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The anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) are important structures to maintain knee stability. The present study aimed to further enrich understandings of the morphology of the cruciate ligaments and explore the relationship between the diameter of ACL and PCL.

Background and purpose: Despite the superior dose conformity of proton therapy, the dose distribution is sensitive to daily anatomical changes, which can affect treatment accuracy. This study evaluated the dose recalculation accuracy of two synthetic computed tomography (sCT) generation algorithms in a commercial treatment planning system. Materials and methods: The evaluation was conducted for head-and-neck, thorax-and-abdomen, and pelvis sites treated with proton therapy. Thirty patients with two cone-beam computed tomography (CBCT) scans each were selected. The sCT images were generated from CBCT scans using two algorithms, Corrected CBCT (corrCBCT) and Virtual CT (vCT). Dose recalculations were performed based on these images for comparison with “ground truth” deformed CTs. Results: The choice of algorithm influenced dose recalculation accuracy, particularly in high dose regions. For head-and-neck cases, the corrCBCT method showed closer agreement with the “ground truth”, while for thorax-and-abdomen and pelvis cases, the vCT algorithm yielded better results (mean percentage dose discrepancy of 0.6 %, 1.3 % and 0.5 % for the three sites, respectively, in the high dose region). Head-and-neck and pelvis cases exhibited excellent agreement in high dose regions (2 %/2 mm gamma passing rate >98 %), while thorax-and-abdomen cases exhibited the largest differences, suggesting caution in sCT algorithm usage for this site. Significant systematic differences were observed in the clinical target volume and organ-at-risk doses in head-and-neck and pelvis cases, highlighting the importance of using the correct algorithm. Conclusions: This study provided treatment site-specific recommendations for sCT algorithm selection in proton therapy. The findings offered insights for proton beam centers implementing adaptive radiotherapy workflows.

Parrondo's paradox refers to the counterintuitive phenomenon whereby two losing strategies, when alternated in a certain manner, can result in a winning outcome. Understanding the optimal sequence in Parrondo's games is of significant importance for maximizing profits in various contexts. However, the current predefined sequences may not adapt well to changing environments, limiting their potential for achieving the best performance. We posit that the optimal strategy that determines which game to play should be learnable through experience. In this Letter, we propose an efficient and robust approach that leverages Q learning to adaptively learn the optimal sequence in Parrondo's games. Through extensive simulations of coin-tossing games, we demonstrate that the learned switching strategy in Parrondo's games outperforms other predefined sequences in terms of profit. Furthermore, the experimental results show that our proposed method can be easily adjusted to adapt to different cases of capital-dependent games and history-dependent games.

This paper studies how the GeoGebra 3D Calculator with augmented reality (AR) serves as a tool for visualizing 3D graphs from functions of two variables in a mid-sized multivariable calculus class. We assessed this AR tool’s usability as a content delivery system. In our study, 39 students in the AR group received instructions on using the GeoGebra 3D Calculator with AR tool, while another 28 students in the control group worked exclusively with PowerPoint slides. Statistical testing reveals that the GeoGebra 3D Calculator with AR proves more effective than PowerPoint slides in terms of usability for teaching and learning multivariable calculus. However, the students’ attitudes and engagement do not show significant differences between the AR and control groups. This preliminary study highlights the crucial role of innovative educational technologies like the GeoGebra 3D Calculator with AR in enhancing the visualization of 3D graphs and other multivariable calculus topics. It suggests an advancement in the evolution of digital tools in education. The increased usability observed in the AR group indicates a promising direction for future educational strategies, especially in fields that require strong spatial visualization skills.

