Percentage Points Research Articles

Relationship between the concepts of “Digital transformation” and “Industry 5.0”: bibliometric analysis

At the current stage of transformational transformations, the problems of transformation of the national economy are extremely relevant. And these questions are especially relevant in the conditions of rapid development of the digital economy. According to estimates by Forbes experts, 67% of the leaders of companies from the Global 2000 list chose digital transformation as a priority goal of their corporate strategy in 2018. According to a study by analysts International Data Corporation, the total global spending on digital technologies will grow by 16.8% annually and amounted to 2.1 trillion dollars in 2019. Research by Huawei and Oxford Economics has shown that intelligent network interaction will trigger the growth of the digital economy, which will reach 23 trillion dollars by 2025. This increase will be 78.3% compared to 2017 (12.9 trillion dollars). By 2025, the share of the digital economy is planned to increase by 7.2 percentage points, or from 17.1 to 24.3% of global GDP. According to the calculations of The Boston Consulting Group experts, the volume of the digital economy by 2035 will amount to 16 trillion dollars USA. In view of this, the purpose of this study is to determine the trends and key directions of digital transformation research in the era of Industry 5.0 based on bibliometric analysis using the VOSviewer software. The article carries out a bibliometric analysis of the relationship between the terms “digital transformation”, “digitalization”,”Industry 4.0”, “Industry 5.0”. It has been established that currently the problems of the digital transformation of the economy in the context of the implementation of the digital strategy of the European Union are gaining special relevance. Digital transformation is a key component of the overall strategy to transform the global economy. Correctly selected digital technologies combined with employee competencies, processes and operations will allow companies to quickly adapt to crisis situations, use promising opportunities to modernize work processes, meet new and constantly changing customer needs, stimulate growth and implement innovative and management solutions. The concept of digital transformation of the national economy in the context of Industry 5.0 should involve the use of digital tools and platforms to transform traditional business processes, improve interaction with customers, introduce innovative technologies and form a digital ecosystem. The main components of the digital transformation of critical infrastructure include: digital technologies (cloud computing, artificial intelligence, big data analytics and the Internet of Things (IoT); organizational changes (restructuring of processes to increase their flexibility, introduction of new methodologies (DevOps, Agile), formation of a digital culture); customer orientation and integration of digital channels. For effective digital transformation of the economy on a practical level, it is advisable to pay attention to such main aspects as: having a clear vision and strategy that meets business goals; involving different groups of stakeholders and ensuring the interest of the entire organization; constant monitoring and evaluation of the implementation of digital transformation initiatives; an adaptive and iterative approach that allows you to navigate the changing digital landscape. It has been proven that the priority direction of research in the future should be the substantiation of the theoretical and methodological provisions of the formation of the digital ecosystem.

Zodasiran, an RNAi Therapeutic Targeting ANGPTL3, for Mixed Hyperlipidemia.

Angiopoietin-like 3 (ANGPTL3) inhibits lipoprotein and endothelial lipases and hepatic uptake of triglyceride-rich lipoprotein remnants. ANGPTL3 loss-of-function carriers have lower levels of triglycerides, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and non-HDL cholesterol and a lower risk of atherosclerotic cardiovascular disease than noncarriers. Zodasiran is an RNA interference (RNAi) therapy targeting expression of ANGPTL3 in the liver. We conducted a double-blind, placebo-controlled, dose-ranging phase 2b trial to evaluate the safety and efficacy of zodasiran in adults with mixed hyperlipidemia (fasting triglyceride level of 150 to 499 mg per deciliter and either an LDL cholesterol level of ≥70 mg per deciliter or a non-HDL cholesterol level of ≥100 mg per deciliter). Eligible patients were randomly assigned in a 3:1 ratio to receive subcutaneous injections of zodasiran (50, 100, or 200 mg) or placebo on day 1 and week 12 and were followed through week 36. The primary end point was the percent change in the triglyceride level from baseline to week 24. A total of 204 patients underwent randomization. At week 24, substantial mean dose-dependent decreases from baseline in ANGPTL3 levels were observed with zodasiran (difference in change vs. placebo, -54 percentage points with 50 mg, -70 percentage points with 100 mg, and -74 percentage points with 200 mg), and significant dose-dependent decreases in triglyceride levels were observed (difference in change vs. placebo, -51 percentage points, -57 percentage points, and -63 percentage points, respectively) (P<0.001 for all comparisons). Other differences in change from baseline as compared with placebo included the following: for non-HDL cholesterol level, -29 percentage points with 50 mg, -29 percentage points with 100 mg, and -36 percentage points with 200 mg; for apolipoprotein B level, -19 percentage points, -15 percentage points, and -22 percentage points, respectively; and for LDL cholesterol level, -16 percentage points, -14 percentage points, and -20 percentage points, respectively. We observed a transient elevation in glycated hemoglobin levels in patients with preexisting diabetes who received the highest dose of zodasiran. In patients with mixed hyperlipidemia, zodasiran was associated with significant decreases in triglyceride levels at 24 weeks. (Funded by Arrowhead Pharmaceuticals; ARCHES-2 ClinicalTrials.gov number, NCT04832971.).

