bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial Activity and Mode of Action of Aspergillus terreus Strain (MTCC9618) Mediated Biosynthesized Silver Nanoparticles-AgNPs Against Staphylococcus aureus and Escherichia coli
Authors: Flifl Abaysew Ayele , Singh Rita Mujumdar
Published: September 2021

Fungal approaches bio-synthesis of silver nanoparticles (AgNPs) have been given attention because of their cost effectiveness and environment friendliness. Therefore, fungal Aspergillus terreus (MTCC 9618) intermediated biosynthesis of AgNPs was conducted, and the SEM, EDS and XRD reports confirmed the synthesis of well-organized, structured and stabilized AgNPs. Antimicrobial efficacy of disk diffusion assay of AgNPs (10, 20 and 30[Formula: see text][Formula: see text]g/ml) dosage was confirmed against Staphylococcus aureus and Escherichia coli and found that has bactericidal effects against both strains. Broth assay was also confirmed through LB broth media and colony forming units (CFU) which defined that biosynthesized AgNPs had much potential effect against gram negative ([Formula: see text]ve) than gram positive ([Formula: see text]ve) due to its peptidoglycan thickness. AgNPs had adverse mode of action on both bacterial strains and resulting was found damaged site on cell wall, necrosis, shrinkage, influx out and ruptures of the cells according to SEM scanning profiles. This study promised to green and economical way of AgNPs biosynthesis along with targeted antimicrobial effects in food preservative, biomedical coating tools, fabrics and pharmaceutical industries.
An Empirical Investigation of Consumers’ Intention for Using Ride-Sharing Applications: Does Perceived Risk Matter?
Authors: Ashrafi Dewan Mehrab , Alam Ismail , Anzum Muzdalifa
Published: September 2021

The study examines the factors impacting the willingness of non-users to use ride-sharing applications from the lens of perceived value and perceived risk. Both perceived value and risk have been proposed and conceptualized as second-order constructs containing their first-order value and risk dimensions. An online questionnaire survey was used to collect the data from 388 respondents in Bangladesh using the convenience sampling method. PLS-SEM was performed, and results revealed that perceived value positively affected non-users’ intention to engage in ride-sharing. Contrarily, consumers’ risk perception was negatively associated with their willingness to adopt ride-sharing applications. Additionally, the impact of consumers’ value perception on consumers’ intention to adopt ride-sharing services was positively moderated by perceived risk. This study leads to a comprehensive understanding of the factors related to perceived value and perceived risk dimensions and provides novel insights to entrepreneurial firms, market specialists and policymakers to reposition the service in the market and foster innovation to make the platforms more readily accessible and reliable through policy reformations. The study adds to the body of literature and enhances theoretical depth by highlighting the multi-dimensional nature of perceived value and risk from the human-centered design thinking approach, which aims to assist two-sided platforms like ride-sharing to understand the factors that influence non-users willingness to engage in ride-sharing services.
Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach
Authors: Cui Yue , Sun Hao , Zhao Yan , Yin Hongzhi , Zheng Kai
Published: September 2021

Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.
The Network Visibility Problem
Authors: Gatmiry Khashayar , Gomez-Rodriguez Manuel
Published: September 2021

Social media is an attention economy where broadcasters are constantly competing for attention in their followers’ feeds. Broadcasters are likely to elicit greater attention from their followers if their posts remain visible at the top of their followers’ feeds for a longer period of time. However, this depends on the rate at which their followers receive information in their feeds, which in turn depends on the broadcasters they follow. Motivated by this observation and recent calls for fairness of exposure in social networks, in this article, we look at the task of recommending links from the perspective of visibility optimization. Given a set of candidate links provided by a link recommendation algorithm, our goal is to find a subset of those links that would provide the highest visibility to a set of broadcasters. To this end, we first show that this problem reduces to maximizing a nonsubmodular nondecreasing set function under matroid constraints. Then, we show that the set function satisfies a notion of approximate submodularity that allows the standard greedy algorithm to enjoy theoretical guarantees. Experiments on both synthetic and real data gathered from Twitter show that the greedy algorithm is able to consistently outperform several competitive baselines.
PySPH: A Python-based Framework for Smoothed Particle Hydrodynamics
Authors: Ramachandran Prabhu , Bhosale Aditya , Puri Kunal , Negi Pawan , Muta Abhinav , Dinesh A. , Menon Dileep , Govind Rahul , Sanka Suraj , Sebastian Amal S. , Sen Ananyo , Kaushik Rohan , Kumar Anshuman , Kurapati Vikas , Patil Mrinalgouda , Tavker Deep , Pandey Pankaj , Kaushik Chandrashekhar , Dutt Arkopal , Agarwal Arpit
Published: September 2021

