Limited Power Research Articles

Eco-FL: Enhancing Federated Learning sustainability in edge computing through energy-efficient client selection

In the realm of edge cloud computing (ECC), Federated Learning (FL) revolutionizes the decentralization of machine learning (ML) models by enabling their training across multiple devices. In this way, FL preserves privacy and minimizes the need for centralized data by processing data near the source. From a communication standpoint, only the model weights are exchanged between devices. By avoiding the need to send data to a centralized location for processing, FL reduces the energy required for data transfer and supports more efficient use of computing resources at the edge. FL is particularly advantageous for resource-constrained devices, such as smartphones and IoT devices. However, this limited computational power and battery capacity and the challenge of energy consumption are critical aspects of FL systems. This paper introduces Eco-FL, an innovative methodology designed to optimize energy consumption in FL systems, in the field of Green Edge Cloud Computing (GECC). Our approach employs a device selection process that considers the entropy of the data held by the devices and their available energy reserves. This ensures that devices with lower energy availability are less likely to participate in the training rounds, prioritizing those with higher energy capacities. To evaluate the efficacy of our methodology, we utilize FedEntropy, an entropy-based aggregation method, alongside established aggregation methods such as FedAvg and FedProx for performance comparison. The effectiveness of Eco-FL in reducing energy consumption without compromising the accuracy of the FL process is demonstrated through analyses conducted on three distinct datasets. These analyses vary the β parameter of the Dirichlet distribution and account for scenarios with both homogeneous and heterogeneous initial device charges. Our findings validate Eco-FL’s potential to enhance the sustainability of FL systems by judiciously managing client participation based on energy criteria, presenting a significant step forward in the development of energy-efficient FL.

Deep learning for predicting fibrotic progression risk in diabetic individuals with metabolic dysfunction-associated steatotic liver disease initially free of hepatic fibrosis

ObjectiveMetabolic dysfunction-associated steatotic liver disease (MASLD) significantly impacts patients with type 2 diabetes mellitus (T2DM), where current non-invasive assessment methods show limited predictive power for future fibrotic progression. This study aims to develop an enhanced deep learning (DL) model that integrates ultrasound elastography images with clinical data, refining the prediction of fibrotic progression in T2DM patients with MASLD who initially exhibit no signs of hepatic fibrosis. MethodsWe enrolled 946 diabetic MASLD patients without advanced fibrosis, confirmed by initial liver stiffness measurements (LSM) below 6.5 kPa. Patients were divided into a training dataset of 671 and a testing dataset of 275. Hepatic shear wave elastography (SWE) images measured liver stiffness, classifying participants based on progression. A DL integrated model (DI-model) combining SWE images and clinical data was trained and its predictive performance compared with individual Image and Tabular models, as well as a logistic regression model on the testing dataset. ResultsFibrotic progression was observed in 18.1 % of patients over three years. During the training phase, the DI-model outperformed other models, achieving the lowest validation loss of 0.161 and highest accuracy of 0.933 through cross-validation. In the testing phase, it demonstrated robust discrimination with AUCs of 0.884 and 0.903 for the receiver operating characteristic and precision-recall curves, respectively, clearly outperforming other models. Shapley analysis identified BMI, LSM, and glycated hemoglobin as critical predictors. ConclusionThe DI-model significantly enhances the prediction of future fibrotic progression in diabetic MASLD patients, demonstrating the benefit of combining clinical and imaging data for early diagnosis and intervention.

Stable Operation Domain Analysis of Back-to-Back Converter Based Flexible Multi-State Switches Considering PLL Dynamics

Flexible multi-state switches (FMSSs), also called soft open point (SOP), are regarded as the core devices in flexible interconnected distribution network. However, due to their more complex control structure and nonlinear characteristics, there is a tight and complex coupling relationship between the transmission power limit and stability of two (or more) converter sides, which brings difficulties to the planning and power flow dispatching control of flexible interconnected distribution networks. In this paper, taking the back-to-back converter based FMSS (BTBC-FMSS) as the research object, the dynamic equivalent circuit connected to the distribution network is constructed, and the transmission active and reactive power expression of the converter on both sides considering PLL dynamics is deduced. The variation rules of transmission power limits on both sides of the device with various structures and control parameters are summarized. Furthermore, the stable operation domain of the BTBC-FMSS is constructed, and the effective measures to improve the transmission power stability of the device are given according to the analysis of the stable operation domain. Finally, a simulation model is built in Matlab/Simulink to verify the effectiveness of the analysis results.

