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Abstract Background Randomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess treatment effects of medical interventions. We aimed to hypothetically pool bodies of evidence (BoE) from RCTs with matched BoE from cohort studies included in the same systematic review. Methods BoE derived from systematic reviews of RCTs and cohort studies published in the 13 medical journals with the highest impact factor were considered. We re-analyzed effect estimates of the included systematic reviews by pooling BoE from RCTs with BoE from cohort studies using random and common effects models. We evaluated statistical heterogeneity, 95% prediction intervals, weight of BoE from RCTs to the pooled estimate, and whether integration of BoE from cohort studies modified the conclusion from BoE of RCTs. Results Overall, 118 BoE-pairs based on 653 RCTs and 804 cohort studies were pooled. By pooling BoE from RCTs and cohort studies with a random effects model, for 61 (51.7%) out of 118 BoE-pairs, the 95% confidence interval (CI) excludes no effect. By pooling BoE from RCTs and cohort studies, the median I 2 was 48%, and the median contributed percentage weight of RCTs to the pooled estimates was 40%. The direction of effect between BoE from RCTs and pooled effect estimates was mainly concordant (79.7%). The integration of BoE from cohort studies modified the conclusion (by examining the 95% CI) from BoE of RCTs in 32 (27%) of the 118 BoE-pairs, but the direction of effect was mainly concordant (88%). Conclusions Our findings provide insights for the potential impact of pooling both BoE in systematic reviews. In medical research, it is often important to rely on both evidence of RCTs and cohort studies to get a whole picture of an investigated intervention-disease association. A decision for or against pooling different study designs should also always take into account, for example, PI/ECO similarity, risk of bias, coherence of effect estimates, and also the trustworthiness of the evidence. Overall, there is a need for more research on the influence of those issues on potential pooling.

Background: The World Health Organization (WHO) recommends the diagnosis of tuberculosis (TB) using molecular tests, such as Xpert MTB/RIF (MTB/RIF) or Xpert Ultra (Ultra). These tests are expensive and resource-consuming, and cost-effective approaches are needed for greater coverage. Methods: We evaluated the cost-effectiveness of pooling sputum samples for TB testing by using a fixed amount of 1,000 MTB/RIF or Ultra cartridges. We used the number of people with TB detected as the indicator for cost-effectiveness. Cost-minimization analysis was conducted from the healthcare system perspective and included the costs to the healthcare system using pooled and individual testing. Results: There was no significant difference in the overall performance of the pooled testing using MTB/RIF or Ultra (sensitivity, 93.9% vs. 97.6%, specificity 98% vs. 97%, p-value > 0.1 for both). The mean unit cost across all studies to test one person was 34.10 international dollars for the individual testing and 21.95 international dollars for the pooled testing, resulting in a savings of 12.15 international dollars per test performed (35.6% decrease). The mean unit cost per bacteriologically confirmed TB case was 249.64 international dollars for the individual testing and 162.44 international dollars for the pooled testing (34.9% decrease). Cost-minimization analysis indicates savings are directly associated with the proportion of samples that are positive. If the TB prevalence is ≥ 30%, pooled testing is not cost-effective. Conclusion: Pooled sputum testing can be a cost-effective strategy for diagnosis of TB, resulting in significant resource savings. This approach could increase testing capacity and affordability in resource-limited settings and support increased testing towards achievement of WHO End TB strategy. [ABSTRACT FROM AUTHOR]

To Pool or Not to Pool? Analyzing Customer-Intensive Services with Strategic Agents In customer-intensive services where service quality increases with service time, service providers commonly pool their agents and give performance bonuses that reward agents for achieving greater customer satisfaction and serving more customers. Conventional wisdom suggests that pooling agents reduce customer wait time whereas performance bonuses motivate agents to produce high-quality services, both of which should boost customer satisfaction. However, in "Pooling Agents for Customer-Intensive Services," Wang, Yang, Cui, Ülkü, and Zhou find that when agents act strategically, they may choose to speed up under pooling in an attempt to serve more customers, thus undermining service quality. If this happens, pooling can backfire and result in both lower customer satisfaction and agent payoff. Consequently, the researchers propose a simple practical solution to restore the efficiency of pooling. They propose pooling a portion of the performance bonuses (incentive pooling) in conjunction with pooling agents (operational pooling). In customer-intensive services where service quality increases with service time, service providers commonly pool their agents and give performance bonuses that reward agents for achieving greater customer satisfaction and serving more customers. Conventional wisdom suggests that pooling agents reduce customer wait time while performance bonuses motivate agents to produce high-quality service, both of which should boost customer satisfaction. However, our queueing-game-theoretic analysis reveals that when agents act strategically, they may choose to speed up under pooling in an attempt to serve more customers, thus undermining service quality. If this happens, pooling can backfire and result in both lower customer satisfaction and agent payoff. We propose a simple solution to resolve this issue: pooling a portion of the performance bonuses (incentive pooling) in conjunction with pooling agents (operational pooling). History: This paper has been accepted for the Operations Research Special Issue on Behavioral Queueing Science. Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2022.2259. Funding: Z. Wang acknowledges support from the National Natural Science Foundation of China [Grants 72001118, 72132007]. [ABSTRACT FROM AUTHOR]

