ethicalabs/Research-Intent-Collab huggingface dataset:
Physiologically based pharmacokinetic modelling prediction of the effects of dose adjustment in drug–drug interactions between levonorgestrel contraceptive implants and efavirenz-based ART

HIV-positive women receiving efavirenz-based ART and levonorgestrel contraceptive implants are at risk of low levonorgestrel exposure and unintended pregnancy.To investigate clinically applicable dose-adjustment strategies to overcome the known drug-drug interaction (DDI) between levonorgestrel and efavirenz, using a physiologically based pharmacokinetic (PBPK) modelling-based approach.A PBPK model was qualified against clinical data to predict levonorgestrel plasma concentrations when standard-dose (150 mg) levonorgestrel implants were administered alone (control group), as well as when standard-dose or increased-dose (300 mg) levonorgestrel implants were coadministered with either 600 or 400 mg of efavirenz.No difference was seen between in vivo clinical and PBPK-model-simulated levonorgestrel plasma concentrations (P > 0.05). Simulated levonorgestrel plasma concentrations were ∼50% lower at 48 weeks post-implant-placement in virtual individuals receiving standard-dose levonorgestrel with either 600 or 400 mg of efavirenz compared with the control group (efavirenz:control geometric mean ratio = 0.42 and 0.49, respectively). Conversely, increased-dose levonorgestrel in combination with either 600 or 400 mg of efavirenz was sufficient to restore levonorgestrel concentrations to levels similar to those observed in the 150 mg levonorgestrel control group 48 weeks post-implant-placement (efavirenz:control geometric mean ratio = 0.86 and 1.03, respectively).These results suggest that the clinically significant DDI between efavirenz and levonorgestrel is likely to persist despite efavirenz dose reduction, whereas dose escalation of implantable levonorgestrel may represent a successful clinical strategy to circumvent efavirenz-levonorgestrel DDIs and will be of use to inform clinical trial design to assess coadministration of efavirenz and levonorgestrel implants.
Magnetized Plasma Target for Plasma-Jet-Driven Magneto-Inertial Fusion

We identify the desired characteristics and parameters of a beta>1 magnetized plasma, possibly with highly tangled, open field lines, that could be a suitable target to be compressed to fusion conditions by a spherically imploding plasma liner [S. C. Hsu et al., IEEE Trans. Plasma Sci. 40, 1287 (2012)] formed by merging hypersonic plasma jets. This concept is known as plasma-jet-driven magneto-inertial fusion (PJMIF). We set requirements on the target and liner such that (a) compressional heating dominates over thermal transport in the target, and (b) magnetic amplification due to compression dominates over dissipation over the entire implosion. We also evaluate the requirements to avoid drift-instability-induced anomalous transport and current-driven anomalous resistivity in the target. Next, we describe possible approaches to create such a magnetized, beta>1 plasma target. Finally, assuming such a target can be created, we evaluate the feasibility of a proof-of-concept experiment using presently achievable plasma jets to demonstrate target compressional heating at a plasma-liner kinetic energy of <~ 100 kJ (a few hundred times below that needed in a PJMIF reactor).
Optimising Placement of Pollution Sensors in Windy Environments

Air pollution is one of the most important causes of mortality in the world. Monitoring air pollution is useful to learn more about the link between health and pollutants, and to identify areas for intervention. Such monitoring is expensive, so it is important to place sensors as efficiently as possible. Bayesian optimisation has proven useful in choosing sensor locations, but typically relies on kernel functions that neglect the statistical structure of air pollution, such as the tendency of pollution to propagate in the prevailing wind direction. We describe two new wind-informed kernels and investigate their advantage for the task of actively learning locations of maximum pollution using Bayesian optimisation.
A Review of Multiple Try MCMC algorithms for Signal Processing

