Size Categories Research Articles

Differences in Use of a Patient Portal Across Sociodemographic Groups: Observational Study of the NHS App in England

Background The adoption of patient portals, such as the National Health Service (NHS) App in England, may improve patient engagement in health care. However, concerns remain regarding differences across sociodemographic groups in the uptake and use of various patient portal features, which have not been fully explored. Understanding the use of various functions across diverse populations is essential to ensure any benefits are equally distributed across the population. Objective This study aims to explore differences in the use of NHS App features across age, sex, deprivation, ethnicity, long-term health care needs, and general practice (GP) size categories. Methods We used weekly NHS App use data from the NHS App dashboard for 6386 GPs in England from March 2020 to June 2022. Negative binomial regression models explored variations in weekly rates of NHS App features used (registrations, log-ins, prescriptions ordered, medical record views, and appointments booked). Outcomes were measured as weekly rates per 1000 GP-registered patients, and we conducted separate models for each outcome. Regression models included all covariates mentioned above and produced incident rate ratios, which we present here as relative percentages for ease of interpretation. GP-level covariate data on sociodemographic variables were used as categorical variables in 5 groups for deprivation (Q1=least deprived practices and Q5=most deprived practices) and 4 groups for all other variables (Q1=least deprived practices and Q4=most deprived practices). Results We found variations in the use of different features overall and across sociodemographic categories. Fully adjusted regression models found lower use of features overall in more deprived practices (eg, Q5 vs Q1: registrations=–34%, log-ins=–34.9%, appointments booked=–39.7%, medical record views=–32.3%, and prescriptions ordered=–9.9%; P<.001). Practices with greater proportions of male patients also had lower levels of NHS App use (eg, Q4 vs Q1: registration=–7.1%, log-in=–10.4%, and appointments booked=–36.4%; P<.001). Larger practices had an overall higher use of some NHS App features (eg, Q4 vs Q1: registration=3.2%, log-ins=11.7%, appointments booked=73.4%, medical record views=23.9%, and prescriptions ordered=20.7%; P<.001), as well as those with greater proportions of White patients (eg, Q4 vs Q1: registration=1.9%, log-ins=9.1%, appointments booked=14.1%, medical record views=28.7%, and prescriptions ordered=130.4%; P<.001). Use patterns varied for practices with greater proportions of patients with long-term health care needs (eg, Q4 vs Q1: registrations=–3.6%, appointments booked=–20%, and medical record views=6%; P≤.001). Conclusions This study highlights that the use of the NHS App features varied across sociodemographic groups. In particular, it is used less by people living in more deprived areas. Tailored interventions and patient support are required to ensure that any benefits from the NHS App are spread equally throughout the population.

Estimation of Marketed Surplus and Marketing Efficiency of Milk in Andhra Pradesh, India

A study was conducted to analyze milk's marketed surplus and marketing efficiency in the Vishakhapatnam and Chittoor districts of Andhra Pradesh. The study randomly selected 80 dairy farmers from four villages in Chittoor and Vishakhapatnam districts. According to the study, average milk production and marketable surplus increase as herd size increases. The average marketed surplus is highest for the large size category (58.75 lit/day) followed by Medium (45.40 lit/day) and small size category (19.01 lit/day). Because of the lowest price spread and highest producer share in consumers' rupee, the most efficient marketing channel was producer-consumer (Channel-I), followed by producer-creameries-consumer (Channel-II). The study suggests that producers' choice of supply network influences their profitability.

Size Classification and Material Sorting of Fine Powders with a Deflector Wheel Air Classifier and an Electrostatic Separator

In this study, a two-dimensional separation of microparticles based on their settling velocity and triboelectric charge ability is achieved using an air classifier for size fractionation and simultaneous charging, followed by an electrostatic separator. In the first part, considerations for enhancing particle classification with high sharpness and low-pressure drops are discussed through improvements in blade design investigated with CFD simulations and validated experimentally. Blades with extended lengths towards the center of the classifier prevent the formation of high-velocity vortices, thereby minimizing the back-mixing of particles and enhancing separation sharpness. This approach also reduces pressure drops associated with these flow vortices. In the second part of the study, the modified blades within the classifier are utilized for two-dimensional separation. Powders from two different materials are fed into the classification system, where particles become triboelectrically charged, mainly through collisions with the walls of the classification system components. Coarse particles are rejected at the wheel and exit the classifier, while differently charged fine particles of the two materials are directed into an electrostatic separator for material sorting. An enrichment of approximately 25–35% for both materials has been achieved on the electrodes of the separator.

