196,426 research outputs found
Dataset supporting the University of Southampton Doctoral Thesis "Social trust in property ownership: a study in the kingdom of Saudi Arabia"
No full textDataset supporting the University of Southampton Doctoral Thesis "Social trust in property ownership: a study in the kingdom of Saudi Arabia".
The dataset is a iUML-B based formal model of the process of ownership. It includes a zipped file:
OwnershipModelling.zip
The data is accessible under CC BY license.
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Microencapsulation of Probiotics for Food Functionalization: An Update on Literature Reviews
No full textFunctional foods comprise the largest growing food category due to both consumer demands and health claims by manufacturers. Probiotics are considered one of the best choices for meeting these demands. Traditionally, the food vehicle for introducing probiotics to consumers was dairy products, and to expand the benefits of probiotics for a wider range of consumers, the need to use other food items was essential. To achieve this goal while maximising the benefits of probiotics, protection methods used during food processing were tackled. The microencapsulation of probiotics is a promising methodology for achieving this function. This review highlights the use of the microencapsulation of probiotics in order to functionalise food items that initially were not considered suitable for probiotication, such as baked products, or to increase their functionality such as dairy products. The co-microencapsulation of probiotics with other functional ingredients such polyphenol, prebiotics, or omega-3 is also highlighted
Social trust in property ownership: a study in the kingdom of Saudi Arabia
No full textOwnership refers to the legal rights, obligations, and interests, all of which are formally recognised by a legal system. The regulation of property ownership dictates access to and control over land and other resources. In modern society, property ownership relies heavily on technology for identifying, record-keeping, and verifying property ownership to ensure compliance with the legal system. However, this reliance on technology introduces challenges such as fraud, incorrect information, and potential challenges like double sales as technology continues to advance. Further challenges can be encountered when it is regulated by several governmental entities to secure ownership. This research focuses specifically on the legal system in the Kingdom of Saudi Arabia, acknowledging the global nature of challenges but tailoring the investigation to the unique context of this region. To address the challenges inherent in the land registration system, the research proposes a STF. Unlike existing approaches that primarily lean on technology, this framework delves into the underlying factors contributing to challenges and identifies key considerations for establishing a safe process for transferring ownership. The framework encompasses four categories of social needs for trusting systems: organisation, provenance, technical, and legal. Each category comprises a set of factors that should be considered during the development of the system. Importantly, this framework is designed in alignment with the interests of stakeholders who regularly engage with the systems and is validated by experts in governmental entities. The validation process is based on qualitative data and analyse using thematic analysis. The STF supports the analysis of user requirement specifications to identify the necessary requirements. The user needs are analysed to identify the requirements needed using the scenario-based approach of transferring ownership, with the knowledge that this scenario avoids the challenges outlined. The identified requirements serve as essential inputs for constructing models using the Event-B formal methods, a mathematical approach that ensures the correctness and consistency of the requirements. The formal model is built using a correctness-by-construction methodology, that allows the deduction of assurances for the trustworthy process of transferring ownership. The model undergoes evaluation by formal method specialists to guarantee the construction of an accurate model through the utilisation of discount expert review. Applying the formal method significantly affects system development. It aids in articulating implicit assumptions and clarifying system requirements. They highlight challenges with system requirements, and their rigour helps to understand the challenge better. Additionally, the formal method reduces implementation time and error by a large amount
Limosilactobacillus reuteri in Health and Disease
No full textLimosilactobacillus reuteri is a microorganism with valuable probiotic qualities that has been widely employed in humans to promote health. It is a well-studied probiotic bacterium that exerts beneficial health effects due to several metabolic mechanisms that enhance the production of anti-inflammatory cytochines and modulate the gut microbiota by the production of antimicrobial molecules, including reuterin. This review provides an overview of the data that support the role of probiotic properties, and the antimicrobial and immunomodulatory effects of some L. reuteri strains in relation to their metabolite production profile on the amelioration of many diseases and disorders. Although the results discussed in this paper are strain dependent, they show that L. reuteri, by different mechanisms and various metabolites, may control body weight and obesity, improve insulin sensitivity and glucose homeostasis, increase gut integrity and immunomodulation, and attenuate hepatic disorders. Gut microbiota modulation by ingesting probiotic L. reuteri strains could be a promising preventative and therapeutic approach against many diseases and disorders
Akkermansia muciniphila, a new generation of beneficial microbiota in modulating obesity: A systematic review
No full textObesity is a complex syndrome and is recognized as the ultimate pathway of many chronic diseases. Studies using Akkermansia muciniphila supplementation strategy have proved to be effective for the prevention and treatment of obesity and other metabolic disorders. Although there are studies that support the protective effect of this strategy, the effects on the prevention of obesity on humans are not clear yet and need more investigation. The aim of this study is to investigate the effect of A. muciniphila administration on modulating obesity. This systematic review was generated from articles published within the last 10 years. All articles were in English and included animal subjects. The review relied on the search engines Google Scholar, Pub Med, Web of Science and Medline using the following keywords: A. muciniphila, next-generation probiotic, new-generation probiotic, obesity, fat mass, body fat and lipid profile. The search has revealed 804 articles with relevant key words. After the exclusion of irrelevant articles, 10 studies were selected based on the criteria. These studies were randomized controlled trials that have shown that A. muciniphila modulates obesity by regulating metabolism and energy hemostasis and improving insulin sensitivity and glucose hemostasis. In addition, studies showed this microorganism enhances low grade inflammation by different mechanisms
Local effects of redundant terrestrial and GPS-based tie vectors in ITRF-like combinations
No full textTie vectors (TVs) between co-located space geodetic
instruments are essential for combining terrestrial reference
frames (TRFs) realised using different techniques. They
provide relative positioning between instrumental reference
points (RPs) which are part of a global geodetic network
such as the international terrestrial reference frame (ITRF).
