Significant Differences In Performance Research Articles

Proposal of a methodology for prediction of heavy metals concentration based on PM2.5 concentration and meteorological variables using machine learning

In this study, we developed a prediction model for heavy metal concentrations using PM2.5 concentrations and meteorological variables. Data was collected from five sites, encompassing meteorological factors, PM2.5, and 18 metals over 2 years. The study employed four analytical methods: multiple linear regression (MLR), random forest regression (RFR), gradient boosting, and artificial neural networks (ANN). RFR was the best predictor for most metals, and gradient boosting and ANN were optimal for certain metals like Al, Cu, As, Mo, Zn, and Cd. Upon evaluating the final model’s predicted values against the actual measurements, differences in the concentration distribution between measurement locations were observed for Mn, Fe, Cu, Ba, and Pb, indicating varying prediction performances among sites. Additionally, Al, As, Cd, and Ba showed significant differences in prediction performance across seasons. The developed model is expected to overcome the technical limitations involved in measuring and analyzing heavy metal concentrations. It could further be utilized to obtain fundamental data for studying the health effects of exposure to hazardous substances such as heavy metals.

How can children best learn map skills? a step-by-step approach

ABSTRACT This study contributes to our understanding of children's development of map-reading skills through a systematic ‘step-by-step approach’. Utilising an ecological research design, we investigated how children perceive and utilise maps in a large-scale outdoor environment. A total of 156 children, aged 5–11 (both boys and girls), participated in the study, tasked with locating ten controls in the terrain using five different types of maps. The study measured accuracy in wayfinding (the number of correctly identified controls) and efficiency in wayfinding (the time spent on the task). The results revealed that children exhibited better mastery of perspective maps compared to symbolic maps, with older children demonstrating significantly greater proficiency with symbolic maps than younger children. Notably, there were no significant differences in performance between boys and girls.

Educating on spatial skills using a paper-folding-and-punched-hole videogame: gameplay data analysis

IntroductionPaper folding and punched hole tests are used to measure spatial abilities in humans. These abilities are relevant since they are associated with success in STEM (Science, Technology, Engineering, and Mathematics). This study addresses the challenge of teaching spatial reasoning skills using an educational videogame, the Paper Folding Reasoning Game.MethodsThe Paper Folding Reasoning Game is an interactive game which presents activities intended to help users train and understand how to fold a paper to get a specific shape (Part I) and the consequence of punching a hole on a folded paper (Part II). This educational videogame can automatically generate paper-folding-and-punched-hole questions with varying degrees of difficulty depending on the number of folds and holes made, thus producing additional levels for training due to its embedded reasoning mechanisms (Part III).ResultsThis manuscript presents the results of analyzing the gameplay data gathered by the Paper Folding Reasoning Game in its three parts. For Parts I and II, the data provided by 225 anonymous unique players are analyzed. For Part III (Mastermode), the data obtained from 894 gameplays by 311 anonymous unique players are analyzed. In our analysis, we found out a significant difference in performance regarding the players who trained (i.e., played Parts I and II) before playing the Mastermode (Part III) vs. the group of players who did not train. We also found a significant difference in players' performance who used the visual help (i.e., re-watch the animated sequence of paper folds) vs. the group of players who did not use it, confirming the effectiveness of the Paper Folding Reasoning Game to train paper-folding-and-punched-hole reasoning skills. Statistically significant gender differences in performance were also found.

