Human Performance Research Articles

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.

Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm3 and 101-300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups. Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm3 nodules in non-emphysema (p = 0.009). AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR. In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs. • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.

Deep Learning Based Lipreading for Video Captioning

Visual speech recognition, often referred to as lipreading, has garnered significant attention in recent years due to its potential applications in various fields such as human-computer interaction, accessibility technology, and biometric security systems. This paper explores the challenges and advancements in the field of lipreading, which involves deciphering speech from visual cues, primarily movements of the lips, tongue, and teeth. Despite being an essential aspect of human communication, lipreading presents inherent difficulties, especially in noisy environments or when contextual information is limited. The McGurk effect, where conflicting audio and visual cues lead to perceptual illusions, highlights the complexity of lipreading. Human lipreading performance varies widely, with hearing-impaired individuals achieving relatively low accuracy rates. Automating lipreading using machine learning techniques has emerged as a promising solution, with potential applications ranging from silent dictation in public spaces to biometric authentication systems. Visual speech recognition methods can be broadly categorized into those that focus on mimicking words and those that model visemes, visually distinguishable phonemes. While word-based approaches are suitable for isolated word recognition, viseme-based techniques are better suited for continuous speech recognition tasks. This study proposes a novel deep learning architecture for lipreading, leveraging Conv3D layers for spatiotemporal feature extraction and bidirectional LSTM layers for sequence modelling. The proposed model demonstrates significant improvements in lipreading accuracy, outperforming traditional methods on benchmark datasets. The practical implications of automated lipreading extend beyond accessibility technology to include biometric identity verification, security surveillance, and enhanced communication aids for individuals with hearing impairments. This paper provides insights into the advancements, challenges, and future directions of visual speech recognition research, paving the way for innovative applications in diverse domains.

Optimizing deep learning-based segmentation of densely packed cells using cell surface markers

BackgroundSpatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or immune reaction, remains a challenge.MethodsWe first assess the performance of 18 deep learning-based cell segmentation models, either pre-trained or trained by us using two public image sets, on a set of immunofluorescence images stained with immune cell surface markers in skin tissue obtained during human herpes simplex virus (HSV) infection. We then further train eight of these models using up to 10,000+ training instances from the current image set. Finally, we seek to improve performance by tuning parameters of the most successful method from the previous step.ResultsThe best model before fine-tuning achieves a mean Average Precision (mAP) of 0.516. Prediction performance improves substantially after training. The best model is the cyto model from Cellpose. After training, it achieves an mAP of 0.694; with further parameter tuning, the mAP reaches 0.711.ConclusionSelecting the best model among the existing approaches and further training the model with images of interest produce the most gain in prediction performance. The performance of the resulting model compares favorably to human performance. The imperfection of the final model performance can be attributed to the moderate signal-to-noise ratio in the imageset.

Taxonomy of performance shaping factors in manufacturing: A systematic literature review

AbstractHuman error in manufacturing can have substantial consequences, including loss of life, injuries, productivity, and financial losses. Human reliability analysis (HRA) methods can be used to evaluate the likelihood of human error in manufacturing tasks and identify potential sources of error. Performance shaping factors (PSFs) are internal and external factors that influence human performance and can affect the likelihood of human reliability estimates in HRA methods. Understanding the impact of PSFs on human performance in manufacturing is essential for developing effective strategies to minimize the likelihood of human error and improve the safety and efficiency of manufacturing processes. This systematic review scrutinizes the literature on PSFs within manufacturing, highlighting HRA applications. Using the PRISMA protocol, studies from 2000 to 2024 across engineering and psychology were examined, culminating in the analysis of 35 pertinent works. The review identifies and contrasts various PSF taxonomies from established HRA methods like SPAR‐H, HEART, CREAM, and THERP, revealing their diverse applications in different manufacturing settings. The review also uncovers a tendency to devise taxonomies through the lens of experts' domain knowledge, particularly tailored to discrete manufacturing contexts. A critical gap is observed in the lack of a uniform PSF framework, with the current literature reflecting a disparate understanding of PSFs' roles, definitions, and interrelations. This absence is further pronounced by the inadequate integration of human factors in the dialogue surrounding Industry 4.0. The analysis points to the necessity of harmonizing PSFs to better assess human reliability amid technological integration. The findings emphasize the need for an industry‐specific PSF framework that aligns with the intricacies of manufacturing operations, thus enabling more accurate HRA outcomes and informing strategies for error reduction and process optimization.