PySPH is an open-source, Python-based, framework for particle methods in general and Smoothed Particle Hydrodynamics (SPH) in particular. PySPH allows a user to define a complete SPH simulation using pure Python. High-performance code is generated from this high-level Python code and executed on either multiple cores, or on GPUs, seamlessly. It also supports distributed execution using MPI. PySPH supports a wide variety of SPH schemes and formulations. These include, incompressible and compressible fluid flow, elastic dynamics, rigid body dynamics, shallow water equations, and other problems. PySPH supports a variety of boundary conditions including mirror, periodic, solid wall, and inlet/outlet boundary conditions. The package is written to facilitate reuse and reproducibility. This article discusses the overall design of PySPH and demonstrates many of its features. Several example results are shown to demonstrate the range of features that PySPH provides.
Exploiting Group Information for Personalized Recommendation with Graph Neural Networks
Authors: Tian Zhiqiang , Liu Yezheng , Sun Jianshan , Jiang Yuanchun , Zhu Mingyue
Published: September 2021

Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared
CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words
Authors: Liu Chao , Xia Xin , Lo David , Liu Zhiwe , Hassan Ahmed E. , Li Shanping
Published: September 2021

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR)-based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning (DL)-based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this article, we proposed an IR-based model CodeMatcher that inherits the advantages of DeepCS (i.e., the capability of understanding the sequential semantics in important query words), while it can leverage the indexing technique in the IR-based model to accelerate the search response time substantially. CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query. We verified its effectiveness on a large-scale codebase with ~41K repositories. Experimental results showed that CodeMatcher achieves an MRR (a widely used accuracy measure for code search) of 0.60, outperforming DeepCS, CodeHow, and UNIF by 82%, 62%, and 46%, respectively. Our proposed model is over 1.2K times faster than DeepCS. Moreover, CodeMatcher outperforms two existing online search engines (GitHub and Google search) by 46% and 33%, respectively, in terms of MRR. We also observed that: fusing the advantages of IR-based and DL-based models is promising; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code.
MICOSE4aPS: Industrially Applicable Maturity Metric to Improve Systematic Reuse of Control Software
Authors: Vogel-Heuser Birgit , Neumann Eva-Maria , Fischer Juliane
Published: September 2021

automated Production Systems (aPS)
are highly complex, mechatronic systems that usually have to operate reliably for many decades. Standardization and reuse of control software modules is a core prerequisite to achieve the required system quality in increasingly shorter development cycles. However, industrial case studies in aPS show that many aPS companies still struggle with strategically reusing software. This paper proposes a metric-based approach to objectively measure the

m
aturity of
i
ndustrial IEC 61131-based
co
ntrol
s
oftwar
e
in aPS (MICOSE4aPS)

to identify potential weaknesses and quality issues hampering systematic reuse. Module developers in the machine and plant manufacturing industry can directly benefit as the metric calculation is integrated into the software engineering workflow. An in-depth industrial evaluation in a top-ranked machine manufacturing company in food packaging and an expert evaluation with different companies confirmed the benefit of efficiently managing the quality of control software.
Towards an Anatomy of Software Craftsmanship
Authors: Sundelin Anders , Gonzalez-huerta Javier , Wnuk Krzysztof , Gorschek Tony
Published: September 2021

Context:
The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective.

Objective:
The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study.

Method:
We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories.

Results:
The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results.

