Real Case Study Research Articles

Optimal charge scheduling and on-board control of an urban electrified BRT fleet considering synthetic representative driving cycles

This paper presents a comprehensive approach for optimal charge scheduling and on-board vehicular control of electrified fleets based on synthetic driving cycles. The proposed approach is conducted within a real case-study in Cairo, Egypt, whereto a representative distance-based driving cycle has been synthesized using K-means clustering over a sliding horizon of gathered data-sets. Two multi-objective problems defining optimal charge scheduling and vehicular control have been formulated to achieve minimal energy consumption and operating cost of the fleet . Non-dominant genetic algorithm (NSGA-II) has been implemented to solve the optimization problems jointly considering fluctuating electricity cost of the grid. The comparative evaluation of results reveals an improvement of 19% and 28% in energy consumption and retention of on-board energy accordingly, with less than 2% mitigation of driveability. Moreover, a reduction of 40.8%, 20%, and 21.9% in fleet size, required charging stations, and annual recharging cost respectively has been realized. The main innovation of this work can be put forward as the ability to address the above-mentioned quadrilateral objectives of electrified fleets in a single comprehensive approach, considering synthetic driving cycles and electricity prices to yield a customized-optimal solution.

Imaging of moho topography with conditional generative adversarial network from observed gravity anomalies

Accurate estimation of Moho topography plays a crucial role in understanding Earth’s structure, geodynamic processes, and resource exploration. This study presents a novel approach that utilizes conditional Generative Adversarial Networks (cGAN) to reveal Moho topography based on observed gravity anomalies. Synthetic training datasets of Moho topography were generated using the FFT filtering method due to the scarcity of true datasets. Spherical prism-based forward gravity modeling was employed to evaluate the resulting gravity anomalies. We compared the performance of our developed deep learning algorithm cGAN (conditional Generative Adversarial Networks) with a traditional inversion technique using various synthetic datasets, and a real case study in southern peninsular India, a geologically diverse region comprising ancient continental tectonic blocks. Bott’s inversion scheme was employed as a verification method for the Moho surface estimation using the presented deep learning model. Using spherical prism-based forward gravity modeling, observed gravity anomalies were corrected for multiple factors such as topography, bathymetry, sediments, crustal heterogeneities, and mantle heterogeneities. By removing these effects, we isolated the gravity contribution solely related to pure Moho undulation. The mean Moho depth and density contrast between the crust and mantle were derived from seismic constraints for improving estimation accuracy. The findings demonstrate the potential of the cGAN and spherical prism-based gravity modeling approach in accurately estimating the Moho topography, offering insights into Earth’s subsurface structures and enhancing our understanding of geodynamic processes and resource exploration efforts.

English Learning Material for Journalism Courses: What are Students’ Needs?

This study aims to analyze student needs for learning materials for journalism courses at Indonesian Language and Literature Education, Toraja Indonesian Christian University. Using a qualitative approach, the study collected data through a series of in-depth interviews, surveys, and focus groups with students enrolled in journalism courses. The main objective of the study was to identify specific aspects of the learning material that students consider important, areas they find challenging, and suggestions for curriculum improvement. The results show that students value a combination of theoretical and practical learning, with particular emphasis on real-life case studies, multimedia skills, and the use of social media in journalism. Students also highlight the need for greater interaction with professional journalistic practitioners and field practice opportunities such as internships. Key difficulties students face include understanding journalistic investigative concepts, ethics, and legal aspects, as well as the development of technical skills such as writing, editing, and media analysis. This research suggests the need for a flexible and responsive curriculum that integrates the latest technology, provides teaching materials relevant to industry developments, and strengthens students' practical skills. These findings contribute to the development of more effective journalism teaching practices, which focus on preparing students for real challenges in the journalism industry.

