An Improved GM(1,1) Model of Marketization of Forest Ecological Compensation based on Health-Cultivation Industry

In the context of supply-side structual reform, the marketization of forest ecological compensation is an inevitable requirement under the market economy. The forest health-cultivation industry, which explores the new method to utilize the forestry resources, is a new direction of forestry firms and is the production of supply-side structural reform. From the perspective of forest health-cultivation industry, this essay explores the possibility of the marketization of forest ecological compensation based on forest health-cultivation industry, and predicts the willingness of people to pay for the ecological service by using the original number of tourists in forestry tourism and leisure industry tourism between 2010 and 2015 and adopting the improved GM (1,1) model. The result shows that people’s willingness is increasing. It is predicted that, by 2023, the number of tourists in forestry tourism and leisure industry tourism will reach 8.98 billion. The development of forest health-cultivation industry has introduced the market mechanism for forestry development, which makes up for the deficiency of the existing compensation mechanism characterized by low compensation standards and single channel of funding. The increase of people’s demand for ecological tourism and the boom of forest health-cultivation industry which provide a new opportunity for the marketization and diversification of forest ecological compensation mechanism.

Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNet

EfficientNet is a recent Deep Convolutional Neural Network (DCNN) architecture intended to be proportionally extendible in depth, width and resolution. Through its variants, it can achieve state of the art accuracy on the ImageNet classification task as well as on other classical challenges. Although its name refers to its efficiency with respect to the ratio between outcome (accuracy) and needed resources (number of parameters, flops), we are studying a method to reduce the original number of trainable parameters by more than 84% while keeping a very similar degree of accuracy. Our proposal is to improve the pointwise (1x1) convolutions, whose number of parameters rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Basically, our tweak consists in grouping filters into parallel branches, where each branch processes a fraction of the input channels. However, by doing so, the learning capability of the DCNN is degraded. To avoid this effect, we suggest interleaving the output of filters from different branches at intermediate layers of consecutive pointwise convolutions. Our experiments with the CIFAR-10 dataset show that our optimized EfficientNet has similar learning capacity to the original layout when training from scratch.

Therapeutic duplication on the general surgical wards

Therapeutic duplication is the practice of prescribing multiple medications for the same indication or purpose without a clear distinction of when one agent should be administered over another. This is a problem that occurs frequently, especially on electronic prescribing records (EPR) as the medication chart is not always reviewed before prescribing. The aim of this Quality Improvement Project (QIP) was to reduce therapeutic duplication to 0% through educating the general surgical team. Prescriptions of all general surgical patients in the surgical wards were reviewed daily for a month. EPR was used to check if there were any duplications or identical class of drug prescribed. Patient documentation was thoroughly checked to rule out if the duplication was intentional. Following this, if duplication was still unclear, the relevant teams would be contacted for clarification. Any unintentional error was removed, and data was collected. The QIP results were presented to the local general surgical meeting and our fellow colleagues were educated on the importance of safe prescribing and on how to prevent prescribing errors. The baseline of therapeutic duplications on the general surgical wards was 9% prior to our first cycle. Following the presentation of data and educating the surgical team at the surgical meeting, the number of errors seemingly reduced, however, there was a jump to 22% of therapeutic duplication on a particular Friday which brought the average of therapeutic duplication to 8.77%. The team was reminded again about the importance of correct prescribing and after the second cycle, the number of errors reduced to 5.29%. For the third audit cycle, the team was presented with the reaudited data and following this, the number of errors dropped down to 3.12%. Therapeutic duplication should never occur as this could cause a risk to patient harm. Through educating the surgical team and reminding our team regularly, the average number of errors reduced by more than half of the original number. In our hospital, the main source of safety net is through pharmacists and nurses, however as shown, this is not enough to prevent all therapeutic errors. A more sustainable intervention such as an alert on EPR prior to prescribing may be required to maintain a low therapeutic duplication average and prevent patient harm.

