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Abstract Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today’s vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don’t know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to query Scopus API; (2: Scoring and method extraction) relevancy and popularity scores calculation and solution method extraction in papers utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and post-analyzes which reveals trends, relevant papers and methods. Comparing the SARBOLD-LLM to manual ground truth using precision, recall, and F1-score metrics, the performance results of AI in the oncology case study are 0.68, 0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes across various domains, showcasing its robustness and effectiveness. The SARBOLD-LLM addresses engineers more than researchers, as it proposes methods and trends without adding pros and cons. It is a useful tool to select which methods to investigate first and comes as a complement to surveys. This can limit the global search and accumulation of knowledge for the end user. However, it can be used as a director or recommender for future implementation to solve a problem.

The vehicle routing problem (VRP) is a combinatorial optimization problem that determines optimal routes to enhance distribution efficiency. One of the most popular strategies in freight distribution is multi-echelon distribution. Multi-echelon distribution networks often apply to supply chain management, land transportation, the maritime industry, aviation, etc., and rely on VRP. This comprehensive review systematically analyses 382 papers retrieved through the Scopus database. We use a bibliometric and network analysis tool to complete a systematic literature mapping identifying key interrelationships and research clusters. The analysis depicts five main research clusters: green logistics and decision analysis, scheduling and inventory optimization, VRP for city logistics, mathematical modeling and optimization, and outbound logistics and customer service, identified based on author keywords of the systematically derived paper pool. Each cluster is provided with foundational knowledge, concepts, theories, and employed techniques. Finally, future studies are suggested to explore more comprehensive investigation in highly discussed domains like city logistics problems in e-commerce, vehicle routing problems for sustainable logistics, and technological advancement-based applications.

For a constellation of agile Earth Observation Satellites (EOS), efficiently scheduling image acquisitions presents a complex decision-making challenge characterized by balancing a multitude of qualitative and quantitative preferences on the imaging requests while considering a high number of operational and temporal constraints. Current research predominantly focuses on the scheduling aspect, often neglecting the fuzzy multi-objective nature of the problem and the near real-time computation requirement. This study proposes an innovative three-stage solution method. Initially, an a priori multi-criteria scoring approach, based on the ELECTRE-III method’s fuzzy pairwise evaluation, is employed to value each potential imaging attempt, addressing the gap in comprehensive pre-scheduling valuation. The problem is then redefined as a Longest Path Problem in a Directed Acyclic Graph with Interdependent and Allowed Nodes (DAG-IAN). This re-conceptualization accommodates unique multi-satellite operational needs and imaging techniques such as stereo and strip acquisitions. We introduce the Extended Longest Path Algorithm (ELPA) for this purpose, which emerges as a novel solution mechanism. The final stage is a decision support system designed to guide decision-makers through the satellite operation’s intricate trade-offs, facilitating iterative enhancements and a deeper understanding of the conflicting objectives through a weight space analysis and a significance test. Our approach not only demonstrates high adaptability and explainability but also shows computationally efficient performance. In smaller problem scenarios, the ELPA closely approximates exact methods while significantly outperforming other approaches in large-scale applications. The research advances state of the art by offering an intuitive, customizable, and scalable framework in the preference integration aspect of the Satellite Image Acquisition Scheduling Problem.

The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.

In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.

The successful implementation of predictive maintenance for offshore wind farms suffers from a poor understanding of the consequential short-term impacts and a lack of research on how to evaluate the cost-efficiency of such efforts. This paper aims to develop a methodology to explore the short-term marginal impacts of predictive maintenance applied to an already existing preventive maintenance strategy. This method will be based on an analysis of the performance of the underlying predictive model and the costs considered under specific maintenance services. To support this analysis, we develop a maintenance efficiency measure able to estimate the efficiency of both the underlying prediction model used for predictive maintenance and the resulting maintenance efficiency. This distinction between the efficiency of the model and the service results will help point out insufficiencies in the predictive maintenance strategy, as well as facilitate calculations on the cost–benefits of the predictive maintenance implementation. This methodology is validated on a realistic case study of an annual service mission for an offshore wind farm and finds that the efficiency metrics described in this paper successfully support cost–benefit estimates.

For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data.

Abstract The extraordinary circumstances of the COVID-19 pandemic led to measures to mitigate the spread of the disease, with lockdowns and mobility restrictions at national and international levels. These measures led to sudden and sometimes dramatic reductions in human activity, including significant reductions in ship traffic in the maritime sector. We report on a reduction of deep-ocean acoustic noise in three ocean basins in 2020, based on data acquired by hydroacoustic stations in the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty. The noise levels measured in 2020 are compared with predicted levels obtained from modelling data from previous years using Gaussian Process regression. Comparison of the predictions with measured data for 2020 shows reductions of between 1 and 3 dB in the frequency range from 10 to 100 Hz for all but one of the stations.

