DIVERSITY AND HABITAT PROFILE OF THE SHALLOW WATER HOLOTHURIANS IN CAMOTES ISLANDS, CENTRAL PHILIPPINES

Species diversity and habitat profile of holothurians in Camotes Islands, Cebu Philippines were studied as baseline data for resource and ecological management. A 150 meter transect was laid in sandy, muddy and rocky substrates of the coastal barangays during the day and night assessments of the four municipalities of Camotes Islands which are San Francisco, Poro, Tudela and Pilar. Physico-chemical instruments and interview guide to the gleaners were used to gather data. Actual collection of specimen and other data was done in every 10 meter distance in the transect where a 1m quadrat was used. Results show that there are 20 species of holothurians belonging to 3 families namely Holothuriidae, Stichopodidae and Synaptidae. There are 2 species found in sandy substrate; 10 in muddy substrate; 2 in pure rocky substrate and 6 found in both rocky and muddy substrates. Results further show that there are 13 common species of Holothurians found in the four municipalities. The most diverse municipality is San Francisco which has 18 species followed by Poro (15); Pilar (14) and Tudela (13). For the distinct species, Holothuria rigida is found only in San Francisco followed by Pearsonothuria graffei (in Tudela and Pilar); Physico-chemical parameters like salinity, range from 23-38 ppt; temperature is 260C-350C, pH 4.5-8.0 both day and night assessments. Substrate grain size analysis shows that 100 microns dominate the amount of substrates in all the barangays, followed by 200 microns and then 400 microns. Keywords: Diversity, Habitat Profile, Holothurians, Camotes Island

The Pan American (1989-05)

https://scholarworks.utrgv.edu/panamerican/1515/thumbnail.jp

The Pan American (1989-02)

https://scholarworks.utrgv.edu/panamerican/1512/thumbnail.jp

The Pan American (1989-04)

https://scholarworks.utrgv.edu/panamerican/1514/thumbnail.jp

Generating Privacy-Compliant, Utility-Preserving Synthetic Tabular and Relational Datasets Through Deep Learning

Due tendenze hanno rapidamente ridefinito il panorama dell'intelligenza artificiale (IA) negli ultimi decenni. La prima è il rapido sviluppo tecnologico che rende possibile un'intelligenza artificiale sempre più sofisticata. Dal punto di vista dell'hardware, ciò include una maggiore potenza di calcolo ed una sempre crescente efficienza di archiviazione dei dati. Da un punto di vista concettuale e algoritmico, campi come l'apprendimento automatico hanno subito un'impennata e le sinergie tra l'IA e le altre discipline hanno portato a sviluppi considerevoli. La seconda tendenza è la crescente consapevolezza della società nei confronti dell'IA. Mentre le istituzioni sono sempre più consapevoli di dover adottare la tecnologia dell'IA per rimanere competitive, questioni come la privacy dei dati e la possibilità di spiegare il funzionamento dei modelli di apprendimento automatico sono diventate parte del dibattito pubblico. L'insieme di questi sviluppi genera però una sfida: l'IA può migliorare tutti gli aspetti della nostra vita, dall'assistenza sanitaria alla politica ambientale, fino alle opportunità commerciali, ma poterla sfruttare adeguatamente richiede l'uso di dati sensibili. Purtroppo, le tecniche di anonimizzazione tradizionali non forniscono una soluzione affidabile a suddetta sfida. Non solo non sono sufficienti a proteggere i dati personali, ma ne riducono anche il valore analitico a causa delle inevitabili distorsioni apportate ai dati. Tuttavia, lo studio emergente dei modelli generativi ad apprendimento profondo (MGAP) può costituire un'alternativa più raffinata all'anonimizzazione tradizionale. Originariamente concepiti per l'elaborazione delle immagini, questi modelli catturano le distribuzioni di probabilità sottostanti agli insiemi di dati. Tali distribuzioni possono essere successivamente campionate, fornendo nuovi campioni di dati, non presenti nel set di dati originale. Tuttavia, la distribuzione complessiva degli insiemi di dati sintetici, costituiti da dati campionati in questo modo, è equivalente a quella del set dei dati originali. In questa tesi, verrà analizzato l'uso dei MGAP come tecnologia abilitante per una più ampia adozione dell'IA. A tal scopo, verrà ripercorsa prima di tutto la legislazione sulla privacy dei dati, con particolare attenzione a quella relativa all'Unione Europea. Nel farlo, forniremo anche una panoramica delle tecnologie tradizionali di anonimizzazione dei dati. Successivamente, verrà fornita un'introduzione all'IA e al deep-learning. Per illustrare i meriti di questo campo, vengono discussi due casi di studio: uno relativo alla segmentazione delle immagini ed uno reltivo alla diagnosi del cancro. Si introducono poi i MGAP, con particolare attenzione agli autoencoder variazionali. L'applicazione di questi metodi ai dati tabellari e relazionali costituisce una utile innovazione in questo campo che comporta l’introduzione di tecniche innovative di pre-elaborazione. Infine, verrà valutata la metodologia sviluppata attraverso esperimenti riproducibili, considerando sia l'utilità analitica che il grado di protezione della privacy attraverso metriche statistiche.Two trends have rapidly been redefining the artificial intelligence (AI) landscape over the past several decades. The first of these is the rapid technological developments that make increasingly sophisticated AI feasible. From a hardware point of view, this includes increased computational power and efficient data storage. From a conceptual and algorithmic viewpoint, fields such as machine learning have undergone a surge and synergies between AI and other disciplines have resulted in considerable developments. The second trend is the growing societal awareness around AI. While institutions are becoming increasingly aware that they have to adopt AI technology to stay competitive, issues such as data privacy and explainability have become part of public discourse. Combined, these developments result in a conundrum: AI can improve all aspects of our lives, from healthcare to environmental policy to business opportunities, but invoking it requires the use of sensitive data. Unfortunately, traditional anonymization techniques do not provide a reliable solution to this conundrum. They are insufficient in protecting personal data, but also reduce the analytic value of data through distortion. However, the emerging study of deep-learning generative models (DLGM) may form a more refined alternative to traditional anonymization. Originally conceived for image processing, these models capture probability distributions underlying datasets. Such distributions can subsequently be sampled, giving new data points not present in the original dataset. However, the overall distribution of synthetic datasets, consisting of data sampled in this manner, is equivalent to that of the original dataset. In our research activity, we study the use of DLGM as an enabling technology for wider AI adoption. To do so, we first study legislation around data privacy with an emphasis on the European Union. In doing so, we also provide an outline of traditional data anonymization technology. We then provide an introduction to AI and deep-learning. Two case studies are discussed to illustrate the field’s merits, namely image segmentation and cancer diagnosis. We then introduce DLGM, with an emphasis on variational autoencoders. The application of such methods to tabular and relational data is novel and involves innovative preprocessing techniques. Finally, we assess the developed methodology in reproducible experiments, evaluating both the analytic utility and the degree of privacy protection through statistical metrics