Effects of partial substitution of enzymatic hydrolysate of poultry by-product meal for fishmeal on the growth performance, hepatic health, antioxidant capacity, and immunity of juvenile largemouth bass (Micropterus salmoides)

A 95-day experiment was conducted to evaluate the feasibility of partially substituting fishmeal with enzymatic hydrolysate of poultry by-product meal (EHPB) as feed for juvenile largemouth bass (Micropterus salmoides). Five diets (EHPB0, EHPB3, EHPB6, EHPB9, and EHPB12) of fishmeal were 45% (0% EHPB, the control group), 41% (3.1% EHPB), 37% (6.2% EHPB), 33% (9.3% EHPB), and 29% (12.4% EHPB), respectively. The initial mean weigh of largemouth bass was 22.35 ± 0.08 g. There were no significant differences in growth performance among all EHPB groups compared with the control (P > 0.05). The growth performance of EHPB3 was significantly better than that of EHPB12 (P < 0.05). EHPB0 had the highest crude protein and moisture content (P < 0.05). Plasma total cholesterol (TC), triglyceride (TG), and lactate dehydrogenase (LDH) were significantly lower under EHPB9 and EHPB12 than the control (P < 0.05). Low-density lipoprotein (LDL) and alkaline phosphatase (ALP) were significantly lower under EHPB12 than the control (P < 0.05). High-density lipoprotein (HDL), total protein (TP), and albumin (ALB) did not significantly differ among diets (P > 0.05). Hepatic glutathione (GSH) content and glutathione peroxidase (GPx) activity were highest for the control (EHPB0). The hepatic superoxide dismutase (SOD) and catalase (CAT) activity levels were significantly lower for all treatments than the control except for EHPB3 (P < 0.05). Keap1 in the Nrf2 pathway was significantly upregulated and cat was significantly downregulated under all treatments compared with the control except for EHPB3 (P < 0.05). Jnk1/2, nf-κb, il-8/10, tnf-α, and traf2 were significantly upregulated under EHPB6 relative to the control (P < 0.05). The diet containing 29% basal fishmeal (12.4% EHPB) did not affect the growth performance of juvenile largemouth bass. However, the optimal levels of the genes related to antioxidant performance and immune function were attained under 3.1% EHPB (41% fishmeal).

Validity and reliability evidence support task-specific metrics for laparoscopic fundoplication.

Laparoscopic hiatal hernia repair (LHHR) is a complex operation requiring advanced surgical training. Surgical simulation offers a potential solution for learning complex operations without the need for high surgical volume. Our goal is to develop a virtual reality (VR) simulator for LHHR; however, data supporting task-specific metrics for this procedure are lacking. The purpose of this study was to develop and assess validity and reliability evidence of task-specific metrics for the fundoplication phase of LHHR. In phase I, structured interviews with expert foregut surgeons were conducted to develop task-specific metrics (TSM). In phase II, participants with varying levels of surgical expertise performed a laparoscopic Nissen fundoplication procedure on a porcine stomach explant. Video recordings were independently assessed by two blinded graders using global and TSM. An intraclass correlation coefficient (ICC) was used to assess interrater reliability (IRR). Performance scores were compared using a Kruskal-Wallis test. Spearman's rank correlation was used to evaluate the association between global and TSM. Phase I of the study consisted of 12 interviews with expert foregut surgeons. Phase II engaged 31 surgery residents, a fellow, and 6 attendings in the simulation. Phase II results showed high IRR for both global (ICC = 0.84, p < 0.001) and TSM (ICC = 0.75, p < 0.001). Significant between-group differences were detected for both global (χ2 = 24.01, p < 0.001) and TSM (χ2 = 18.4, p < 0.001). Post hoc analysis showed significant differences in performance between the three groups for both metrics (p < 0.05). There was a strong positive correlation between the global and TSM (rs = 0.86, p < 0.001). We developed task-specific metrics for LHHR and using a fundoplication model, we documented significant reliability and validity evidence. We anticipate that these LHHR task-specific metrics will be useful in our planned VR simulator.