In this study, we investigate the attitudes of students towards an immersive educational instruction method used to augment in-person biology laboratories. Specifically, we employ the Oculus Quest 2 in a mid-sized biology classroom setting to conduct a virtual heart dissection instructional class using Virtual Reality (VR). We engaged with 23 students from a Singaporean secondary school to gather insights into their experiences and perspectives. The focus of this study is twofold: firstly, to assess the potential of VR technology, particularly the Oculus Quest 2, as a tool for enhancing learning in biology, and secondly, to evaluate the feasibility of replacing traditional in-person laboratory settings with VR-based learning environments. By examining the students’ experiences, this research sheds light on the effectiveness of VR technology in enhancing academic engagement and understanding of this specific content taught in a standardized biology syllabus. Our findings reveal that Oculus Quest 2 with VR can significantly stimulate student interest in this topic. The study provides insights into how VR as part of educational technology can transform the traditional learning experience, offering a more interactive and engaging approach to education in biology. These preliminary results have important implications for the future of educational methodologies in science and technology subjects.

Abstract Endothelial dysfunction is a critical initiating factor contributing to cardiovascular diseases, involving the gut microbiome-derived metabolite trimethylamine N-oxide (TMAO). This study aims to clarify the time-dependent molecular pathways by which TMAO mediates endothelial dysfunction through transcriptomics and metabolomics analyses in human microvascular endothelial cells (HMEC-1). Cell viability and reactive oxygen species (ROS) generation were also evaluated. TMAO treatment for either 24H or 48H induces reduced cell viability and enhanced oxidative stress. Interestingly, the molecular signatures were distinct between the two time-points. Specifically, few Gene Ontology biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were modulated after a short (24H) compared to a long (48H) treatment. However, the KEGG signalling pathways namely “tumour necrosis factor (TNF)” and “cytokine-cytokine receptor interaction” were downregulated at 24H but activated at 48H. In addition, at 48H, BPs linked to inflammatory phenotypes were activated (confirming KEGG results), while BPs linked to extracellular matrix (ECM) structural organisation, endothelial cell proliferation, and collagen metabolism were repressed. Lastly, metabolic profiling showed that arachidonic acid, prostaglandins, and palmitic acid were enriched at 48H. This study demonstrates that TMAO induces distinct time-dependent molecular signatures involving inflammation and remodelling pathways, while pathways such as oxidative stress are also modulated, but in a non-time-dependent manner.

Lifelong learning is crucial for equipping the workforce to navigate a volatile, uncertain, complex, and ambiguous (VUCA) world. Despite its importance, resistance to enrolling in lifelong learning courses persists. This exploratory study examines the exposure to and engagement with government-sponsored courses among two distinct groups: individuals who opt for these courses and those who select alternative courses. We employed comparative statistical analysis to identify the primary factors influencing course awareness and selection. Our findings underscore the enduring influence of traditional media in promoting course awareness. Additionally, personal interest and availability of subsidies emerged as significant determinants of course selection. Based on these insights, we propose policy recommendations to enhance the effectiveness of these courses. This empirical study contributes to the understanding of the dynamics of lifelong learning in Singapore, providing valuable insights for policy and practice.

Compared with the variables separation method and integral-transform method, Green’s function method is much more applicable and thus of great practical significance. Based on the general steps of Green’s function method, a new method is originally raised to get Green’s function in oil and gas flow problems through underground formations. After that, the transient pressure solution is get for flow in a spherical reservoir by using this method. This case enlarged the application of Greenʼs method function, and this work can also be a good foundation for the solution of more and more complicated flow problems in oil and gas reservoirs.

The throughput and latency bottleneck in accessing system resources is prevalent in all communication systems. Likewise, communication overhead in modern computer systems is a vital limiting factor in their performance. In this Letter, we propose a routing strategy to improve communication in networks based on Parrondo's paradox. We show that random switching between the shortest-path algorithm and making the local optimum choice (greedy algorithm) yields a significant reduction in total transmission weight compared to when the shortest-path and greedy algorithms are operated separately. This effect recapitulates Parrondo's paradox, where two games/strategies are losing when played alone but create a winning outcome or optimum results when combined in a certain manner. The performance of the switching strategy is further validated under various parameters, and the results indicate that the effect is more remarkable with an increase in the number of packets and the number of nodes in the system. The proposed routing strategy enhances efficiency and scalability in modern computer and communication systems.

