Original Dimensions Research Articles

Embedded System‐Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model

ABSTRACTThe disease of malaria, transmitted by female Anopheles mosquitoes, is highly contagious, resulting in numerous deaths across various regions. Microscopic examination of blood cells remains one of the most accurate methods for malaria diagnosis, but it is time‐consuming and can produce inaccurate results occasionally. Due to machine learning and deep learning advances in medical diagnosis, improved diagnostic accuracy can now be achieved while costs can be reduced compared to conventional microscopy methods. This work utilizes an open‐source dataset with 26 161 blood smear images in RGB for malaria detection. Our preprocessing resized the original dimensions of the images into 64 × 64 due to the limitations in computational complexity in developing embedded systems‐based malaria detection. We present a novel embedded system approach using 119 154 trainable parameters in a lightweight 17‐layer SqueezeNet model for the automatic detection of malaria. Incredibly, the model is only 1.72 MB in size. An evaluation of the model's performance on the original NIH malaria dataset shows that it has exceptional accuracy, precision, recall, and F1 scores of 96.37%, 95.67%, 97.21%, and 96.44%, respectively. Based on a modified dataset, the results improved further to 99.71% across all metrics. Compared to current deep learning models, our model significantly outperforms them for malaria detection, making it ideal for embedded systems. This model has also been rigorously tested on the Jetson Nano B01 edge device, demonstrating a rapid single image prediction time of only 0.24 s. The fusion of deep learning with embedded systems makes this research a crucial step toward improving malaria diagnosis. In resource‐constrained settings, the model's lightweight architecture and accuracy enhancements hold great promise for addressing the critical challenge of malaria detection.

Anti-Shrinkage Technique for Feline Skin Samples

Surgical resection of subcutaneous neoplasms with clear margins is crucial for preventing local recurrence and avoiding adjuvant treatments. However, the evaluation of surgical margins often differs significantly from the histopathological assessment due to tissue shrinkage, which can result in inaccurate therapeutic assessments and unreliable patient prognoses. In this study, ten feline cadavers were utilized. Six 50 mm diameter specimens were collected from three regions (thorax, flank, femur) and measured at three time points: T0 (excision time), T1 (10 min after incision), and T2 (at least 48 h after sample collection and formalin fixation). Samples in the study group were stretched and fixed on a cork plate with pinpoint needles after excision to restore their original dimensions. All specimens exhibited a similar trend. After 48 h of formalin fixation, the control specimens showed significant shrinkage, with a reduction of 25.73% in radius and 26.32% in diameter. In contrast, the study specimens demonstrated minimal changes, with a radius reduction of −0.28% and no change in diameter. The results indicate that all feline skin specimens experienced significant shrinkage of approximately one-quarter from their pre-incisional size. Stretching and pinning the excised tissues allowed for the restoration and maintenance of original dimensions even after formalin fixation. This technique represents a valid and practical approach to minimize tissue shrinkage.

Design System of Solar-Powered Safety Lamp Integrated Proximity Sensor as a Solution for Accident Prevention at Blind Spot Areas

The curved contour of the road creates blind spots which increase the risk of traffic accidents. Throughout 2019, there were 107,500 accident cases recorded in Indonesia. Therefore, efforts are needed to provide road infrastructure that supports driving safety, such as traffic signs to reduce the risk of accidents in blind spot areas. This research aims to design a smart safety light warning system that is integrated with solar-based street lighting. The design uses a design-based research approach referring to several established standards including Indonesia National Standard (SNI) 7931: 2008, SNI IEC 60529:2014, Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 13 of 2014, Manual on Uniform Traffic Control Devices (MUTCD) issued by the Federal Highway Administration (FHWA) of the United States Department of Transportation (USDOT), and Article 115 of Minister of Transportation Republic of Indonesia Regulation PM 27 of 2018. This study used Design-Based Research (DBR) as a research methodology. The result of this study is a prototype design and development of smart safety light warning system which can be optimized. Validation of the design and circuit is conducted using prototyping modeling 1:20 from the original dimensions to measure the function and performance of the circuit. The prototype is capable of turning on warning lights and siren notifications when detecting objects with a maximum distance of 350 cm as measured by the HC – SR04 sensor. Charging the stored solar panels via the accumulator allows the technology to continue working for 45 hours without charging. For power efficiency purposes, the technology is equipped with an LDR (Light Dependent Resistor) sensor with a minimum limit of 341-lux which regulates the lighting. The development of these safety lights will increase driver awareness when passing through blind spot areas and have positive implications for reducing the number of accidents in Indonesia.

