A review of predictive factors of student success in and satisfaction with online learning

Students perceive online courses differently than traditional courses. Negative perceptions can lead to unfavourable learning outcomes including decreased motivation and persistence. Throughout this review, a broad range of factors that affect performance and satisfaction within the online learning environment for adult learners will be examined including learning outcomes, instructional design and learner characteristics, followed by suggestions for further research, and concluding with implications for online learning pertinent to administrators, instructors, course designers and students. Online learning may not be appropriate for every student. Identifying particular characteristics that contribute to online success versus failure may aid in predicting possible learning outcomes and save students from enrolling in online courses if this type of learning environment is not appropriate for them. Furthermore, knowing these learner attributes may assist faculty in designing quality online courses to meet students’ needs. Adequate instructional methods, support, course structure and design can facilitate student performance and satisfaction.

Predictive And Prognostic Role Of Biological Markers In Neuroendocrine Neoplasia And Evaluation Of Activity And Safety Of Second Line Treatments In Neuroendocrine Carcinoma Patients

BACKGROUND Neuroendocrine neoplasia (NEN) are divided in well differentiated G1,G2 and G3 neuroendocrine tumors (NETs) and G3 neuroendocrine carcinomas (NECs). For the latter no standard therapy in second-line is available and prognosis is poor. METHODS Primary aim was to evaluate new prognostic and predictive biomarkers (WP1-3). In WP4 we explored the activity of FOLFIRI and CAPTEM as second-line in NEC patients in a multicenter non-comparative phase II trial RESULTS In WP1-2 we found that 4 of 6 GEP-NEC patients with a negative 68Ga-PET/CT had a loss of expression of RB1. In WP3 on 47 GEP-NENs patients the presence of DLL3 in 76.9% of G3 NEC correlate with RB1-loss (p<0.001), negative 68Ga-PET/CT(p=0.001) and a poor prognosis. In the WP4 we conducted a multicenter non-comparative phase II trial to explore the activity of FOLFIRI or CAPTEM in terms of DCR, PFS and OS given as second-line in NEC patients. From 06/03/2017 to 18/01/2021 53 out of 112 patients were enrolled in 17 of 23 participating centers. Median follow-up was 10.8 (range 1.4 – 38.6) months. The 3-month DCR was 39.3% in the FOLFIRI and 32.0 % in the CAPTEM arm. The 6-months PFS rate was 34.6% ( 95%CI 17.5-52.5) in FOLFIRI and 9.6% (95%CI 1.8-25.7) in CAPTEM group. In the FOLFIRI subgroup the 6-months and 12-months OS rate were 55.4% (95%CI 32.6-73.3) and 30.3% (CI 11.1-52.2) respectively. In CAPTEM arm the 6-months and 12-months OS rate were 57.2% (95%34.9-74.3) and 29.0% (95%10.0-43.3). The miRNA analysis of 20 patients compared with 20 healthy subjects shows an overexpression of miRNAs involved in staminality , neo-angiogenesis and mitochontrial anaerobic glycolysis activation. CONCLUSION WP1-3 support the hypothesis that G3NECs carrying RB1 loss is associated with a DLL3 expression highlighting a potential therapeutic opportunity. Our study unfortunately didn’t met the primary end–point but the results are promising

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

The mesophotic zone of the Mediterranean Sea: spatial definition, biodiversity, and predictive models

