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Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supporting information sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint–peer-reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer-reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape., Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review This study analyzes the full text content of the bioRxiv preprint repository, identifying field-specific patterns and changes that occur during publication, and providing a search tool that can identify the published papers that are most similar to a given bioRxiv or medRxiv preprint.

To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China’s commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available., Author summary Combination therapies are a promising approach to counter complex diseases such as cancers. Two key difficulties in the design of effective cancer combination therapies are the large number of available drugs and the heterogeneity of cancers which render exhaustive laboratory studies impractical. In recent years, sophisticated signaling pathway models that can predict responses to combination treatments at a molecular level have been developed. This motivates the question of how one can leverage mechanistic models to identify candidates for novel combination treatments. In this paper we propose a combination treatment optimization framework which employs a large-scale pan-cancer pathway model. We formulate treatment optimization problems for single cell lines and heterogeneous populations of cancer cells. We further investigate sequential treatment plans and combine an existing evolutionary algorithm with an efficient Hamiltonian Monte-Carlo based sampling scheme. During extensive simulation studies our approach identified combination therapies which are predicted to be more effective than conventional treatments. We hope that one day in silico experiments will be used to identify a small set of promising treatment candidates which can then form a starting point for laboratory studies, allowing for an efficient use of limited resources and accelerated discovery of effective therapies.

The strain state in 3D space is usually expressed by the conventional method of combining three linear and shear strains. Due to the obvious differences between the first two strains, it is necessary to uncover their properties when describing deformation, studying yield and failure, and developing test apparatus or equipment. The difficulties encountered in the above work would be greatly simplified if strain states could be expressed in a single strain form, namely including only linear or shear strains. As a start, this paper explores the meaning and nature of strain states. Then, based on the hypothesis of small deformations, two strain state expressions, the linear strain expression method (LSEM) and shear strain expression method (SSEM), were established for incompressible materials with only linear strain and shear strain as parameters respectively. Furthermore, conditions, implementation steps and specific forms for the application of SSEM in 1D, 2D and 3D strain states are obtained. As an example, two representations based on tetragonal pyramid and rotating tetrahedron are especially given. Therefore, conventional strain representation methods can be expressed as a combination of line strains in a certain direction or a combination of characteristic shear strains. The results of this paper provide a new way for understanding deformation characteristics, revealing yielding process, establishing constitutive models, and developing testing apparatus or equipment.

PurposeThe aim of the paper is to identify management practices that are characteristic for SMEs that achieve market success measured by their business performance in last their years of their operation analyze the relationships between management practices applied in small and medium-sized enterprises and their success measured by their business performance drawing on the data from 2710 SMEs operating on the Polish market.Approach/Methodology/DesignA cluster analysis was used to distinguish homogenous SME groups in view of their management practices. We examined differences between groups in terms of their business performance. The HINoV algorithm allowed six variables to be selected out of 32 management practices chosen initially for testing, with these variables providing the basis for grouping. Modal values and medians were calculated for 17 business performance measures in the three clusters produced. The subsequent analysis of those findings was focused on capturing significant differences.FindingsIn the group of 2710 Polish SMEs, it was possible to verify that there existed an association between management practices in the field of modern HRM, computer systems supporting management and the company’s economic performance, as measured by an increase in net revenue and number of customers over the last three years. In clusters where the above mentioned practices were appreciated, modal and median values of the increase reported in net revenue and number of customers were significantly higher.Practical implicationsThe research has shown that at a time marked by a shortage of highly skilled personnel one should pay particular attention to building an integrated and committed team of workers and to employee empowerment. The research has also shown that SME managers monitor only a fraction of basic business performance measures, which may prove to be a major risk to SMEs.Originality/ValuePrevious studies have been largely conducted in a fragmentary manner, i.e. they were concerned with the relationships between the application of some practices (strategic management, BPR, entrepreneurial orientation, monitoring, etc.) and selected business effectiveness measures. In this paper, the research covered SME management practices from a variety of areas which were then compared with the entrepreneurs’ assessment as to whether the company’s economic condition changed over the last three years. It is also the first attempt in post-socialist economies to identify those SME management practices that are related to better economic results.

