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This paper presents an innovative mathematical model of solid fuel gasification, which is not described in the available literature. The calculation of the components of the heterogeneous phase (including both solid and gaseous phases) as well as the calculation of the homogeneous phase (only gaseous components) is based on the balance of the total amounts of carbon, oxygen, hydrogen, and nitrogen entering the reactor space. Additionally, this paper introduces a new method for calculating the composition of the gaseous phase, based on reducing the heterogeneous mixture (composed of solid and gaseous phases) to a homogeneous gaseous phase. This approach to calculating the gaseous phase composition in the solid fuel gasification process has not been found by the authors in the cited literature. This paper also presents a model for calculating the heterogeneous and gaseous phases using the number of moles that participate in the assumed chemical reactions of the solid fuel gasification process. This approach to calculating the composition of the heterogeneous and gaseous phases of the solid fuel gasification process is also not represented in the cited literature. For comparison with the literature data, municipal solid waste (MSW) and cashew nut shell (Cashew Shell Char (CNSC)) were used as fuels in the calculation of gasification composition. The results of the calculation of the gaseous phase composition using the model presented in the paper show good agreement with the data from the literature. The calculation of the composition of the heterogeneous mixture during the steam gasification of MSW (α = 0.4) shows the presence of a solid phase (carbon) up to approximately 735 °C. At that temperature, the synthetic gas contains only gaseous components: CO = 33.10%, H2 = 52.70%, CH4 = 2.54%, CO2 = 4.97, H2O = 5.93% and N2 = 0.76%. Increasing the temperature above 735 °C eliminates the solid phase from the equilibrium mixture. The literature data on solid fuel gasification generally do not consider the proportion of the solid phase (carbon) in the equilibrium mixture. To satisfy the material balance at the input and output of the gasification reactor, it is necessary to determine the proportion of the solid phase (carbon) in the equilibrium mixture. Since the proportion of the solid phase (carbon) in the heterogeneous equilibrium mixture can only be determined through measurement, the development and application of a mathematical model in engineering practice is of great importance, so this developed model can be considered a useful tool for simulating the influence of process parameters on gas characteristics.

The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic articles containing both the terms “machine learning” and “marketing” in their titles, which yields a pool of papers. These papers have been processed using the Supabase platform. The process has included steps like text refinement and feature extraction. In addition, our study uses two key ML methodologies: topic modeling through NMF and a comparative analysis utilizing the k-means clustering algorithm. Through this analysis, three distinct clusters emerged, thus clarifying how ML techniques are influencing marketing strategies, from enhancing customer segmentation practices to optimizing the effectiveness of advertising campaigns.

Human trafficking thrives in the shadows, and the rise of social media has provided traffickers with a powerful and unregulated tool. This paper delves into how these criminals exploit online platforms to target and manipulate vulnerable populations. A thematic analysis of existing research explores the tactics used by traffickers on social media, revealing how algorithms can be manipulated to facilitate exploitation. Furthermore, the paper examines the limitations of current regulations in tackling this online threat. The research underscores the urgent need for collaboration between governments and researchers to combat algorithmic exploitation. By harnessing data analysis and machine learning, proactive strategies can be developed to disrupt trafficking networks and protect those most at risk.

This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and is applicable to Markov chains demonstrating structural regularity. Proportional lumpability extends this idea, proposing that the transition rates of a Markov chain can be modified by certain factors, resulting in a lumpable new Markov chain. This concept facilitates the derivation of precise performance indices for the original process. This paper establishes the well-defined nature of the problem of computing the coarsest proportional lumpability that refines a given initial partition, ensuring a unique solution exists. Additionally, a polynomial time algorithm is introduced to solve this problem, offering valuable insights into both the concept of proportional lumpability and the broader realm of partition refinement techniques. The effectiveness of proportional lumpability is demonstrated through a case study that consists of designing a model to investigate selfish mining behaviors on public blockchains. This research contributes to a better understanding of efficient approaches for handling large stochastic models and highlights the practical applicability of proportional lumpability in deriving exact performance indices.

