Extensive Testing Research Articles

Early prediction of COVID-19 infection using data mining and multi machine learning algorithms

The fields of artificial intelligence (AI) and machine learning (ML) have attracted significant interest and investment from a diverse range of industries, especially during the last several years. Despite the fact that AI methods have been used extensively and put through extensive testing in the healthcare industry, the recently discovered coronavirus disease (COVID-19) necessitates the use of these methods in order to prevent the emergence of the disease. The proposed system is based on six ML algorithms to predict COVID-19 infection as random forest (RF) algorithm, naive bayes (NB) algorithm, support vector machine (SVM) algorithm, decision tree (DT) algorithm, multi-layer perceptron (MLP), and k-nearest neighbor (KNN). It is based on two steps: first, we uploaded the dataset to train the model. Then, we test our model on those cases to work directly after making a trained classifier so it can directly discover with automatic COVID-19 prediction state of a patient suspected or not. The proposed system results showed the high accuracy of NB, DT, and SVM as 98.646%. Besides the better time to build the model and early predict the state of patients is 31 ms of the NB algorithm.

Innovative Hierarchical Control of Multiple Microgrids: Cheetah Meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques.This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights.HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities.The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600V-bus According to IEEE519-2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 milliseconds. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility

Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information.

Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.

Thoracolumbar Fascia and Lumbar Muscle Stiffness in Athletes with A History of Hamstring Injury

The purpose of this study was to examine the differences in thoracolumbar fascia (TLF) and lumbar muscle modulus in individuals with and without hamstring injury using shear wave elastography (SWE). Thirteen male soccer players without a previous hamstring injury and eleven players with a history of hamstring injury performed passive and active (submaximal) knee flexion efforts from 0°, 45° and 90° angle of knee flexion as well as an active prone trunk extension test. The elastic modulus of the TLF, the erector spinae (ES) and the multifidus (MF) was measured using ultrasound SWE simultaneously with the surface electromyography (EMG) signal of the ES and MF. The TLF SWE modulus was significantly (p < 0.05) higher in the injured group (range: 29.86 ± 8.58 to 66.57 ± 11.71 kPa) than in the uninjured group (range: 17.47 ± 9.37 to 47.03 ± 16.04 kPa). The ES and MF modulus ranged from 14.97 ± 4.10 to 66.57 ± 11.71 kPa in the injured group and it was significantly (p < .05) greater compared to the uninjured group (range: 11.65 ± 5.99 to 40.49 ± 12.35 kPa). TLF modulus was greater than ES and MF modulus (p < 0.05). Active modulus was greater during the prone trunk extension test compared to the knee flexion tests and it was greater in the knee flexion test at 0° than at 90° (p < 0.05). The muscle EMG was greater in the injured compared to the uninjured group in the passive tests only (p < 0.05). SWE modulus of the TLF and ES and MF was greater in soccer players with previous hamstring injury than uninjured players. Further research could establish whether exercises that target the paraspinal muscles and the lumbar fascia can assist in preventing individuals with a history of hamstring injury from sustaining a new injury.

Deepfake Detection System

Abstract: Because deep fake technology allows for the creation of incredibly realistically modified material that has the potential to mislead viewers and possibly cause instability across a range of businesses, it poses a severe threat to modern society. These days, detecting such modified content is crucial to maintaining the trustworthiness and integrity of digital media. In response, this research proposes a robust deep learning-based technique for detecting deep fakes in videos. Our method uses the Deep fake Detection Challenge dataset, which contains both real and deep fake films, to train and assess our deep learning model. We want to provide a dependable solution that can differentiate between artificially generated information and actual content using stateof-the-art neural networks. Artificial intelligence-generated phony videos are more likely to spread thanks to deep learning algorithms, which raises serious concerns about official blackmail, terrorist propaganda, revenge pornography, and political manipulation. To alleviate these concerns, our approach is designed to automatically identify several forms of deep forgery, including replacement and re-enactment techniques. By means of extensive testing and analysis, we demonstrate the effectiveness of our deep fake detection system in precisely distinguishing bogus films from authentic ones. Our system, which combines cutting-edge deep learning methods with a large dataset, offers a promising way to lessen the hazards related to the spread of deep fake technologies

The influence of gender and sport on popliteal angle and dorsiflexion in junior high school students.