The analysis of brain-imaging data requires complex and often non-linear transformations to support findings on brain function or pathologies. And yet, recent work has shown that variability in the choices that one makes when analyzing data can lead to quantitatively and qualitatively different results, endangering the trust in conclusions1–3. Even within a given method or analytical technique, numerical instabilities could compromise findings4–7. We instrumented a structural-connectome estimation pipeline with Monte Carlo Arithmetic8, 9, a technique to introduce random noise in floating-point computations, and evaluated the stability of the derived connectomes, their features10, 11, and the impact on a downstream analysis12, 13. The stability of results was found to be highly dependent upon which features of the connectomes were evaluated, and ranged from perfectly stable (i.e. no observed variability across executions) to highly unstable (i.e. the results contained no trustworthy significant information). While the extreme range and variability in results presented here could severely hamper our understanding of brain organization in brain-imaging studies, it also leads to an increase in the reliability of datasets. This paper highlights the potential of leveraging the induced variance in estimates of brain connectivity to reduce the bias in networks alongside increasing the robustness of their applications in the detection or classification of individual differences. This paper demonstrates that stability evaluations are necessary for understanding error and bias inherent to scientific computing, and that they should be a component of typical analytical workflows.

As the largest carbon dioxide emitter, China is working towards the direction of a green economy. As an irreplaceable part of establishing a green economy, the low-carbon city pilot (LCCP) policy is implemented in many large cities in China, and the scope of implementation will be further expanded. However, to date, there has been an absence of empirical studies basing on prefecture-level cities about the evaluation of China’s LCCP policy. Evaluating and optimizing the LCCP policy is constructive to achieve the goal of China’s green economic transition. In this paper, we evaluated the effect of the LCCP policy on China’s low-carbon economic transition by using the difference-in-difference (DID) approach which can effectively alleviate endogenous problems and better evaluate this effect and the panel data of 210 prefecture-level cities in China from 2008 to 2016. The empirical analysis revealed that the LCCP policy inhibited China’s low-carbon economic transition in general. Specifically, the policy worked well in the eastern region but failed in the central region and western region by studying the regional heterogeneity and influence mechanism. The reason is that the LCCP policy can stimulate low-carbon innovation with the help of innovation offset effects in the eastern region, but it failed to do so in the central region and western region. In addition, this paper analyzed the performance of three types of policy tools adopted by local governments to implement the policy, we found that market-economic tools are valuable to improving the low-carbon economic transition in pilot areas, but command-mandatory tools and voluntary tools have failed to achieve the expected objectives. The research results of this article can provide policy recommendations for optimizing the low-carbon policy and provide a reference for countries that are determined to develop a green economy.

In their Commentary paper, Villaverde and Massonis (On testing structural identifiability by a simple scaling method: relying on scaling symmetries can be misleading) have commented on our paper in which we proposed a simple scaling method to test structural identifiability. Our scaling invariance method (SIM) tests for scaling symmetries only, and Villaverde and Massonis correctly show the SIM may fail to detect identifiability problems when a model has other types of symmetries. We agree with the limitations raised by these authors but, also, we emphasize that the method is still valuable for its applicability to a wide variety of models, its simplicity, and even as a tool to introduce the problem of identifiability to investigators with little training in mathematics.

