bluuebunny/crossref_metadata_2025 huggingface dataset:
The historical footprint and future challenges of water-energy-food nexus research: a bibliometric review towards sustainable development
Authors: Han Xinxueqi , Zhao Yong , Gao Xuerui , Wang Yubao , Jiang Shan , Zhu Yongnan , An Tingli
Published: January 2021

The water–energy–food (WEF) nexus has emerged as a frontier issue in interdisciplinary research and is one of the most complex sustainability challenges that the world is faced with today. In this review, we ask: (i) how can the interdependent relationships among water, energy, and food resources be identified? (ii) what methods have been applied to understand these relationships? and (iii) what are the future opportunities and challenges for the WEF nexus development? To answer these questions, we provide a critical assessment of the relevant literature from Web of Science database on WEF nexus published between 2008 and 2019 using a bibliometric analysis. Using the resulting 396 published articles, we systematically reviewed the concept and the bibliometric characteristics of the WEF nexus research to assess the development footprint. Based on the most popular topics and research methods found in these publications, we discussed the major research limitations as well as future opportunities and challenges for WEF research. An examination of internal and external relationships among topics showed that the three most recent hot areas of WEF nexus research include (i) water, energy, and food, (ii) policy-making and resource management, and (iii) system models and methods. Specifically, considering that no one method can solve all problems, we innovatively summarized the application scope and the advantages and disadvantages of each method, with a particular focus on the WEF nexus models. This was undertaken to support readers in choosing a scientific method to analyze the specific WEF nexus related issues. We anticipate that complex interdependence mechanisms, data uncertainty, analytical model development, and in-depth policy implementation will pose the greatest challenges for future WEF nexus research; however, these challenges will also generate better research opportunities. This bibliometric review highlights that to increase understanding of complex WEF systems and formulating optimal strategies to manage them is of great significance for environmental and social sustainability.
Triple Cyclic Codes Over 𝔽q + u𝔽q
Authors: Yao Ting , Zhu Shixin , Pang Binbin
Published: January 2021

Let [Formula: see text], where [Formula: see text] is a power of a prime number [Formula: see text] and [Formula: see text]. A triple cyclic code of length [Formula: see text] over [Formula: see text] is a set that can be partitioned into three parts that any cyclic shift of the coordinates of the three parts leaves the code invariant. These codes can be viewed as [Formula: see text]-submodules of [Formula: see text]. In this paper, we study the generator polynomials and the minimum generating sets of this kind of codes. Some optimal or almost optimal linear codes are obtained from this family of codes. We present the relationship between the generators of triple cyclic codes and their duals. As a special class of triple cyclic codes, separable codes over [Formula: see text] are discussed briefly in the end.
Optimal Placement of Sensors for Controlling Smart Base Isolation Systems
Authors: Mohebbi Mohtasham , Dadkhah Hamed
Published: January 2021

Smart base isolation composed of a low-damping base isolation and a supplemental magneto-rheological (MR) damper is one of the most effective semi-active control systems to protect the structures against earthquake. In this study, optimal placement of sensors is determined by using a mixed-integer genetic algorithm for best possible performance. The results show significant effect of sensor configuration on the control system performance, revealing that the sensor configuration should be taken as an important factor in design process of smart base isolation. Besides, although optimal determination of sensor placement improves the control system performance, a high number of sensors is needed to measure structure responses. A method using the least number of sensors is proposed in order to reduce the control cost. The results show the effectiveness of this method in achieving near-optimal response and reducing the control cost.
Indirect Damage Identification in Bridges Based on Dynamic Tyre Pressure Monitoring
Authors: Kumar G. Sai , Krishnanunni C. G. , Rao B. N.
Published: January 2021

This paper deals with an indirect health monitoring strategy for bridges using an instrumented vehicle. Thermodynamic principles are used to relate the change in Vehicle–Bridge-Interaction (VBI) forces to the change in dynamic tyre pressure. The damage identification process involves two stages. In the first stage, the unknown tyre model parameters are estimated using Bayesian inference based on the calibration data. The approach uses a Stein variational gradient descent implementation of the Bayes rule to quantify the uncertainty in the estimated tyre parameters. In the second stage, the calibrated tyre model is used to reconstruct the change in VBI force from measured tyre pressure data considering a damaged bridge. It is observed that damage present in the bridge produces notable changes in VBI force. Contour plots based on VBI force and natural frequency are developed for damage detection. The reconstructed VBI force change is used to quantify damage using the contour plots. Further, the least square estimation approach is adopted for damage identification by defining appropriate objective functions and imposing constraints on the damage indicators. The damage is estimated by minimizing the objective function using Cuckoo search algorithm. Numerical experiments reveal that the developed method could be used for accurate damage identification in the presence of measurement noise, uncertainty in estimated tyre parameters, and the uncertainty in bridge model parameters.
Accelerating dark energy universe with LRS Bianchi type-I space-time
Authors: Solanke Y. S. , Pawar D. D. , Dagwal V. J.
Published: January 2021

