Reduction In Average Time Research Articles

Establishing automatic coating paths: empirical study for steel component production

The higher the complexity and size of H-shaped steel components, the more productivity challenges a coating factory encounters, particularly when catering to the intricate demands of customer-oriented industries. The aim of this research was to devise efficient coating paths for H-shaped steel components, replacing labour-intensive manual processes. A review of the existing literature revealed methodologies involving parameter adjustment, configuration recognition and optimisation of coating paths for H-shaped steel components. Using a comprehensive database of H-shaped steel components, an algorithm was developed to identify components accurately (>90% accuracy) and subsequently streamline the coating process. Through a comparative evaluation of 99 diverse scenarios of H-shaped steel coating, the results showed an average time reduction of 24.6% compared with manual coating methods. This finding bears significance given the industry’s challenges with a scarcity of skilled labour and elevated coating expenses, both of which hinder smooth business operations for practitioners.

The Development and Impact the First Irish Nurse-Led Lumbar Puncture Clinic in a Regional Specialist Memory Clinic

Abstract Background A Regional Specialist Memory service assesses complex cases of cognitive impairment, requiring a comprehensive diagnostic assessment. Lumbar punctures (LP's) are the definitive procedure to identify Alzheimer's Disease. The highest limitation to LP access is shortage and availability of specially trained medical staff. Cognitive centres across Europe have been training nurses (Advanced Nurse Practitioner) to perform LPs since 2006. A nurse led service was established in April 2023 in a large Irish teaching hospital. Methods This analysis data represents a preliminary 5-month audit of an ANP Led LP service (ALLPS) compared to prior service provision (ALLPS n=40, pre-ALLPS n=20). Waiting time for LP prior to ALLPS averaged 25 weeks, which reduced by over 50% (12 weeks). Results CSF results were identified in half the time with ALLPS (3 weeks versus 6 weeks). The initiation of the ALLPS reduced the timeframe to diagnosis from 12 weeks to 5 weeks. This led to an average wait time reduction from date of CSF result to disclosure of diagnosis of 53% (15 weeks pre-ALLPS to 8 weeks). The ALLPS is pioneering the use of a seated upright LP procedural position, reported to be comfortable by this cohort. Analysis of the nurse led LP procedure is comparative with medical LP services. Almost 40% were achieved on 1st attempt, a further 28% on 2nd attempt, with 18% on 3rd attempt. The ANP successfully completed 75% of LPs in this initial cohort with 9 patients requiring supervisor LP performance. One case required x-ray guided LP. Conclusion Appointment of a dedicated ALLPS service has reduced waiting times for diagnostic intervention, leading to early diagnosis, accessing post diagnostic support, meeting best practice guidelines outlined by the National Dementia Office.

A machine-learning-based column generation heuristic for electric bus scheduling

Bus scheduling in public transit consists in determining a set of bus schedules to cover a set of timetabled trips at minimum cost. This planning process has evolved recently with the advent of electric buses that introduce constraints related to vehicle autonomy and battery charging process. In particular, column-generation algorithms have regained popularity for solving problems similar to the one considered in this paper, namely, the multi-depot electric vehicle scheduling problem (MDEVSP) with a piecewise linear charging function and capacitated charging stations. To tackle large-scale MDEVSP instances, we design a column generation (CG) heuristic that relies on reduced-sized networks to generate the bus schedules. The reduction is achieved by selecting a priori a subset of the arcs. Multiple selection techniques are studied: some are based on a greedy heuristic and others exploit a supervised learning algorithm relying on a graph neural network. It turns out that combining both selection types yields the best computational results. On 405 artificial instances involving between 568 and 1474 trips and generated from real bus lines in Montreal, the network reduction technique produced an average computational time reduction of 71.6% (compared to the same CG heuristic but without network reduction), while deteriorating solution cost by an average of 2.2%. On 8 larger instances containing more than 2500 trips on average, the proposed solution method also provided an average time saving of 52.5% with an average gap of 4.2% thanks to a transfer learning approach.

