Explore the vast range of titles available.

With the rapid evolution of scientific research, there are a huge volume of papers published every year and the number of scholars is also growing fast. How to effectively predict the scientific impact has become an important research problem, attracting the attention of researchers in various fields, and it is of great significance in improving research efficiency and assisting in decision-making and scientific evaluation. In this paper, we propose a new framework to perform a systematical survey of scientific impact prediction research. Specifically, we take the four common academic entities into account: papers, scholars, venues and institutions. We reviewed all the prediction tasks reported in the literature in detail; the input features are divided into six groups: paper-related, author-related, venue-related, institution-related, network-related and altmetrics-related. Moreover, we classify the forecasting methods into mathematical statistics-based, traditional machine learning-based, deep learning-based and graph-based, and subdivide each category according to the characteristics. Finally, we discuss open issues and existing challenges, and provide potential research directions.

PurposePredicting the impact of social entrepreneurship is crucial as it can help social entrepreneurs to determine the achievement of their social mission and performance. However, there is a lack of existing social entrepreneurship models to predict social enterprises' social impacts. This paper aims to propose the social impact prediction model for social entrepreneurs using a data analytic approach.Design/methodology/approachThis study implemented an experimental method using three different algorithms: naive Bayes, k-nearest neighbor and J48 decision tree algorithms to develop and test the social impact prediction model.FindingsThe accurate result of the developed social impact prediction model is based on the list of identified social impact prediction variables that have been evaluated by social entrepreneurship experts. Based on the three algorithms' implementation of the model, the results showed that naive Bayes is the best performance classifier for social impact prediction accuracy.Research limitations/implicationsAlthough there are three categories of social entrepreneurship impact, this research only focuses on social impact. There will be a bright future of social entrepreneurship if the research can focus on all three social entrepreneurship categories. Future research in this area could look beyond these three categories of social entrepreneurship, so the prediction of social impact will be broader. The prospective researcher also can look beyond the difference and similarities of economic, social impacts and environmental impacts and study the overall perspective on those impacts.Originality/valueThis paper fulfills the need for the Malaysian social entrepreneurship blueprint to design the social impact in social entrepreneurship. There are none of the prediction models that can be used in predicting social impact in Malaysia. This study also contributes to social entrepreneur researchers, as the new social impact prediction variables found can be used in predicting social impact in social entrepreneurship in the future, which may lead to the significance of the prediction performance.

To improve the hit probability of tank firing at high speed, an adaptive robust control strategy considering the exterior ballistic process is proposed based on constraint-following control, which is used to predict the impact point of projectile. First, the rigid-flexible coupling multibody system dynamics model of moving tank is constructed with nonlinear factors and uncertainty such as the flexible barrel, contact clearance, road excitation, and so on. Second, based on Udwadia-Kalaba theory, bidirectional stability constraints and constrained following error are constructed, and an adaptive robust controller considering model uncertainties and external disturbance is designed. Then, by combining the ordinary differential equation of exterior ballistic with the controller, the impact-point control of projectile is realized through coordinate transformation and geometric relations. Finally, the co-simulation demonstrates that it is feasible to integrate the exterior ballistic differential equation into the control system. And the proposed method has strong robustness while the tank is moving at high speed, which can effectively restrain the nonlinear factors and uncertainty, and meet the requirements of impact-point prediction.

Biofuels derived from microalgae is an emerging technology that can supply fuel demand and alleviate greenhouse gas emissions. However, exclusively producing biofuels from microalgae remains to be commercially unsustainable because of its high investment and operating costs. A promising opportunity to address this are algal bio-refineries. Nonetheless, there is still a need to verify the environmental sustainability of this system along its entire process chain, from raw material acquisition to end-of-life. This study utilizes a life-cycle perspective approach to assess the sustainability of the algal bio-refinery and developed environmental impact prediction model using artificial intelligence, particularly adaptive neuro fuzzy inference system. Results will indicate the environmental impacts of a bio-refinery system identifying its major hotspots on different environmental impact categories. Results show that in the investigated proposed algal bio-refinery, the transesterification process had a huge contribution on the overall environmental impact having over 51.5 % of the total weight. In addition, ANFIS results showed the correlation of input parameters with respect to the environmental impact of the system. The model also indicated that there is a perfect correlation between the two parameters. The model and its accuracy should be further validated with the use of real data.

Close-in weapon system (CIWS) obligates to intercept incoming high-speed targets by shooting numerous bullets within a short time. This study proposes a firing command generation method for CIWS by deriving an approximate closed-form trajectory for a bullet launched from CIWS. Based on the closed-form trajectory solution, an impact point prediction method is developed for moving targets. A predicted impact point (PIP), firing angles, and expected time-of-flight (TOF) can be obtained here. By serving those firing angles as a baseline firing command, we also propose a firing correction algorithm to improve interception performance against maneuvering targets. To this end, correcting firing angles are obtained for a given static impact error using the previous engagement data. It is shown that they can also be used for calibrating CIWS in the presence of an aiming error. A likely prediction error is introduced to deal with the impact error caused by the target maneuver. Engagement simulations are performed between a single CIWS and anti-ship missiles (ASMs) to demonstrate that the proposed algorithms can effectively intercept high-speed targets.