The mesophotic zone is frequently defined as ranging between 30-40 and 150 m depth. However, these borders are necessarily imprecise due to variations in the penetration of light along the water column related to local factors. Moreover, density of data on mesophotic ecosystems vary along geographical distance, with temperate latitudes largely less explored than tropical situations. This is the case of the Mediterranean Sea, where information on mesophotic ecosystems is largely lower with respect to tropical situations. The lack of a clear definition of the borders of the mesophotic zone may represent a problem when information must be transferred to the policy that requires a coherent spatial definition to plan proper management and conservation measures. The present thesis aims at providing information on the spatial definition of the mesophotic zone in the Mediterranean Sea, its biodiversity and distribution of its ecosystems. The first chapter analyzes information on mesophotic ecosystems in the Mediterranean Sea to identify gaps in the literature and map the mesophotic zone in the Mediterranean Sea using light penetration estimated from satellite data. In the second chapter, different visual techniques to study mesophotic ecosystems are compared to identify the best analytical method to estimate diversity and habitat extension. In the third chapter, a set of Remotely Operated vehicles (ROV) surveys performed on mesophotic assemblages in the Mediterranean Sea are analyzed to describe their taxonomic and functional diversity and environmental factors influencing their structure. A Habitat Suitability Model is run in the fourth chapter to map the distribution of areas suitable for the presence of deep-water oyster reefs in the Adriatic-Ionian area. The fifth chapter explores the mesophotic zone in the northern Gulf of Mexico providing its spatial and vertical extension of the mesophotic zone and information on the diversity associated with mesophotic ecosystems.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.

Research of predictive biomarkers to anti-CD22 antibody-drug conjugate treatment in B-cell acute lymphoblastic leukemia

Background: The treatment of B-cell acute lymphoblastic leukemia (B-ALL) has been enriched by novel agents targeting surface markers CD19 and CD22. Inotuzumab ozogamicin (INO) is a CD22-calicheamicin conjugated monoclonal antibody approved in the setting of relapse/refractory (R/R) B-ALL able to induce a high rate of deep responses, not durable over time. Aims: This study aims to identify predictive biomarkers to INO treatment in B- ALL by flow cytometric analysis of CD22 expression and gene expression profile. Materials and methods: Firstly, the impact on patient outcome in 30 R/R B-ALL patients of baseline CD22 expression in terms of CD22 blast percentage and CD22 fluorescent intensity (CD22-FI) was explored. Secondly, baseline gene expression profile of 18 R/R B-ALL patient samples was analyzed. For statistical analysis of differentially expressed genes (DEGs) patients were divided in non-responders (NR), defined as either INO-refractory or with duration of response (DoR) < 3 months, and responders (R). Gene expression results were analyzed with Ingenuity pathway analysis (IPA). Results: In our patient set higher CD22-FI, defined as higher quartiles (Q2-Q4), correlated with better patient outcome in terms of CR rate, OS and DoR, compared to lower CD22-FI (Q1). CD22 blast percentage was less able to discriminate patients’ outcome, although a trend for better outcome in patients with CD22 ≥ 90% could be appreciated. Concerning gene expression profile, 32 genes with corrected p value <0.05 and absolute FC ≥2 were differentially expressed in NR as compared to R. IPA upstream regulator and regulator effect analysis individuated the inhibition of tumor suppressor HIPK2 as causal upstream condition of the downregulation of 6 DEGs. Conclusions: CD22-FI integrates CD22-percentage on leukemic blasts for a more comprehensive target pre-treatment evaluation. Moreover, a unique pattern of gene expression signature based on HIPK2 downregulation was identified, providing important insights in mechanisms of resistance to INO.

Tumor burden as assessed by 18 F FDG PET scan as predictive biomarker for immune checkpoint blockers in advanced NSCLC - a multi centric study