Brain tumour is a fatal disease and its diagnosis is a difficult procedure for radiologists due to the heterogeneous behaviour of tumour cells. Diagnosing brain tumour using MRI at early stage is essential by attaining higher accuracy with minimum error function. Based on this insight, the goal of the proposed research is to create a deep learning architecture that will aid in the automatic detection of brain tumour utilising two-dimensional MRI slices. The proposedSquare Array Filtering(SAF) approach is applied to the acquired input image in order to remove such noisy contents. To obtain a noiseless image, an array in square grid format is generated to update the missing pixel values of an image using SAF. This research employs the Kernel K-means clustering (K2C) model for segmenting the injured region, with the weighted mean enhancement methodology efficiently removing noise from the image and the K2C method isolating the infected areas. The proposed Optimization empowered Hierarchical Residual VGGNet19 (HR-VGGNet19) model is designed to explore the discriminative information with the help of convolution layer employed in it. The proposed model combines both the low level and high-level features with the help of HR-VGGNet19 network and obtains the output class. The performance metric shows the proposed methodology outperforms better than the state-of-art methods. The proposed methodology is implemented in the working platform of MATLAB in terms of evaluation metrics like accuracy, sensitivity, specificity, PPV, NPV, FPR, FNR and FDR. [ABSTRACT FROM AUTHOR]

Since tick-borne diseases (TBDs) incidence, both in human and animal populations, is increasing worldwide, there is the need to assess the presence, distribution and prevalence of tick-borne pathogens. Reliable estimates on tick-borne pathogens (TBPs) prevalence represent the public health foundation to create risk maps and take effective prevention and control actions against TBDs. Tick surveillance consists of collecting and testing (usually in pools) thousands of specimens. Construction and analysis of tick pools represent a challenge due to the complexity of tick-borne pathogens and tick-borne diseases ecology. The aim of this study is to provide a practical guideline on appropriate pooling strategies and statistical analysis of infection prevalence through: (i) reporting the different pooling strategies and statistical methodologies commonly used to calculate pathogen prevalence in tick populations and (ii) practical comparison between statistical methods utilising a real dataset of infection prevalence in ticks collected in Northern Italy. Reporting detailed information on tick pool composition and size is as important as the correct TBPs prevalence estimation. Among the prevalence indexes, we suggest using maximum-likelihood estimates of pooled prevalence instead of minimum infection rate or pool positivity rate given the merits of the method and availability of software. [ABSTRACT FROM AUTHOR]

India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. [ABSTRACT FROM AUTHOR]

Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a Variational AutoEncoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements. [ABSTRACT FROM AUTHOR]

Trichomonosis is a venereal disease of cattle caused by the protozoan Tritrichomonas foetus. T. foetus infection in cattle herds can be economically costly for cattle producers; therefore, testing is important for detection of the agent. Given that bulls are considered to be subclinical carriers of T. foetus, it is important to detect T. foetus infection prior to movement and/or breeding season. We have described previously the development of an updated set of PCR primers and probes that offer increased sensitivity of T. foetus detection in preputial washings collected in PBS by utilizing reverse-transcription real-time PCR (RT-rtPCR) that targets the 5.8S ribosomal RNA of the T. foetus organism. Here, we report improvements in the updated RT-rtPCR reagents as well as the evaluation of testing of pooled preputial washings. We found that up to 5 preputial washings can be pooled, similar to routine testing practices (InPouch culture), without reducing the sensitivity of detection of T. foetus. [ABSTRACT FROM AUTHOR]

This article introduces an innovative theoretical conception of the corporate agency of international organizations (IOs). Existing rationalist and constructivist accounts attribute IO agency to the influence of intra-organizational agents. Drawing on general conceptions of corporate agency in International Relations, sociology, and philosophy, we elucidate how IOs can develop corporate agency, even if the member states prepare and adopt all organizational decisions themselves. In line with recent studies on international political authority, we replace the IO-as-bureaucracy model with the more comprehensive concept of IOs-as-governors. To establish the micro-foundations of IO agency, we adopt a bottom-up perspective and outline how, and under which conditions, IO agency arises from the interaction of constituent actors. Irrespective of any specific institutional design, IOs become actors in their own right whenever they gain action capability and autonomy. They acquire action capability whenever their members pool governance resources like the right to regulate certain activities or to manage common funds and authorize IOs to deploy these resources. IOs gain autonomy whenever they affect organizational decisions. Both dimensions of IO agency are variable and open to empirical enquiry. To illustrate our argument, we refer to the United Nations Security Council and other IOs with member-driven decision processes. [ABSTRACT FROM AUTHOR]

Hypertensive retinopathy (HR) results from the microvascular retinal changes triggered by hypertension, which is the most common leading cause of preventable blindness worldwide. Therefore, it is necessary to develop an automated system for HR detection and evaluation using retinal images. We aimed to propose an automated approach to identify and categorize the various degrees of HR severity. A new network called the spatial convolution module (SCM) combines cross-channel and spatial information, and the convolution operations extract helpful features. The present model is evaluated using publicly accessible datasets ODIR, INSPIREVR, and VICAVR. We applied the augmentation to artificially increase the dataset of 1200 fundus images. The different HR severity levels of normal, mild, moderate, severe, and malignant are finally classified with the reduced time when compared to the existing models because in the proposed model, convolutional layers run only once on the input fundus images, which leads to a speedup and reduces the processing time in detecting the abnormalities in the vascular structure. According to the findings, the improved SVM had the highest detection and classification accuracy rate in the vessel classification with an accuracy of 98.99% and completed the task in 160.4 s. The ten-fold classification achieved the highest accuracy of 98.99%, i.e., 0.27 higher than the five-fold classification accuracy and the improved KNN classifier achieved an accuracy of 98.72%. When computation efficiency is a priority, the proposed model’s ability to quickly recognize different HR severity levels is significant.