The Effect of Gamification on Employee Boredom and Performance†

ABSTRACTThis study investigates the effect of gamification on employee boredom and performance in a repetitive work process. In video games, loot is unpredictable, intermittent rewards used to motivate players to repeat boring actions. In a 2 × 1 laboratory experiment, I examine how gamification, featuring nonmonetary loot point rewards, may impact boredom and performance. I find that individuals have mixed opinions. On the one hand, they recognize the emotional value of gamification and find the repetitive work process more attractive. On the other hand, they experience a violation of fairness even though the point rewards do not impact their monetary payoff. My findings help reconcile the seemingly contradictory predictions from two sets of motivation theories. While some conventional theories (e.g., equity theory, expectancy theory, and agency theory) suggest that unpredictable rewards negatively affect motivation, both the reinforcement theory of motivation and findings from neuroscience research indicate a bright side to those rewards. Due to the countervailing effects, I do not find a significant difference in either boredom or performance between conditions. My results show that when gamifying repetitive work processes with unpredictable rewards such as loot points, managers need to address fairness concerns while maintaining the motivational properties of gamification.This article is protected by copyright. All rights reserved.

Transverse Analysis of Box Girders with Corrugated Steel Webs

The utilisation of box girders with corrugated steel webs (CSWs) represents an innovative approach to bridge superstructure design that has garnered substantial popularity worldwide, with a notable prevalence in both Asia and Europe. Compared with traditional box girders, they avoid web cracking, improving the prestressing efficiency and bridge spanning ability. As an innovative box girder, a corrugated web can increase the cantilever length and transverse stiffness, and at the same time, it reduces the dead weight of the bridge deck. However, little research has been conducted on the mechanical properties of this novel spine-like box girder with CSWs, especially its transverse performance, although it has been used in many applications. In this paper, the effect of the web form on the behaviour of box girders is introduced. Therefore, three representative three-dimensional (3D) finite-element models (i.e., corrugated web box girder, flat web box girder, and ordinary equivalent concrete web box girder) have been established to quantitatively investigate the influence of corrugated web stiffness on transverse stress under the action of gravity and vehicle loads. Generally, significant differences in the mechanical performance of box girders with CSWs have been observed compared with conventional box girders with concrete webs. Additionally, parametric studies to investigate the influences of the corrugation dimensions (in term of the corrugation height, web thickness, panel width, web height and elastic modulus) on the transverse stiffness of such bridges are analyzed. The results show that a new stiffness formula can be put forward to consider the effect of web height, and a high-strength steel web needs to be developed urgently for box girders with CSWs in the near future. Overall, the results of this investigation can be used as a reference for transverse designing and segmental construction of similar projects.

An evaluative study of the induction policy’s impact on teachers’ performance in Khyber Pakhtunkhwa, Pakistan

This research aimed to analyse the performance of teachers who have received a professional educational degree versus those who have not, as per the new induction policy introduced in Khyber Pakhtunkhwa in 2019. Data were collected from 20 male teachers using the nonparticipant observation technique and ten principals via semi-structured interviews. Participants were selected through convenient sampling from 10 different primary schools in the Dir Upper district. The quantitative data were analysed using the independent sample t-test, while thematic analysis was used for the interviewed data. The findings showed a significant difference in teachers' performance with and without professional degrees for teaching and management skills. However, no significant difference was found in teachers' performance with and without professional degrees for discipline and regularity and interpersonal relations between teachers with and without professional degrees. The study suggests that teachers' performance can be improved by providing them with training in a real classroom environment with a robust monitoring mechanism while ensuring high discipline and regularity in the school. Further, to enhance interpersonal relationships, teachers must be educated on the significance of teamwork, cooperative work within the school, integration of modern information and communication technology (ICT), and interactive and student-centred teaching pedagogies.

Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization

Federated learning (FL) has been proposed as a privacy-preserving distributed learning paradigm, which differs from traditional distributed learning in two main aspects: the systems heterogeneity, meaning that clients participating in training have significant differences in systems performance including CPU frequency, dataset size, and transmission power, and the statistical heterogeneity, indicating that the data distribution among clients exhibits Non-Independent Identical Distribution. Therefore, the random selection of clients will significantly reduce the training efficiency of FL. In this article, we propose a client selection mechanism considering both systems and statistical heterogeneity, which aims to improve the time-to-accuracy performance by trading off the impact of systems performance differences and data distribution differences among the clients on training efficiency. First, client selection is formulated as a combinatorial optimization problem that jointly optimizes systems and statistical performance. Then, we generalize it to a submodular maximization problem with knapsack constraint, and propose the Iterative Greedy with Partial Enumeration (IGPE) algorithm to greedily select the suitable clients. Then, the approximation ratio of IGPE is analyzed theoretically. Extensive experiments verify that the time-to-accuracy performance of the IGPE algorithm outperforms other compared algorithms in a variety of heterogeneous environments.

