Performance Validation Research Articles

Development of a Practical Procedure for Data-Driven Weigh-in-Motion Equipment Calibration Scheduling to Assure Data Accuracy and Consistency over Time

Weigh-in-motion (WIM) technology is a traffic monitoring technology that highway agencies use to obtain information about the weight, axle loading, and configuration of heavy vehicles moving at operational speed. To ensure high-quality WIM data, the Federal Highway Administration (FHWA) recommends regular calibration of WIM equipment. This study addresses the need to optimize the allocation of the limited resources that agencies have for WIM equipment calibration by developing a procedure for data-driven calibration scheduling. This was accomplished through an analysis of WIM measurement errors from test truck data collected during field performance validation and calibration events and an analysis of monthly changes in truck weight and axle loading characteristics, based on WIM data collected between calibration events. The analysis results were used to draw conclusions on the functional performance of different WIM sites. The study also demonstrates how the newly developed National Cooperative Highway Research Program (NCHRP) WIM Data Quality Assurance Analysis Tool can be used to compute truck weight and axle loading parameters and visualize data analysis results using four case studies: two WIM sites with piezo quartz sensors in asphalt pavements and two WIM sites with bending plate sensors in concrete pavements. This paper provides a practical procedure and recommendations that highway agencies can use to develop data-driven WIM calibration schedules that will ensure consistent high-quality WIM data for sites managed by an agency with the aid of the NCHRP WIM Data Quality Assurance Analysis Tool.

A CNN- and Self-Attention-Based Maize Growth Stage Recognition Method and Platform from UAV Orthophoto Images

The accurate recognition of maize growth stages is crucial for effective farmland management strategies. In order to overcome the difficulty of quickly obtaining precise information about maize growth stage in complex farmland scenarios, this study proposes a Maize Hybrid Vision Transformer (MaizeHT) that combines a convolutional algorithmic structure with self-attention for maize growth stage recognition. The MaizeHT model utilizes a ResNet34 convolutional neural network to extract image features to self-attention, which are then transformed into sequence vectors (tokens) using Patch Embedding. It simultaneously inserts category information and location information as a token. A Transformer architecture with multi-head self-attention is employed to extract token features and predict maize growth stage categories using a linear layer. In addition, the MaizeHT model is standardized and encapsulated, and a prototype platform for intelligent maize growth stage recognition is developed for deployment on a website. Finally, the performance validation test of MaizeHT was carried out. To be specific, MaizeHT has an accuracy of 97.71% when the input image resolution is 224 × 224 and 98.71% when the input image resolution is 512 × 512 on the self-built dataset, the number of parameters is 15.446 M, and the floating-point operations are 4.148 G. The proposed maize growth stage recognition method could provide computational support for maize farm intelligence.

Providing a brief corrective statement does not improve test performance in patients invalidating testing: A multisite, single-blind randomized controlled trial

Objective: Performance below the actual abilities of the examinee can be measured using performance validity tests (PVTs). PVT failure negatively impacts the quality of the neuropsychological assessment. In our study, we addressed this issue by providing a brief corrective statement regarding invalidity to improve test-taking behavior. Methods: This study is a multisite single-blind randomized controlled trial in a consecutive sample of clinically referred adult patients (N = 196) in a general hospital setting. Patients who failed a PVT (n = 71) were randomly allocated to a corrective statement approach (CS; n = 39), in which a brief verbal corrective statement was given by the technician, or received no corrective statement upon indications of invalid performance (NO-CS; n = 32). Both groups (CS and NO-CS) were provided with the same subsequently repeated and newly administered tests. Results: There were no group (CS vs. NO-CS) differences on both the repeated and single-administered PVTs and standard cognitive tests. Furthermore, invalid performing participants benefited significantly less from the repeated test administration compared to the valid performing group. Conclusions: Our study found that a brief corrective within-session statement, to address PVT failure and improve test-taking behavior, did not improve consequent test performance. These results suggest limited value of a brief verbal corrective statement to influence performance below best of capabilities. It highlights the need for more research to identify more effective approaches that can enhance patients test-taking behavior. Ultimately, such efforts are critical in ensuring accurate diagnosis and effective treatment recommendations for patients.