Longitudinal objective assessment of speech in Multiple Sclerosis

BackgroundRemote objective tests may supplement in-clinic examination to better inform treatment decisions. Previous cross-sectional studies presented objective speech metrics as potential markers of Multiple Sclerosis (MS) disease progression. ObjectiveTo examine the short-term stability and long-term sensitivity of speech metrics to MS progression. MethodsWe prospectively recorded speech from people with MS at baseline, six, twelve weeks, and at ten months or longer after baseline (1y+). Only people with a definite diagnosis of MS and without other potential causes of dysarthria were included. Speech tasks comprehended 1) a sustained vowel /a/, 2) saying the days of the week, 3) repeating the non-word pa-ta-ka multiple times as fast as possible, 4) reading the Grandfather Passage, and 5) telling a personal story. We selected speech metrics of interest according to their association with MS presence, correlation with general disability, and short-term metric stability in the absence of disease progression. Selected speech metrics were analysed for short- versus long-term changes in the whole MS cohort and in the clinically stable versus progression subgroups at 1y+. ResultsSixty-nine people with MS participated (76.8 % female, age mean 47.5 ± 11.1 SD, EDSS median 3.5, interquartile range 3.5). Twenty-six unique speech metrics satisfied the suitability criteria. On average, reading rate improved 3.5 % for all people with MS and 6.5 % for slow readers with MS from baseline to the six-week, driven by a reduction in pauses. At 1y+, participants showed a 3.1 % average reduction in vocalization time during the reading task, which was similar in the progression (n = 29) and non-progression (n = 40) groups and thus unrelated to disease progression. Both findings are in the opposite direction of what would be generally expected for deterioration in speech performance and might be attributable to familiarity and training effects. Other speech metrics showed either negligible change or a similar variability between short-term and long-term differences. ConclusionMost individual long-term changes were small and within short-term variability intervals, irrespective of clinical disease progression. Familiarity and practice effects might have blunted the measurement of change. The present lack of longitudinal sensitivity of speech in MS contradicts previous cross-sectional findings and requires further investigation.

WP4.2 - Covid positivity: the pandemic surgical referral telephone triage legacy lives on

Abstract Introduction Covid restrictions provided opportunities for novel approaches to patient assessment. Innovative telephone triage of surgical referrals at Great Western Hospital, a UK District General Hospital, resulted in reduction in those patients reviewed and discharged the same day from 56% to 23.6%. Furthermore, 28.4% of admissions were avoided by triage. This novel work was adapted for the Ambulatory Emergency Care (AEC) department of Yeovil District Hospital, in response to exponential increases in Primary Care and Emergency Department referrals. Aims To develop a telephone triage system to get the right patient to the right place at the right time, reviewed by the right team. Methods Over the two-week study period (18/9–29/9/2023) a dedicated triage telephone line enabled a discussion between referring clinician and an experienced surgeon. Four outcomes were offered:AdviceOutpatient review within 1 weekHot clinic review within 24-48 hoursSame-day review by the on-call team Prospective referral data (18/9–29/9/2023) and retrospective non-triage data (1/9–14/9/2023) were gathered. Triage effects were measured by outcome comparison with non-triage data. Results A total of 193 patients were discussed. 48% were Emergency Department referrals, 25% from General Practitioners. Same-day reviews reduced by 35%. Overall, the on-call workload decreased by 68%. Return to AEC for review reduced by 99%. Reduction in average time spent by patients in AEC from 6.9 to 1.29 hours, saved up to 6 bed-days per day and facilitated significant cost savings. Conclusions Novel approaches can be cost effective and positively impact patient care.