Using metabolic data to investigate the role of brood size in the development of endothermy

Altricial songbirds transform themselves from naked poikilotherms to fully feathered endothermic homeotherms over a matter of days from hatching to fledging. The ontogeny of endothermy is a developmental milestone for birds that not only face warmer average temperatures, but also increasingly frequent cold snaps and extreme weather. The timing of development of endothermy has been studied in altricial birds for over half a century. However, the determinants and constraints of the onset of endothermy are not yet fully understood. We experimentally investigated whether brood size influences the ontogeny of endothermic heat production in 4–8 day‐old nestling blue tits Cyanistes caeruleus in southern Sweden. The thermogenic response to a cooling challenge (15°C) increased with age overall. We found that 8‐day‐old nestlings from reduced broods had a slightly increased capacity for endothermic heat production compared to enlarged broods. This difference cannot be explained by body mass because this trait did not differ between brood size categories. Although a metabolic response was present in most nestlings by day 6, it was brief, not lasting more than a few minutes, and not sufficient to maintain a stable body temperature in any age group. Our study shows that incipient endothermy is present at an early age in nestling blue tits and may advance faster in reduced broods, but that individual nestlings lack sufficient insulation and thermogenic performance to maintain homeothermy independently during the first week of life.

Sphenoid Ridge in Fetuses: Size Analysis, Classification, and Clinical Implications

Objective: To identify the size and angle of the sphenoid ridge (SR) in fetuses. Methods: Skull bases of 32 fetuses (11 males / 21 females) aged 17 to 32 weeks of gestations were included the study. The angle of SR (SRA), length of LW (SRL), and also LW widths at the midline (SRW-ML), at the midpoint (SRW-MP), and at the lateral point (SRW-L) were measured. Results: SRL, SRA, SRW-ML, SRW-MP, and SRW-L were measured as 22.98±4.62 mm, 150.69±8.05 degrees, 6.57±1.46 mm, 4.68±1.07 mm, and 2.68±0.71 mm, respectively. No significant difference was observed between the measurements in terms of sex and sides. Apart from SRA, the parameters were greater in the third-trimester fetuses than the second-trimester fetuses. SRA was similar in both trimester fetuses. SRA did not alter with advancing gestational weeks, but the other increased. In fetuses, only one configuration regarding SRA types was observed. Type A was observed in all fetuses. Linear function was calculated as y=1.411 + 0.902×age for SRL, y=−0.137 + 0.281×age for SRW-ML, and y=1.024 + 0.069×age for SRW-L. Conclusion: Our study provides beneficial data for neurosurgeons and anatomists to understand the development of SR in the prenatal period. The authors' regression equations for determining the growth dynamics of the ridge may be used to estimate SR parameters.

A coarse aggregate particle size classification method by fusing 3D multi‐view and graph convolutional networks

A coarse aggregate particle size classification method by fusing 3D multi‐view and graph convolutional networks

Tracking Microplastics Contamination in Drinking Water Supply Chain in Haikou, China: From Source to Household Taps

The presence of microplastics (MPs) in aquatic environments has become a significant global concern due to their potential adverse effects on human health. This study aimed to investigate the contamination of MPs throughout the drinking water supply chain in Haikou City, China, and to conduct risk assessments regarding the relationship between MPs contamination and human health. The results revealed that the abundance of MPs in raw, treated, and tap water was 0.6 ± 0.6, 5.2 ± 2.7, and 1.2 ± 1.1 particles·L−1, respectively. Fragments were identified as the most prevalent shape across all samples, with the size category of 20–50 μm showing the highest abundance of MPs. Among the 11 types of polymers identified, polyethylene and polypropylene accounted for 50% and 29%, respectively. The potential risk index values were significantly higher for treated water (370.26) and tap water (303.85) compared to raw water (13.46), suggesting that plastic pipes may be a key contributor to MPs contamination in drinking water. Therefore, efforts should be directed toward developing pipes with low release rates of MPs, as well as improving detection methods for smaller particles and accurately assessing associated risks.