Recently, object detection has become a research hotspot in computer vision, which often detects regular images with small viewing angles. In order to obtain a field of view without blind spots, fisheye cameras, which have distortions and discontinuities, have come into use. The fisheye camera, which has a wide viewing angle, and an unmanned aerial vehicle equipped with a fisheye camera are used to obtain a field of view without blind spots. However, distorted and discontinuous objects appear in the captured fisheye images due to the unique viewing angle of fisheye cameras. It poses a significant challenge to some existing object detectors. To solve this problem, this paper proposes a PGDS-YOLOv8s model to solve the issue of detecting distorted and discontinuous objects in fisheye images. First, two novel downsampling modules are proposed. Among them, the Max Pooling and Ghost’s Downsampling (MPGD) module effectively extracts the essential feature information of distorted and discontinuous objects. The Average Pooling and Ghost’s Downsampling (APGD) module acquires rich global features and reduces the feature loss of distorted and discontinuous objects. In addition, the proposed C2fs module uses Squeeze-and-Excitation (SE) blocks to model the interdependence of the channels to acquire richer gradient flow information about the features. The C2fs module provides a better understanding of the contextual information in fisheye images. Subsequently, an SE block is added after the Spatial Pyramid Pooling Fast (SPPF), thus improving the model’s ability to capture features of distorted, discontinuous objects. Moreover, the UAV-360 dataset is created for object detection in fisheye images. Finally, experiments show that the proposed PGDS-YOLOv8s model on the VOC-360 dataset improves mAP@0.5 by 19.8% and mAP@0.5:0.95 by 27.5% compared to the original YOLOv8s model. In addition, the improved model on the UAV-360 dataset achieves 89.0% for mAP@0.5 and 60.5% for mAP@0.5:0.95. Furthermore, on the MS-COCO 2017 dataset, the PGDS-YOLOv8s model improved AP by 1.4%, AP50 by 1.7%, and AP75 by 1.2% compared with the original YOLOv8s model.

Various measures have been implemented around the world to prevent the spread of SARS-CoV-2. A potential tool to reduce disease transmission is regular mass testing of a high percentage of the population, possibly with pooling (testing a compound of several samples with one single test). We develop a compartmental model to study the applicability of this method and compare different pooling strategies: regular and Dorfman pooling. The model includes isolated compartments as well, from where individuals rejoin the active population after some time delay. We develop a method to optimize Dorfman pooling depending on disease prevalence and establish an adaptive strategy to select variable pool sizes during the course of the epidemic. It is shown that optimizing the pool size can avert a significant number of infections. The adaptive strategy is much more efficient, and may prevent an epidemic outbreak even in situations when a fixed pool size strategy can not.

Pooling layers are essential building blocks of convolutional neural networks (CNNs), to reduce computational overhead and increase the receptive fields of proceeding convolutional operations. Their goal is to produce downsampled volumes that closely resemble the input volume while, ideally, also being computationally and memory efficient. Meeting both these requirements remains a challenge. To this end, we propose an adaptive and exponentially weighted pooling method: adaPool. Our method learns a regional-specific fusion of two sets of pooling kernels that are based on the exponent of the Dice-Sørensen coefficient and the exponential maximum, respectively. AdaPool improves the preservation of detail on a range of tasks including image and video classification and object detection. A key property of adaPool is its bidirectional nature. In contrast to common pooling methods, the learned weights can also be used to upsample activation maps. We term this method adaUnPool. We evaluate adaUnPool on image and video super-resolution and frame interpolation. For benchmarking, we introduce Inter4K, a novel high-quality, high frame-rate video dataset. Our experiments demonstrate that adaPool systematically achieves better results across tasks and backbones, while introducing a minor additional computational and memory overhead. [ABSTRACT FROM AUTHOR]

We study the interaction between insurance and financial markets. Individuals who differ only in risk have access to insurance contracts offered by a monopolist and can also save through a competitive market. We show that an equilibrium always exists in that economy and identify an externality imposed on the insurer's decision by the endogeneity of prices in the financial market. We argue that, because of that externality and in contrast to the case of pure contract theory, equilibrium always exhibits under-insurance even for the riskiest agents in the economy and may even exhibit pooling. Importantly, the externality does not disrupt the single crossing property of the economy. • We study the interaction between insurance and financial markets. • Individuals who differ only in risk have access to insurance contracts offered by a monopolist and can also save through a competitive market. • An equilibrium always exists. • An externality is imposed on the insurer's decision by the endogeneity of prices in the financial market. • Equilibrium always exhibits under-insurance even for the riskiest agents in the economy and may even exhibit pooling. • The externality does not disrupt the single crossing property of the economy. [ABSTRACT FROM AUTHOR]