DIGWO-N-BEATS: An evolutionary time series prediction method for situation prediction

Situation awareness is a key technology in many decision systems, but its performance bottleneck in the third step, namely situation prediction, has not been overcome yet. This step involves time series prediction and commonly uses deep learning methods. However, there are significant performance differences among these methods, and the tuning of their hyperparameters has not been thoroughly investigated. To address these challenges, a novel prediction method named Distributed Improved Gray Wolf Optimizer-Neural Basis Expansion Analysis for Time-Series (DIGWO-N-BEATS) is proposed for situation prediction tasks. First, an architecture based on N-BEATS, which is a cutting-edge paradigm, is formulated for modeling situation value time series. Second, a novel improved evolutionary algorithm, which can converge in parallel, is proposed to optimize thirteen hyperparameters and the model structures of N-BEATS. The experiment results demonstrate that DIGWO-N-BEATS outperforms the six most competitive baselines, reducing the average MAPE on two real-world situation awareness datasets and two time-series prediction datasets by 8.18%, 1.12%, 9.92%, and 4.98%, respectively. Furthermore, DIGWO-N-BEATS exhibits good convergence in hyperparameter optimization tasks and scalability.

Analisis Perbandingan Kinerja Keuangan Lembaga Amil Zakat Yayasan Amal Sosial Ash Shohwah Malang Sebelum dan Selama Pandemi Covid-19

This research aims to compare the financial performance of LAZ YASA Malang before and during the Covid-19 pandemic. The research method used is quantitative research with a comparative approach. The data obtained is secondary data consisting of the financial ratios of LAZ YASA Malang for the period 2018-2021. The results of the research show that there is no significant difference in the financial performance of LAZ YASA Malang before and during the Covid-19 pandemic. However, most of the financial ratios of LAZ YASA Malang have shown improvement and are considered good and efficient according to the interpretation of OPZ financial ratios. Ratios such as fundraising costs, current ratio, quick ratio, growth of zakat, growth of infaq, growth of ZIS, and growth of allocation indicate a decline in performance during the Covid-19 pandemic. This provides an opportunity for LAZ YASA Malang to evaluate and enhance their financial performance in managing and distributing zakat, infaq, and Shadaqah funds. This research contributes to the understanding of the financial performance of zakat amil institutions during the Covid-19 pandemic. The findings can be used as a reference for LAZ YASA Malang to optimize the management and distribution of zakat, infaq, and Shadaqah funds. Additionally, this research provides valuable information for further research and development in the field of financial performance of zakat amil institutions.

Combined Replacement of Fishmeal and Fish Oil by Poultry Byproduct Meal and Mixed Oil: Effects on the Growth Performance, Body Composition, and Muscle Quality of Tiger Puffer.