Introduction Only a proportion of patients with advanced NSCLC benefit from Immune checkpoint blockers (ICBs). No biomarker is validated to choose between ICBs monotherapy or in combination with chemotherapy (Chemo-ICB) when PD-L1 expression is above 50%. The aim of the present study is to validate the biomarker validity of total Metabolic Tumor Volume (tMTV) as assessed by 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography ([18F]FDG-PET) Material and methods This is a multicentric retrospective study. Patients with advanced NSCLC treated with ICBs, chemotherapy plus ICBs and chemotherapy were enrolled in 12 institutions from 4 countries. Inclusion criteria was a positive PET scan performed within 42 days from treatment start. TMTV was analyzed at each center based on a 42% SUVmax threshold. High tMTV was defined ad tMTV>median Results 493 patients were included, 163 treated with ICBs alone, 236 with chemo-ICBs and 94 with CT. No correlation was found between PD-L1 expression and tMTV. Median PFS for patients with high tMTV (100.1 cm3) was 3.26 months (95% CI 1.94–6.38) vs 14.70 (95% CI 11.51–22.59) for those with low tMTV (p=0.0005). Similarly median OS for pts with high tMTV was 11.4 months (95% CI 8.42 – 19.1) vs 33.1 months for those with low tMTV (95% CI 22.59 – NA), p .00067. In chemo-ICBs treated patients no correlation was found for OS (p = 0.11) and a borderline correlation was found for PFS (p=0.059). Patients with high tMTV and PD-L1 ≥ 50% had a better PFS when treated with combination of chemotherapy and ICBs respect to ICBs alone, with 3.26 months (95% CI 1.94 – 5.79) for ICBs vs 11.94 (95% CI 5.75 – NA) for Chemo ICBs (p = 0.043). Conclusion tMTV is predictive of ICBs benefit, not to CT benefit. tMTV can help to select the best upfront strategy in patients with high tMTV.

Analysis of extracellular vesicles from patients with advanced pancreatic cancer identifies miRNAs with predictive value for treatment with gemcitabine + nab-paclitaxel

Pancreatic cancer (PC) is the seventh leading cause of cancer death. Despite recent therapy advancements, 5-year survival is 11%. Resistance to therapy is common, and no predictive factors, except for BRCA1/2 and PALB2 mutations, can drive treatment selection. Based on the easy isolation of extracellular vesicles (EVs) from blood and the role of EV-borne miRNAs in chemoresistance, we analyzed EVs and their miRNA content in order to identify predictive factors. First, we analyzed samples from 28 PC patients and 7 healthy subjects, in order to establish methods for isolation and analysis of EVs and their miRNA content. We observed a significantly different expression of 28 miRNAs, including oncogenic or tumor suppressor miRNAs, showing the ability of our approach to detect candidate biomarkers. Then, we analyzed samples of 21 advanced PC patients, collected before first-line treatment with gemcitabine + nab-paclitaxel, and compared findings in responders and non-responders. EVs have been analyzed with Nanoparticle tracking analysis, flow cytometry and RNA-Seq; then, laboratory results have been matched with clinical data. Nanoparticle tracking analysis did not show any significant difference. Flow cytometry showed a lower expression of SSE4 and CD81 in responders. Finally, miRNA analysis showed 25 upregulated and 19 downregulated miRNAs in responders. In particular, in responders we observed upregulation of miR-141-3p, miR-141-5p, miR-200a-3p, miR-200b-3p, miR-200c-3p, miR-375-3p, miR-429, miR-545-5p. These miRNAs have targets with a previously reported role in PC. In conclusion, we show the feasibility of the proposed approach to identify EV-derived biomarkers with predictive value for therapy with gemcitabine + nab-paclitaxel in PC. Our findings highlight the possibility to exploit liquid biopsy for personalized treatment in PC, in order to maximize chances of response and patients’ outcome. These findings are worthy of further investigation: in the same setting, with different chemotherapy schedules, and in different disease settings such as preoperative therapy.

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.

Enhancing predictive capability of models for solubility and permeability in polymers and composites

The interpretation of phase equilibrium and mass transport phenomena in gas/solvent - polymer system at molten or glassy state is relevant in many industrial applications. Among tools available for the prediction of thermodynamics properties in these systems, at molten/rubbery state, is the group contribution lattice-fluid equation of state (GCLF-EoS), developed by Lee and Danner and ultimately based on Panayiotou and Vera LF theory. On the other side, a thermodynamic approach namely non-equilibrium lattice-fluid (NELF) was proposed by Doghieri and Sarti to consistently extend the description of thermodynamic properties of solute polymer systems obtained through a suitable equilibrium model to the case of non-equilibrium conditions below the glass transition temperature. The first objective of this work is to investigate the phase behaviour in solvent/polymer at glassy state by using NELF model and to develop a predictive tool for gas or vapor solubility that could be applied in several different applications: membrane gas separation, barrier materials for food packaging, polymer-based gas sensors and drug delivery devices. Within the efforts to develop a predictive tool of this kind, a revision of the group contribution method developed by High and Danner for the application of LF model by Panayiotou and Vera is considered, with reference to possible alternatives for the mixing rule for characteristic interaction energy between segments. The work also devotes efforts to the analysis of gas permeability in polymer composite materials as formed by a polymer matrix in which domains are dispersed of a second phase and attention is focused on relation for deviation from Maxwell law as function of arrangement, shape of dispersed domains and loading.