This paper explores the potential of combining different isotope systems from different tissues to improve resolution when reconstructing breastfeeding and weaning practices (BWP) in archaeology. Additionally, we tested whether changes in diet can be detected in deciduous teeth. Rib collagen samples from 22 infants/children from the archaeological site of Bacuranao I (Mayabeque, Cuba) were processed for nitrogen (δ15N) and carbon (δ13Cco) stable isotopes and assessed using a Bayesian model (WARN). In addition, enamel of 48 teeth from 30 infants/children were analyzed for oxygen (δ18Oen) and carbon (δ13Cen) stable isotopes. Data revealed that the timing of weaning cannot be characterized precisely by analyzing either δ18O or δ15N. While a depletion in both δ15N and δ13Cco is only evident after one year, the WARN model suggested that the weaning process started at around 3 months and ended around 1.7 years. Most teeth were enriched in δ18Oen compared to deciduous incisors, suggesting a breastfeeding signal. However, a high variability in δ18O was found between similar teeth from the same individuals. Higher enrichment in δ18Oen, and variability, was observed in tissues formed during the first six months of life. A δ13C enrichment of 1.0‰ was observed among deciduous teeth and ribs. While most individuals enriched in δ15N showed enrichment in δ13C, the δ18O values were more variable. Our data suggests that stable isotopes of deciduous teeth, especially δ13Cen, can be used to detect changes in diet during the weaning process. It is also possible that the δ18O enrichment observed in M1 is influenced by the effects of cooking techniques on weaning foods. The combination of multiple isotope systems and tissues overcome some of the limitations posed by single tissue approaches.

Online virtual museum tours combine museum authority and an academic approach with the diversity and interactivity of online resources; such tours have become an essential resource for online scientific research and education. Many important museums around the world are developing this type of online service. Comprehensive evaluation of such tours is, however, urgently needed to ensure effectiveness. This paper establishes a heuristic evaluation scale based on the literature. Taking the online virtual tour of the Exhibition of Architecture of the Forbidden City as a case study, confirmatory factor analysis was then carried out to improve the scale. Interviews were conducted to discuss and analyze the research results. The developed evaluation scale has four dimensions: authenticity, interaction, navigation, and learning. The results from the case study showed, first, that the exhibition had visual authenticity, but the behavioral authenticity was insufficient; second, the exhibition was generally interactive, but this aspect could be improved by enriching the links; third, the lack of effective navigation design for the exhibit was the main factor affecting experience quality. Fourth, the exhibition was informative and supported learning, but needs further improvement to the quantity and quality of information provided. Finally, the interviews revealed that the online exhibition did not entirely support people of different ages and abilities, so it needs further improvement to be wholly inclusive.

Economic globalization has swept the whole world. To focus on their main business, enterprises that are referred to as original equipment manufacturers (OEMs) outsource non-core production activities to contract manufacturers (CMs). By constructing a two-level supply chain consisting of two competing OEMs and one upstream CM, the strategic interaction of the OEMs between outsourcing and purchasing is studied. Specifically, the CM can offer custom- and predefined modes of original equipment manufacturing (namely, CO mode and PO mode, respectively). The former mode enables OEMs to determine product quality, while the latter only allows them to purchase from several quality configurations. The results show that, first, since the CO mode allows the adopter to lead the product design, whether to choose this mode depends on the required R&D cost. Interestingly, however, a lower R&D cost does not necessarily result in the adoption of the CO mode if the product quality difference is small under the PO mode. Second, the optimal purchasing strategy of an OEM is indifferent to the outsourcing mode (CO and PO) of its rival but significantly affected by the quality cost. However, compared to the PO mode, choosing the CO mode would cause the competitor to suffer more profit losses. Third, differing from the prior literature, this paper finds that when the downstream OEM can make quality decisions, although this may lead to profit loss of the contract manufacturer in some channels, it could benefit the CM overall.

Instance segmentation is more challenging and difficult than object detection and semantic segmentation. It paves the way for the realization of a complete scene understanding, and has been widely used in robotics, automatic driving, medical care, and other aspects. However, there are some problems in instance segmentation methods, such as the low detection efficiency for low-resolution objects and the slow detection speed of images with complex backgrounds. To solve these problems, this paper proposes an instance segmentation method with multi-scale attention, which is called a Hybrid Kernel Mask R-CNN. Firstly, the hybrid convolution kernel is constructed by combining different kernels and groups, which can complement each other to extract rich information. Secondly, a multi-scale attention mechanism is designed by assign weights to different convolution kernels, which can retain more important information. After the introduction of our strategy, the network is more inclined to focus on the low-resolution objects in the image. The proposed method achieves the best accuracy over the anchor-based method. To verify the universality of the model, we test Hybrid Kernel Mask R-CNN on Balloon, xBD and COCO datasets. The test results exceed the state of art methods. And the visualization results show our method can extract low-resolution objects effectively.

