Traditional DP Research Articles

Polymeric Carbon Nitride Edged with Spatially Isolated Donor and Acceptor for Sunlight‐Driven H2O2 Synthesis and In‐Situ Utilization

AbstractOn‐site H2O2 activation attracts much attention in energy conversion and environment remediation et al., yet remains challenging in its highly efficient and sustainable synthesis. Herein, we grafted a pair of spatially isolated donor (methoxyphenyl unit) and acceptor (anthraquinone unit) in polymeric carbon nitride edges, which induce directional electron‐hole transfer to the two spatially separated dual active centers. Specifically, photogenerated electrons in the anthraquinone unit facilitate the 2e− ORR, while the methoxyphenyl unit, which gathers photogenerated holes, enables rapid 4e− WOR. More impressively, the anthraquinone unit also exhibits strong proton extraction capabilities to boost the generation of *OOH intermediates and H2O2. Consequently, the synthesized donor‐polymeric carbon nitride‐acceptor (DPA) catalyst shows a remarkable H2O2 yield of 6497.1 μM h−1 g−1 in pure water, surpassing traditional DP and PA catalysts. Because of its high efficiency, the H2O2 product can efficiently degrade and mineralize various organic contaminants in a continuous‐flow self‐Fenton reactor under sunlight irradiation. Our work presents an unprecedented approach to designing photocatalysts with efficient H2O2 synthesis and practical application from a molecular engineering perspective.

Polymeric Carbon Nitride Edged with Spatially Isolated Donor and Acceptor for Sunlight-Driven H2O2 Synthesis and In-Situ Utilization.

On-site H2O2 activation attracts much attention in energy conversion and environment remediation et al., yet remains challenging in its highly efficient and sustainable synthesis. Herein, we grafted a pair of spatially isolated donor (methoxyphenyl unit) and acceptor (anthraquinone unit) in polymeric carbon nitride edges, which induce directional electron-hole transfer to the two spatially separated dual active centers. Specifically, photogenerated electrons in the anthraquinone unit facilitate the 2e- ORR, while the methoxyphenyl unit, which gathers photogenerated holes, enables rapid 4e- WOR. More impressively, the anthraquinone unit also exhibits strong proton extraction capabilities to boost the generation of *OOH intermediates and H2O2. Consequently, the synthesized donor-polymeric carbon nitride-acceptor (DPA) catalyst shows a remarkable H2O2 yield of 6497.1 μM h-1 g-1 in pure water, surpassing traditional DP and PA catalysts. Because of its high efficiency, the H2O2 product can efficiently degrade and mineralize various organic contaminants in a continuous-flow self-Fenton reactor under sunlight irradiation. Our work presents an unprecedented approach to designing photocatalysts with efficient H2O2 synthesis and practical application from a molecular engineering perspective.

Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data

Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport.

Research on Plug-in Hybrid Electric Vehicle (PHEV) Energy Management Strategy with Dynamic Planning Considering Engine Start/Stop

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine’s operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy.

Effect of enhancement of hepatic metabolic and glucose homeostasis by gut microbiome regulation of DP in obese mice

The traditional medicine DP is generally used to treat obesity‐related disease and plays central role in the improvement of high‐fat and high‐cholesterol‐induced obesity. In order to reveal the systematic mechanisms of DP on obesity‐related disease, we induced the HFHC diet for 12 weeks in C57BL/6 mice and administrated DP for 6 weeks. After that, we performed non‐targeted lipidomics analysis using mass spectrometry and integrated analysis with various clinical marker of liver. The DP prescription altered liver inflammation status and ameliorated insulin resistance (IR) by regulating GLP‐1 secretion. Additionally, the DP alleviated non‐alcoholic fatty liver disease (NAFLD) through reduce of CD1d‐restricted lipid antigen coupled with γδ‐T cells. More importantly, we have found using microbiome analysis that these mechanisms were regulated by gut microbiota which has been changed by DP treatment. Comprehensively, DP is considered to be an effective herbal decoctions for obesity‐induced systemic hepatic inflammation, glucose homeostasis, and hepatic metabolic homeostasis by regulating microbiome. Therefore, due to the potential therapeutic effects of DP, it is expected to be widely used for the treatment of NAFLD in Korean and Chinese medicine.