The main purpose of this paper is to investigate LRS Bianchi type I metric in the presence of perfect fluid and dark energy. In order to obtain a deterministic solution of the field equations we have assumed that the two sources of the perfect fluid and dark energy interact minimally with separate conservation of their energy–momentum tensors as well EoS parameter of the perfect fluid is assumed to be constant. In addition to this we have used a special law of variation of Hubble parameter proposed by Berman that yields constant deceleration parameter. For the two different constant values of deceleration we have obtained two different cosmological models. The physical behaviors of both the models have been discussed by using MATLAB.
Quantitative Evaluation of the Seismic Reinforcement Effect Based on Observed Microtremors
Authors: Baoyintu  , Kawase Hiroshi
Published: January 2021

To validate the seismic retrofitting of buildings quantitatively, we observed microtremors in the Uji campus buildings at Kyoto University. We compared the dynamic characteristics derived from the resonant frequency of microtremors in a five-story steel-frame structure and a three-story reinforced-concrete (RC) building before and after retrofitting. We calculated the weight of the building from the retrofitting design plan and constructed a numerical model of the structure by estimating the building stiffness that fits the resonant frequency. Assuming the characteristics of the restoring force for each story of the numerical model from previous studies, we calculated the non-linear response of the building to the strong motions predicted for earthquakes occurring at the Obaku fault system that runs right in front of the Uji campus. We compared the response of the numerical models constructed before and after retrofitting to verify the effects of the seismic retrofitting. We found that the proposed method of dynamic response model construction using microtremor observations is effective when the reinforcement members are rigidly fixed to the existing structure, as is the case for RC buildings.
Identifying Linear Models in Multi-Resolution Population Data Using Minimum Description Length Principle to Predict Household Income
Authors: Amornbunchornvej Chainarong , Surasvadi Navaporn , Plangprasopchok Anon , Thajchayapong Suttipong
Published: January 2021

One shirt size cannot fit everybody, while we cannot make a unique shirt that fits perfectly for everyone because of resource limitations. This analogy is true for policy making as well. Policy makers cannot make a single policy to solve all problems for all regions because each region has its own unique issue. At the other extreme, policy makers also cannot make a policy for each small village due to resource limitations. Would it be better if we can find a set of largest regions such that the population of each region within this set has common issues and we can make a single policy for them? In this work, we propose a framework using regression analysis and Minimum Description Length (MDL) to find a set of largest areas that have common indicators, which can be used to predict household incomes efficiently. Given a set of household features, and a multi-resolution partition that represents administrative divisions, our framework reports a set
C
*
of largest subdivisions that have a common predictive model for population-income prediction. We formalize the problem of finding
C
*
and propose an algorithm that can find
C
*
correctly. We use both simulation datasets as well as a real-world dataset of Thailand’s population household information to demonstrate our framework performance and application. The results show that our framework performance is better than the baseline methods. Moreover, we demonstrate that the results of our method can be used to find indicators of income prediction for many areas in Thailand. By adjusting these indicator values via policies, we expect people in these areas to gain more incomes. Hence, the policy makers will be able to make policies by using these indicators in our results as a guideline to solve low-income issues. Our framework can be used to support policy makers in making policies regarding any other dependent variable beyond income in order to combat poverty and other issues. We provide the R package, MRReg, which is the implementation of our framework in the R language. The MRReg package comes with a documentation for anyone who is interested in analyzing linear regression on multi-resolution population data.
Core Interest Network for Click-Through Rate Prediction
Authors: Xu En , Yu Zhiwen , Guo Bin , Cui Helei
Published: January 2021

In modern online advertising systems, the click-through rate (CTR) is an important index to measure the popularity of an item. It refers to the ratio of users who click on a specific advertisement to the number of total users who view it. Predicting the CTR of an item in advance can improve the accuracy of the advertisement recommendation. And it is commonly calculated based on users’ interests. Thus, extracting users’ interests is of great importance in CTR prediction tasks. In the literature, a lot of studies treat the interaction between users and items as sequential data and apply the recurrent neural network (RNN) model to extract users’ interests. However, these solutions cannot handle the case when the sequence length is relatively long, e.g., over 100. This is because of the vanishing gradient problem of RNN, i.e., the model cannot learn a users’ previous behaviors that are too far away from the current moment. To address this problem, we propose a new Core Interest Network (CIN) model to mitigate the problem of a long sequence in the CTR prediction task with sequential data. In brief, we first extract the core interests of users and then use the refined data as the input of subsequent learning tasks. Extensive evaluations on real dataset show that our CIN model can outperform the state-of-the-art solutions in terms of prediction accuracy.
Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living
Authors: Akbari Ali , Grimsley Reese , Jafari Roozbeh
Published: January 2021