Background: Blood safety is a crucial health concern, and it is still a problem on a worldwide scale. Pool-testing approach is to combine multiple samples and test them as one pool. A pool-testing strategy can shorten the screening time and increase the test rate, especially during times of inadequate reporting speed and limited test availability. Aim: This research aimed to assess the effectiveness of the pool-testing strategy in Egyptian blood banks and to develop a pooling test to be used in the routine screening of blood donors. We suggest that the use of pooling for blood screening is beneficial, especially in our country, which has a high rate of hepatitis infection and recent extensive government control measures, as well as the importance of potential cost savings in Egypt. Subjects and methods: Sera from 619 volunteer blood donors approaching Suez Canal University Hospital's Blood Transfusion Department, were screened individually for Syphilis and viral infectious markers HBV, HCV, and HIV by ELISA, then pooled into pools of five and retested. Results: The results showed sensitivity, specificity, and accuracy of syphilis and HBV tests were 100% on pooled samples while HCV test showed sensitivity 71%, specificity 100%, and accuracy 99.68%. Conclusion: Our present study demonstrated the importance of a pooling protocol and that a pooling scheme of five samples is helpful for mass screening for syphilis and viral markers in subjects from large populations as blood donors without loss of accuracy. [ABSTRACT FROM AUTHOR]

The concept of sharing, enabled by emerging technologies, is playing an increasingly important role in contributing to a transformation toward more sustainable transportation. This study aimed to contribute to the growing body of literature on on-demand transportation services, with a particular emphasis on sharing or pooling a ride when using services such as transportation-network companies (TNCs) and microtransit. The study conducted a shared mobility survey of over 2,500 respondents from selected locales across Texas—ranging from large urban areas to small cities and rural areas. We analyzed the survey data in detail using extensive statistical analysis and inferential techniques and adopted an analysis approach toward implementation-oriented research to address the gap between theory and practice. Demographic, as well as geographic and built-environment, factors were found to play an important role in determining whether users will opt for a shared or pooled service and/or how they perceive these alternatives. The findings highlight the importance of improving safety and security, increasing awareness of the benefits of ride-sharing, and designing appropriate policy measures to promote sustainable mobility. We identified potential operational improvements, government policies, and employer programs to improve shared-ride services and encourage their use, such as reducing uncertainty in shared rides and minimizing inconvenience for passengers. A critical finding was the need to prioritize operational improvements in shared-ride trips over solely relying on financial incentives to induce behavior change. Enhanced public awareness and education were also determined to be crucial regardless of the nature of improvements, policies, or programs that are implemented. [ABSTRACT FROM AUTHOR]

Background: Due to supply chain shortages of reagents for real-time (RT)-PCR for SARS-CoV-2 and increasing demand on technical staff, an end-to-end data automation strategy for SARS-CoV-2 sample pooling and singleton analysis became necessary in the summer of 2020. Methods: Using entirely open source software tools—Linux, bash, R, RShiny, ShinyProxy, and Docker—we developed a modular software application stack to manage the preanalytical, analytical, and postanalytical processes for singleton and pooled testing in a 5-week time frame. Results: Pooling was operationalized for 81 days, during which time 64 pooled runs were performed for a total of 5320 sample pools and approximately 21 280 patient samples in 4:1 format. A total of 17 580 negative pooled results were released in bulk. After pooling was discontinued, the application stack was used for singleton analysis and modified to release all viral RT-PCR results from our laboratory. To date, 236 109 samples have been processed avoiding over 610 000 transcriptions. Conclusions: We present an end-to-end data automation strategy connecting 11 devices, one network attached storage, 2 Linux servers, and the laboratory information system. [ABSTRACT FROM AUTHOR]

The testing capacity of the laboratory is paramount for better control of the pandemic caused by SARS-CoV-2. The pooling method is promising to increase testing capacity, and the use of direct NAAT-based detection of SARS-CoV-2 on a non-invasive specimen such as saliva will ultimately accelerate the testing capacity. This study aims to validate the pooling-of-four method to quadruple the testing capacity using RNA-extraction-free saliva specimens. In addition, we intend to investigate the preferable stage of pooling, including pre- or post-heating. The compatibility of this approach was also tested on five commercial kits. Saliva specimens stored at −80 °C for several months were proven viable and were used for various tests in this study. Our findings revealed that pooling-of-four specimens had an overall agreement rate of 98.18% with their individual testing. Moreover, we proved that the pooling procedure could be conducted either pre- or post-heating, with no discordance and no significant difference in Ct values generated. When compared to other commercial detection kits, it demonstrated an overall agreement greater than 85%, which exhibits broad compatibility and ensures easy adaptability in clinical settings. This method has been proven reliable and increases the testing capacity up to fourfold. [ABSTRACT FROM AUTHOR]

Transformer-based NLP models have achieved state-of-the-art results in many NLP tasks including text classification and text generation. However, the layers of these models do not output any explicit representations for texts units larger than tokens (e.g. sentences), although such representations are required to perform text classification. Sentence encodings are usually obtained by applying a pooling technique during fine-tuning on a specific task. In this paper, a new sentence encoder is introduced. Relying on an autoencoder architecture, it was trained to learn sentence representations from the very beginning of its training. The model was trained on bilingual data with variational Bayesian inference. Sentence representations were evaluated in downstream and linguistic probing tasks. Although the newly introduced encoder generally performs worse than well-known Transformer-based encoders, the experiments show that it was able to learn to incorporate linguistic information in the sentence representations. [ABSTRACT FROM AUTHOR]

Multisite field experiments using the (generalized) randomized block design that assign treatments to individuals within sites are common in education and the social sciences. Under this design, there are two possible estimands of interest and they differ based on whether sites or blocks have fixed or random effects. When the average treatment effect is assumed to be identical across sites, it is common to omit site by treatment interactions and "pool" them into the error term in classical experimental design. However, prior work has not addressed the consequences of pooling when site by treatment interactions are not zero. This study assesses the impact of pooling on inference in the presence of nonzero site by treatment interactions. We derive the small sample distributions of the test statistics for treatment effects under pooling and illustrate the impacts on rejection rates when interactions are not zero. We use the results to offer recommendations to researchers conducting studies based on the multisite design. [ABSTRACT FROM AUTHOR]