Assistive technologies (ATs) often have a high-dimensionality of possible movements (e.g., assistive robot with several degrees of freedom or a computer), but the users have to control them with low-dimensionality sensors and interfaces (e.g., switches). This paper presents the development of an open-source interface based on a sequence-matching algorithm for the control of ATs. Sequence matching allows the user to input several different commands with low-dimensionality sensors by not only recognizing their output, but also their sequential pattern through time, similarly to Morse code. In this paper, the algorithm is applied to the recognition of hand gestures, inputted using an inertial measurement unit worn by the user. An SVM-based algorithm, that is aimed to be robust, with small training sets (e.g., five examples per class) is developed to recognize gestures in real-time. Finally, the interface is applied to control a computer’s mouse and keyboard. The interface was compared against (and combined with) the head movement-based AssystMouse software. The hand gesture interface showed encouraging results for this application but could also be used with other body parts (e.g., head and feet) and could control various ATs (e.g., assistive robotic arm and prosthesis).

In this paper, I propose a conceptual framework for law and artificial intelligence (AI) that is based on ideas derived from systems and control theory. The approach considers the relationship between the input to an AI-controlled system and the system’s output, which may affect events in the real-world. The approach aims to add to the current discussion among legal scholars and legislators on how to regulate AI, which focuses primarily on how the output, or external behavior of a system, leads to actions that may implicate the law. The goal of this paper is to show that not only is the systems output an important consideration for law and AI but so too is the relationship between the systems input to its desired output, as mediated through a feedback loop (and other control variables). In this paper, I argue that ideas derived from systems and control theory can be used to provide a conceptual framework to help understand how the law applies to AI, and particularly, to algorithmically based systems.

As a hot topic in the field of library and information, the research on topic recognition and trend prediction has been paid close attention by academic circles. This paper uses a systematic literature review, bibliometric analyses and classification methods. Through a systematic literature review, 96 studies about topic identification and evolution prediction models are selected from the CNKI database. By using VOSviewer to conduct bibliometric analyses, the key research content and themes are revealed. Through the classification method, EXCEL is used to summarize models and algorithms used in the literature comprehensively. It is found that topic identification models and algorithms can be divided into four categories: ① Topic model based on LDA and related derivative models. ② Machine learning and deep learning methods. ③ Methods based on reference relation. ④ Text mining methods. Trend prediction models and algorithms mainly cover two categories: ① deep learning or machine learning models and algorithms based on time sequence; ② link prediction algorithms based on complex network. At the same time, we have also summarized the common index system involved in each study and the way to evaluate the effectiveness of the method, thus this paper comprehensively reveals the application progress in academic circles of topic identification and prediction models and algorithms from the last 10 years and beyond, based on the CNKI database. The purpose is to determine the most popular models and algorithms applied in research, generalize the corresponding indicator systems and validation methods, and finally provide references for model choice or evaluation when identifying and predicting topics in the future. Thus, this paper can help us to understand the overall progress made in text analysis research, and provides a useful reference for selecting and applying the appropriate models, algorithms and indicators.

The demand for a reliable, cheap and environmentally friendly source of energy makes the integration of renewable energy into power networks a global challenge. Furthermore, reliability, as one of the core elements of efficient and cost-effective energy management options, is still among the dominant factors/techniques that receive more attention for realistic penetrations of renewable energy into the electricity grid. This paper proposes an efficient way of energy management for a grid-connected microgrid. The grid-connected microgrid used in the analysis consists of solar photovoltaic (P.V.) and battery. In this microgrid configuration, oftentimes, the output power might not be equal to the system demand; in this regard, it is expected that the mismatch between these output powers is not zero. However, to reduce the mismatch between demand and supply to be close to zero, this paper proposes strategies of increasing the rated power of solar, battery and grid separately and combining them with a view of finding the cheapest option among these strategies. The results have shown that the cost increment for different options is USD 280.792, 84.48 and 48.204 for storage, P.V. and grid, respectively. These have shown that the storage option is the most expansive option for improving P.V. grid-connected microgrids. This is followed immediately by the P.V. option, which is weather dependent. On the other hand, the grid option is the cheapest option for system reliability improvement. This paper is expected to be useful to both new researchers and experts who are working in energy management with an emphasis on the reliability aspect.