Wearable systems have unlocked new sensing paradigms in various applications such as human activity recognition, which can enhance effectiveness of mobile health applications. Current systems using wearables are not capable of understanding their surroundings, which limits their sensing capabilities. For instance, distinguishing certain activities such as attending a meeting or class, which have similar motion patterns but happen in different contexts, is challenging by merely using wearable motion sensors. This article focuses on understanding user's surroundings, i.e., environmental context, to enhance capability of wearables, with focus on detecting complex activities of daily living (ADL). We develop a methodology to automatically detect the context using passively observable information broadcasted by devices in users’ locale. This system does not require specific infrastructure or additional hardware. We develop a pattern extraction algorithm and probabilistic mapping between the context and activities to reduce the set of probable outcomes. The proposed system contains a general ADL classifier working with motion sensors, learns personalized context, and uses that to reduce the search space of activities to those that occur within a certain context. We collected real-world data of complex ADLs and by narrowing the search space with context, we improve average F1-score from 0.72 to 0.80.
Enabling Cost-Effective Population Health Monitoring By Exploiting Spatiotemporal Correlation
Authors: Chen Dawei , Wang Jiangtao , Ruan Wenjie , Ni Qiang , Helal Sumi
Published: January 2021

Because of its important role in health policy-shaping, population health monitoring (PHM) is considered a fundamental block for public health services. However, traditional public health data collection approaches, such as clinic-visit-based data integration or health surveys, could be very costly and time-consuming. To address this challenge, this article proposes a cost-effective approach called Compressive Population Health (CPH), where a subset of a given area is selected in terms of regions within the area for data collection in the traditional way, while leveraging inherent spatial correlations of neighboring regions to perform data inference for the rest of the area. By alternating selected regions longitudinally, this approach can validate and correct previously assessed spatial correlations. To verify whether the idea of CPH is feasible, we conduct an in-depth study based on spatiotemporal morbidity rates of chronic diseases in more than
500
regions around London for over 10 years. We introduce our CPH approach and present three extensive analytical studies. The first confirms that significant spatiotemporal correlations do exist. In the second study, by deploying multiple state-of-the-art data recovery algorithms, we verify that these spatiotemporal correlations can be leveraged to do data inference accurately using only a small number of samples. Finally, we compare different methods for region selection for traditional data collection and show how such methods can further reduce the overall cost while maintaining high PHM quality.
Unbiased Measurement of Feature Importance in Tree-Based Methods
Authors: Zhou Zhengze , Hooker Giles
Published: January 2021

We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biased towards increasing the importance of features with more potential splits. We show that by appropriately incorporating split-improvement as measured on out of sample data, this bias can be corrected yielding better summaries and screening tools.
A Differentially Private Incentive Design for Traffic Offload to Public Transportation
Authors: Niu Luyao , Clark Andrew
Published: January 2021

Increasingly large trip demands have strained urban transportation capacity, which consequently leads to traffic congestion and rapid growth of greenhouse gas emissions. In this work, we focus on achieving sustainable transportation by incentivizing passengers to switch from private cars to public transport. We address the following challenges. First, the passengers incur inconvenience costs when changing their transit behaviors due to delay and discomfort, and thus need to be reimbursed. Second, the inconvenience cost, however, is unknown to the government when choosing the incentives. Furthermore, changing transit behaviors raises privacy concerns from passengers. An adversary could infer personal information (e.g., daily routine, region of interest, and wealth) by observing the decisions made by the government, which are known to the public. We adopt the concept of differential privacy and propose privacy-preserving incentive designs under two settings, denoted as two-way communication and one-way communication. Under two-way communication, passengers submit bids and then the government determines the incentives, whereas in one-way communication, the government simply sets a price without acquiring information from the passengers. We formulate the problem under two-way communication as a mixed integer linear program and propose a polynomial-time approximation algorithm. We show the proposed approach achieves truthfulness, individual rationality, social optimality, and differential privacy. Under one-way communication, we focus on how the government should design the incentives without revealing passengers’ inconvenience costs while still preserving differential privacy. We formulate the problem as a convex program and propose a differentially private and near-optimal solution algorithm. A numerical case study using the Caltrans Performance Measurement System (PeMS) data source is presented as evaluation. The results show that the proposed approaches achieve a win-win situation in which both the government and passengers obtain non-negative utilities.
𝜆ₛ: computable semantics for differentiable programming with higher-order functions and datatypes
Authors: Sherman Benjamin , Michel Jesse , Carbin Michael
Published: January 2021

Deep learning is moving towards increasingly sophisticated optimization objectives that employ higher-order functions, such as integration, continuous optimization, and root-finding. Since differentiable programming frameworks such as PyTorch and TensorFlow do not have first-class representations of these functions, developers must reason about the semantics of such objectives and manually translate them to differentiable code.