Data-driven predictive maintenance is typically based on collected data from multiple sensors or industrial systems over a period of time, where historical and real-time data are combined as input to black-box machine learning models. In the current study we provide a case study of a major manufacturing company of large industrial equipment. We investigate the opportunity to utilize the manufacturing state of the equipment alone to predict future conditions. The production data contain information about the errors or defects in the equipment found in production. The defects are potentially repaired before the equipment is installed. We present a proactive approach based on interpretable machine learning models, where the production data are used to predict maintenance, which creates the opportunity to prevent future maintenance. The solution is easily translated into a simple set of rules that can be used to separate critical production errors from non-critical production errors. Identifying critical production errors potentially prevents future and more expensive repairs of errors detected in inspections after the equipment has been installed. Our paper contributes to the literature on predictive maintenance in two ways. Firstly, we show the viability of a more proactive approach utilizing production data to prevent future maintenance. Secondly, we demonstrate the applicability of interpretable machine learning models to understand the relationship between the features of the production errors and the later inspection errors.

This paper presents a novel approach to the joint proactive and reactive planning of deliveries by an unmanned aerial vehicle (UAV) fleet. We develop a receding horizon-based approach to contingency planning for the UAV fleet's mission. We considered the delivery of goods to spatially dispersed customers, over an assumed time horizon. In order to take into account forecasted weather changes that affect the energy consumption of UAVs and limit their range, we propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. These rules are used in the course of designing the contingency plans related to the need to implement an emergency return of the UAV to the base or handling of ad hoc ordered deliveries. Due to the nonlinearity of the environment's characteristics, both constraint programming and genetic algorithm paradigms have been implemented. Because of the NP-difficult nature of the considered planning problem, conditions have been developed that allow for the acceleration of calculations. The multiple computer experiments carried out allow for comparison representatives of the approximate and exact methods so as to judge which approach is faster for which size of the selected instance of the UAV mission planning problem.

On 18 June 2020 and 4 March 2021, very energetic low-frequency underwater T-wave signals (2 to 25 Hz) were recorded at the Comprehensive Nuclear-Test-Ban Treaty (CTBT) International Monitoring System (IMS) hydrophone stations in the Pacific Ocean (Stations HA11 and HA03) and the South Atlantic Ocean (Station HA10). This work investigates the long-range (megameters) propagation of these T-waves. Their sources were three powerful submarine earthquakes in the Kermadec Trench and Islands, located at approximately 6000, 8800, and 15100 km from Stations HA11, HA03, and HA10, respectively. Arrival time and back azimuth of the recorded T-waves were estimated using the Progressive Multi-Channel Correlation algorithm installed on the CTBT Organization (CTBTO) virtual Data Exploitation Centre (vDEC). Different arrivals within the duration of the earthquake signals were identified, and their correlations were also analyzed. The data analysis at HA03 and HA10 revealed intriguing T-wave propagation paths reflecting, refracting, or even transmitting through continents, as well as T-wave excitation along a chain of seamounts. The analysis also showed much higher transmission loss (TL) in the propagation paths to HA11 than to HA03 and HA10. Moreover, strong discrepancies between expected and measured back azimuths were observed for HA11, and a three-dimensional (3D) parabolic equation model was utilized to identify the cause of these differences. Numerical results revealed the importance of 3D effects induced by the Kermadec Ridge, Fiji archipelago, and Marshall Islands on T-wave propagation to HA11. This analysis can guide future improvements in underwater event localization using the CTBT-IMS hydroacoustic sensor network.

The most effective way to combat β-thalassemias is to prevent the birth of children with thalassemia major. Therefore, a cost-effective screening method is essential to identify β-thalassemia traits (BTT) and differentiate normal individuals from carriers. We considered five hematological parameters to formulate two separate scoring mechanisms, one for BTT detection, and another for joint determination of hemoglobin E (HbE) trait and BTT by employing decision trees, Naïve Bayes classifier, and Artificial neural network frameworks on data collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, India. We validated both the scores on two different data sets and found 100% sensitivity of both the scores with their respective threshold values. The results revealed the specificity of the screening scores to be 79.25% and 91.74% for BTT and 58.62% and 78.03% for the joint score of HbE and BTT, respectively. A lower Youden’s index was measured for the two scores compared to some existing indices. Therefore, the proposed scores can obviate a large portion of the population from expensive high-performance liquid chromatography (HPLC) analysis during the screening of BTT, and joint determination of BTT and HbE, respectively, thereby saving significant resources and cost currently being utilized for screening purpose.

With increasing interest in reducing fuel consumption and related pollution caused by transportation services, a speed optimization problem that derives speeds of a vehicle given a fixed route to customers has become significant, given that fuel consumption of vehicles is a function of their speeds. Following this interest, we address a speed optimization problem under time-dependent travel conditions. Motivated by the fact that altering the fixed route in response to the time-dependent condition would contribute to minimizing fuel consumption for transportation services, a routing decision is also involved in the problem formulation. To solve this problem, we propose an exact approach with approximation schemes to handle its complexity. Through the experiments, we verify the performance of the proposed approach in terms of finding near-optimal solutions within a short computation time as well as how the routing decision contributes to minimizing fuel consumption for transportation services.