Unsupervised Feature Extraction From Raw Data for Gesture Recognition With Wearable Ultralow-Power Ultrasound.

Wearable ultrasound (US) is a novel sensing approach that shows promise in multiple application domains, and specifically in hand gesture recognition (HGR). In fact, US enables to collect information from deep musculoskeletal structures at high spatiotemporal resolution and high signal-to-noise ratio, making it a perfect candidate to complement surface electromyography for improved accuracy performance and on-the-edge classification. However, existing wearable solutions for US-based gesture recognition are not sufficiently low power for continuous, long-term operation. On top of that, practical hardware limitations of wearable US devices (limited power budget, reduced wireless throughput, and restricted computational power) set the need for the compressed size of models for feature extraction and classification. To overcome these limitations, this article presents a novel end-to-end approach for feature extraction from raw musculoskeletal US data suited for edge computing, coupled with an armband for HGR based on a truly wearable (12 cm2, 9 g), ultralow-power (ULP) (16 mW) US probe. The proposed approach uses a 1-D convolutional autoencoder (CAE) to compress raw US data by 20× while preserving the main amplitude features of the envelope signal. The latent features of the autoencoder are used to train an XGBoost classifier for HGR on datasets collected with a custom US armband, considering armband removal/repositioning in between sessions. Our approach achieves a classification accuracy of 96%. Furthermore, the proposed unsupervised feature extraction approach offers generalization capabilities for intersubject use, as demonstrated by testing the pretrained encoder on a different subject and conducting posttraining analysis, revealing that the operations performed by the encoder are subject-independent. The autoencoder is also quantized to 8-bit integers and deployed on a ULP wearable US probe along with the XGBoost classifier, allowing for a gesture recognition rate ≥ 25 Hz and leading to 21% lower power consumption [at 30 frames/s (FPS)] compared to the conventional approach (raw data transmission and remote processing).

Energy Management Considering both Efficiency Optimization and Lifetime Balance of Multi-stack FCS

Abstract With the increase of energy transformation and environmental protection awarenesses, fuel cells, as a clean and efficient energy conversion technology, have been widely adopted in transportation and stationary power supply applications. A single fuel cell has a limited power capability. On the other hand, the multiple fuel cells are combined into a multi-stack fuel cell system (MFCS), which provides a high power output and modular design. However, the energy management of such multi-stack systems faces challenges such as uneven power distribution, inconsistent stack life, and low overall efficiency. Current multi-stack fuel cell control systems often optimize single objectives, either efficiency or lifetime balance produce the optimized energy management strategy. This may not suffice for the overall system benefit. For example, if only efficiency is optimized by energy management, the failure to consider lifetime balance may result in uneven life degradation for each fuel cell, and vice versa. To address the above issues, this paper proposes an energy management strategy for multi-stack fuel cell systems that considers dual objectives, which can simultaneously ensure the optimal co-optimization of the output efficiency and stack life for each stack, thereby improving the overall operating efficiency and lifetime of the system. The effectiveness and practicality of the proposed energy management control strategy were verified on a 60kW dual-stack PEMFC fixed power generation system developed by SeeEx.