Plozasiran, an RNA Interference Agent Targeting APOC3, for Mixed Hyperlipidemia.

Persons with mixed hyperlipidemia are at risk for atherosclerotic cardiovascular disease due to an elevated non-high-density lipoprotein (HDL) cholesterol level, which is driven by remnant cholesterol in triglyceride-rich lipoproteins. The metabolism and clearance of triglyceride-rich lipoproteins are down-regulated through apolipoprotein C3 (APOC3)-mediated inhibition of lipoprotein lipase. We carried out a 48-week, phase 2b, double-blind, randomized, placebo-controlled trial evaluating the safety and efficacy of plozasiran, a hepatocyte-targeted APOC3 small interfering RNA, in patients with mixed hyperlipidemia (i.e., a triglyceride level of 150 to 499 mg per deciliter and either a low-density lipoprotein [LDL] cholesterol level of ≥70 mg per deciliter or a non-HDL cholesterol level of ≥100 mg per deciliter). The participants were assigned in a 3:1 ratio to receive plozasiran or placebo within each of four cohorts. In the first three cohorts, the participants received a subcutaneous injection of plozasiran (10 mg, 25 mg, or 50 mg) or placebo on day 1 and at week 12 (quarterly doses). In the fourth cohort, participants received 50 mg of plozasiran or placebo on day 1 and at week 24 (half-yearly dose). The data from the participants who received placebo were pooled. The primary end point was the percent change in fasting triglyceride level at week 24. A total of 353 participants underwent randomization. At week 24, significant reductions in the fasting triglyceride level were observed with plozasiran, with differences, as compared with placebo, in the least-squares mean percent change from baseline of -49.8 percentage points (95% confidence interval [CI], -59.0 to -40.6) with the 10-mg-quarterly dose, -56.0 percentage points (95% CI, -65.1 to -46.8) with the 25-mg-quarterly dose, -62.4 percentage points (95% CI, -71.5 to -53.2) with the 50-mg-quarterly dose, and -44.2 percentage points (95% CI, -53.4 to -35.0) with the 50-mg-half-yearly dose (P<0.001 for all comparisons). Worsening glycemic control was observed in 10% of the participants receiving placebo, 12% of those receiving the 10-mg-quarterly dose, 7% of those receiving the 25-mg-quarterly dose, 20% of those receiving the 50-mg-quarterly dose, and 21% of those receiving the 50-mg-half-yearly dose. In this randomized, controlled trial involving participants with mixed hyperlipidemia, plozasiran, as compared with placebo, significantly reduced triglyceride levels at 24 weeks. A clinical outcomes trial is warranted. (Funded by Arrowhead Pharmaceuticals; MUIR ClinicalTrials.gov number NCT04998201.).