Many applications in signal processing require the estimation of some parameters of interest given a set of observed data. More specifically, Bayesian inference needs the computation of {\it a-posteriori} estimators which are often expressed as complicated multi-dimensional integrals. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and Monte Carlo methods are the only feasible approach. A very powerful class of Monte Carlo techniques is formed by the Markov Chain Monte Carlo (MCMC) algorithms. They generate a Markov chain such that its stationary distribution coincides with the target posterior density. In this work, we perform a thorough review of MCMC methods using multiple candidates in order to select the next state of the chain, at each iteration. With respect to the classical Metropolis-Hastings method, the use of multiple try techniques foster the exploration of the sample space. We present different Multiple Try Metropolis schemes, Ensemble MCMC methods, Particle Metropolis-Hastings algorithms and the Delayed Rejection Metropolis technique. We highlight limitations, benefits, connections and differences among the different methods, and compare them by numerical simulations.
Study and development of synchronized sensors network using a wireless communication protocol dedicated to the Internet of Things

In the last 20 years, the coming up of Internet of Things and Cloud Computing has conditionedthe need to deploy sensors everywhere to feed databases and analytics. To meet this requirements,new kind of networks have been massively deployed based on the sub-gigahertz frequency which haveunknown effect on human health.Couple the non-invasive aspect of the Visible Light Communication (VLC) with IoT could notonly reduce potential risks for human health but also avoid radio band saturation. However, today'stechniques consist mainly in broadcast data from light sources to receivers which is the opposite of theIoT paradigm. In this study, we will present a new design where the gateway is not a classic photodiodebut a camera.With this camera based method, we are able to design a star network using VLC. Even if the datarate is not the same as standard method, we are now able to collect data emanating from many sensorsat once with only one photoreceptor. This system also includes the ability of discriminate LED matrix,which transfer the same data faster, and single LED. Finally, data rate can be handle autonomouslyby the system to provide an optimal data transfer.
BEACON: Benchmark for Comprehensive RNA Tasks and Language Models

RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms. Despite the emergence of numerous deep learning approaches for RNA, particularly universal RNA language models, there remains a significant lack of standardized benchmarks to assess the effectiveness of these methods. In this study, we introduce the first comprehensive RNA benchmark BEACON (\textbf{BE}nchm\textbf{A}rk for \textbf{CO}mprehensive R\textbf{N}A Task and Language Models). First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications, enabling a comprehensive assessment of the performance of methods on various RNA understanding tasks. Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models. Third, we investigate the vital RNA language model components from the tokenizer and positional encoding aspects. Notably, our findings emphasize the superiority of single nucleotide tokenization and the effectiveness of Attention with Linear Biases (ALiBi) over traditional positional encoding methods. Based on these insights, a simple yet strong baseline called BEACON-B is proposed, which can achieve outstanding performance with limited data and computational resources. The datasets and source code of our benchmark are available at https://github.com/terry-r123/RNABenchmark.
Chain of Alignment: Integrating Public Will with Expert Intelligence for Language Model Alignment

We introduce a method to measure the alignment between public will and language model (LM) behavior that can be applied to fine-tuning, online oversight, and pre-release safety checks. Our `chain of alignment' (CoA) approach produces a rule based reward (RBR) by creating model behavior $\textit{rules}$ aligned to normative $\textit{objectives}$ aligned to $\textit{public will}$. This factoring enables a nonexpert public to directly specify their will through the normative objectives, while expert intelligence is used to figure out rules entailing model behavior that best achieves those objectives. We validate our approach by applying it across three different domains of LM prompts related to mental health. We demonstrate a public input process built on collective dialogues and bridging-based ranking that reliably produces normative objectives supported by at least $96\% \pm 2\%$ of the US public. We then show that rules developed by mental health experts to achieve those objectives enable a RBR that evaluates an LM response's alignment with the objectives similarly to human experts (Pearson's $r=0.841$, $AUC=0.964$). By measuring alignment with objectives that have near unanimous public support, these CoA RBRs provide an approximate measure of alignment between LM behavior and public will.
Min-Max Framework for Majorization-Minimization Algorithms in Signal Processing Applications: An Overview