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids.

In this paper, a heuristic approach to doubly fed induction generator (DFIG) protection and low voltage ride through (LVRT) is carried out. DFIG-based wind systems are rapidly penetrating the power generation section. Despite their advantages, their direct coupling grid makes them highly sensitive to symmetrical faults. A well-known solution to this is the crowbar method of DFIG protection. This paper provides a method to determine the optimal crowbar resistance value, to ensure a strong trade-off between the rotor current and DC voltage transients. Further, since the crowbar method requires disconnection from the grid, the linear quadratic regulator (LQR) is applied to the system. This is to ensure fault ride through compliance with recent grid code requirements. The well-known PSO, as well as the recently developed African vultures optimization algorithm (AVOA), was applied to the problem. The first set of results show that for severe symmetrical voltage dips, the AVOA provides a good option for crowbar magnitude optimization, whereas PSO performed better for moderately severe dips. Secondly, when the LQR was optimized via the AVOA, it exhibited superiority over the conventional PSO-based PI controller. This superiority was in terms of rotor current transient magnitude, DC voltage transient magnitude, and reactive power steady-state ripple and were in the order of 67.5%, 20.35%, and 37.55%, respectively. When comparing the crowbar method and the LQR, it was observed that despite the LQR exhibiting superiority in terms of transient performance, the crowbar method offered a unanimously superior settling time.

The detection of obstacles of intelligent driving vehicles becomes a primary condition to ensure safe driving. LiDAR and camera are the main sensors for intelligent vehicles to obtain information about their surroundings, and they each have their own benefits in terms of object detection. LiDAR can obtain the position and geometric structure of the object, and camera is very suitable for object recognition, but the reliance on environmental perception by a single-type sensor can no longer meet the detection requirements in complex traffic scenes. Therefore, this paper proposes an improved AVOD fusion algorithm for LiDAR and machine vision sensors. The traditional NMS (non-maximum suppression) algorithm is optimized using a Gaussian weighting method, while the 3D-IoU pose estimation loss function is introduced into the target frame screening module to upgrade the 2D loss function to 3D and design the 3D-IoU criterion. By comparing the detection accuracy of the algorithm proposed in this paper with that of the traditional method, it has been found that the improved AVOD fusion algorithm significantly improved the detection efficiency and the detection accuracy reached 96.3%. The algorithm proposed in this paper can provide a new approach for object detection of intelligent driving vehicles.

As is well known, complex intuitionistic fuzzy preference relation can describe the fuzzy characters of things in more detail and comprehensively and is very useful in dealing with decision-making problems that include periodic or recurring phenomena. However, sometimes, a decision-maker may provide incomplete judgments in a complex intuitionistic fuzzy preference relation because of a lack of knowledge, time pressure, and the decision-makers’ limited expertise related to the problem domain. In such cases, it would be sensible not to force the expert to express “false” preferences over these objects. Consequently, how to define incomplete complex intuitionistic fuzzy preference relations and to estimate their missing elements in an incomplete complex intuitionistic fuzzy preference relation becomes a necessary step in a decision-making process. In this paper, the concept of incomplete complex intuitionistic fuzzy preference relation is introduced and its properties are discussed. Meanwhile, the multiplicative consistent incomplete complex intuitionistic fuzzy preference relations are defined. Secondly, estimating algorithms are developed to estimate the missing elements in the acceptable incomplete complex intuitionistic fuzzy preference relations. Finally, an expert weight determination algorithm and the group decision-making algorithms based on incomplete complex intuitionistic fuzzy preference relations are established. The solving process of the algorithms is illustrated by an example, the practicability of the algorithms is verified, the advantages and disadvantages of two group decision-making algorithms are compared and analyzed, and the simulation verification of incomplete complex intuitionistic fuzzy system is carried out by MATLAB software. The framework proposed in this paper effectively generalizes and enriches the previous works and has a good application prospect.