Carbon Pricing Impacts on Four Pollutants: A Cross-Country Analysis

Research on climate change mitigation has increasingly considered carbon pricing, with these efforts concentrating on reductions in carbon dioxide (CO2) emissions. Our comprehensive cross-country analysis extends this focus by quantitatively evaluating the effects of carbon pricing on four major pollutants: CO2, nitrous oxide (N2O), methane (CH4), and particulate matter (PM). We use regressions and introduce entropy balancing to this research area. Analyzing data from 132 countries from 1992 to 2019, we find that carbon pricing is associated with an average annual reduction in CO2 emissions by 3 percentage points. A one-unit increase in a coverage-weighted carbon price is associated with reductions in N2O emissions by approximately 0.1 percentage points. A shorter panel for 2010–2017 shows a larger impact of 0.3 percentage points for PM. These findings underline the efficacy of carbon pricing not just in curtailing CO2 but in significantly mitigating other harmful pollutants on a global scale. Reductions in pollutants beyond CO2 provide further motivation for policymakers to pursue carbon pricing.

Evolution of Industrial Quality Parameters of Wheat during Storage in White and Colored Silo Bags: A Field-Scale Study

Over the past two decades, the silo bag system has gained popularity for storing grains and by-products under hermetic conditions. However, the impact of higher temperatures in the outer grain layer on key industrial parameters, such as wheat baking quality, remains insufficiently understood. Traditional silo bags are black on the inside and white on the outside to reflect sunlight, but colored bags, recently introduced to the market, absorb more heat, potentially warming the grain and causing damage. This study aimed to assess the effect of grain strata and bag color on grain temperature and quality under field conditions. Results showed a significant surface temperature increase in colored bags compared to white ones, approximately 3 °C, which affected the temperature of the peripheral grain layer. Moisture content slightly increased (0.2 percentage points) in the outer grain layer. However, many industrial quality parameters (protein content, P/L, W, and loaf volume) and the germination test for wheat, showed no significant differences between colored and white bags or between different strata after 120 days of storage, although the falling number increased and wet gluten decreased. These findings suggest that, despite surface temperature differences, the overall industrial quality of wheat remains unaffected by external bag coloration. The influence of ambient temperature on the peripheral layer was estimated to affect approximately 5–10% of the grain mass, indicating that adverse impacts on grain quality may go unnoticed without implementing stratified sampling techniques.

Unionization of retired workers in Europe

We shed light on an understudied group: retirees in unions. Using representative individual-level data of 19 European countries, we find that the share of retirees in unions and the union density of retirees increased between 2008 and 2020. Econometric analyses indicate that on average retired workers’ probability of union membership is 17 percentage points lower than that of active workers, with some variation among countries. This finding is consistent with social custom models and cost–benefit considerations, but it partly questions the inter-generational solidarity model put forward in the literature. We further find that some determinants of union membership differ between active and retired workers; for instance, the (former) status of being a full-time or blue-collar worker is only statistically significant for active but not for retired workers. Overall, standard membership models better explain the unionization of active than retired workers.

Enabling patient-physician continuity in Swedish primary care: the importance of a named GP.

Continuity of care is important for patients with chronic conditions. Assigning patients to a named GP may increase continuity. To examine if patients who were registered with a named GP at the onset of their first chronic disease had higher continuity at subsequent visits than patients who were only registered at a practice. Registry-based observational study in Region Skåne, Sweden. The study population included 66,063 patients registered at the same practice at least 1 year before the first chronic condition onset in 2009-2015. We compared patients registered with a named GP with patients only registered at a practice over a four-year follow-up period. The primary outcome was the Usual Provider of Care (UPC) index, for all visits and for visits related to the chronic disease. Secondary outcomes were the number of GP, nurse and out-of-hours visits, ED visits, hospital admissions, and mortality. We used linear regression models, adjusted for patient characteristics (using entropy balancing weights) and for practice-level fixed effects. Patients with a named GP at onset had 3-4 percentage points higher UPC, but the difference decreased and was not statistically significant after adjusting for patient and practice characteristics. Patients with a named GP made more visits, though not for the chronic condition. There were no statistically significant differences for the other outcomes. Registration with a GP at onset does not imply higher continuity at visits and is not linked to other relevant outcomes for patients diagnosed with their first chronic condition.