Conclusion:
We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.
SPI: Automated Identification of Security Patches via Commits
Authors: Zhou Yaqin , Siow Jing Kai , Wang Chenyu , Liu Shangqing , Liu Yang
Published: September 2021

Security patches in open source software, providing security fixes to identified vulnerabilities, are crucial in protecting against cyber attacks. Security advisories and announcements are often publicly released to inform the users about potential security vulnerability. Despite the
National Vulnerability Database (NVD)
publishes identified vulnerabilities, a vast majority of vulnerabilities and their corresponding security patches remain beyond public exposure, e.g., in the open source libraries that are heavily relied on by developers. As many of these patches exist in open sourced projects, the problem of curating and gathering security patches can be difficult due to their hidden nature. An extensive and complete security patches dataset could help end-users such as security companies, e.g., building a security knowledge base, or researcher, e.g., aiding in vulnerability research.

To efficiently curate security patches including undisclosed patches at large scale and low cost, we propose a deep neural-network-based approach built upon commits of open source repositories. First, we design and build security patch datasets that include 38,291 security-related commits and 1,045
Common Vulnerabilities and Exposures (CVE)
patches from four large-scale C programming language libraries. We manually verify each commit, among the 38,291 security-related commits, to determine if they are security related.

We devise and implement a deep learning-based security patch identification system that consists of two composite neural networks: one commit-message neural network that utilizes pretrained word representations learned from our commits dataset and one code-revision neural network that takes code before revision and after revision and learns the distinction on the statement level. Our system leverages the power of the two networks for Security Patch Identification. Evaluation results show that our system significantly outperforms SVM and K-fold stacking algorithms. The result on the combined dataset achieves as high as 87.93% F1-score and precision of 86.24%.

We deployed our pipeline and learned model in an industrial production environment to evaluate the generalization ability of our approach. The industrial dataset consists of 298,917 commits from 410 new libraries that range from a wide functionalities. Our experiment results and observation on the industrial dataset proved that our approach can identify security patches effectively among open sourced projects.
hyper.deal: An Efficient, Matrix-free Finite-element Library for High-dimensional Partial Differential Equations
Authors: Munch Peter , Kormann Katharina , Kronbichler Martin
Published: September 2021

This work presents the efficient, matrix-free finite-element library
hyper.deal
for solving partial differential equations in two up to six dimensions with high-order discontinuous Galerkin methods. It builds upon the low-dimensional finite-element library
deal.II
to create complex low-dimensional meshes and to operate on them individually. These meshes are combined via a tensor product on the fly, and the library provides new special-purpose highly optimized matrix-free functions exploiting domain decomposition as well as shared memory via
MPI-3.0
features. Both node-level performance analyses and strong/weak-scaling studies on up to 147,456 CPU cores confirm the efficiency of the implementation. Results obtained with the library
hyper.deal
are reported for high-dimensional advection problems and for the solution of the Vlasov–Poisson equation in up to six-dimensional phase space.
Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines
Authors: Graziotin Daniel , Lenberg Per , Feldt Robert , Wagner Stefan
Published: September 2021

A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this article, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.
Parallel Test Prioritization
Authors: Zhou Jianyi , Chen Junjie , Hao Dan
Published: September 2021

Although regression testing is important to guarantee the software quality in software evolution, it suffers from the widely known cost problem. To address this problem, existing researchers made dedicated efforts on test prioritization, which optimizes the execution order of tests to detect faults earlier; while practitioners in industry leveraged more computing resources to save the time cost of regression testing. By combining these two orthogonal solutions, in this article, we define the problem of parallel test prioritization, which is to conduct test prioritization in the scenario of parallel test execution to reduce the cost of regression testing.

Different from traditional sequential test prioritization, parallel test prioritization aims at generating a set of test sequences, each of which is allocated in an individual computing resource and executed in parallel. In particular, we propose eight parallel test prioritization techniques by adapting the existing four sequential test prioritization techniques, by including and excluding testing time in prioritization.