The effect of e-cigarettes on cognitive function: a scoping review.

Much research has been conducted on the acute effects of nicotine on human cognitive performance, demonstrating both enhancing and impairing cognitive effects. With the relatively recent introduction of electronic cigarettes ('e-cigarettes') as a smoking cessation device, little is known about the cognitive effects of e-cigarettes specifically, either as a nicotine replacement device or in the absence of nicotine. The purpose of this review was to present an overview of evidence from empirical studies on the effect of e-cigarettes on cognitive function. Guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines (PRISMA-ScR), SCOPUS, PubMed, and EBSCOhost were searched from 2006, the year e-cigarettes were introduced, to June 2023 for relevant papers, along with reference lists checked for additional papers. Seven experimental and four cross-sectional survey studies were identified and included. The majority of the studies only include regular and current cigarette smokers and primarily assessed the acute cognitive effect of e-cigarettes relative to nicotine. While the findings primarily suggest either no or positive effect of e-cigarettes on cognition in cigarette smokers, associations between e-cigarettes and cognitive impairments in memory, concentration and decision making were reported in both cigarette smokers and never-smokers. The acute cognitive effect of e-cigarettes on regular cigarette smokers appears minimal. However, long-term cognitive effect and their effects on never-smokers are unclear. Given that the increased numbers of e-cigarette users are non-smokers and/or adolescents, research with those naïve to nicotine and a developmentally vulnerable adolescent population on its long-term effect is needed.

Simulation analysis of low back forces in Snatch and Clean & Jerk movements via digital human modelling.

Weightlifting is an Olympic sport for dynamic strength and power, and requires the execution of different lifting techniques It is important to analyze the forces subjected to the lower back during weightlifting movements to prevent injuries. Digital Human Modeling (DHM) is a powerful tool that can be used to analyze and optimize the performance of humans while doing their work or activities. The purpose of this study is to present a simulation analysis of the lower back forces during the execution of two weightlifting techniques: Snatch (SN) and Clean & Jerk (CJ), with different loads and for both genders. Digital Human modelling through JACK simulation package was used analyze the forces exerted on the lumbosacral area (L5-S1) of the lower back in order to determine the risk for low back injuries. The level of compression and shear forces recommended by the literature have been set as thresholds. The simulaitons were performed in male and female models, with loads from 20-100kg. The results show that any weight higher than 60 kg in both movements poses risk for the weightlifters in terms of compression and shear forces. It has been observed that weightlifters can lift greater loads in the CJ technique compared to the SN technique. Furthermore, females are able to lift higher loads with lower risk of injuries. Weightlifting is a high-risk activity due to the high levels of shear and compression forces that the body is exposed to during the lifting techniques. Digital Human Modeling holds significant value due to their ability to facilitate the exploration of diverse conditions within a safe environment, devoid of any potential harm to human subjects.

New task-new results? How the gaze cone is influenced by the method of measurement.

Perceiving direct gaze-the perception of being looked at-is important in everyday life. The gaze cone is a concept to define the area in which observers perceive gaze as direct. The most frequently used methods to measure direct gaze threshold fall into two broad groups: First, a variant of the method of constant stimuli, firstly introduced by Gibson and Pick (The American Journal of Psychology, 76, 386-394, 1963). Second, a variant of the method of adjustment, firstly introduced by Gamer and Hecht (Journal of Experimental Psychology: Human Perception and Performance, 33, 705-715, 2007). Previous studies found a considerable range of thresholds, and although some influences on thresholds are already known (uncertainty, clinical groups), thresholds often vary for no apparent reason. Another important method is a triadic gaze-perception task, which usually finds triadic gaze direction judgments to be overestimated. In two experiments, we compare the method of adjustment with the method of constant stimuli. Experiment 1additionally examines the influence of the overestimation effect found in the triadic task. Results indicate that thresholds are larger when measured by the method of adjustment than by constant stimuli. Furthermore, Experiment 1finds a nonlinear overestimation factor, indicating that gaze directions near 0° are less overestimated than larger eccentricities. Correcting the thresholds with individually obtained overestimation factors widens the gaze cone but does not eliminate the average difference between the methods of adjustments and constant stimuli.

Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties. We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images. The performance of the ARD-Net framework was evaluated for numerous low count realizations using fidelity-based qualitative metrics, task-based segmentation, and quantitative metrics. Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat-pad were subjected to preclinical [18F]-Fluorodeoxyglucose (FDG)-PET/CT imaging. SC-PET images were derived from a 10min static FDG-PET acquisition, 50min post administration of FDG, and were resampled to generate four distinct LC-PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC-PET count-level. ARD-Net was trained and optimized using 48 preclinical FDG-PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD-Net was benchmarked against two leading DL-based methods (Residual UNet, RU-Net; and Dilated Network, D-Net) and non-DL methods (Non-Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity-based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer-based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics-derived features were utilized as a measure of quality assurance (QA) in comparison to true SC-PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity-based and task-based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non-parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD-Net against benchmarked methods with significance at adjusted p-value ≤0.01. ARD-Net-generated SC-PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon-count levels compared to benchmarked DL and non-DL methods. In terms of task-based quantitative accuracy evaluated by SUVMeanand SUVPeak, ARD-Net exhibited less than 5% median absolute bias for SUVMean compared to true SC-PET and lower degree of variability compared to benchmarked DL and non-DL based methods in generating SC-PET. Additionally, ARD-Net-generated SC-PET images displayed higher degree of concordance to SC-PET images in terms of radiomics features compared to non-DL and other DL approaches. Finally, the ensemble score suggested that ARD-Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction). ARD-Net provides a robust framework to generate SC-PET from LC-PET images. ARD-Net generated SC-PET images exhibited superior performance compared other DL and non-DL approaches in terms of image-fidelity based metrics, task-based segmentation metrics, and minimal bias in terms of task-based quantification performance for preclinical PET imaging.

Human resource challenges in health systems: evidence from ten African countries.

Sub-Saharan Africa has fewer medical workers per capita than any region of the world, and that shortage has been highlighted consistently as a critical constraint to improving health outcomes in the region. This paper draws on newly available, systematic, comparable data from ten countries in the region to explore the dimensions of this shortage. We find wide variation in human resources performance metrics, both within and across countries. Many facilities are barely staffed, and effective staffing levels fall further when adjusted for health worker absences. However, caseloads-while also varying widely within and across countries-are also low in many settings, suggesting that even within countries, deployment rather than shortages, together with barriers to demand, may be the principal challenges. Beyond raw numbers, we observe significant proportions of health workers with very low levels of clinical knowledge on standard maternal and child health conditions. This work demonstrates that countries may need to invest broadly in health workforce deployment, improvements in capacity and performance of the health workforce, and on addressing demand constraints, rather than focusing narrowly on increases in staffing numbers.

Towards decision-making support for complex socio-technical system safety assessment: A hybrid model combining FRAM and dynamic Bayesian networks

Effectively managing system safety and resilience in critical infrastructures requires addressing emergent risks and critical resonances. This study introduces a quantitative model that merges Monte Carlo Simulation (MCS) of the Functional Resonance Analysis Method (FRAM) and Dynamic Bayesian Network (DBN) to assess technological, organizational, and human performance variability in complex social-technical systems. FRAM identifies system taxonomy and functional variability, while MCS pinpoints critical coupling, supplying prior probabilities for functional resonance. This information feeds into the DBN, facilitating the modeling of causal relationships and probabilistic inferences regarding risk uncertainties and system resonances. The model underwent rigorous testing and validation on a preheater cyclone system within the cement industry. This process involved the utilization of historical field data gathered from six prominent cement industries and input from thirty subject matter experts. The integrated approach deeply analyzes emerging risk indicators, unveiling interactions within organizational, human, and technical subsets, alongside performance variability. Furthermore, the model incorporates system learning parameters, aiding decision-making under uncertainty. These findings advance system safety and resilience management, offering insights for risk assessment and accident prevention across diverse scenarios in complex socio-technical systems.