Optimal Operation Solution for Public Bicycles Based on Genetic Algorithm

Aiming at the various problems with the scheduling of urban public bicycles, this paper conducted a research on the shortest path between rental points and employed the Floyd algorithm to find the optimal route. Based on the conditions of limited number of bicycle transportation vehicles and in different time slots the bicycle rental points were required to restore to the original number of bicycles, a constraint scheduling model was established according to the bicycle supply-demand relationships of the rental points, and the Genetic Algorithm (GA) was used to solve the model to find the shortest path. In terms of balancing the bicycles at each rental point, this paper re-distributed the initial bicycles according to the different demands of each rental point in different time slots, and solved the problem using the solution of the first problem to obtain the optimal vehicle route. This research is a useful reference for solving difficulties in public bicycle scheduling.

Entanglement-Based Feature Extraction by Tensor Network Machine Learning

It is a hot topic how entanglement, a quantity from quantum information theory, can assist machine learning. In this work, we implement numerical experiments to classify patterns/images by representing the classifiers as matrix product states (MPS). We show how entanglement can interpret machine learning by characterizing the importance of data and propose a feature extraction algorithm. We show on the MNIST dataset that when reducing the number of the retained pixels to 1/10 of the original number, the decrease of the ten-class testing accuracy is only O (10–3), which significantly improves the efficiency of the MPS machine learning. Our work improves machine learning’s interpretability and efficiency under the MPS representation by using the properties of MPS representing entanglement.

Sea Surface Object Detection Algorithm Based on YOLO v4 Fused with Reverse Depthwise Separable Convolution (RDSC) for USV

Unmanned surface vehicles (USVs) have been extensively used in various dangerous maritime tasks. Vision-based sea surface object detection algorithms can improve the environment perception abilities of USVs. In recent years, the object detection algorithms based on neural networks have greatly enhanced the accuracy and speed of object detection. However, the balance between speed and accuracy is a difficulty in the application of object detection algorithms for USVs. Most of the existing object detection algorithms have limited performance when they are applied in the object detection technology for USVs. Therefore, a sea surface object detection algorithm based on You Only Look Once v4 (YOLO v4) was proposed. Reverse Depthwise Separable Convolution (RDSC) was developed and applied to the backbone network and feature fusion network of YOLO v4. The number of weights of the improved YOLO v4 is reduced by more than 40% compared with the original number. A large number of ablation experiments were conducted on the improved YOLO v4 in the sea ship dataset SeaShips and a buoy dataset SeaBuoys. The experimental results showed that the detection speed of the improved YOLO v4 increased by more than 20%, and mAP increased by 1.78% and 0.95%, respectively, in the two datasets. The improved YOLO v4 effectively improved the speed and accuracy in the sea surface object detection task. The improved YOLO v4 algorithm fused with RDSC has a smaller network size and better real-time performance. It can be easily applied in the hardware platforms with weak computing power and has shown great application potential in the sea surface object detection.

The Agony of Choice: Acceptance, Efficiency, and Psychometric Properties of Questionnaires With Different Numbers of Response Options

Questionnaires are one of the most important tools in psychological assessment, yet the impact of different numbers of response options on psychometric properties of questionnaires is limited. This study extends existing research by analyzing respondents’ acceptance of and the efficiency of different numbers of response options and replicate findings on reliability and validity. We studied these questions in 540 respondents who filled out the Big Five Inventory–2 and the Emotion Regulation Questionnaire. Two response options, 11 response options and the visual analog scale showed disadvantages in acceptance compared with the original number of response options. The completion time increased by 1.7s per item when moving from 2 to 11 response options. Cronbach’s alpha (but not ordinal alpha based on polychoric covariance) was lowest for two response options. Validity was unaffected. Overall, compared with the typical choice of five or seven response options, fewer or more response options resulted in disadvantages.