In this study, we tackle a key scheduling problem in a robotic arm-based food processing system, where multiple conveyors—an infeed conveyor that feeds food items to robotic arms and two tray lane conveyors, on which trays to batch food items are placed—are implemented. The target scheduling problem is to determine what item on an infeed conveyor belt is picked up by which robotic arm at what position, and on which tray the picked up item will be placed. This problem involves critical constraints, such as sequence-dependent processing time and dynamic item and tray positions. Moreover, due to the speed of the infeed conveyor and latency in the information about entering items into the system, this scheduling problem must be solved in near real time. To address these challenges, we propose a scheduling solution that first decomposes the original scheduling problem into sub-problems, where a sub-problem formulated as a goal program schedules robotic arms only for a single tray. The performance of the proposed solution approach is then tested under a simulation environment, and from the experiments, the proposed approach produces acceptable performance.

In this study, principal component analysis and k-means clustering (PCAKM) methods for synthetic aperture radar (SAR) data are analyzed to reduce the sensitivity caused by changes in the parameters and input images of the algorithm, increase the accuracy, and make an improvement in the computation time, which are advantageous for scoring in the follow-up plan. Although there are many supervised methods described in the literature, unsupervised methods may be more appropriate in terms of computing time, data scarcity, and explainability in order to supply a trustworthy system. We consider the PCAKM algorithm, which is used as a benchmark method in many studies when making comparisons. Error metrics, computing times, and utility functions are calculated for 22 modified PCAKM regarding difference images and filtering methods. Various images with different characteristics affect the results of the configurations. However, it is evident that the PCAKM becomes less sensitive and more accurate for both the overall results and image results. Scoring by utilizing these results and other map information is a gap and innovation. Obtaining a change map in a fast, explainable, more robust and less sensitive way is one of the aims of our studies on scoring points in the follow-up plan.

Low-income countries are persistently suffering from last-mile logistics issues in healthcare supply chains. Therefore, it is high time to explore technological applications to overcome such inadequacies. The faster speed, low maintenance cost, and absence of road dependency in unmanned aerial vehicles (UAV) have popularized them as an alternative to road delivery. Hence, it is suggested as a solution to overcome the persisting distribution inefficiencies in healthcare logistics of low-income countries. According to the case study analysis conducted on the Sri Lankan vaccine cold chain, incorporating UAVs increases truck-space utilization and reduces the time consumed, cost incurred, and carbon dioxide emission in a delivery round. Moreover, the most suitable way to cover the initial setup cost of an unmanned aerial system (UAS) is by receiving aid from international donors. The capital cost also can be covered by government investments or via service outsourcing only if the number of flights per year is increased. Moreover, a homogenous (i.e., only UAV) solution was revealed to be more beneficial than a heterogeneous (i.e., truck and UAV) solution. However, due to the lack of technology literacy and willingness to change in low-income countries, it is recommended to initially execute a heterogeneous solution and expand to a homogeneous plan in the future years. However, it was evident that for a mixed-fleet solution to be advantageous, drone characteristics play a vital role. Hence, a UAV with specifications ideal for the use case must be utilized to garner the maximum benefits. Nevertheless, it was apparent that with the right implementation plan, UAVs possess the potential to overcome the shortcomings in the healthcare logistics of low-income countries.

Objective: Leptin (Lep) plays a crucial role in controlling food intake and energy expenditure. Defective Lep/LepRb-signaling leads to fat accumulation, massive obesity, and the development of diabetes. We serendipitously noticed spontaneous development of obesity similar to LepR-deficient (db/db) mice in offspring from a C57BL/6J breeding and transmittance of the phenotype in a Mendelian manner. Candidate gene sequencing revealed a spontaneous point mutation in the LepRb gene. We investigated leptin responsiveness, leptin receptor signaling and metabolic phenotype of this novel LepRb mutant mouse variant. Methods: Overexpression and functional tests of the mutant LepRb in 3T3 cells. Measurement of leptin responsiveness in hypothalamic nuclei, glucose tolerance, food uptake and energy expenditure in the mutant mice. Results: The mutation results in the exchange of a glycine for serine (G506S) and introduces an alternative splice acceptor which, when used, encodes for a protein with a 40aa deletion that is retained in the cytoplasm. LepRb signaling was abrogated in the hypothalamic ventromedial nucleus (VMN) and dorsomedial nucleus (DMN), but only partially reduced in the hypothalamic arcuate nucleus (ARC) of LepRbG506S/G506S mice, most likely due to differential splicing in neurons located in the respective regions of the hypothalamus. Extensive metabolic characterization of these mice revealed interesting differences in the control of food intake, glucose tolerance, energy expenditure, and fat accumulation in LepRbG506S/G506S compared with LepRb-deficient db/db mice. Conclusions: This study provides further insight into differences of the leptin responsiveness in VMN, DMN, and ARC and its metabolic consequences. Keywords: Leptin receptor mutation, Ventromedial nucleus, Dorsomedial nucleus, Arcuate nucleus, Energy expenditure, Glucose tolerance