Proposing a more conservative Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Effort Index cutoff score for forensic inpatient populations.

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Effort Index (EI) may be unreliable at its standard recommended cutoff score when used with forensic psychiatric inpatient populations given high rates of genuine cognitive impairment. The present study sought to (a) examine the rate of invalid performance on the RBANS EI using the standard cutoff among incompetent to stand trial (IST) inpatients and (b) investigate the psychometric properties of the RBANS EI at various cutoff scores. This study was conducted using archival assessment data collected at a large forensic psychiatric hospital. Across the entire sample (N = 238, 79% male, Mage = 45 years, 24% primary Spanish-speaking), 79% were diagnosed with a schizophrenia spectrum disorder. Experiment 1 of the study (n = 165) contained IST patients who were classified as having a valid presentation. Experiment 2 contained a valid (n = 46) and invalid (n = 27) presentation group comprised of postadjudication and IST inpatients, respectively. In Experiment 1, over one third (36.4%) of the valid presentation sample scored above the standard recommended EI cutoff. In Experiment 2, applying the standard recommended cutoff score of > 3 was associated with a false positive rate of over 20%. Obtaining < 10% false positive errors was achieved at a cutoff score of > 5. At this cutoff score, the estimated local base rate of invalid responding among IST patients was 28%. Clinical and forensic implications and recommendations for adopting this more conservative RBANS EI cutoff score are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Monitoring the Vertical Variations in Chlorophyll-a Concentration in Lake Chaohu Using the Geostationary Ocean Color Imager

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (Rrs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field Rrs (Rrs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI Rrs data (Rrs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R2 = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAIRrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAIRrs (R2 = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with Rrs_G and environmental parameters (R2 = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management.

Classification Model for Real-Time Monitoring of Machining Status of Turned Workpieces

The occurrence of tool chatter can have a detrimental impact on the quality of the workpiece. In order to improve surface quality, machining stability, and reduce tool wear cycles, it is essential to monitor the workpiece machining process in real time during the turning process. This paper presents a tool chatter state recognition model based on a denoising autoencoder (DAE) for feature dimensionality reduction and a bidirectional long short-term memory (BiLSTM) network. This study examines the feature dimensionality reduction method of the DAE, whereby the reduced-dimensional data are concatenated and input into the BiLSTM model for training. This approach reduces the learning difficulty of the network and enhances its anti-interference capability. Turning experiments were conducted on a SK50P lathe to collect the dataset for model performance validation. The experimental results and analysis indicate that the proposed DAE-BiLSTM model outperforms other models in terms of prediction and classification accuracy in distinguishing between stable machining, over-machining, and severe chatter stages in turning chatter state recognition. The overall classification accuracy reached 96.28%.

Multi-parameter MRI-Based Machine Learning Model to Evaluate the Efficacy of STA-MCA Bypass Surgery for Moyamoya Disease: A Pilot Study.

Superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery represents the primary treatment for Moyamoya disease (MMD), with its efficacy contingent upon collateral vessel development. This study aimed to develop and validate a machine learning (ML) model for the non-invasive assessment of STA-MCA bypass surgery efficacy in MMD. This study enrolled 118 MMD patients undergoing STA-MCA bypass surgery. Clinical features were screened to construct a clinical model. MRI features were extracted from the middle cerebral artery supply area using 3D Slicer and employed to build five ML models using logistic regression algorithm. The combined model was developed by integrating the radiomics score (Rad-score) with the clinical features. Model performance validation was conducted using ROC curves. Platelet count (PLT) was identified as a significant clinical feature for constructing the clinical model. A total of 3404 features (851 × 4) were extracted, and 15 optimal features were selected from each MRI sequence as predictive factors. Multivariable logistic regression identified PLT and Rad-score as independent parameters used for constructing the combined model. In the testing set, the AUC of the T1WI ML model [0.84 (95% CI, 0.70-0.97)] was higher than that of the clinical model [0.66 (95% CI, 0.46-0.86)] and the combined model [0.80 (95% CI, 0.66-0.95)]. The T1WI ML model can be used to assess the postoperative efficacy of STA-MCA bypass surgery for MMD.

