Unpacking the intellectual structure of ecosystem research in innovation studies

In light of the scattered nature of extant ecosystem literature, we seek to (1) identify the intellectual structure and theoretical roots of ecosystem literature within the broader field of innovation, and (2) take stock of how ecosystem research enriches and extends the innovation literature. Based on a bibliometric analysis of innovation and management journals over the past 30 years, we identify three lineages of ecosystem research within the “umbrella” of innovation studies. The first lineage finds its theoretical roots in the literature of product and architecture innovation. The second follows the lineage of open innovation and innovation strategy. The third adopts the traditions of regional innovation and entrepreneurship. Further, we offer an extended-boundary model that features two sets of synopses, explicating ecosystems' contributions to the field of innovation. The first set demonstrates how the ecosystem concept extends the three lineages with its unique attributes from a meta-organisational perspective, i.e., consumption-side synergies, boundary spanning and self-organisation. The second indicates how the concept may be repositioned on the foundational economic theories, i.e., Schumpeterian Innovation and Transaction Cost Economics, and advance how it may integrate the two schools and extend their research implications vis-à-vis profiting from, organising for, and outcomes of innovation. We contribute by organising the fragmented ecosystem literature into an integrative framework, forging stronger links with traditional innovation theories, and offering a cumulative theoretical basis for this promising concept to further flourish in the field of innovation studies.     

Effect of methods of demagnetization on the energy required to magnetize steel

In this investigation the first series of tests consisted of obtaining zero flux in two varieties of steel from various maximum flux values and by three different methods. B-H curves were obtained after demagnetization by the three methods and from the maximum value of these B-H curves hysteresis loops were taken.One method of demagnetizing was that suggested by the U. S. Bureau of Standards, which consists of current reversals through a magnetizing winding, in which current values are gradually reduced to zero. The second method consisted of applying a negative current of such an amount that the resultant value of flux would be such that when the current was released the flux value would go to zero. The third method was substantially the same as the second except the zero flux was approached from the direction of opposite polarity.The resultant B-H curves proved to be different from each other and to depend upon not only their origin but the previous history of the material as regards methods of demagnetization.The areas between the curves were evaluated.It was found that in cases of two of the three typical B-H curves the B-log H curves gave straight lines for part of the range involved.A second series of tests consisted of taking a set of hysteresis loops and repeating the determinations, using a greater number of steps to find if by taking a greater number of steps there might be a greater indicated energy loss. The results of this group of tests were not sufficiently definite to warrant conclusions.     

Determination of fructan exohydrolase activity in the crude extracts of plants

BackgroundThe roots of chicory and the tubers of Jerusalem artichoke are used for the production of inulin. However, a quality of tubers and roots, i.e. the content of inulin, monosaccarides and disaccharides, depends on the activity of enzymes implicated in the metabolism of inulin. The knowledge on the changes of activities of inulin synthesizing and degrading enzymes is limited during plant sprouting, growth and dormancy. It happens due to complicated measurements of the product of enzymatic reaction in the presence of crude plant extract. Fructan exohydrolase (β-d-fructan fructohydrolase, FEH, EC 3.2.1.80) is an enzyme responsible for the hydrolysis of fructans in plants. For fructose as the reaction product measurement, a high-performance liquid chromatography is usually used. The aim of the study was to choose a simple and suitable method for FEH activity determination and the measurement of fructose in the presence of plant extracts.ResultsTwo chemical methods, i.e. copper(II)–neocuproine and 3,5-dinitrosalicylic acid, and the enzymatic one based on the reactions catalyzed by hexokinase, phosphoglucose isomerase and glucose-6-phosphate dehydrogenase were used. Enzymatic method was found to be suitable for FEH activity determination in plant extracts, and on the contrary to chemical methods no interference effects of compounds from crude plant extracts were observed.ConclusionEnzymatic method is applicable for the routine analysis and will allow performing the investigations without special equipment on the inulin degrading enzyme in biotechnologically important crops.     