ШЛИФОВАНИЕ ПОВЕРХНОСТИ ВОССТАНОВЛЕННЫХ ПОРОШКОВЫМИ ТРУДНООБРАБАТЫВАЕМЫМИ МАТЕРИАЛАМИ

The article discusses the use of impregnated abrasive wheels for grinding the surface of recovered hard-to-process powder materials. The paper investigates the formation of a rough surface during internal grinding of cylinders of marine engines after restoration to their original dimensions, with powder materials of grades PG-CP2, PG-10H-0.2 containing nickel, chromium, iron and other hard-to-work metals, which during the restoration process create a special protective metal layer on the surface of the cylinders, the processing of which is very difficult It's difficult. Therefore, the grinding of the internal surfaces of the restored cylinders of marine engines is a complex technological process that proceeds in strict compliance with all technological operations, in stages. According to the results of the study, the influence of the rotation speed of the workpiece on the roughness of the inner surface of the sleeve, accompanied by grinding with impregnated grinding wheels, was revealed. It has been experimentally confirmed that when treated with impregnated abrasive wheels, the roughness of the polished surface decreases by about 1.5-1.8 times.

Phylogeographic structure of Italian Formica pratensis (Retzius 1783) populations in the framework of the species Eurasian range

AbstractThe phylogeography and demographical history of Italian Formica pratensis populations were examined and compared with the Eurasian-wide dataset available for this species and the other red wood ant species Formica lugubris. Forty-eight workers belonging to eight populations from both Alps and Apennines were analysed sequencing a 1.5-kilobase mitochondrial DNA fragment, including the cytochrome b gene and part of the NADH dehydrogenase subunit 6 gene. A total of 127 sequences were screened, scoring 53 different haplotypes amongst all specimens, with five new haplotypes discovered in the Italian populations. All the Italian haplotypes clustered in a monophyletic clade, underlining a clear phylogeographical separation of this group from the other Eurasian groups and suggesting a glacial separate forest refugia and different post-glacial colonisation patterns. The haplotypes from the Alps and the Apennines showed a high genetic proximity, pointing out an ancient (Pleistocene) wide distribution of this species across all these areas and common ancestral lineages. No shared haplotypes were scored between Northern and Central Apennine populations, but the low inter-population genetic distance indicated similar post-glacial selective processes acting on these groups. The diversity we recorded may be influenced by the actual fragmentation of F. pratensis populations across its entire Eurasian range, and by the limited geographical origin and sample dimension of the dataset analysed. Future studies with a more extensive sampling in the Alps and Eastern Europe are needed to confirm our result.

Extended Representation Learning Based Neural Network Model for Outlier Detection

Outlier detection problems have drawn much attention in recent times for their variety of applications. An outlier is a data point that is different from the rest of the data and can be detected based on some measure. In recent years, Artificial Neural Networks (ANN) have been used extensively for finding outliers more efficiently. This method is highly competitive with other methods currently in use such as similarity searches, density-based approaches, clustering, distance-based approaches, linear methods, etc. In this paper, we have proposed an extended representation learning based neural network. This model follows a symmetric structure like an autoencoder where the dimensions of the data are initially increased from their original dimensions and then reduced. Root mean square error is used to compute the outlier score. Reconstructed error is calculated and analyzed to detect the possible outliers. The experimental findings are documented by applying it to two distinct datasets. The performance of the proposed model is compared to several state-of-art approaches such as Rand Net, Hawkins, LOF, HiCS, and Spectral. Numerical results show that the proposed method outperforms all of these methods in terms of 5 validation scores, Accuracy (AC), Precision (P), Recall, F1 Score, AUC score.