Instance generation tool for on-demand transportation problems

We present REQreate, a tool to generate instances for on-demand transportation problems. Such problems consist of optimizing vehicle routes according to passengers’ demand for transportation under space and time restrictions (called requests). REQreate is flexible and can be configured to generate instances for a variety of problems types in this problem class. In this paper, we exemplify this with the Dial-a-Ride Problem (DARP) and On-demand Bus Routing Problem (ODBRP). In most of the literature, researchers either test their solution algorithms with instances based on artificial networks or they perform real-life case studies on instances derived from a specific city or region. Furthermore, locations of requests for on-demand transportation problems are mostly randomly chosen according to a uniform distribution, rather than being derived from actual data.The aim of REQreate is to overcome any shortcomings from synthetic or specific instances. Rather than relying on artificial or limited data, we retrieve real-world street networks from OpenStreetMaps (OSM). To the best of our knowledge, this is the first tool to make use of real-life networks to generate instances for an extensive catalog of existing and upcoming on-demand transportation problems. Additionally, we present a simple method that can be embedded in the instance generation process to produce distinct urban mobility patterns. We perform an analysis with real-life data sets reported by rideshare companies and compare them with properties of synthetic instances generated with REQreate. Another contribution of this work is the introduction of the concept of instance similarity that serves as support to create a set of diverse instances, in addition to properties (size, dynamism, urgency, and geographic dispersion) that can be used to comprehend which characteristics of the problem instances have an impact on the performance quality (or efficiency) of a solution algorithm.

Pilot plant approach combining photocatalysis and adsorption for antibiotics removal from slaughterhouse and urban wastewater treatment plant effluents

The main objective of this research is to perform a real case-study of antibiotic decontamination from real slaughterhouse and wastewater effluents by applying a sequential treatment of TiO2/UV–vis photocatalysis and adsorption. More precisely, the removal of five classes of antibiotics: enrofloxacin, sulfadiazine, trimethoprim, azithromycin, amoxicillin and amoxicillin degradation products was studied in an 80 L/h photocatalytic and adsorption pilot-scale plant over multiple cycles, operating in semi-continuous mode. The results exhibited significant removal rates, ranging from 77% to 100% for the slaughterhouse effluent, and 61–89% for the wastewater treatment plant effluent, thus demonstrating that the treatment is more effective when it is applied directly in the emission source, previous to antibiotic dilution in the municipal collector. Using the two processes sequentially results in greater efficiency than when they are used in isolation. After photocatalysis, antibiotic degradation products are easily adsorbed, since they have more affinity for the adsorbent, and the presence of competing compounds decreases considerably. After applying several cycles of treatment, the adsorption performance remains almost constant. By contrast, the photocatalysis performance decreased, which was attributed to catalyst agglomeration determined by Asymmetric Flow Field-Flow Fractionation (AF4) coupled with Dynamic Light Scattering (DLS) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS).

A Techno-Economic-Environmental Feasibility Study of Residential Solar Photovoltaic/Biomass Power Generation for Rural Electrification: A Real Case Study

To avert climate change, there has been a rise in the usage of green energy sources that are also beneficial to the environment. To generate sustainable energy in a financially and technically efficient manner, our research attempts to close the gaps. The potential of green sources like photovoltaic (PV) and biomass for a rural community southwest of Sohag Al Gadida City, Sohag, Egypt, is examined in this research considering its techno-economic (TE) and eco-friendly feasibility. The HOMER Pro v3.14 package is used as a scaling and optimization instrument, to calculate the price of the PV/biomass setup and the size and characteristics of its parts. This is to estimate the corresponding electrical production and reduce the total annual cost for the customer. The suggested system structure is validated through the presentation of simulation outcomes and evaluations utilizing MATLAB/SIMULINK R2022a. In addition, a TE-environmental investigation of the optimized PV/biomass structure is performed. The optimum structure is carefully chosen from the best four configurations using the demand predilection by analogy to the perfect technique based on the generation cost, operation cost, energy production, and renewable fraction. The results also indicate that using hybrid PV/biomass is an attractive choice with the initial capital cost (ICC: USD 8.144), net present cost (NPC: USD 11,026), a low cost of energy (LCOE: 0.184 USD/kWh), and the high renewable fraction (RF: 99.9%) of the system. The annual CO2 emission performance of a PV/biomass system is much better than that of the grid alone and PV/diesel. This method might be applied in rural areas in other developing countries.