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

BackgroundThe detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article.Results2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating.ConclusionThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

Exploring characteristics of and transitions between mental states within the Multi‐Action Plan model in high‐level judo

AbstractThe Multi‐Action Plan (MAP) model presents an idiosyncratic framework for human performance. MAP research has categorised four Performance Types (PTs; T1–T4) using self‐paced activities. Specifically, T1–4 present four distinct mental states experienced by athletes during performance, differing, among other aspects, in the level of effort and perceived control. However, transitions between PTs are yet to be empirically investigated. This study aimed to examine MAP in the hyperdynamic open‐skill combat context of judo. We explored how judoka experienced PTs and the transition processes between these mental states. Six British, high‐level judoka (n = 6; Mdnage = 19) were interviewed using a retrospective semi‐structured interview schedule. Interpretative phenomenological analysis indicated that judo presents a hyperdynamic difficult‐to‐predict performance context due to the opponent and fighting rhythm. Second, participants strongly related to T2 and T3 and less to T1 and T4. They also reported experiences ‘between’ T2 and T3. Lastly, participants reported common transition pathways (e.g., T2–T3), typical transition cues, and specific trainable strategies to optimise performance. Findings are discussed in relation to existing literature. Lastly, we present limitations, applied, and research considerations.

Shades of gravity - effects of planetary gravity levels on electrocortical activity and neurocognitive performance.

The plans of international space agencies to return to the Moon and explore deep space, including Mars, highlight the challenges of human adaptation and stress the need for a thorough analysis of the factors that facilitate, limit and modify human performance under extreme environments. This study investigates the influence of partial gravity on behavioural (error rate and reaction time) and neuronal parameters (event-related potentials) through parabolic flights. Brain cortical activity was assessed using EEG from 18 participants who solved a neurocognitive task, consisting of a mental arithmetic task and an auditory oddball paradigm, during Earth (1G), Lunar (0.16G + 0.25G) and Martian gravity (0.38G + 0.5G) for 15 consecutive parabolas. Data shows higher electrocortical activity in Earth gravity compared to Lunar and Martian gravity in the parietal lobe. No differences in participants' performance were found among the gravity levels. Event-related potentials displayed gravity-dependent variations, though limited stimuli recording suggests caution in interpretation. Data suggests a threshold between Earth and Martian gravity within the different gravities responsible for physiological changes, but it seems to vary greatly between individuals. The altered neuronal communication could be explained with a model developed by Kohn and Ritzmann in 2018. The increasing intracranial pressure in weightlessness changes the properties of the cell membrane of neurons and leads to a depolarisation of the resting membrane potential. The findings underscore the individuality of physiological changes in response to gravity alterations, signalling the need for further investigations in future studies.

Re-evaluating the Need for Routine Maximal Aerobic Capacity Testing within Fighter Pilots.

