Surrogate Data Research Articles

Solvation phenomena in ternary system tetramethylurea-methylacetamide-water: Insights from volumetric, compressibility and FTIR analysis

The properties of the ternary systems N,N,N',N'-tetramethylurea - N-methylacetamide - water were investigated using Fourier-transform infrared spectroscopy (FTIR), volumetric and compression measurements. Densities and sound velocities were determined in order to obtain the apparent molar volumes (VΦ) and apparent molar isentropic compressions (▪S,Φ). These values were then extrapolated to infinite dilution. Additionally, interaction parameters were calculated from the McMillan-Mayer theory. The studies were conducted at 288.15, 298.15, and 308.15 K, at atmospheric pressure (0.1 MPa). The concentration ranges of N-methylacetamide were 2, 4, 6, and 8 moles per kilogram of pure water, and for N,N,N',N'-tetramethylurea from 0 to around 0.35 moles per kilogram of solvent. Discrete changes in isentropic compression were observed. This is the result of the alignment of plots of ▪S,Φ0 as a function of NMA concentration. The results for N,N,N',N'-tetramethylurea were compared with analogous data for the system containing n-butylurea, which is an isomeric compound but exhibits different hydration behaviour. Additionally, large volumetric interaction coefficients were observed, indicating strong interactions between urea derivatives and NMA. This suggests the possibility of strong interactions between protein destabilizers and the protein backbone, differing from those observed for protein structure stabilizers. The observation contributes to understanding the mechanism of osmolyte action and their influence on protein stability.

Pioneering Museum Data Transformation: Unifying Legacy Systems and Enhancing Data Integrity

In the digital era, museums confront the challenge of modernising legacy data systems to align with current standards. Part of the RECODE (Rethinking Collections Data Ecosystems) Programme (Dupont et al. 2022) examines the complex transition from disparate departmental object models to a unified system, reflecting the broader museum community's struggle with data standardisation. The case study centres on consolidating five distinct data models accumulated over two decades, each tailored to different departmental needs under various constraints. The data mapping process identifies and aligns similar fields across models, facilitating the integration of analogous data points and exposing redundancies and unique practices embedded over years. Major challenges include data complexity, diversity, and quality issues (cleaning, standardizing, and deduplicating), compounded by the massive scale of the data: currently, the Natural History Museum London (NHM) has 262,831,590 records across 8,175 fields, and a total volume of 12 TB of data, which define both the ongoing data modelling work and our future data migration. A key innovation in our process is the immediate visibility of data issues that became apparent upon migrating to Amazon Web Services (AWS). AWS serves as the staging environment for our transition to the new NHM Collections Management System (CMS) and offers an unprecedented platform for directly addressing these challenges. It enables us to query all our data—across all departments and fields—in just seconds. This capability provides a comprehensive view that was previously unattainable. The core of this presentation is sharing "lessons learned" from navigating the intricacies of an ongoing CMS transition within a museum. The endeavor to untangle and unify diverse data models is a common challenge (IEEE Big Data Governance and Metadata Management Industry Connections Activity 2020,Wu et al. 2022, Wu et al. 2021,Little et al. 2022,Woodburn et al. 2022) highlighting the importance of community engagement and knowledge exchange. Our findings underscore the necessity of cross-departmental collaboration and the benefits of a data-driven data modelling approach. By sharing our journey and the developed comprehensive object model (Collier and Woodburn 2022), we aim to contribute valuable insights to museums undergoing similar transformations. The RECODE Programme sheds light on the practical aspects of CMS modernisation through the analytics and knowledge derived from over two decades of collections data and data use.

Atmospheric black carbon in the metropolitan area of La Paz and El Alto, Bolivia: concentration levels and emission sources