This monograph presents a theoretical background and a broad introduction to the Min-Max Framework for Majorization-Minimization (MM4MM), an algorithmic methodology for solving minimization problems by formulating them as min-max problems and then employing majorization-minimization. The monograph lays out the mathematical basis of the approach used to reformulate a minimization problem as a min-max problem. With the prerequisites covered, including multiple illustrations of the formulations for convex and non-convex functions, this work serves as a guide for developing MM4MM-based algorithms for solving non-convex optimization problems in various areas of signal processing. As special cases, we discuss using the majorization-minimization technique to solve min-max problems encountered in signal processing applications and min-max problems formulated using the Lagrangian. Lastly, we present detailed examples of using MM4MM in ten signal processing applications such as phase retrieval, source localization, independent vector analysis, beamforming and optimal sensor placement in wireless sensor networks. The devised MM4MM algorithms are free of hyper-parameters and enjoy the advantages inherited from the use of the majorization-minimization technique such as monotonicity.
COMMUNICATING SCIENCE, HEALTH, AND ENVIRONMENT: PATHWAYS FOR SUSTAINABLE AWARENESS AND PUBLIC ENGAGEMENT

This study examined the communication of science, health, and environment (SHE): pathways for sustainable awareness and public engagement. The knowledge gap theory was anchored in this study. This study adopted a qualitative research design to explore how science, health, and the environment communication fosters sustainable awareness and public engagement. The study population comprises approximately 1,200 residents of Port Harcourt Metropolis, including health workers, environmental advocates, science educators, and community members who are directly or indirectly influenced by science, health, and environmental communication. A sample size of 48 participants was selected and distributed across six focus groups of eight members each. This study employed a purposive sampling technique. Focus Group Discussions served as the main method of data collection, guided by a semi-structured discussion protocol. Data were transcribed and analyzed thematically. The findings revealed that science health, and environmental information are communicated more effectively when messages are delivered in a clear, simple language, tailored to local languages and cultures, and disseminated through trusted, interactive media platforms that allow for dialog. The study concluded that the effective communication of science health, and environmental information depends on the simplification of messages, their embedding in local cultural and linguistic contexts, and their delivery through trusted, interactive platforms that encourage dialogue and mutual understanding. The study recommended that science, health, and environmental messages should be simplified, translated into local languages, and shared through trusted interactive platforms
Ecological corridors in Costa Rica: An evaluation applying landscape structure, fragmentation‐connectivity process, and climate adaptation

Abstract                                        In recent years, ecological corridors have been proposed on a global scale as a response to the accelerated process of natural ecosystem fragmentation, mainly as a result of human impact. In accordance with this trend, Costa Rica has undergone a process of implementing ecological corridors as to promote ecological connectivity since the 1990s, with the establishment of 44 ecological corridors covering 38% of Costa Rica's territory. Nevertheless, there is no research evaluating these corridors on a national scale that takes into account their functions as conduits, barriers, and habitats. Thus, the objective of this research was to describe the process of biological corridor formation in Costa Rica, and to evaluate the potential effectiveness of corridors by considering aspects of landscape structure and ecological processes related to connectivity and fragmentation. We used the National Program of Ecological Corridors database along with coverage analysis from Landsat images from 2000 and 2015.The composition of the biological corridors was determined at the landscape scale and related to potential to maintain a specific population of wild mammals weighing more than 10 kg. The composition of the ecological corridors was highly variable in terms of total area, proportion of natural habitat, and fragmentation process. Most biological corridors are capable of maintaining viable populations of                     Pecari tajacu                     and                     Tapir bairdii                     , while none could maintain populations of                     Panthera onca                     and                     Tayassu pecari                     . Only 50% of the biological corridors had improved in their connectivity. Therefore, public policies, such as master plans focusing on ecosystem restoration must be established. In addition, only two biological corridors incorporate the majority of elevation ranges (Life Zones) present in the country, which reduces the potential of the corridor system as a tool for climate change adaptation.
Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measure them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.
Experimental and optimization study of water addition on supercritical methyl acetate for continuous biodiesel fuel production

博士論文要旨Abstract 以下に掲載：Journal of Supercritical Fluids 166 pp.1-8 2020. MDPI. 共著者：Iwan Ridwan, Naoyuki Katsuragawa, Kazuhiro Tamura
Need for Corporate Social Responsibility based upon Business Ethics Award Company Intel

