Powerful Model Research Articles

DRM LISTS CREATED WITH CHATGPT: ANALYSING RECOGNITION MEMORY WITH CLIMATE-CHANGE THEMED LISTS

Machine learning and natural language processing have led to the development of powerful language models such as ChatGPT, which can generate consistent and human-like responses to a wide range of queries. In many domains, ChatGPT provides appropriate responses to given commands. One of the aims of this study is to investigate the use of these association lists, such as the Deese-Roediger-McDermott (DRM) lists popular in cognitive psychology studies, by ChatGPT by giving the necessary instructions. The same method was then used to create association lists around a specific topic (climate change). The results of the first study showed that participants gave more false answers when discriminating whether critical words were presented during the test phase than when related and unrelated words were presented. This finding shows that DRM lists generated by ChatGPT can be used to search for memory errors. In line with the literature, false answers for critical words were predominantly rated as ‘remember’. The results of the second study, which was applied to the lists created on the topic of climate change and compared the responses of the groups with the climate denial scores, show that there is no significant difference in the emergence of false memories between the two groups. The level of climate change denial did not significantly affect the participants’ responses to the critical words in the climate- related lists. The low level of climate denial in the sample is a limitation of this study. It is recommended that future studies compare memory performance across an appropriate sample.

Repair of genomic interstrand crosslinks

Genomic interstrand crosslinks (ICLs) are formed by reactive species generated during normal cellular metabolism, produced by the microbiome, and employed in cancer chemotherapy. While there are multiple options for replication dependent and independent ICL repair, the crucial step for each is unhooking one DNA strand from the other. Much of our insight into mechanisms of unhooking comes from powerful model systems based on plasmids with defined ICLs introduced into cells or cell free extracts. Here we describe the properties of exogenous and endogenous ICL forming compounds and provide an historical perspective on early work on ICL repair. We discuss the modes of unhooking elucidated in the model systems, the concordance or lack thereof in drug resistant tumors, and the evolving view of DNA adducts, including ICLs, formed by metabolic aldehydes.

Optimization of long-term incubation of precision-cut kidney slices

Precision-cut kidney slices (PCKS) provide a powerful model to close the gap between in vivo and in vitro research. Publications by various authors favor different incubation conditions, media, and antibiotics, that have not yet been compared in a standardized manner. After preparation, rat-PCKS were incubated in a total of nine combinations of incubation media and antibiotics for four days. We found that a combination of DMEM/F-12 and gentamicin showed the highest levels of viability. Utilizing both qualitative and quantitative methods, we observed stable levels of cellular viability for 10 days when incubated in the most suitable medium combination of DMEM and gentamicin. Additionally, a calcein acetoxymethyl/ethidium homodimer-1 based live/dead staining, analysis of total protein content and lactate dehydrogenase (LDH) were explored to assess both short- and long-term tissue viability. PCKS showed a significant decrease in total protein content, leveling off at around 60% over the duration of 10 days. To be able to evaluate viability irrespective of decreases in total protein detected, we chose to utilize the alamarBlue Cell Viability Assay. Quantifying both intra- and extracellular activity of LDH, while using different concentrations of ethanol as a positive control, we explored enzyme content as a parameter for cell membrane damage and cytotoxicity in PCKS. Overall, we showed that PCKS are suitable for both short- and long-term observation by optimizing incubation parameters, with numerous possibilities for other assays and methods in future studies.

Competitive Game Model and Evolutionary Strategy Analysis of Green Power and Thermal Power Generation

Competitive Game Model and Evolutionary Strategy Analysis of Green Power and Thermal Power Generation

Enhancing Support Vector Machine Performance: A Hybrid Approach with Davidon-Fletcher-Powell Algorithm and Elephant Herding Optimization (EHO-DFP) for Parameter Optimization

Support Vector Machines (SVMs) have gained prominence in machine learning for their capability to establish optimal decision boundaries in high-dimensional spaces. SVMs are powerful machine learning models but can encounter difficulties in achieving optimal performance due to challenges such as selecting appropriate kernel parameters, handling uncertain data, and adapting to complex decision boundaries.. This paper introduces a novel hybrid approach to enhance the performance of Support Vector Machines (SVM) through the integration of the Davidon-Fletcher-Powell (DFP) optimization algorithm and Elephant Herding Optimization (EHO) for parameter tuning. SVM, a robust machine learning algorithm, relies on effective hyperparameter selection for optimal performance. The proposed hybrid model synergistically leverages DFP's efficiency in unconstrained optimization and EHO's exploration-exploitation balance inspired by elephant herding behavior. The fusion of these algorithms address the challenges associated with traditional optimization methods. The hybrid model offers improved convergence towards the global optimum. Experimental results demonstrate the efficacy of the approach, showcasing enhanced SVM performance in terms of minimum 3.3% accuracy and 3.4% efficiency. This research contributes to advancing the field of metaheuristic optimization in machine learning, providing a promising avenue for effective parameter optimization in SVM applications.