Exploring value relevance of EVA over different stages of business lifecycle

Purpose This study aims to determine whether the financial ecosystem is reliable for evaluating a company’s financial performance at various lifecycle stages or whether value-based performance metrics like Economic Value Added (EVA) are the most comprehensive indicators regardless of the company’s lifecycle stage. The evaluation will analyse the importance of value-based and accounting performance benchmarks (EVA, Return on Capital Employed, Return on Equity and Earnings per Share) in creating shareholder value, as measured by market value added, throughout the company’s lifecycle. Design/methodology/approach This study is based on a robust empirical analysis of 228 Indian firms listed on the National Stock Exchange Nifty 500 Index from 2006 to 2023. By considering firms of different sizes and ages, the authors aim to capture and analyse any distinct impact across various stages of their lifecycles. Using quantile regression for analysis equips the authors to effectively address extreme events such as the 2008 global financial crisis and the COVID-19 pandemic, making the method particularly suitable for conditionally distributed samples based on size and age. Findings The research reveals that various performance indicators are crucial at different stages of a company’s lifecycle for generating wealth for shareholders. However, considering the significance of the EVA measure, it is recommended that policymakers standardize the calculation of EVA and mandate its disclosure. Originality/value The authors comprehensively analysed the economic value addition relative to a company’s size and age, going beyond previous studies that focused solely on specific size categories. This offers managers valuable insights into aligning business performance measures with the various stages of the business lifecycle.

Health impact of the Serious Accidents Punishment Act in Korean occupational settings

Abstract Background Interrupted time series analysis serves as a valuable study design for assessing the efficacy of public health interventions implemented at a specific moment in time at the population level. We aim to elucidate the effects of the Serious Accidents Punishment Act implemented in February 2021 in Korea by analyzing changes in the incidence rates of occupational diseases. Methods The monthly data from patients with occupational diseases was extracted from the National Health Insurance Service Cohort database from 2011 to 2023. An Interrupted time series analysis with Poisson segmented regression was used to compare the rates before and after the Serious Accidents Punishment Act introduction in both outpatients and inpatients. Adjustments or stratification variables included sex, age, company size, and injury-related international classification of diseases codes (Injury, poisoning, and certain other consequences of external causes, S00-T88). Results Based on injury rates, men’s inpatient injury rates remained stable, but outpatient rates decreased after the act implementation, while women’s injury rates consistently declined in both inpatient and outpatient settings. The Interrupted time series regression analysis indicates that the Serious Accidents Punishment Act led to a significant reduction in both outpatient and inpatient injury rates for men and women, with a marked decline in outpatient rates and hospital admissions among women post-intervention. Conclusions The Interrupted time series regression results suggest that the Serious Accidents Punishment Act has been effective in reducing injury-related healthcare usage, highlighting the necessity of gender-specific interventions and the potential impact on lowering inpatient visits, although causality remains uncertain without further analysis. Key messages • The Serious Accidents Punishment Act significantly reduced occupational injury rates in Korea, especially among women, demonstrating effective public health intervention. • Interrupted time series analysis reveals Serious Accidents Punishment Act’s impact on reducing healthcare usage for occupational injuries, highlighting the need for gender-specific approaches.

Dementia severity and advanced pressure injury procedures and prognosis: A nationwide study in Japan.

In older adults, pressure injuries (PIs) are common and, despite advancements in PI care, are associated with increased complications in patients with dementia for whom standardized treatments are unavailable. Herein, we aimed to examine the influence of dementia status on the care practice pattern and healing outcomes for PI on discharge. This retrospective cohort study used data from the Diagnosis Procedure Combination database for 2014-2015 and the Annual Report for Functions of Medical Institutions and included patients aged ≥65 years. The DESIGN-R (depth, exudates, size, inflammation/infection, granulation, necrosis and rating) classification system was used to determine PI severity, and the Dementia Scale was used to assess dementia status. The measured outcomes included advanced PI care and healing upon discharge. Multivariable logistic regression, accounting for hospital-related clustering, was used to examine the association of dementia with these outcomes. Among 20 386 patients from 1198 hospitals included in the analysis, 32.5%, 20.1% and 47.3% had no, mild and severe dementia, respectively. After adjustment for patients' and hospital characteristics, compared with those without dementia, patients with severe dementia were significantly less likely to undergo advanced treatments, such as skin grafts (adjusted odds ratio [aOR], 0.62 [95% confidential interval [CI], 0.40-0.97]; P = 0.034) and flap surgeries (aOR, 0.57 [95% CI, 0.42-0.77]; P < 0.001), and had a reduced likelihood of PI healing (aOR, 0.80 [95% CI, 0.72-0.90]; P < 0.001). Severe dementia was associated with poor PI healing outcomes, indicating potential treatment disparities. Thus, PI treatment should be provided according to the severity of dementia. Geriatr Gerontol Int 2024; ••: ••-••.