An N - policy M/M/1 queueing model for energy saving mechanism in Networks

The conservation of power in wireless sensor networks (WSNs) is critical due to the difficulty of replacing or recharging batteries in remote sensor nodes. Additionally, sensor node failure is inevitable in WSNs. In order to overcome these difficulties, this study proposes an N-policy M/M/1 queuing system model with an unstable server. This model offers insightful information for improving performance, maximizing energy use, and prolonging the lifespan of WSNs. The study looks into how different parameters and N-values affect the system's performance by examining the average system size under various scenarios. Important performance parameters are taken into consideration in the suggested model, including the total system size and the average number of data packets in the busy, idle, and down stages. These measurements aid in the comprehension of the behavior of the system and direct the N-policy optimization process to reduce setup, holding, server downtime, and operating state expenses. The analytical results are supported by numerical representations that show system size is reduced by higher repair and service rates and increased by higher breakdown rates. The cost function is estimated using MATLAB simulations, which also find the ideal N-value to reduce the overall predicted cost per unit of time. The findings demonstrate that an ideal N-policy can greatly enhance system performance and energy efficiency, guaranteeing the WSN functions well even in the face of node failure and power limitations. Based on in-depth numerical analysis and performance evaluation, this paper offers a thorough framework for improving WSN efficiency and reliability through strategic N-policy implementation.

A Critical Analysis of the Regulations and Process of Appointment of Vice-Chancellors at Higher Education Institutions in Pakistan

In post Eighteenth Amendment era higher education became a provincial subject, limiting the Federal HEC's role to maintaining standards. However, only two provinces have established their Higher Education Commissions with limited power. Various university acts have loopholes regarding appointments of vice- chancellors, leading to some appointments being nullified by higher courts due to procedural non-compliance. The lack of clear rules for higher level appointments negatively impacts lower levels, necessitating a robust legislative framework. It is crucial to appoint permanent vice-chancellors for all vacant positions in Higher Education Institutions. An effective and efficient regulatory and legislative framework for the higher education sector should involve all major stakeholders, including the Federal Government, Provincial Governments, university representatives, the National Finance Commission, and the Council of Common Interest. This collaboration is essential to address the current gaps and improve governance in higher education.

개념 디자인에서의 AI의 활용에 관한 연구

Machine learning(ml) was already developed in the 1940s, but its application value was low due to limited computational power and lack of data. Since then, based on advancements in computer technology and data accumulation, artificial intelligence, particularly machine learning, has gained interdisciplinary attention as a tool for providing insights in the field of design. Recently, numerous prior studies on image generation using artificial intelligence have emerged, reflecting this growing interest. However, from a practical standpoint, there are several criticisms: the images generated by machine learning often lack the resolution required by designers, and they fall short in providing original images. It is worth examining whether the perspectives of these prior studies are becoming teleologically biased. Therefore, this study aims to explore the gap between interdisciplinary discussions and practical applications, seeking to bridge this gap and integrate AI-generated image creation into practical use. This research is based on a literature review of Generative Adversarial Networks(GANs) in machine learning(ml), examining whether the images generated are acceptable from the perspective of product designers. Based on this, we will use text mining methods to perform clustering, information extraction, and sentiment analysis to understand consumer attitudes. Through this approach, we aim to explore the potential use of AI in product design from the standpoint of consumers and practitioners rather than an interdisciplinary perspective.

VegaEdge: Edge AI confluence for real-time IoT-applications in highway safety

Traditional highway safety and monitoring solutions, reliant on surveillance cameras, face limitations due to their dependence on high-speed internet connectivity and the remote processing of Artificial Intelligence (AI) algorithms. This reliance introduces latency, undermining the real-time detection and analysis crucial for highway applications. The fusion of AI with the Internet of Things (IoT) opens new avenues for highway safety and surveillance innovation. Yet, most existing solutions are confined to vehicle detection and tracking, hindered by edge-IoT platforms’ limited power and processing capabilities. Addressing these limitations, this paper presents VegaEdge, an AI framework optimized for edge-IoT devices capable of real-time vehicle detection and tracking, trajectory forecasting, and identifying anomalous driving behaviors, such as road departures, sudden stops, and hazardous merges. A novel lightweight anomaly detection algorithm based on trajectory prediction is used for identifying hazardous driving on highways. VegaEdge demonstrates its versatility and efficiency across various traffic conditions and roadway configurations and has been evaluated on platforms like the Nvidia Jetson Orin and Xavier NX. The Nvidia Jetson Orin processes up to 738 trajectories per second and detects up to 140 vehicles in a single frame. Additionally, the Carolinas Anomaly Dataset (CAD) an extension of the Carolinas Highway Dataset (CHD) is introduced. While CHD consists of standard highway vehicle videos and trajectories, CAD includes video data of anomalous driving behaviors, providing a crucial resource for enhancing anomaly detection algorithms. CAD is available at https://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set.