This paper gathers the set of very long baseline interferometry
(VLBI)–global positioning system (GPS) local ties
performed at the observatory of Medicina (Northern Italy)
during the years 2001–2006 and discusses some important
aspects related to the usage of co-location ties in the combinations
of TRFs. Two measurement approaches of local
survey are considered here: a GPS-based approach and a
classical approach based on terrestrial observations (i.e.
angles, distances and height differences). The behaviour of terrestrial local ties, which routinely join combinations of
space geodetic solutions, is compared to that of GPS-based
local ties. In particular, we have performed and analysed different
combinations of satellite laser ranging (SLR), VLBI
and GPS long term solutions in order to (i) evaluate the
local effects of the insertion of the series of TVs computed
at Medicina, (ii) investigate the consistency of GPS-based
TVs with respect to space geodetic solutions, (iii) discuss
the effects of an imprecise alignment of TVs from a local to
a global reference frame. Results of ITRF-like combinations
show that terrestrial TVs originate the smallest residuals in
all the three components. In most cases, GPS-based TVs fit
space geodetic solutions very well, especially in the horizontal
components (N, E). On the contrary, the estimation of
the VLBI RP Up component through GPS technique appears
to be awkward, since the corresponding post fit residuals
are considerably larger. Besides, combination tests including
multi-temporal TVs display local effects of residual redistribution,
when compared to those solutions where Medicina
TVs are added one at a time. Finally, the combination of
TRFs turns out to be sensitive to the orientation of the local
tie into the global frame
Formal modeling of trust in AI-driven autonomous delivery vehicles
No full textTrust modeling is critical for the safe deployment of autonomous systems, yetexisting approaches that rely primarily on historical performance data fail to capture dynamic operational contexts and real-time agent capabilities. This paper introduces a formal framework for modeling actual trust in Autonomous Delivery Vehicles (ADVs)—a context-aware trust model that evaluates an agent’s current ability, knowledge state, and commitment to task completion rather than relying solely on past behavior. We present a systematic refinement-based approach using Event-B formal methods to model trust in ADV task delegation scenarios.Our methodology progresses through five refinement levels, transitioning froman untrusted baseline model to a comprehensive trust framework that integratesthree key dimensions: (1) strategic trust (capability verification), (2) epistemictrust (knowledge-based assessment), and (3) commitment trust (availability andwillingness evaluation). Each refinement level addresses specific failure modesidentified in traditional delegation systems where tasks may be assigned to incapable, unknown, or unavailable vehicles. The formal model is verified using the Rodin theorem prover with 93 proof obligations, achieving 90% automatic verification. Our approach demonstrates how actual trust can be systematicallyintegrated into autonomous systems through correctness-by-construction refinement, ensuring that task assignments occur only when trust conditions areformally verified. The framework provides a foundation for trustworthy task delegation in multi-agent autonomous systems and offers insights for developing reliable AI-driven delivery networks
Comparison of neural networks based on accuracy and robustness in identifying impact location for structural health monitoring applications
No full textStructural health monitoring systems must provide accuracy and robustness in predicting the structure’s health using the minimum intervention to ensure commercial viability. Characterization of impact is useful in assessing its severity, deciding if detailed damage analysis is necessary, and re-evaluating the present health of the structure under monitoring with better confidence. In this characterization process, the impact location is significant since some positions within a structure are more sensitive to damage. The inherent noise and uncertainties present in the sensor response pose a substantial hurdle to estimating the external impact correctly. This paper quantitatively compares three of the widely used neural networks, namely, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory network (LSTM), to estimate impact location from the lead zirconate titanate (PZT) sensor response. For this purpose, a square aluminum plate of 500 × 500 mm was equipped with four PZT sensors; each placed 100 mm away in both the plate directions from a corner and impact loads were given on a grid covering the whole plate. The PZT responses were used to train the three neural networks under study here, and their estimations were compared based on the Mean Absolute Error (MAE). In addition, increasing Gaussian noise was added to the PZT responses, and the robustness of the three neural networks was monitored. It was found that the ANN gives better accuracy with a Mean Absolute Error of 22 mm compared to Convolutional Neural Network (MAE = 31 mm) and Long Short-Term Memory (MAE = 25 mm). However, CNN is more robust when encountering noise with a 2% reduction in accuracy, while LSTM and ANN lost 7% and 11% accuracy, respectively
Dr. Duane M. Jackson, Morehouse College, July 2011
No full textThis video is a conversation with Dr. Duane M. Jackson. Dr. Jackson talks about his paper, "Recall and the Serial Position Effect: The Role of Primacy and Recency on Accounting Students' Performance." Jackie Daniel, AUC Woodruff Library, is the interviewer
"Reflections on the subject of Emigration from Europe with a view to Settlement in the United States" By M. Carey.
No full text"Reflections on the subject of Emigration from Europe with a view to Settlement in the United States: containing bried sketches of the moral and political character of those states.
By M. Carey, member of the American philosophical, and of the American Antiquarian Society, and author of The Olive Branch, Cindiciae Hibernicae, essays on banking, on political economy, and on internal improvement.
To which are now added the English editor's comments on the subject; together with Important Advice to Emigrants, and Cautions Against Impositions Practiced in the Outports
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