Objective: This study compared the aerosol and splatter diameter and count numbers produced by a dental mouth prop with a suction holder device and a saliva ejector during ultrasonic scaling in a clinical setting. Methodology: Fluorescein dye was placed in the dental equipment irrigation reservoirs with a mannequin, and an ultrasonic scaler was employed. The procedures were performed three times per device. The upper and bottom board papers were placed on the laboratory platform. All processes used an ultrasonic scaler to generate aerosol and splatter. A dental mouth prop with a suction holder and a saliva ejector were also tested. Photographic analysis was used to examine the fluorescein samples, followed by image processing in Python and assessment of the diameter and count number. For device comparison, statistics were used with an independent t-test. Result: When using the dental mouth prop with a suction holder, the scaler produced aerosol particles that were maintained on the upper board paper (mean ± SD: 1080 ± 662 µm) compared to on the bottom board paper (1230 ± 1020 µm). When the saliva ejector was used, it was found that the diameter of the aerosol on the upper board paper was 900 ± 580 µm, and the diameter on the bottom board paper was 1000 ± 756 µm. Conclusion: There was a significant difference in the aerosol and splatter particle diameter and count number between the dental mouth prop with a suction holder and saliva ejector (p < 0.05). Furthermore, the results revealed that there was a statistically significant difference between the two groups on the upper and bottom board papers.

The key priority of metallurgical industry development is expanding the range and improving the quality of bar products and their major component, steel wire. Continuous wire rod mills with multi-roll passes have been developed and implemented over the past decades. These include mills with four-roll passes with mutually perpendicular rolls. The specific feature of mills with complex passes is the impossibility of conduct and the direct measurement of the workpiece dimensions in several directions during rolling. The paper studies the development of a system for indirect control over complex section geometry by adjusting the interstand space tension with simultaneous compensation for changes in rolling forces. The paper contribution is the first justification of a technique for the control over the indirect rolled product section on mills with multi-roll passes based on theoretical and experimental research. Analytical and experimental dependencies between the metal pressure on the rolls and the semifinished rolled product temperature, rolling speed, and single drawing have been obtained for various steel grades. The impact of process factors on the rolled product section geometry when rolling in stands with four-roll passes has first been studied. The automated control system implementing the proposed technique has passed pilot tests on a continuous five-stand mill. The processes occurring in closed-loop speed and torque control systems under controlling and disturbing effects have been experimentally studied. Implementing the proposed algorithms indirectly confirmed the reduced impact of tension and pressure on the section geometry.

Typically, devices work to improve life quality, measure parameters, and make decisions. They also signalize statuses, and take actions accordingly. When working, they measure different values. These are to be compared against thresholds. Some time ago, vision systems came into play. They use camera(s) to deliver(s) images to a processor module. The received images are processed to perform detections (typically, they focus to detect objects, pedestrians, mopeds, cyclists, etc.). Images are analyzed and thresholds are used to compare the computed values. The important thing is that images are affected by noise. Therefore, the vision system performance can be affected by weather in some applications (for example, in automotive). An interesting case in this domain is when the measured/computed values show small variations near the threshold (not exceeding) but very close to it. The system is not able to signalize/declare a state in this case. It is also important to mention that changing the threshold does not guarantee solving the problem in any future case, since this may happen again. This paper proposes the Alpha-Beta family of filters as a solution to this problem. The members can track a signal based on measured values. This reveals errors when the tracked-signal’s first derivative changes sign. These errors are used in this paper to bypass the threshold problem. Since these errors appear in both situations (when the first derivative decreases from positive to negative and increases from negative to positive), the proposed method works when the observed data are in the vicinity of the threshold but above it.