Social media represent an important source of news for many users. They are, however, affected by misinformation and they might be playing a role in the growth of political polarization. In this paper, we create an agent based model to investigate how policing content and backlash on social media (i.e. conflict) can lead to an increase in polarization for both users and news sources. Our model is an advancement over previously proposed models because it allows us to study the polarization of both users and news sources, the evolution of the audience connections between users and sources, and it makes more realistic assumptions about the starting conditions of the system. We find that the tendency of users and sources to avoid policing, backlash and conflict in general can increase polarization online. Specifically polarization comes from the ease of sharing political posts, intolerance for opposing points of view causing backlash and policing, and volatility in changing one’s opinion when faced with new information. On the other hand, it seems that the integrity of a news source in trying to resist the backlash and policing has little effect.

The Monitoring and Analysis Centre for the fight against money laundering is a valid financial information body which is responsible for collecting, analysing and providing financial information and conducting international exchanges of financial information for relevant undertakings. By constructing the analysis of the monitoring of the local and foreign currency and of the data transmission subsystem in the plan for the transitional period against In the light of the above, the Commission will continue to monitor the implementation of the acquis in the light of the progress made in implementing the acquis future new systems. The purpose of this paper is to study the research and implementation of the security control of the anti-money laundering monitoring and analysis system. This article studies the application of decision tree analysis technology in the anti-money laundering monitoring system of insurance companies to achieve the purpose of improving the anti-money laundering monitoring technology and capabilities of insurance companies. The emergence of data mining technology provides a new system solution for anti-money laundering monitoring. For insurance anti-money laundering, how to find potential money laundering cases in suspicious and large surrender transactions is key. The experimental data show that the decision tree method is the best predictor of the customer category between the insurance application and the surrender days. The decision tree analysis technology has greatly improved the security monitoring capabilities of the insurance in the anti-money laundering monitoring system. Experimental data shows that the security control capabilities of the anti-money laundering monitoring and analysis system make the accuracy of the entire model reach 95%, the accuracy of large and suspicious transactions reaches 88.6%, and the correct classification of customers is about 99.6%.

This paper proposes a multiple criteria group decision making with individual preferences (MCGDM-IP) to address the robot selection problem (RSP). Four objective criteria elicitation approaches, namely, Shannon entropy approach, CRITIC approach, distance-based approach, and ideal-point approach, are proposed to indicate individual decision makers. A preliminary group decision matrix is therefore formulated. Both preferential differences representing the preference degrees among different robots, and preferential priorities representing the favorite ranking of robots for each individual decision maker, are analyzed to propose a revised group decision matrix. A satisfaction index is developed to manifest the merits of the proposed MCGDM-IP. An illustrative example using the data drawn from previous literature is conducted to indicate the effectiveness and validity of MCGDM-IP. The results demonstrate that the MCGDM-IP could generate a more satisfactory scheme to evaluate and select industrial robots, with an improvement of group satisfactory level as 2.12%.

Motivated by the two-decade-long scientific debate over the existence of the ego-depletion effect, our paper contributes to exploring the scope conditions of ego-depletion theory. Specifically, in a randomized experiment, we depleted students’ self-control with a cognitively demanding task that required students’ effort. We measured the effect of depleted self-control on a subsequent task that required self-control to not engage in fraudulent cheating behavior—measured with an incentivized dice-roll task—and tested ego-depletion in a large-scale preregistered field experiment that was similar to real-life situations. We hypothesized that treated students would cheat more. The data confirms the hypothesis and provides causal evidence of the ego-depletion effect. Our results provide new insights into the scope conditions of ego-depletion theory, contribute methodological information for future research, and offer practical guidance for educational policy.