Electro-Hydraulic Variable-Speed Drive Network Technology—First Experimental Validation

The improvement of the energy efficiency of hydraulic systems remains an essential challenge for industry, and the demand for more sustainable solutions is increasing. A main focus in this endeavor is the ability to eliminate or strongly reduce the use of throttle control valves which have been the preferred control element in industrial hydraulic systems for decades. Components have been subject to continuous evolution, and current industrial grade hydraulic pumps and motors are both efficient and reliable. Even though few percentages of energy efficiency can still be achieved, the main achievements in terms of efficiency are associated with novel system designs rather than further development of components. An area subject to increasing attention is the field of variable-speed displacement control, allowing to avoid the main control valve throttle losses. Systems using this technology are, however, mainly developed as standalone drive systems, necessitating maximum force, speed, and power installed in each axis, with limited hydraulic power distribution capability compared to valve-controlled systems. An emerging field addressing this challenge is that of so-called electro-hydraulic variable-speed drive networks, which allow to completely eliminate the use of control valves and enable power sharing both electrically and hydraulically, potentially reducing the necessary installed power in many cases. The idea of such a technology was first proposed in 2022, and so far developments reported in the literature have mainly been of a theoretical nature. This article presents the first ever experimental results for a dual-cylinder electro-hydraulic variable-speed drive network prototype. The prototype was developed for an industrial application, but has initially been implemented in a laboratory testbench. Extensive data acquisition has been conducted while subject to the associated industrial motion cycle, under different load conditions. The data obtained are further used in combination with models to predict the total efficiency of the drive network prototype under higher loads than what could be achieved in the laboratory, suggesting a total efficiency from the electric supply to the cylinder pistons of 68%. Re-configuring the prototype to a known standalone drive system structure implies comparable efficiencies. Finally, the drive network is theoretically compared to a valve drive solution, generally suggesting that the prototype drive network can provide efficiency improvements of at least 40% in comparison.

Allele-specific binding variants causing ChIP-seq peak height of histone modification are not enriched in expression QTL annotations

BackgroundGenome sequence variants affecting complex traits (quantitative trait loci, QTL) are enriched in functional regions of the genome, such as those marked by certain histone modifications. These variants are believed to influence gene expression. However, due to the linkage disequilibrium among nearby variants, pinpointing the precise location of QTL is challenging. We aimed to identify allele-specific binding (ASB) QTL (asbQTL) that cause variation in the level of histone modification, as measured by the height of peaks assayed by ChIP-seq (chromatin immunoprecipitation sequencing). We identified DNA sequences that predict the difference between alleles in ChIP-seq peak height in H3K4me3 and H3K27ac histone modifications in the mammary glands of cows.ResultsWe used a gapped k-mer support vector machine, a novel best linear unbiased prediction model, and a multiple linear regression model that combines the other two approaches to predict variant impacts on peak height. For each method, a subset of 1000 sites with the highest magnitude of predicted ASB was considered as candidate asbQTL. The accuracy of this prediction was measured by the proportion where the predicted direction matched the observed direction. Prediction accuracy ranged between 0.59 and 0.74, suggesting that these 1000 sites are enriched for asbQTL. Using independent data, we investigated functional enrichment in the candidate asbQTL set and three control groups, including non-causal ASB sites, non-ASB variants under a peak, and SNPs (single nucleotide polymorphisms) not under a peak. For H3K4me3, a higher proportion of the candidate asbQTL were confirmed as ASB when compared to the non-causal ASB sites (P < 0.01). However, these candidate asbQTL did not enrich for the other annotations, including expression QTL (eQTL), allele-specific expression QTL (aseQTL) and sites conserved across mammals (P > 0.05).ConclusionsWe identified putatively causal sites for asbQTL using the DNA sequence surrounding these sites. Our results suggest that many sites influencing histone modifications may not directly affect gene expression. However, it is important to acknowledge that distinguishing between putative causal ASB sites and other non-causal ASB sites in high linkage disequilibrium with the causal sites regarding their impact on gene expression may be challenging due to limitations in statistical power.