A comparative study of the imaging capability of quasi-null and dipole-dipole electrode configurations over an elongated, dipping, semi-infinite conducting body

The effect of the dipping and the finiteness of a 2D prismatic body were investigated in Electrical Resistivity Tomography (ERT) measurements. For this purpose a free-air analogue modeling laboratory was designed. This enabled to control the parameters of the body and carry out measurements in field conditions as this is the most reliable method to verify the field applicability of an array. The imaging capacity of the best four-electrode traditional array, the Dipole-dipole (Dp) was compared to that of special configurations, three members of the series of the γ11n quasi-null arrays. This is the first attempt to study in field circumstances the imaging capacity of quasi-null arrays whose theoretical background is very different from that of the traditional arrays. In the lack of available processing codes that can invert the field data of these arrays a new routine, the Res2D-Hu code was used. Dp array proved to be robust, insensitive to both dipping and finiteness of the body, and very good in imaging shallow objects. The results of the γ11n arrays are less certain, but they may detect deeper bodies and resolve the model also vertically. Due to the complementary nature of the Dp and γ11n arrays, the joint interpretation of their images is recommended. In the present study - with a minimally increased time investment in relation to the individual Dp measurement - the joint interpretation enabled the following aspects: (1) separate clearly the real anomaly from possibly artificial ones; (2) make clear that the investigated model is not exactly 2D, it ends close to the first profile; (3) delineate the model better both horizontally and vertically and (4) detect the body from a larger depth than it would have been possible by the application of only the traditional array. In order to make the above points explicit, investigations had to be done besides the traditional Dp array by the γ116.

QP for indefiniteness: With special reference to Sinhala and Chinese

This paper proposes a QP account for indefiniteness in a range of languages, with QP being situated between DP and NumP. This proposal differs from the traditional DP framework crucially in two aspects: (i) definiteness and indefiniteness are expressed by DP and QP, respectively, and their co-occurrence is theoretically allowed, under due circumstances; (ii) both [Spec, DP] and [Spec, QP] can serve as the landing sites for an XP under a wh-movement, which can thus be associated with both definiteness and indefiniteness. Crucially, the postulation of QP accounts for several nominal phenomena in the Sinhala language, which pose serious challenges to the traditional DP framework.First, Sinhala features the existence of indefinite articles as well as the absence of definite ones; second, an indefinite article can co-occur with a demonstrative in one and the same nominal phrase; third, NP necessarily precedes [numeral+(classifier)], regardless of its (in-)definiteness; fourth, indefinite quantifiers exhibit disparities along the line of their (in-)sensitivity toward the feature of animacy. All these phenomena can be adequately captured by the proposal of QP; specifically, the first three are explained by the DP–QP division whereas the fourth one by the QP–NumP distinction. The DP proposal is further supported by the need to have both DP–QP and QP–NumP divisions in the Chinese language, as well as the intuitive account it provides for the “double definiteness” in Scandinavian languages. In sum, the QP postulation is strongly motivated by cross-lingual evidence for the account of definiteness and indefiniteness as two separate but related unary features instead of binary value of the same feature, e.g. [+/−DEF].

ConTPL: Controlling Temporal Privacy Leakage in Differentially Private Continuous Data Release.

In many real-world systems, such as Internet of Thing, sensitive data streams are collected and analyzed continually. To protect privacy, a number of mechanisms are designed to achieve ϵ-differential privacy for processing sensitive streaming data, whose privacy loss is rigorously controlled within a given parameter ϵ. However, most of the existing studies do not consider the effect of temporal correlations among the continuously generated data on the privacy loss. Our recent work reveals that, the privacy loss of a traditional DP mechanism (e.g., Laplace mechanism) may not be bounded by ϵ due to temporal correlations. We call such unexpected privacy loss Temporal Privacy Leakage (TPL). In this demonstration, we design a system, ConTPL, which is able to automatically convert an existing differentially private streaming data release mechanism into one bounding TPL within a specified level. ConTPL also provides an interactive interface and real-time visualization to help data curator understand and explore the effect of different parameters on TPL.