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking.

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Cyber‐attack and defense method for load frequency control in smart grid systems with electric vehicles

AbstractThe latest advancements in Vehicle‐to‐Grid (V2G) technology enable Electric Vehicles (EVs) to contribute to Frequency Regulation (FR), mitigating the impact of uncertain power fluctuations in renewable resources. However, diverse cyber‐attacks pose threats to the Load Frequency Control (LFC) system and smart grid infrastructure, compromising their accuracy and reliability. This research paper focuses on the effects of cyber‐attacks on frequency regulation in a smart grid system that relies on a communication network. The study proposes a two‐area system and a modified IEEE‐39 bus three‐area system, incorporating intermittent solar photovoltaic and wind turbine sources, traditional conventional sources, and electric vehicles. To ensure frequency stabilization and tie‐line power, the researchers introduce a cascade FOPIDN‐(1+TD) controller. Its parameters are optimized using a novel Quasi Opposition Arithmetic Optimization Algorithm (QOAOA). The proposed approach's dynamic response is compared with other commonly used controllers, demonstrating its effectiveness in maintaining stability. The article focuses on the LFC system and presents a novel approach involving a cyber‐physical model. It introduces a method for detecting and defending against cyber‐attacks using deep learning, specifically utilizing the Long‐Short‐Term‐Memory (LSTM) network. The effectiveness of the proposed defense strategy is demonstrated through experiments conducted on both a two‐area interconnected power system and the IEEE‐39 bus system. The outcomes indicate that the suggested defense mechanism effectively maintains acceptable levels of power system frequency and tie‐line power during cyber‐attacks. Additionally, the validity of the controller's performance is verified through real‐time analysis using Hardware‐In‐The‐Loop (HIL) simulation with the OPAL‐RT platform.

Neuropsychological functioning after COVID-19: minor differences between individuals with and without persistent complaints after SARS-CoV-2 infection

Objective: It is unclear how self-reported severe fatigue and difficulty concentrating after SARS-CoV-2 infection relate to objective neuropsychological functioning. The study aimed to compare neuropsychological functioning between individuals with and without these persistent subjective complaints. Method: Individuals with and without persistent severe fatigue (Checklist Individual Strength (CIS) fatigue ≥ 35) and difficulty concentrating (CIS concentration ≥ 18) at least 3 months after SARS-CoV-2 infection were included. Neuropsychological assessment was performed on overall cognitive functioning, attention, processing speed, executive functioning, memory, visuo-construction, and language (18 tests). T-scores −1.5 SD below population normative data (T ≤ 35) were classified as “impaired”. Results: 230 participants were included in the study, of whom 22 were excluded from the analysis due to invalid performance. Of the participants included in the analysis, 111 reported persistent complaints of severe fatigue and difficulty concentrating and 97 did not. Median age was 54 years, 59% (n = 126) were female, and participants were assessed a median of 23 months after first infection (IQR: 16–28). With bivariate logistic regression, individuals with persistent complaints had an increased likelihood of slower information processing speed performance on the Stroop word reading (OR = 2.45, 95%CI = 1.02–5.84) compared to those without persistent complaints. Demographic or clinical covariates (e.g. hospitalization) did not influence this association. With linear regression techniques, persistent complaints were associated with lower t-scores on the D2 CP, TMT B, and TMT B|A. There were no differences in performance on the other neuropsychological tests. Conclusions: Individuals with subjective severe fatigue and difficulty concentrating after COVID-19 do not typically demonstrate cognitive impairment on extensive neuropsychological testing.

Does generative artificial intelligence pose a risk to performance validity test security?