Velocity sensitivity refers to the possibility and degree of reservoir permeability decline caused by the migration of various particles in the reservoir rock due to the increase in fluid flow velocity and the blockage of pore channels. To improve the development results of M reservoir in Karamay Oil field, two reservoir cores were selected to carry out velocity sensitivity experiments. The permeability of No. 1 core decreases obviously when the flow rate is greater than 0.04 mL/min. Therefore, it can be considered that the injection rate of velocity sensitivity is between 0.04 and 0.06 mL/min, and the displacement rate should be less than 0.04 mL/min in the core displacement experiment. When the flow rate is greater than 0.5 mL/min, the permeability of No. 2 core decreases significantly. This is mainly due to the high permeability and critical velocity of No. 2 core. The study can provide a basis for the selection of displacement velocity in core displacement experiments, and also provide a reference for the determination of reasonable injection-production velocity in actual production.

As one of the unconventional oil and gas resources, tight oil is of great development prospect all over the world. The characterization of tight reservoir has important guiding significance for overcoming the problems in exploration as well as improving the development effect. As one of the characteristics of reservoir cores, the specific surface area is very important for the characterization of tight reservoirs. In this study, based on mercury injection data of tight reservoir core from Changqing Oilfield, through the establishment of equal diameter pore model, the specific surface area of pores corresponding to different radii is calculated, respectively, and the overall specific surface area of the core is obtained. Through the comprehensive evaluation of the mercury injection data and the calculation results, it is found that the pores with the medium radius (0.009–0.178 μm) have the greatest contribution to the pore volume, followed by the pores with smaller radius (0.004–0.007 μm), and the pores with larger radius (0.268–53.835 μm) have the least contribution to the pore volume. However, the pores with smaller radius (0.004–0.089 μm) have the greatest contribution to the specific surface area, followed by the pore with larger radius (0.133–6.666 μm), and the specific surface area of individual pores in the middle range (8.917 μm) has the least contribution. Therefore, the adsorption loss of surfactant and so on must be considered in the process of tight oil development. In the development process, a series of main technologies such as fracturing, new water/gas injection, and horizontal well development should be explored. Through the overall design and scale implementation of reservoir scale, the investment cost of unit-producing reserves can be effectively reduced, and ultimately, the maximum benefit of tight oil development can be realized.

In this study, we have developed a webXR tool that helps students visualise 3D graphs from functions of two variables through the use of simple, practical and cost-effective Google Cardboard for use in the classroom. Further, we have assessed Google Cardboard’s usability as a content delivery system in a mid-sized multivariable calculus class with 36 students, and 40 other students in another class as the control group. We also attempt to assess if Google Cardboard is better than PowerPoint slides, shown on flat screen computers, in terms of students’ attitudes and engagement towards the teaching and learning of multivariable calculus. Our results suggest that Google Cardboard functions better than PowerPoint slides when encouraging students’ attitudes and engagement towards learning multivariable calculus. At the same time, Google Cardboard as a content delivery system does not appear to differ from PowerPoint slides in terms of its usability.

Amidst the fourth industrial revolution, marked by swift technological advancements and changing economic environments, lifelong learning has risen as an essential cornerstone for developing people and society. Adult education, with a particular focus on skills learning, is vital in equipping individuals with the necessary competencies to navigate the dynamic demands of the modern workforce. This paper provides a qualitative analysis and commentary on the case study of Singapore’s SkillsFuture movement, an exemplary national initiative to promote skills learning among adults. Intending to reach a wide audience in educational science, we investigate the effectiveness and impact of this comprehensive programme and its implications for other countries. This article contributes to educational science and policy development by illustrating the importance of investing in adult education and skills development. By comprehensively studying the SkillsFuture experience, we offer valuable insights into establishing effective and inclusive lifelong learning ecosystems to foster a culture of continuous learning, equipping individuals to adapt and thrive in a volatile, uncertain, complex, and ambiguous global landscape.