To investigate the performance of the eight parallel test prioritization techniques, we conducted an extensive study on 54 open-source projects and a case study on 16 commercial projects from
Baidu
, a famous search service provider with 600M monthly active users. According to the two studies, parallel test prioritization does improve the efficiency of regression testing, and cost-aware additional parallel test prioritization technique significantly outperforms the other techniques, indicating that this technique is a good choice for practical parallel testing. Besides, we also investigated the influence of two external factors, the number of computing resources and time allowed for parallel testing, and find that more computing resources indeed improve the performance of parallel test prioritization. In addition, we investigated the influence of two more factors, test granularity and coverage criterion, and find that parallel test prioritization can still accelerate regression testing in parallel scenario. Moreover, we investigated the benefit of parallel test prioritization on the regression testing process of continuous integration, considering both the cumulative acceleration performance and the overhead of prioritization techniques, and the results demonstrate the superiority of parallel test prioritization.
SPEX: A Generic Framework for Enhancing Neural Social Recommendation
Authors: Li Hui , Li Lianyun , Xv Guipeng , Lin Chen , Li Ke , Jiang Bingchuan
Published: September 2021

Social Recommender Systems (SRS) have attracted considerable attention since its accompanying service, social networks, helps increase user satisfaction and provides auxiliary information to improve recommendations. However, most existing SRS focus on social influence and ignore another essential social phenomenon, i.e., social homophily. Social homophily, which is the premise of social influence, indicates that people tend to build social relations with similar people and form influence propagation paths. In this article, we propose a generic framework Social PathExplorer (SPEX) to enhance neural SRS. SPEX treats the neural recommendation model as a black box and improves the quality of recommendations by modeling the social recommendation task, the formation of social homophily, and their mutual effect in the manner of multi-task learning. We design a Graph Neural Network based component for influence propagation path prediction to help SPEX capture the rich information conveyed by the formation of social homophily. We further propose an uncertainty based task balancing method to set appropriate task weights for the recommendation task and the path prediction task during the joint optimization. Extensive experiments have validated that SPEX can be easily plugged into various state-of-the-art neural recommendation models and help improve their performance. The source code of our work is available at: https://github.com/XMUDM/SPEX.
Error Bounded Line Simplification Algorithms for Trajectory Compression: An Experimental Evaluation
Authors: Lin Xuelian , Ma Shuai , Jiang Jiahao , Hou Yanchen , Wo Tianyu
Published: September 2021

Nowadays, various sensors are collecting, storing, and transmitting tremendous trajectory data, and it is well known that the storage, network bandwidth, and computing resources could be heavily wasted if raw trajectory data is directly adopted. Line simplification algorithms are effective approaches to attacking this issue by compressing a trajectory to a set of continuous line segments, and are commonly used in practice. In this article, we first classify the error bounded line simplification algorithms into different categories and review each category of algorithms. We then study the data aging problem of line simplification algorithms and distance metrics from the views of aging friendliness and aging errors. Finally, we present a systematic experimental evaluation of representative error bounded line simplification algorithms, including both compression optimal and sub-optimal methods, in terms of commonly adopted perpendicular Euclidean, synchronous Euclidean, and direction-aware distances. Using real-life trajectory datasets, we systematically evaluate and analyze the performance (compression ratio, average error, running time, aging friendliness, and query friendliness) of error bounded line simplification algorithms with respect to distance metrics, trajectory sizes, and error bounds. Our study provides a full picture of error bounded line simplification algorithms, which leads to guidelines on how to choose appropriate algorithms and distance metrics for practical applications.
Efficient Multi-modal Hashing with Online Query Adaption for Multimedia Retrieval
Authors: Zhu Lei , Zheng Chaoqun , Lu Xu , Cheng Zhiyong , Nie Liqiang , Zhang Huaxiang
Published: September 2021

Multi-modal hashing supports efficient multimedia retrieval well. However, existing methods still suffer from two problems: (1) Fixed multi-modal fusion. They collaborate the multi-modal features with fixed weights for hash learning, which cannot adaptively capture the variations of online streaming multimedia contents. (2) Binary optimization challenge. To generate binary hash codes, existing methods adopt either two-step relaxed optimization that causes significant quantization errors or direct discrete optimization that consumes considerable computation and storage cost. To address these problems, we first propose a Supervised Multi-modal Hashing with Online Query-adaption method. A self-weighted fusion strategy is designed to adaptively preserve the multi-modal features into hash codes by exploiting their complementarity. Besides, the hash codes are efficiently learned with the supervision of pair-wise semantic labels to enhance their discriminative capability while avoiding the challenging symmetric similarity matrix factorization. Further, we propose an efficient Unsupervised Multi-modal Hashing with Online Query-adaption method with an adaptive multi-modal quantization strategy. The hash codes are directly learned without the reliance on the specific objective formulations. Finally, in both methods, we design a parameter-free online hashing module to adaptively capture query variations at the online retrieval stage. Experiments validate the superiority of our proposed methods.
Enhancing Search-based Testing with Testability Transformations for Existing APIs
Authors: Arcuri Andrea , Galeotti Juan P.
Published: September 2021