Abstract. Black carbon (BC) is a major component of submicron particulate matter (PM), with significant health and climate impacts. Many cities in emerging countries lack comprehensive knowledge about BC emissions and exposure levels. This study investigates BC concentration levels, identifies its emission sources, and characterizes the optical properties of BC at urban background sites of the two largest high-altitude Bolivian cities: La Paz (LP) (3600 m above sea level) and El Alto (EA) (4050 m a.s.l.), where atmospheric oxygen levels and intense radiation may affect BC production. The study relies on concurrent measurements of equivalent black carbon (eBC), elemental carbon (EC), and refractory black carbon (rBC) and their comparison with analogous data collected at the nearby Chacaltaya Global Atmosphere Watch Station (5240 m a.s.l). The performance of two independent source apportionment techniques was compared: a bilinear model and a least-squares multilinear regression (MLR). Maximum eBC concentrations were observed during the local dry season (LP: eBC = 1.5 ± 1.6 µg m−3; EA: 1.9±2.0 µg m−3). While eBC concentrations are lower at the mountain station, daily transport from urban areas is evident. Average mass absorption cross sections of 6.6–8.2 m2 g−1 were found in the urban area at 637 nm. Both source apportionment methods exhibited a reasonable level of agreement in the contribution of biomass burning (BB) to absorption. The MLR method allowed the estimation of the contribution and the source-specific optical properties for multiple sources, including open waste burning.

Reconstructing the Ocean State Using Argo Data and a Data-Driven Method

Abstract Over the past two decades, Argo profiles have provided unprecedented insight into the global patterns of space and time variability of ocean temperature and salinity, significantly reducing associated uncertainties. However, analyzing such assessments during the pre-Argo period remains challenging due to the scarcity of observations in many regions. From the Argo period, a set of dominant three-dimensional patterns can be estimated using Empirical Orthogonal Function (EOF) analysis, which helps to fill in observational gaps. From the associated principal components, temporal fluctuations can be observed, aiming to build a catalog of possible ocean state trajectories. To map pre-Argo observations, EOFs are used in a data assimilation framework that uses this catalog to feed an analog prediction and provide reanalysis. In this study, a new data-driven interpolation method called RedAnDA (Reduced-space Analog Data Assimilation) was tested in the tropical Pacific Ocean. RedAnDA was first validated through an Observing System Simulation Experiment (OSSE) approach, using synthetic observations extracted from a model simulation. It was subsequently applied to a real historical dataset and compared with other available reanalysis products. Overall, the reconstructed temperature field showed variability consistent with the OSSE true field and other reanalysis products in the real data application. Further improvements are needed to optimally estimate uncertainty, but RedAnDA already combines valuable information about state predictability, observational sampling, and unresolved scale issues.

Suggested guide to using lactate gap as a surrogate marker in the diagnosis of ethylene glycol overdose.

Ethylene glycol (EG) poisoning, if not diagnosed rapidly, can lead to poor patient outcomes. Gas Chromatography (GC) is primarily used for EG quantitation which is rarely available, and the turn-around time may be prolonged. Most lactate results from point-of care (POCT) methods are falsely elevated in EG poisoning compared with automated chemistry analyser results. In combination the lactate gap (POCT-Automated chemistry) can be used as surrogate marker in just about all laboratories to indicate likely EG toxicity and guide treatment. A male presented by ambulance to hospital with severe agitation requiring mechanical ventilation to facilitate ongoing management. Venous blood gas analysis confirmed a high anion gap metabolic acidosis (HAGMA) with an elevated lactate. The lactate and osmolarity measured in the laboratory showed a normal lactate and high osmolarity, giving a large osmolar gap. The patient was immediately commenced on renal replacement therapy for presumed EG poisoning to minimize kidney injury, and the treatment continued for 19 hours. A very high EG concentration was confirmed by GC the next day. An elevated lactate gap along with a HAGMA and osmolar gap can provide rapid surrogate laboratory data indicating EG poisoning enabling timely treatment and better patient outcomes.

Design and Construction of an Arduino-Driven Weight-Based Fish Sorting Tool

This research aims to design and develop a weight-based fish sorting tool that uses a strain gauge sensor under Arduino Uno control. We designed this tool to enhance the effectiveness and accuracy of the fish sorting process, focusing on three size categories: small, medium, and large. The system consists of a strain gauge sensor that measures the weight of the fish, an HX711 module that converts analog data to digital, a servo motor to move the fish, and a conveyor that moves the fish through the sorting process. We conducted testing to measure the accuracy of the system in determining the fish's weight and its success in classifying it based on its size. The test results show that this device has an average accuracy rate of 99.5%. However, minor differences in weight measurements suggest the need for further improvements. Overall, the fishing industry has enormous potential to implement this tool to increase productivity and reduce reliance on manual methods.