Understanding how textual information impacts financial market volatility has been one of the growing topics in financial econometric research. In this paper, we aim to examine the relationship between the volatility measure that is extracted from GARCH modelling and textual news information both publicly available and from subscription, and the performances of the two datasets are compared. We utilize a latent Dirichlet allocation method to capture the dynamic features of the textual data overtime by summarizing their statistical outputs, such as topic distributions in documents and word distributions in topics. In addition, we transform various measures representing the popularity and diversity of topics to form predictors for a rolling regression model to assess the usefulness of textual information. The proposed method captures the statistical properties of textual information over different time periods and its performance is evaluated in an out-of-sample analysis. Our results show that the topic measures are more useful for predicting our volatility proxy, the unexplained variance from the GARCH model than the simple moving average. The finding indicates that our method is helpful in extracting significant textual information to improve the prediction of stock market volatility.

In this work, we proposed a hybrid pointer network (HPN), an end-to-end deep reinforcement learning architecture is provided to tackle the travelling salesman problem (TSP). HPN builds upon graph pointer networks, an extension of pointer networks with an additional graph embedding layer. HPN combines the graph embedding layer with the transformer’s encoder to produce multiple embeddings for the feature context. We conducted extensive experimental work to compare HPN and Graph pointer network (GPN). For the sack of fairness, we used the same setting as proposed in GPN paper. The experimental results show that our network significantly outperforms the original graph pointer network for small and large-scale problems. For example, it reduced the cost for travelling salesman problems with 50 cities/nodes (TSP50) from 5.959 to 5.706 without utilizing 2opt. Moreover, we solved benchmark instances of variable sizes using HPN and GPN. The cost of the solutions and the testing times are compared using Linear mixed effect models. We found that our model yields statistically significant better solutions in terms of the total trip cost. We make our data, models, and code publicly available https://github.com/AhmedStohy/Hybrid-Pointer-Networks.

There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946–2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.

Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language. ML is used to find the important attributes of DCO-OFDM that influence the optimum DC bias. It is shown here that the optimum DC bias is a function of several factors including, the minimum, the standard deviation, and the maximum value of the bipolar OFDM signal, and the constellation size. Next, linear and polynomial regression algorithms are successfully applied to predict the optimum DC bias value. Results show that polynomial regression of order 2 can predict the optimum DC bias value with a coefficient of determination of 96.77% which confirms the effectiveness of the prediction.

Some evidence suggests that people behave more cooperatively and generously when observed or in the presence of images of eyes (termed the ‘watching eyes’ effect). Eye images are thought to trigger feelings of observation, which in turn motivate people to behave more cooperatively to earn a good reputation. However, several recent studies have failed to find evidence of the eyes effect. One possibility is that inconsistent evidence in support of the eyes effect is a product of individual differences in sensitivity or susceptibility to the cue. In fact, some evidence suggests that people who are generally more prosocial are less susceptible to situation-specific reputation-based cues of observation. In this paper, we sought to (1) replicate the eyes effect, (2) replicate the past finding that people who are dispositionally less prosocial are more responsive to observation than people who are more dispositionally more prosocial, and (3) determine if this effect extends to the watching eyes effect. Results from a pre-registered study showed that people did not give more money in a dictator game when decisions were made public or in the presence of eye images, even though participants felt more observed when decisions were public. That is, we failed to replicate the eyes effect and observation effect. An initial, but underpowered, interaction model suggests that egoists give less than prosocials in private, but not public, conditions. This suggests a direction for future research investigating if and how individual differences in prosociality influence observation effects.