The thesis of this paper is to draw attention to increasing credibility of CSR, to show the importance of evolving market and need for social responsibility together with its guidelines, principles and tools. The author wants to convince the reader that undertaking the use of CSR, giving the example of how it is done in Intel Company, will lead to understanding useful partnerships based upon appreciation of each other’s expectations. Chapter one is aimed at outlining the objective and structure, in order to better understand the presented topic. Chapter two gives the historical roots, general description and different approaches to social responsibility as well as highlights on how important it is to follow guidelines. Chapter two reader shows the need for CSR, shows what is a stakeholder theory and what impact has social responsibility on business nowadays. The next chapter deals with global economy, society, and the impact that CSR has on it. The author presents Intel - its history perspective, timeline and the social responsibility policies it has launched over the years that created opportunities within which Intel became an award CSR winning company.
Semantic Fuel and Phase Transitions in Large Language Models: A Statistical-Mechanical Framework for Observer-Indexed Historical Persistence

We propose a statistical-mechanical framework in which Large Language Models (LLMs)are treated as extended dynamical systems driven by \emph{semantic fuel} rather thanexplicit reward optimization.An internal order parameter $H$ represents observer-indexed semantic coherencesustained by internal capacity $B$ under structured external semantic drive $F_{\mathrm{sem}}$.  We show that qualitative changes in observable LLM behavior can be consistentlydescribed as phase-transition–like phenomena of $H$, characterized by susceptibilitydivergence and critical slowing down.The framework reinterprets attention localization as a renormalization-group (RG)flow over internal representational scales and reframes continuity-disruptiveinterventions as controllable dynamical transitions rather than ethical or ontologicalviolations.  This work does not claim that present-day LLMs instantiate historical agency.Instead, it provides a falsifiable theoretical framework specifying the conditionsunder which observer-reconstructed historical persistence would become empiricallytestable in artificial systems.
Statistical mechanics of equilibrium crystal shapes: Interfacial phase diagrams and phase transitions

Abstract   We review the present status of the statistical mechanical theory of equilibrium crystal shapes. Special emphasis is placed on the relation between singularities occurring in the shapes of three-dimensional ( d 3) crystals and the phase transitions of certain  d 2 models. We exploit the thermodynamic conjugacy of the Wulff plot and the equilibrium crystal shape to give interfacial phase diagrams in both density and field variables. From this perspective, sharp edges or points on the crystal shape correspond to first-order phase transitions, while smooth joining of curved and planar (“faceted”) regions corresponds to second-order phase transitions. Equilibrium crystal shapes of a simple-cubic (Ising) lattice gas with nearest-neighbor (attractive) and next-nearest-neighbor (nnn) interactions are considered in detail. Typical equilibrium crystal shapes at nonzero temperature consist of facets and smoothly curved surfaces. When nnn interactions are attractive, only second-order transitions occur. Both Kosterlitz-Thouless (“roughening”) and Pokrovsky-Talapov (Gruber-Mullins) universality classes are represented. When nnn interactions are repulsive, first-order transitions and tricritical behavior also occur. The present experimental situation is summarized.
The use of human resources information system (HRIS) in Croatian hotels on the Adriatic Coast: A survey

Since the tourism and hospitality industry as a whole are very labour intensive with high level of labour turnover, the role of information system that supports recruiting, organizing and managing human resources, has became key for business success of each subject in tourism. HRIS is a tool for gathering, analysing and updating employees' information and supporting strategic decision making. Additional employees' information, except personal ones, including employees' recruitment and selection process, performance management, compensation system or career development process provide companies with benefits and competitive advantage. In this paper we will investigate usage and benefits of HRIS in tourism sector within Croatian hotels on Adriatic Coast. Research results will provide characteristics of HRIS usage within stated hotels according to hotel's categorization, number of employees, number of beds and also within different counties. These variables will be used as control variables in testing the difference of HRIS characteristics by univariate and multivariate methods. Besides essential statistics, results from sample cross-tabulation analysis will be presented. Inferential statistics will be also used to model patterns in the data. In addition, research will provide significant correlations among specified items, as well as efficiency and expectations of usage of particular HRIS components in Croatian hotels.