Construction Algorithm Research Articles

Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission

One of the important directions for improving modern Wide Area Networks is efficient and secure packet routing. Efficient routing is often based on using the shortest paths, while ensuring security involves preventing the possibility of packet interception. The work is devoted to improving the security of data transmission in IP networks. A new approach is proposed to minimize the number of distrustful nodes on the path of IP packet transmission. By a distrustful node, we mean a node that works correctly in terms of hardware and software and fully implements its data transport functions, but from the point of view of its organizational subordination, we are not sure that the node will not violate security rules to prevent unauthorized access and interception of data. A distrustful node can be either a transit or an end node. To implement this approach, we modified Dijkstra’s shortest path tree construction algorithm. The modified algorithm ensures that we obtain a path that will pass only through trustful nodes, if such a path exists. If there is no such path, the path will have the minimum possible number of distrustful intermediate nodes. The number of intermediate nodes in the path was used as a metric to obtain the shortest path trees. Routing tables of routers, built on the basis of trees obtained using a modified algorithm, provide increased security of data transmission, minimizing the use of distrustful nodes.

A Unified Approach to Coreset Learning.

Coreset of a given dataset and loss function is usually a small weighed set that approximates this loss for every query from a given set of queries. Coresets have shown to be very useful in many applications. However, coresets' construction is done in a problem-dependent manner and it could take years to design and prove the correctness of a coreset for a specific family of queries. This could limit coresets' use in practical applications. Moreover, small coresets provably do not exist for many problems. To address these limitations, we propose a generic, learning-based algorithm for construction of coresets. Our approach offers a new definition of coreset, which is a natural relaxation of the standard definition and aims at approximating the average loss of the original data over the queries. This allows us to use a learning paradigm to compute a small coreset of a given set of inputs with respect to a given loss function using a training set of queries. We derive formal guarantees for the proposed approach. Experimental evaluation on deep networks and classic machine learning problems show that our learned coresets yield comparable or even better results than the existing algorithms with worst case theoretical guarantees (that may be too pessimistic in practice). Furthermore, our approach applied to deep network pruning provides the first coreset for a full deep network, i.e., compresses all the networks at once, and not layer by layer or similar divide-and-conquer methods.

农业环境高精度三维点云地图构建方法研究

In agricultural operation scenarios, the diversity of farmland terrain, crops and other forms, as well as uncertain factors such as weather changes and crop growth during agricultural operation, can have an impact on the construction of high-precision maps. To address these challenges and analyze operational scenarios based on the characteristics of agricultural scenarios, this paper proposes a point cloud map construction algorithm for plant point removal and locatability estimation. Based on the existing Simultaneous Localization and Mapping (SLAM) framework, plant point removal and locatability estimation are improved. Firstly, Red, Green, Blue (RGB) images and Near Infrared (NIR) images are fused to identify and remove plant point clouds, preserving effective inter frame matching information, reducing the impact of dynamic points on inter frame matching, and achieving high front-end motion estimation accuracy. Then, the localization estimation method based on learning is used to determine the motion estimation status and determine whether to execute the backend optimization algorithm. Finally, the back-end optimization algorithm based on Factor graph is designed, and the Factor graph, constraint relationship and optimization function are constructed to optimize the pose of all frames. The optimized map construction algorithm reduces the re projection errors between field roads, paths, and crop rows by 10.27%, 20.76%, and 14.36% compared to before optimization. To verify the actual operational effectiveness of the point cloud map construction algorithm, the hardware part of the multi-sensor information collection system was designed, and sensor internal and external parameter calibration were also carried out. A map information collection vehicle was built and field experiments were conducted. The results showed that the positioning error of the point cloud map construction method proposed in this paper is less than 0.5°, and the cumulative error of 30 m translation is less than 12 cm, which meets the actual operational requirements.

Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part 1: Event detection for cryoseismology