This study aimed to evaluate the effects of combined replacement of fishmeal (FM) and fish oil (FO) with poultry byproduct meal (PBM) and mixed oil (MO, poultry oil: coconut oil = 1 : 1) on growth performance, body composition and muscle quality of tiger puffer (Takifugu rubripes). Fish with an average initial body weight of 14.29 g were selected for the feeding experiment. FM accounting for 0%, 5%, and 10% of the diet was replaced by PBM. For each grade of FM replacement, 5% FO or MO was used as added oil. The six experimental diets were designated as FO-FM, MO-FM, FO-5PBM, MO-5PBM, FO-10PBM, and MO-10PBM, respectively. Each treatment was performed in triplicate with 30 fish per replicate. The feeding period was 45 days. There was no significant difference in growth performance among the groups. Dietary supplementation of both PBM and MO had marginal effects on whole-fish proximate composition, except that dietary MO supplementation significantly increased the liver moisture content. In serum, there were no significant differences in contents of triglyceride, total cholesterol, total bile acid, and protein carbonyl among groups, but the malondialdehyde content was reduced by MO. The fatty acid composition in fish mirrored those in the diets, but the omega-3 sparing effects of saturated and monounsaturated fatty acid in MO can still be observed. Dietary PBM and MO had marginal effects on free amino acid composition and texture of fish muscle, but exerted complicated effects on the muscle volatile flavor compound composition. In conclusion, combined fishmeal (10% of the diet) and fish oil (5% of the diet) replacement with poultry byproduct and mixed oil (poultry oil + coconut oil) had no adverse effects on the growth performance and body proximate composition of farmed tiger puffer. However, these replacements changed the muscle flavor compound profile.

Effects of Dietary Alfalfa Meal Supplementation on the Growth Performance, Nutrient Apparent Digestibility, Serum Parameters, and Intestinal Microbiota of Raccoon Dogs (Nyctereutes procyonoides).

The raccoon dog (Nyctereutes procyonoides) is a typical omnivore possessing wide dietary adaptability and tolerance to rough feeding, which may be attributed to its intestinal microbiota. The study aimed to investigate the effect of dietary alfalfa meal levels on the growth performance, nutrient apparent digestibility, serum parameters, and intestinal microbiota of raccoon dogs. Sixty raccoon dogs were randomly divided into four dietary treatments containing 0% (AM0), 5% (AM5), 10% (AM10), and 15% (AM15) alfalfa meal for a 60-day experiment. The results showed that compared to raccoon dogs fed the AM0 diet, those fed the AM5 and AM10 diets had no significant difference in growth performance, while those fed the AM15 diet experienced a significant decrease. Raccoon dogs fed the AM5 diet had no significant effect on the nutrient apparent digestibility. Dietary supplementation with alfalfa meal significantly decreased serum urea levels and increased the antioxidant capacity of raccoon dogs. The intestinal microbiome analysis showed that the richness and diversity of colonic microbiota significantly increased in the AM15 group. With the increase in dietary alfalfa meal levels, the relative abundance of fiber-degrading bacteria in the colon of raccoon dogs, such as Treponema, Phascolarctobacterium, and Christensenellaceae R-7 group, increased. However, the relative abundance of pathogenic bacteria, including Anaerobiospirillum, decreased. In conclusion, the inclusion of 5% alfalfa meal in the raccoon dogs' diet had no effect on growth performance, but it exhibited the potential to improve serum antioxidant capacity and intestinal microbiota. This indicates that raccoon dogs have a certain tolerance to the addition of alfalfa meal in their diet.

Effect of Seed Extraction Period and Germination Temperature on Viability of Pinus patula Under Controlled Conditions

A number of coniferous species have demonstrated varied cone responses to the temperature intensities for seed release and subsequent seed germination behaviour. This study investigated the interactions of cone physical characteristics (weight and width), exposure duration (6,12 and 24 hours) at a fixed extraction temperature (65°C) and the germination temperature (22, 27 and 32°C) on seed quality of Pinus patula. The experimental design was a factorial experiment (4×3×3) laid down in a completely randomized design (CRD), with thirty-six treatments replicated 3 times. Analysis of the difference in means from the three factor effects from ANOVA was performed using R Statistical software. Where significant differences were observed, post hoc tests were carried out to separate means using the Tukey test at 5 % significance level. Results showed significant (p=0.001) differences in germination performance as a result of cone characteristics, extraction exposure periods, and germination chamber conditions. Seeds extracted from heavy cones and exposed to germination temperature of 32℃ demonstrated the highest germination percent at 90% while the lowest was 20% from light cones exposed to similar germination temperature conditions. Negative and significant correlation coefficients were observed in germination of seeds from narrow and light cones, thus cone sorting for heavy and wide cones was recommended for better germination performance of P. patula seed germplasm.