We present a differentiable programming language, λ

S

, that is the first to deliver a semantics for higher-order functions, higher-order derivatives, and Lipschitz but nondifferentiable functions. Together, these features enableλ

S

to expose differentiable, higher-order functions for integration, optimization, and root-finding as first-class functions with automatically computed derivatives. λ

S

’s semantics is computable, meaning that values can be computed to arbitrary precision, and we implement λ

S

as an embedded language in Haskell.

We use λ

S

to construct novel differentiable libraries for representing probability distributions, implicit surfaces, and generalized parametric surfaces – all as instances of higher-order datatypes – and present case studies that rely on computing the derivatives of these higher-order functions and datatypes. In addition to modeling existing differentiable algorithms, such as a differentiable ray tracer for implicit surfaces, without requiring any user-level differentiation code, we demonstrate new differentiable algorithms, such as the Hausdorff distance of generalized parametric surfaces.
On the semantic expressiveness of recursive types
Authors: Patrignani Marco , Martin Eric Mark , Devriese Dominique
Published: January 2021

Recursive types extend the simply-typed lambda calculus (STLC) with the additional expressive power to enable diverging computation and to encode recursive data-types (e.g., lists). Two formulations of recursive types exist: iso-recursive and equi-recursive. The relative advantages of iso- and equi-recursion are well- studied when it comes to their impact on type-inference. However, the relative semantic expressiveness of the two formulations remains unclear so far. This paper studies the semantic expressiveness of STLC with iso- and equi-recursive types, proving that these formulations are equally expressive. In fact, we prove that they are both as expressive as STLC with only term-level recursion. We phrase these equi-expressiveness results in terms of full abstraction of three canonical compilers between these three languages (STLC with iso-, with equi-recursive types and with term-level recursion). Our choice of languages allows us to study expressiveness when interacting over both a simply-typed and a recursively-typed interface. The three proofs all rely on a typed version of a proof technique called approximate backtranslation. Together, our results show that there is no difference in semantic expressiveness between STLCs with iso- and equi-recursive types. In this paper, we focus on a simply-typed setting but we believe our results scale to more powerful type systems like System F.
Asynchronous effects
Authors: Ahman Danel , Pretnar Matija
Published: January 2021

We explore asynchronous programming with algebraic effects. We complement their conventional synchronous treatment by showing how to naturally also accommodate asynchrony within them, namely, by decoupling the execution of operation calls into signalling that an operation’s implementation needs to be executed, and interrupting a running computation with the operation’s result, to which the computation can react by installing interrupt handlers. We formalise these ideas in a small core calculus, called λ
æ
. We demonstrate the flexibility of λ
æ
 using examples ranging from a multi-party web application, to preemptive multi-threading, to remote function calls, to a parallel variant of runners of algebraic effects. In addition, the paper is accompanied by a formalisation of λ
æ
’s type safety proofs in Agda, and a prototype implementation of λ
æ
 in OCaml.
Modeling and analyzing evaluation cost of CUDA kernels
Authors: Muller Stefan K. , Hoffmann Jan
Published: January 2021

General-purpose programming on GPUs (GPGPU) is becoming increasingly in vogue as applications such as machine learning and scientific computing demand high throughput in vector-parallel applications. NVIDIA's CUDA toolkit seeks to make GPGPU programming accessible by allowing programmers to write GPU functions, called kernels, in a small extension of C/C++. However, due to CUDA's complex execution model, the performance characteristics of CUDA kernels are difficult to predict, especially for novice programmers.

This paper introduces a novel quantitative program logic for CUDA kernels, which allows programmers to reason about both functional correctness and resource usage of CUDA kernels, paying particular attention to a set of common but CUDA-specific performance bottlenecks. The logic is proved sound with respect to a novel operational cost semantics for CUDA kernels. The semantics, logic and soundness proofs are formalized in Coq. An inference algorithm based on LP solving automatically synthesizes symbolic resource bounds by generating derivations in the logic. This algorithm is the basis of RaCuda, an end-to-end resource-analysis tool for kernels, which has been implemented using an existing resource-analysis tool for imperative programs. An experimental evaluation on a suite of CUDA benchmarks shows that the analysis is effective in aiding the detection of performance bugs in CUDA kernels.
Dijkstra monads forever: termination-sensitive specifications for interaction trees
Authors: Silver Lucas , Zdancewic Steve
Published: January 2021