The predictive role of the human gut microbiota in covid-19: implications for disease severity and post-covid syndrome

The COVID-19 pandemic, sparked by the SARS-CoV-2 virus, stirred global comparisons to historical pandemics. Initially presenting a high mortality rate, it later stabilized globally at around 0.5-3%. Patients manifest a spectrum of symptoms, necessitating efficient triaging for appropriate treatment strategies, ranging from symptomatic relief to antivirals or monoclonal antibodies. Beyond traditional approaches, emerging research suggests a potential link between COVID-19 severity and alterations in gut microbiota composition, impacting inflammatory responses. However, most studies focus on severe hospitalized cases without standardized criteria for severity. Addressing this gap, the first study in this thesis spans diverse COVID-19 severity levels, utilizing 16S rRNA amplicon sequencing on fecal samples from 315 subjects. The findings highlight significant microbiota differences correlated with severity. Machine learning classifiers, including a multi-layer convoluted neural network, demonstrated the potential of microbiota compositional data to predict patient severity, achieving an 84.2% mean balanced accuracy starting one week post-symptom onset. These preliminary results underscore the gut microbiota's potential as a biomarker in clinical decision-making for COVID-19. The second study delves into mild COVID-19 cases, exploring their implications for ‘long COVID’ or Post-Acute COVID-19 Syndrome (PACS). Employing longitudinal analysis, the study unveils dynamic shifts in microbial composition during the acute phase, akin to severe cases. Innovative techniques, including network approaches and spline-based longitudinal analysis, were deployed to assess microbiota dynamics and potential associations with PACS. The research suggests that even in mild cases, similar mechanisms to hospitalized patients are established regarding changes in intestinal microbiota during the acute phase of the infection. These findings lay the foundation for potential microbiota-targeted therapies to mitigate inflammation, potentially preventing long COVID symptoms in the broader population. In essence, these studies offer valuable insights into the intricate relationships between COVID-19 severity, gut microbiota, and the potential for innovative clinical applications.

Identification of outcome-oriented cut-offs for copy number alterations in multiple myeloma: predictive biomarkers with improved prognostic accuracy

Aim of the present study was to develop a statistical approach to define the best cut-off Copy number alterations (CNAs) calling from genomic data provided by high throughput experiments, able to predict a specific clinical end-point (early relapse, 18 months) in the context of Multiple Myeloma (MM). 743 newly diagnosed MM patients with SNPs array-derived genomic and clinical data were included in the study. CNAs were called both by a conventional (classic, CL) and an outcome-oriented (OO) method, and Progression Free Survival (PFS) hazard ratios of CNAs called by the two approaches were compared. The OO approach successfully identified patients at higher risk of relapse and the univariate survival analysis showed stronger prognostic effects for OO-defined high-risk alterations, as compared to that defined by CL approach, statistically significant for 12 CNAs. Overall, 155/743 patients relapsed within 18 months from the therapy start. A small number of OO-defined CNAs were significantly recurrent in early-relapsed patients (ER-CNAs) - amp1q, amp2p, del2p, del12p, del17p, del19p -. Two groups of patients were identified either carrying or not ≥1 ER-CNAs (249 vs. 494, respectively), the first one with significantly shorter PFS and overall survivals (OS) (PFS HR 2.15, p<0001; OS HR 2.37, p<0.0001). The risk of relapse defined by the presence of ≥1 ER-CNAs was independent from those conferred both by R-IIS 3 (HR=1.51; p=0.01) and by low quality (< stable disease) clinical response (HR=2.59 p=0.004). Notably, the type of induction therapy was not descriptive, suggesting that ER is strongly related to patients’ baseline genomic architecture. In conclusion, the OO- approach employed allowed to define CNAs-specific dynamic clonality cut-offs, improving the CNAs calls’ accuracy to identify MM patients with the highest probability to ER. As being outcome-dependent, the OO-approach is dynamic and might be adjusted according to the selected outcome variable of interest.