A review of intracranial aneurysm imaging modalities, from CT to state-of-the-art MR.

Traditional guidance for intracranial aneurysm (IA) management is dichotomized by rupture status. Fundamental to ruptured aneurysm management is the detection and treatment of subarachnoid hemorrhage, along with securing the aneurysm by the safest technique. On the other hand, unruptured aneurysms first require a careful assessment of natural history versus treatment risk, including an imaging assessment of aneurysm size, location, and morphology, along with additional evidence-based risk factors such as smoking, hypertension, and family history. Unfortunately, a large proportion of ruptured aneurysms are in the lower risk size category (<7mm), putting a premium on discovering a more refined non-invasive biomarker to detect and stratify aneurysm instability prior to rupture. In this review of aneurysm work-up, we cover the gamut of established imaging modalities (e.g., CT, CTA, DSA, FLAIR, 3D-TOF-MRA, CE-MRA) as well as more novel MR techniques (MR-VWI, DCE-MRI, CFD). Additionally, we evaluate the current landscape of AI software and their integration into diagnostic and risk stratification pipelines for IAs. These advanced MR techniques, increasingly complemented with AI models, offer a paradigm shift by evaluating factors beyond size and morphology, including vessel wall inflammation, permeability, and hemodynamics. Additionally, we provide our institution's scan parameters for many of these modalities as reference. Ultimately, this review provides an organized, up-to-date summary on the array of available modalities/sequences for IA imaging to help build protocols focused on IA characterization.ABBREVIATIONS: IA = intracranial aneurysm; LP = lumbar puncture; UIA = unruptured intracranial aneurysm; VWI = vessel wall imaging; 3DRA = 3D Rotational Angiography.

Self-compassion and postpartum depression in mothers: A meta-analysis

Purpose: The aim of this study was to provide evidence on a strong relationship between self-compassion and postpartum depression. In addition, it was also to synthesize evidence of the application of population, intervention, comparison, and outcome (PICO) procedures quantitatively. Previous researches demonstrated that low self-compassion was a significant factor in postpartum mothers experiencing psychological distress. This is due to a lack of understanding and appreciation on the positive aspects of oneself that leads to the onset of postpartum depression. Method: This study used a meta-analysis with a statistical method that combined the results of several studies on a specific research question. A meta-analysis provides a comprehensive summary of all evidence related to the topic that employed to more accurate and reliable conclusions than only from a single study. The researchers conducted a comprehensive search for databases, include Scopus, Web of Science, Science Direct, and Proquest from February 2023 to July 2023. Result: A total of nine articles were identified for inclusion in the review from 3320 articles. The analysis using the DerSinomian and Laird (DL) estimator model resulted an estimated effect size of 0.586 (p &lt; 0.001; 95% CI = -0.668 - -0.504). Although the meta-analysis results showed a strong negative correlation between the variables, heterogeneity and the risk of publication bias must be considered. Conclusion: The meta-analysis of self-compassion and postpartum depression variables indicates a negative correlation with a moderate effect size category. It is in accordance with the findings of nine articles that also demonstrated a negative correlation.

Advancing the Understanding of Microplastic Weathering: Insights from a Novel Polarized Light Scattering Approach.