This article presents a pioneering mathematical model, fuzzy bipolar hypersoft (FBHS) sets, which combines the bipolarity of parameters with the fuzziness of data. Motivated by the need for a comprehensive framework capable of addressing uncertainty and variability in complex phenomena, our approach introduces a novel method for representing both the presence and absence of parameters through FBHS sets. By employing two mappings to estimate positive and negative fuzziness levels, we bridge the gap between bipolarity, fuzziness, and parameterization, allowing for more realistic simulations of multifaceted scenarios. Compared to existing models like bipolar fuzzy hypersoft (BFHS) sets, FBHS sets offer a more intuitive and user-friendly approach to modeling phenomena involving bipolarity, fuzziness, and parameterization. This advantage is underscored by a detailed comparison and a practical example illustrating FBHS sets’ superiority in modeling such phenomena. Additionally, this paper provides an in-depth exploration of fundamental FBHS set operations, highlighting their robustness and applicability in various contexts. Finally, we demonstrate the practical utility of FBHS sets in problem-solving and introduce an algorithm for optimal object selection based on available information sets, further emphasizing the advantages of our proposed framework.

Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future.

This paper presents a novel complete design procedure to convert a maximum power point tracking (MPPT) algorithm into a control system. The MPPT algorithm can be tuned by employing any control system design. In this paper, we adopted Bode diagrams using the criteria of module and phase as the power electronics specialists are habituated with such concepts. The MPPT control transfer functions were derived using the average state equations and small-signal analysis. The control loops were derived for power and voltage control loops. The design procedure was applied to the well-known perturb and observe (P&O) and incremental conductance (IC) algorithms, returning the P&O based on PI and IC based on PI algorithms. Such algorithms were evaluated through simulation and experimental results. Additionally, we showed that the proposed design methodology can optimize energy harvesting, allowing algorithms to have outstanding tracking factors (above 99%) and adaptability characteristics.

There has been a significant focus on predictive policing systems, as law enforcement agents embrace modern technology to forecast criminal activity. Most developed nations have implemented predictive policing, albeit with mixed reactions over its effectiveness. Whilst at its inception, predictive policing involved simple heuristics and algorithms, it has increased in sophistication in the ever-changing technological environment. This paper, which is based on a literature survey, examines predictive policing over the last decade (2010 to 2020). The paper examines how various nations have implemented predictive policing and also documents the impediments to predictive policing. The paper reveals that despite the adoption of predictive software applications such as PredPol, Risk Terrain Modelling, HunchLab, PreMap, PRECOBS, Crime Anticipation System, and Azevea, there are several impediments that have militated against the effectiveness of predictive policing, and these include low predictive accuracy, limited scope of crimes that can be predicted, high cost of predictive policing software, flawed data input, and the biased nature of some predictive software applications. Despite these challenges, the paper reveals that there is consensus by the majority of the researchers on the importance of predictive algorithms on the policing landscape.

Some of the most powerful spatial analysis software solutions (Oracle, Google Earth Engine, PostgreSQL + PostGIS, etc.) are currently performing geometric calculations directly on the ellipsoid (a quadratic surface that models the earth shape), with a double purpose: to attain a high degree of accuracy and to allow the full management of large areas of territory (countries or even continents). It is well known that both objectives are impossible to achieve by means of the traditional approach using local mathematical projections and Cartesian coordinates. This paper demonstrates in a quantitative methodological way that most of the spatial analysis software products make important deviations in calculations regarding to geodesics, being the users unaware of the magnitude of these inaccuracies, which can easily reach meters depending on the distance. This is due to the use of ellipsoid calculations in an approximate way (e.g., using a sphere instead of an ellipsoid). This paper presents the implementation of two algorithms that solve with high accuracy (less than 100 nm) and efficiently (few iterations) two basic geometric calculations on the ellipsoid that are essential to build more complex spatial operators: the intersection of two geodesics and the minimum distance from a point to a geodesic.