Upcycling and reclaiming of low quality or stored coal combustion fly ashes could enable to tap into a voluminous resource of supplementary cementitious materials (SCMs) for low-carbon blended cements. Low reactivity fly ashes are usually either too crystalline or too coarse. Beneficiation treatments for coarse fly ashes comprise size classification or milling processes to extract or produce fine size fractions of higher pozzolanic reactivity. This article compares the effect of size classification and milling treatments on the reactivity of a siliceous fly ash (FA). The intrinsic chemical reactivity is assessed using the R3 heat release test method. The results showed significant increases of 57 and 40% for fine classified and milled fly ash compared to the initial fly ash, respectively. In addition heat release and portlandite consumption were measured for blended cements with 30 wt.% Portland cement replacement by the fly ashes. Both test results are combined to calculate the degree of reaction of the fly ashes over time in blended cement. The results demonstrate a strong effect of particle size on fly ash reactivity and degree of reaction. It is shown that increasing the inherent reactivity of fly ashes is an effective way of both accelerating compressive strength gain and enhancing late age strength with fine classified fly ashes reaching equivalent strength as neat Portland cement by 28 days and attaining a strength activity index of 137% by 90 days.

A zoning approach that divides an area of interest into multiple sub-areas can be a systemic and strategic solution to safely deploy a fleet of unmanned aerial vehicles (UAVs) for package delivery services. Following the zoning approach, a UAV can be assigned to one of the sub-areas, taking sole ownership and responsibility of the sub-area. As a result, the need for collision avoidance between units and the complexity of relevant operational activities can be minimized, ensuring both safe and reliable execution of the tasks. Given that the zoning approach involves the demand-server allocation decision, the service quality to customers can also be improved by performing the zoning properly. To illuminate the benefits of the zoning approach to UAV operations from a systemic perspective, this study applies clustering techniques to derive zoning solutions under different scenarios and examines the performance of the solutions using a simulation model. The simulation results demonstrate that the zoning approach can improve the safety of UAV operations, as well as the quality of service to demands.

We compare two models of two-level facility location problems for network design in autonomous ground vehicle (AGV) operations. The two-level model consists of locations for charging stations (main facilities), as well as for storage locations (substations). Demand points will represent processing locations. In both formulations, demands are assigned to substations and substations are assigned to main facilities. The formulations differ in whether each connection between a facility and a substation is counted once in absolute terms, or once per demand. These represent two different views, in which transfer between a main facility and substation is carried out either in bulk, e.g. using a shuttle, or by each AGV independently. Selected experimental results are presented for geometric networks and networks consisting of uniformly distributed points on a regular mesh. These results indicate that the two formulations lead to vastly different network designs in terms of the number of facilities and connectivity.

In this article, we analyze the models quantifying the bullwhip effect in supply chains with stochastic lead times and find advantages and disadvantages of their approaches to the bullwhip problem. Moreover, using computer simulation, we find interesting insights into the bullwhip behavior for a particular instance of a multi-echelon supply chain with constant customer demands and random lead times. We confirm the recent finding of Michna and Nielsen that under certain circumstances lead time signal processing is by itself a fundamental cause of bullwhip effect just like demand-signal processing is. The simulation also shows that in this supply chain the delay parameter of demand forecasting smooths the bullwhip effect at the manufacturer level much faster than the delay parameter of lead time forecasting. Additionally, in the supply chain with random demands, the reverse behavior is observed, that is, the delay parameter of lead time forecasting smooths bullwhip effect at the retailer stage much faster than the delay parameter of demand forecasting. At the manufacturer level, the delay parameter of demand forecasting and the delay parameter of lead time forecasting dampen the effect with a similar strength.

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner.

To reduce CO2 emissions from the building industry, one option is to replace cement in specific applications with alternative binders. The Carbstone technology is based on the reaction of calcium- and magnesium-containing minerals with CO2 to form carbonate binders. Mixes of carbon steel slag and stainless-steel slag, with tailored particle size distributions, were compacted with a vibro-press and subsequently carbonated in an autoclave to produce carbonated steel slag pavers. The carbonated materials sequester 100–150 g CO2/kg slag. Compressive and tensile splitting strength of the resulting pavers were determined, and the ratio was found to be comparable to that of concrete. The environmental performance of the Carbstone pavers, with an average tensile splitting strength of 3.6 MPa, was found to be in compliance with Belgian and Dutch leaching limit values for construction materials. In addition, leaching results for a concrete mix made with aggregates of crushed Carbstone pavers (simulating the so-called “second life” of pavers) demonstrate that the pavers can be recycled as aggregates in cement-bound products after their product lifetime.

Accelerated carbonation is a rapidly developing technology that is attracting attention as it uses CO2 as a binder to make construction materials. Originally stemming from geochemical and environmental research into CO2 sequestration or waste remediation, accelerated carbonation has been developed into a technology that enables to transform alkaline precursors into products that meet technical requirements for use as aggregates or shaped blocks. Alkaline precursors can be manufactured from primary resources or derived from industrial residues: a.o. metallurgical slags, incineration ashes and concrete recycling residues are prone to carbonate under controlled conditions. Moist carbonation of shaped Ca-silicate rich precursors at elevated curing temperature and CO2 concentration or pressure has delivered the most promising results so far. This letter presents an overview of current accelerated carbonation approaches to make carbonate bonded construction materials from alkaline residues. The general carbonation mechanism is explained and two application routes are exemplified: i.e. production of lightweight aggregates and compact blocks by accelerated moist carbonation.