The aim of the study was to assess factors affecting the popliteal angle and foot dorsiflexion, in particular gender. The subjects were 142 students from the 2nd and 3rd year of Poznań junior high schools. The participants included 57 girls and 87 boys. Three raters examined each subject: a specialist in orthopaedics, a resident doctor and a physical therapy student. Foot dorsal flexion was tested in a supine position with lower limbs extended. Next, dorsal flexion was evaluated with the knee and hip in 90 degrees of flexion. Finally, a passive knee extension (PKE) test was carried out. The significance of the PKE test is that the lower the angle the more flexible the hamstrings. This is because the PKE measurement is the distance to the right angle, that is a full knee extension with the hip flexed. The non-parametric test (Mann-Whitney) and the Student's t-test showed differences between the female and male gender in the measurements of the popliteal angle (p < .05000). The correlation was negative, which means that the hamstrings are more flexible in girls. No differences were found between gender and passive foot dorsiflexion and dorsiflexion with a flexed hip and knee. No differences were found between the group with the extended PE curriculum and the group with the standard number of PE classes in the range of motion of foot dorsiflexion and the value of the popliteal angle. Girls between 13 and 15years old have a significantly larger hamstring flexibility, which is confirmed by the tests of the popliteal angle. No differences were found in dorsiflexion between girls and boys who have not been trained using a training model.

Decoding BCOR-ITD Sarcomas: Case Report of a Rare Pediatric Tumor With Challenges in Diagnosis.

Sarcomas characterized by BCOR gene alterations, are a distinct clinico-pathological group of high-grade tumors, that represent 5% of small round cell tumors without EWSR or FUS fusion. Diverse genetic alterations characterize this group, including BCOR-CCNB3 gene fusion being the most common alteration and less frequently internal tandem duplications (ITDs). We present a compelling case of a 3-year-old girl diagnosed with a high-grade nasoethmoidal sarcoma exhibiting BCOR-ITD. The diagnostic process illustrates the histological and immunophenotypic spectrum, requiring an extensive immunohistochemical panel and diverse molecular tests for accurate classification. Additionally, this case highlights the challenges in detecting BCOR-ITDs using different NGS panels, advocating for alternative molecular approaches. Our patient after 10 months since diagnosis is alive with progressive disease. This emphasizes the urgency for ongoing research to refine diagnostic methods and develop effective therapeutic strategies for these rare and aggressive tumors.