Summer Antarctic expeditions in seasonal stations as analogs for long-duration space exploration missions: A critical review

This critical review aims to compare the conditions of summer Antarctic expeditions in seasonal stations with key characteristics of long-duration space exploration missions (LDSEM). Utilizing NASA's Analog Assessment Tool and data from the COMNAP Antarctic Station Catalogue, along with scientific literature, the review identifies significant parallels for LDSEM analog research. We assess how seasonal and year-round stations differ and highlight aspects of where seasonal stations serve as a better or worse analog for LDSEM. Key findings include that while summer expeditions allow for more feasible evacuations than winter-overs, their access to medical care is more limited. Crowdedness in summer stations with shared rooms better represents LDSEM conditions than the lower density of winter-over settings. Varying daylight hours in summer stations provide a closer parallel to Mars or Moon surface missions than the continuous darkness of winter-over conditions. Additionally, constant hazards, risk management strategies, isolation, sensory deprivation, workload, leadership structures, autonomy, and communication challenges in summer stations align well with LDSEM scenarios. Conclusively, we propose a shift in perceptions, recognizing seasonal Antarctic expeditions as a valuable analog of planetary LDSEM with several advantages over traditionally accepted winter-over settings. Further comparative and longitudinal studies between seasonal and year-round Antarctic stations should be pursued to enhance LDSEM analog research and support interdisciplinary collaboration. This approach will not only advance progress in space exploration research but also improve the quality of life and safety in remote and extreme environments.

Exploiting Spatial Ionic Dynamics in Solid-State Organic Electrochemical Transistors for Multi-Tactile Sensing and Processing.

The human nervous system inspires the next generation of sensory and communication systems for robotics, human-machine interfaces (HMIs), biomedical applications, and artificial intelligence. Neuromorphic approaches address processing challenges; however, the vast number of sensors and their large-scale distribution complicate analog data manipulation. Conventional digital multiplexers are limited by complex circuit architecture and high supply voltage. Large sensory arrays further complicate wiring. An 'in-electrolyte computing' platform is presented by integrating organic electrochemical transistors (OECTs) with a solid-state polymer electrolyte. These devices use synapse-like signal transport and spatially dependent bulk ionic doping, achieving over 400 times modulation in channel conductance, allowing discrimination of locally random-access events without peripheral circuitry or address assignment. It demonstrates information processing from 12 tactile sensors with a single OECT output, showing clear advantages in circuit simplicity over existing all-electronic, all-digital implementations. This self-multiplexer platform offers exciting prospects for circuit-free integration with sensory arrays for high-quality, large-volume analog signal processing.

InfoFlowNet: A multi-head attention-based self-supervised learning model with surrogate approach for uncovering brain effective connectivity

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, which gauges the directional influences among brain regions, is pivotal for these studies. This research introduces InfoFlowNet, a novel self-supervised learning model designed to infer causal relationships in electroencephalogram (EEG) data, aiming at capturing the complex information flow among brain regions. Specifically, InfoFlowNet employs convolution operations and multi-head self-attention mechanisms with a masking feature, which ignores self-connections to focus on inter-regional dynamics. Additionally, a new causal magnitude estimation is introduced by assessing the impact of shuffled surrogate data on signal prediction to quantify causal influence. Experiments using synthetic data and real EEG datasets demonstrate that InfoFlowNet effectively uncovers time-varying causal relationships. Compared with the Granger causality model (GCM) and the temporal causal discovery framework (TCDF), InfoFlowNet shows superior sensitivity in detecting significant causal edges. For instance, in a psychomotor vigilance task, InfoFlowNet identified 8 out of 16 significant causal edges, significantly outperforming GCM and TCDF. Furthermore, InfoFlowNet successfully passes the whiteness test on the model's residuals, and advanced analyses illustrate the advantages of utilizing multi-head attention and masking mechanisms to capture effective connectivity. In sum, InfoFlowNet represents a significant advancement in the analysis of effective connectivity, offering a deeper understanding of brain network dynamics. The model's ability to discern complex causal interactions supports its potential application in broader neuroscientific research and applications.

Ultra-fast Digital DPC Yielding High Spatio-temporal Resolution for Low-Dose Phase Characterization.

