Seven Simple Steps for Log Analysis in AI Systems

This article describes the design of a framework for sound synthesis in LC, a new computer music language we prototyped, together with concrete code examples. Unlike existing unit-generator languages, LC provides objects as well as library functions and methods that can directly represent microsounds and related manipulations that are involved in microsound synthesis. Furthermore, LC is equipped with traditional unit generators, and these two different abstractions can collaborate seamlessly. Although the framework for microsound synthesis itself is not particularly bound to the entire language design of LC, such seamless unification between the traditional concept of unit generators and LC’s microsound synthesis framework contribute to making LC’s programming model for microsound synthesis simpler and terser in comparison with existing unit-generator languages. These features of LC’s entire sound-synthesis framework can help computer musicians to creatively explore the domain of microsound synthesis and would also be beneficial for further research in computer music language as a design exemplar.

This research explores the nuanced differences in texts produced by AI and those written by humans, aiming to elucidate how language is expressed differently by AI and humans. Through comprehensive statistical data analysis, the study investigates various linguistic traits, patterns of creativity, and potential biases inherent in human-written and AIgenerated texts. The significance of this research lies in its contribution to understanding AI's creative capabilities and its impact on literature, communication, and societal frameworks. By examining a meticulously curated dataset comprising 500K essays spanning diverse topics and genres, generated by LLMs, or written by humans, the study uncovers the deeper layers of linguistic expression and provides insights into the cognitive processes underlying both AI and human-driven textual compositions. The analysis revealed that human-authored essays tend to have a higher total word count on average than AI-generated essays but have a shorter average word length compared to AIgenerated essays, and while both groups exhibit high levels of fluency, the vocabulary diversity of Human authored content is higher than AI generated content. However, AIgenerated essays show a slightly higher level of novelty, suggesting the potential for generating more original content through AI systems. The study also identifies a lower prevalence of gender bias in AI-generated texts but a higher presence of biased topics overall. These findings highlight the strengths and limitations of AI in text generation and the importance of considering multiple approaches for comprehensive analysis. The paper addresses challenges in assessing the language generation capabilities of AI models and emphasizes the importance of datasets that reflect the complexities of human-AI collaborative writing. Through systematic preprocessing and rigorous statistical analysis, this study offers valuable insights into the evolving landscape of AI-generated content and informs future developments in natural language processing (NLP).

This special issue of Software Tools for Technology Transfer comprises extended versions of selected papers from the 26th edition of the International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2020). The focus of this conference series is tools and algorithms for the rigorous analysis of software and hardware systems, and the papers in this special cover the spectrum of current work in this field.

The integration of Fast Healthcare Interoperability Resources (FHIR) with generative artificial intelligence (GenAI) systems is accelerating across the healthcare landscape, particularly within pharmacy ecosystems responsible for medication dispensing, formulary management, prior authorization, and real-time benefit determination. While FHIR provides a standardized framework for interoperable data exchange, GenAI introduces new risks such as prompt injection, hallucinated clinical assertions, privacy violations, model inversion, and inadvertent leakage of protected health information (PHI). These risks become more pronounced when GenAI models are connected to high-sensitivity medication APIs used by payers, pharmacies, and clinical systems. This paper presents a comprehensive security architecture for FHIR-GenAI integration within pharmacy workflows. It proposes a multilayer strategy incorporating Zero Trust, privacy-preserving computation, API governance, model guardrails, and secure machine-learning pipelines. Code examples are provided to illustrate key enforcement mechanisms. The analysis demonstrates that secure implementation of GenAI in pharmacy systems must harmonize conventional enterprise security controls with new GenAI-specific risks while honoring regulatory requirements such as HIPAA, HITECH, and HITRUST CSF.

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.

The three papers in this special issue focus on rigorous modeling and analysis of cyber-physical systems.

Agent technology is widely recognized as a new paradigm for design of concurrent software and systems. The aim of this paper is to give a mathematical foundation for design and analysis of multi-agent systems by means of a Petri-net-based model. The proposed model, called PN 2, is based on place/transition nets (P/T nets), which is one of the simplest classes of Petri nets. The main di.erence between PN 2s and P/T nets is that each token, representing an agent, is also a P/T net. State equation and invariants are known as standard techniques for the analysis of P/T nets. As the first step of mathematical analysis of PN 2s, we define these for PN 2s, and show how the invariants are computed in an efficient way.

Seismic Reservoir Characterization, also known as reservoir geophysics, has evolved over the past several years into a multi-disciplinary, business-critical function in most ED&P organizations. Sheriff defines reservoir geophysics as "The use of geophysical methods to assist in delineating or describing a reservoir or monitoring the changes in a reservoir as it is produced." Reservoir geophysics is applied across a wide spectrum of the oilfield life cycle from discovery and early development to tertiary recovery. One critical part of this process is careful analysis and understanding of petrophysical properties from well logs and core data (seismic petrophysics). The purpose of this paper is to illustrate why seismic petrophysics is so important and to show how carefully constructed synthetic models can help the geoscientist interpret acoustic and elastic impedance inversion from seismic data. Introduction Well logs are sometimes viewed by geophysicists as "hard data" and not subjected to the same level of scrutiny as the seismic data. This can be a mistake because well logs are susceptible to errors from a number of sources. In this presentation we will examine some of the processes and procedures that allow well logs to be correctly used in Seismic Reservoir Characterization. The basic steps in seismic petrophysics analysis are:Collect and organize input dataPerform geophysical log interpretation for volume minerals, porosity, and fluidsDetermine fluid properties (oil API, brine salinity, etc.) and reservoir pressuretemperaturePerturb reservoir properties using rock physics effective medium models (pseudo-well modeling)Compute synthetic seismic tracesGenerate trend curves and crossplotsCreate graphics and digital output files. Geophysical Well Log Analysis Well log analysis for geophysics differs in several important ways from standard log analysis. In most cases well logs are obtained for the purpose of estimating recoverable hydrocarbon volumes. Therefore the zone of interest is mainly the producing interval(s). For geophysics, well logs form the basis for relating seismic properties to the reservoir. While we are still concerned about producing intervals, we also need good information about all of the rock through which the seismic waves have passed. Therefore our zone of interest is much larger and encompasses basically everything from the surface to total depth. In all cases the log data will require some editing, normalization, and interpretation before they can be used in a reservoir study. Several specific analysis steps will be followed:De-spike and filter to remove or correct anomalous data pointsNormalize logs from all of the selected wells to determine the appropriate ranges and cutoffs for porosity, clay content, water resistivity, etc.Compute the volumetric curves such as total porosity, Vclay, and SwCalibrate the volumetric curves to core data if availableCorrect sonic and density logs for mud filtrate invasion if neededCompute Vshear on all wells. Missing log curves can often be computed with a reasonable degree of certainty. There are two major ways this is done. The first is through application of modern rock physics principles.