In this paper, we develop two algorithms to solve a nonlinear system of symmetric equations. The first is an algorithm based on modifying two Broyden–Fletcher–Goldfarb–Shanno (BFGS) methods. One of its advantages is that it is more suitable to effectively solve a small-scale system of nonlinear symmetric equations. In contrast, the second algorithm chooses new search directions by incorporating an approximation method of computing the gradients and their difference into the determination of search directions in the first algorithm. In essence, the second one can be viewed as an extension of the conjugate gradient method recently proposed by Lv et al. for solving unconstrained optimization problems. It was proved that these search directions are sufficiently descending for the approximate residual square of the equations, independent of the used line search rules. Global convergence of the two algorithms is established under mild assumptions. To test the algorithms, they are used to solve a number of benchmark test problems. Numerical results indicate that the developed algorithms in this paper outperform the other similar algorithms available in the literature.

In this paper, we study the knapsack problem with conflict pair constraints. After a thorough literature survey on the topic, our study focuses on the special case of bipartite conflict graphs. For complete bipartite (multipartite) conflict graphs, the problem is shown to be NP-hard but solvable in pseudo-polynomial time, and it admits an FPTAS. Extensions of these results to more general classes of graphs are also presented. Further, a class of integer programming models for the general knapsack problem with conflict pair constraints is presented, which generalizes and unifies the existing formulations. The strength of the LP relaxations of these formulations is analyzed, and we discuss different ways to tighten them. Experimental comparisons of these models are also presented to assess their relative strengths. This analysis disclosed various strong and weak points of different formulations of the problem and their relationships to different types of problem data. This information can be used in designing special purpose algorithms for KPCC involving a learning component.

The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments.

This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author’s choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.

Nowadays, Unmanned Aerial Vehicles (UAV) are frequently present in the civilian environment. However, proper implementations of different solutions based on these aircraft still face important challenges. This article deals with multi-UAV systems, forming aerial networks, mainly employed to provide Internet connectivity and different network services to ground users. However, the mission duration (hours) is longer than the limited UAVs’ battery life-time (minutes). This paper introduces the UAV replacement procedure as a way to guarantee ground users’ connectivity over time. This article also formulates the practical UAV replacements problem in moderately large multi-UAV swarms and proves it to be an NP-hard problem in which an optimal solution has exponential complexity. In this regard, the main objective of this article is to evaluate the suitability of heuristic approaches for different scenarios. This paper proposes betweenness centrality heuristic algorithm (BETA), a graph theory-based heuristic algorithm. BETA not only generates solutions close to the optimal (even with 99% similarity to the exact result) but also improves two ground-truth solutions, especially in low-resource scenarios.

The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm 2 (VASIA2) algorithms were used as well as the database of visual ice observations accumulated in the course of 15 Arctic expeditions. The comparison was performed in line with the SIC gradation (in tenths) into very open (1−3), open (4−6), close (7−8), very close and compact (9−10,10) ice, separately for summer and winter seasons. On average, in summer NT underestimates SIC by 0.4 tenth as compared to ship observations, while ASI and VASIA2 by 0.3 tenth. All three algorithms overestimate total SIC in regions of very open ice and underestimate it in regions of close, very close, and compact ice. The maximum average errors are typical of open ice regions that are most common in marginal ice zones. In winter, NT and ASI also underestimate SIC on average by 0.4 and 0.8 tenths, respectively, while VASIA2, on the contrary, overestimates by 0.2 tenth against the ship data, however, for open and close ice the average errors are significantly higher than in summer. In the paper, we also estimate the impact of ice melt stage and presence of new ice and nilas on SIC derived from NT, ASI, and VASIA2.