A Bayesian Approach for Lifetime Modeling and Prediction with Multi-Type Group-Shared Missing Covariates

In the field of reliability engineering, covariate information shared among product units within a specific group (e.g., a manufacturing batch, an operating region), such as operating conditions and design settings, exerts substantial influence on product lifetime prediction. The covariates shared within each group may be missing due to sensing limitations and data privacy issues. The missing covariates shared within the same group commonly encompass a variety of attribute types, such as discrete types, continuous types, or mixed types. Existing studies have mainly considered single-type missing covariates at the individual level, and they have failed to thoroughly investigate the influence of multi-type group-shared missing covariates. Ignoring the multi-type group-shared missing covariates may result in biased estimates and inaccurate predictions of product lifetime, subsequently leading to suboptimal maintenance decisions with increased costs. To account for the influence of the group-shared missing covariates with different structures, a new flexible lifetime model with multi-type group-shared latent heterogeneity is proposed. We further develop a Bayesian estimation algorithm with data augmentation that jointly quantifies the influence of both observed and multi-type group-shared missing covariates on lifetime prediction. A tripartite method is then developed to examine the existence, identify the correct type, and quantify the influence of group-shared missing covariates. To demonstrate the effectiveness of the proposed approach, a comprehensive simulation study is carried out. A real case study involving tensile testing of molding material units is conducted to validate the proposed approach and demonstrate its practical applicability.

Global food supply chain resilience assessment: A case in the United Kingdom

With the development of globalisation, countries such as the United Kingdom (UK) heavily rely on shipping for food imports and supplies. With the unpredictable impacts of black swan events, such as the Coronavirus disease (COVID-19) outbreak and the armed conflict between Ukraine and Russia, which are traditional food export countries, global freight supply chains could dramatically change, which significantly affects the global food supply chain (FSC) resilience, requiring new solutions to be found. Therefore, it is vital to assess a country’s food importing system by sea to ensure its FSC resilience. However, it is challenging to analyse different food supply resilience levels on a national scale given the high uncertainties in the key relevant elements (e.g. food supplies and transportation) influencing FSC resilience. This paper aims to develop a new method to assess FSC resilience at a country level by pioneering the combination of the two most relevant attributes, its production-to-supply ratio and shipping transport connectivity, used to present food supply security separately in the current literature. Within this context, food production and import distance affecting the connectivity of various food supplies are selected to estimate FSC resilience in this study. The findings, including a new index framework to assess national food resilience, significantly contribute to a country’s food security and the rational development of countermeasures and policies when necessary. To demonstrate the significance of the findings, the resilience of the UK FSC is first evaluated in a real case study, followed by a comparative study with Canada, Australia, Democratic Republic of the Congo (DRC) to identify the strengths and weaknesses of the FSCs of different countries. The findings of the studies can be used to monitor FSC resilience of the countries and provide rational policies for enhancing FSC resilience.

CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

Thermal model of DC conductors for railway traction networks: A hosting capacity assessment

In this paper, the authors have adapted existing thermal models for the calculation of conductor temperature for use in electrical distribution systems for railway applications in multi-train environments. For this purpose, the authors have used existing standards such as IEEE 738 for overhead lines and have proposed a resolution methodology based on the difference equation model for cables. The authors have opted for a simplified method since the priority in this case is the resolution in systems with multiple stations, lines, and trains. For this same reason, they have chosen to decouple the electrical and thermal calculations so that the electrical variables calculated through load flows constitute the input to the thermal calculation problem. The paper reviews the existing formulation and proposes models for specific use in railway applications in a multi-train environment. A detailed analysis of the results obtained in a real case study is presented as a validation element.

Economic Impact of Lean Healthcare Implementation on the Surgical Process.

The objective of this study was to analyse and detail surgical process improvement activities that achieve the highest economic impact. Over 4 years, a team of technicians and healthcare professionals implemented a set of Lean surgical process improvement projects at Vall d'Hebron University Hospital (VHUH), Barcelona, Spain. Methods employed in the study are common in manufacturing environments and include reducing waiting and changeover time (SMED), reducing first time through, pull, and continuous flow. Projects based on these methods now form part of the daily routine in the surgical process. The economic impact on the hospital's surgical activity budget was analysed. Process improvements have led to annual operational savings of over EUR 8.5 million. These improvements include better patient flow, better management of information between healthcare professionals, and improved logistic circuits. The current cultural shift towards process management in large hospitals implies shifting towards results-based healthcare, patient-perceived value (VBHC), and value-added payment. A Lean project implementation process requires long-term stability. The reason a considerable number of projects fail to complete process improvement projects is the difficulty involved in establishing the project and improving management routines. Few studies in the literature have investigated the economic impact of implementing Lean management a posteriori, and even fewer have examined actual cases. In this real case study, changes to surgical block management were initiated from stage zero. After being carefully thought through and designed, changes were carried out and subsequently analysed.