INTRODUCTION: There is a current belief in aviation suggesting that aerobic training may reduce G-tolerance due to potential negative impacts on arterial pressure response. Studies indicate that increasing maximal aerobic capacity (<mml:math id="ie1" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂ max) through aerobic training does not hinder G-tolerance. Moreover, sustained centrifuge training programs revealed no instances where excessive aerobic exercise compromised a trainee's ability to complete target profiles. The purpose of this review article is to examine the current research in the hope of establishing the need for routine <mml:math id="ie2" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂-max testing in air force pilot protocols.METHODS: A systematic search of electronic databases including Google Scholar, PubMed, the Aerospace Medical Association, and Military Medicine was conducted. Keywords related to "human performance," "Air Force fighter pilots," "aerobic function," and "maximal aerobic capacity" were used in various combinations. Articles addressing exercise physiology, G-tolerance, physical training, and fighter pilot maneuvers related to human performance were considered. No primary data collection involving human subjects was conducted; therefore, ethical approval was not required.RESULTS: The <mml:math id="ie3" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂-max test provides essential information regarding a pilot's ability to handle increased Gz-load. It assists in predicting G-induced loss of consciousness by assessing anti-G straining maneuver performance and heart rate variables during increased G-load.DISCUSSION: <mml:math id="ie4" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂-max testing guides tailored exercise plans, optimizes cardiovascular health, and disproves the notion that aerobic training hampers G-tolerance. Its inclusion in air force protocols could boost readiness, reduce health risks, and refine training for fighter pilots' safety and performance. This evidence-backed approach supports integrating <mml:math id="ie5" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂-max testing for insights into fitness, risks, and tailored exercise.Zeigler Z, Acevedo AM. Re-evaluating the need for routine maximal aerobic capacity testing within fighter pilots. Aerosp Med Hum Perform. 2024; 95(5):273-277.

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary characteristics and the individual difference between subjects, how to obtain the effective signal features of the EEG for practical application is still a challenging task. In this article, we investigate the EEG feature learning problem and propose a novel temporal feature learning method based on amplitude-time dual-view fusion for automatic sleep staging. First, we explore the feature extraction ability of convolutional neural networks for the EEG signal from the perspective of interpretability and construct two new representation signals for the raw EEG from the views of amplitude and time. Then, we extract the amplitude-time signal features that reflect the transformation between different sleep stages from the obtained representation signals by using conventional 1-D CNNs. Furthermore, a hybrid dilation convolution module is used to learn the long-term temporal dependency features of EEG signals, which can overcome the shortcoming that the small-scale convolution kernel can only learn the local signal variation information. Finally, we conduct attention-based feature fusion for the learned dual-view signal features to further improve sleep staging performance. To evaluate the performance of the proposed method, we test 30-s-epoch EEG signal samples for healthy subjects and subjects with mild sleep disorders. The experimental results from the most commonly used datasets show that the proposed method has better sleep staging performance and has the potential for the development and application of an EEG-based automatic sleep staging system.

Proximity to rewards modulates parameters of effortful control exertion.

The now-classic goal-gradient hypothesis posits that organisms increase effort expenditure as a function of their proximity to a goal. Despite nearly a century having passed since its original formulation, goal-gradient-like behavior in human cognitive performance remains poorly understood: Are we more willing to engage in costly cognitive processing when we are near, versus far, from a goal state? Moreover, the computational mechanisms underpinning these potential goal-gradient effects-for example, whether goal proximity affects fidelity of stimulus encoding, response caution, or other identifiable mechanisms governing speed and accuracy-are unclear. Here, in two experiments, we examine the effect of goal proximity, operationalized as progress toward the completion of a rewarded task block, upon task performance in an attentionally demanding oddball task. Supporting the goal-gradient hypothesis, we found that participants responded more quickly, but not less accurately, when rewards were proximal than when they were distal. Critically, this effect was only observed when participants were given information about goal proximity. Using hierarchical drift diffusion modeling, we found that these apparent goal-gradient performance effects were best explained by a collapsing bound model, in which proximity to a goal reduced response caution and increased information processing. Taken together, these results suggest that goal gradients could help explain the oft-observed fluctuations in engagement of cognitively effortful processing, extending the scope of the goal-gradient hypothesis to the domain of cognitive tasks. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prediction of subcutaneous drug absorption − Development of novel simulated interstitial fluid media for predictive subcutaneous in vitro assays

Media that mimic physiological fluids at the site of administration have proven to be valuable in vitro tools for predicting in vivo drug release, particularly for routes of administration where animal studies cannot accurately predict human performance. The objective of the present study was to develop simulated interstitial fluids (SISFs) that mimic the major components and physicochemical properties of subcutaneous interstitial fluids (ISFs) from preclinical species and humans, but that can be easily prepared in the laboratory and used in in vitro experiments to estimate in vivo drug release and absorption of subcutaneously administered formulations. Based on data from a previous characterization study of ISFs from different species, two media were developed: a simulated mouse-rat ISF and a simulated human-monkey ISF. The novel SISFs were used in initial in vitro diffusion studies with a commercial injectable preparation of liraglutide. Although the in vitro model used for this purpose still requires significant refinement, these two new media will undoubtedly contribute to a better understanding of the in vivo performance of subcutaneous injectables in different species and will help to reduce the number of unnecessary in vivo experiments in preclinical species by implementation in predictive in vitro models.