Abstract. Cryoseismology is a powerful toolset for progressing the understanding of the structure and dynamics of glaciers and ice sheets. It can enable the detection of hidden processes such as brittle fracture, basal sliding, transient hydrological processes, and calving. Addressing the challenge of detecting signals from many different processes, we present a novel approach for the semi-automated detection of events and event-like noise, which is well-suited for use as Part 1 of a workflow where unsupervised machine learning will be used as Part 2 (Latto et al., 2024) to facilitate the main reconnaissance of diverse detected event types. Implemented in the open-source and widely used ObsPy Python package, the multi-STA/LTA algorithm constructs a hybrid characteristic function from a set of short-term average (sta)–long-term average (lta) pairs (refer to Sect. 2 in the main text for an explanation of how uppercase and lowercase STA/sta and LTA/lta abbreviations are differentiated). We apply the algorithm to data from a seismic array deployed on the Whillans Ice Stream (WIS) in West Antarctica (austral summer 2010–2011) to form a “catch-all” catalogue of events and event-like noise. The new algorithm compares favorably with standard approaches, yielding a diversity of seismic events, including all previously identified stick-slip events (Pratt et al., 2014), teleseisms, and other noise-type signals. In terms of a catalogue overview, we investigate a partial association of seismicity with the tidal cycle and a slight association with ice temperature changes of the Antarctic summer. The new algorithm and workflow will assist in the comparison of different glacier environments using seismology, the identification of process change over time, and the targeting of possible subsequent high-resolution studies.

Construction of Algorithm for English Learners to Read Foreign Literature Based on Machine Translation Technology

This study focuses on the development of an algorithm aimed at assisting English learners in comprehending foreign literature through the utilization of machine translation technology. With the growing importance of multilingual proficiency in a globalized world, the ability to read and understand literature in foreign languages is increasingly valued. However, language barriers often hinder learners' comprehension and appreciation of such texts. To address this challenge, our research endeavors to construct an algorithm that leverages machine translation techniques to facilitate the reading process for English learners. By integrating advanced translation algorithms and natural language processing methods, the proposed system aims to provide accurate and contextually relevant translations of foreign literature into English. Additionally, the algorithm will incorporate features tailored to the needs of English learners, such as vocabulary assistance, grammatical explanations, and cultural context annotations, to enhance their reading experience and comprehension levels. Through rigorous experimentation and evaluation, the effectiveness and usability of the algorithm will be assessed, with the ultimate goal of empowering English learners to engage more deeply with foreign literature and broaden their linguistic and cultural horizons.

Construction of Algorithms for English Learners to Read Foreign Literature Based on Machine Translation Technology

The construction of algorithms for English learners to read foreign literature based on machine translation technology involves several key steps. Initially, the algorithm utilizes machine translation to convert the foreign text into English, ensuring accessibility for learners. Next, it employs natural language processing (NLP) techniques to enhance the readability and comprehension of the translated text, such as simplifying complex sentences, clarifying ambiguous phrases, and providing annotations or explanations for cultural or linguistic nuances. Additionally, the algorithm may incorporate adaptive learning mechanisms to personalize the reading experience, adjusting the difficulty level and content based on the learner's proficiency and preferences. This paper explores the development and implementation of algorithms designed to facilitate English learners' comprehension of foreign literature using machine translation technology. Leveraging Probability Path analysis and classification techniques, investigate the effectiveness of various algorithms in improving translation accuracy, fluency, and comprehension for English learners. Through the Probability Path analysis, we delve into the complexities of language structures, translation probabilities, learner interaction, and linguistic adaptation, illuminating the intricate dynamics of machine translation in the context of language learning. Additionally, classification results provide insights into the performance of different algorithms in translating diverse literary genres, showcasing their potential to support English learners in comprehending foreign texts. The findings reveal translation accuracies ranging from 87.8% to 92.0%, with corresponding fluency scores ranging from 4.0 to 4.6. Additionally, algorithms result in comprehension improvements of 23% to 30% for English learners.

Seneca: Taint-Based Call Graph Construction for Java Object Deserialization

Object serialization and deserialization are widely used for storing and preserving objects in files, memory, or database as well as for transporting them across machines, enabling remote interaction among processes and many more. This mechanism relies on reflection, a dynamic language that introduces serious challenges for static analyses. Current state-of-the-art call graph construction algorithms do not fully support object serialization/deserialization, i.e., they are unable to uncover the callback methods that are invoked when objects are serialized and deserialized. Since call graphs are a core data structure for multiple types of analysis (e.g., vulnerability detection), an appropriate analysis cannot be performed since the call graph does not capture hidden (vulnerable) paths that occur via callback methods. In this paper, we present Seneca, an approach for handling serialization with improved soundness in the context of call graph construction. Our approach relies on taint analysis and API modeling to construct sound call graphs. We evaluated our approach with respect to soundness, precision, performance, and usefulness in detecting untrusted object deserialization vulnerabilities. Our results show that Seneca can create sound call graphs with respect to serialization features. The resulting call graphs do not incur significant runtime overhead and were shown to be useful for performing identification of vulnerable paths caused by untrusted object deserialization.