A zebrafish gephyrinb mutant distinguishes synaptic and enzymatic functions of Gephyrin

Gephyrin is thought to play a critical role in clustering glycine receptors at synapses within the central nervous system (CNS). The main in vivo evidence for this comes from Gephyrin (Gphn)-null mice, where glycine receptors are depleted from synaptic regions. However, these mice die at birth, possibly due to impaired molybdenum cofactor (MoCo) synthesis, an essential role Gephyrin assumes throughout an animal. This complicates the interpretation of synaptic phenotypes in Gphn-null mice and raises the question whether the synaptic and enzymatic functions of Gephyrin can be investigated separately. Here, we generated a gephyrinb zebrafish mutant, vo84, that almost entirely lacks Gephyrin staining in the spinal cord. gephyrinbvo84 mutants exhibit normal gross morphology at both larval and adult stages. In contrast to Gphn-null mice, gephyrinbvo84 mutants exhibit normal motor activity and MoCo-dependent enzyme activity. Instead, gephyrinbvo84 mutants display impaired rheotaxis and increased mortality in late development. To investigate what may mediate these defects in gephyrinbvo84 mutants, we examined the cell density of neurons and myelin in the spinal cord and found no obvious changes. Surprisingly, in gephyrinbvo84 mutants, glycine receptors are still present in the synaptic regions. However, their abundance is reduced, potentially contributing to the observed defects. These findings challenge the notion that Gephyrin is absolutely required to cluster glycine receptors at synapses and reveals a new role of Gephyrin in regulating glycine receptor abundance and rheotaxis. They also establish a powerful new model for studying the mechanisms underlying synaptic, rather than enzymatic, functions of Gephyrin.

Assessing Power and Hierarchy in Cyberspace: An Approach of Power Transition Theory

This study explores the application of Power Transition Theory (PTT) to cyberspace, aiming to establish a comprehensive framework for understanding and measuring cyber power. Utilizing PTT’s national power model, the research treats states as rational and unitary actors, integrating the rational actor model to assess state behavior in cyberspace. The objectives include defining cyber power, developing a novel metric for its evaluation, and categorizing states within a hierarchical structure of cyber power. By analyzing key components such as data resources, digital economic strength, and cyber political capacity, the study provides a nuanced understanding of cyber power dynamics. The results demonstrate that the traditional IR theories retain relevance in the cyber domain, offering a valuable lens for comprehending global cyber governance and geopolitical competition. This foundational work sets the stage for future analyses of power transitions within cyberspace, highlighting the critical interplay between traditional power metrics and emerging digital landscapes.

Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction.

Signal peptides (SPs) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) and prompt-based learning provide a new opportunity for SP prediction, especially for the categories with limited annotated data. We present a parameter-efficient fine-tuning (PEFT) framework for SP prediction, PEFT-SP, to effectively utilize pretrained PLMs. We integrated low-rank adaptation (LoRA) into ESM-2 models to better leverage the protein sequence evolutionary knowledge of PLMs. Experiments show that PEFT-SP using LoRA enhances state-of-the-art results, leading to a maximum Matthews correlation coefficient (MCC) gain of 87.3% for SPs with small training samples and an overall MCC gain of 6.1%. Furthermore, we also employed two other PEFT methods, prompt tuning and adapter tuning, in ESM-2 for SP prediction. More elaborate experiments show that PEFT-SP using adapter tuning can also improve the state-of-the-art results by up to 28.1% MCC gain for SPs with small training samples and an overall MCC gain of 3.8%. LoRA requires fewer computing resources and less memory than the adapter tuning during the training stage, making it possible to adapt larger and more powerful protein models for SP prediction.

A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs.