In the scanning transmission electron microscope, both phase imaging of beam-sensitive materials and characterization of a material's functional properties using in situ experiments are becoming more widely available. As the practicable scan speed of 4D-STEM detectors improves, so too does the temporal resolution achievable for both differential phase contrast (DPC) and ptychography. However, the read-out burden of pixelated detectors, and the size of the gigabyte to terabyte sized data sets, remain a challenge for both temporal resolution and their practical adoption. In this work, we combine ultra-fast scan coils and detector signal digitization to show that a high-fidelity DPC phase reconstruction can be achieved from an annular segmented detector. Unlike conventional analog data phase reconstructions from digitized DPC-segment images yield reliable data, even at the fastest scan speeds. Finally, dose fractionation by fast scanning and multi-framing allows for postprocess binning of frame streams to balance signal-to-noise ratio and temporal resolution for low-dose phase imaging for in situ experiments.

Comparison of Postoperative Analgesic Effectiveness of Bupivacaine and Bupivacaine Plus Dexmedetomidine Wound Infiltration in Abdominal Surgeries under General Anesthesia

Abdominal surgeries were major surgical procedures that were performed at any teaching hospital. Pain control was a major concern during intra-operative as well as post-operative periods in these patients. Objective: To compare post-operative analgesic effectiveness of bupivacaine and bupivacaine plus dexmedetomidine wound infiltration in abdominal surgeries under General Anesthesia. Methods: This randomized controlled trial was conducted at the Department of Anesthesia, Sahiwal Teaching Hospital Sahiwal from 1st April, 2024 till 31st May 2024. Sixty-four patients underwent a pre-operative assessment on the day before surgery. Both Groups received wound infiltration with studied drugs at the end of surgery. After surgery, patients were assessed for pain using a Visual Analog Scale (VAS) and data was collected and analyzed using Statistical Package for the Social Sciences (SPSS) version 26.0. Results: The mean post-operative analgesia duration of the patients on bupivacaine was 11.78 ± 1.64 but the mean post-operative analgesia duration of the patient on bupivacaine plus dexmedetomidine was 19.19 ± 2.49. (2-tailed significance 0.001). The mean opioid consumption in mg of the patients in bupivacaine was 20.69 ± 4.31 but the mean opioid consumption in mg of the patient in bupivacaine plus dexmedetomidine was 10.88 ± 4.53. (2-tailed significance 0.001). In bupivacaine, patients with bradycardia were 0% and patients without bradycardia were 100% but in bupivacaine plus dexmedetomidine, patients with bradycardia were 15.6% and patients without bradycardia were 84.4%. Conclusions: There was a difference in the analgesic effectiveness of dexmedetomidine when added to bupivacaine in wound infiltration in abdominal surgeries.

Stability of infraslow correlation structure in time-shifted intracranial EEG signals.

It is increasingly understood that the epilepsies are characterized by network pathology that can span multiple spatial and temporal scales. Recent work indicates that infraslow (<0.2Hz) envelope correlations may form a basis for distant spatial coupling in the brain. We speculated that infraslow correlation structure may be preserved even with some time lag between signals. To this end, we studied intracranial EEG (icEEG) data collected from 22 medically refractory epilepsy patients. For each patient, we selected hour-long background, awake icEEG epochs before and after antiseizure medication (ASM) taper. For each epoch, we selected 5,000 random electrode contact pairs and estimated magnitude-squared coherence (MSC) below 0.15Hz of band power time-series in the traditional EEG frequency bands. Using these same contact pairs, we shifted one signal of the pair by random durations in 15-s increments between 0 and 300s. We aggregated these data across all patients to determine how infraslow MSC varies with duration of lag. We further examined the effect of ASM taper on infraslow correlation structure. We also used surrogate data to empirically characterize MSC estimator and to set optimal parameters for estimation specifically for the study of infraslow activity. Our empirical analysis of the MSC estimator showed that hour-long segments with MSC computed using 3-min windows with 50% overlap was sufficient to capture infraslow envelope correlations while minimizing estimator bias and variance. The mean MSC decreased monotonically with increasing time lag until 105s of lag, then plateaued between 106 and 300s. Significantly nonzero infraslow envelope MSC was preserved in all frequency bands until about 1min of time lag, both pre- and post-ASM taper. We also saw a slight, but significant increase in infraslow MSC post-ASM taper, consistent with prior work. These results provide evidence for the feasibility of examining infraslow activity via its modulation of higher-frequency activity in the absence of DC-coupled recordings. The use of surrogate data also provides a general methodology for benchmarking measures used in network neuroscience studies. Finally, our study points to the clinical relevance of infraslow activity in assessing seizure risk.