Hypospadias Reconstruction Training: Development of an Ex-Vivo Model for Objective Evaluation of Surgical Skills

To objectively evaluate technical skill acquisition in hypospadias repair procedures during surgical training using noninvasive wearable sensor technology. We combined subjective video evaluations with objective electromyography (EMG) measurements in a hands-on hypospadias training course. Surgeons wore wireless EMG and accelerometer sensors on their dominant hand while performing tasks on ex-vivo cadaveric calf penises. The study focused on 4 skills as follows: urethral mobilization, dorsal inlay graft harvest/implantation, meatal-based flap urethroplasty, and dorsal plication. Machine learning techniques analyzed muscle activation patterns and attributes for assessing surgical precision. The course included 18 participants (10 female, 8 males; average age 40.18±8.46 years) categorized as novice (n=10, <3 years' experience), intermediate (n=5, 3-5 years), and expert (n=3, >5 years). Video evaluations did not reveal significant differences due to short-term training. However, EMG measurements showed significant reductions in average EMG power, total time, dominant frequency, and cumulative muscle workload after training. Additionally, the mean power spectral density of the EMG signal decreased notably post-training. This study presents a structured approach for hypospadias training and highlights the effectiveness of wearable sensor technology for objective skill assessment. While video evaluations did not detect significant changes, EMG data provided measurable differences in skill acquisition, suggesting that wearable sensors could enhance objective evaluations of surgical proficiency in residency programs.

Learning-based CU partition prediction for fast panoramic video intra coding

In recent years, Virtual Reality (VR) technology has been applied in various aspects of life. As an indispensable component of VR, panoramic video offers viewers immersive experiences, thereby attracting widespread attention. The high resolution of panoramic video poses significant challenges for its storage and transmission, requiring efficient video compression. The Versatile Video Coding (VVC) exhibits superior coding efficiency, albeit at the expense of a several dozen-fold increase in encoding complexity. In this paper, we propose a fast Coding Unit (CU) partition algorithm for intra-coding based on Support Vector Machine (SVM), which selects appropriate features for online training of multiple classifiers based on frame texture characteristics. Additionally, in response to distortion issues in panoramic video coding, we divide video frames into different regions and adjust the Quantization Parameter (QP) values of CUs within each region to improve the encoding efficiency. Experimental results show that the proposed method achieves an average encoding time reduction of 46.21%, with an average BDBR increase of only 1.01%, achieving a good trade-off between time-saving and encoding efficiency.

Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression.

Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-DixonDL). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed. Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-DixonDL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-DixonDL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-DixonDL (P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-DixonDL technique (P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-DixonDL. Interreader agreement between VIBE-Dixon and VIBE-DixonDL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXONDL was observed in both the precontrast (P = 0.025) and postcontrast images (P < 0.001). Also, an increase of splenic SNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.34 and P = 0.003, respectively). Similarly, an increase of pancreas CNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.557 and P = 0.026, respectively). The prospectively accelerated, DL-enhanced VIBE with Dixon fat suppression was clinically feasible. It enabled a 52% reduction in breath-hold time and provided superior image quality, diagnostic confidence, and pancreatic lesion conspicuity. This technique might be especially useful for patients with limited breath-hold capacity.

Periodic hydration of paddy rice in mass transfer, physical and thermodynamic properties

Hydration is an essential stage in the rice parboiling process, and it can be a time-consuming. We evaluated the effects of periodic operation in paddy rice hydration to reduce the processing time. Periodic hydration with temperature modulation (10 °C amplitude and 12 min periods) was compared with isothermal hydration (conventional) at mean temperatures of 25, 35, 45, 55, and 65 °C. The process comparison was based on mass transfer, process time reduction, change in grain microstructure and thermodynamic properties. The periodic process increased water absorption, with a 41.47 % average reduction in process time. The Diffusion model satisfactorily predicted the moisture uptake experimental data (R2 > 0.97). The significant increase (p < 0.05) in the 27.38 % average in the model parameter indicated that the periodic operation favored the mass transfer phenomenon. Micrographs of hydrated grains revealed that the hydrations at 55 e 65 °C favored starch gelatinization, being able to reduce a subsequent step in the process (steaming). The temperature modulation did not alter the grain microstructure when both processes were compared at the same temperature. However, it interfered with the thermodynamic properties of the system. Periodic operation revealed mean values higher to ΔH# and mean values lower to ΔS# and ΔG#.

Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model

One of the preconditions for efficient cloud computing services is the continuous availability of services to clients. However, there are various reasons for temporary service unavailability due to routine maintenance, load balancing, cyber-attacks, power management, fault tolerance, emergency incident response, and resource usage. Live Virtual Machine Migration (LVM) is an option to address service unavailability by moving virtual machines between hosts without disrupting running services. Pre-copy memory migration is a common LVM approach used in cloud systems, but it faces challenges due to the high rate of frequently updated memory pages known as dirty pages. Transferring these dirty pages during pre-copy migration prolongs the overall migration time. If there are large numbers of remaining memory pages after a predefined iteration of page transfer, the stop-and-copy phase is initiated, which significantly increases downtime and negatively impacts service availability. To mitigate this issue, we introduce a prediction-based approach that optimizes the migration process by dynamically halting the iteration phase when the predicted downtime falls below a predefined threshold. Our proposed machine learning method was rigorously evaluated through experiments conducted on a dedicated testbed using KVM/QEMU technology, involving different VM sizes and memory-intensive workloads. A comparative analysis against proposed pre-copy methods and default migration approach reveals a remarkable improvement, with an average 64.91% reduction in downtime for different RAM configurations in high-write-intensive workloads, along with an average reduction in total migration time of approximately 85.81%. These findings underscore the practical advantages of our method in reducing service disruptions during live virtual machine migration in cloud systems.

Branch-Price-and-Cut algorithms for the team orienteering problem with interval-varying profits

This paper studies the team orienteering problem (TOP), wherein each vertex requires two visits, and the service profit of each vertex depends on the time interval between these two visits. Motivated by scenarios like perishable product delivery, home health care scheduling, and earth observation satellite scheduling, the paper addresses two variants: the periodic orienteering problem with a focus on the number of days (NoDs) and the team orienteering problem with attention to a combination of time windows (CoTWs). It develops mixed-integer linear programming (MILP) models for both variants and devises exact Branch-Price-and-Cut (BPC) algorithms tailored to their block diagonal structures. Furthermore, this paper proposes two strategies to improve algorithm performance. A simplification strategy streamlines the directed graph network by removing redundant vertices and arcs without compromising optimality, thereby accelerating the solution of pricing problems. Additionally, a matheuristic algorithm is proposed to obtain integer solutions quickly. Computational results demonstrate the effectiveness of these algorithms, showcasing their superiority over CPLEX. The BPC algorithm, coupled with the simplification strategy, exhibits an average computational time reduction of 70 % compared to the basic strategy. The effectiveness of the matheuristic algorithm is also confirmed. Finally, the findings presented herein offer valuable insights for refining and applying BPC algorithms.

Comparative study of percutaneous endoscopic lumbar decompression and traditional revision surgery in the treatment of symptomatic adjacent segment degeneration

ObjectiveThe objective of this study is to evaluate and compare the surgical outcomes and complications of Percutaneous Endoscopic Lumbar Decompression (PELD) and traditional revision surgery in treating symptomatic Adjacent Segment Degeneration (ASD). This comparison aims to delineate the advantages and disadvantages of these methods, assisting spine surgeons in making informed surgical decisions.Methods66 patients with symptomatic ASD who failed conservative treatment for more than 1 month and received repeated lumbar surgery were retrospectively collected in the study from January 2015 to November 2018, with the average age of 65.86 ± 11.04 years old. According to the type of surgery they received, all the patients were divided in 2 groups, including 32 patients replaced the prior rod in Group A and 34 patients received PELD at the adjacent level in Group B. Patients were followed up routinely and received clinical and radiological evaluation at 3, 6, 12 months and yearly postoperatively. Complications and hospital costs were recorded through chart reviews.ResultsThe majority of patients experienced positive surgical outcomes. However, three cases encountered complications. Notably, Group B patients demonstrated superior pain relief and improved postoperative functional scores throughout the follow-up period, alongside reduced hospital costs (P < 0.05). Additionally, significant reductions in average operative time, blood loss, and hospital stay were observed in Group B (P < 0.05). Notwithstanding these benefits, three patients in Group B experienced disc re-herniation and underwent subsequent revision surgeries.ConclusionsWhile PELD offers several advantages over traditional revision surgery, such as reduced operative time, blood loss, and hospital stay, it also presents a higher likelihood of requiring subsequent revision surgeries. Future studies involving a larger cohort and extended follow-up periods are essential to fully assess the relative benefits and drawbacks of these surgical approaches for ASD.