One common cause of vision loss after retinal detachment surgery is retinal scarring. Here the authors demonstrate that a bio-functional polymer can prevent retinal scarring by suppression of epithelial-mesenchymal transition and hyper-proliferation of retinal pigment epithelial cells.

Abstract For a certain kind of decision event, the decision maker does not know the internal mechanism and knowledge information of the decision events.When this kind of decision events gives multiple selection branches, it is found that there is a decision psychological tendency to find the most common features by comparing the selection branches. Based on this, a zero-knowledge decision making (ZKDM) method is proposed. By defining the feature points and feature sets of the selection branches of the decision events, the characteristic moments of the system are constructed and the branch with the most common characteristics is obtained. It is observed that through the findings of investigation the probability of arriving at the correct choice based on the ZKDM method is high. The effectiveness of the ZKDM method may be related to the fact that the designers of decision events usually determine the correct selection branch first, before changing it to design other branches. A questionnaire survey of 279 respondents reveals that more than half of them actually adopt such a design idea. Furthermore, a separate questionnaire survey of 465 decision-makers reveal that 19.14% of the respondents clearly adopt ZKDM.

This review paper provides the conceptualization and development of augmented reality (AR) environment for education by featuring implementations in physics education. The use of AR creates an environment designed to fully incorporate next-generation AR-aided notes, virtual laboratory and interactive problem-based learning with real-time automated generation of application-centric scenarios. This can be carried out via the fusion and technologizing of pre-existing teaching materials (such as books and notes) using AR and be mobile device friendly to fully leverage on learning beyond classrooms. Such a method is proposed to give students the access to resources anytime, anywhere without the spatial and temporal restrictions of synchronous-learning. This review discusses the advances of AR as an important tool in physics education, identify potential challenges and envisions the future by surveying recent trends and reviews. We provide perspective on practical AR implementation and evaluation for educators and school administrator, and potential academic advances through physics education research for researchers.

Augmented reality and virtual reality, collectively called extended reality (XR), has made substantial strides in the education sector in both theory and practice. Existing active research focuses on implementation by educators to teach real-world phenomena, and for students to learn through an immersive experience. This article surveys existing research in XR with special focus on the implications of immersive extended realities for teaching and learning engineering mathematics in institutes of higher learning. We also survey various interactive multimedia associated with XR before examining the implications of XR as an educational tool for existing mathematics pedagogy. Finally, the contribution of this scoping review is to provide an adaptable framework on XR implementation for educators, and potential academic advances for researchers.

Abstract Background Dormancy is widespread in nature, but while it can be an effective adaptive strategy in fluctuating environments, the dormant forms are costly due to the inability to breed and the relatively high energy consumption. We explore mathematical models of predator-prey systems, in order to assess whether dormancy can be an effective adaptive strategy to outcompete perennially active (PA) prey, even when both forms of the dormitive prey (active and dormant) are individually disadvantaged. Results We develop a dynamic population model by introducing an additional dormitive prey population to the existing predator-prey model which can be active (active form) and enter dormancy (dormant form). In this model, both forms of the dormitive prey are individually at a disadvantage compared to the PA prey and thus would go extinct due to their low growth rate, energy waste on the production of dormant prey, and the inability of the latter to grow autonomously. However, the dormitive prey can paradoxically outcompete the PA prey with superior traits and even cause its extinction by alternating between the two losing strategies. We observed higher fitness of the dormitive prey in rich environments because a large predator population in a rich environment cannot be supported by the prey without adopting an evasive strategy, that is, dormancy. In such environments, populations experience large-scale fluctuations, which can be survived by dormitive but not by PA prey. Conclusion We show that dormancy can be an effective adaptive strategy to outcompete superior prey, recapitulating the game-theoretic Parrondo’s paradox, where two losing strategies combine to achieve a winning outcome. We suggest that the species with the ability to switch between the active and dormant forms can dominate communities via competitive exclusion.