A Novel Fracturing Fluid Based on Functionally Modified Nano-Silica-Enhanced Hydroxypropyl Guar Gel

Considering the damage caused by conventional fracturing fluid in low-permeability reservoirs, a novel fracturing fluid (FNG) combining hydroxypropyl guar (HPG) and functionally modified nano-silica (FMNS) was prepared. The properties of heat/shear resistance, rheological property, proppant transportation, and formation damage were evaluated with systematic experiments. The results showed that the viscosities of FNG before and after the heat/resistance were 1323 mPa·s and 463 mPa·s, respectively, while that of conventional HPG gel was 350 mPa·s. FNG is a pseudoplastic strong gel with a yield stress of 12.9 Pa, a flow behavior index of 0.54, an elastic modulus of 16.2 Pa, and a viscous modulus of 6.2 Pa. As the proportions of proppant mass in further sections transported with FNG were higher than those transported with HPG gel, FNG could transport the proppant better than HPG gel at high temperatures. Because of the amphiphilic characteristics of FMNS, the surface/interface properties were improved by the FNG filtrate, resulting in a lower oil permeability loss rate of 10 percentage points in the matrix than with the filtrated HPG gel. Due to the considerable residual gel in broken HPG gel, the retained conductivity damaged with broken FNG was 9.5 percentage points higher than that damaged with broken HPG gel. FNG shows good potential for reducing formation damage during fracturing in low-permeability reservoirs in China.

YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model.

Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale pavement distresses and high costs in detection task, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (pavement distress). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the large separable kernel attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road damages while reducing the computational load. Furthermore, we introduced lightweight shared convolution detection head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP reaches an improvement of 1.4 percentage points from the baseline. In addition, experimental results on the RoadDamage dataset show that the mAP increased by 4.2% and this algorithm has good robustness. This method can provide a reference for the automatic detection method of pavement distress.

Using machine‐learning methods in meta‐analyses: An empirical application on consumer acceptance of meat alternatives

AbstractMeta‐analyses are widely used in various academic fields, including applied economics. However, the high labor intensity involved in paper searching and small sample sizes remain two dominant limiting factors. We conducted a meta‐analysis of studies on consumer preferences for plant‐based and lab‐grown meat alternatives using machine‐learning techniques at both the data collection and the data analysis phases. We demonstrated that machine learning reduces the workload in the manual title‐abstract screen phase by 69% accounting for 24% of total workload in data collection. We also found that machine learning improves out‐of‐sample of sample prediction accuracy by 48–78 percentage points when compared to econometric model. Notably, we showed that integrating machine learning can also improve the predictive performance of econometric methods, thereby improving their out‐of‐sample predictions. Our empirical findings further revealed that demand for meat alternatives is higher among younger consumers, especially when the products displayed benefit information.

Small oases below the canopy: The cooling effects of water-filled tree holes on the local microclimate in oak-dominated stands

The relevance of forests in mitigating biodiversity loss and the negative effects of climate change on the biota is largely shaped by below-canopy microclimatic conditions, which are buffered, cooler and more humid compared to adjoining open habitats. Water-filled tree holes (dendrotelms, DTs) are special tree-related microhabitats that host semi-aquatic communities and are important substrates or water sources for numerous taxa (e.g., birds, mammals, insects, fungi, bryophytes). Despite their microrefugia-role might be more pronounced under climate change-induced drought events, little is known about their microclimate and its potential drivers.In an abandoned Turkey oak-dominated coppice stand in Hungary, the microclimates of 80 paired dendrotelms and adjacent forest plots were measured in the summer of 2021. Compared to nearby open areas, under closed forests, air temperature maxima were up to 5 °C cooler, DTs had an additional negative offset of 2.5 °C and increased air humidity by 15 percentage points (5% vs. 20 %). The local microclimate of DTs and the adjacent closed forest differed significantly in means and extremes of air temperature, relative humidity and vapour pressure deficit, which was mainly driven by DT entrance area and water volume and seemed independent from local stand structure. On the contrary, nearby below-canopy microclimate was shaped by the distance from forest edge, incoming light and tree density. Water volume in DTs also influenced local forest microclimates up to 0.5–1 m from the entrances.As the frequency and severity of extreme weather events such as heatwaves are likely to intensify due to climate change, the importance of microhabitats and habitat trees for invertebrates and vertebrates will increase further, and will additionally contribute to the microclimatic refugia role of forests. Active protection of veteran trees under the scheme of continuous cover forestry or conservation-oriented management is essential facing climate change to mitigate its negative effects on forest biodiversity.