ENTRECTINIB–INDUCED ACUTE RIGHT HEART FAILURE IN A PATIENT WITH METASTATIC LUNG ADENOCARCINOMA

Abstract We report a case report of a 45–year–old woman with a recent diagnosis of stage IV lung adenocarcinoma (PDL1 60%) with brain metastases, who started entrectinib 600 mg orally once daily. Four days after entrectinib initiation, the patient activated the emergency medical services for acute onset of marked fatigue and hypotension. The ECG performed by the emergency unit showed a marked ST–segment elevation in the infero–lateral leads. Therefore, emergent coronary angiography was performed and no evidence of coronary disease was reported. Subsequently, the patient was admitted to the Coronary Care Unit. Clinical evaluation revealed labile hemodynamics, as the patient presented mental confusion, persistent hypotension (BP 90/50 mmHg) and bradycardia (HR 30 bpm). Laboratory testing showed elevated NT–pro BNP, troponin I and transaminases, normal CRP. The electrocardiogram showed persistent diffuse ST–segment elevation. Transthoracic echocardiogram showed severe dilatation and hypokinesia of the right ventricular (GLS VDx –4.5%), with diastolic “D–shape”, normal left ventricular ejection fraction, no significant valvular disease, circumferential pericardial effusion. Clinical, laboratory and instrumental findings suggested a condition of acute right heart failure. We performed urgent computed tomography pulmonary angiography (CTPA) which resulted negative for acute pulmonary embolism. Hence, supportive therapy with dobutamine, norepinephrine and fluid resuscitation was started, with subsequent hemodynamic stabilization. Since no other etiology was demonstrated by the extensive testing, we attributed the acute right ventricular dysfunction to the recent introduction of entrectinib therapy. Therefore, the drug was discontinued. During the hospitalization we observed a rapid normalization of electrocardiogram and right ventricle function by transthoracic echocardiogram, accompanied by a progressive improvement of laboratory parameters. In order to better define right ventricle function and dimension, the patient underwent cardiac magnetic resonance imaging which showed a normal right ventricle ejection fraction (RV EF 52%), the absence of myocardial edema or late gadolinium enhancement and normal volume of the right ventricle. After six days of hospitalization the patient was discharged. Our case report adds to the limited available literature of cardiac toxicity of entrectinib.

Investigation of lightweight gypsum based on montmorillonite nanoclay with enhanced insulation properties

PurposeNowadays, thermal comfort plays a prominent role in contemporary construction practices. Appropriate thermal insulation not only offers energy efficiency benefits in buildings but also enhances occupant well-being, comfort, and productivity. Therefore, a comprehensive understanding of the thermal properties of building materials is essential. This research aims to prepare and investigate a lightweight gypsum-based composite incorporating nano montmorillonite with advanced thermal insulation properties, considering both quality and cost-effectiveness while ensuring environmental compatibility.Design/methodology/approachThis study adopts a laboratory experimental approach. A gypsum sample (without additives) and seven samples of gypsum combined with varying percentages of sodium and calcium montmorillonite nanoclays undergo extensive testing and analysis. Subsequently, the properties of these samples are compared.FindingsThe results indicate that adding montmorillonite nanoclays to gypsum composites reduces the density of the tested samples and increases their porosity. Moreover, the thermal conductivity coefficient decreases in these samples, significantly improving the thermal insulation properties of the lightweight gypsum plaster. This improvement is more pronounced in samples containing sodium montmorillonite nanoclay compared to calcium-based samples. Additionally, the investigations reveal that compressive strength decreases with the addition of montmorillonite to the samples.Originality/valueIn this research, laboratory experiments were conducted to investigate the physical and mechanical properties of gypsum plaster with varying percentages of sodium and calcium montmorillonite nanoclays. The studied properties include density, porosity, thermal conductivity coefficient, and compressive strength. Additionally, stress-strain diagrams, elastic modulus, and initial and secondary critical stresses were analyzed for each specimen.

Cardiac amyloidosis is not a single disease: a multiparametric comparison between the light chain and transthyretin forms.