Observer-based control for singular nonhomogeneous Markov jump systems with packet losses

This paper is concerned with the observer-based H_∞ control for a class of singular Markov jump systems over a finite-time interval, where the transition probability (TP) is time-varying and is limited to a convex hull. Due to the limited capacity of network medium, packet losses are presented in the underlying systems. Firstly, using a stochastic Lyapunov functional, a sufficient condition on singular stochastic H_∞ finite-time boundedness for the corresponding closed-loop error systems is provided. Subsequently, a linear matrix inequality (LMI) condition on the existence of the H_∞ observer-based controller is developed from a new perspective. Finally, three numerical examples are provided to illustrate the effectiveness of the proposed controller design method, wherein it is shown that the proposed method yields less conservative results than those in the literature.     

High temperature and UV-C treatments affect stilbenoid accumulation and related gene expression levels in Gnetum parvifolium

BackgroundGnetum parvifolium stems and roots have been used for a long time in traditional Chinese medicines. Stilbenes are bioactive compounds present in G. parvifolium plants, and they possess antioxidative and anticancer properties. However, little is known about the responses of G. parvifolium stilbene biosynthetic pathways to stress conditions. Therefore, we investigated stilbene biosynthesis, including the expression of relevant genes, in G. parvifolium exposed to high-temperature and ultraviolet-C treatments.ResultsHigh temperatures did not influence the accumulation of total stilbenes in stems but decreased stilbene concentrations in roots at 3 h, with a subsequent restoration to control levels. In contrast, ultraviolet irradiation induced the accumulation of total stilbenes in stems but not in roots. We also observed that high temperatures inhibited the production of resveratrol and piceatannol in G. parvifolium stems and roots, whereas ultraviolet treatments initially inhibited their accumulation (up to 6 h) but induced their production at later time points. Analyses of specific genes (i.e., PAL, C4H, 4CL, STS, and CYP) revealed that their expression levels generally increased in stress-treated stems and roots, although there was some variability in the expression profiles during treatments.ConclusionsOur results indicated that high temperatures and ultraviolet irradiation differentially affect the biosynthesis of specific stilbenes in G. parvifolium stems and roots. Therefore, cultivating G. parvifolium seedlings under optimal stress conditions may increase the biosynthesis of specific stilbene compounds.     

New methods for compression of MP double array by compact management of suffixes

Minimal Prefix (MP) double array is an efficient data structure for a trie. However, its space efficiency is degraded by the non-compact management of suffixes. This paper presents three methods to compress the MP double array. The first two methods compress the MP double array by accommodating short suffixes inside the leaf nodes, and pruning leaf nodes corresponding to the end marker symbol. These methods achieve size reduction of up to 20%, making insertion and deletion faster at the same time while maintaining the retrieval time of O(1). The third method eliminates empty spaces in the array that holds suffixes, and improves the maximum size reduction further by about 5% at the cost of increased insertion time. Compared to a Ternary Search Tree, the key retrieval of the compressed MP double array is 50% faster and its size is 3–5 times smaller.     

VAD-assisted multitask transformer framework for emotion recognition and intensity prediction on suicide notes

Detecting suicidal tendencies and preventing suicides is an important social goal. The rise and continuance of emotion, the emotion category, and the intensity of the emotion are important clues about suicidal tendencies. The three determinants of emotion, viz. Valence, Arousal, and Dominance (VAD) can help determine a person’s exact emotion(s) and its intensity. This paper introduces an end-to-end VAD-assisted transformer-based multi-task network for detecting emotion (primary task) and its intensity (auxiliary task) in suicide notes. As part of this research, we expand the utility of the emotion-annotated benchmark dataset of suicide notes, CEASE-v2.0, by annotating all its sentences with emotion intensity labels. Empirical results show that our multi-task method performs better than the corresponding single-task systems, with the best attained overall Mean Recall (MR) of 65.25% on the emotion task. On a similar task, we improved MR by 8.78% over the existing state-of-the-art system. We evaluated our approach on three benchmark datasets for three different tasks. We observed that the introduced method consistently outperformed existing state-of-the-art approaches on the studied datasets, demonstrating its capacity to generalize to other downstream correlated tasks. We qualitatively examined our model’s output by comparing it to the labeling of a psychiatrist.     