Effectiveness of Virtual Reality Training in Teaching Personal Protective Equipment Skills

Training on the proper use of personal protective equipment (PPE) is critical for infection prevention among health care workers. Traditional methods, such as face-to-face and video-based training, can strain resources and present challenges. To determine the effectiveness of 360° virtual reality (VR) training for PPE donning and doffing compared with face-to-face and video training in enhancing the PPE use skills of prospective health care practitioners. A blinded, prospective, and randomized noninferiority clinical trial was conducted from August to December 2021 at Teikyo University School of Medicine in Tokyo, Japan, with a mixed population of medical students. Participants were second- to fourth-year medicine, medical technology, or pharmacy students aged 20 years or older with no prior PPE training. Participants were randomized into 1 of 3 training groups (VR, face-to-face, or video) based on their enrollment order. An intention-to-treat analysis was conducted. A 30-minute lecture on PPE procedures was delivered to all participants before the training. After the lecture, the VR group trained with an immersive 360° VR tool, the face-to-face group trained with actual PPE, and the video group trained by watching video footage on a computer and a projector. After 3 days, a standardized practical skills test was administered. The primary outcome was the mean score on a 20-point practical skills test, and the secondary outcome was the percentage of correct execution. A total of 91 participants were recruited and randomized into 3 groups: VR (n = 30), face-to-face (n = 30), and video (n = 31) training. After excluding 1 participant due to illness, 90 participants (mean [SD] age, 24.2 [3.15] years; 54 males [60.0%]) completed the assessment. The mean (SD) scores were 17.70 (2.10) points for the VR group, 17.57 (2.45) points for the face-to-face group, and 15.87 (2.90) points for the video group. The VR group demonstrated no significant difference in performance from the face-to-face group. However, the VR group had significantly higher effectiveness than the video group (17.70 vs 15.87 points; P = .02). Results of this trial indicate that VR training was as effective as face-to-face training in enhancing PPE donning and doffing skills and was superior to video training. The findings suggest that VR training is a viable resource-conserving training option. Japan Registry of Clinical Trials Identifier: jRCT103021029.

Linking morphology, performance, and habitat utilization: adaptation across biologically relevant ‘levels’ in tamarins

BackgroundBiological adaptation manifests itself at the interface of different biologically relevant ‘levels’, such as ecology, performance, and morphology. Integrated studies at this interface are scarce due to practical difficulties in study design. We present a multilevel analysis, in which we combine evidence from habitat utilization, leaping performance and limb bone morphology of four species of tamarins to elucidate correlations between these ‘levels’.ResultsWe conducted studies of leaping behavior in the field and in a naturalistic park and found significant differences in support use and leaping performance. Leontocebus nigrifrons leaps primarily on vertical, inflexible supports, with vertical body postures, and covers greater leaping distances on average. In contrast, Saguinus midas and S. imperator use vertical and horizontal supports for leaping with a relatively similar frequency. S. mystax is similar to S. midas and S. imperator in the use of supports, but covers greater leaping distances on average, which are nevertheless shorter than those of L. nigrifrons.We assumed these differences to be reflected in the locomotor morphology, too, and compared various morphological features of the long bones of the limbs. According to our performance and habitat utilization data, we expected the long bone morphology of L. nigrifrons to reflect the largest potential for joint torque generation and stress resistance, because we assume longer leaps on vertical supports to exert larger forces on the bones. For S. mystax, based on our performance data, we expected the potential for torque generation to be intermediate between L. nigrifrons and the other two Saguinus species. Surprisingly, we found S. midas and S. imperator having relatively more robust morphological structures as well as relatively larger muscle in-levers, and thus appearing better adapted to the stresses involved in leaping than the other two. ConclusionThis study demonstrates the complex ways in which behavioral and morphological ‘levels’ map onto each other, cautioning against oversimplification of ecological profiles when using large interspecific eco-morphological studies to make adaptive evolutionary inferences.