Search-based software testing (SBST) has been shown to be an effective technique to generate test cases automatically. Its effectiveness strongly depends on the guidance of the fitness function. Unfortunately, a common issue in SBST is the so-called
flag problem
, where the fitness landscape presents a plateau that provides no guidance to the search. In this article, we provide a series of novel
testability transformations
aimed at providing guidance in the context of commonly used API calls (e.g., strings that need to be converted into valid date/time objects). We also provide specific transformations aimed at helping the testing of REST Web Services. We implemented our novel techniques as an extension to
EvoMaster
, an SBST tool that generates system-level test cases. Experiments on nine open-source REST web services, as well as an industrial web service, show that our novel techniques improve performance significantly.
Coupling Friction with Visual Appearance
Authors: Andrews Sheldon , Nassif Loic , Erleben Kenny , Kry Paul G.
Published: September 2021

We present a novel meso-scale model for computing anisotropic and asymmetric friction for contacts in rigid body simulations that is based on surface facet orientations. The main idea behind our approach is to compute a direction dependent friction coefficient that is determined by an object's roughness. Specifically, where the friction is dependent on asperity interlocking, but at a scale where surface roughness is also a visual characteristic of the surface. A GPU rendering pipeline is employed to rasterize surfaces using a shallow depth orthographic projection at each contact point in order to sample facet normal information from both surfaces, which we then combine to produce direction dependent friction coefficients that can be directly used in typical LCP contact solvers, such as the projected Gauss-Seidel method. We demonstrate our approach with a variety of rough textures, where the roughness is both visible in the rendering and in the motion produced by the physical simulation.
Visual Simulation of Soil-Structure Destruction with Seepage Flows
Authors: Wang Xu , Fujisawa Makoto , Mikawa Masahiko
Published: September 2021

This paper introduces a method for simulating soil-structure coupling with water, which involves a series of visual effects, including wet granular materials, seepage flows, capillary action between grains, and dam breaking simulation. We develop a seepage flow based SPH-DEM framework to handle soil and water particles interactions through a momentum exchange term. In this framework, water is seen as a seepage flow through porous media by Darcy's law; the seepage rate and the soil permeability are manipulated according to drag coefficient and soil porosity. A water saturation-based capillary model is used to capture various soil behaviors such as sandy soil and clay soil. Furthermore, the capillary model can dynamically adjust liquid bridge forces induced by surface tension between soil particles. The adhesion model describes the attraction ability between soil surfaces and water particles to achieve various visual effects for soil and water. Lastly, this framework can capture the complicated dam-breaking scenarios caused by overtopping flow or internal seepage erosion that are challenging to simulate.
Neural UpFlow
Authors: Roy Bruno , Poulin Pierre , Paquette Eric
Published: September 2021

We present a novel up-resing technique for generating high-resolution liquids based on scene flow estimation using deep neural networks. Our approach infers and synthesizes small- and large-scale details solely from a low-resolution particle-based liquid simulation. The proposed network leverages neighborhood contributions to encode inherent liquid properties throughout convolutions. We also propose a particle-based approach to interpolate between liquids generated from varying simulation discretizations using a state-of-the-art bidirectional optical flow solver method for fluids in addition with a novel key-event topological alignment constraint. In conjunction with the neighborhood contributions, our loss formulation allows the inference model throughout epochs to reward important differences in regard to significant gaps in simulation discretizations. Even when applied in an untested simulation setup, our approach is able to generate plausible high-resolution details. Using this interpolation approach and the predicted displacements, our approach combines the input liquid properties with the predicted motion to infer semi-Lagrangian advection. We furthermore showcase how the proposed interpolation approach can facilitate generating large simulation datasets with a subset of initial condition parameters.
Contact and Physical Interaction
Authors: Hogan Neville
Published: September 2021