Online Platform as an Alternative for International Multidisciplinary Medical Conferences During the Novel Coronavirus Pandemic (Preprint)

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has stunted medical education activities with most conferences being cancelled or postponed. To continue professional education during this crisis, an alternative is conducting online conferences with live streaming and an audience interaction platform. OBJECTIVE The unprecedented COVID-19 pandemic has affected human connection globally. Conventional conferences have been replaced by online conferences. However, online conferencing has its challenges and limitations. This manuscript reports the logistics and preparations required for converting an international onsite multidisciplinary conference into a completely online conference within 3 weeks during this pandemic. METHODS The program was revised, and teleconference system, live recording, director system setup, and broadcast platform were arranged to enable the online conference. RESULTS We used YouTube and WeChat for the online conference. Of the 24 hours of the conventional conference, 21.5 hours were retained in the online conference (89.6%) with live broadcasting. The conference was attended by 71.7% of the original international faculty and 71.6% of the overall faculty. In total, 61 presentations (92% of the original number) were delivered. A special session “Dialysis access management under the impact of viral epidemics” was added to replace precongress workshops and competitions. The conference received 1810, 1452, and 1008 visits on YouTube and 6777, 4623, and 3100 visits on WeChat on days 1, 2, and 3, respectively. CONCLUSIONS Switching a conventional onsite conference to a completely online format within a short period is a feasible way to maintain professional education in a socially responsible manner during a pandemic.

Recent hybrid speciation at the origin of the narrow endemic Pulmonaria helvetica

Background and Aims Hybridization is known to drive plant speciation through the establishment of homoploid or allopolyploid hybrid species. Here we investigate the origin of Pulmonaria helvetica, a narrow endemic species described across a restricted area of Switzerland that was entirely covered by ice during the last glacial maximum. This species presents an original number of chromosomes (2n = 24) and morphological traits suggestive of a hybrid origin. Methods We sequenced a plastid locus and 1077 double-digest restriction-site-associated DNA (ddRAD) loci in 67 individuals from across the distribution range of P. helvetica and candidate progenitor species growing in the same area. Assignment of genotypes to main genetic clusters within and among taxa using STRUCTURE tested whether P. helvetica represents a genetically differentiated lineage and addressed the hypothesis of its hybrid origin. Comparative ecological modelling further addressed possible niche differentiation among taxa. Key Results Pulmonaria helvetica was highlighted as a genetically homogeneous species distinct from co-occurring taxa. Consistent with a scenario of hybrid speciation, it presented clear evidence of balanced admixture between Pulmonaria officinalis (2n = 16) and Pulmonaria mollis s.l. (2n = 18, 22), which was also highlighted as a maternal progenitor based on plastid sequences. Limited genetic structure within the maternal progenitor is consistent with an origin of P. helvetica through either homoploid hybridization with considerable karyotype changes or via complex scenarios of allopolyploidy involving a dysploid taxon of P. mollis s.l. Comparative niche modelling indicated non-significant ecological differences between P. helvetica and its progenitors, supporting intrinsic factors resulting from hybridization as main drivers of speciation. Conclusions Hybridization appears as a major process having promoted the postglacial origin of the narrow endemic P. helvetica, suggesting hybrid speciation as an effective process that rapidly produces new species under climate changes.

Fitting Precipitation Particle Size–Velocity Data to Mixed Joint Probability Density Function with an Expectation Maximization Algorithm

This paper proposes an estimation method of joint size and terminal velocity distribution on the basis of sampling data of precipitation particles containing multiple types. Assuming that the velocity follows the normal distribution and the size follows the gamma distribution, the method searches a locally maximum logarithmic likelihood within a realistic parameter range using the expectation–maximization algorithm. Several test populations were prepared with a realistic number of elements, and then the method was evaluated by retrieving the populations from their sample. The results showed that the original parameters were successfully estimated in most cases of the test population containing some of liquids, graupels, and rimed and unrimed aggregates. The original number of elements was also estimated with an adjustment of the number of elements in a manner such that each of their minority fractions exceeded a threshold. Applied to the two-dimensional disdrometer observation data, the method was helpful to discard frequently observed erroneous data with unrealistically large fall velocity.