The pattern of a few abundant species and many rarer species is a defining characteristic of communities worldwide. These abundant species are often referred to as dominant species. Yet, despite their importance, the term dominant species is poorly defined and often used to convey different information by different authors. Based on a review of historical and contemporary definitions we develop a synthetic definition of dominant species. This definition incorporates the relative local abundance of a species, its ubiquity across the landscape, and its impact on community and ecosystem properties. A meta-analysis of removal studies shows that the loss of species identified as dominant by authors can significantly impact ecosystem functioning and community structure. We recommend two metrics that can be used jointly to identify dominant species in a given community and provide a roadmap for future avenues of research on dominant species. In our review, we make the case that the identity and effects of dominant species on their environments are key to linking patterns of diversity to ecosystem function, including predicting impacts of species loss and other aspects of global change on ecosystems.

To solve the problem of difficulty in predicting the impact point clearly and promptly during projectile flight, this paper proposes an improved ballistic-impact-point prediction method. A certain type of high-spinning tailed projectile is taken as the research object for online real-time landing point prediction research. This study comprehensively utilizes the real-time radar measurement data and the geomagnetic data measured by the bomb-carried geomagnetic sensor. It applies the four-degree-of-freedom ballistic model to predict the landing point. First, the roll angular velocity is calculated based on the geomagnetic data, after which the radar real-time measurement data are segmentally fitted using the improved crayfish algorithm. Then, the fitted parameters are substituted into the four-degree-of-freedom ballistic model. Finally, the C-K method is used to identify the aerodynamic parameters, and the identified aerodynamic parameters are used for fallout prediction. The simulation results show a small deviation between the predicted and actual impact points using the improved ballistic-impact-point prediction method.

Increasing extreme climatic events threaten the functioning of terrestrial ecosystems 1 , 2 . Because soil microbes govern key biogeochemical processes, understanding their response to climate extremes is crucial in predicting the consequences for ecosystem functioning 3 , 4 . Here we subjected soils from 30 grasslands across Europe to four contrasting extreme climatic events under common controlled conditions (drought, flood, freezing and heat), and compared the response of soil microbial communities and their functioning with those of undisturbed soils. Soil microbiomes exhibited a small, but highly consistent and phylogenetically conserved, response under the imposed extreme events. Heat treatment most strongly impacted soil microbiomes, enhancing dormancy and sporulation genes and decreasing metabolic versatility. Microbiome response to heat in particular could be predicted by local climatic conditions and soil properties, with soils that do not normally experience the extreme conditions being imposed being most vulnerable. Our results suggest that soil microbiomes from different climates share unified responses to extreme climatic events, but that predicting the extent of community change may require knowledge of the local microbiome. These findings advance our understanding of soil microbial responses to extreme events, and provide a first step for making general predictions about the impact of extreme climatic events on soil functioning. Soils from 30 grasslands across Europe were subjected to 4 contrasting extreme climatic events under drought, flood, freezing and heat conditions, with the results suggesting that soil microbiomes from different climates share unified responses to extreme climatic events.

1. Predictions of the identities and ecological impacts of invasive alien species are critical for risk assessment, but presently we lack universal and standardized metrics that reliably predict the likelihood and degree of impact of such invaders (i.e. measurable changes in populations of affected species). This need is especially pressing for emerging and potential future invaders that have no invasion history. Such a metric would also ideally apply across diverse taxonomic and trophic groups. 2. We derive a new metric of invader ecological impact that blends: (i) the classic Functional Response (FR; consumer per capita effect) and Numerical Response (NR; consumer population response) approaches to determining consumer impact, that is, the Total Response (TR = FR × NR), with; (ii) the Tarker-Lonsdale equation' for invader impact, where Impact = Range × Abundance × Effect (per capita effect), into; (iii) a new metric, Relative Impact Potential (RIP), where RIP = FR × Abundance. The RIP metric is an invader/native ratio, where values > 1 predict that invader ecological impact will occur, and increasing values above 1 indicate increasing impact. In addition, the invader/invader RIP ratio allows comparisons of the ecological impacts of different invaders. 3. Across a diverse range of trophic and taxonomic groups, including predators, herbivores, animals and plants (22 invader/native systems with 47 individual comparisons), high-impact invaders were significantly associated with higher FRs compared to native trophic analogues. However, the RIP metric substantially improves this association, with 100% predictive power of high-impact invaders. 4. Further, RIP scores were significantly and positively correlated with two independent ecological impact scores for invaders, allowing prediction of the degree of impact of invasive alien species with the RIP metric. Finally, invader/invader RIP scores were also successful in identifying and associating with higher impacting invasive alien species. 5. Synthesis and applications. The Relative Impact Potential metric combines the per capita effects of invaders with their abundances, relative to trophically analogous natives, and is successful in predicting the likelihood and degree of ecological impact caused by invasive alien species. As the metric constitutes readily measurable features of individuals, populations and species across abiotic and biotic context-dependencies, even emerging and potential future invasive alien species can be assessed. The Relative Impact Potential metric can be rapidly utilized by scientists and practitioners and could inform policy and management of invasive alien species across diverse taxonomic and trophic groups.

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/ , which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.