Weathering is a significant process that alters the properties of microplastics (MPs) and consequently affects their environmental behaviors. In this study, we introduced a novel approach based on polarized light scattering technique, which offers advantages in terms of rapid, high-throughput, and submicron-sized detection. This technique was successfully applied to characterize the weathered MPs after a 180-day laboratory simulation of coastal environments. By employing polarization measurements, we obtained a 46-dimensional matrix data set for the weathered MP fragments and subsequently processed them using a backpropagation neural network. The successful extraction of effective polarization pulses confirmed the presence of MP fragments within the size range of 0.2-60 μm, yielding total accuracies for size classification ranging from 78.9 to 86.9%. Furthermore, this technique achieved an overall accuracy of 93.8% in classifying MPs with different weathering degrees and polymer types, revealing polarization parameters associated with size and morphological changes play a dominant role in characterizing the weathering process of MPs. Compared with conventional approaches, the novel polarized light scattering approach holds great promise for rapid, high-throughput, and accurate characterization of MPs with small sizes. The findings of this study provided new insights into how MPs change after long-term weathering in aquatic environments.

Spatial distribution and fecundity of the cinnamon shrimp, Macrobrachium acanthurus (Wiegmann, 1836) (Crustacea: Decapoda: Palaemonidae) from Jequitinhonha River (BA), Brazil

ABSTRACT Macrobrachium acanthurus is an amphidromous palaemonid shrimp exploited by artisanal fishing in Brazil that presents limited published information on its population biology. Therefore, this study aimed to assess the spatial distribution and fecundity of M. acanthurus in the Jequitinhonha River (BA). Collections occurred at four points in the river downstream of the Itapebi Hydroelectric Powerplant (P1–P4). The abundance and size of the demographic categories analysed (male morphotypes, non-ovigerous females, and ovigerous females) and fecundity were compared among points. We also examined the relationship between female body size and fecundity. A total of 938 individuals were collected during the sampling period. The highest abundances were observed downstream (P3 and P4). The sizes of males and ovigerous females were greater at points farthest upstream. Three male morphotypes were identified, and their proportions varied among points. Fecundity was higher upstream, and there was a positive correlation between female size and number of eggs. All population parameters indicate a longitudinal gradient towards the estuary, with strong evidence of amphidromy. This is the first study with a polymorphic species to consider morphotypes as a population structure component. The results represent an important contribution to a better understanding of the amphidromous life cycle of palaemonid shrimp.

Surgical treatment of cesarean scar pregnancy based on the three-category system: a retrospective analysis

BackgroundCesarean scar pregnancy (CSP), a distinct form of ectopic pregnancy, presents challenges in effective management. It is categorized into three subtypes according to the location of placental implantation and the thickness of the myometrium at the uterine scar. Nevertheless, the optimal choice of treatment modalities for these subtypes remains largely unexamined.MethodsIn this retrospective analysis, we investigated the cases of 130 patients diagnosed with CSP who underwent diverse treatment approaches, namely ultrasound-guided dilation and curettage (D&C), hysteroscopic surgery alone or in combination with laparoscopic surgery (HCoLC), or uterine artery embolization (UAE) followed by curettage. Clinical data were meticulously retrieved from medical records and follow-up data, and a comparative analysis of relevant indicators was carried out across the different CSP subtypes.ResultsFrom January 2017 to December 2021, 35 patients underwent D&C, 85 underwent HCoLC, and 10 received UAE as a pretreatment. In the D&C group, the success rates for Type I and Type II CSP were 64.29% (18/28) and 14.28% (1/7), respectively. Significant differences were observed between the success and failure groups in terms of gestational sac size and clinical classification. Compared to Type I CSP, Type II CSP exhibited significantly longer surgical durations and higher hospitalization costs (P < 0.05). Three patients classified as Type III underwent simultaneous hysteroscopic evacuation of cesarean scar pregnancy and laparoscopic repair of the cesarean scar defect, achieving a 100% success rate in their initial treatment. HCoLC showed no significant differences in surgical duration and hospitalization costs but had higher success rates and shorter hospital stays compared to the D&C and UAE groups (P < 0.05). The UAE group had significantly longer surgical durations, higher hospitalization costs, and a higher incidence of postoperative complications (P < 0.05). However, these factors did not result in improved surgical success rates.ConclusionThe classification of CSP and the measurement of gestational sac are of crucial importance in determining the most appropriate surgical intervention strategy. For patients diagnosed with Type I CSP, D&C and hysteroscopy are reliable treatment choices. In cases with larger gestational sacs or Type II CSP, hysteroscopy, either alone or in combination with laparoscopy, is regarded as a reliable and effective treatment approach. In patients with type III CSP, lesion excision with uterine repair is the recommended treatment.

