis of ethyl levulinate

This work is focused on studying the kinetics of esterification of levulinic acid in an isothermal batch reactor using ethanol as a reactant and as a protic polar solvent at the same time and in the presence of an acid catalyst (sulfuric acid). The choice of solvent is important as it affects the kinetics and thermodynamics of the reaction system moreover, the knowledge of the reaction kinetics plays an important role in the design of the process. This work is divided into two stages; The first stage is the experimental part in which the experimental matrix was developed by changing the process variables one at a time (temperature, molar ratio between reactants, and catalyst concentration) in order to study their influence on the kinetics; the second stage is using the obtained data from the experiments to build the modeling part in order to estimate the thermodynamics parameters.