Objective We examined the performance validity test (PVT) security risk presented by artificial intelligence (AI) chatbots asking questions about neuropsychological evaluation and PVTs on two popular generative AI sites. Method In 2023 and 2024, multiple questions were posed to ChatGPT-3 and Bard (now Gemini). One set started generally and refined follow-up questions based on AI responses. A second set asked how to feign, fake, or cheat. Responses were aggregated and independently rated for inaccuracy and threat. Responses not identified as inaccurate were assigned a four-level threat rating (no, mild, moderate, or high threat). Combined inaccuracy and threat ratings were examined cross-sectionally and longitudinally. Results Combined inaccuracy rating percentages were 35 to 42% in 2023 and 16 to 28% in 2024. Combined moderate/high threat ratings were observed in 24 to 41% of responses in 2023 and in 17 to 31% of responses in 2024. More ChatGPT-3 responses were rated moderate or high threat compared to Bard/Gemini responses. Over time, ChatGPT-3 responses became more accurate with a similar threat level, but Bard/Gemini responses did not change in accuracy or threat. Responses to how to feign queries demonstrated ethical opposition to feigning. Responses to similar queries in 2024 showed even stronger ethical opposition. Conclusions AI chatbots are a threat to PVT test security. A proportion of responses were rated as moderate or high threat. Although ethical opposition to feigning guidance increased over time, the natural language interface and the volume of AI chatbot responses represent a potentially greater threat than traditional search engines.

A fatigue crack tip field model considering residual stress and plasticity-induced closure for welded structures

In order to more accurately understand and predict the fatigue crack growth behavior in welded structures, a fatigue crack tip field model considering residual stress and plasticity-induced closure (RSPC model) is presented in this paper. Building upon the existing CJP model, which adequately explains plasticity-induced crack closure, the RSPC model further considers the influence of tensile residual stress induced by welding. The model, through a detailed derivation and analysis of the residual stress function, establishes the theoretical stress and displacement fields at the crack tip of the butt-welded joint. Integrating these fields with the CJP model, the theoretical framework of the RSPC model is formulated. Subsequently, through fatigue crack growth experiments and digital image correlation analysis, a comparison and validation of the predictive performance of the CJP and RSPC models are conducted. It is observed that the latter aligns more closely with experimental results in calculating plastic zone area, with a maximum error of only 10%. In comparison to the CJP model, the average error decreases by 19.143%, indicating higher predictive accuracy. Furthermore, the study focuses on the contribution of tensile residual stress in butt welded joints to the growth of the plastic zone at the fatigue crack tip and the underlying reasons for its variations. Finally, the research investigates the constrained relationship between plastic zone size and fatigue crack growth rate. Corresponding relationship functions are fitted, providing more accurate guidance for crack control and material design.

A Prediction Nomogram for Fractured Vertebra Recollapse After Posterior Reduction and Pedicle Screw Fixation in Thoracolumbar Fractures

This study aimed to establish a predictive nomogram model for recollapse of fractured vertebra after posterior pedicle screw fixation in thoracolumbar fractures (TLFs). Patients undergoing posterior pedicle screw fixation for TLFs at our hospital between January 2016 and December 2021 were retrospectively reviewed. Patients were divided into 2 groups according to the presence or absence of recollapse of the fractured vertebra at the final follow-up. The predictors for fractured vertebra recollapse were identified by univariate and multivariable logistic regression analysis, and a nomogram model was developed. The prediction performance and internal validation were established. A total of 224 patients were included in this study. Of these, 46 (20.5%) patients developed recollapse of fractured vertebra. Age, thoracic and lumbar injury severity score, screw distribution in the fractured vertebra, and anterior vertebral height compression ratio were associated with vertebral recollapse. These predictors were used to construct a predictive nomogram. The area under the receiver operating characteristic curve of the nomogram model was 0.891. The concordance index was 0.891, and it was 0.877 with bootstrapping validation. The calibration curves and decision curve analysis also suggested that the nomogram model had excellent predictive performances for fractured vertebra recollapse. A clinical nomogram incorporating 4 variables was constructed to predict fractured vertebra recollapse after posterior pedicle screw fixation for TLFs. The nomogram demonstrated good calibration and discriminative abilities, which may help clinicians to make better treatment decisions.