To delineate a powerful reservoir model, rock type identification is an essential task. Recognizing intervals with promising reservoir quality in a heterogeneous reservoir, such as the Pab Formation, using well logs is critical for better exploration, because coring programs are always impractical due to time and cost constraints. Rock types are described by specific log responses, which are ultimately distinguished with the help of electrofacies. The current study uses a cluster analysis technique for the evaluation of reservoir rock types in the identified sand units. K-means cluster analysis is employed to define electrofacies, which are ultimately classified into four rock types on the basis of reservoir quality, from bad to excellent. Rock typing using cluster analysis has been done for four wells, and a correlation has been made to depict changes in electrofacies. From well-to-well correlation, it can be inferred that the reservoir quality of the Pab Formation at the lower portion of Zamzama-02 and 05 wells is excellent and is defined by rock type 4. The Zamzama-03 well in the southwestern region, on the other hand, has good to moderate reservoir quality, as demonstrated by dominating rock types 3 and 2, respectively. The applied prediction technique to the studied field provides continuous rock type identification for the entire reservoir. Using this methodology in defining rock type is cost-effective, requires less time in the demarcation of zones of interest, and is more accurate than manual analysis of the heterogeneous and thick Pab Formation. The studied approach is not only useful in the exploitation of the heterogeneous Pab Formation but also can be applied to other heterogeneous sandstone reservoirs elsewhere.

The Interplay of TLR-NFκB Signalling Pathway and Functional Immune-Related Enzymes in the Inflammatory Response of Ciona robusta.

The close phylogenetic relationship between ascidians (Tunicata) and vertebrates makes them a powerful model for studying the innate immune system. To better understand the nature and dynamics of immune responses and the mechanisms through which bacterial infections are detected and translated into inflammation in Ciona robusta, we applied an approach combining in vivo lipopolysaccharide (LPS) stimulation, immune-labelling techniques and functional enzymatic analyses. The immunohistochemistry showed that Toll-like receptor 4 (TLR4) and nuclear factor kappa B (NFκB) were expressed during the inflammatory pharynx response 4 h post-LPS, with the formation of nodules in pharynx vessel lumen. Also, the endothelium vessels were involved in the inflammatory response. Observations of histological sections from naive and buffer-inoculated ascidians confirmed an immuno-positive response. Enzyme immune parameters-which included the activity of phenoloxidase, glutathione peroxidase, lysozyme, alkaline phosphatase and esterase-showed up-modulation 4 h after LPS injection, confirming their participation during ascidian inflammatory response. These findings provide new insights into the mechanisms underlying the LPS-induced C. robusta response and suggest that a broad innate immune mechanism, as in vertebrates, is involved in the regulation of inflammatory responses. Further findings in this direction are needed to cover knowledge gaps regarding the organized set of molecular and cellular networks involved in universal immune interactions with pathogens.

The axillary lymphoid organ - an external, experimentally accessible immune organ in the zebrafish.

Lymph nodes and other secondary lymphoid organs play critical roles in immune surveillance and immune activation in mammals, but the deep internal locations of these organs make it challenging to image and study them in living animals. Here, we describe a previously uncharacterized external immune organ in the zebrafish ideally suited for studying immune cell dynamics in vivo, the axillary lymphoid organ (ALO). This small, translucent organ has an outer cortex teeming with immune cells, an inner medulla with a mesh-like network of fibroblastic reticular cells along which immune cells migrate, and a network of lymphatic vessels draining to a large adjacent lymph sac. Noninvasive high-resolution imaging of transgenically marked immune cells can be carried out in the lobes of living animals, and the ALO is readily accessible to external treatment. This newly discovered tissue provides a superb model for dynamic live imaging of immune cells and their interaction with pathogens and surrounding tissues, including blood and lymphatic vessels.

Modelling the Solar Cells Temperature and Power Output from PV Panels under Dusty Conditions

Abstract Almost all available power and temperature models of PV panels can only predict the power output and the cell temperature under clean conditions. The objective of this research is to study the effect of dust accumulation on the solar cells temperature as well as to model the solar cells temperature and the power output from PV panels under dusty conditions. An experimental setup has been developed in this study, which consists of a PV panel that contains two thermocouples embedded inside the panel, in order to measure the cells' temperatures. The cells temperatures, solar irradiance received by the panel and the power output from the panel have been measured for 5 consecutive weeks in Cairo, Egypt. The actual solar irradiance received by the cells, i.e. Gcell, was calculated using the power model of PV panels. Afterwards, the cell temperature was calculated based on the cells temperature model and the actual solar irradiance received by the cells. It has been found that the difference between the measured and the calculated cell temperature was not more than 3.71% during the time of experiments. Therefore, it can be concluded that the influence of dust on the power output as well as the cell temperature can be calculated using the power model and the cell temperature model under clean conditions, but the solar irradiance in these models should be replaced by the actual solar irradiance received by the cells, i.e. Gcell.