Modeling and Model Predictive Control of an Industrial Hydraulic System

This thesis aims to illustrate the construction of a mathematical model of a hydraulic system, oriented to the design of a model predictive control (MPC) algorithm. The modeling procedure starts with the basic formulation of a piston-servovalve system. The latter is a complex non linear system with some unknown and not measurable effects that constitute a challenging problem for the modeling procedure. The first level of approximation for system parameters is obtained basing on datasheet informations, provided workbench tests and other data from the company. Then, to validate and refine the model, open-loop simulations have been made for data matching with the characteristics obtained from real acquisitions. The final developed set of ODEs captures all the main peculiarities of the system despite some characteristics due to highly varying and unknown hydraulic effects, like the unmodeled resistive elements of the pipes. After an accurate analysis, since the model presents many internal complexities, a simplified version is presented. The latter is used to linearize and discretize correctly the non linear model. Basing on that, a MPC algorithm for reference tracking with linear constraints is implemented. The results obtained show the potential of MPC in this kind of industrial applications, thus a high quality tracking performances while satisfying state and input constraints. The increased robustness and flexibility are evident with respect to the standard control techniques, such as PID controllers, adopted for these systems. The simulations for model validation and the controlled system have been carried out in a Python code environment.

Acquisition of Vibration Signals for Predictive Maintenance: a Practical Case

As predictive maintenance becomes more and more relevant in industrial environment, the possible range of applications for this maintenance strategy grows. The progresses in components technology and their reduction in price, together with the late years' advances in machine learning and in computational power, are making the implementation of predictive maintenance possible in plants where it would have previously been unreasonably costly. This is leading major pharmaceutical industries to explore the possibility of the application of condition monitoring systems on progressively less and less critical equipment. The focus of this thesis is on the implementation of a system to gather vibrational data from the motors installed in a pre-existing machine using off-the-shelf components. The final goal for the system is to provide the necessary vibration data, in the form of frequency spectra, to a machine learning system developed by IMA Digital, which will be leveraging such data to predict possible upcoming faults and to give the final client all the information necessary to plan maintenance activity according to the estimated machine condition.

Egfr Activating Mutations And Their Association With Response To Platinum-doublet Chemotherapy In Brazilian Non-small Cell Lung Cancer Patients.

The aim of the study was to analyze the frequency of epidermal growth factor receptor (EGFR) mutations in Brazilian non-small cell lung cancer patients and to correlate these mutations with response to benefit of platinum-based chemotherapy in non-small cell lung cancer (NSCLC). Our cohort consisted of prospective patients with NSCLCs who received chemotherapy (platinum derivates plus paclitaxel) at the [UNICAMP], Brazil. EGFR exons 18-21 were analyzed in tumor-derived DNA. Fifty patients were included in the study (25 with adenocarcinoma). EGFR mutations were identified in 6/50 (12 %) NSCLCs and in 6/25 (24 %) adenocarcinomas; representing the frequency of EGFR mutations in a mostly self-reported White (82.0 %) southeastern Brazilian population of NSCLCs. Patients with NSCLCs harboring EGFR exon 19 deletions or the exon 21 L858R mutation were found to have a higher chance of response to platinum-paclitaxel (OR 9.67 [95 % CI 1.03-90.41], p = 0.047). We report the frequency of EGFR activating mutations in a typical southeastern Brazilian population with NSCLC, which are similar to that of other countries with Western European ethnicity. EGFR mutations seem to be predictive of a response to platinum-paclitaxel, and additional studies are needed to confirm or refute this relationship.