Multivariable calculus is a subject undertaken by engineering students as a core module at the freshman level. One of the intended learning outcomes (ILOs) in multivariable calculus is to gain an intuition for visualizing three-dimensional surfaces and deducing their properties. For students to visualize more complex multivariable calculus concepts, a virtual reality (VR) application has been created. Tapping on existing infrastructures, we investigate the effectiveness of visualization through VR usage vis-à-vis a two-dimensional digital screen. We have conducted a controlled trial on a group of N=119 students across two groups. The first group (control group) comprises students who participated in an online quiz (as a baseline test). The second group (treatment group) is given two sets of tests, the first is the same baseline test that the control group participated in, before administering the test questions on the VR platform (termed the treatment test) to the same group of students. Our analysis reveals that students, in general, perform better on questions pertaining to the identification of the sign of partial derivatives in the treatment test, but for other intended learning outcomes linked to other questions, students have performance similar to the baseline test. Furthermore, low-progress students in the treatment group exhibited improvement after the treatment. Our work here has the potential to be developed into a future-ready smart classroom through VR usage.

Quantum game theory has stimulated some interest in recent years with the advancement of quantum information theory. This interest has led to a resurgence of quantum Parrondo's games. With two losing games combining to give a winning game, this paradoxical idea is known as Parrondo's paradox. By using chaotic switching between the two losing quantum games, we show that it is possible to achieve Parrondo's paradox involving a quantum walker playing two-sided quantum coin tossing games. Furthermore, we show that the framework of chaotic switching in quantum coin tosses can be applied to encryption. This is a proposal to deploy a quantum coin toss with chaotic switching for semiclassical encryption.

The subject of probability and statistics is easily dismissed by students as assemblages of formulae to be rote-memorized. We propose here an integration of simulation-based activities with certain mathematical paradoxes using patchwork assessment to first-year undergraduates, such that they can better appreciate the real-world context of probability and statistics. The proposed examples alongside various facilitation skills for the instructor are discussed. We also provide an original spreadsheet simulation program in Excel and Visual Basic for Applications to reproduce the numerical experiments. This program is capable of running Monte Carlo simulations for all three seminal Parrondo's paradox variants, and can be easily used by students and instructors; moreover, the computed datasets and code are fully-transparent, thereby allowing interactive discussions, modifications and extensions, and further analyses. Our findings suggest that the proposed teaching strategy is useful, and we hope that this work will initiate the significant adoption of paradoxical simulations in teaching practice. The interactive program is freely available on open science framework.

The advent of social media and technologies augmenting social communication has dramatically amplified the role of rumor spreading in shaping society, via means of misinformation and fact distortion. Existing research commonly utilize contagion mechanisms, statistical mechanics frameworks, or complex-network opinion dynamics models. In this paper, we incorporate information distortion and polarization effects into an opinion dynamics model based on information entropy, modeling imprecision in human memory and communication, and the consequent progressive drift of information toward subjective extremes. Simulation results predict a wide variety of possible system behavior, heavily dependent on the relative trust placed on individuals of differing social connectivity. Mass-polarization toward a positive or negative consensus occurs when a synergistic mechanism between preferential trust and polarization tendencies is sustained; a division of the population into segregated groups of different polarity is also possible under certain conditions. These results may aid in the analysis and prediction of opinion polarization phenomena on social platforms, and the presented agent-based modeling approach may aid in the simulation of complex-network information systems.

We present an efficient and scalable system to predict emergency department (ED) patient volume in hospitals using publicly available Google Trends search data. Search volume data are retrieved for a selected set of context-relevant query keywords with refinements, on which a series of correlation analyses are performed, and a multiple regression predictive model is constructed. We also develop a software suite to enable convenient access to data visualization and prediction capabilities by medical and administrative staff. A preliminary demonstration of the method and software is presented with data from a large public hospital as a form of validation. This paper enables informed resource and manpower allocation in hospitals and thus improved ability to respond to patient influx surges, and importantly, can serve as a key mitigation measure against worsening ED congestion problems that plague hospitals.