This paper extends the Dijkstra monad framework, designed for writing specifications over effectful programs using monadic effects, to handle termination sensitive specifications over interactive programs. We achieve this by introducing base specification monads for non-terminating programs with uninterpreted events. We model such programs using interaction trees, a coinductive datatype for representing programs with algebraic effects in Coq, which we further develop by adding trace semantics. We show that this approach subsumes typical, simple proof principles. The framework is implemented as an extension of the Interaction Trees Coq library.
Deciding ω-regular properties on linear recurrence sequences
Authors: Almagor Shaull , Karimov Toghrul , Kelmendi Edon , Ouaknine Joël , Worrell James
Published: January 2021

We consider the problem of deciding ω-regular properties on infinite traces produced by linear loops. Here we think of a given loop as producing a single infinite trace that encodes information about the signs of program variables at each time step. Formally, our main result is a procedure that inputs a prefix-independent ω-regular property and a sequence of numbers satisfying a linear recurrence, and determines whether the sign description of the sequence (obtained by replacing each positive entry with “+”, each negative entry with “−”, and each zero entry with “0”) satisfies the given property. Our procedure requires that the recurrence be simple, i.e., that the update matrix of the underlying loop be diagonalisable. This assumption is instrumental in proving our key technical lemma: namely that the sign description of a simple linear recurrence sequence is almost periodic in the sense of Muchnik, Sem'enov, and Ushakov. To complement this lemma, we give an example of a linear recurrence sequence whose sign description fails to be almost periodic. Generalising from sign descriptions, we also consider the verification of properties involving semi-algebraic predicates on program variables.
A pre-expectation calculus for probabilistic sensitivity
Authors: Aguirre Alejandro , Barthe Gilles , Hsu Justin , Kaminski Benjamin Lucien , Katoen Joost-Pieter , Matheja Christoph
Published: January 2021

Sensitivity properties describe how changes to the input of a program affect the output, typically by upper bounding the distance between the outputs of two runs by a monotone function of the distance between the corresponding inputs. When programs are probabilistic, the distance between outputs is a distance between distributions. The Kantorovich lifting provides a general way of defining a distance between distributions by lifting the distance of the underlying sample space; by choosing an appropriate distance on the base space, one can recover other usual probabilistic distances, such as the Total Variation distance. We develop a relational pre-expectation calculus to upper bound the Kantorovich distance between two executions of a probabilistic program. We illustrate our methods by proving algorithmic stability of a machine learning algorithm, convergence of a reinforcement learning algorithm, and fast mixing for card shuffling algorithms. We also consider some extensions: using our calculus to show convergence of Markov chains to the uniform distribution over states and an asynchronous extension to reason about pairs of program executions with different control flow.
Ordinal Approximation for Social Choice, Matching, and Facility Location Problems Given Candidate Positions
Authors: Anshelevich Elliot , Zhu Wennan
Published: January 2021

In this work, we consider general facility location and social choice problems, in which sets of agents A and facilities F are located in a metric space, and our goal is to assign agents to facilities (as well as choose which facilities to open) to optimize the social cost. We form new algorithms to do this in the presence of only
ordinal information
, i.e., when the true costs or distances of the agents from the facilities are
unknown
, and only the ordinal preferences of the agents for the facilities are available. The main difference between our work and previous work in this area is that, while we assume that only ordinal information about agent preferences is known, we also know the exact locations of the possible facilities F. Due to this extra information about the facilities, we are able to form powerful algorithms that have small
distortion
, i.e., perform almost as well as omniscient algorithms (which know the true numerical distances between agents and facilities) but use only ordinal information about agent preferences. For example, we present natural social choice mechanisms for choosing a single facility to open with distortion of at most 3 for minimizing both the total and the median social cost; this factor is provably the best possible. We analyze many general problems including matching,
k
-center, and
k
-median, and we present black-box reductions from omniscient approximation algorithms with approximation factor β to ordinal algorithms with approximation factor 1+2β doing this gives new ordinal algorithms for many important problems and establishes a toolkit for analyzing such problems in the future.
Oceanic Frontogenesis
Authors: McWilliams James C.
Published: January 2021

Frontogenesis is the fluid-dynamical processes that rapidly sharpen horizontal density gradients and their associated horizontal velocity shears. It is a positive feedback process where the ageostrophic, overturning secondary circulation in the cross-front plane accelerates the frontal sharpening until an arrest occurs through frontal instability and other forms of turbulent mixing. Several well-known types of oceanic frontal phenomena are surveyed, their impacts on oceanic system functioning are assessed, and future research is envisioned.
Historical Estimates of Surface Marine Temperatures
Authors: Kent Elizabeth C. , Kennedy John J.
Published: January 2021