This article reviews approaches to controlling robots undergoing physical contact and dynamic interaction with objects in the world. Conventional motion control is compared with a hybrid combination of position and force control. Several challenges are reviewed, most importantly the problems of instability: dynamic instability due to coupling, and static instability due to exerting force. Energetically passive interactive dynamics addresses the former; a minimum stiffness proportional to the force exerted addresses the latter. Actuators, which dominate the robot's interactive dynamics, are briefly surveyed, including series elastic, variable-stiffness, and emerging designs. A comparison with human performance is made. A bioinspired approach to controlling interactive dynamics (mechanical impedance or admittance) is reviewed. Robot configuration profoundly modulates apparent inertia, whereas force feedback control has minimal influence. Superimposing first-order mechanical impedances simplifies controlling many degrees of freedom. It manages redundancy while preserving passivity (unlike null-space projection methods) and enables seamless operation into and out of singular configurations.
Control of the Stefan System and Applications: A Tutorial
Authors: Koga Shumon , Krstic Miroslav
Published: September 2021

The so-called Stefan system describes the dynamical model of the liquid–solid phase change in materials ranging from water and ice in the polar caps to metal casting and additive manufacturing (3D printing). The mathematical structure is given by a partial differential equation (PDE) with a moving boundary governed by a scalar ordinary differential equation. Because of the system's moving-boundary nature, control of the Stefan model is unconventional even within the class of otherwise challenging PDE control problems. The second decade of the twenty-first century has witnessed remarkable advances in control design for the Stefan system. Such advances carry significant potential in several areas of technology. In this article, we briefly review the principal literature on control of the Stefan model, along with the associated basics of the PDE analysis of the model and select applications. Principal ideas from our work on control design, stability analysis, and the maintenance of physical phase constraints are given sufficient attention and tutorial treatment so that the article can serve as a self-contained point of entry into the growing subject of boundary control of the Stefan system using the method of PDE backstepping.
Remembrances of a Honey Bee Biologist
Authors: Seeley Thomas D.
Published: September 2021

Thomas Seeley's research has focused on analyzing the collective intelligence and natural lives of honey bees. This account describes how the author encountered honey bees as a boy and became a beekeeper; how he switched his career path from medicine to biology to study the behavior and social life of honey bees; and how he focuses on understanding how a honey bee colony functions when it lives in the wild, rather than in a beekeeper's hive. He has shown how a honey bee colony works as a single decision-making unit to adaptively allocate its foragers among flower patches and to choose its nesting site in a hollow tree. These findings buttress the view that, in some social insect species, the colony is a group-level vehicle of gene survival. Beyond his research, he has written three books to synthesize these findings for biologists and share these discoveries with beekeepers.
Trust Within the Workplace: A Review of Two Waves of Research and a Glimpse of the Third
Authors: Dirks Kurt T. , de Jong Bart
Published: September 2021

Over the past quarter century, trust has emerged as a core concept in organizational psychology and organizational behavior. We review the body of research amassed over that period using a field evolutionary lens and identify two “waves” that have shaped and progressed the field in specific and important ways: Wave 1, establishing foundational building blocks; Wave 2, questioning assumptions and examining alternatives. For each wave, we identify what has been learned and identify key questions that still need to be addressed. We also suggest researchers will need to evolve the fundamental questions asked in order to maintain the momentum of the literature into the next quarter century, and we speculate about what these might look like. Finally, as a result of recent organizational developments and societal disruptions, we anticipate the emergence of a third wave, aimed at examining their implications for trust in the workplace.
From Traditional Research to Responsible Research: The Necessity of Scientific Freedom and Scientific Responsibility for Better Societies
Authors: Tsui Anne S.
Published: September 2021