Systemic amyloidosis represents a heterogeneous group of diseases resulting from amyloid fibre deposition. The purpose of this study is to establish a differential diagnosis algorithm targeted towards the two most frequent subtypes of CA. We prospectively included all consecutive patients with ATTR and AL evaluated between 2018 and 2022 in two centres in a score derivation cohort and a different validation sample. All patients had a complete clinical, biomarker, electrocardiographic, and imaging evaluation. Confirmation of the final diagnosis with amyloid typing was performed according to the current international recommendations. The study population included 81 patients divided into two groups: ATTR (group 1, n=32: 28 variant and 4 wild type) and AL (group 2, n=49). ATTR patients were younger (50.7±13.9 vs. 60.2±7.3years, P=0.0001), and significantly different in terms of NT-proBNP [ATTR: 1472.5ng/L (97-4218.5) vs. AL 8024ng/L (3058-14069) P=0.001], hs-cTn I [ATTR: 10ng/L (4-20) vs. AL 78ng/L (32-240), P=0.0002], GFR [ATTR 95.4mL/min (73.8-105.3) vs. AL: 68.4mL/min (47.8-87.4) P=0.003]. At similar left ventricular (LV) wall thickness and ejection fraction, the ATTR group had less frequently pericardial effusion (ATTR: 15% vs. AL: 33% P=0.0027), better LV global longitudinal strain (ATTR: -13.1%±3.5 vs. AL: -9.1%±4.3 P=0.04), RV strain (ATTR: -21.9%±6.2 vs. AL: -16.8%±6 P=0.03) and better reservoir function of the LA strain (ATTR: 22%±12 vs. AL: 13.6%±7.8 P=0.02). Cut-off points were calculated based on the Youden method. We attributed to 2 points for parameters having an AUC>0.75 (NT-proBNP AUC 0.799; hs-cTnI AUC 0.87) and 1 point for GFR (AUC 0.749) and TTE parameters (GLS AUC 0.666; RV FWS AUC 0.649, LASr AUC 0.643). A score of equal or more than 4 points has been able to differentiate between AL and ATTR (sensitivity 80%, specificity 62%, AUC=0.798). The differential diagnosis score system was applied to the validation cohort of 52 CA patients showing a sensitivity of 81% with specificity of 77%. CA is a complex entity and requires extensive testing for a positive diagnosis. This study highlights a series of non-invasive checkpoints, which can be useful in guiding the decision-making process towards a more accurate and rapid differential diagnosis.

DMGM: deformable-mechanism based cervical cancer staging via MRI multi-sequence.

This study aims to leverage a deep learning approach, specifically a deformable convolutional layer, for staging cervical cancer using multi-sequence MRI images. This is in response to the challenges doctors face in simultaneously identifying multiple sequences, a task that computer-aided diagnosis systems can potentially improve due to their vast information storage capabilities.&#xD;&#xD;Approach: To address the challenge of limited sample sizes, we introduce a sequence enhancement strategy to diversify samples and mitigate overfitting. We propose a novel deformable ConvLSTM module that integrates a deformable mechanism with ConvLSTM, enabling the model to adapt to data with varying structures. Furthermore, we introduce the deformable multi-sequence guidance model (DMGM) as an auxiliary diagnostic tool for cervical cancer staging.&#xD;&#xD;Main Results: Through extensive testing, including comparative and ablation studies, we validate the effectiveness of the deformable ConvLSTM module and the DMGM. Our findings highlight the model's ability to adapt to the deformation mechanism and address the challenges in cervical cancer tumor staging, thereby overcoming the overfitting issue and ensuring the synchronization of asynchronous scan sequences. The research also utilized the multi-modal data from BraTS 2019 as an external test dataset to validate the effectiveness of the proposed methodology presented in this study.&#xD;&#xD;Significance: The DMGM represents the first deep learning model to analyze multiple MRI sequences for cervical cancer, demonstrating strong generalization capabilities and effective staging in small dataset scenarios. This has significant implications for both deep learning applications and medical diagnostics. The source code will be made available subsequently.

Do analysts play a monitoring role? Evidence from exogenous changes in litigation risk

AbstractThis paper examines whether securities litigation and sell‐side equity analysts play a substitutive versus complementary role as an external governance mechanism. We expect the 1999 ruling on the stricter interpretation of the Private Securities Litigation Reform Act to make it easier for firms headquartered in the Ninth Circuit to defend against securities class actions filed by shareholders, resulting in weaker protection from a litigation channel and stronger demand for analyst activity. Using a difference‐in‐differences research design, we find that analyst coverage increases as litigation risk decreases in the Ninth Circuit firms. Moreover, we find that analyst earnings forecast accuracy increases for firms in the Ninth Circuit following the ruling, consistent with analysts exerting greater efforts to perform their monitoring role. In cross‐sectional tests, we find that the substitution relation between litigation risk and analyst research is more pronounced in firms with a high level of ex‐ante litigation risk. We conduct extensive robustness tests, including another legal event exogenously increasing the risk of litigation, to gain confidence in our interpretations. We contribute to the literature by documenting causal evidence that analysts have greater incentives to provide meaningful equity research as gatekeepers in times of weaker investor protection by securities litigation.