Advancing disease identification in fava bean crops: A novel deep learning solution integrating YOLO-NAS for precise rust

A significant concern is the economic impact of agricultural diseases on the world’s crop production. The disease significantly reduces agricultural production across the world. Loss of nutrients caused by parasite infection of leaves, pods, and roots–the pathogenic agent that causes fava bean rust disease–decreases crop health. This work addresses this requirement by offering an innovative deep-learning model approach for early identification and classification of fava bean rust disease. The suggested method uses the effectiveness of modern YOLO-based object detection architectures like You Only Look Once –Neural Architecture Search (YOLO-NAS) L, YOLO-NASM, and YOLO-NASS, Faster Region-based Convolutional Neural Network (Faster R-CNN), and RetinaNet. An inclusive dataset of 3296 images of various lighting and background situations was selected for extensive model training. Each model underwent thorough training and adjusted parameters through careful experimentation. The models’ comparative studies found significant performance differences. The precision for YOLO-NASL was 82.10%; for YOLO-NASM, it was 84.80%; for YOLO-NASS, it was 83.90%; for Faster R-CNN, it was 75.51%; and for RetinaNet, it was 73.74%. According to the evaluation, model complexity and detection accuracy are directly correlated. YOLO-NASL, YOLO-NASM, and YOLO-NASS showed remarkable mean average precision values of 90.90%, 94.10%, and 92.60%, respectively, and became highly functional models. The fastest model was YOLO-NASS. Its satisfying recognition speed made real-time detection possible in particular applications. The YOLO-NASM model, which shows an extraordinary state-of-the-art performance, represents the pinnacle of our work. Its mean average precision (mAP@0.5) was 94.10%, with notable values of 90.84%, 96.96%, and 84.80% for the F1-score, Recall, and precision, respectively. This investigation addresses a critical need in agricultural disease management, aligning with broader global efforts toward sustainable agriculture. Our studies add to the knowledge about precision agriculture and inspire practical, long-lasting disease management techniques in the agricultural industry. The real-time performance of the system will need to be improved, and satellite imagery integration may be considered in the future to provide more comprehensive coverage.

Modeling correlated pairs of mammogram images.

Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.

Performance comparison of 6 in-hospital patient monitoring systems in the detection and alarm of ventricular cardiac arrhythmias

BackgroundPatient monitoring devices are critical in alerting potential cardiac arrhythmias during hospitalization; however, there are concerns of alarm fatigue due to high false alarm rates. ObjectiveTo evaluate sensitivity and false alarm rate of hospital-based continuous electrocardiographic (ECG) monitoring technologies. MethodsSix commonly-used multiparameter bedside monitoring systems available in the United States were evaluated: B125M (GE HealthCare), ePM10 and iPM12 (Mindray), Efficia and IntelliVue (Philips), and Life Scope (Nihon Kohden). Sensitivity was tested using ECG recordings containing 57 true ventricular tachycardia (VT) events. False positive rate testing used 205 patient-hours of ECG recordings containing no cardiac arrhythmias. Signals from ECG recordings were fed to devices simultaneously; high-severity arrhythmia alarms were tracked. Sensitivity to true VT events and false positive rates were determined. Differences were assessed using Fisher’s exact tests (sensitivity) and Z-tests (false positive rates). ResultsB125M raised 56 total alarms for 57 annotated VT events and had the highest sensitivity (98%, P<0.05), followed by iPM12 (84%), Life Scope (81%), Efficia (79%), ePM10 (77%), and IntelliVue (75%). B125M raised 20 false alarms, significantly lower (P<0.0001) than iPM12 (284), Life Scope (292), IntelliVue (304), ePM10 (324), and Efficia (493). The most common false alarm was VT, followed by non-sustained VT. ConclusionWe found significant performance differences among multiparameter bedside ECG monitoring systems using previously-collected recordings. B125M had the highest sensitivity in detecting true VT events and lowest false alarm rate. These results can assist in minimizing alarm fatigue and optimizing patient safety by careful selection of in-hospital continuous monitoring technology.

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.