Subspace design of low-rank estimators for higher-order statistics

Higher-order statistics (HOS) are well known for their robustness to additive Gaussian noise and ability to preserve phase. HOS estimates, on the other hand, have been criticized for high complexity and the need for long data in order to maintain small variance. Since rank reduction offers a general principle for reduction of estimator variance and complexity, we consider the problem of designing low-rank estimators for HOS. We propose three methods for choosing the transformation matrix that reduces the mean-square error (MSE) associated with the low-rank HOS estimates. We also demonstrate the advantages of using low-rank third-order moment estimates for blind system estimation. Results indicate that the full rank MSE corresponding to some data length N can be attained by a low-rank estimator corresponding to a length significantly smaller than N.     

Compositional question answering: A divide and conquer approach

This paper describes how questions can be characterized for question answering (QA) along different facets and focuses on questions that cannot be answered directly but can be divided into simpler ones so that they can be answered directly using existing QA capabilities. Since individual answers are composed to generate the final answer, we call this process as compositional QA. The goal of the proposed QA method is to answer a composite question by dividing it into atomic ones, instead of developing an entirely new method tailored for the new question type. A question is analyzed automatically to determine its class, and its sub-questions are sent to the relevant QA modules. Answers returned from the individual QA modules are composed based on the predetermined plan corresponding to the question type. The experimental results based on 615 questions show that the compositional QA approach outperforms the simple routing method by about 17%. Considering 115 composite questions only, the F-score was almost tripled from the baseline.     

Microbial transformation of ginsenosides extracted from Panax ginseng adventitious roots in an airlift bioreactor

BackgroundGinsenoside is the most important secondary metabolite in ginseng. Natural sources of wild ginseng have been overexploited. Although root culture can reduce the length of the growth cycle of ginseng, the number of species of ginsenosides is reduced and their contents are lower in the adventitious roots of ginseng than in the roots of ginseng cultivated in the field.ResultsIn this study, 147 strains of β-glucosidase-producing microorganisms were isolated from soil. Of these, strain K35 showed excellent activity for converting major ginsenosides into rare ginsenosides, and a NCBI BLAST of its 16S rDNA gene sequence showed that it was most closely related to Penicillium sp. (HQ608083.1). Strain K35 was used to ferment the adventitious root extract, and the fermentation products were analyzed by high-performance liquid chromatography. The results showed that the content of the rare ginsenoside CK was 0.253 mg mL^-1 under the optimal converting conditions of 9 d of fermentation at pH 7.0 in LL medium, which was significantly higher than that in the adventitious roots of ginseng.ConclusionThese findings may not only solve the problem of low productivity of metabolite in ginseng root culture but may also result in the development of a new valuable method of manufacturing ginsenoside CK.     

Improving the retrieval effectiveness of very short queries

This paper describes an automatic approach designed to improve the retrieval effectiveness of very short queries such as those used in web searching. The method is based on the observation that stemming, which is designed to maximize recall, often results in depressed precision. Our approach is based on pseudo-feedback and attempts to increase the number of relevant documents in the pseudo-relevant set by reranking those documents based on the presence of unstemmed query terms in the document text. The original experiments underlying this work were carried out using Smart 11.0 and the lnc.ltc weighting scheme on three sets of documents from the TREC collection with corresponding TREC (title only) topics as queries. (The average length of these queries after stoplisting ranges from 2.4 to 4.5 terms.) Results, evaluated in terms of P@20 and non-interpolated average precision, showed clearly that pseudo-feedback (PF) based on this approach was effective in increasing the number of relevant documents in the top ranks. Subsequent experiments, performed on the same data sets using Smart 13.0 and the improved Lnu.ltu weighting scheme, indicate that these results hold up even over the much higher baseline provided by the new weights. Query drift analysis presents a more detailed picture of the improvements produced by this process.     