Nonadiabatic Field with Triangle Window Functions on Quantum Phase Space.

Recent progress on the constraint coordinate-momentum phase space (CPS) formulation of finite-state quantum systems has revealed that the triangle window function approach is an isomorphic representation of the exact population-population correlation function of the two-state system. We use the triangle window (TW) function and the CPS mapping kernel element to formulate a novel useful representation of discrete electronic degrees of freedom (DOFs). When it is employed with nonadiabatic field (NaF) dynamics, a new variant of the NaF approach (i.e., NaF-TW) is proposed. The NaF-TW expression of the population of any adiabatic state is always positive semidefinite. Extensive benchmark tests of model systems in both the condensed phase and gas phase demonstrate that the NaF-TW approach is able to faithfully capture the dynamical interplay between electronic and nuclear DOFs in a broad region, including where the states remain coupled all the time, as well as where the bifurcation characteristic of nuclear motion is important.

Vesicle and reaction-diffusion hybrid modeling with STEPS

Vesicles carry out many essential functions within cells through the processes of endocytosis, exocytosis, and passive and active transport. This includes transporting and delivering molecules between different parts of the cell, and storing and releasing neurotransmitters in neurons. To date, computational simulation of these key biological players has been rather limited and has not advanced at the same pace as other aspects of cell modeling, restricting the realism of computational models. We describe a general vesicle modeling tool that has been designed for wide application to a variety of cell models, implemented within our software STochastic Engine for Pathway Simulation (STEPS), a stochastic reaction-diffusion simulator that supports realistic reconstructions of cell tissue in tetrahedral meshes. The implementation is validated in an extensive test suite, parallel performance is demonstrated in a realistic synaptic bouton model, and example models are visualized in a Blender extension module.

A Dynamic Programming Approach to the Collision Avoidance of Autonomous Ships

The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among the various possible approaches, dynamic programming is a promising tool for optimizing collision avoidance maneuvers. This paper presents a DP formulation for the collision avoidance of autonomous vessels. We set up the problem framework, formulate it as a multi-stage decision process, define cost functions and constraints focusing on the actual requirements a marine maneuver must comply with, and propose a solution algorithm leveraging parallel computing. Additionally, we present a greedy approximation to reduce algorithm complexity. We put the proposed algorithms to the test in realistic navigation scenarios and also develop an extensive test on a large set of randomly generated scenarios, comparing them with the RRT* algorithm using performance metrics proposed in the literature. The results show the potential benefits of an autonomous navigation or decision support framework.

Design and Implementation of a Standardised Clinical Decision Support Algorithm for Fever

This study aims to design a decision support tool to assist in providing quality treatment that is consistent with World Health Organization (WHO) and Nigerian National guidelines. The system is designed to allow clinicians to administer care within their competent level working from one algorithm. The system will help them to identify emergencies associated with fever symptoms and to recommend stabilisation actions before a referral is made. This new system allows structured assessment of patients who should receive optimal care and improved data transmission to the next caregiver. In this study, we used an explanatory approach, starting with the quantitative data collection phase which is the administration of questionnaires and Pre and Post questionnaires followed by qualitative data from focus group discussions over the clinician experience using the Clinical Decision Support System (CDSS). Focus group discussions were performed to authenticate the quantitative data to have a more holistic view of the CDSS. Using elements of the decision support system together with the clinician's decision showed that the clinicians felt that they worked more systematically and communicated more effectively with others. They felt more professional when using the decision support system. 73% of clinicians reported using CDSS in almost every consultation and 93% used the CDSS in the majority of their consultations during the three-month testing period. The mean total test score before the CDSS was 2.5 and this increased by the end of the test period to a mean score of 9.6, an improvement of 74.4%. The results of this study showed that with the help of a decision support system, patients were properly identified and stabilised before they were referred, and the clinicians stayed on their competency level. It allowed caregivers to interact professionally without bias. However, the decision support system requires more extensive testing to enhance the evidence base relating to the vital parameters and the use of the decision support system.