By shining a light on the previously neglected combination of public service media (PSM) and the audience perspective, this paper adds to the debate on (algorithmic) news personalization. While news personalization may offer new opportunities, it can clearly also conflict with the PSM mission of universality of access, reach, and content. This empirical study compares the audience perspective on the news personalization of users and non-users of public service news in Germany, France, and the UK. Overall, the findings of the online survey show that users of public service news in Germany and the UK—in comparison to non-users of these services—perceive more risks such as missing out on certain topics or viewpoints, place greater value on a shared public sphere, and more strongly prefer a news selection primarily made by professional news editors. In France, however, the differences between users and non-users of public service news are rarely significant, which is interpreted against the background of the different media systems and the role of PSM. The findings add to the understanding of what public service news audiences expect PSM organizations to provide while keeping the difficult balance between personalization and universality.

This paper explores the emerging and evolving landscape for metrics in smart cities in relation to big data challenges. Based on a review of the research literature, the problem of “synthetic quantitative indicators„ along with concerns for “measuring urban realities„ and “making metrics meaningful„ are identified. In response, the purpose of this paper is to advance the need for innovating metrics for smarter, more interactive and responsive cities in addressing and mitigating algorithmic-related challenges on the one hand, and concerns associated with involving people more meaningfully on the other hand. As such, the constructs of awareness, learning, openness, and engagement are employed in this study. Using an exploratory case study approach, the research design for this work includes the use of multiple methods of data collection including survey and interviews. Employing a combination of content analysis for qualitative data and descriptive statistics for quantitative data, the main findings of this work support the need for rethinking and innovating metrics. As such, the main conclusion of this paper highlights the potential for developing new pathways and spaces for involving people more directly, knowingly, and meaningfully in addressing big and small data challenges for the innovating of urban metrics.

This paper shows the implementation and performance analysis of motion control algorithms for a 3D Concrete Printing system based on an industrial robotic arm. This work is part of the project of digital fabrication of low-cost housing. Regarding the technological architecture integrated in the present work, the hardware devices used are the EPSON C12 industrial robot arm and the RC700-A controller, and the software tools are Fusion 360 and RC+. To evaluate the point extraction and sequencing algorithms of a 3D structure and the motion control algorithms of the robot arm, three test wall models were designed: a semi-circle with horizontal undulations, a semi-circle with vertical undulations and an orthohedron without undulations. For the performance analysis, 140 trajectory times were extracted for each test model. The extracted values are the trajectory time intervals of the wall model envelope for each layer and of the internal trajectory of the wall model infill. Due to the increasing and decreasing trends of time for specific cases, it was concluded that in the working area of the robot arm, there are parts in which the robot is more efficient and therefore the joints offer less inertia for certain types of movements, for example straight or curved movements and short or long movements.

Some might say that the eigenproblem is one of the examples people discovered by looking at the sky and wondering. Even though it was formulated to explain the movement of the planets, today it has become the ansatz of solving many linear and nonlinear problems. Formulation in the terms of the eigenproblem is one of the key tools to solve complex problems, especially in the area of molecular geometry. However, the basic concept is difficult without proper preparation. A review paper covering basic concepts and algorithms is very useful. This review covers the basics of the topic. Definitions are provided for defective, Hermitian, Hessenberg, modal, singular, spectral, symmetric, skew-symmetric, skew-Hermitian, triangular, and Wishart matrices. Then, concepts of characteristic polynomial, eigendecomposition, eigenpair, eigenproblem, eigenspace, eigenvalue, and eigenvector are subsequently introduced. Faddeev–LeVerrier, von Mises, Gauss–Jordan, Pohlhausen, Lanczos–Arnoldi, Rayleigh–Ritz, Jacobi–Davidson, and Gauss–Seidel fundamental algorithms are given, while others (Francis–Kublanovskaya, Gram–Schmidt, Householder, Givens, Broyden–Fletcher–Goldfarb–Shanno, Davidon–Fletcher–Powell, and Saad–Schultz) are merely discussed. The eigenproblem has thus found its use in many topics. The applications discussed include solving Bessel’s, Helmholtz’s, Laplace’s, Legendre’s, Poisson’s, and Schrödinger’s equations. The algorithm extracting the first principal component is also provided.