Surface temperature documents our changing climate, and the marine record represents one of the longest widely distributed, observation-based estimates. Measurements of near-surface marine air temperature and sea-surface temperature have been recorded on platforms ranging from sailing ships to autonomous drifting buoys. The raw observations show an imprint of differing measurement methods and are sparse in certain periods and regions. This review describes how the real signal of global climate change can be determined from these sparse and noisy observations, including the quantification of measurement method–dependent biases and the reduction of spurious signals. Recent progress has come from analysis of the observations at increasing levels of granularity and from accounting for artifacts in the data that depend on platform types, measurement methods, and environmental conditions. Cutting across these effects are others caused by how the data were recorded, transcribed, and archived. These insights will be integrated into the next generation of global products quantified with validated estimates of uncertainty and the dependencies of its correlation structure. Further analysis of these records using improved data, metadata, and methods will certainly uncover more idiosyncrasies and new ways to improve the record.
Relaxation Analysis of LixNi0.8Co0.1Mn0.1O2 after Lithium Extraction to High-Voltage Region (x ≤ 0.12)
Authors: Kang Jian , Takai Shigeomi , Yabutsuka Takeshi , Yao Takeshi
Published: January 2021

After the lithium extraction from LiNi0.8Co0.1Mn0.1O2 (NCM-811) to high-voltage region, structural variation has been investigated during relaxation process. When the lithium ions are extracted to x ≤ 0.12 for Li
x
Ni0.8Co0.1Mn0.1O2, H3 phase is mainly observed in addition to small amount of H2 phase. For highly lithium extraction up to x = 0.09 or 0.06, excess amount of H3 phase is created at the charging which turns into H2 at the relaxation. On the other hand, no significant variation is observed for the sample of x = 0.12. In comparison with LiNiO2, less amount of transformation from H3 to H2 occurs during the relaxation for NCM-811. For all the samples, Ni interlayer distance of H3 phase decreases with relaxation time, which is presumably due to the increase in valence of Ni for diminishment of lithium concentration in the remaining H3 phase.
Self-Triggered Model Predictive Control for Perturbed Underwater Robot Systems
Authors: Xu Zhongxian , He Lile , He Ning , Qi Lipeng
Published: January 2021

Aiming at solving the control problem of a constrained and perturbed underwater robot, a control method was proposed by combining the self-triggered mechanism and the nonlinear model predictive control (NMPC). The theoretical properties of the kinematic model of the underwater robot, as well as the corresponding MPC controller, are first studied. Then, a novel technique for determining the next update moment of both the optimal control problem and the system state is developed. It is further rigorously proved that the proposed algorithm can (1) stabilize the closed-loop underwater robot system, (2) reduce the time of solving the optimal control problem and (3) save the information transfer resources. Finally, a case study is provided to show the effectiveness of the developed researched scheme.
Biomechanical Properties in Different Types of Thin Corneas in Menoufia Population
Authors: Galal Zaky Adel , Ellakwa Amin Faisal , Basiony Ahmed Ibrahim
Published: January 2021

Background. To evaluate and compare corneal hysteresis (CH) and corneal resistance factor (CRF) in normal thin (NT) healthy corneas with central corneal thickness (CCT) of 470–500 μm with matched thickness in keratoconus suspect (KCS) and keratoconus (KC) eyes. Methods. A total of 103 eyes in three groups were included prospectively: NT, KCS, and KC groups based on clinical examination and Pentacam findings. Corneal hysteresis (CH) and corneal resistance factor (CRF) were measured using the ocular response analyzer (ORA). CCT, CH, and CRF were compared between the three groups and statistically analyzed by variance tests. Results. The three groups consisted of 44 NT, 26 KCS, and 33 KC. The mean CH measured was 8.689 ± 1.775, 9.051 ± 1.1190, and 8.129 ± 0.8539 mmHg in NT, KCS, and KC eyes, respectively. The mean CRF was 8.441 ± 1.663, 8.337 ± 1.114, and 7.2422 ± 1.3110 mmHg in NT, KCS, and KC eyes, respectively. Within the range of central corneal thickness (470–500 μm), only mean CRF was statistically significantly different between the NT and KC (P<0.05); there was no statistically significant difference between NT and KCS, nor was the mean CH between each group (P>0.05). Conclusions. CRF only can be helpful in differentiating KC from NT eyes; KCS could not be predicted with either corneal biomechanical metrics. There was no benefit from CH in differentiating between the three study groups.
Modulation of Short-Chain Fatty Acids as Potential Therapy Method for Type 2 Diabetes Mellitus
Authors: Tang Ruiqi , Li Lanjuan
Published: January 2021

In recent years, the relationship between intestinal microbiota (IM) and the pathogenesis of type 2 diabetes mellitus (T2DM) has attracted much attention. The beneficial effects of IM on the metabolic phenotype of the host are often considered to be mediated by short-chain fatty acids (SCFAs), mainly acetate, butyrate, and propionate, the small-molecule metabolites derived from microbial fermentation of indigestible carbohydrates. SCFAs not only have an essential role in intestinal health but might also enter the systemic circulation as signaling molecules affecting the host’s metabolism. In this review, we summarize the effects of SCFAs on glucose homeostasis and energy homeostasis and the mechanism through which SCFAs regulate the function of metabolically active organs (brain, liver, adipose tissue, skeletal muscle, and pancreas) and discuss the potential role of modulation of SCFAs as a therapeutic method for T2DM.
Impact of Geological Factors on Marine Shale Gas Enrichment and Reserve Estimation: A Case Study of Jiaoshiba Area in Fuling Gas Field
Authors: Yu Siyu , Wang Xixin , Li Shaohua , Liu Yuming , Xiao Liming , Liu Xin , Zhao Wen , Zhang Jiaen
Published: January 2021