دور استراتيجيات إدارة الموارد البشرية في التميز التنظيمي دراسة تطبيقية على العاملين في صندوق تنمية الموارد البشرية بالمملكة العربية السعودية

This study aimed to examine the impact of human resource management strategies, including job analysis, human resource planning, training, performance evaluation, and compensation, on achieving organizational excellence in its various dimensions such as leadership excellence, strategic excellence, organizational structure excellence, individual excellence, and organizational culture excellence at the Human Resources Development Fund in the Kingdom of Saudi Arabia. The study sample consisted of 262 employees out of a total of 578, and the study followed a descriptive-analytical approach, utilizing the SPSS program for data analysis. The study found statistically significant evidence of the impact of human resource management strategies on achieving organizational excellence at the Human Resources Development Fund in the Kingdom of Saudi Arabia. The study recommended the continuous development of effective and well-thought-out human resource management strategies, covering all stages of the employee lifecycle, to optimize the utilization of human resources and achieve organizational performance and objectives.

Enhancing entrepreneurial strategy skills in Saudi universities employees: The impact of psychological capital

Psychological capital is recognized as a positive and unique factor that plays a crucial role in human resource development and performance management. It has the potential to increase employees’ efforts towards achieving organizational goals and improving their entrepreneurial strategy skills. The objective of this study was to examine the contribution of psychological capital in enhancing the entrepreneurial strategy skills of employees in Saudi universities. The study employed a descriptive approach, specifically utilizing the survey study method. The study sample was intentionally selected from different categories within the study population. Data was collected from 530 participants using two questionnaires. The findings revealed that employees exhibited an average level of psychological capital, while their practice of entrepreneurial strategy skills was rated as poor. The study also demonstrated that psychological capital significantly contributes to enhancing employees’ entrepreneurial strategy skills. Furthermore, statistically significant differences were observed in the psychological capital of employees across certain variables, such as personal and functional aspects. The average level of psychological capital among employees indicates the need for further development in this area. By focusing on enhancing psychological capital, organizations can effectively improve the entrepreneurial strategy skills of their employees. It is clear that investing in the psychological capital of employees can lead to significant improvements in their entrepreneurial strategy skills. This highlights the potential for organizations to foster a more entrepreneurial mindset and approach among their staff members. Additionally, the study’s findings underscore the need to tailor interventions and development programs to address specific aspects of psychological capital that may vary across different employees. Overall, the study emphasizes that psychological capital is a valuable resource that should be nurtured and developed within the organizational context. By doing so, organizations can not only enhance the entrepreneurial strategy skills of their employees but also cultivate a more resilient, motivated, and engaged workforce. This has the potential to contribute to the overall success and innovation of Saudi universities and similar institutions.

Emotional Intelligence: Catalyst for enhanced human resource management performance

This study investigates the pivotal role of emotional intelligence (EI) in optimizing human resource management (HRM) outcomes. Through a mixed-method approach combining quantitative surveys and qualitative interviews, the research explores the correlation between EI competencies and various HRM effectiveness metrics. Findings reveal a significant positive relationship between EI levels among HR professionals and key performance indicators such as employee engagement, leadership effectiveness, and conflict resolution. Furthermore, qualitative insights underscore the nuanced ways in which EI influences HRM practices, emphasizing its role in fostering empathetic communication, facilitating constructive feedback, and mitigating workplace tensions. These results have profound implications for organizational HR strategies, suggesting the integration of EI development programs into HRM training initiatives. By cultivating EI competencies among HR practitioners, organizations can enhance their capacity to nurture a positive work environment, foster stronger employee relationships, and ultimately achieve greater organizational success.