Quantum word embedding for machine learning

The accelerated progress in quantum computing has enabled a new form of machine intelligence that runs on quantum hardware, which holds great promise for more powerful computational models in various learning tasks. An emergent application of Quantum Machine Intelligence (QMI) is Quantum Natural Language Processing (QNLP). This paper proposes a multi-dimensional, finite automaton model for quantum word embedding (QWE) via the Galois field. We demonstrated the model to three applications: (1) English vocabulary, (2) amino acid-based genetic codes, and (3) DNA-based genetic codes. The numerical results obtained from the proposed algorithm for the English vocabulary indicate that it produces more representative word features than Word2Vec based on the word distance metric. Second, the proposed algorithm is also utilized to model RNA-Protein interaction based on the latent distance of a given molecule, which is demonstrated on three large datasets, namely RPI369, RPI1807, and RPI2241. Finally, two embedding techniques for DNA-based genetic codes are proposed in this work: Two-state Lackadaisical Encoding (TCE) and Topological-Cyclic Encoding (TLE). These techniques enable extracting relevant features for the efficacy score of gRNAs used in the CRISPR-Cas 9 system, demonstrated on 15 datasets, compared to 12 mathematical features. We make our implementation available at https://github.com/namnguyen0510/Quantum-Embedding-of-Word/tree/main.

Comparative Analysis of Digital Self-Interference Cancellation Methods for Simultaneous Transmit and Receive Arrays

This paper provides a comprehensive theoretical derivation of the residual noise power and effective isotropic isolation (EII) signal models in baseband digital cancellation, aperture-level digital cancellation, and beamforming spatial cancellation models in simultaneous transmit and receive (STAR) arrays. We simulated and analyzed the isolation performance and self-interference (SI) noise cancellation of STAR systems using digital SI cancellation (SIC) and beamforming in a 32-element planar array. The simulation results show that in the absence of SIC or baseband digital SIC, the EII obtained by receive adaptive beamforming (ABF) is 20 dB higher than that obtained by transmit ABF. On the basis of aperture-level digital SIC, the EII obtained from transmit ABF and receive ABF is basically the same. The EII obtained by baseband digital SIC is 22.5 dB and 41.1 dB lower than that of transmit ABF and receive ABF, respectively. Therefore, baseband digital SIC technology is not required when using transmit ABF and receive ABF.

Comparing comparison: Smith and Davidson

Comparison, as a method in the study of religion, has received considerable criticism in recent years. Much of the discussion addresses what we call the dilemma of religious comparison: either similarities between the cases compared are real and the differences accidental, or the differences are real and the similarities merely apparent. The first path is held to artificially impose similarity by wrongly privileging one of the things being compared, which imposes an essentialist view, taking its characteristic as the measure of all comparable cases. The other path exaggerates differences between comparands, undermining the very premise of comparison. We argue that this apparent dilemma presupposes a representational conception of meaning, namely that the task of comparison is to discover what kind of thing is represented by the cases being compared. However, there are other conceptions of meaning that might can potentially avoid or resolve the dilemma, by reorientating the basic conception of comparison, as opposed to entering into details of recent critical debates. This article argues that interpretationism, an approach to meaning associated with philosopher Donald Davidson does just this. JZ Smith did not explicitly adopt any semantic theory. But we demonstrate that he implicitly endorsed all of its main elements in his work on comparison. Smith’s writings represent a rich and detailed model of how comparison can be carried out under an interpretationist semantics. This article has two important implications for the study of religion. First, it dissolves the dilemma of religious comparison, suggesting a firmer theoretical and methodological basis for comparative studies. Second, it underlines that Smith’s work serves as a powerful and practical model of comparative method, as constructed on this basis. As a corollary, an interdisciplinary side-benefit, reading Smith in light of Davidson also makes important contributions to philosophy of language.