Topic of balancing assembly lines is of great interest for researchers and industry practitioners due to the significant impact it has on increasing productivity and efficiency of manufacturing systems. Robots are widely applied in manufacturing industries for assembly processes. Wide literature has been reported on balancing of robotic assembly lines with single and mixed models. Researchers have extensively used heuristics and metaheuristics to solve these problems due to their NP-hard nature. However, no work has been reported on how to balance a robotic assembly line with multiple models (MuRALB) with batch production. This problem is highly relevant for large-scale assembly of products found, e.g. the automotive industry. To authors’ knowledge, this is the first attempt to solve this problem. This research proposes a novel heuristic to solve type II MuRALB problem. Type II problem deals with minimizing the cycle time for a fixed set of robots. Heuristic is implemented, and method for scheduling batched production with related setup times for a robotic assembly line is presented, and based on the analysis conducted, advantage of batching is presented. Proposed heuristic is tested on a set of new five datasets, and performance of this heuristic and batching is presented in detail.

Globalization is on the agenda as a cause of job insecurity, but knowledge regarding the relationship between globalization, management practices, and employee job insecurity is in fact limited. This study aims to investigate this multilevel association, defining globalization by the interrelated aspects of competition, liberalization, and technological innovation, and management by offensive practices such as external collaboration, organizational change, numerical flexibility, and work intensification. Questionnaire data were collected in 2010 from 3392 employees of 569 Danish companies and analyzed with multilevel modeling. The results show that international collaboration and numerical flexibility are associated with higher job insecurity. The level of job insecurity varies with occupational position, age group, and company size. Because globalization and the management of globalization are associated with employee job insecurity, companies should react in a timely way to globalization pressures: for example, by involving employees, communicating clearly, and supporting employees with lower education.

This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows a pre-planned separate route linking points defined by the customer location and service periods when ordered deliveries are carried out. Customer order specifications and their services time windows as well as vehicle travel times are dynamically recognized over time. The objective is to maximize a number of newly introduced or modified requests, being submitted dynamically throughout the assumed time horizon, but not compromising already considered orders. Therefore, the main question is whether a newly reported delivery request or currently modified/corrected one can be accepted or not. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries as well as preventive maintenance requests are scheduled and implemented according to a cyclically repeating sequence. It is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism enabling to handle the fuzzy nature of variables through an algebraic approach. Computational results show that the proposed solution outperforms commonly used computer simulation methods.

Unmanned aerial vehicle (UAV) routing is transitioning from an emerging topic to a growing research area as the 3D flexible utilization of airspace, promogulated by UAVs, is a potential game changer in solving the urban air mobility challenge by allowing to reshape transportation and logistics in the future. This has revealed a need to classify different types of research and examine the general characteristics of the research area. This research aims to assist in identifying the main topics and emerging research streams and provides a published overview of the current state and contributions to the area of the UAV routing problem (UAVRP) and a general categorization of the vehicle routing problem (VRP) followed by a UAVRP classification with a graphical taxonomy based on the analysis of UAVRP current status. To achieve this, an analysis of the existing research contributions promulgated in this domain is conducted. This analysis is used to identify the current state of UAVRP and the gaps related to the UAVs’ flight dynamics and weather conditions, which significantly influence the fuel consumption of the UAV when modeling the UAVRP.

The multi-satellite image acquisition scheduling problem is traditionally seen as a complex optimization problem containing a generic objective function that represents the priority structure of the satellite operator. However, the majority of literature neglect the collective and contemporary effect of factors associated with the operational goal in the objective function, i.e., uncertainty in cloud cover, customer priority, image quality criteria, etc. Consequently, the focus of the article is to integrate a real-time scoring approach of imaging attempts that considers these aspects. This is accomplished in a multi-satellite planning environment, through the utilization of the multi-criteria decision making (MCDM) models, Elimination and Choice Expressing Reality (ELECTRE-III) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the formulation of a binary linear programming model. The two scoring approaches belong to different model classes of MCDM, respectively an outranking approach and a distance to ideal point approach, and they are compared with a naive approach. Numerical experiments are conducted to validate the models and illustrate the importance of criteria neglected in previous studies. The results demonstrate the customized behaviour allowed by MCDM methods, especially the ELECTRE-III approach.

Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.

The main goal of this paper is to construct objectives and attributes of the service centers location problem. This problem is often found within production management as a location problem, when designing for example supply chains or manufacturing layouts. The main contribution of this paper is constructing related location indicators. Since there was no similar literature in this discipline, so a Delphi survey applied to quantify expert’s attitudes about location problem of Agricultural Service Center (ASC) and construct location selection attributes and also ASC objectives. A TOPSIS survey is done to rank extracted attributes to import in fuzzy analytical hierarchy process (AHP) study. Then a fuzzy AHP technique is applied to compute the weight of these most important attributes using four objectives, which obtained by Delphi technique too. In the simplest form with this assumption that all objectives have the same priority, the results illustrated that the service, cost, speed, and ASC profit are first to last important objectives, respectively. At the end of this paper, the multi-choice goal programming method recommended to use when the priorities of objectives are not the same. Finally, the weight of ASC location attributes computed to consider in ASC location problem.