Video surveillance is gaining popularity in numerous applications, including facility management, traffic monitoring, crowd analysis, and urban security. Despite the increasing demand for closed-circuit television (CCTV) and related infrastructure in public spaces, there remains a notable lack of readily-deployable automated surveillance systems. In this study, we present a low-cost and efficient approach that integrates the use of computational object recognition to perform fully-automated identification, tracking, and counting of human traffic on camera video streams. Two software implementations are explored and the performance of these schemes is compared. Validation against controlled and non-controlled real-world environments is also demonstrated. The implementation provides automated video analytics for medium crowd density monitoring and tracking, eliminating labor-intensive tasks traditionally requiring human operation, with results indicating great reliability in real-life scenarios.

Data centers are mission-critical infrastructures. There are strict service level requirements and standards imposed on operators and maintainers to ensure reliable run-the-clock operation. In the context of thermal management and data hall environmental control, the formation of hot and cold spots around server cabinets are especially undesirable, and can result in overheating, lifespan reductions, and performance throttling in the former and condensation damage in the latter. In this paper, we present a comprehensive multi-pronged methodology in data center environmental control, comprising computational fluid dynamics (CFD) simulation-aided predictive design first-stage approach, and a complementing Internet of Things (IoT) reactive management system that autonomously monitors and regulates fluctuations in thermal parameters. The novel hybrid methodology is demonstrated on various test scenarios derived from real-world context, and prototypes of the IoT system have been experimentally validated. The approach is shown to be efficient in eliminating unfavourable environmental variations and provides an enhanced understanding of common design problems and respective mitigation measures.

Building Information Modeling (BIM) has been increasingly used in coordinating the different mechanical, electrical, and plumbing (MEP) services in the construction industries. As the construction industries are slowly adapting to BIM, the use of 2D software may become obsolete in the future as MEP services are technically more complicated to coordinate, due to respective services’ codes of practice to follow and limit ceiling height. The 3D MEP designs are easy to visualize before installing the respective MEP services on the construction site to prevent delay in the construction process. The aid of current advanced technology has brought BIM to the next level to reduce manual work through automation. Combining both innovative technology and suitable management methods not only improves the workflow in design coordination, but also decreases conflict on the construction site and lowers labor costs. Therefore, this paper tries to explore possible advance technology in BIM and management strategies that could help MEP services to increase productivity, accuracy, and efficiency with a lower cost of finalizing the design of the building. This will assist the contractors to complete construction works before the targeted schedule and meet the client’s expectations.

Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.

Abstract In this work, the cooperation problem between two populations in a periodically varying environment is discussed. In particular, the two‐population prisoner's dilemma game with periodically oscillating payoffs is discussed, such that the time‐average of these oscillations over the period of environmental variations vanishes. The possible overlaps of these oscillations generate completely new dynamical effects that drastically change the phase space structure of the two‐population evolutionary dynamics. Due to these effects, the emergence of some level of cooperators in both populations is possible under certain conditions on the environmental variations. In the domain of stable coexistence the dynamics of cooperators in each population form stable cycles. Thus, the cooperators in each population promote the existence of cooperators in the other population. However, the survival of cooperators in both populations is not guaranteed by a large initial fraction of them.

Abstract It has been shown that the tumor population growth dynamics in a periodically varying environment can drastically differ from the one in a fixed environment. Thus, the environment of a tumor can potentially be manipulated to suppress cancer progression. Diverse evolutionary processes play vital roles in cancer progression and accordingly, understanding the interplay between these processes is essential in optimizing the treatment strategy. Somatic evolution and genetic instability result in intra‐tumor cell heterogeneity. Various models have been developed to analyze the interactions between different types of tumor cells. Here, models of density‐dependent interaction between different types of tumor cells under fast periodical environmental changes are examined. It is illustrated that tumor population densities, which vary on a slow time scale, are affected by fast environmental variations. Finally, the intriguing density‐dependent interactions in metastatic castration‐resistant prostate cancer (mCRPC) in which the different types of tumor cells are defined with respect to the production of and dependence on testosterone are discussed.