Correlates of creativity: an association between creativity, personality, and intelligence

ABSTRACT The present study aimed to explore the association between creativity, intelligence, and personality. The study recruited sixty healthy volunteers in the age range of 20–40 years from Bengaluru city (formerly Bangalore), South India, and administered tests for fluid intelligence (Raven’s Standard Progressive Matrices), personality (Big Five NEO-FFI), and divergent thinking (Wallach & Kogan Creativity Test). Findings revealed that intelligence and openness to experience were positively correlated with the fluency, flexibility, and originality dimensions of creativity. The hierarchical multiple regression analysis suggested openness and fluid intelligence significantly predicted creativity. This suggests that individuals who are more open to new experiences continue to integrate new and diverse information into their existing knowledge base. When combined with contextual and emotional stimuli, this integration process can lead to more varied and innovative responses to divergent thinking tasks.

Machine-assisted social psychology hypothesis generation.

Social psychology research projects begin with generating a testable idea that relies heavily on a researcher's ability to assimilate, recall, and accurately process available research findings. However, an exponential increase in new research findings is making the task of synthesizing ideas across the multitude of topics challenging, which could result in important overlooked research connections. In this research, we leverage the fact that social psychology research is based on verbal models and employ large natural language models to generate hypotheses that can aid social psychology researchers in developing new research hypotheses. We adopted two methodological approaches. In the first approach, we fine-tuned the third-generation generative pre-trained transformer (GPT-3) language model on thousands of abstracts published in more than 50 social psychology journals in the past 55 years as well as on preprint repositories (PsyArXiv). Social psychology experts rated model- and human-generated hypotheses similarly on the dimensions of clarity, originality, and impact. In the second approach, without fine-tuning, we generated hypotheses using GPT-4 and found that social psychology experts rated these generated hypotheses as higher in quality than human-generated hypotheses on dimensions of clarity, originality, impact, plausibility, and relevance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Devising a methodology for prototyping convex-concave parts using reverse-engineering technology providing the predefined geometric accuracy of their manufacturing

The object of this paper is the geometric accuracy of the acquired portrait from the 3D printed full-scale sample in the reduced size of the convex-concave parts compared to the correspondingly reduced ideal one. The subject of research is reverse engineering and additive technology for manufacturing convex-concave parts for mechanical engineering (ME). A new prototyping methodology for convex-concave parts of mechanical engineering objects has proposed. Underlying the methodology is the use of the scaled (by reducing in size) ideal portrait of the 3D scanned original part. The decision to start the production is made by comparing the geometry of the portrait acquired from the 3D printed sample in reduced size with the ideal one, provided that the values are within the tolerance range. The following results were obtained. A design and technological analysis of the blade of the pumped hydroelectric power station was performed, after which a 3D scanner and a 3D printer were selected. A 3D scan of the blade with the formation of a portrait in the STL format file was implemented, as well as its refinement into an ideal one. From the geometric features and shapes of the blade, as well as the technical characteristics of the 3D printer, the percentage of reducing the sample for printing (by 75 % of the original dimensions) was calculated. According to the rated dimensions of the original part and the reduced sample, the tolerance field was set for the size: 0.6 mm and 0.25 mm, respectively, at 12 quality of the part’s manufacturing accuracy. Inspection of the printed sample and comparison with the correspondingly reduced ideal portrait revealed a deviation from –0.123 to +0.120 mm, which is within the defined tolerance field for the manufactured reduced sample. The results of experimental studies confirmed the adequacy of the proposed methodology for prototyping the mechanical engineering parts and verified the theoretical foundations of reverse engineering for convex-concave parts of any large size by using a proportional reduction in the size of finished portraits

Dimensional changes in free gingival grafts at implant sites in the reconstructed mandible: a retrospective study.