Individual tree segmentation from UAS Lidar data based on hierarchical filtering and clustering

ABSTRACT Accurate Individual Tree Segmentation (ITS) is fundamental to fine-scale forest structure and management studies. Light detection and ranging (Lidar) from Unoccupied Aerial Systems (UAS) has shown strengths in ITS and tree parameter estimation at stand and landscape scales. However, dense woodlands with tightly interspersed canopies and highly diverse tree species challenge the performance of ITS, and current research has not delved into the impact of mixed tree species and different leaf conditions on algorithm accuracy. Therefore, this study firstly evaluates the performance of open-source ITS methods, including both deep learning and non-deep learning algorithms, on data with mixed tree species and different leaf conditions, then proposes a hierarchical filtering and clustering (HFC) algorithm to mitigate the influence and improve the robustness. Hierarchical filtering consists of intensity filtering, Singular Value Decomposition (SVD) filtering, and Statistical Outlier Removal (SOR). Hierarchical clustering involves point-based clustering, cluster merging, and filtered point assignment. Through experiments on three distinct UAS Lidar datasets of forests with mixed tree species and different leaf conditions, HFC achieved the optimal segmentation results while maintaining high robustness. The variations of F1-score are 1–3 percentage points for mixed tree species and 1–2 percentage points for different leaf conditions across different datasets.

A pragmatic randomized trial of mailed fecal immunochemical testing to increase colorectal cancer screening among low-income and minoritized populations.

Colorectal cancer (CRC) screening is underused, particularly among low-income and minoritized populations, for whom the coronavirus disease 2019 (COVID-19) pandemic has challenged progress in achieving equity. A hub-and-spoke model was used. The hub was a nonacademic organization and the spokes were three community health center (CHC) systems overseeing numerous clinic sites. Via a cluster-randomized trial design, nine clinic sites were randomized to intervention and 16 clinic sites were randomized to usual care. Patient-level interventions included invitation letters, mailed fecal immunochemical tests (FITs), and call/text-based reminders. Year 1 intervention impact, which took place during the COVID-19 pandemic, was assessed as the proportion completing screening among individuals not up to date at baseline, which compared intervention and nonintervention clinics accounting for intraclinic cluster variation; confidence intervals (CIs) around differences not including 0 were interpreted as statistically significant. Among 26,736 patients who met eligibility criteria, approximately 58% were female, 55% were Hispanic individuals, and 44% were Spanish speaking. The proportion completing screening was 11.5 percentage points (ppts) (95% CI, 6.1-16.9 ppts) higher in intervention versus usual care clinics. Variation in differences between intervention and usual care clinics was observed by sex (12.6 ppts [95% CI, 7.2-18.0 ppts] for females; 8.8 ppts [95% CI, 4.7-13.9 ppts] for males) and by racial and ethnic group (13.8 ppts [95% CI, 7.0-20.6 ppts] for Hispanic individuals; 13.0 ppts [95% CI, 3.6-22.4 ppts] for Asian individuals; 11.3 ppts [95% CI, 5.8-16.8 ppts] for non-Hispanic White individuals; 6.1 ppts [95% CI, 0.8-10.4 ppts] for Black individuals). A regional mailed FIT intervention was effective for increasing CRC screening rates across CHC systems serving diverse, low-income populations.

Cost-Effectiveness of Test-and-Treat Strategies to Reduce the Antibiotic Prescription Rate for Acute Febrile Illness in Primary Healthcare Clinics in Africa.