Optimal scheduling system for reservoir electric power generation based on functional dynamic programming algorithm

In order to improve the solving efficiency, functional analysis is applied to DP. Based on the characteristics of high abstraction and generality, this improved DP can eliminate a large number of repeated calculating procedures. Then it can reduce the dynamic program calculating scale and shorten the calculating time obviously compared to the traditional DP.

Simplifying GPS Trajectory Data with Enhanced Spatial-Temporal Constraints

Raw GPS trajectory data are often very large and use up excessive storage space. The efficiency and accuracy of activity patterns analysis or individual–environment interaction modeling using such data may be compromised due to data size and computational needs. Line generalization algorithms may be used to simplify GPS trajectories. However, traditional algorithms focus on geometric characteristics of linear features. Trajectory data may record information beyond location. Examples include time and elevation, and inferred information such as speed, transportation mode, and activities. Effective trajectory simplification should preserve these characteristics in addition to location and orientation of spatial-temporal movement. This paper proposes an Enhanced Douglas–Peucker (EDP) algorithm that implements a set of Enhanced Spatial-Temporal Constraints (ESTC) when simplifying trajectory data. These constraints ensure that the essential properties of a trajectory be preserved through preserving critical points. Further, this study argues that speed profile can uniquely identify a trajectory and thus it can be used to evaluate the effectiveness of a trajectory simplification. The proposed ESTC-EDP simplification method is applied to two examples of GPS trajectory. The results of trajectory simplification are reported and compared with that from traditional DP algorithm. The effectiveness of simplification is evaluated.

Single-leg revenue management with downsell and delayed decision making

In this paper, we study the impact of downsell in leg revenue management. Downsell happens when a customer purchases a lower fare than she was looking for. We aim to minimize the losses in revenue that arise from this situation. We reformulate the traditional DP formulation to account for this behavior, and show significant revenue gains compared to the traditional DP formulation. Next, we aim to improve customer booking simulation by assuming that customers may postpone their decision to book. Using a surprisingly easy reformulation of our DP strategy we ensure that cheaper classes will never open after they get closed, guaranteeing to customers that booking now is better than doing so in the future. Our results also show that when more than one eighth of passengers postpone their bookings, revenue gains are reported. While this paper is specific to airline revenue management, this practice can also be applied for other industries that practice revenue management, such as the hospitality or car rental industries.

Lifestyle Patterns and Weight Status in Spanish Adults: The ANIBES Study.

Limited knowledge is available on lifestyle patterns in Spanish adults. We investigated dietary patterns and possible meaningful clustering of physical activity, sedentary behavior, sleep time, and smoking in Spanish adults aged 18–64 years and their association with obesity. Analysis was based on a subsample (n = 1617) of the cross-sectional ANIBES study in Spain. We performed exploratory factor analysis and subsequent cluster analysis of dietary patterns, physical activity, sedentary behaviors, sleep time, and smoking. Logistic regression analysis was used to explore the association between the cluster solutions and obesity. Factor analysis identified four dietary patterns, “Traditional DP”, “Mediterranean DP”, “Snack DP” and “Dairy-sweet DP”. Dietary patterns, physical activity behaviors, sedentary behaviors, sleep time, and smoking in Spanish adults aggregated into three different clusters of lifestyle patterns: “Mixed diet-physically active-low sedentary lifestyle pattern”, “Not poor diet-low physical activity-low sedentary lifestyle pattern” and “Poor diet-low physical activity-sedentary lifestyle pattern”. A higher proportion of people aged 18–30 years was classified into the “Poor diet-low physical activity-sedentary lifestyle pattern”. The prevalence odds ratio for obesity in men in the “Mixed diet-physically active-low sedentary lifestyle pattern” was significantly lower compared to those in the “Poor diet-low physical activity-sedentary lifestyle pattern”. Those behavior patterns are helpful to identify specific issues in population subgroups and inform intervention strategies. The findings in this study underline the importance of designing and implementing interventions that address multiple health risk practices, considering lifestyle patterns and associated determinants.