A variety of monodisperse superparamagnetic iron oxide particles (SPIOs) was designed in which the surface was modified by PEGylation with mono- or bifunctional poly(ethylene oxide)amines (PEG). Using 125I-labeled test proteins (transferrin, albumin), the binding and exchange of corona proteins was studied first in vitro. Incubation with 125I-transferrin showed that with increasing grade of PEGylation the binding was substantially diminished without a difference between simply adsorbed and covalently bound protein. However, after incubation with excess albumin and subsequently whole plasma, transferrin from the preformed transferrin corona was more and more lost from SPIOs in the case of adsorbed proteins. If non-labeled transferrin was used as preformed corona and excess 125I-labeled albumin was added to the reaction mixtures with different SPIOs, a substantial amount of label was bound to the particles with initially adsorbed transferrin but little or even zero with covalently bound transferrin. These in vitro experiments show a clear difference in the stability of a preformed hard corona with adsorbed or covalently bound protein. This difference seems, however, to be of minor importance in vivo when polymer-coated 59Fe-SPIOs with adsorbed or covalently bound 125I-labeled mouse transferrin were injected intravenously in mice. With both protein coronae the 59Fe/125I-labelled particles were cleared from the blood stream within 30 min and appeared in the liver and spleen to a large extent (>90%). In addition, after 2 h already half of the 125I-labeled transferrin from both nanodevices was recycled back into the plasma and into tissue. This study confirms that adsorbed transferrin from a preformed protein corona is efficiently taken up by cells. It is also highlighted that a radiolabelling technique described in this study may be of value to investigate the role of protein corona formation in vivo for the respective nanoparticle uptake.

51Cr-labeled, superparamagnetic, iron oxide nanoparticles (51Cr-SPIOs) and 65Zn-labeled CdSe/CdS/ZnS-quantum dots (65Zn-Qdots) were prepared using an easy, on demand, exchange-labeling technique and their particokinetic parameters were studied in mice after intravenous injection. The results indicate that the application of these heterologous isotopes can be used to successfully mark the nanoparticles during initial distribution and organ uptake, although the 65Zn-label appeared not to be fully stable. As the degradation of the nanoparticles takes place, the individual transport mechanisms for the different isotopes must be carefully taken into account. Although this variation in transport paths can bring new insights with regard to the respective trace element homeostasis, it can also limit the relevance of such trace material-based approaches in nanobioscience. By monitoring 51Cr-SPIOs after oral gavage, the gastrointestinal non-absorption of intact SPIOs in a hydrophilic or lipophilic surrounding was measured in mice with such high sensitivity for the first time. After intravenous injection, polymer-coated, 65Zn-Qdots were mainly taken up by the liver and spleen, which was different from that of ionic 65ZnCl2. Following the label for 4 weeks, an indication of substantial degradation of the nanoparticles and the release of the label into the Zn pool was observed. Confocal microscopy of rat liver cryosections (prepared 2 h after intravenous injection of polymer-coated Qdots) revealed a colocalization with markers for Kupffer cells and liver sinusoidal endothelial cells (LSEC), but not with hepatocytes. In J774 macrophages, fluorescent Qdots were found colocalized with lysosomal markers. After 24 h, no signs of degradation could be detected. However, after 12 weeks, no fluorescent nanoparticles could be detected in the liver cryosections, which would confirm our 65Zn data showing a substantial degradation of the polymer-coated CdSe/CdS/ZnS-Qdots in the liver.

During the last decades of nanoparticles research, many nanomaterials have been developed for applications in the field of bio-labelling. For the visualization of transport processes in the body, organs and cells, luminescent quantum dots (QDs) make for highly useful diagnostic tools. However, intercellular routes, bio-distribution, metabolism during degradation or quantification of the excretion of nanoparticles, and the study of the biological response to the QDs themselves are areas which to date have not been fully investigated. In order to aid in addressing those issues, CdSe/CdS/ZnS QDs were radioactively labelled, which allows quantification of the QD concentration in the whole body or in ex vivo samples by γ-counting. However, the synthesis of radioactively labelled QDs is not trivial since the coating process must be completely adapted, and material availability, security and avoidance of radioactive waste must be considered. In this contribution, the coating of CdSe/CdS QDs with a radioactive 65ZnS shell using a modified, operator-safe, SILAR procedure is presented. Under UV illumination, no difference in the photoluminescence of the radioactive and non-radioactive CdSe/CdS/ZnS colloidal solutions was observed. Furthermore, a down-scaled synthesis for the production of very small batches of 5 nmol QDs without loss in the fluorescence quality was developed. Subsequently, the radio-labelled QDs were phase transferred by encapsulation into an amphiphilic polymer. γ-counting of the radioactivity provided confirmation of the successful labelling and phase transfer of the QDs.