Transgender Women With Suppressed Testosterone Display Lower Burden of Coronary Disease Than Matched Cisgender Men.

Cardiovascular disease (CVD) in transgender women (TW) may be affected by gender-affirming hormone therapy (GAHT) and HIV, but few data compare TW on contemporary GAHT to well-matched controls. We compared CVD burden and biomarker profiles between TW and matched cisgender men (CM). Adult TW on GAHT (n = 29) were recruited for a cross-sectional study (2018-2020). CM (n = 48) from the former Multicenter AIDS Cohort Study were matched 2:1 to TW on HIV serostatus, age ±5 years, race/ethnicity, BMI category and antiretroviral therapy (ART) type. Cardiac parameters were measured by CT and coronary atherosclerosis by coronary CT angiography; sex hormone and biomarker concentrations were measured centrally from stored samples. Overall, median age was 53 years and BMI 29 kg/m2; 69% were non-white. All participants with HIV (71%) had viral suppression on ART. Only 31% of TW had testosterone suppression (<50 ng/dL, TW-S). Traditional CVD risk factors were similar between groups, except that TW-S had higher BMI than TW with non-suppressed testosterone (TW-T). TW-S had no evidence of non-calcified coronary plaque or advanced coronary stenosis, whereas TW-T and CM had similar burden. TW had lower prevalence of any coronary plaque, calcified plaque and mixed plaque than CM, regardless of testosterone concentrations and HIV serostatus. Estradiol but not testosterone concentrations moderately and negatively correlated with the presence of coronary plaque and stenosis. Small sample size limited statistical power. Older TW with suppressed total testosterone on GAHT had no CT evidence of non-calcified coronary plaque or advanced coronary stenosis. Longitudinal studies to understand relationships between GAHT and CVD risk in TW are needed.

Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid

The growing popularity of plug-in hybrid electric vehicles (PHEVs) is due to their environmental advantages. But uncoordinated charging of a large number of PHEVs can lead to a significant surge in peak loads and higher charging costs for PHEV owners. To end this, this paper introduces an innovative approach to address the issue by proposing a multi-objective weighting control for coordinated charging of PHEVs in a future smart grid, which aims to find an economically optimal solution while also considering load stabilization with large-scale PHEV penetration. Technical constraints related to the owner’s demand and power limitations are considered. In the proposed approach, the charging behavior of PHEV owners is modeled by a normal distribution. It is observed that owners typically start charging their vehicles when they arrive home and stop charging when they go to their workplace. The charging cost is then calculated based on the tiered electricity price and charging power. By adjusting the cost weighting factor and the load stability weighting factor in the multi-objective function, the grid allows for flexible weight selection between the two objectives. This approach effectively encourages owners to actively participate in coordinated charging scheduling, which sets it apart from existing works. The algorithm offers better robustness and adaptability for large-scale PHEV penetration, making it highly relevant for the future smart grid. Finally, numerical simulations are presented to demonstrate the desirable performance of theory and simulation.