Resource sharing in general is a means of solving the problem of infrequent and, thus, inefficient utilization of expensive or scarce resources. In this paper, we present an approach to run shared EV-charging infrastructures in the context of commercial real-estate facilities. Collaborating EV-charger owners thereby create a pool of chargers for shared use. In our work, we consider aspects of economic viability, desirability and technical feasibility as prerequisites of a successful solution. We formally prove that the basic economic potential of the proposed pooling with regard to overall infrastructure utilization is given. In order to operate the shared pool of charging points at a given location, the corresponding management software must fulfil specific requirements. Our prototype implementation that was realized as an extension of the open-source system Open E-Mobility demonstrates the technical feasibility of the sharing idea in a user-friendly way. Questionnaires and personal interviews conducted with owners of small and medium-sized businesses revealed that they would share charging stations if it helped overcome availability bottlenecks, thus improving customer and employee perception. [ABSTRACT FROM AUTHOR]

We are concerned with the development of effective training strategies for light-weight deep networks, which facilitates harnessing the power of deep learning on hand-held devices. A vast literature has focused on enhancing the performance of deep convolution models (CNNs) by introducing changes in the model architecture and/or developing new training procedures. Recent literature has witnessed a growing focus on pooling layers, one example for this is wavelet pooling. Pooling is often adopted by CNNs to enlarge the receptive field, yet traditional pooling methods cause information loss which undermines further operations such as feature extraction. By capitalizing on spectral information, wavelet pooling (WP) has addressed this drawback in neighborhood pooling, and we have demonstrated its positive impact on light-weight CNN architectures such as MobileNets in previous research. In this paper, we report the following significant observation: Including all sub-bands of the wavelet decomposition does not necessarily yield higher accuracies than using certain sub- set of sub-bands, i.e., less is more. By identifying which sub-bands to be included in the pooling process for every image during training and testing, we propose a new training and inference procedures, namely, Matched Wavelet Pooling (MWP), for light-weight CNN architectures, and in particular, MobileNets. As an example, the image classification application is considered. On two widely adopted datasets, the proposed MWP algorithm consistently outperform previous pooling methods and attained an average of 20% accuracy increase compared to the recent literature.

Acquisition of adequate transfer of passive immunity (ATPI) by calves depends on the absorption of sufficient mass of colostral immunoglobulin G (IgG). Several studies report conflicting evidence regarding the ability of feeding pooled colostrum to achieve ATPI. Pooling colostrum is practical and efficient for some dairies, and recommendations are required to prevent failure of transfer of passive immunity (FTPI) in calves following pooling. This study aimed to determine the effect of pooling colostrum on serum IgG concentrations, FTPI, and preweaning mortality in calves. A prospective study was performed on two conventional Jersey dairy farms where heat treatment of colostrum occurred in the same colostrum processing kitchen. Four to 10 cows contributed to colostrum pools. A sample of the colostrum pool fed to the calves and serum from calves at 24–72 h was collected for IgG concentration determination by single radial immunodiffusion assay. Multivariable and logistic regression analyses were performed to evaluate factors that predicated serum IgG concentrations and the probability of FTPI, respectively. A Cox proportional hazard model analysis was performed to determine risk factors for mortality over the preweaning period. A total of 164 calves fed 28 colostrum pools were enrolled. Birth weight, number of colostrum feedings and pool IgG concentrations were significant predictors of calf serum IgG concentrations at 24–72 h, whereas the number of colostrum feedings and age at bleeding to determine passive transfer status were not significant predictors of calf serum IgG concentrations at 24–72 h. The prevalence of FTPI was 4.9%. Birth weight, pool IgG concentrations, number of colostrum feedings, and age at bleeding to check for passive transfer status were not significant predictors of the probability of FTPI at 24–72 h. The incidence of mortality was 4.3%. Passive transfer status was not a predictor of mortality. Our study demonstrates the effect of pooling colostrum on serum IgG concentrations even in herds where colostrum with higher median colostrum IgG concentrations is fed to calves. The results emphasize the recommendations to assess pooled colostrum IgG concentrations before feeding calves.

Shared ride services allow riders to share a ride to a common destination. They include ridesharing (carpooling and vanpooling); ridesplitting (a pooled version of ridesourcing/transportation network companies); taxi sharing; and microtransit. In recent years, growth of Internet-enabled wireless technologies, global satellite systems, and cloud computing - coupled with data sharing – are causing people to increase their use of mobile applications to share a ride. Some shared ride services, such as carpooling and vanpooling, can provide transportation, infrastructure, environmental, and social benefits. This paper reviews common shared ride service models, definitions, and summarises existing North American impact studies. Additionally, we explore the convergence of shared mobility; electrification; and automation, including the potential impacts of shared automated vehicle (SAV) systems. While SAV impacts remain uncertain, many practitioners and academic research predict higher efficiency, affordability, and lower greenhouse gas emissions. The impacts of SAVs will likely depend on the number of personally owned automated vehicles; types of sharing (concurrent or sequential); and the future modal split among public transit, shared fleets, and pooled rides. We conclude the paper with recommendations for local governments and public agencies to help in managing the transition to highly automated vehicles and encouraging higher occupancy modes.