Semiconductor quantum dots (QD) and superparamagnetic iron oxide nanocrystals (SPIO) have exceptional physical properties that are well suited for biomedical applications in vitro and in vivo. For future applications, the direct injection of nanocrystals for imaging and therapy represents an important entry route into the human body. Therefore, it is crucial to investigate biological responses of the body to nanocrystals to avoid harmful side effects. In recent years, we established a system to embed nanocrystals with a hydrophobic oleic acid shell either by lipid micelles or by the amphiphilic polymer poly(maleic anhydride-alt-1-octadecene) (PMAOD). The goal of the current study is to investigate the uptake processes as well as pro-inflammatory responses in the liver after the injection of these encapsulated nanocrystals. By immunofluorescence and electron microscopy studies using wild type mice, we show that 30 min after injection polymer-coated nanocrystals are primarily taken up by liver sinusoidal endothelial cells. In contrast, by using wild type, Ldlr-/- as well as Apoe-/- mice we show that nanocrystals embedded within lipid micelles are internalized by Kupffer cells and, in a process that is dependent on the LDL receptor and apolipoprotein E, by hepatocytes. Gene expression analysis of pro-inflammatory markers such as tumor necrosis factor alpha (TNFα) or chemokine (C-X-C motif) ligand 10 (Cxcl10) indicated that 48 h after injection internalized nanocrystals did not provoke pro-inflammatory pathways. In conclusion, internalized nanocrystals at least in mouse liver cells, namely endothelial cells, Kupffer cells and hepatocytes are at least not acutely associated with potential adverse side effects, underlining their potential for biomedical applications.

Besides the lung and skin, the gastrointestinal (GI) tract is one of the main targets for accidental exposure or biomedical applications of nanoparticles (NP). Biological responses to NP, including nanotoxicology, are caused by the interaction of the NP with cellular membranes and/or cellular entry. Here, the physico-chemical characteristics of NP are widely discussed as critical determinants, albeit the exact mechanisms remain to be resolved. Moreover, proteins associate with NP in physiological fluids, forming the protein corona potentially transforming the biological identity of the particle and thus, adding an additional level of complexity for the bio–nano responses.Here, we employed amorphous silica nanoparticles (ASP) and epithelial GI tract Caco-2 cells as a model to study the biological impact of particle size as well as of the protein corona. Caco-2 or mucus-producing HT-29 cells were exposed to thoroughly characterized, negatively charged ASP of different size in the absence or presence of proteins. Comprehensive experimental approaches, such as quantifying cellular metabolic activity, microscopic observation of cell morphology, and high-throughput cell analysis revealed a dose- and time-dependent toxicity primarily upon exposure with ASP30 (Ø = 30 nm). Albeit smaller (ASP20, Ø = 20 nm) or larger particles (ASP100; Ø = 100 nm) showed a similar zeta potential, they both displayed only low toxicity. Importantly, the adverse effects triggered by ASP30/ASP30L were significantly ameliorated upon formation of the protein corona, which we found was efficiently established on all ASP studied. As a potential explanation, corona formation reduced ASP30 cellular uptake, which was however not significantly affected by ASP surface charge in our model. Collectively, our study uncovers an impact of ASP size as well as of the protein corona on cellular toxicity, which might be relevant for processes at the nano–bio interface in general.

With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g. size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs’ fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction under the given conditions. The mission plans are created in such a manner that they are suitable to be sent to Air Traffic Control for flight approval.

To cover an area of interest by an autonomous vehicle, such as an Unmanned Aerial Vehicle (UAV), planning a coverage path which guides the unit to cover the area is an essential process. However, coverage path planning is often problematic, especially when the boundary of the area is complicated and the area contains several obstacles. A common solution for this situation is to decompose the area into disjoint convex sub-polygons and to obtain coverage paths for each sub-polygon using a simple back-and-forth pattern. Aligned with the solution approach, we propose a new convex decomposition method which is simple and applicable to any shape of target area. The proposed method is designed based on the idea that, given an area of interest represented as a polygon, a convex decomposition of the polygon mainly occurs at the points where an interior angle between two edges of the polygon is greater than 180 degrees. The performance of the proposed method is demonstrated by comparison with existing convex decomposition methods using illustrative examples.

One of the main applications of ceria (CeO2) is its use in glass polishing. About 16,000 tonnes of rare earth oxides, which is about 10% of total rare earth production, are used for polishing applications. The waste generated in glass polishing contains rare earths, along with other impurities. In this study, two different glass polishing waste samples were characterized and two different processes were proposed for the complete recovery of rare earths from polishing waste, i.e., an acid-based process and an alkali-based process. The polishing waste samples were characterized with inductively coupled plasma optical emission spectrometry (ICP-OES), X-ray fluorescence spectroscopy (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), thermo-gravimetric analysis (TGA) and particle size analysis. Chemical analysis showed that sample A (CeO2-rich waste from plate glass polishing) contained a high amount of impurities compared to sample B (CeO2-rich waste from mirror polishing). XRD analysis showed that sample B contained CeO2, LaO0.65F1.7 and LaPO4 compounds, whereas sample A contained CaCO3 in addition to rare earth compounds. SEM-EDX analysis showed the presence of alumino-silicates in sample A. Leaching experiments were carried out at 75 °C at different acid concentrations for the recovery of rare earths from polishing waste samples. The leaching results showed that it is difficult to dissolve rare earths completely in acid solutions due to the presence of fluorides and phosphates. Hence, undissolved rare earths in the leach residue were further recovered by an alkali treatment with NaOH. In another approach, polishing waste samples were directly treated with NaOH at 500 °C. After alkali treatment followed by water leaching, rare earths can be completely dissolved during acid leaching. Rare earths from polishing waste can be recovered completely by both the acid-based process and the alkali-based process.

Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial--hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.

PURPOSE:In-vitro evaluation of the feasibility of 4D real time tracking of endovascular devices and stenosis treatment with a magnetic particle imaging (MPI) / magnetic resonance imaging (MRI) road map approach and an MPI-guided approach using a blood pool tracer. MATERIALS AND METHODS:A guide wire and angioplasty-catheter were labeled with a thin layer of magnetic lacquer. For real time MPI a custom made software framework was developed. A stenotic vessel phantom filled with saline or superparamagnetic iron oxide nanoparticles (MM4) was equipped with bimodal fiducial markers for co-registration in preclinical 7T MRI and MPI. In-vitro angioplasty was performed inflating the balloon with saline or MM4. MPI data were acquired using a field of view of 37.3×37.3×18.6 mm3 and a frame rate of 46 volumes/sec. Analysis of the magnetic lacquer-marks on the devices were performed with electron microscopy, atomic absorption spectrometry and micro-computed tomography. RESULTS:Magnetic marks allowed for MPI/MRI guidance of interventional devices. Bimodal fiducial markers enable MPI/MRI image fusion for MRI based roadmapping. MRI roadmapping and the blood pool tracer approach facilitate MPI real time monitoring of in-vitro angioplasty. Successful angioplasty was verified with MPI and MRI. Magnetic marks consist of micrometer sized ferromagnetic plates mainly composed of iron and iron oxide. CONCLUSIONS:4D real time MP imaging, tracking and guiding of endovascular instruments and in-vitro angioplasty is feasible. In addition to an approach that requires a blood pool tracer, MRI based roadmapping might emerge as a promising tool for radiation free 4D MPI-guided interventions.

This study describes the recovery and removal of heavy metals through washing from fine grained materials, recovered from mixed industrial waste (IW) from a landfill site. During this first screening, solutions of different concentrations of three inorganic acids (H2SO4, HCl and HNO3) were tested as possible lixiviants. It has been shown that the extraction of most heavy metals should take place at a well defined pH range in order to avoid dissolution of the matrix material (consisting mainly of Si, Fe, Al, Ca). Furthermore, the buffering effect of the studied material was shown to be stronger for smaller particle sizes, due to larger specific contact surfaces with the lixiviants and differences in composition.

Technological developments combined with increasing levels of competition related to the ongoing globalization imply that firms find themselves in dynamic, changing environments that call for dynamic capabilities. This challenges the internal human and organizational resources of firms in general and in particular their ability to develop firm-specific innovative capabilities through employee participation and creation of innovative workplaces. In this article, we argue that national institutional conditions can play an enhancing or hampering role in this. Especially the norms and values governing relations between employers and employees are expected to be of vital importance. This article will follow a resource-based perspective on developing dynamic capabilities in order to test the importance of enhancing human and organizational capabilities for innovation in firms. In particular, the article will focus on some of the important institutional conditions in Danish firms derived from the Nordic model, such as the formal and informal relations of cooperation between employers and employees in firms and their function in building capabilities for innovation. The foundation of the empirical analysis is a survey that collected information from 601 firms belonging to the private urban sector in Denmark. The survey was carried out in late 2010.

The human epithelial cell adhesion molecule (EpCAM) is highly expressed in a variety of clinical tumour entities. Although an antibody against EpCAM has successfully been used as an adjuvant therapy in colon cancer, this therapy has never gained wide-spread use. We have therefore investigated the possibilities and limitations for EpCAM as possible molecular imaging target using a panel of preclinical cancer models. Twelve human cancer cell lines representing six tumour entities were tested for their EpCAM expression by qPCR, flow cytometry analysis and immunocytochemistry. In addition, EpCAM expression was analyzed in vivo in xenograft models for tumours derived from these cells. Except for melanoma, all cell lines expressed EpCAM mRNA and protein when grown in vitro. Although they exhibited different mRNA levels, all cell lines showed similar EpCAM protein levels upon detection with monoclonal antibodies. When grown in vivo, the EpCAM expression was unaffected compared to in vitro except for the pancreatic carcinoma cell line 5072 which lost its EpCAM expression in vivo. Intravenously applied radio-labelled anti EpCAM MOC31 antibody was enriched in HT29 primary tumour xenografts indicating that EpCAM binding sites are accessible in vivo. However, bound antibody could only be immunohistochemically detected in the vicinity of perfused blood vessels. Investigation of the fine structure of the HT29 tumour blood vessels showed that they were immature and prone for higher fluid flux into the interstitial space. Consistent with this hypothesis, a higher interstitial fluid pressure of about 12 mbar was measured in the HT29 primary tumour via "wick-in-needle" technique which could explain the limited diffusion of the antibody into the tumour observed by immunohistochemistry.