Discrete-time event-triggered H-infinity stabilization for networked cascade control systems with uncertain delay

This paper is concerned with the problem of discrete-time event-triggered H_∞ control for networked cascade control systems (NCCSs) with time-varying network-induced delay. First of all, an event-triggered scheme is introduced to this system for reducing the unnecessary waste of limited network bandwidth resources. Considering the effect of time-varying delay, a new mathematical model for this system is constructed. In this paper, based on the model and Lyapunov functional method, the co-design method of event-triggered parameter, state feedback primary controller and secondary controller with H_∞ performance is derived via linear matrix inequality technique. To illustrate the effectiveness of the proposed method, a simulation example considering a main steam temperature cascade control system is given. The proposed method emphasizes the application in the corresponding industrial control systems, it can be found that this method is superior to the one in some existing references, and the provided example demonstrates the effectiveness of the co-design method in the networked cascade control systems with event-triggered scheme.     

Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data

In the early diagnosis of the Coronavirus disease (COVID-19), it is of great importance for either distinguishing severe cases from mild cases or predicting the conversion time that mild cases would possibly convert to severe cases. This study investigates both of them in a unified framework by exploring the problems such as slight appearance difference between mild cases and severe cases, the interpretability, the High Dimension and Low Sample Size (HDLSS) data, and the class imbalance. To this end, the proposed framework includes three steps: (1) feature extraction which first conducts the hierarchical segmentation on the chest Computed Tomography (CT) image data and then extracts multi-modality handcrafted features for each segment, aiming at capturing the slight appearance difference from different perspectives; (2) data augmentation which employs the over-sampling technique to augment the number of samples corresponding to the minority classes, aiming at investigating the class imbalance problem; and (3) joint construction of classification and regression by proposing a novel Multi-task Multi-modality Support Vector Machine (MM-SVM) method to solve the issue of the HDLSS data and achieve the interpretability. Experimental analysis on two synthetic and one real COVID-19 data set demonstrated that our proposed framework outperformed six state-of-the-art methods in terms of binary classification and regression performance.     

An extension of the general Struble's method for solving an nth order nonlinear differential equation when the corresponding unperturbed equation has some repeated eigenvalues

This paper attempts to show the more suitability of the extended general Struble's technique than the unified Krylov-Bogoliubov-Mitropolskii (KBM) method in solving the problems that occur during the critical conditions. Recently a critically damped condition of an nth, n=2,3, … order weakly nonlinear autonomous ordinary differential equation has been investigated by the unified KBM method, in which the corresponding unperturbed equation has some real (negative) repeated eigenvalues. But there are more important critical conditions, which are still untouched. One of them occurs when a pair of complex eigenvalues is equal to another. It is complicated to formulate as well as to utilize the KBM method to investigate this condition. However, the extended general Struble's technique is applicable to both autonomous and non-autonomous systems. Solutions obtained for different critical conditions as well as for different initial conditions show a good agreement with the numerical solutions. The method is illustrated by an example of a fourth-order nonlinear differential equation whose unperturbed equation has repeated complex eigenvalues. A steady-state solution is determined for the non-autonomous equation. Moreover, a critical condition of a fourth-order nonlinear equation is investigated when two real eigenvalues of the unperturbed equation are non-positive and equal.     

Exploiting context-awareness and multi-criteria decision making to improve items recommendation using a tripartite graph-based model