Shared mobility with pooled rides is the linchpin for leveraging vehicle automation and electrification to reduce congestion and emissions and to create livable urban communities. The sharing of rides is older than horse-and-buggy travel. Recent innovations make sharing easier, more convenient, and more efficient. Innovative mobility services premised on pooling can lower travel costs, mitigate congestion, and reduce greenhouse gas emissions. They also offer travelers more mobility choices between the traditional bookends of auto ownership and public transit. While the realm of shared mobility is vast, including shared bikes, scooters, and cars, the focus of this chapter is on pooled services—placing more people in a single vehicle. Doing so unlocks huge economic, social, and environmental benefits. The motivation for pooling is simple. First and foremost is economics. Cars are among the most underused capital assets in our economy, sitting empty 95 percent of the time and carrying one individual much of the remaining time. If a car were used more than 5 percent of the time, and if that car carried two, three, or four passengers, the cost per rider would drop dramatically. The benefits go well beyond cheaper mobility. Because the car would be carrying multiple riders who might otherwise be driving, there would also be fewer vehicles on the road, less parking space required, less air pollution, and reduced energy use and greenhouse gas emissions. Given that the world has more than 1 billion cars and light trucks, the potential for major reductions in pollution and greenhouse gases is huge—in the United States and also most other countries. The transition to a future where many rides are shared is now possible. What remains to be seen is whether and under what conditions people will be willing to make the transition.

Convolutional Neural Network is one of the famous members of the deep learning family of neural network architectures, which is used for many purposes, including image classification. In spite of the wide adoption, such networks are known to be highly tuned to the training data (samples representing a particular problem), and they are poorly reusable to address new problems. One way to change this would be, in addition to trainable weights, to apply trainable parameters of the mathematical functions, which simulate various neural computations within such networks. In this way, we may distinguish between the narrowly focused task-specific parameters (weights) and more generic capability-specific parameters. In this paper, we suggest a couple of flexible mathematical functions (Generalized Lehmer Mean and Generalized Power Mean) with trainable parameters to replace some fixed operations (such as ordinary arithmetic mean or simple weighted aggregation), which are traditionally used within various components of a convolutional neural network architecture. We named the overall architecture with such an update as a hyper-flexible convolutional neural network. We provide mathematical justification of various components of such architecture and experimentally show that it performs better than the traditional one, including better robustness regarding the adversarial perturbations of testing data. [ABSTRACT FROM AUTHOR]

One of the essential layers in most Convolutional Neural Networks (CNNs) is the pooling layer, which is placed right after the convolution layer, effectively downsampling the input and reducing the computational power required. Different pooling methods have been proposed over the years, each with its own advantages and disadvantages, rendering them a better fit for different applications. We introduce a benchmark between many of these methods that highlights an optimal choice for different scenarios depending on each project's individual needs, whether it is detail retention, performance, or overall computational speed requirements. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has led to the rapid development and launch of several commercial RT-PCR-based assays for identification of SARSCoV-2. However, there is need for peer-reviewed evaluation of these assays that can support their clinical performance. In this study, we, therefore, conduct an in-house evaluation of the automated Cobas 68000 RT-PCR assay in detecting SARS-CoV-2 infections using different pooling techniques. Methods: An observational study is conducted to evaluate the clinical performance of the Cobas 6800 SARS-CoV-2 assay in comparison with the Labgun Exofast RT-PCR kit, using both pooled and non-pooled sample techniques. A total of 300 nasopharyngeal swab samples, 40 known positive samples and 260 negative samples, are used for pooling, while the performance is evaluated in three different sample pool sizes of 4, 5, and 6. Results: The sensitivity and specificity of the Cobas 6,800 was 100% when compared to the comparable assay. The sample pooling technique showed that specificity was 100% in all pool sizes and the sensitivity varied from 95% in the 6-pooled sample to 100% in both the 5- and 4-pooled samples. The lower limit of detection was verified as 25 copies/ml for un-pooled samples, and, therefore, the limit of detection was 100, 125, and 150 copies/ml for the 4, 5, and 6 sample pools, respectively. Strong correlation was observed between the Ct values of the target genes of both assays. Conclusion: Cobas 6800 RT-PCR assay is a reliable platform for qualitative and rapid detection of SARS-CoV-2 and can be effectively utilized for pooling of samples with highly efficient performance when disease prevalence is lower. [ABSTRACT FROM AUTHOR]

Background: Randomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess treatment effects of medical interventions. We aimed to hypothetically pool bodies of evidence (BoE) from RCTs with matched BoE from cohort studies included in the same systematic review.Methods: BoE derived from systematic reviews of RCTs and cohort studies published in the 13 medical journals with the highest impact factor were considered. We re-analyzed effect estimates of the included systematic reviews by pooling BoE from RCTs with BoE from cohort studies using random and common effects models. We evaluated statistical heterogeneity, 95% prediction intervals, weight of BoE from RCTs to the pooled estimate, and whether integration of BoE from cohort studies modified the conclusion from BoE of RCTs.Results: Overall, 118 BoE-pairs based on 653 RCTs and 804 cohort studies were pooled. By pooling BoE from RCTs and cohort studies with a random effects model, for 61 (51.7%) out of 118 BoE-pairs, the 95% confidence interval (CI) excludes no effect. By pooling BoE from RCTs and cohort studies, the median I2 was 48%, and the median contributed percentage weight of RCTs to the pooled estimates was 40%. The direction of effect between BoE from RCTs and pooled effect estimates was mainly concordant (79.7%). The integration of BoE from cohort studies modified the conclusion (by examining the 95% CI) from BoE of RCTs in 32 (27%) of the 118 BoE-pairs, but the direction of effect was mainly concordant (88%).Conclusions: Our findings provide insights for the potential impact of pooling both BoE in systematic reviews. In medical research, it is often important to rely on both evidence of RCTs and cohort studies to get a whole picture of an investigated intervention-disease association. A decision for or against pooling different study designs should also always take into account, for example, PI/ECO similarity, risk of bias, coherence of effect estimates, and also the trustworthiness of the evidence. Overall, there is a need for more research on the influence of those issues on potential pooling. [ABSTRACT FROM AUTHOR]