Geological factors are key elements to control shale gas enrichment and influence the accurate estimation of shale gas reserve. However, the impact of the main geological factors, such as porosity, mineralogy, and organic matter, on marine shale gas enrichment and reserve calculation has not yet been fully clarified. Herein, we measured gas adsorption, porosity, mineralogical composition, and total organic carbon content of the marine shale samples from the Jiaoshiba area of Fuling gas field in Sichuan Basin, South China, and investigate the relationships between the geological factors and the adsorbed gas content. The results show that adsorbed gas content is positively correlated with total organic carbon and porosity; the larger specific surface area of samples with more clay minerals essentially contributes to shale gas enrichment. Additionally, the sealing of faults imposes a significant impact on shale gas accumulation. The probability volume method was applied to calculate the shale gas reserve. The reserves of P90 (the most pessimistic reserve), P50 (the most likely reserve), and P10 (the most optimistic reserve) were calculated, respectively, which provides useful information to reduce the risk in shale gas development.
Study on Numerical Simulation of Geometric Elements of Blasting Funnel Based on PFC5.0
Authors: Gao Wenle , Zhang Zehua , Li Baojie , Li Kunpeng
Published: January 2021

Based on the particle flow code (PFC2D) within the discrete element method (DEM), the rock mass model was established according to the site rock conditions and the rock mass blasting was simulated by the explosive particle expansion method. The influence of various parameters (the peak pressure action coefficient of the borehole wall, explosive particle buried depth, and charge mode) in the explosive particle expansion method on blasting effect was investigated. Furthermore, the relationship between the various parameters and the geometry size of the blasting crater was obtained. By comparing the size of blasting crater in the field blasting test and numerical simulation example, the reliability of rock mass blasting simulated by the explosive particle expansion method using PFC is verified. The result shows that this paper provides a reliable new numerical simulation method for rock mass blasting and can be used to guide field blasting.
A New Framework to Address BIM Interoperability in the AEC Domain from Technical and Process Dimensions
Authors: Ren Ran , Zhang Jiansong
Published: January 2021

Building information modelling (BIM) is an integrated informational process and plays a key role in enabling efficient planning and control of a project in the architecture, engineering, and construction (AEC) domain. Industry foundation classes‐ (IFC‐) based BIM allows building information to be interoperable among different BIM applications. Different stakeholders take different responsibilities in a project and therefore keep different types of information to meet project requirements. In this paper, the authors proposed and adopted a six‐step methodology to support BIM interoperability between architectural design and structural analysis at both AEC project level and information level, in which (1) the intrinsic and extrinsic information transferred between architectural models and structural models was analyzed and demonstrated by a business process model and notation (BPMN) model that the authors developed; (2) the proposed technical routes with different combinations and their applications to different project delivery methods provided new instruments to stakeholders in industry for efficient and accurate decision making; (3) a new material‐centered invariant signature with portability can improve information exchange between different data formats and models to support interoperable BIM applications; and (4) a newly developed formal material information representation and checking method was tested on a case study where its efficiency was demonstrated to outperform (i) proprietary representations and information checking method based on a manual operation, and (ii) the model view definition (MVD)‐based information checking method. The proposed invariant signature‐based material information representation and checking method brings a better efficiency for information transfer between architectural design and structural analysis, which can have significant positive effects on a project delivery due to the frequent and iterative update of a project design. This improves the information transfer and coordination between architects and structural engineers and therefore the efficiency of the whole project. The proposed method can be extended and applied to other application phases and functions such as cost estimation, scheduling, and energy analysis.
Dynamics Modeling and Simulation of a Net Closing Mechanism for Tether-Net Capture
Authors: Si Jiyue , Pang Zhaojun , Du Zhonghua , Fu Jie
Published: January 2021