To evaluate the dimensional changes in free gingival grafts (FGG) at implant sites in mandibular reconstruction patients. Patients who received FGG 4 months after implant placement in the reconstructed mandible with no keratinized mucosa (KM) present were invited for re-examination after 36.7 ± 16.8 months (3.06 ± 1.4 years). Immediately after graft extraction (T0), graft width (GW), graft length (GL), graft thickness (GT), graft dimension (GD), and vertical bone height were documented. Re-examination (T1) included clinical examinations (GW, GL, GD, peri-implant probing depths, and modified Sulcus Bleeding Index), radiographic examination (marginal bone level), and medical chart review. Twenty patients and 62 implants (47 in fibula flaps and 15 in iliac flaps) were included. A significant decrease in GW (51.8%), GL (19.2%), and GD (60.2%), were found between T0 and T1 (p < .001). The univariate analysis showed that GW change was not significantly associated with reconstruction technique, baseline GL, baseline GT, baseline GD, implant location, or type of prosthesis. Implant survival rate of 100% was observed at follow-up. Within the limitations of the study, free gingival grafts at implant sites in the reconstructed mandible undergo dimensional change that result in a reduction of approximately 60% of the original graft dimension. Graft width decreased over 50%. FGG is the standard of care intervention for increasing the amount of KM around implants. This study was the first to evaluate the dimensional change in FGG at implant sites in mandibular reconstruction patients after a medium-term follow-up. Clinical trial registration is not applicable as this study comprehends a retrospective analysis.

3D-printed β-TCP/Ti6Al4V composite scaffolds for bone regeneration: Process parameter optimization and evaluation

A β-TCP/Ti6Al4V composite scaffold with interconnected macro porous architecture was fabricated using Direct Ink Writing (DIW). Pluronic F-127 and de-ionized water was used as binder and solvent for ink preparation. The present work was carried out to study the rheological behavior of the composite bioceramic ink and to investigate DIW process parameters such as Ti6Al4V proportion, infill percentage and extrusion pressure. The Box-Behnken response surface methodology, ANOVA, sensitivity, desirability approach are used for the experimental, statistical and numerical optimization of the parameters suitable for DIW. The output responses such as dimensional error of the fabricated scaffold from the original dimensions and compressive strength are considered for multi-objective optimization. The result defined that the optimal values are solid loading 55 %v/v (40 %v/v of β-TCP, 15 %v/v of Ti6Al4V) and 45 %v/v of Pluronic gel, 98 % infill rate and 6.36 bar pressure. The dimensional error and compressive strength of the scaffold printed at the optimized conditions are found as 1.88 % and 19 MPa with macro and micro pores suitable for bone regeneration with satisfactory biocompatibility assed via MTT assay.

High Specific Surface Area TiO2 Aerogels Incorporated in situ with Co/Mn Oxides as Stable Electrochemical Capacitor Electrodes

Nanocomposite aerogels comprised of TiO2 and metal (Co and Mn) oxides are synthesized via an in situ sol–gel method in this study, and their structural, compositional and electrochemical properties are evaluated for possible applications as electrodes in energy storage devices. The inclusion of metallic oxides into TiO2 aerogels hinder the formation of titania crystalline phases, preserved particle sizes close to their original dimensions and yielded higher specific surface areas compared to pure TiO2 aerogels after heat treatment. High specific surface areas in aerogels positively affect the electrochemical properties, allowing a high electrochemical activity of the electrodes, in addition to intensifying the transport of ions and solvents through the mesoporous network of this material. Evaluation of the electrochemical properties of the aerogel‐based nanocomposites involves galvanostatic charge–discharge, cyclic voltammetry, and impedance spectroscopy. The nanocomposites exhibit enhanced electrochemical properties and stable performance within the range suitable for supercapacitor applications, as indicated by the Ragone chart. Notably, aerogels with higher incorporation of cobalt and manganese oxides in TiO2 aerogels exhibit significantly elevated specific surface areas, reaching 562 and 555 m2 g−1, respectively. These values are notably high for nanocomposites, underscoring the potential of these electroactive materials for electrochemical capacitors.