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Background: Instruments to critically appraise randomised controlled trials (RCTs) are based on evidence from meta-epidemiological studies. We aim to conduct a meta-epidemiological study on the average bias associated with reported methodological trial characteristics such as random sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting, and compliance of RCTs in nutrition research.Methods: We searched the Cochrane Database of Systematic Reviews, for systematic reviews of RCTs, published between 01 January 2010 and 31 December 2019. We combined the estimates of the average bias (e.g. ratio of risk ratios [RRR] or differences in standardised mean differences) in meta-analyses using the random-effects model. Subgroup analyses were conducted to investigate the potential differences among the RCTs with low versus high/unclear risk of bias with respect to the different types of interventions (e.g. micronutrients, fatty acids, dietary approach), outcomes (e.g. mortality, pregnancy outcomes), and type of outcome (objective, subjective). Heterogeneity was assessed through I2 and τ2, and prediction intervals were calculated.Results: We included 27 Cochrane nutrition reviews with 77 meta-analyses (n = 927 RCTs). The available evidence suggests that intervention effect estimates may not be exaggerated in RCTs with high/unclear risk of bias (versus low) judgement for sequence generation (RRR 0.97, 95% CI 0.93 to 1.02; I2 = 28%; τ2 = 0.002), allocation concealment (RRR 1.00, 95% CI 0.96 to 1.04; I2 = 27%; τ2 = 0.001), blinding of participants and personnel (RRR 0.95, 95% CI 0.91 to 1.00; I2 = 23%; τ2 = 0), selective reporting (RRR 0.97, 95% CI 0.92 to 1.02; I2 = 24%; τ2 = 0), and compliance (RRR 0.95, 95% CI 0.89 to 1.02; I2 = 0%; τ2 = 0). Intervention effect estimates seemed to be exaggerated in RCTs with a high/unclear risk of bias judgement for blinding of outcome assessment (RRR 0.81, 95% CI 0.70 to 0.94; I2 = 26%; τ2 = 0.03), which was predominately driven by subjective outcomes, and incomplete outcome data (RRR 0.92, 95% CI 0.88 to 0.97; I2 = 22%; τ2 = 0.001). For continuous outcomes, no differences were observed, except for selective reporting.Conclusions: On average, most characteristics of nutrition RCTs may not exaggerate intervention effect estimates, but the average bias appears to be greatest in trials of subjective outcomes. Replication of this study is suggested in this field to keep this conclusion updated. [ABSTRACT FROM AUTHOR]

Background: Despite continuing technological advances, the cost for large-scale genotyping of a high number of samples can be prohibitive. The purpose of this study is to design a cost-saving strategy for SNP genotyping. We suggest making use of pooling, a group testing technique, to drop the amount of SNP arrays needed. We believe that this will be of the greatest importance for non-model organisms with more limited resources in terms of cost-efficient large-scale chips and high-quality reference genomes, such as application in wildlife monitoring, plant and animal breeding, but it is in essence species-agnostic. The proposed approach consists in grouping and mixing individual DNA samples into pools before testing these pools on bead-chips, such that the number of pools is less than the number of individual samples. We present a statistical estimation algorithm, based on the pooling outcomes, for inferring marker-wise the most likely genotype of every sample in each pool. Finally, we input these estimated genotypes into existing imputation algorithms. We compare the imputation performance from pooled data with the Beagle algorithm, and a local likelihood-aware phasing algorithm closely modeled on MaCH that we implemented. Results: We conduct simulations based on human data from the 1000 Genomes Project, to aid comparison with other imputation studies. Based on the simulated data, we find that pooling impacts the genotype frequencies of the directly identifiable markers, without imputation. We also demonstrate how a combinatorial estimation of the genotype probabilities from the pooling design can improve the prediction performance of imputation models. Our algorithm achieves 93% concordance in predicting unassayed markers from pooled data, thus it outperforms the Beagle imputation model which reaches 80% concordance. We observe that the pooling design gives higher concordance for the rare variants than traditional low-density to high-density imputation commonly used for cost-effective genotyping of large cohorts. Conclusions: We present promising results for combining a pooling scheme for SNP genotyping with computational genotype imputation on human data. These results could find potential applications in any context where the genotyping costs form a limiting factor on the study size, such as in marker-assisted selection in plant breeding. [ABSTRACT FROM AUTHOR]

Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi‐species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local‐scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously. We show how this challenge can be overcome by incorporating preexisting range information into MSOMs, yielding a "biogeographic multi‐species occupancy model" (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey and entire avian communities in forests and pastures of Colombia's West Andes. Compared with traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species‐specific inference even for never‐observed species. Incorporating preexisting range data enables principled partial pooling of information across species in large‐scale MSOMs. Our biogeographic framework for multi‐species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false absences are modeled in an occupancy framework. [ABSTRACT FROM AUTHOR]