The Pan American (1989-03)

https://scholarworks.utrgv.edu/panamerican/1513/thumbnail.jp

Stochastic and Reactive Methods for the Determination of Optimal Calibration Intervals

The length of calibration intervals of measurement instrumentations can be determined by means of several techniques. In this paper three different methods are compared for the establishment of optimal calibration intervals of atomic clocks. The first one, is based on a stochastic model, and provides the estimation of the calibration interval also in the transient situation, while the others, attain to the class of the so–called reactive methods, which determine the value of the optimal interval on the basis of the last calibration outcomes. Algorithms have been applied to experimental data and obtained results have been compared in order to determine the most effective technique. Since the analyzed reactive methods present a large transient time, a new algorithm is proposed and applied to the available data

The governmentality of corporate (un)sustainability: the case of the ILVA steel plant in Taranto (Italy)

The present research aims to investigate the role of states in governing the sustain- ability trajectories and decisions of companies and their local communities. Draw- ing on Dean’s (Governmentality: power and rule in modern society, SAGE, London, 2009) “analytics of government” as the theoretical framework, the paper focuses on detecting how the Italian Government “problematised” the sustainability-related risks associated with the ILVA steel plant in Taranto, whose levels of pollution have worried both the Italian authorities and the European Union Commission. The anal- ysis also considers the “regimes of governance” under which the risks have been addressed and then explains the “utopian ideal” that the Italian Government tried to achieve by allowing the company to continue its activity, contrary to the Italian Judiciary’s provision to halt the hot working area of the steel plant in July 2012. Patterns related to Dean’s framework were identi ed through an iterative process of manual elaborative coding of the o cial documents ascribable to the main actors involved in governing the sustainability-related risks at ILVA. The ndings show that the Italian Government took its decisions on ILVA in the name of relevant risks of unemployment, economic development and territorial competitiveness. The Ital- ian Government adopted several practices of governance to make these risks more “visible” and to silence the environmental and health risks that, otherwise, would have emphasised the unsustainability of the business activities. The paper extends the growing body of research that investigates corporate (un)sustainability practices by showing how states may directly in uence sustainability-related corporate risks in the name of a higher public interest