Inappropriate antibiotic use increases selective pressure, contributing to antimicrobial resistance. Point-of-care rapid diagnostic tests (RDTs) would be instrumental to better target antibiotic prescriptions, but widespread implementation of diagnostics for improved management of febrile illnesses is limited. Our study aims to contribute to evidence-based guidance to inform policymakers on investment decisions regarding interventions that foster more appropriate antibiotic prescriptions, as well as to address the evidence gap on the potential clinical and economic impact of RDTs on antibiotic prescription. A country-based cost-effectiveness model was developed for Burkina Faso, Ghana and Uganda. The decision tree model simulated seven test strategies for patients with febrile illness to assess the effect of different RDT combinations on antibiotic prescription rate (APR), costs and clinical outcomes. The incremental cost-effectiveness ratio (ICER) was expressed as the incremental cost per percentage point (ppt) reduction in APR. For Burkina Faso and Uganda, testing all patients with a malaria RDT was dominant compared to standard-of-care (SoC) (which included malaria testing). Expanding the test panel with a C-reactive protein (CRP) test resulted in an ICER of $0.03 and $0.08 per ppt reduction in APR for Burkina Faso and Uganda, respectively. For Ghana, the pairwise comparison with SoC-including malaria and complete blood count testing-indicates that both testing with malaria RDT only and malaria RDT + CRP are dominant. The use of RDTs for patients with febrile illness could effectively reduce APR at minimal additional costs, provided diagnostic algorithms are adhered to. Complementing SoC with CRP testing may increase clinicians' confidence in prescribing decisions and is a favourable strategy.

Supplemental Nutrition Assistance Program and food expenditures: Evaluating California's cash‐out policy

AbstractThis paper investigates how Supplemental Nutrition Assistance Program (SNAP) eligibility affects food expenditures. A 2019 policy change in California granted SNAP eligibility to previously ineligible Supplemental Security Income (SSI) recipients. Using the Consumer Expenditure Survey, we find that after the policy change, affected SSI recipients increased their “food at home” budget share between 2.5 and 4.3 percentage points ($120 to $206 per quarter). The SNAP effect on total food expenditures is dampened by a decrease in “food away from home” which SNAP benefits cannot be spent on.

A Randomized Phase 2 Trial of Felzartamab in Antibody-Mediated Rejection.

Antibody-mediated rejection is a leading cause of kidney-transplant failure. The targeting of CD38 to inhibit graft injury caused by alloantibodies and natural killer (NK) cells may be a therapeutic option. In this phase 2, double-blind, randomized, placebo-controlled trial, we assigned patients with antibody-mediated rejection that had occurred at least 180 days after transplantation to receive nine infusions of the CD38 monoclonal antibody felzartamab (at a dose of 16 mg per kilogram of body weight) or placebo for 6 months, followed by a 6-month observation period. The primary outcome was the safety and side-effect profile of felzartamab. Key secondary outcomes were renal-biopsy results at 24 and 52 weeks, donor-specific antibody levels, peripheral NK-cell counts, and donor-derived cell-free DNA levels. A total of 22 patients underwent randomization (11 to receive felzartamab and 11 to receive placebo). The median time from transplantation until trial inclusion was 9 years. Mild or moderate infusion reactions occurred in 8 patients in the felzartamab group. Serious adverse events occurred in 1 patient in the felzartamab group and in 4 patients in the placebo group; graft loss occurred in 1 patient in the placebo group. After week 24, resolution of morphologic antibody-mediated rejection was more frequent with felzartamab (in 9 of 11 patients [82%]) than with placebo (in 2 of 10 patients [20%]), for a difference of 62 percentage points (95% confidence interval [CI], 19 to 100) and a risk ratio of 0.23 (95% confidence interval [CI], 0.06 to 0.83). The median microvascular inflammation score was lower in the felzartamab group than in the placebo group (0 vs. 2.5), for a mean difference of -1.95 (95% CI, -2.97 to -0.92). Also lower was a molecular score reflecting the probability of antibody-mediated rejection (0.17 vs. 0.77) and the level of donor-derived cell-free DNA (0.31% vs. 0.82%). At week 52, the recurrence of antibody-mediated rejection was reported in 3 of 9 patients who had a response to felzartamab, with an increase in molecular activity and biomarker levels toward baseline levels. Felzartamab had acceptable safety and side-effect profiles in patients with antibody-mediated rejection. (Funded by MorphoSys and Human Immunology Biosciences; ClinicalTrials.gov number, NCT05021484; and EUDRACT number, 2021-000545-40.).

Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex image backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for pitaya orchards. Initially, DeepLabV3+ utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with a Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pyramid Pooling (ASPP) module. This improvement enhances the spatial feature representation of feature maps, thereby sharpening the segmentation boundaries. Additionally, an Efficient Channel Attention Network (ECANet) mechanism is integrated with the lower-level features of MobileNetV2 to reduce computational complexity and refine the clarity of target boundaries. The paper also designs a navigation path extraction algorithm, which fits the road mask regions segmented by the model to achieve precise navigation path recognition. Experimental findings show that the enhanced DeepLabV3+ model achieved a Mean Intersection over Union (MIoU) and average pixel accuracy of 95.79% and 97.81%, respectively. These figures represent increases of 0.59 and 0.41 percentage points when contrasted with the original model. Furthermore, the model’s memory consumption is reduced by 85.64%, 84.70%, and 85.06% when contrasted with the Pyramid Scene Parsing Network (PSPNet), U-Net, and Fully Convolutional Network (FCN) models, respectively. This reduction makes the proposed model more efficient while maintaining high segmentation accuracy, thus supporting enhanced operational efficiency in practical applications. The test results for navigation path recognition accuracy reveal that the angle error between the navigation centerline extracted using the least squares method and the manually fitted centerline is less than 5°. Additionally, the average deviation between the road centerlines extracted under three different lighting conditions and the actual road centerline is only 2.66 pixels, with an average image recognition time of 0.10 s. This performance suggests that the study can provide an effective reference for visual navigation in smart agriculture.

Screening for atrial fibrillation in patients with symptoms of cognitive impairment

Abstract Introduction A diagnosis of atrial fibrillation increases the risk for dementia (1). Large register studies including approximately 400,000 patients with atrial fibrillation, have suggested that oral anti-coagulation treatment is associated with a 30-50% reduction of dementia development (2). Still, there are no studies investigating the true prevalence of atrial fibrillation in patients with cognitive problems. Purpose We studied the incidence of silent atrial fibrillation in patients with symptoms of cognitive impairment, participation rate in atrial fibrillation screening and the tolerance of the screening method in this specific population. Method Patients ≥70 years of age, referred from primary care for cognitive investigation at a specialised memory clinic, were invited to undergo a long-term ECG recording using an ECG-patch for two weeks. Patients with a known diagnosis of atrial fibrillation or who already were prescribed oral anti-coagulation treatment were excluded. Participants with detected atrial fibrillation (&amp;gt;30 seconds) were referred to a cardiologist for follow-up and oral anti-coagulation treatment was initiated according to clinical guidelines. Results During the inclusion period n=734 patients were examined for symptoms of cognitive impairment at a memory clinic. Of those, n=191 (26%) had already known atrial fibrillation and n=2 (0.3%) had oral anti-coagulation treatment for other reasons. Of the patients eligible for inclusion n=255/541 (47%) agreed to participate in screening for atrial fibrillation. Participants had a median age of 77 (IQR 74-82) years and median CHA2DS2-VASc score 3 (IQR 3-5). Long-term ECG was recorded for a median of 13.7 (IQR 12.6-14.0) days. Among the 255 participants n=14 (6%) had new atrial fibrillation detected. Total prevalence of atrial fibrillation in the population undergoing cognitive investigation increased with two percent units after screening to 28%. Conclusion The incidence of atrial fibrillation is remarkably high in patients with symptoms of cognitive impairment. Participation rate in screening is high in the selected population and the screening method is well-tolerated.