Tether-net is a promising active debris removal technique, and a closing mechanism can ensure the reliable wrapping of space debris by using tether-net. This study focuses on the dynamics model of the split closing mechanism and the sliding joint between thread and ring. First, a new kind of closing mechanism is proposed, which drives the closing thread to close the net mouth through the split masses, and the mass-spring-damper method is used to model tether-net. Thereafter, for the first time, the model of thread-ring sliding joint is proposed based on the mass-spring-damper method, which can be used to simulate the closing process of tether-net. Finally, one-edge closure experiment of the net is carried out and the experimental results are compared with the simulation results, and the closing process of the tether-net is simulated by using the thread-ring sliding joint. Results reveal that the thread-ring sliding joint can be used to simulate the relative slip between the thread and the ring, and the tether-net can wrap the target reliably in a short time by using the split closing system. The split closing mechanism can make it possible for the tether-net to close successfully, whether it starts to work before or after the net contacts with the target.
Modal Analysis of Beam Structures with Random Field Models at Multiple Scales
Authors: Feng Dan
Published: January 2021

Structure material properties are heterogeneous in nature and would be characterized with different statistics at different length scales due to the spatially averaging effects. This work develops a framework for the modal analysis of beam structures with random field models at multiple scales. In this framework, the random field theory is adopted to model heterogeneous material properties, and the cross‐correlations between material properties are explicitly considered. The modal parameters of a structure are then evaluated using the finite element (FE) method with the simulated heterogeneous material properties taken as input. With the aid of Monte Carlo simulation, the modal parameters are analyzed in a probabilistic manner. In addition, to accommodate the necessity of different mesh sizes in FE models, an approach of evaluating random field parameters and generating random field material properties at different length scales is developed. The performance of the proposed framework is demonstrated through the modal analysis of a simply supported beam, where the formulation of the multiscale random field approach is validated and the effects of heterogeneous material properties on modal parameters are analyzed. Parametric studies on the random field parameters, including the coefficient of variation and the scale of fluctuation, are also conducted to further investigate the relations between the random field parameters at different scales.
Extrapolating Prognostic Factors of Primary Curative Resection to Postresection Recurrences Hepatocellular Carcinoma Treatable by Radiofrequency Ablation
Authors: Ma Hui , Li Zhongchen , Yuan Jia , Zhang Lan , Xie Xiaoying , Yin Xin , Chen Rongxin , Ren Zhenggang
Published: January 2021

Objective. Recurrence after curative resection for hepatocellular carcinoma (HCC) is a major cause of death from this disease. Factors of primary curative resection are available and potential in the prognosis of follow-up treatment. Our aim was to assess the prognostic significance of primary curative resection factors in recurrent HCC patients undergoing radiofrequency ablation therapy (RFA). Methods. In this retrospective study, we assessed 235 patients who underwent limited RFA of HCC recurrences (

tumors
≤
5
 
cm

;

nodules
≤
3

) after primary curative resection. Factors of primary curative resection were collected, and overall survival and recurrence-free survival were evaluated by the Kaplan-Meier method. Univariate and multivariate analyses were used to identify significant prognostic factors. Results. After a median follow-up of 36 months, 54 patients died, and 128 patients had hepatic recurrence. On univariate analyses, patients whose primary tumors were less differentiated (

p
=
0.032

and

p
=
0.048

) and required less time to recur (

p
=
0.013

and

p
=
0.001

) after curative resection displayed poorer overall survival and higher recurrence rates following RFA. On multivariate analyses, the pathologic tumor grade (

p
=
0.026

and

p
=
0.038

) and recurrence-free survival after primary curative resection (

p
=
0.028

and

p
<
0.001

) emerged as independent risk factors of survival and HCC recurrence. Conclusions. Primary tumor differentiation and time to recurrence after curative resection are viable prognostic factors of overall survival and further recurrence risk in patients undergoing RFA of recurrent HCC.
Experimental Investigation on Flow Field Characteristics of Impinging-Film Cooling
Authors: Zhang Jingyu , Jiang Ping , Yuan Ce , He Xiaomin
Published: January 2021

This paper describes an experimental investigation on flow field characteristics of impinging-film cooling. Particle Image Velocimetry (PIV) technology has been applied to observe the effect of blowing ratio (

0.04
≤
M
≤
0.3

), temperature ratio (

0.73
≤

T

u

≤
0.91

), jet-to-plate pitch (

1.6
≤

Z

n

≤
3.2

), and spacing of impinging holes (

1.94
≤

Y

n

≤
3.5

) on the flow field patterns in an impinging-film cooling test rig under atmospheric pressure. Experiment results show that the near-wall entrained vortex at the downstream of the slit moves downstream of the test rig as the blowing ratio increases, which increases the effective protection length of the film. While the vortex at the end of the inducting slab is stronger, this will increase the mixing in the shear layer. The radial size of the near-wall entrained vortex tends to decrease as the temperature ratio increases at the low blow ratio, and the entrainment effect on the downstream of the slit becomes smaller, causing the separation zone to decrease. Increasing the jet-to-plate pitch, the size of the near-wall entrained vortex increases, and the thickness of the film layer increases, this strengthens the separation effect of the near-wall airflow from the wall surface. The larger the spacing of the impinging holes, the more uneven the velocity distribution of the film.