Comparative analysis of evolving artificial neural network and reinforcement learning in stochastic optimization of multireservoir systems

ABSTRACTDynamic programming (DP) has been among the most popular techniques for solving multireservoir problems since the early 1960s. However, DP and DP-based methods suffer from two serious issues: namely, the curses of modelling and dimensionality. Later, reinforcement learning (RL) was introduced to overcome some deficiencies in the traditional DP mainly related to the curse of modelling, but it still encounters the curse of dimensionality in larger systems. Recently, the artificial neural network has emerged as an effective approach to solve stochastic optimization of reservoir systems with high flexibility. In this paper, we develop a single-step evolving artificial neural network (SENN) model that overcomes the curses of modelling and dimensionality in a multireservoir system. Furthermore, a novel efficient allocation technique is developed to ease the allocation of water among different users. A two-reservoir system in Karkheh Basin, Iran, is applied to derive and test the methods. Elitist-mutated particle swarm optimization is used to train the network. A comparison of the results with Q-learning shows the superiority of the SENN, especially during drought periods. Moreover, the SENN performs better in producing more hydropower energy in the system. Thus, the main contributions of this research are (1) development of SENN applications to multireservoir systems, (2) a comparative analysis between SENN and Q-learning especially in prolonged drought conditions and (3) a proposed efficient optimal allocation technique using the simulation method.Editor D. Koutsoyiannis; Associate editor E. Toth

Dynamic Programming-Based Energy Management System for Range-Extended Electric Bus

The heavy computational burden associated with the application of the traditional DP strategy to the energy management of range-extended electric buses poses a serious problem. On the basis of one Chinese typical urban bus driving cycle, an optimal control strategy is designed according to the SOC consumption trend, which is optimized by the DP algorithm. The dissipative energy and the energy-traction efficiency are our evaluation indices. The energy efficiencies of the powertrain system and components are analyzed by the energy flow diagram method. The results show that when the range-extended electric bus runs 35 Chinese typical urban bus driving cycles, the energy consumption and the energy efficiency of the powertrain system, which are optimized by the traditional DP strategy, can reach 2844.28 MJ and 31.29%, respectively. Compared with the traditional bus, the energy consumption can be reduced by 31.08%. The energy consumption and the energy efficiency of the powertrain system, which are based on one driving cycle optimal strategy, can reach 2857.69 MJ and 31.14%, respectively. The energy consumption is 0.47% higher than that with the traditional DP strategy, but the computation time is reduced by 96.85%.

Synthesis, characterization and histomorphometric analysis of cellular response to a new elastic DegraPol® polymer for rabbit Achilles tendon rupture repair

Tendon rupture repair is a surgical field where improvements are still required due to problems such as repeat ruptures, adhesion formation and joint stiffness. In the current study, a reversibly expandable and contractible electrospun tube based on a biocompatible and biodegradable polymer was implanted around a transected and conventionally sutured rabbit Achilles tendon. The material used was DegraPol® (DP), a polyester urethane. To make DP softer, more elastic and surgeon-friendly, the synthesis protocol was slightly modified. Material properties of conventional and new DP film electrospun meshes are presented. At 12 weeks post-surgery, tenocyte and tenoblast density, nuclei and width, collagen fibre structure and inflammation levels were analyzed histomorphometrically. Additionally, a comprehensive histological scoring system by Stoll et al. (2011) was used to compare healing outcomes. Results showed that there were no adverse reactions of the tendon tissue following the implant. No differences were found whether the DP tube was applied or not for both traditional and new DP materials. As a result, the new DP material was shown to be an excellent carrier for delivery of growth factors, stem cells and other agents responsible for tendon healing.