Building a 3D Reconstruction System for Construction Scenes using Deep Learning Techniques

Due to the developing intricacy and size of construction projects and the way that construction plan the executives is still generally performed physically, many ventures run over financial plan and into legitimate difficulty as an immediate consequence of timetable deferrals. Albeit the result of existing 3D reconstruction techniques is many times a model with immense openings, bends, or foggy segments, the result of man-made intelligence-based 3D remaking procedures is commonly a model with straightforward disengaged pieces that are portrayed as 3D boxes. Hence, overall, these algorithmic systems are lacking for certified use. The focal goal of this study is to apply the creation inadequately organized network system to 3D diversion by setting up a generative seriously organized network model to a related state, subsequently influencing the possibility of the fundamental 3D redirection model. Late 2D pictures are expected as models with no previous information on the 3D concealed shape or reference experiences. The revelations of a standard benchmark for 3D diversion procedures uncover that this algorithmic construction beats state of the art methods. Exploratory results show that our algorithmic system defeats state of the art 3D replication procedures on a standard benchmark dataset for 3D changes.

Fast, parallel, and cache-friendly suffix array construction

PurposeString indexes such as the suffix array (sa) and the closely related longest common prefix (lcp) array are fundamental objects in bioinformatics and have a wide variety of applications. Despite their importance in practice, few scalable parallel algorithms for constructing these are known, and the existing algorithms can be highly non-trivial to implement and parallelize.MethodsIn this paper we present caps-sa, a simple and scalable parallel algorithm for constructing these string indexes inspired by samplesort and utilizing an LCP-informed mergesort. Due to its design, caps-sa has excellent memory-locality and thus incurs fewer cache misses and achieves strong performance on modern multicore systems with deep cache hierarchies.ResultsWe show that despite its simple design, caps-sa outperforms existing state-of-the-art parallel sa and lcp-array construction algorithms on modern hardware. Finally, motivated by applications in modern aligners where the query strings have bounded lengths, we introduce the notion of a bounded-context sa and show that caps-sa can easily be extended to exploit this structure to obtain further speedups. We make our code publicly available at https://github.com/jamshed/CaPS-SA.

A Versatile Approach for Adaptive Grid Mapping and Grid Flex-Graph Exploration with a Field-Programmable Gate Array-Based Robot Using Hardware Schemes.

Robotic exploration in dynamic and complex environments requires advanced adaptive mapping strategies to ensure accurate representation of the environments. This paper introduces an innovative grid flex-graph exploration (GFGE) algorithm designed for single-robot mapping. This hardware-scheme-based algorithm leverages a combination of quad-grid and graph structures to enhance the efficiency of both local and global mapping implemented on a field-programmable gate array (FPGA). This novel research work involved using sensor fusion to analyze a robot's behavior and flexibility in the presence of static and dynamic objects. A behavior-based grid construction algorithm was proposed for the construction of a quad-grid that represents the occupancy of frontier cells. The selection of the next exploration target in a graph-like structure was proposed using partial reconfiguration-based frontier-graph exploration approaches. The complete exploration method handles the data when updating the local map to optimize the redundant exploration of previously explored nodes. Together, the exploration handles the quadtree-like structure efficiently under dynamic and uncertain conditions with a parallel processing architecture. Integrating several algorithms into indoor robotics was a complex process, and a Xilinx-based partial reconfiguration approach was used to prevent computing difficulties when running many algorithms simultaneously. These algorithms were developed, simulated, and synthesized using the Verilog hardware description language on Zynq SoC. Experiments were carried out utilizing a robot based on a field-programmable gate array (FPGA), and the resource utilization and power consumption of the device were analyzed.

Grammar-based test suite construction using coverage-directed algorithms over LR-graphs

In grammar-based testing, the test suites that drive the system under test are typically constructed from a given context-free grammar through a set of derivations that jointly satisfy some coverage criterion. In this paper, we describe and evaluate a new algorithm that instead constructs test suites from a set of valid paths that cover all edges in a labeled directed graph corresponding to an LR-automaton that accepts the language of the grammar. Vertices in this graph correspond to states in the LR-automaton; two vertices are connected by an edge iff the top of the LR-automaton’s stack can change from one state to the other, either by shifting a terminal or non-terminal symbol (push edges), or by reducing with a grammar rule (pop edges). The algorithm constructs a unique reduction path for each pop edge in the graph. These reduction paths are recursively embedded into each other, and any unresolved non-terminal push edges are substituted by shortest derivations for the non-terminal symbol. The algorithm can work with different types of LR-automata, including LR(0)- and LR(1)-automata, and can successfully generate a test suite from an LR-graph even if the underlying LR-automaton construction leads to shift/reduce or reduce/reduce conflicts. The algorithm only constructs valid paths over the LR-graphs that correspond to sentences in the language and thus generates only positive tests. We therefore also describe mutations to the positive paths that are guaranteed to generate negative tests without needing any further verification by an oracle.Our algorithm is substantially more efficient than an earlier algorithm that explores LR-graphs with two consecutive breadth-first graph traversals and our experimental evaluation shows that it scales to large production-quality grammars. It is robust against random choices made to resolve ambiguity in the construction of the tests, while the code coverage of the different test suite variants is relatively uniform. Finally, our evaluation shows that the negative test suites constructed by path mutation identify more faults in a set of student grammars than those constructed by rule mutation.

Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model

ObjectivesCOVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on multiple machine learning algorithms and validated its effectiveness.MethodsA cross-sectional method was used to examine the depression status of individuals quarantined at home during the epidemic via the network. Characteristics included variables on sociodemographics, COVID-19 and its prevention and control measures, impact on life, work, health and economy after the city was sealed off, and PHQ-9 scale scores. The home-quarantined subjects were randomly divided into training set and validation set according to the ratio of 7:3, and the performance of different machine learning models were compared by 10-fold cross-validation, and the model algorithm with the best performance was selected from 15 models to construct and validate the depression prediction model for home-quarantined subjects. The validity of different models was compared based on accuracy, precision, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC), and the best model suitable for the data framework of this study was identified.ResultsThe prevalence of depression among home-quarantined individuals during the epidemic was 31.66% (202/638), and the constructed Adaboost depression prediction model had an ACC of 0.7917, an accuracy of 0.7180, and an AUC of 0.7803, which was better than the other 15 models on the combination of various performance measures. In the validation sets, the AUC was greater than 0.83.ConclusionsThe Adaboost machine learning algorithm developed in this study can be used to construct a depression prediction model for home-quarantined individuals that has better machine learning performance, as well as high effectiveness, robustness, and generalizability.

High-order iterative rearrangement transform for time–frequency characterization of bearing fault impact

Time–frequency analysis (TFA) can effectively characterize features of non-stationary signals. Traditional TFA algorithms construct signal models in the time domain and make the assumption that the instantaneous characteristics of each component are continuous. However, the instantaneous frequency (IF) of the transient signal is discontinuous in the time domain and exhibits a multifaceted relationship with time, such as shock, vibration wave, damped sound wave, etc. Additionally, in most existing TFA methods, low-order group delay (GD) is used to describe transient signals, which leads to unsatisfactory energy concentration and calculation accuracy. To address about issues, a novel TFA technique, termed high-order iterative rearrangement transform (HOIRT), is developed in this research. First, the signal model is defined within the frequency domain, and the frequency ridge of the transient signal is described by a high-order GD (HOGD), which is similar to the IF. Second, a HOGD-based iterative synchrosqueezing operator is defined to reassign time–frequency coefficients into the GD trajectories along the time direction. Finally, the HOGD-based frequency extraction operator is constructed to only retain the target time–frequency information of the transient signal from the rearranged results, such that the noise interference is eliminated and the energy-concentrated TFR is obtained. A simulation signal with nonlinear GDs is employed to illustrate the effectiveness of the HOIRT. Compared with the other seven typical TFA algorithms, the developed technique has the smallest calculation error and Rényi entropy, showing that the HOIRT has the highest accuracy and energy concentration. Analysis result of the bearing fault impact signal shows that the proposed HOIRT can display the time when pulses occur while ensuring high time–frequency resolution, making it suitable for detecting bearing faults.

Multi-association evidential feature selection and its application to identifying schizophrenia

Granular Computing (GrC)-based feature selection can remove redundant features from a massive amount of data and improve the efficiency of information processing. However, the existing method of neighborhood-based information granule only considers the distance between samples, ignoring other significant relationships existing between them. To fill this gap, this paper proposes a novel feature selection approach based on two-step multi-association neighborhood evidence entropy. This approach is constructed in three phases. Firstly, adaptive k value corresponding to each sample in sparse representation method is determined. Sparse correlation and distance measure are fused to form a multi-association information granule. Then, the samples in the multi-association information granule are estimated and weak-related information is removed to constitute a two-step multi-association information granule. Secondly, sparse correlation information is processed using Dempster-Shafer evidence theory, and a new credibility-based function is developed. In addition, the credibility is used to construct a novel neighborhood evidence entropy, which can effectively reflect the uncertainty of data. Thirdly, the proposed neighborhood evidence entropy is applied to assess the importance of features. As a result, several vital features are selected. The experimental results on twelve datasets demonstrate that the effectiveness of the proposed method is superior to other algorithms in construction of information granules and classification accuracy, respectively. Finally, the proposed method is applied to the selection of brain regions in schizophrenia. It can effectively analyze the lesions of schizophrenia and improve the prediction of the disorder. The code is available at https://github.com/fxx-Aurora/TMAE-FS/tree/main.