Background Several meta-analyses of the relationship between endothelial nitric oxide synthase (eNOS) T-786C gene polymorphism and chronic kidney disease (CKD) have been published. However, the results of these studies were inconsistent, and it is undetermined whether sample sizes are sufficient to reach a definite conclusion. Objective To elucidate the relationship between T-786C and CKD by combining previous studies with our case-control sample and incorporate trial sequential analysis (TSA) to verify whether the sample size is adequate to draw a definite conclusion. Methods PubMed and Embase databases were searched for relevant articles on eNOS T-786C and CKD before February 28, 2021. TSA was also incorporated to ascertain a conclusion. A total of 558 hemodialysis cases in the case-control study was recruited from nine dialysis centers in the northern area of Taiwan in 2020. Additionally, 640 healthy subjects of the control group, with estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m2, were selected from participants of the annual elderly health examination program at the Tri-Service General Hospital. The functional analysis was based on eQTL data from GTExPortal. Results After screening with eligibility criteria, 15 papers were included and eventually combined in a meta-analysis. The result of the TSA showed that the sample size for Caucasians was adequate to ascertain the correlation between eNOS T-786C and CKD but was insufficient for Asians. Therefore, we added our case-control samples (n = 1198), though not associated with CKD (odds ratio [OR] = 1.01, 95% confidence interval [CI] = 0.69–1.46), into a meta-analysis, which supported that eNOS T-786C was significantly associated with CKD in Asians (OR = 1.39, 95% CI = 1.04–1.85) by using an adequate cumulative sample size (n = 4572) analyzed by TSA. Data of eQTL from GTEx showed that T-786C with the C minor allele exhibited relatively lower eNOS mRNA expression in whole blood, indicating the hazardous role of eNOS T-786C in CKD. Conclusions eNOS T-786C genetic polymorphism was of conclusive significance in the association with CKD among Asians in our meta-analysis. Our case-control samples play a decisive role in changing conclusions from indefinite to definite.

In this paper, a new mathematical model is formulated that describes the interaction between uninfected cells, infected cells, viruses, intracellular viral RNA, Cytotoxic T-lymphocytes (CTLs), and antibodies. Hence, the model contains certain biological relations that are thought to be key factors driving this interaction which allow us to obtain precise logical conclusions. Therefore, it improves our perception, that would otherwise not be possible, to comprehend the pathogenesis, to interpret clinical data, to control treatment, and to suggest new relations. This model can be used to study viral dynamics in patients for a wide range of infectious diseases like HIV, HPV, HBV, HCV, and Covid-19. Though, analysis of a new multiscale HCV model incorporating the immune system response is considered in detail, the analysis and results can be applied for all other viruses. The model utilizes a transformed multiscale model in the form of ordinary differential equations (ODE) and incorporates into it the interaction of the immune system. The role of CTLs and the role of antibody responses are investigated. The positivity of the solutions is proven, the basic reproduction number is obtained, and the equilibrium points are specified. The stability at the equilibrium points is analyzed based on the Lyapunov invariance principle. By using appropriate Lyapunov functions, the uninfected equilibrium point is proven to be globally asymptotically stable when the reproduction number is less than one and unstable otherwise. Global stability of the infected equilibrium points is considered, and it has been found that each equilibrium point has a specific domain of stability. Stability regions could be overlapped and a bistable equilibria could be found, which means the coexistence of two stable equilibrium points. Hence, the solution converges to one of them depending on the initial conditions.

Cyber insurance is a risk management option to cover financial losses caused by cyberattacks. Researchers have focused their attention on cyber insurance during the last decade. One of the primary issues related to cyber insurance is estimating the premium. The effect of network topology has been heavily explored in the previous three years in cyber risk modeling. However, none of the approaches has assessed the influence of clustering structures. Numerous earlier investigations have indicated that internal links within a cluster reduce transmission speed or efficacy. As a result, the clustering coefficient metric becomes crucial in understanding the effectiveness of viral transmission. We provide a modified Markov-based dynamic model in this paper that incorporates the influence of the clustering structure on calculating cyber insurance premiums. The objective is to create less expensive and less homogenous premiums by combining criteria other than degrees. This research proposes a novel method for calculating premiums that gives a competitive market price. We integrated the epidemic inhibition function into the Markov-based model by considering three functions: quadratic, linear, and exponential. Theoretical and numerical evaluations of regular networks suggested that premiums were more realistic than premiums without clustering. Validation on a real network showed a significant improvement in premiums compared to premiums without the clustering structure component despite some variations. Furthermore, the three functions demonstrated very high correlations between the premium, the total inhibition function of neighbors, and the speed of the inhibition function. Thus, the proposed method can provide application flexibility by adapting to specific company requirements and network configurations.