Video-blogs (Vlogs)-based Project: A Meta Analysis

This study aims to analyze the relationship between using video-blogs (vlogs)-based projects on student academic achievement through project-based learning. This study applied a meta-analysis through comprehensive sources, either from the international or the Indonesian index by considering certain criteria. The data sources in this research were obtained from twenty-seven studies that had met the inclusion criteria. The inclusion criteria used are 1) research published in 2020-2024; 2) research must be obtained from the Google Scholar database; ScienceDirect; ERIC; Wiley and IEEE; 3) Publications indexed by SINTA, Scopus, and Web of Science; 4) publications must be related to Vlog-based project; 5) the research has a value (R); (t) or (f) and 6) sample size (N) ≥ 30 students. The study showed that the Vlog-based project significantly influenced students' academic achievement (z = 8,042; p &lt; 0.001; CI 95 % [ 0.526; 0.962]. The effect size in this study is a high effect size category (rRE = 1,024). In this meta-analysis, the data presented is accurate, valid, and representative, free from publication bias. This finding explains that V-logs-based project-based learning positively affects students' academic achievement. This study shows that using Vlogs-based projects has a positive impact on students' academic achievement, and it encourages teachers to enhance students' academic performance. Furthermore, the Vlogs-based project motivates students to engage more actively and creatively with the learning materials.

A meta-analysis of the relationship between overtime work and neck musculoskeletal disorders in Chinese occupational population

Objective: To systematically evaluate the effect of overtime work on neck musculoskeletal disorders of occupational population in China. Methods: In August 2023, the literatures on the relationship between overtime work and neck musculoskeletal disorders were searched in China National Knowledge Infrasture (CNKI), WanFang Database, Chinese Science & Technology Journal Database (VIP), China Biomedical Literature Service System (SinoMed) and PubMed. The search scope was Chinese and English literatures published before June 30, 2023. The Quality of literature studies was evaluated using the Agency for Healthcare Research and Quality (AHRQ) cross-sectional study quality evaluation criteria. The Stata 15.0 software was used to calculate combined OR values, and subgroup analysis and meta regression were used to explore the sources of heterogeneity. Results: A total of 32 literatures were included, all of which were in Chinese, with a total sample size of 36003 people, and the literature quality scores were 5-7 points. Heterogeneity test showed that there was no heterogeneity between the literatures (I(2)=20.3%, P=0.156), so fixed effect model was selected to calculate the combined OR value. The combined OR value of overtime work on neck musculoskeletal disorders was 1.51 (95%CI: 1.42-1.60, P<0.001). Subgroup analysis showed that the combined OR of occupational population <35 years old group was higher than that in the ≥35 years old group (P<0.05). But there were no significant differences in the combined OR values in the subgroups classified by average years of service, male proportion, type of OR values, sample size and industry classification (P>0.05) . Conclusion: Overtime work is a risk factor for neck musculoskeletal disorders in Chinese occupational population. Working hours should be rationally arranged to reduce the prevalence of neck musculoskeletal disorders.

Manipulator with Integrated Flexible Tactile Sensing Arrays for Kiwifruit Ripeness and Size Classification.

Fruit grading for ripeness and size is an essential process in the supply chain. Incorrect grading can easily lead to spoiled and degraded fruits entering the market, reducing consumers' confidence in purchasing. At the same time, it is easy to cause the fruit supply chain to reduce profits, unreasonable resource allocation, and related practitioners' income. The current mainstream machine vision grading and manual grading in the production line have dilemmas such as susceptibility to environmental interference, inconsistent grading standards, high cost, and labor shortage. To overcome these problems, this study proposes an integrated flexible tactile sensing array (3 × 4) manipulator for efficient, stable, low-cost, and accurate ripeness and size grading of kiwifruit. The flexible sensing manipulator grasps the kiwifruit, detects the hardness of the kiwifruit by relying on tactile sensing, and determines the ripeness level based on the hardness. The size of the kiwifruit is also differentiated according to whether there is a significant change in the resistance of the topmost sensing unit of the flexible pressure sensor array. The 0, 1, 2, 3, 4, and 5 anomalies that may occur in actual production were tested and combined with machine learning KNN, SVM, and RF algorithms for data modeling and grading. The results show that the lowest accuracy of 0, 1, 2, 3, 4, and 5 possible outliers is 86.67% (KNN), 95.83% (SVM), and 92.5% (RF), respectively. KNN has the lowest classification effect, and SVM has the best. This study overcomes the drawbacks of inefficient destructive detection and unstable manual detection and makes up for the vulnerability of single machine vision to interference from environmental factors. This study can alleviate the challenges caused by fruit wastage and promote the sustainable production and consumption of the fruit industry chain.