Construction and Analysis of Structure-Preserving Numerical Algorithm for Two-Dimensional Damped Nonlinear Space Fractional Schrödinger equation

Construction and Analysis of Structure-Preserving Numerical Algorithm for Two-Dimensional Damped Nonlinear Space Fractional Schrödinger equation

DiagPara: Volumetric parameterization with energy-minimizing pair of diagonal surfaces from given boundaries

An efficient approach for constructing volumetric parameterization with internal energy-minimizing pair of diagonal surfaces from given boundaries is presented. The expression for the internal energy of one diagonal surface pair is formulated. Then necessary and sufficient conditions for trivariate tensor-product Bézier volume with minimal internal energy of the diagonal surface pair are derived. From the derived conditions, an efficient construction algorithm for single- and multi-block Bézier volumes with minimal diagonal internal energy by the Lagrange multiplier method is presented. The auxiliary linear system only needs to be solved once. As soon the explicit computation masks are obtained, the interior control points of the volume are then directly calculated. Experimental results are given to illustrate the volumetric parameterization effects of the presented approach.

Innovative solar distillation system with prismatic absorber basin: Experimental analysis and LSTM machine learning modeling coupled with great wall construction algorithm

Enhancing the vaporization surface area of the solar distillers through the implementation of a novel configurations based solar distiller represents a cost-efficient strategy for maximizing the distilled water output of conventional solar stills. The study introduces a pioneering wicked prismatic-shaped solar distiller equipped with wick materials and feed spaying nozzles, aimed at augmenting vaporization rates inside the still trough and, consequently, increasing the yield of freshwater. Two solar distillers with double slope covers are constructed and tested include a modified solar still with a prismatic basin, two incline covers, and spraying nozzles (MSS) and a reference double slope solar still (RSS). Furthermore, we have constructed a hybrid artificial intelligence framework, integrating a long short-term memory (LSTM) neural network fine-tuned through the utilization of great wall construction algorithm (GWCA). This model has been designed for the purpose of forecasting both the saltwater temperature and the associated freshwater product within the two examined solar distillers, whereas, the time, solar flux, wind velocity, and ambient temperature are considered as inputs. GWCA is effectively employed to optimize the LSTM model by determining the optimal parameter values to enhance its performance. The experimental results revealed that the daily freshwater production for the MSS reached 7.94 kg/m²/day, while the RSS achieved 5.31 kg/m²/day. This represents a substantial 49.53% improvement when compared to the RSS. Additionally, the daily energy efficiency of the MSS and RSS was assessed at 57.40% and 39.80%, respectively, whereas the daily exergy efficiency was 3.80% and 2.20%, respectively, signifying a notable 44.23% and 72.74% increase the energetic and exergetic efficiencies over RSS. Furthermore, the prediction findings demonstrated that during the testing phase, the coefficient of determination for saltwater temperature prediction of the MSS was calculated at 0.996 for LSTM-GWCA and 0.963 for LSTM. In the case of freshwater product prediction, these values were 0.983 for LSTM-GWCA and 0.922 for LSTM, respectively.

Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

Computer vision-based real-time monitoring for swivel construction of bridges: from laboratory study to a pilot application

PurposeThe swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.Design/methodology/approachThis study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.FindingsIn the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.Originality/valueIn a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Improved bounds for the bracketing number of orthants or revisiting an algorithm of Thiémard to compute bounds for the star discrepancy

We improve the best known upper bound for the bracketing number of d-dimensional axis-parallel boxes anchored in 0 (or, put differently, of lower left orthants intersected with the d-dimensional unit cube [0,1]d). More precisely, we provide a better estimate for the cardinality of an algorithmic bracketing cover construction due to Eric Thiémard, which forms the core of his algorithm to approximate the star discrepancy of arbitrary point sets from Thiémard (2001) [22]. Moreover, the new upper bound for the bracketing number of anchored axis-parallel boxes yields an improved upper estimate for the bracketing number of arbitrary axis-parallel boxes in [0,1]d. In our upper bounds all constants are fully explicit.