A Solution to Measure the Flow Rate of Wet Gas

Traditionally the mixture of fluids at the wellhead is separated into its various phases. Proper separation and accurate measurement of each of the component phases is critical in determining production capabilities. Gas is separated from the liquid by passing the emitted well fluids through a separator. The phases are then further separated and measured independently. Using a flowmeter rather than separators to determine the gas and liquid phases gives a very significant reduction in CAPEX in the evaluation of new wells. This paper covers the key benefits that wet gas meters provide in comparison with expensive separators, multiphase metering and traditional single phase DP producing devices such as orifice plates.

Efficient Query-by-Content Audio Retrieval by Locality Sensitive Hashing and Partial Sequence Comparison

This paper investigates suitable indexing techniques to enable efficient content-based audio retrieval in large acoustic databases. To make an index-based retrieval mechanism applicable to audio content, we investigate the design of Locality Sensitive Hashing (LSH) and the partial sequence comparison. We propose a fast and efficient audio retrieval framework of query-by-content and develop an audio retrieval system. Based on this framework, four different audio retrieval schemes, LSH-Dynamic Programming (DP), LSH-Sparse DP (SDP), Exact Euclidian LSH (E2LSH)-DP, E2LSH-SDP, are introduced and evaluated in order to better understand the performance of audio retrieval algorithms. The experimental results indicate that compared with the traditional DP and the other three compititive schemes, E2LSH-SDP exhibits the best tradeoff in terms of the response time, retrieval accuracy and computation cost.

Deposition of Highly Dispersed Gold on Alumina Support

Gold catalysts of Au/Al2O3, prepared by the methods of deposition precipitation (DP, AuCl3 was the precursor of gold) and chemical vapor deposition (CVD, an organic gold complex was the precursor), were tested in the reaction of CO oxidation at subambient temperatures. The activity observed for prepared catalysts generally increased with their gold dispersion characterized by transmission electron microscopy. Finely dispersed Au/Al2O3 (with dAu<5 nm) catalysts may be easily prepared by the CVD method. Au/Al2O3 samples prepared by the traditional DP method contained many large gold crystallites (dAu>7 nm). They originated, during the impregnation step, from the occlusion of gold complex solution in pores of Al2O3. However, small gold particles (dAu∼3.5 nm) may be deposited on Al2O3 by a modified DP method featuring prealkalinization of the Al2O3 surface. Furthermore, a mild hydrogen reduction is effective in eliminating the incubation period required for catalysts freshly prepared by the DP method. Characterization of the temperature-programmed reduction revealed that the function of reduction was to remove poisonous chloride ions from deposited gold crystallites.

Plausibility functions in continuous speech recognition: The VINICS system

We propose a new approach to pheneme-based continuous speech recognition when a time function of plausibility of observing each phoneme is given. We introduce a criterion for best sentence, related to the sum of plausibilities of individual symbols composing the sentence. Based on the idea of making use of a high plausibility region to reduce the computation load while keeping optimality, our method finds the most plausible sentences relating to the input speech, given the plausibility, μ a,n of observing each phoneme a at each time slot n. Two optimization procedures are defined to deal with the following embedded search processes: (1) find the best path connecting peaks of the plausibility functions of two successive symbols, and (2) find the best time transition slot index for two given peaks. Dynamic programming is used in these two procedures. Since the best path finding algorithm does not search slot by slot, the recognition is highly efficient. Experimental results with the VINICS system show that the method gives a better recognition precision while requiring about 1/20 computing time, compared to traditional DP based methods. The experimental system obtained a 95% sentence recognition rate on a speaker-dependent test.

Strategies for meeting user demands: An international perspective

The role of the Data Processing or Information Department is to bridge the gap between the available information technology and the use and users of that technology in the organization at large. Changes in available technology and changes in the nature or scale of information use engender changes in the way the Information Department carries out its mediating role. The growth in the volume of end-user requests for systems, and the character of the systems requested, have made traditional DP methods inadequate. This has led to changes in the organization of work in these departments. This paper presents international survey data on a number of different strategies that have been used to enable Information Departments better to meet enduser demands. It considers factors which lead to the adoption of different strategies in different circumstances. Data from USA, Japan, UK and Scandinavia are used to examine some of the social and cultural factors which can influence the organization of the Information Department.