FSDC: Flow Samples and Dimensions Compression for Efficient Detection of DNS-over-HTTPS Tunnels

This paper proposes an innovative approach capitalized on the distinctive characteristics of command and control (C&amp;C) beacons, namely, time intervals and frequency between consecutive unique connections, to compress the network flow dataset. While previous studies on the same matter used single technique, we propose a multi-technique approach for efficient detection of DoH tunnels. We use a baseline public dataset, CIRA-CIC-DoHBrw-2020, containing over a million network flow properties and statistical features of DoH, tunnels, benign DoH and normal browsing (HTTPS) traffic. Each sample is represented by 33 features with a timestamp. Our methodology combines star graph and bar plot visualizations with supervised and unsupervised learning techniques. The approach underscores the importance of C&amp;C beacon characteristic features in compressing a dataset and reducing a flow dimension while enabling efficient detection of DoH tunnels. Through compression, the original dataset size and dimensions are reduced by approximately 95% and 94% respectively. For supervised learning, RF emerges as the top-performing algorithm, attaining precision and recall scores of 100% each, with speed increase of ≈6796 times faster in training and ≈55 in testing. For anomaly detection models, OCSVM emerges as the most suitable choice for this purpose, with precision (88.89) and recall (100). Star graph and bar graph models also show a clear difference between normal traffic and DoH tunnels. The reduction in flow sample size and dimension, while maintaining accuracy, holds promise for edge networks with constrained resources and aids security analysts in interpreting complex ML models to identify Indicators of Compromise (IoC).

Private Query Release via the Johnson-Lindenstrauss Transform

We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma. The key idea is to randomly project the query answers to a lower dimensional space so that the distance between any two vectors of feasible query answers is preserved up to an additive error. Then we answer the projected queries using a simple noise-adding mechanism, and lift the answers up to the original dimension. Using this method, we give, for the first time, purely differentially private mechanisms with optimal worst case sample complexity under average error for answering a workload of $k$ queries over a universe of size $N$. As other applications, we give the first purely private efficient mechanisms with optimal sample complexity for computing the covariance of a bounded high-dimensional distribution, and for answering 2-way marginal queries. We also show that, up to the dependence on the error, a variant of our mechanism is nearly optimal for every given query workload.

Exploring Age Differences in Absorption and Enjoyment during Story Listening

Using naturalistic spoken narratives to investigate speech processes and comprehension is becoming increasingly popular in experimental hearing research. Yet, little is known about how individuals engage with spoken story materials and how listening experiences change with age. We investigated absorption in the context of listening to spoken stories, explored predictive factors for engagement, and examined the utility of a scale developed for written narratives to assess absorption for auditory materials. Adults aged 20–78 years (N = 216) participated in an online experimental study. Participants listened to one of ten stories intended to be engaging to different degrees and rated the story in terms of absorption and enjoyment. Participants of different ages rated the stories similarly absorbing and enjoyable. Further, higher mood scores predicted higher absorption and enjoyment ratings. Factor analysis showed scale items approximately grouped according to the original scale dimensions, suggesting that absorption and enjoyment experiences may be similar for written and spoken stories, although certain items discriminated less effectively between stories intended to be more or less engaging. The present study provides novel insights into how adults of different ages engage in listening and supports using naturalistic speech stimuli in hearing research.

Is the ambiguity of emotion multidimensional? The ambiguous valence, activation and origin of emotions

Mixed emotions remain a fascinating, yet still understudied phenomenon. All of the previous research has focused solely on ambivalence, studying only the mix of positivity and negativity in emotions (the dimensions of valence). We sum up the already existing knowledge about the dimensional approach to ambivalence and its consequences. Based directly on this knowledge, we introduce a new theoretical model describing ambiguity in four additional dimensions (apart from valence), grouped into two bivariate spaces: origin (dimensions of automaticity and reflectiveness) and activation (arousal and subjective significance). Both of these spaces have never been studied before in the context of ambiguity and mixed feelings. Future implications of the new model are discussed, including any potential impact on the methodology of research and the possible advantages in understanding and describing emotional experiences.