My research journey spans a period of exciting new theories informing innovative practices in businesses and other organizations and a period littered with concerns about the research-practice gap, questionable research practices, and a strong emphasis on the number of publications in top journals for hiring and promotions. These recent developments led to the dilution of both scientific freedom and scientific responsibility in our scientific work. I offer my research journey to illustrate the importance of both, with the most recent endeavor in a global responsible research movement to produce useful and credible knowledge that will enable business and management practices to serve all stakeholders and to solve the world's most challenging problems. Emerging institutional changes in business schools, journals, associations, and accreditation agencies provide hope that scholars will soon find the conditions favorable for both freedom and responsibility to support their aspiration to pursue research that will contribute to better societies and meaningful careers.
Systems Biology of the Vasopressin V2 Receptor: New Tools for Discovery of Molecular Actions of a GPCR
Authors: Chen Lihe , Jung Hyun Jun , Datta Arnab , Park Euijung , Poll Brian G. , Kikuchi Hiroaki , Leo Kirby T. , Mehta Yash , Lewis Spencer , Khundmiri Syed J. , Khan Shaza , Chou Chung-Lin , Raghuram Viswanathan , Yang Chin-Rang , Knepper Mark A.
Published: September 2021

Systems biology can be defined as the study of a biological process in which all of the relevant components are investigated together in parallel to discover the mechanism. Although the approach is not new, it has come to the forefront as a result of genome sequencing projects completed in the first few years of the current century. It has elements of large-scale data acquisition (chiefly next-generation sequencing–based methods and protein mass spectrometry) and large-scale data analysis (big data integration and Bayesian modeling). Here we discuss these methodologies and show how they can be applied to understand the downstream effects of GPCR signaling, specifically looking at how the neurohypophyseal peptide hormone vasopressin, working through the V2 receptor and PKA activation, regulates the water channel aquaporin-2. The emerging picture provides a detailedframework for understanding the molecular mechanisms involved in water balance disorders, pointing the way to improved treatment of both polyuric disorders and water-retention disorders causing dilutional hyponatremia.
Study on the Clinical Characteristics and Treatment of Obstetric Hypertension with Sequence Pattern Mining Algorithm
Authors: Li Jiawei
Published: September 2021

Aiming at the difficulty in setting the support threshold for sequential pattern mining algorithms and improving the effectiveness of the support threshold setting without the guidance of domain experts’ experience, an improved SPADE (sequential pattern discovery using equivalence classes) algorithm is proposed. By analyzing the relationship between the number of frequent sequences and the support threshold, the support threshold is dynamically selected. Using the electronic medical record data from a medical centre, the time-series relationship of the drugs taken by hypertension patients was extracted as the drug sequence dataset. By determining the optimal support threshold of the dataset, the frequent sequence set is mined, and the sequence rules are generated from the obtained regular sequences to visualize the sequence rules. The sequence rules of medication for hypertensive patients were combined with the patients’ physical indicators for the recommendation. For patients with obstetric hypertension, a combination of nifedipine and captopril is recommended. Through the comparison of the observation group and control group, we study the curative effect of various drugs. The results showed that the total effective rate of the observation group was about 96.6%; compared with the control group, the result indicated that the difference was significant (

P

 < 0.05). The comparison of blood pressure levels between the two groups after treatment also showed that the results of the observation group were ideal (

P

 < 0.05). In addition, the incidence of postpartum haemorrhage and perinatal complications in the observation group was also significantly reduced (

P

 < 0.05). Therefore, the combination of medication for pregnancy hypertension syndrome can effectively improve the treatment effect of the disease and reduce the rate of postpartum haemorrhage and the incidence of perinatal complications.
DNA Hypomethylation of DOCK1 Leading to High Expression Correlates with Neurologic Deterioration and Poor Function Outcomes after Spontaneous Intracerebral Hemorrhage
Authors: Gao Yufeng , Fu Xiaojie , Yu Lie , Zhang Di , Lu Zhengfang , Cui Kefei , Liu Chang , Man Jiang , Wang Jianping
Published: September 2021