Unveiling the ototoxic effects of paraquat on zebrafish larva

Paraquat is a widely utilized herbicide in agricultural fields posing a significant impact on human health and the environment due to its potent oxidant properties. Rampant paraquat usage leads to serious health hazards to farmers and the ecosystem, particularly the water bodies. Paraquat exposure can damage dopaminergic neurons causing Parkinson's disease in humans and other animal models. Extensive research has been done regarding the mode of action, pathophysiology and molecular mechanisms of paraquat-induced Parkinson's disease. Meanwhile, the ototoxic effect of paraquat remains poorly understood. Potential ototoxins can cause sensorineural hearing loss, one of the most common sensory disabilities in humans. In this study, we investigated the harmful effects of paraquat on neuromast hair cells in zebrafish larvae, a powerful model organism for auditory research. We treated sub-lethal concentrations (125 μM to 1000 μM) of paraquat to 3 and 4 dpf zebrafish larvae to investigate its ototoxic effects via rheotaxis behavioral assay, neuromast staining and scanning electron microscopy. The behavioral assay findings showed a drastic decline in the rheotaxis behavior in all the concentrations of paraquat-treated larvae. Furthermore, DASPEI neuromast vital staining displayed a dose-dependent reduction in the neuromast hair cells as we increased the paraquat concentration. The scanning electron microscope data revealed the significant shortening of kinociliary length, a decrease in stereociliary density and changes in semilunar peridermal cell morphology signifying the damaging effects of paraquat at the cellular level. Collectively, the behavioral, anatomical and morphological studies highlight the potential ototoxic effects of paraquat on zebrafish neuromast hair cells, further signifying its potential role in causing hearing loss in humans.

Modelling and optimization method for energy saving of computer numerical control machine tools under operating condition

The widespread application of machine tools in industry results in very high energy consumption. In order to save energy, it is an effective means to reduce the processing power while improving the processing efficiency of machine tools. Since milling parameters directly affect machine performance, the selection of suitable milling parameters is particularly important. In this regard, this study proposes an integrated modelling and optimization method for energy saving of computer numerical control (CNC) machine tools under operating condition. Firstly, the operating condition of the CNC machine are analyzed and the processing power and efficiency mathematical models are established based on the four milling parameters (n, fz, ae, ap). Subsequently, the Pareto optimal solution is introduced to establish an optimization model integrating the processing power and time. To solve the optimization problem, an adaptive chaotic multi-objective chimp algorithm with fast convergence and satisfactory optimization is proposed considering three aspects: search space, convergence factor, and chaotic mapping. Afterwards, further degrees of freedom and sensitivity analyses were carried out on the constructed model using the Sobol method. Finally, comparative analysis and validation are conducted through simulation cases and actual processing experiments, respectively. The results show that both validation scenarios have the same conclusion. Compared with three existing algorithms, the proposed method reduces the processing power by 5.63 %, 4.65 % and 4.51 % and improves the processing efficiency by 27.88 %, 15.11 % and 15.31 %, respectively. Based on the above enhancements, the energy consumption is reduced by 58.21 %, 45.54 % and 41.45 % respectively.

Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California

Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows.

Joint Modeling of Wind Speed and Power via a Nonparametric Approach

Power output from wind turbines is influenced by wind speed, but the traditional theoretical power curve approach introduces uncertainty into wind energy forecasting models. This is because it assumes a consistent power output for a given wind speed. To address this issue, a new nonparametric method has been proposed. It uses K-means clustering to estimate wind speed intervals, applies kernel density estimation (KDE) to establish the probability density function (PDF) for each interval and employs Monte Carlo simulation to predict power output based on the PDF. The method was tested using data from the MERRA-2 database, covering five wind farms in Brazil. The results showed that the new method outperformed the conventional estimation technique, improving estimates by an average of 47 to 49%. This study contributes by (i) proposing a new nonparametric method for modeling the relationship between wind speed and power; (ii) emphasizing the superiority of probabilistic modeling in capturing the natural variability in wind generation; (iii) demonstrating the benefits of temporally segregating data; (iv) highlighting how different wind farms within the same region can have distinct generation profiles due to environmental and technical factors; and (v) underscoring the significance and reliability of the data provided by the MERRA-2 database.

Developing GA-FuL: A Generic Wide-Purpose Library for Computing with Geometric Algebra

The Geometric Algebra Fulcrum Library (GA-FuL) version 1.0 is introduced in this paper as a comprehensive computational library for geometric algebra (GA) and Clifford algebra (CA), in addition to other classical algebras. As a sophisticated software system, GA-FuL is useful for practical applications requiring numerical or symbolic prototyping, optimized code generation, and geometric visualization. A comprehensive overview of the GA-FuL design is provided, including its core design intentions, data-driven programming characteristics, and extensible layered design. The library is capable of representing and manipulating sparse multivectors of any dimension, scalar kind, or metric signature, including conformal and projective geometric algebras. Several practical and illustrative use cases of the library are provided to highlight its potential for mathematical, scientific, and engineering applications. The metaprogramming code optimization capabilities of GA-FuL are found to be unique among other software systems. This allows for the automated production of highly efficient code, based on powerful geometric modeling formulations provided by geometric algebra.