Nonlinear Analysis of Phase-Locked Loop

Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities

Previous analyses of RTPV space power systems published by the authors were based on a number of approximations employed to permit early dissemination of preliminary results pending availability of fuller experimental data needed to conduct more rigorous analyses. The shortcomings of the previously published analyses are addressed in the paper, which describes revised analyses based on experimental data of IR filter reflectivities and PV cell quantum efficiencies and current-voltage characteristics. The paper applies the revised test data and analyses to an illustrative converter design, using an OSC-generated computer program that also includes the effect of shadowing and ohmic losses by the cell's optimized grid lines, the use of accurate active-area fractions representing detailed converter designs, and the reduction in the required heat source-to-emitter heat flux due to radiation reflected by the inactive filter area and absorbed by the emitter. It details the effect of each analytical change on the converter's performance. The analytical results confirm that EDTEK's improved filters have greatly enhanced the performance of the RTPV generator. They also demonstrate that accurate accounting for the effect of reflection from the inactive filter area and absorption by the emitter results in a significant performance improvement. But they show that these improvements are not large enough to compensate for the adverse effect of replacing the previously used theoretical equations for the converter's open-circuit voltage and fill factor with measured values of those parameters achieved to date.

Analysis and design of common types of buildings to resist earthquake forces is done by the method described in the Uniform Building Code. Approximate formulas for natural periods of fundamental mode of buildings are available and are used in the static method of aseismic design of Uniform Building Code. However, some building codes in the seismic regions require dynamic analysis of tall buildings and other important structures. The dynamic analysis of multistory structures is laborious and time consuming because of the many degrees of freedom involved. Modern electronic computers have made possible the dynamic analysis of multidegree freedom systems which was prohibitive by hand calculations. However, when the number of degrees of freedom is large, the dynamic analysis requires the use of digital computers of large capacities and the computer time used can be very expensive. In the modal method of dynamic analysis, the natural periods and mode shapes for various modes of vibrations are determined by computer analysis. The objective in this paper is to develop simplified methods for determining natural periods of vibrations of multistory buildings so that expensive computer time is kept to a minimum. Since the aseismic design by dynamic analysis require a few preliminary trials before final satisfactory design is achieved, the simplified methods can save laborious computer calculations in the preliminary design. The simplified methods discussed in this paper are developed using the concept of an elastic shear wave equation in solid uniform bars. A numerical example is presented to compare the simplified methods with the more rigorous analysis.

Direct current (DC) distribution systems and DC microgrids are becoming a reliable and efficient alternative energy system, compatible with the DC nature of most of the distributed energy resources (DERs), storage devices and loads. The challenging problem of redesigning an autonomous DC-grid system in view of using energy storage devices to balance the power produced and absorbed, by applying simple decentralized controllers on the electronic power interfaces, is investigated in this paper. To this end, a complete nonlinear DC-grid model has been deployed that includes different DC-DERs, two controlled parallel battery branches, and different varying DC loads. Since many loads in modern distribution systems are connected through power converters, both constant power loads and simple resistive loads are considered in parallel. Within this system, suitable cascaded controllers on the DC/DC power converter interfaces to the battery branches are proposed, in a manner that ensures stability and charge sharing between the two branches at the desired ratio. To achieve this task, inner-loop current controllers are combined with outer-loop voltage, droop-based controllers. The proportional-integral (PI) inner-loop current controllers include damping terms and are fully independent from the system parameters. The controller scheme is incorporated into the system model and a globally valid nonlinear stability analysis is conducted; this differs from small-signal linear methods that are valid only for specific systems, usually via eigenvalue investigations. In the present study, under the virtual cost of applying advanced Lyapunov techniques on the entire nonlinear system, a rigorous analysis is formulated to prove stability and convergence to the desired operation, regardless of the particular system characteristics. The theoretical results are evaluated by detailed simulations, with the system performance being very satisfactory.

In this paper, a smart platform-based log analysis and parameter-modifying tool for naval combat systems (NCSs) is proposed. To support the development of NCSs, the log-analysis and parameter-modifying tool provides several other functions, such as node information gathering and remote log gathering. Applications called smartWatcher (for smart devices) and smartAgent (for nodes) communicate to gather node information and log files. The developer of the NCS can modify parameters to resolve errors in the NCS based on results of the log analysis. Simulation results show the availability and effectiveness of the smart platform for NCSs.

Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP (www.neurowrap.org), which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.