This research investigates an integrated problem of transshipment for cross-filling and imputation for missing demand data. Transshipment for cross-filling has proved effective in mitigating shortages with relatively low inventory, thus reducing resource consumption in inventory management. Although accurate demand data are critical for cross-filling decision making, some demand data are inevitably incomplete. These missing data should be treated for effective transshipment operations. Despite the importance, these missing data issues have not been adequately studied for transshipment problems. This paper addresses how transshipment can be conducted under missing demand conditions. A novel integrated problem is established to combine demand-data imputation processes and transshipment decisions. Imputation strategies and new algorithms suitable for transshipment are developed to handle missing demand data. Diverse demand and transshipment cases are analyzed for cost-effectiveness. The analysis uncovers that conventional straightforward imputation methods result in inferior transshipment decisions. The study also reveals that imputed values should be adjusted to appropriate levels for transshipment to be effective. The strong interplay between imputation processes and shortage prevention is also discovered for transshipment with missing demand. This study demonstrates how inventory transshipment can be carried out successfully with appropriate treatment of missing demand data in practice. [ABSTRACT FROM AUTHOR]

Appointment schedules, in essence, balance supply and demand and are often employed in settings where resources are scarce and thus a high utilization is realized (e.g., healthcare). Whereas most of the existing literature focuses on the single-server case, a framework is developed to study appointment scheduling in multiserver settings. Relying on phase-type approximations, general service-time distributions are modeled, which are fed into a recursive approach allowing evaluation and optimization of an objective function that balances expected waiting times and idle times. Studying optimized schedules for multiple servers reveals that the start and end of a session can deviate greatly from the dome-shaped pattern as established for the single-server case. Furthermore, a comparison of various multiserver setups shows that significant performance gains can be achieved when servers are pooled. This allows an explicit quantification of the cost of continuity of care. In addition, session overtime as well as early finish of servers can be incorporated in the approach; the benefits of the additional flexibility that a multiserver setting provides are summarized. For the stationary plateau of the dome, to which the optimal interarrival times converge, steady-state appointment schedules are obtained by exploiting the embedded Markov chain; these schedules are shown and argued to converge quickly to optimal solutions obtained in a heavy-traffic regime. In this regime, algebraic solutions are derived, which provide interesting managerial guidelines when the pooling of servers is considered in appointment scheduling. This paper was accepted by Bariş Ata, stochastic models and simulation. [ABSTRACT FROM AUTHOR]

Background: We tested the utility of mini-pool PCR testing for the rational use of PCR consumables in screening for CoViD-19. Methods: After pilot experiments, 3-samples pool size was selected. Onestep RT-PCR was performed. The samples in the mini-pool having COVID gene amplification were tested individually. Results: 1548 samples tested in 516 mini-pools resulted 396 mini-pools as negative and 120 as positive. Upon individual testing, 110 samples tested positive and 9 were inconclusive. 876 PCR reactions were performed to test 1548 samples, saving 43% PCR reagents. Centres with low prevalence resulted in most saving on reagents (50%), while centres with high prevalence resulted in more test reactions. Testing of individual samples resulted in delays in reporting. Conclusion: Pooling can increase lab capacity, however, pooling delays results and cause degradation of samples. [ABSTRACT FROM AUTHOR]

Horizontal collaboration is one of the most effective strategies applied to address issues related to sustainability and the adverse effects of commodity distribution. Although it has received increased attention in recent years, no synthesis has been conducted to present the main practices and sustainability indicators used to assess the performance of horizontal collaboration. In addition, the factors that can further improve the performance of horizontal collaboration in terms of sustainability have not been presented in any literature review. Thus, we expose, in this paper, a review of the existing studies dealing with horizontal collaboration, and we define the key indicators used to measure its performance. The most commonly used practices of horizontal collaboration are also illustrated. In fact, the main objectives of the present study are to highlight the impact of certain factors on the success of collaboration and to identify several guidelines for researchers and companies wishing to implement horizontal collaboration. [ABSTRACT FROM AUTHOR]

We consider the problem of representation learning for graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks such as segmentation. While encoder-decoder architectures like U-Nets have been successfully applied to image pixel-wise prediction tasks, similar methods are lacking for graph data. This is because pooling and up-sampling operations are not natural on graph data. To address these challenges, we propose novel graph pooling and unpooling operations. The gPool layer adaptively selects some nodes to form a smaller graph based on their scalar projection values. We further propose the gUnpool layer as the inverse operation of the gPool layer. Based on our proposed methods, we develop an encoder-decoder model, known as the graph U-Nets. Experimental results on node classification and graph classification tasks demonstrate that our methods achieve consistently better performance than previous models. Along this direction, we extend our methods by integrating attention mechanisms. Based on attention operators, we proposed attention-based pooling and unpooling layers, which can better capture graph topology information. The empirical results on graph classification tasks demonstrate the promising capability of our methods. [ABSTRACT FROM AUTHOR]

Universal Chiplet Interconnect Express (UCIe) is an open industry standard interconnect for developing an open chiplet ecosystem, where chiplets from any supplier can be packaged anywhere in an interoperable manner. This article delves into the architectural, circuit, channel, and packaging aspects that we developed that has been adopted in the UCIe 1.0 Specification. We present our results based on our channel and circuit implementation studies. [ABSTRACT FROM AUTHOR]