Design and Mechanical Validation of Commercially Viable, Personalized Passive Prosthetic Feet

Abstract Current high-performance prosthetic feet work well for many users, but the low resolution of size and stiffness categories may limit walking performance for certain users. A line of prosthetic feet with a high resolution of sizes and stiffnesses, designed through amputee-specific personalization, could provide clinical and economic value. The lower leg trajectory error (LLTE) design framework facilitates the design of high-performance, amputee-specific prosthetic feet; however, previous foot prototypes were not designed to satisfy the economic, mechanical, and aesthetic requirements for commercial adoption. The aims of this work were to understand how a personalized, affordable prosthetic foot can align with the clinical-commercial ecosystem, innovate a viable future product, and inform other prosthesis designers of considerations required to connect innovation to real-world implementation. We evaluated needs by identifying how products, capital, and services flow between stakeholders, and we elucidated design requirements for a personalized prosthetic foot that can be manufactured, distributed, and clinically provided. Based on material properties and manufacturing process capabilities, computer numerically controlled (CNC) machining of Nylon 6/6 satisfies these requirements. We present a novel parametric foot architecture that can be CNC machined, fits within a commercial foot shell, and can be designed for individual users’ body characteristics and activity levels. Prototypes made using the new foot design behaved as anticipated (1–12% error in modeled displacement), satisfied industry-standard strength (ISO 10328) and mechanical performance (AOPA dynamic heel/keel) requirements, and elicited positive feedback from both amputees and prosthetists.

Optimizing Wrist Splint Fitting Parameters Through Artificial Intelligence Analysis.

Introduction The current method for determining the appropriate wrist splint size in the clinical setting relies on measuring wrist circumference, but this approach often fails to ensure optimal fit. This study evaluates additional hand features using 3-dimensional (3D) scanned data and Artificial Intelligence (AI) to improve the fit of pre-fabricated wrist splints. We hypothesize that wrist and forearm widths can provide a more accurate fitting than wrist circumference alone. Materials and methods We recruited 54 healthy volunteers to be scanned. Each volunteer was fitted with a standard wrist brace (Short Arm Brace, Ossur, Iceland), and 3D data from their hands were collected using an infrared-based 3D scanner (Einscan Pro, Shining3D, China). The 3D scanned data were then analyzed to identify and measure 14 distinct hand features. To explore the relationship between these hand features and the optimal splint size, we generated a categorical correlation map. This map identified hand features that were most strongly correlated with splint size categories (small, medium, large). Subsequently, we developed a classification algorithm to predict the appropriate splint size based on the correlated hand features. We utilized three different machine learning models for this purpose: Extreme Gradient Boosting (XGB) Classifier, RandomForestClassifier, and Support Vector Classifier (SVC). Each of these classifiers was trained and evaluated to determine their accuracy and effectiveness in predicting the correct splint size. Results Wrist width showed the highest classification accuracy (91%) for both the XGB Classifier and RandomForestClassifier. The measurements including hand wrist width, mid-forearm width, and hand crease line width also performed well with the XGB Classifier, achieving an accuracy of 90%. The SVCshowed consistent performance across various feature sets, with the highest accuracy of 81% for the measurements. Overall, these findings suggest that wrist width is the most predictive feature for splint size classification, with additional features providing minimal enhancement. Conclusions Artificial intelligence, combined with 3D scanning, can accurately predict wrist splint size from a single image acquisition, enabling contactless, personalized fitting.This approach can improve patient outcomes by enhancing the fit of prefabricated splints.