Clinical manifestations of EVALI in adolescents and the impact of e-cigarettes on the respiratory disease

In recent years there has been a significant increase in the popularity of electronic cigarettes among adolescents and young adults worldwide. E-cigarettes have reached the market without the extensive preclinical toxicology testing or long-term safety studies required for conventional therapeutics and medical devices. Their effectiveness as a smoking cessation intervention, their impact at the population level and whether they are less harmful than combustible tobacco products are highly controversial. Studies show measurable adverse biological effects on organ and cell health in humans, animals and in vitro. E-cigarette use also increases the risk of respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma. There is a serious risk of complications associated with e-cigarette use, such as EVALI (E-cigarette or Vaping Product Use-Associated Lung Injury) - serious lung damage associated with, especially for adolescents. This paper reviews the effects of e-cigarettes on respiratory health.

Baseline clinical and MRI risk factors for hamstring reinjury showing the value of performing baseline MRI and delaying return to play: a multicentre, prospective cohort of 330 acute hamstring injuries

ObjectivesStudies identifying clinical and MRI reinjury risk factors are limited by relatively small sample sizes. This study aimed to examine the association between baseline clinical and MRI findings with the...

Inventory Management System

Inventory management systems cater to the demands of businesses by providing efficient inventory operations via user-friendly interfaces for supplier administration, product categorization, inventory tracking, and transaction recording. This web application facilitates online stock and inventory management for a particular business or retailer. The project makes it simple for the management of the company to keep track of, record, and maintain its goods inventory. The system will have a modular architecture to enable future improvements and changes, with a focus on scalability, security, and user-friendliness. It will also include strong search and filtering capabilities, role-based access control, and support for many transaction types. It guarantees dependability and efficiency, includes extensive testing and documentation, and finally gives companies a strong instrument to streamline inventory procedures and enhance overall operations. The Inventory Management System will include cutting-edge capabilities such inventory Management Systems, with features like automated stock replenishment, real-time inventory synchronization across multiple locations or warehouses, and integration, represent a substantial investment in raising a company's competitiveness through cost savings, improved customer satisfaction, and optimized inventory processes

Does an aptitude for surgery exist and can we predict it? an experimental study.

The selection and allocation of surgeons with a greater potential for high surgical performance are essential aspects of improving the quality, safety and effectiveness of surgical procedures. Objective of this trial was to determine the existence of basic skills and traits that would predict better performance in surgery, and those predictive factors that constitute a driving force in different stages of training. The randomized crossover training trial took place from January 2021 to December 2021 and was conducted at an educational training center for minimally invasive surgery. A total of n=87 physicians (residents and experts) from surgical disciplines and n=239 fifth-year medical students were studied. The participants underwent extensive neuropsychological testing and surgical training, which was performed with conventional as well as robot-assisted laparoscopy by way of identical brief tasks conducted six times in a randomized crossover setting. Main Outcome was the latent factor structure of "psychomotor skills", "personality" and "motivation" based on structural equation modeling. The training performance of both students and physicians was significantly explained by the interaction of the three factors (explained variance: 8.2% for students, 23.8% for physicians). In students, motivation (explained variance 8.4%) and personality (explained variance 4.5%) revealed the highest contribution to surgical training performance (explained variance through psychomotor skills 1.1%). In physicians, psychomotor skills (explained variance 27.4%) made the greatest contribution to surgical training performance (explained variance through motivation 2.3%; explained variance through personality 10.5%). The study showed that surgical performance is sensitive to, and fragile in regard of non-surgery-related general individual traits. This aligns with the notion that early selection of surgeons with prospects of high surgical performance is possible, and perhaps even necessary in order to keep up with future demands on the medical system.