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Thailand Research Fund (TRF

Social indeterminacy and Quine's indeterminacy thesis

This article examines whether Willard Van Orman Quine’s indeterminacy thesis can be sustained. The argument from above, Quine argues, can derive indeterminacy as its conclusion. I will argue that the indeterminacy claim cannot be sustained. I further argue that Quine changed the formulation of the underdetermination of theory by evidence (UTE) argument from what Duhem said to the Quine/Pierce meaning verification view, in order use the new formulation of UTE to imply indeterminacy. Given all that, we see when we apply the old UTE argument we only arrive at underdetermination of theory by evidence, and that applies to all sciences, philosophy and knowledge, including philosophy of language

Exploring Searle's Social Ontology

In this short article, I will explore John Searle’s social ontology project from the perspective of social epistemology. The outcome of my analysis is that language is decisive for the collective acquisition and production of knowledge. I agree with Searle regarding the exposure of language as a central constitutive component of social forms of knowledge, a component that plays a significant role in the development of social epistemology

Exploring Searle's Social Ontology

In this short article, I will explore John Searle’s social ontology project from the perspective of social epistemology. The outcome of my analysis is that language is decisive for the collective acquisition and production of knowledge. I agree with Searle regarding the exposure of language as a central constitutive component of social forms of knowledge, a component that plays a significant role in the development of social epistemology

Operation of Molecular Machines in Soft Matter Systems

Inspired by the motion in living organisms, where the collective action of biological molecular machines transforms chemical energy into activity, artificial molecular motors are designed to have controllable motion and responsiveness to external stimuli. These molecular machines, ranging from molecular motors to switches and rotaxanes, offer a spectrum of complex functions valuable for developing adaptive materials at the nanoscale. To fully harness their molecular motion for functional work, these machines are integrated into soft material systems, which possess intrinsic properties to transfer motion. In return, this integration initiates an interplay between the machines and their surroundings. While there is a strong foundation for understanding the effects of the environment in solution systems, most responsive systems are built upon the integration of molecular machines and soft matter. This thesis explores the dynamic potential of artificial molecular machines as they are incorporated into different soft matter environments, including liquid crystals, micelles, and vesicular membranes. The focus is on overcrowded alkene-based molecular motors, known for their ability to undergo unidirectional rotation activated by light. We studied the interplay between environmental properties and the operations of molecular machines within these soft matter media, demonstrating the concept of molecular motors as probes for membrane stiffness. This exploration has inspired further investigation into rotaxanes, demonstrating the transmission of chiral information in liquid crystals, where macroscopic effects are induced by the dynamics of nanoscale rotaxanes

Sequencing, Analysis, and Annotation of Expressed Sequence Tags for Camelus dromedarius

Despite its economical, cultural, and biological importance, there has not been a large scale sequencing project to date for Camelus dromedarius. With the goal of sequencing complete DNA of the organism, we first established and sequenced camel EST libraries, generating 70,272 reads. Following trimming, chimera check, repeat masking, cluster and assembly, we obtained 23,602 putative gene sequences, out of which over 4,500 potentially novel or fast evolving gene sequences do not carry any homology to other available genomes. Functional annotation of sequences with similarities in nucleotide and protein databases has been obtained using Gene Ontology classification. Comparison to available full length cDNA sequences and Open Reading Frame (ORF) analysis of camel sequences that exhibit homology to known genes show more than 80% of the contigs with an ORF>300 bp and ~40% hits extending to the start codons of full length cDNAs suggesting successful characterization of camel genes. Similarity analyses are done separately for different organisms including human, mouse, bovine, and rat. Accompanying web portal, CAGBASE (http://camel.kacst.edu.sa/), hosts a relational database containing annotated EST sequences and analysis tools with possibility to add sequences from public domain. We anticipate our results to provide a home base for genomic studies of camel and other comparative studies enabling a starting point for whole genome sequencing of the organism

A comprehensive approach to the design of advanced well completions

Advanced Well Completions (AWCs) employing Downhole Flow Control (DFC) technology such as Inflow Control Devices (ICDs), Interval Control Valves (ICVs),Autonomous Inflow Control Devices (AICDs) and/or Annular Flow Isolations (AFIs) provide a practical solution to the challenges normally encountered by conventional wells. Both oilfield operating companies and several researchers have developed workflows to identify the optimum well location and field development well configuration. However, all these approaches do not at present consider optimising advanced well completions employing DFCs. The objective of this thesis is to provide an automated, comprehensive workflow to identify the optimum advanced well completion design that ensures an optimum well performance throughout the well’s and field’s life. This study starts by describing the history of ICD, AICD, ICV and AFI development with emphasis on the (near and) fully commercially available types and their areas of application. The thesis then reviews the flow performance of available ICD, ICV and AICD types. It reviews the available advanced completion modelling techniques and their historical development. This allows provision of guidelines on how to model DFC technologies performance when combined with AFIs over the well’s life. It shows how the value of such well-construction options can be quantified using these tools. The thesis introduces a novel workflow outlining the process of designing ICD completions with or without AFIs for different well architectures applied in different reservoir types for production or injection purposes. The workflow incorporates: the ICD restriction sizing; the requirement for AFI, their frequency and distribution; the impact of ICD reliability throughout the life of the well, the effect of uncertainty on the design parameters, installation risks and the resulting economic value. This workflow is then extended to the design and evaluation of AICD completions, through identification of the optimum control of water and excess gas production. The value and applicability of the proposed workflow is verified using synthetic and real field case studies. The latter include three oil fields (H-Field, S-Field and U-Field), one thin oil column/gas condensate field (NH-Field) and a gas field (C-Field). These cases also illustrated the value which can be gained from the application of Downhole Flow Control technologies

Indirect feedback alignment in deep learning for cognitive agent modeling: enhancing self-confidence analytics in the workplace

The innovative application of indirect feedback alignment (IFA) in deep learning enhances workplace self-confidence analytics through cognitive agent modeling. IFA addresses the challenge of credit assignment in multi-layer neural networks, offering a more efficient and biologically plausible alternative to traditional backpropagation methods. The paper delves into the integration of IFA in workplace dynamics, focusing on the development of a state-determined system to describe and analyze the dynamics of self-confidence, self-concept, self-esteem, and self-efficacy among employees. Utilizing a combination of endogenous and exogenous factors, the study presents a comprehensive model that captures the complex interplay of these factors in professional settings. The research further conducts experiments to observe and analyze the behavior and pattern formation among real workers in various settings, demonstrating the practical implications of the theoretical model. The findings highlight the potential of IFA in enhancing and accelerating the components of deep learning associated with self-confidence in the workplace, contributing significantly to the fields of neural computation and cognitive psychology. The proposed method was tested in various situations to assess its alignment with the core concepts of workplace self-confidence. Mathematical analysis was employed to explore feasible equilibrium conditions and compatible cases found in the literature