The effect of visual speech cues on neural tracking of speech in 10-month-old infants.

While infants' sensitivity to visual speech cues and the benefit of these cues have been well-established by behavioural studies, there is little evidence on the effect of visual speech cues on infants' neural processing of continuous auditory speech. In this study, we investigated whether visual speech cues, such as the movements of the lips, jaw, and larynx, facilitate infants' neural speech tracking. Ten-month-old Dutch-learning infants watched videos of a speaker reciting passages in infant-directed speech while electroencephalography (EEG) was recorded. In the videos, either the full face of the speaker was displayed or the speaker's mouth and jaw were masked with a block, obstructing the visual speech cues. To assess neural tracking, speech-brain coherence (SBC) was calculated, focusing particularly on the stress and syllabic rates (1-1.75 and 2.5-3.5Hz respectively in our stimuli). First, overall, SBC was compared to surrogate data, and then, differences in SBC in the two conditions were tested at the frequencies of interest. Our results indicated that infants show significant tracking at both stress and syllabic rates. However, no differences were identified between the two conditions, meaning that infants' neural tracking was not modulated further by the presence of visual speech cues. Furthermore, we demonstrated that infants' neural tracking of low-frequency information is related to their subsequent vocabulary development at 18 months. Overall, this study provides evidence that infants' neural tracking of speech is not necessarily impaired when visual speech cues are not fully visible and that neural tracking may be a potential mechanism in successful language acquisition.

Multiresolution analysis of the US stock market nonlinear dynamics based on surrogate data and singular value decomposition

Multiresolution analysis of the US stock market nonlinear dynamics based on surrogate data and singular value decomposition

(Invited) Analog Computing with High Precision and Programmability Enabled by Memristors

While digital computing dominates the technological landscape, analog computing distinguishes itself with superior energy efficiency and high throughput. However, its historical limitation in precision and programmability has confined its application to specific and low-precision domains, notably in neural networks. The escalating challenge posed by the analog data deluge calls for versatile analog platforms. These platforms must not only exhibit exceptional efficiency but also boast reconfigurability and precision.Recent breakthroughs in analog devices, such as memristors, have laid the groundwork for unparalleled analog computing capabilities. Leveraging the multifaceted role of memristors, we introduce memristive field-programmable analog arrays (FPAAs), mirroring the functionality of their digital counterparts, field-programmable digital arrays (FPGAs). To elevate precision, we delve into the origins of reading noise, successfully mitigating it and achieving an unparalleled 2048 conductance levels in individual memristors—equivalent to 11 bits per cell, setting a record precision among diverse memory types. Acknowledging the persistent demand for single or double precision in various applications, we propose and develop a circuit architecture and programming protocol. This innovation enables analog memories to attain arbitrarily high precision with minimal circuit overhead. Our experimental validation involves a memristor System-on-Chip fabricated in a standard foundry, demonstrating significantly improved precision and power efficiency compared to traditional digital systems.The co-design approach presented empowers low-precision analog devices to perform high-precision computing within a programmable platform. This demonstration underscores the transformative potential of analog computing, transcending historical limitations and ushering in a new era of precision and efficiency.

Implementation of microcontroller board on a sustainable and degradable PLA/flax composite substrate: a case study

In this paper, we present a novel polylactic-acid/flax-composite substrate and the implementation of a demonstrator: a microcontroller board based on commercial design. The substrate is developed for printed circuit board (PCB) applications. The pre-preg is biodegradable, reinforced, and flame-retarded. The novel material was developed to counter the increasing amount of e-waste and to improve the sustainability of the microelectronics sector. The motivation was to present a working circuit in commercial complexity that can be implemented on a rigid substrate made of natural, bio-based materials with a structure very similar to the widely used Flame Retardant Class 4 (FR4) substrate at an early technological readiness level (2–3). The circuit design is based on the Arduino Nano open-source microcontroller board design so that the demonstration could be programmable and easy to fit into education, IoT applications, and embedded designs. During the work, the design was optimized at the level of layout. The copper-clad pre-preg was then prepared and processed with subtractive printed wiring technology and through hole plating. The traditional surface mounting methodology was applied for assembly. The resulting yield of PCB production was around 50%. Signal analysis was successful with analogue data acquisition (voltage) and low-frequency (4 kHz) tests, indistinguishable from sample FR4 boards. Eventually, the samples were subjected to highly accelerated stress test (HAST). HAST tests revealed limitations compared to traditional FR4 printed circuit materials. After six cycles, the weight loss was around 30% in the case of PLA/Flax, and as three-point bending tests showed, the possible ultimate strength (25 MPa at a flexural state) was reduced by 80%. Finally, the sustainability aspect was assessed, where we found that ∼95 vol% and ∼90 wt% of the traditional substrate can be substituted, significantly easing the load of waste on the environment.