Localized Path Planning for Mobile Robots Based on a Subarea-Artificial Potential Field Model.

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm.

OEM: An operation-aware event matching algorithm for content-based Pub/Sub systems

Due to the continuous expansion of the user base and the heightened demands for quality of service (QoS), real-time data distribution has encountered significant challenges in event matching within large-scale content-based publish/subscribe (CPS) systems. Many efficient algorithms have been proposed to enhance the matching performance. However, most existing algorithms scarcely consider operation characteristics, which often leads to performance degradation due to a multitude of repetitive operations, such as comparisons, additions, and assignments. In this paper, we propose an Operation-aware Event Matching algorithm called OEM, which takes into account the efficiency of CPU operations, SIMD instructions, and multi-thread architecture. First, we introduce a bitset-based subscription cache mechanism (SCM), which enables the efficient execution of bitwise OR operations during the matching process. Furthermore, we propose two optimizations, namely an optimal skewness-aware cell grouping (OSCG) strategy and an attribute-based clustering mechanism (ACM), to enhance the efficiency of OEM. These optimizations effectively address the challenges posed by data skewness, low cache efficiency, and high dimensionality. In addition, we establish a performance analysis model that characterizes the trade-off between memory consumption and performance improvement. We conducted extensive experiments to evaluate the performance of OEM. Compared to six state-of-the-art algorithms, namely REIN, Ada-REIN, TAMA, OpIndex, PEM and fgPEM, OEM achieves an average reduction in matching time by 40.4×, 38.5×, 12.0×, 33.1×, 27.8×, and 25.5×, respectively.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Designing a Deep Neural Network engine for LLC block reuse prediction to mitigate Soft Error in Multicore

Last level cache (LLC), a major contender of chip area, exhibits the highest sensitivity to soft error. Block reuse prediction is used to exhibit selective protection of LLC blocks. However, traditional reuse predictors fail to accurately understand the cache access pattern. This work explores machine learning for block reuse prediction that realizes the features that affect reuse likelihood and therefore, better predicts the reusable blocks. To off-load the reuse calculation from the processing cores, a dedicated reuse predictor engine is designed. This engine implements a deep neural network with unsupervised learning and calculates reuse probabilities offline without affecting the execution path. Priority blocks with high reuse likelihood are protected using replications in shared LLC. Low priority blocks are invalidated to reduce their vulnerable time. A replication aware replacement policy is also developed to protect the replicated/reserved blocks from eviction. The scheme is evaluated in Multi2Sim 5.0 simulation framework with SPEC-CPU benchmarks considering bulk-CMOS, FDSOI and FinFET technologies. The results indicate 41.29% and 43.14% reductions in miss rate, 92.29% and 89.56% reductions in vulnerability, 5.73% and 6.31% increase in write-back rate, 17.55% and 16.27% reductions in average memory access time, 2.17% and 1.54% increase in Instructions-per-cycle (IPC) and 1.31% and 0.77% reductions in normalized-execution-cycle over the baseline (with SECDED protection) for integer and floating point benchmarks respectively. Marginal increase in power consumption by 1.33% and dynamic energy by 0.43% are observed at 6.32% area overhead.