System identification techniques -- projection pursuit regression models (PPRs) and convolutional neural networks (CNNs) -- provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron's receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron's receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons., 22 pages

Quantum signature is the use of the principles of quantum computing to establish a trusted communication between two parties. In this paper, a quantum signature scheme using amplitude amplification techniques will be proposed. To secure the signature, the proposed scheme uses a partial diffusion operator and a diffusion operator to hide/unhide certain quantum states during communication. The proposed scheme consists of three phases, preparation phase, signature phase and verification phase. To confuse the eavesdropper, the quantum states representing the signature might be hidden, not hidden or encoded in Bell states. It will be shown that the proposed scheme is more secure against eavesdropping when compared with relevant quantum signature schemes.

In this paper, we presentautopsych, a novel online tool that allows school assessment experts, test developers, and researchers to perform routine psychometric analyses and equating of student test data and to examine the effect of student demographic and group conditions on student test performance. The app extends current open-source software by providing (1) extensive embedded result narration and summaries for written reports, (2) improved handling of partial credit data via customizable item-person Wright maps, (3) customizable item- and person-flagging systems, (4) item-response theory model constraints and controls, (5) many-facets Rasch analysis to examine item bias, (6) Rasch fixed item equating for mapping student ability across test forms, (7) tabbed spreadsheet outputs and immediate options for secondary data analysis, (8) customizable graphical color schemes, (9) extended ANOVA analysis for examining group differences, and (10) inter-rater reliability analyses for the verifying the consistency of rater scoring systems. We present the app’s architecture and functionalities and test its performance with simulated and real-world small-, medium-, and large-scale assessment data. Implications and planned future developments are also discussed.

The diversified management ability of the non-family members in the top management teams (TMTs) can significantly increase the research and development (R&D) investment of the family firms. However, existing studies focus on family characteristics. To bridge the gap, this study explored the R&D investment propensity for family firms from the perspective of non-family members’ participation in TMTs. Based on the upper echelons and the socioemotional wealth theory, this paper incorporated the non-economic goals that influence strategic decisions on family firms into the analytical framework. According to the questionnaire data of Chinese private enterprises, the Tobit regression model was used to analyze the influence of family members on R&D investment decisions under non-economic goal orientations. The results indicated that the preference for control and influence among family members weakens the positive effect of non-family managers on R&D investment, while the preferences for status perception and social responsibility strengthen the positive effect.

In this paper, we assume that the supply chain for new energy vehicles (NEVs) consists of a manufacturer and N parts suppliers, considering that the R&D investment of both manufacturer and suppliers will affect the market demand of NEVs and NEVs credit, we construct decentralized and centralized decision-making models under the dual-credit policy to study the R&D investment strategy of supply chain enterprises. Furthermore, considering that suppliers can form alliances, we establish bargaining game models under the conditions of the non-alliance and alliance of suppliers, and discuss the coordination strategy for the NEVs supply chain. It is found that, under the dual-credit policy, the higher the credit coefficient of technology improvement, the higher the transaction price of credits, and the higher the R&D investment of supply chain. Dual-credit policy can effectively encourage NEVs supply chain to increase R&D investment, improve NEV technology level, and improve the profit of supply chain. Under the dual-credit policy, the increment profit distribution strategy based on a bargaining game model can coordinate the NEVs supply chain. When suppliers separately negotiate with the manufacturer, bringing the negotiation sequence forward, the supplier can get more profits. However, as the manufacturer has the right to determine the negotiation sequence, the supplier can only get the profit of the last round of negotiation, and the manufacturer can get excess profit. Forming a suppliers alliance can solve this problem effectively, and increase the profit of all suppliers when the alliance`s negotiating power is improved to a certain threshold.