Analysis of ecological literacy level and creative thinking skills of college students

Higher education institution is one of educational institutions that serve as a place of cultural values, and norms expected to become a foundation to foster caring attitudes toward the environment. These attitudes are accordingly meant to develop ecological literacy in order to overcome environmental problems which are likely to be minimized by combining ecological literacy and creative thinking skills. A quantitative descriptive with correlational study was employed in this research. There were 275 college students from three areas in Indonesia, namely Jakarta, Sumatera, and Gorontalo involved as respondents of the research. Data was collected by means of Google Forms consisting of tests and opinions. The ecological literacy was analyzed using dimensions of caring, practical competence, and knowledge while the creative thinking skills were analyzed using dimensions of fluency, originality, flexibility, and elaboration. This research is limited to students in the Department of Science and Non-Science. Findings of the research indicates that the ecological literacy level is in the medium category, whereas the creative thinking skills are good. Female students have a higher average score in ecological literacy and creative thinking skills compared to that of male students. Thus, there is a positive relationship between ecological literacy and creative thinking skills.

What’s the difference between measures of wellbeing, quality of life, resilience, and coping? An umbrella review and concept map of 155 measures of positive mental health

The multitude of definitions, models, and measures of positive mental health has hindered academic precision and therefore the ongoing scientific evolution of this important area. This umbrella review aimed to synthesize the theoretical landscape of positive mental health, by reviewing measures that were designed to capture overarching, multi-dimensional concepts of positive and adaptive states of mental health (i.e., wellbeing, quality of life, and resilience/coping), and interrogating their underlying dimensions (e.g., vitality, autonomy) and design features (e.g., response scales, item valence). Our search identified 155 measures of positive mental health with a total of 410 constituent dimensions. Using thematic analysis, we consolidated these 410 original dimensions into a set of 21 themes. These themes were transformed into a concept map to illustrate their inter-relationship with the overarching concepts of positive mental health as identified in this review. Our results point to a lack of consensus on the underlying dimensions and measurement approaches for investigating positive mental health, with singular measures failing to capture its breadth, resulting in an unwieldy situation for ongoing scientific inquiry.

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.

An Empirical Investigation of the Three-Pathway Model and its Contribution to the Gambling Disorder Psychopathology.

Blaszczynski and Nower (2002) proposed a theoretical model that leads to problem gambling via three pathways: (1) operant conditioning; (2) emotional vulnerability; and (3) impulsivity and psychopathy. In the current investigation, we explored the relationship between these three putative causative dimensions and clinical core features of Gambling Disorder (GD): gambling craving, gambling-related cognitive distortions, gambling (wagering) behavior, and gambling severity. Data on 343 people with disordered gambling were analyzed. Measures representing the three pathways were analyzed using principal component analysis (PCA). The PCA generated three profiles. The original dimension of impulsivity/psychopathy was divided into two parts; the impulsivity-related traits were combined with symptoms of depression and anxiety to form one single component representing a volatile emotional, cognitive and behavioral style, named the Affect-instability component. The other two components were Psychopathy and Operant Behavior. Linear regression models for each PCA component found that the Affect-instability component was associated with all core features of GD, i.e., craving, cognitive distortions, gambling behavior and severity (standardized Β range: 0.298-0.448, all p < 0.001). Operant Behavior was significantly associated with gambling behavior (standardized Β=-0.137, p = 0.038) and gambling severity (standardized Β=-0.157, p = 0.006). Psychopathy was associated only with gambling cognitive distortions (standardized Β=-0.300, p < 0.001), suggesting a wider dimension of cognitive challenges in GD. An instability component encompassing emotional and cognitive dysregulation was the strongest predictor of all clinical features of GD. The correlation between operant conditioning and gambling severity suggests that behavioral conditioning plays a role in the persistence of maladaptive gambling.