Abstract Many predators produce dormant offspring to escape harsh environmental conditions, but the evolutionary stability of this adaptation has not been fully explored. Like seed banks in plants, dormancy provides a stable competitive advantage when seasonal variations occur, because the persistence of dormant forms under harsh conditions compensates for the increased cost of producing dormant offspring. However, dormancy also exists in environments with minimal abiotic variation—an observation not accounted for by existing theory. Here it is demonstrated that dormancy can out‐compete perennial activity under conditions of extensive prey density fluctuation caused by overpredation. It is shown that at a critical level of prey density fluctuations, dormancy becomes an evolutionarily stable strategy. This is interpreted as a manifestation of Parrondo's paradox: although neither the active nor dormant forms of a dormancy‐capable predator can individually out‐compete a perennially active predator, alternating between these two losing strategies can paradoxically result in a winning strategy. Parrondo's paradox may thus explain the widespread success of quiescent behavioral strategies such as dormancy, suggesting that dormancy emerges as a natural evolutionary response to the self‐destructive tendencies of overpredation and related biological phenomena.

In this paper, we present a virtual learning laboratory environment for undergraduate mathematics education using an inquiry-based learning approach. The Visible Thinking pedagogical framework is also suggested to achieve a good complement to traditional lecture-tutorial systems. The virtual laboratory is implemented in an open-access Java interactive software. We demonstrate a viable instruction procedure, providing a set of virtual laboratory activities with real-world applications spanning signal processing, data science and analytics, sustainable infrastructure engineering, and theoretical physics. A preliminary study on a pilot cohort indicates that the proposed virtual laboratory can enhance students' learning. The virtual laboratory implementation is scalable and can be easily expanded in scope to other mathematical topics; transitioning to a tablet-based system for use in smart classrooms is also readily achieved. The Java interactive software is freely available on Open Science Framework.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

Background Prior studies have demonstrated the association of air pollution with cardiovascular deaths. Singapore experiences seasonal transboundary haze. We investigated the association between air pollution and acute myocardial infarction (AMI) incidence in Singapore. Methods and Results We performed a time‐stratified case‐crossover study on all AMI cases in the Singapore Myocardial Infarction Registry (2010–2015). Exposure on days where AMI occurred (case days) were compared with the exposure on days where AMI did not occur (control days). Control days were chosen on the same day of the week earlier and later in the same month and year. We fitted conditional Poisson regression models to daily AMI incidence to include confounders such as ambient temperature, rainfall, wind‐speed, and Pollutant Standards Index. We assessed relationships between AMI incidence and Pollutant Standards Index in the entire cohort and subgroups of individual‐level characteristics. There were 53 948 cases. Each 30‐unit increase in Pollutant Standards Index was association with AMI incidence (incidence risk ratio [IRR] 1.04, 95% CI 1.03–1.06). In the subgroup of ST‐segment–elevation myocardial infarction the IRR was 1.00, 95% CI 0.98 to 1.03, while for non–ST‐segment–elevation myocardial infarction, the IRR was 1.08, 95% CI 1.05 to 1.10. Subgroup analyses showed generally significant. Moderate/unhealthy Pollutant Standards Index showed association with AMI occurrence with IRR 1.08, 95% CI 1.05 to 1.11 and IRR 1.09, 95% CI 1.01 to 1.18, respectively. Excess risk remained elevated through the day of exposure and for >2 years after. Conclusions We found an effect of short‐term air pollution on AMI incidence, especially non–ST‐segment–elevation myocardial infarction and inpatient AMI. These findings have public health implications for primary prevention and emergency health services during haze.