337 Bridging the Literacy Gap for Surgical Consents: An AI-Human Expert Collaborative Approach

INTRODUCTION: Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impedes patients’ comprehension. Health literacy is linked to patient outcomes, making it essential to address these issues. METHODS: Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4's potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. RESULTS: Analysis of 15 academic medical centers' consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P &lt; 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American's reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. CONCLUSIONS: This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Moreover, the framework we utilize here can be employed broadly to improve the accessibility of all patient-facing literature in medicine, emphasizing clear communication and mitigating disparities related to health literacy barriers. Ensuring AI technologies are safely incorporated into clinical practice is crucial to reach a wide range of patients, including the most vulnerable.

Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach

Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients’ comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4’s potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers’ consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American’s reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers.

Improved Naked Mole-Rat Algorithm Based on Variable Neighborhood Search for the N-Queens Problem

Solving problems via artificial intelligence techniques has widely prevailed in different aspects. Implementing artificial intelligence optimization algorithms for NP-hard problems is still challenging. In this manuscript, we work on implementing the Naked Mole-Rat Algorithm (NMRA) to solve the n-queens problems and overcome the challenge of applying NMRA to a discrete space set. An improvement of NMRA is applied using the aspect of local search in the Variable Neighborhood Search algorithm (VNS) with 2-opt and 3-opt. Introducing the Naked Mole Rat algorithm based on variable neighborhood search (NMRAVNS) to solve N-queens problems with different sizes. Finding the best solution or set of solutions within a plausible amount of time is the main goal of the NMRAVNS algorithm. The improvement of the proposed algorithm boosts the exploitation capability of the basic NMRA and gives a greater possibility, with the emerging search strategies, to find the global best solution. This algorithm proved successful and outperformed other algorithms and studies with a remarkable target. A detailed comparison is performed, and the data results are presented with the relevant numbers and values. NMRA and NMRAVNS comparisons are implemented and recorded. Later on, a comparison between the Meerkat Clan Algorithm, Genetic Algorithm, Particle Swarm Optimization, and NMRAVNS is tested and presented. Finally, NMRAVNS is evaluated against the examined genetic-based algorithm and listed to prove the success of the proposed algorithm. NMRAVNS outperformed previous findings and scored competitive results with a high number of queen sizes, where an average time reduction reached about 87% of other previous findings.

Efficacy of binary mixtures and antagonistic effect of Azadirachta indica on Afranamum melegueta against helminth parasites of fish in Epe lagoon

This research aims to investigate the efficacy of binary mixtures and interaction of extracts of Azadirachta indica and Afranamum melegueta in reference with praziquantel at Fish Low Observed Effect Concentration (LOEC) on classes of fish parasite species. An in-vitro study was conducted using binary extract mixtures of A.melegueta and A.indica (ratios 1:3, 1:1 and 3:1) respectively on each of the classes of fish helminth parasites and saline solution was used as the control. Average Survival Time (AST) and percentage Reduction in Survival Time (%RST) as measure of sensitivity were estimated for the parasites at varying toxicities (fish 96 h LC5, LC10, LC50) with reference to praziquantel. Saponin and Terpenoid had the highest presence in ethanol mix (water) in A.indica and A.melegueta. The sensitivities of the parasites to the extracts and praziquantel in decreasing order is; A.melegueta (singly) > Praziquantel > 3:1 mix > 1:1 mix > 1:3 mix > A.indica (singly). Increase in the proportion of A. indica decreases the combined effect of the mixtures on the parasites, thereby, decreasing the effect of A. melegueta when acting singly. Tenuisentis sp, an acanthocephalan showed sensitivity of RST >80 % against A. melegueta (singly) similar to praziquantel, the sensitivity was reduced to RST <80 % with 3:1 mix, and significantly reduced to RST close to 60 % with 1:3 mix. Other parasite species had similar trend. There was significant correlation (r > 0.70) between parasite sensitivities to praziquantel and A.melegueta (singly) as well as its binary mixtures at the fish Low Observed Effect Concentration (LOEC). This indicates that the binary extract mixtures could serve as potential alternatives to the use of chemotherapy considering their potency and wide margin of fish host safety.