An improved MPSP‐based path‐following control method for USV with input disturbances

AbstractThis study proposes an improved model predictive static programming (MPSP) based path‐following control method for unmanned surface vessel (USV) subject to input disturbances. The method addresses the challenges of accurate USV dynamics modeling, unpredictable maritime environments, and limited power and energy systems. A trajectory generator is designed to construct smooth reference trajectories, and the MPSP algorithm is adapted to handle path‐following problems while considering state and input constraints. An event‐triggered mechanism is introduced to reduce computational burden and conserve energy. Comparative simulations demonstrate the superiority of the proposed method over open‐loop tracking and the original MPSP approach in terms of tracking accuracy, disturbance rejection, and overall control performance. The improved MPSP‐based control method offers a robust and efficient solution for USV path‐following tasks, ensuring accurate tracking even in the presence of environmental disturbances and system uncertainties.

Prevalence and Risk Factors of Child Marriage Among Madhesi Women in Nepal's Terai Region.

Child marriage is a detrimental practice with negative consequences for girls' health and well-being. This study aims to identify the prevalence of child marriage and associated risk factors within the Madhesi community. A cross-sectional study was conducted in a sub-metropolitan city of Lumbini Province, Nepal, in 2023. A total of 352 married women were randomly selected using simple random sampling and interviewed using a structured questionnaire between March 28th and April 17th. Descriptive categorical variables were expressed as proportions. Risk factors associated with child marriage were measured using the Chi-square test and multivariate logistic regression analysis. All statistical analyses were performed using SPSS version 26. The majority of respondents (36.1%) were in the age group 21-25, with a mean age of 25.90 years ± 4.6 years. Most respondents (48.9%) received only basic-level education, with the majority (72.4%) reporting their parents as having no formal education. The primary family occupation was farming (40.1%). The prevalence of child marriage was high (77.0%), with a median marriage age of 18 years. Logistic regression analysis revealed significant risk factors including lower education levels of respondents and their parents, husband's occupation, lack of awareness about the legal marriage age, limited decision-making power regarding marriage, belief in the dowry system, discussions with friends about child marriage, and occurrence of child marriage among friends. Despite legal prohibition, child marriage remains prevalent in the Madhesi community. Educational interventions targeting women and parents, empowering women with decision-making authority, and addressing the dowry system are crucial for reducing this harmful practice.

Can we compute the worst voltage case under power flows uncertainty at TSO-DSO interfaces?

This paper proposes the new problem of calculating the worst-voltage-case (WVC) under power flows uncertainty, particularly at interfaces between transmission and distribution grid operators. The goal of the calculation is to verify if the realization of the worst uncertainty scenario leads to abnormal voltages, or assure where the voltage instability is not a risk, and inform the transmission system operator. The problem is formulated as a tailored non-convex, nonlinear optimal power flow (OPF) with complementarity or equilibrium constraints (ECs). The latter model the generator switch between under voltage control and under reactive power limit. The problem is converted into a mathematically equivalent nonlinear programming OPF problem in which ECs are handled as penalty terms in the objective function. The paper has conducted a series of experiments on the 60-bus Nordic system, which have pointed out the challenges associated to such computations to obtain meaningful results and unveiled new insights. The findings and scalability are confirmed using a real-world 1203-bus system.

National Identity and the Limits of Platform Power in the Global Economy

Abstract Among the defining features of the contemporary global economy are the digital disruption of economic sectors and the accompanying political and regulatory conflicts. Across the world, multinational technology firms have mobilized consumers as a key ally in these conflicts, a critical element of the platform power they wield. In this article, I examine how non-consumer identities can limit the exercise of platform power by such firms. By synthesizing the concept of platform power with research on political consumerism and national identity, I argue that activating national identity can generate opposition to policies favorable to multinational technology firms and, in turn, curtail their ability to appeal to public support. Empirically, this article uses an online, nationally representative survey fielded in Canada. I explore the determinants of support for global regulatory cooperation and the domestic policy status quo, as well as the causal effect of consumer and national identity framing using vignette experiments across three issue areas: banking, telecommunications, and taxation. The findings reveal that activating consumer identities consistently shifts support but the effect of national identity is more variable. This article thus contributes to scholarship on the digital economic transformation and the exercise of business power in the global economy.

UAV‐aided distribution line inspection using double‐layer offloading mechanism

AbstractWith the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost.