Integrating useful input information is essential to provide efficient recommendations to users. In this work, we focus on improving items ratings prediction by merging both multiple contexts and multiple criteria based research directions which were addressed separately in most existent literature. Throughout this article, Criteria refer to the items attributes, while Context denotes the circumstances in which the user uses an item. Our goal is to capture more fine grained preferences to improve items recommendation quality using users’ multiple criteria ratings under specific contextual situations. Therefore, we examine the recommenders’ data from the graph theory based perspective by representing three types of entities (users, contextual situations and criteria) as well as their relationships as a tripartite graph. Upon the assumption that contextually similar users tend to have similar interests for similar item criteria, we perform a high-order co-clustering on the tripartite graph for simultaneously partitioning the graph entities representing users in similar contextual situations and their evaluated item criteria. To predict cluster-based multi-criteria ratings, we introduce an improved rating prediction method that considers the dependency between users and their contextual situations, and also takes into account the correlation between criteria in the prediction process. The predicted multi-criteria ratings are finally aggregated into a single representative output corresponding to an overall item rating. To guide our investigation, we create a research hypothesis to provide insights about the tripartite graph partitioning and design clear and justified preliminary experiments including quantitative and qualitative analyzes to validate it. Further thorough experiments on the two available context-aware multi-criteria datasets, TripAdvisor and Educational, demonstrate that our proposal exhibits substantial improvements over alternative recommendations approaches.     

Mixed H∞ and passivity control for a class of stochastic nonlinear sampled-data systems

In this paper, we consider the problem of mixed H_∞ and passivity control for a class of stochastic nonlinear systems with aperiodic sampling. The system states are unavailable and the measurement is corrupted by noise. We introduce an impulsive observer-based controller, which makes the closed-loop system a stochastic hybrid system that consists of a stochastic nonlinear system and a stochastic impulsive differential system. A time-varying Lyapunov function approach is presented to determine the asymptotic stability of the corresponding closed-loop system in mean-square sense, and simultaneously guarantee a prescribed mixed H_∞ and passivity performance. Further, by using matrix transformation techniques, we show that the desired controller parameters can be obtained by solving a convex optimization problem involving linear matrix inequalities (LMIs). Finally, the effectiveness and applicability of the proposed method in practical systems are demonstrated by the simulation studies of a Chua’s circuit and a single-link flexible joint robot.     

Resilient control based on disturbance observer for nonlinear singular stochastic hybrid system with partly unknown Markovian jump parameters

In this paper, the composite anti-disturbance resilient control is considered for nonlinear singular stochastic hybrid system with partly unknown Markovian jump parameters under multiple disturbances. Three kinds of disturbances are included in the studied system. One is generated by an external system and it enters the hybrid system from the channel of the control input. The other one is stochastic white noise. And the third one is the external unknown time-varying disturbance and it is supposed to be H₂ norm bounded. By combining the disturbance-observer-based-control scheme, H_∞ control technique and resilient control method, a composite anti-disturbance resilient controller is constructed to attenuate and eliminate the affection of these disturbances, and ensures the whole closed-loop system regular, impulse free and stochastically stable with the corresponding control performance. Then, some sufficient conditions and the gains of the controller and observer are obtained by using Lyapunov function method and the linear matrix inequalities (LMIs) technique. Finally, two numerical examples are given to show the effectiveness of presented method.     

H2/H∞ filtering for networked systems with data transmission time-varying delay,data packet dropout and sequence disorder

This paper investigates an H₂/H_∞ filter designing for networked systems perturbed by multiple noises. The measurement transmission from the sensor to the remote filter is completed via a communication network in simultaneously presenting of data transmission time-varying delays, data packet dropout and data sequence disorder. Since the filter will receive delayed and disordered information, a zero-order-hold (ZOH) or a logical-ZOH (LZOH) is firstly employed for resorting the chaos data sequence. Afterwards, a hybrid H₂/H_∞ filtering scheme is designed for accurately estimating the target output. By Itô formula and a novel free-weight method, the almost surely mean square exponentially stable (ASMSES) condition of the error system is conveniently obtained and the corresponding filter design method is finally presented. By the proposed method, not only the ASMSES with a pre-scheduled H₂/H_∞ performance can be achieved, but also the convergence rate of overall system is pre-regulable. In addition, it has been point out the dynamic filtering performance of LZOH scheme should be better than ZOH ones due to less time-varying delays are introduced and more latest measurement information are employed. Numerical examples are provided to demonstrate the effectiveness of the proposed methods.