Objective. Spontaneous intracerebral hemorrhage (ICH) is a blood clot arising in the brain parenchyma in the absence of trauma or surgery and accounts for 10% to 15% of all strokes, leading to higher rates of mortality and morbidity than either ischemic stroke or subarachnoid hemorrhage. We sought to investigate the potential association of DOCK1 with neurological deficits and outcomes in patients with spontaneous ICH. Methods. Identification of methylation-regulated differentially expressed genes (MeDEGs) between ICH patients and matched controls was performed by analyzing the raw data from the GSE179759 and GSE125512 datasets deposited in the Gene Expression Omnibus. A total of 114 patients who were admitted to our hospital for spontaneous ICH were retrospectively analyzed, with 108 healthy volunteers who had received physical examinations at the same period as controls. The mRNA expression of DOCK1 was determined by quantitative real-time polymerase chain reaction (qRT-PCR). The hematoma volume was calculated according to the Coniglobus formula. The severity of neurological deficits was evaluated using National Institutes of Health Stroke Scale (NIHSS) scores and function outcomes were evaluated by modified Rankin Scale (mRS) scores. Results. A total of 15 MeDEGs between ICH patients and matched controls were identified. The mRNA expression of DOCK1 was remarkably higher in the serum samples of patients with spontaneous ICH than in the healthy controls. According to hematoma volume after ICH attack, small (<10 mL), medium (10 to 30 mL), and large (>30 mL) groups were arranged. The proportions of male patients and patients aged ≥60 years were significantly higher in the large group than in the small and medium groups (

P
<
0.05

). The mRNA expression of DOCK1 was significantly higher in the large group than in the small and medium groups (

P
<
0.05

). According to NIHSS scores, mild (NIHSS scores ≤15), moderate (NIHSS scores from 16 to 30), and severe (NIHSS scores from 31 to 45) groups were classified. It was observed that the severe group had higher proportions of male patients and patients aged ≥60 years than the mild and moderate groups (

P
<
0.05

). The severe group exhibited a higher mRNA expression of DOCK1 than the mild and moderate groups (

P
<
0.05

). According to mRS scores, higher proportions of male patients and patients aged ≥60 years were observed in the unfavorable group than the favorable group (

P
<
0.05

). The patients in the unfavorable group showed an elevated DOCK1 mRNA expression compared to those in the favorable group (

P
<
0.05

). Conclusion. The study provided evidence that male gender, older age, and higher DOCK1 mRNA expression were related to higher admission hematoma volume, neurologic deterioration, and poor function outcomes in patients with spontaneous ICH.
Application of Nanooptics in Photographic Imagery and Medical Imaging
Authors: Liu Yunrun , Pang Na , Cai Yunzhou , Yang Yanqing , Zeng Chunyu , Wang Yuehong
Published: September 2021

Background. At present, with the continuous development of nanotechnology, great changes have taken place in people’s lives in medical treatment, production, daily leisure, and so on. Nanooptical technology is entirely based on nanotechnology that laser and visible light are limited to submicron structures (nanopores, nanoslits, and nanoneedles). Due to the great development potential of nanooptical technology in nanoscale sensors, TOF camera applications, THz imaging technology, and other imaging equipment materials and applications, people have been interested in it, recently. Scope and Approach. In this review, the importance of good practices for nanooptical technology used in equipment as both nanometer scale sensors and optical auxiliary equipment is described. Based on recent reports, this work discussed the development of nanooptical technology in daily photography and medical imaging from both the positive and the negative sides and compared the engineering techniques. Key Findings and Conclusions. As a kind of new optical technology, nanooptical technology can produce the plasmonic effect under the intense collision of atoms and electrons in nanostructures. It has significant effects in superresolution nanolithography, high-density data storage, near-field optics, and other fields. Although the current nanooptic technology is not extremely mature, the results obtained from current works are pointing out that nanooptical technology is the future of daily imaging and medical imaging, and it also will play a positive role in the improvement of people’s health and ecological environment quality. As a trend, nanooptical technology is developing in the direction of energy-saving, portability, high efficiency, and low pollution, and in the upsurge of environmental protection in the world, nanooptical technology will surely achieve amazing development in the field of daily photography and medical imaging. Under the huge market demand and innovation power, nanophotonics technology will cover all emerging technologies that share the same research field with it and take advantage of each technology (terahertz, cell and molecular microscopy, and nanoscale probes) to develop an unprecedented new century in nanoscience. The future trends of research contain finding new imaging equipment with nanostructure, designing nanooptical products, and improving engineering techniques.