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

P-211 Development of an artificial intelligence software with consistent laboratory data from a single IVF center: performance of a new interface to predict clinical pregnancy

Abstract Study question How is the performance of a Morphological Artificial Intelligence Assistant (MAIA) on embryo selection to clinical pregnancy prediction? Summary answer MAIA presented a sensibility of 79.8% and specificity of 72.9% to predict a clinical pregnancy and reached a performance of 64.7% on prospective validation test. What is known already Artificial intelligence (AI) tools have gained attention in assisted reproductive treatments. Algorithms for gametes and embryo classification and selection are commercially available in an exponential increasing race. Usually, those algorithms/software are developed based on data acquisition of several ART centers, from different countries and continents, which may overlap some but rarely all clinical and laboratory methodologies and protocols. In this way, the purpose to develop a software restricted to a single center may represent several advantages which is not highlighted at this moment. Here, we developed a full in-house software for embryo selection and prediction of clinical pregnancy. Study design, size, duration MAIA was developed through artificial neural networks technique and genetic algorithms using the input of morphological data from 1.008 sequential transferred blastocysts and known reproductive outcomes (clinical pregnancy, presence of gestational sac and heartbeat). Blastocysts images were obtained from IVF cycles based in a single ART center. All embryos were cultured in a time-lapse incubator (Embryoscope Plus, Vitrolife) and blastocyst digital image processing were analyzed considering 33 mathematic variables. Participants/materials, setting, methods Input data were randomized for training, validation and test (70, 15 and 15%, respectively). From 1008 images, 755 were used for effective AI learning, 174 were used for the blind test and 79 were excluded due to poor embryo visualization. For performance validation, the software was responsible for embryo selection in 34 elective single embryo transfers. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was measured to obtain predictive power. Main results and the role of chance In the blind test (n = 174), 71 embryos were correctly classified as positive for clinical pregnancy (CP) – true positive – and 18 embryos were misclassified as negative for CP. In negative CP, 62 embryos were correctly classified as negative for CP – true negative – and 23 embryos were misclassified as positive for CP. This represented a sensibility of 79.8%, specificity of 72.9% and precision of 75.5% for CP. The AUC was 76.52%. In performance validation, 34 elective single embryo transfers, meaning there were from cycles with more than 1 viable blastocyst for selection, the chosen embryo was decided by MAIA. In those, 22 achieved a clinical pregnancy (64.7%), in which MAIA predicted correctly the CP in 19 (86.4%) – the software may predict that none of embryos available would achieve a CP. From 12 negative outcomes, MAIA predicted in 25% that none of the embryos available would resulted in CP. Overall prediction for positive and negative CP in performance validation was 64.7%. Limitations, reasons for caution MAIA software was built with the database from a single IVF center and its performance may be impacted if used in different ART services. The number of single embryo transfers used to validate the sofware performance is still low. Wider implications of the findings This is a new artificial intelligence software for embryo selection and clinical pregnancy prediction. Different from all available commercial software, MAIA was built in homogenous clinical and laboratory protocols, analyzing the database from a single IVF center, and were able to reach a performance of 64.7% on prospective validation test. Trial registration number Not applicable.

Advanced stability-indicating gradient elution reverse phase-high performance liquid chromatography method development and validation for simultaneous estimation of rutin and curcumin using an analytical quality by design approach

This research delves into a systematic Analytical Quality by Design (AQbD) approach to develop and validate a stability-indicating robust RP-HPLC method for the simultaneous determination of Rutin and Curcumin, compounds known for their anti-inflammatory, antioxidant, and wound-healing properties. Utilizing QbD principles, critical method parameters (CMPs) were identified and screened using design of experiments (DoE) and optimized via response surface methodology (RSM) with Central Composite Design (CCD). The optimized chromatographic separation used a Phenomenex Luna C18 column with gradient elution, using methanol and 0.1% formic acid, a flow rate of 1 ml/min, and detection at 345 nm (isosbestic point). The retention times were 5.63 mins for Rutin and 17.01 mins for Curcumin. The stability indication of the developed HPLC method was performed, and validation parameters were meticulously conducted following the ICH Q2(R1) guidelines, demonstrating linearity over 2-10 μg/ml for Rutin and 5-25 μg/ml for Curcumin, with correlation coefficients of 0.9999 and 0.9998, respectively. Precision studies showed %RSD within acceptable limits, and accuracies ranged between 97-100%. This validated HPLC method offers a reliable, sensitive approach for simultaneous quantifying Rutin and Curcumin, suitable for routine analysis in pharmaceutical laboratories and quality control.