Transfer entropy on collective motion with undeclared loose leader–follower (LLF) structure

During an emergency or evacuation, individuals focus on the movement of their neighbors and follow those in sight, even without social affiliation, forming the undeclared Loose Leader-Follower (LLF) group, rather than the usual assumption of following certain rules in most of evacuation models. Inspired by the real-life experience and experimental data, the study develops a novel stochastic process-based evacuation model, integrated the LLF structure in collective motion to clarify the mechanism underlying individual interaction and refine the walking process according to attainable information. Transfer entropy (TE), a model-free measure of the direction of information flow, is used to detect the role of the individuals in the LLF structure from the time-series reconstructed motion information (e.g., the walking coordinate series). To reduce the error of the role detection, according to the 3σ principle for TE values, the detection threshold is decided based on the surrogate walking data. For simulation of their motion decision in the LLF structure, the TE is integrated into the walking behavior mode (TE-POMDP) using the Partially Observable Markov Decision Process (POMDP), that produces their motion behavior under the typical attained information condition. The results show that the TE-POMDP can provide more realistic trajectories. Additionally, we find that a ‘Delta-like’ formation of leading group distribution was presented and remained stable in the procession after a short period of confusion in early stage.

Assembling and testing optoelectronic system to record and process signals from fiber-optic sensors

The given research presents assembling and testing optoelectronic system to record and process signals from fiber-optic sensors. The main optoelectronic systems to record and process the signals from fiber-optic sensors are light source controller and optical power detector. There was assembled controller diagram, which apart from light source includes current source for its adequate operation, as well as the systems necessary for stabilizing its working point. The scheme was modelled for specifying nominal and maximum operation criteria. Construction has been designed in the way, that light source controller includes structures of the current regulation and stabilization super luminescent diode (SLED) and temperature stabilization. Apart from that, there was assembled the microsystem of optical power detector additionally to the light detector, which includes the microsystems of intensification and filtration of the signal measured, processing analog data into digital form, microcontroller, used for preliminary data analysis. Data of optoelectronic systems diagram to record and process the signals from fiber-optic sensors has high response speed, low noise level and sufficient progress.

DOA Estimation on One-Bit Quantization Observations through Noise-Boosted Multiple Signal Classification.

Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data.

Delving into the Complexity of Analogical Reasoning: A Detailed Exploration with the Generalized Multicomponent Latent Trait Model for Diagnosis.

Research on analogical reasoning has facilitated the understanding of response processes such as pattern identification and creative problem solving, emerging as an intelligence predictor. While analogical tests traditionally combine various composition rules for item generation, current statistical models like the Logistic Latent Trait Model (LLTM) and Embretson's Multicomponent Latent Trait Model for Diagnosis (MLTM-D) face limitations in handling the inherent complexity of these processes, resulting in suboptimal model fit and interpretation. The primary aim of this research was to extend Embretson's MLTM-D to encompass complex multidimensional models that allow the estimation of item parameters. Concretely, we developed a three-parameter (3PL) version of the MLTM-D that provides more informative interpretations of participant response processes. We developed the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D), which is a statistical model that extends Embretson's multicomponent model to explore complex analogical theories. The GMLTM-D was compared with LLTM and MLTM-D using data from a previous study of a figural analogical reasoning test composed of 27 items based on five composition rules: figure rotation, trapezoidal rotation, reflection, segment subtraction, and point movement. Additionally, we provide an R package (GMLTM) for conducting Bayesian estimation of the models mentioned. The GMLTM-D more accurately replicated the observed data compared to the Bayesian versions of LLTM and MLTM-D, demonstrating a better model fit and superior predictive accuracy. Therefore, the GMLTM-D is a reliable model for analyzing analogical reasoning data and calibrating intelligence tests. The GMLTM-D embraces the complexity of real data and enhances the understanding of examinees' response processes.