Low-dose atropine for high myopia: An observational cohort study

PurposeTo describe and compare the spherical equivalent progression of pediatric patients with high myopia undergoing treatment with 0.01 % atropine and those not receiving any myopia therapy. DesignRetrospective, descriptive cohort study MethodsIn this IRB-approved study, we analyzed the spherical equivalent (SE) progression in children 6–13 years old treated with 0.01 % atropine and those who were not treated for myopia, initial SE of ≤-6 D, astigmatism of ≤2 D, no syndromic conditions, and records of cycloplegic refraction measurements without missing more than one year. ResultsSeventy-five eyes were included, with 87 % belonging to Hispanic patients. The baseline SE (D) was -7.66 ± 1.40 in the atropine group and -7.90 ± 1.38 in the control group. Over three years, atropine-treated eyes demonstrated slower SE progression compared to controls (P = 0.002). Analysis of yearly progression rates (D/year) revealed notably slower progression in the atropine group during the second (P = 0.04) and third (P = 0.02) years. ConclusionOur findings suggest that low-dose atropine may be beneficial as a first-line treatment for high myopia due to its favorable safety profile and potential efficacy. Due to limited statistical power, findings should be interpreted cautiously.

Relationship between facility type and bulk tank milk bacteriology, udder health, udder hygiene, and milk production on Vermont organic dairy farms

The primary objective of this cross-sectional observational study was to determine whether bulk tank milk quality, udder health, udder hygiene and milk production outcomes were associated with facility type on organic dairies. A secondary objective was to identify other management-related risk factors associated with bulk tank milk quality, udder health, udder hygiene, and milk production on organic dairy herds in Vermont. We aimed to enroll 40 farms, to compare herds using the 2 most common systems (freestalls, tiestalls) for housing organic dairy cattle in the state with those using a bedded pack during the non-grazing season (typically November-May). Two general styles of bedded packs were observed: cultivated bedded packs and untilled deep bedded packs. Due to the limited number of herds using bedded packs to house lactating dairy cattle in Vermont, we combined untilled and cultivated bedded packs to describe udder hygiene, milk quality, and udder health on these loose-housing systems deeply-bedded with organic material. The study was completed on 21 farms (5 bedded packs, 6 freestalls, 10 tiestalls) before interruption due to the COVID-19 pandemic. Data captured from Dairy Herd Improvement Association records from the test closest to the date of the farm visit included average somatic cell score (SCS), standardized 150-d milk (pounds), % cows with current high SCS (SCS ≥ 4.0), % cows with newly elevated SCS (previous SCS < 4.0 to current ≥ 4.0), and % cows with chronically elevated SCS (SCS ≥ 4.0 last 2 tests). Multivariable linear regression models were used to describe outcomes by facility type, but suffered from limited statistical power due to small group sample sizes. Unconditional comparisons failed to find statistically significant differences between farms grouped by facility type in metrics captured from Dairy Herd Improvement Association test data, bulk tank milk somatic cell count (BTSCC) and aerobic culture data, or udder hygiene scores. A secondary analysis was conducted using univariate linear regression to identify associations between herd management factors and outcomes for all 21 farms combined. Although not all differences found were statistically significant in this secondary analysis combining all farms, numeric differences that may be biologically important are reported showing farms with deeper bedding had a lower BTSCC, lower newly elevated SCS, lower chronically elevated SCS, lower elevated current SCS, lower average SCS, and better udder hygiene metrics. Farms with lower mean udder hygiene scores had numerically lower chronically elevated SCS, lower elevated current SCS, and lower average SCS. We could not reject the null hypothesis that milk quality and udder health outcomes did not differ by facility type, and this does not preclude the existence of biological differences in these outcomes between facility types. The current study provides insight on factors affecting bulk tank milk quality, udder health and hygiene measures on organic dairy farms in Vermont. Bedded packs may be a viable option for confinement housing during the winter non-grazing season for pasture-based herds interested in a loose-housing system in the Northeastern US, but more research such as longitudinal studies with a larger sample size is needed to test this hypothesis.