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

Wind turbine blades are the core components responsible for efficient wind energy conversion and ensuring stability. To address challenges in wind turbine blade damage detection using image processing techniques such as complex image backgrounds, decreased detection performance due to high image resolution, prolonged inference time, and insufficient recognition accuracy, this study introduces an enhanced wind turbine blade damage detection model named WTDB-YOLOv8. Firstly, by incorporating the GhostCBS and DFSB-C2f modules, the aim is to reduce the number of model parameters while enhancing feature extraction capability. Secondly, by integrating the MHSA-C2f module, which incorporates a multi-head self-attention mechanism, the focus on global information is enabled, thereby mitigating irrelevant background interference and reducing the impact of complex backgrounds on damage detection. Lastly, adopting the Mini-BiFPN structure improves the retention of features for small target objects in shallow networks and reinforces the propagation of these features in deep networks, thereby enhancing the detection accuracy of small target damage and reducing false negative rates. Through training and testing on the Wind Turbine Blade Damage Dataset (WTBDD), the WTDB-YOLOv8 model achieves an average precision of 98.3%, representing a 2.2 percentage point improvement over the original YOLOv8 model. Particularly noteworthy is the increase in precision from 93.1% to 97.9% in small target damage detection. Moreover, the total parameter count of the model decreases from 3.22 million in YOLOv8 to 1.99 million, marking a reduction of 38.2%. Therefore, the WTDB-YOLOv8 model not only enhances the detection performance and efficiency of wind turbine blade damage but also significantly reduces the model parameter count, showcasing its practical advantages in engineering applications.

Virtual Scribes and Physician Time Spent on Electronic Health Records

Time on the electronic health record (EHR) is associated with burnout among physicians. Newer virtual scribe models, which enable support from either a real-time or asynchronous scribe, have the potential to reduce the burden of the EHR and EHR-related documentation. To characterize the association of use of virtual scribes with changes in physicians' EHR time and note and order composition and to identify the physician, scribe, and scribe response factors associated with changes in EHR time upon virtual scribe use. Retrospective, pre-post quality improvement study of 144 physicians across specialties who had used a scribe for at least 3 months from January 2020 to September 2022, were affiliated with Brigham and Women's Hospital and Massachusetts General Hospital, and cared for patients in the outpatient setting. Data were analyzed from November 2022 to January 2024. Use of either a real-time or asynchronous virtual scribe. Total EHR time, time on notes, and pajama time (5:30 pm to 7:00 am on weekdays and nonscheduled weekends and holidays), all per appointment; proportion of the note written by the physician and team contribution to orders. The main study sample included 144 unique physicians who had used a virtual scribe for at least 3 months in 152 unique scribe participation episodes (134 [88.2%] had used an asynchronous scribe service). Nearly two-thirds of the physicians (91 physicians [63.2%]) were female and more than half (86 physicians [59.7%]) were in primary care specialties. Use of a virtual scribe was associated with significant decreases in total EHR time per appointment (mean [SD] of 5.6 [16.4] minutes; P < .001) in the 3 months after vs the 3 months prior to scribe use. Scribe use was also associated with significant decreases in note time per appointment and pajama time per appointment (mean [SD] of 1.3 [3.3] minutes; P < .001 and 1.1 [4.0] minutes; P = .004). In a multivariable linear regression model, the following factors were associated with significant decreases in total EHR time per appointment with a scribe use at 3 months: practicing in a medical specialty (-7.8; 95% CI, -13.4 to -2.2 minutes), greater baseline EHR time per appointment (-0.3; 95% CI, -0.4 to -0.2 minutes per additional minute of baseline EHR time), and decrease in the percentage of the note contributed by the physician (-9.1; 95% CI, -17.3 to -0.8 minutes for every percentage point decrease). In 2 academic medical centers, use of virtual scribes was associated with significant decreases in total EHR time, time spent on notes, and pajama time, all per appointment. Virtual scribes may be particularly effective among medical specialists and those physicians with greater baseline EHR time.