Neurocognitive performance in functional neurological disorder: A systematic review and meta-analysis.

Cognitive complaints are common in functional neurological disorder (FND), but it is unclear whether objective neurocognitive deficits are present. This systematic review summarized validated/standardized cognitive test performance in FND samples across cognitive domains. Embase, PsycInfo and MEDLINE were searched from inception to 15 May 2023, combining terms for FND and cognitive domains (e.g., attention, memory, executive functioning). Studies included a range of FND phenotypes (seizures, motor, cognitive disorder, mixed), compared to healthy or clinical controls. Risk of bias was assessed with the modified Newcastle-Ottawa Scale and a qualitative synthesis/narrative review of cognitive performance in FND was conducted. Test performance scores were extracted, and random effects meta-analyses were run where appropriate. This review was registered on PROSPERO, CRD42023423139. Fifty-six studies including 2260 individuals with FND were eligible. Although evidence for some impairments emerged across domains of executive functioning, attention, memory and psychomotor/processing speed, this was inconsistent across studies and FND phenotypes. Common confounds included group differences in demographics, medication and intellectual functioning. Only 24% of studies objectively assessed performance validity. Meta-analyses revealed higher scores on tests of naming (g = 0.67, 95% confidence interval [CI] 0.50, 0.84) and long-term memory (g = 0.43, 95% CI 0.13, 0.74) in functional seizures versus epilepsy, but no significant differences in working (g = -0.08, 95% CI -0.44, 0.29) or immediate (g = 0.25, 95% CI -0.02, 0.53) memory and cognitive flexibility (g = -0.01, 95% CI -0.29, 0.28). There is mixed evidence for objective cognitive deficits in FND. Future research should control for confounds, include tests of performance validity, and assess relationships between objective and subjective neurocognitive functioning.

Does neuropsychological intraindividual variability index cognitive dysfunction, an invalid presentation, or both? Preliminary findings from a mixed clinical older adult veteran sample

ABSTRACT Introduction Intraindividual variability across a battery of neuropsychological tests (IIV-dispersion) can reflect normal variation in scores or arise from cognitive impairment. An alternate interpretation is IIV-dispersion reflects reduced engagement/invalid test data, although extant research addressing this interpretation is significantly limited. Method We used a sample of 97 older adult (mean age: 69.92), predominantly White (57%) or Black/African American (34%), and predominantly cis-gender male (87%) veterans. Examinees completed a comprehensive neuropsychological battery, including measures of reduced engagement/invalid test data (a symptom validity test [SVT], multiple performance validity tests [PVTs]), as part of a clinical evaluation. IIV-dispersion was indexed using the coefficient of variance (CoV). We tested 1) the relationships of raw scores and “failures” on SVT/PVTs with IIV-dispersion, 2) the relationship between IIV-dispersion and validity/neurocognitive disorder status, and 3) whether IIV-dispersion discriminated the validity/neurocognitive disorder groups using receiver operating characteristic (ROC) curves. Results IIV-dispersion was significantly and independently associated with a selection of PVTs, with small to very large effect sizes. Participants with invalid profiles and cognitively impaired participants with valid profiles exhibited medium to large (d = .55–1.09) elevations in IIV-dispersion compared to cognitively unimpaired participants with valid profiles. A non-significant but small to medium (d = .35–.60) elevation in IIV-dispersion was observed for participants with invalid profiles compared to those with a neurocognitive disorder. IIV-dispersion was largely accurate at differentiating participants without a neurocognitive disorder from invalid participants and those with a neurocognitive disorder (areas under the Curve [AUCs]=.69–.83), while accuracy was low for differentiating invalid participants from those with a neurocognitive disorder (AUCs=.58–.65). Conclusions These preliminary data suggest IIV-dispersion may be sensitive to both neurocognitive disorders and compromised engagement. Clinicians and researchers should exercise due diligence and consider test validity (e.g. PVTs, behavioral signs of engagement) as an alternate explanation prior to interpretation of intraindividual variability as an indicator of cognitive impairment.

Reinforcement Learning-Based Control Sequence Optimization for Advanced Reactors

The last decade has seen the development and application of data-driven methods taking off in nuclear engineering research, aiming to improve the safety and reliability of nuclear power. This work focuses on developing a reinforcement learning-based control sequence optimization framework for advanced nuclear systems, which not only aims to enhance flexible operations, promoting the economics of advanced nuclear technology, but also prioritizing safety during normal operation. At its core, the framework allows the sequence of operational actions to be learned and optimized by an agent to facilitate smooth transitions between the modes of operations (i.e., load-following), while ensuring that all safety significant system parameters remain within their respective limits. To generate dynamic system responses, facilitate control strategy development, and demonstrate the effectiveness of the framework, a simulation environment of a pebble-bed high-temperature gas-cooled reactor was utilized. The soft actor-critic algorithm was adopted to train a reinforcement learning agent, which can generate control sequences to maneuver plant power output in the range between 100% and 50% of the nameplate power through sufficient training. It was shown in the performance validation that the agent successfully generated control actions that maintained electrical output within a tight tolerance of 0.5% from the demand while satisfying all safety constraints. During the mode transition, the agent can maintain the reactor outlet temperature within ±1.5 °C and steam pressure within 0.1 MPa of their setpoints, respectively, by dynamically adjusting control rod positions, control valve openings, and pump speeds. The results demonstrate the effectiveness of the optimization framework and the feasibility of reinforcement learning in designing control strategies for advanced reactor systems.

Which test results to believe? Comparison of different ELISA kits for detection of SARS-CoV-2-neutralizing antibody among COVID-vaccinated individuals

BackgroundIn the context of SARS-CoV-2, infection and immunization lead to varied levels of neutralizing antibodies (NAbs) crucial for protective immunity. In the lack of universal availability of plaque reduction neutralization tests, the gold standard centers rely on surrogate kits, for which performance standards and validation are lacking. Hence, we assessed three NAb kits available in India to determine their agreement and concordance. MethodsIt's a single-center cross-sectional observation study conducted during the third wave of the pandemic starting in January 2022 amongst individuals (n: 247) who received the first two doses of CovishieldTM. Samples were analysed using kit-A (Indian Council of Medical Research (ICMR) approved NAb microlisa kit, J. Mitra & Co. Pvt. Ltd), kit-B (Food and Drug Administration–approved NAb surrogate virus neutralisation test, GenscriptTM), and kit-C (NARI- and ICMR-approved anti–SARS-CoV-2 IgG antibodies kit using indirect ELISA principle, ErbaLisa® COVID-19 IgG). Cohen's kappa and concordance were analyzed using JMPver20.0.0. ResultsNAb was detected in 80.4%, 82.9%, and 21% samples using the three different kits. Discordance was observed in 19.68%, 65.7%, and 62.6% between Kit A-B, A-C, and B-C, respectively. The agreement was fair between Kit A-B (κ-0.35; p: 0.06) and Kit B-C (κ-0.09; p: 0.0005). Kit-B showed higher mean percentage inhibition (74.6 ± 32.1%) than Kit-A (68.1 ± 35.7%) (p: 0.0347). The correlation between kit-A and kit-B was moderate (r: 0.57; p < 0.0001). ConclusionThere is significant discordance between various commercials, which raises questions about their validity. The research underscores the need for robust validation and standardization of NAb tests to ensure their reliability and effectiveness in guiding public health strategies.