An extension of PARAFAC to analyze multi-group three-way data

This paper introduces a novel methodology for analyzing three-way array data with a multi-group structure. Three-way arrays are commonly observed in various domains, including image analysis, chemometrics, and real-world applications. In this paper, we use a practical case study of process modeling in additive manufacturing, where batches are structured according to multiple groups. Vast volumes of data for multiple variables and process stages are recorded by sensors installed on the production line for each batch. For these three-way arrays, the link between the final product and the observations creates a grouping structure in the observations. This grouping may hamper gaining insight into the process if only some of the groups dominate the controlled variability of the products. In this study, we develop an extension of the PARAFAC model that takes into account the grouping structure of three-way data sets. With this extension, it is possible to estimate a model that is representative of all the groups simultaneously by finding their common structure. The proposed model has been applied to three simulation data sets and a real manufacturing case study. The capability to find the common structure of the groups is compared to PARAFAC and the insights into the importance of variables delivered by the models are discussed.

An optimization model for just-in-time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time

PurposeAlthough extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.Design/methodology/approachIn order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.FindingsA real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.Originality/valueThis study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

KarstNSim: A graph-based method for 3D geologically-driven simulation of karst networks

In karst aquifers, groundwater flow is highly influenced by the interconnected underground cavities and conduits that form the karst network. Modeling karst flows requires the use of spatially distributed approaches accounting for these networks. Their exploration is, however, often complex, and mapping them using indirect methods such as geophysical ones has proven challenging. To overcome these limitations, stochastically simulating discrete karst networks should account for the uncertainties on conduit position and geometry. Only a few existing methods can reproduce realistic and diverse karst morphologies. In this work, we propose a new algorithm, KarstNSim, for simulating discrete karst networks, that incorporates field data to generate a range of possible karst network geometries. It relies on the computation of the shortest path between the inlets and outlets of the network with the use of an anisotropic cost function defined on an n-nearest neighbor graph conformal to geological and structural heterogeneities. This cost function represents the physico-chemical processes that govern speleogenesis – such as erosion and chemical weathering – providing simplified control over the geometry of the generated networks. Our approach reproduces the vadose-phreatic partition visible in the karst networks, by generating sub-vertical conduits in the unsaturated zone and sub-horizontal ones in the saturated part. It encompasses geological parameters such as inception surfaces, fractures, permeability, and solubility of layers, along with considering the hydrological context of recharge by assigning relative weights to the inlets. We simulate various synthetic models to demonstrate the influence of different input parameters on the spatial organization of the past and present karst flows. We also apply KarstNSim to a real case study – the Ribeaucourt system (Meuse, France) – and use available data to generate realistic karst networks. The lack of knowledge on the Ribeaucourt network geometry is alleviated by the use of multiple scenarios corresponding to different sets of weights in the cost function, resulting in a variety of network morphologies which represent the uncertainty on the real geometry. The geometrical and topological metrics computed on the simulated networks generally overlap with those of real ones, suggesting the reliability of the method.

Reducing black-box nonlinear state-space models: A real-life case study

A known challenge when building nonlinear models from data is to limit the size of the model in terms of the number of parameters. Especially for complex nonlinear systems, which require a substantial number of state variables, the classical formulation of the nonlinear part (e.g. through a basis expansion) tends to lead to a rapid increase in the model size. In this work, we propose two strategies to counter this effect:1) The introduction of a novel nonlinear-state selection algorithm. The method relies on the non-parametric nonlinear distortion analysis of the Best Linear Approximation framework to identify the state variables which are the most impacted by nonlinearities. Pre-selecting only the most appropriate states when constructing the nonlinear terms results in a considerable reduction of the model size.2) The use of so-called ‘decoupled’ functions directly in the model estimation procedure. While it is known that function decoupling can reduce the model size in a secondary step, we show how a decoupled formulation can be imposed to advantage from the start. The results of this approach are benchmarked with the state-of-the-art a posteriori decoupling technique.Our strategies are demonstrated on real-life data of a multiple-input, multiple-output (MIMO) ground vibration test of an F-16 aircraft, a prime complex and nonlinear dynamic system.

Risk adjustment-based statistical control charts with a functional covariate

ABSTRACT In real applications, there are situations in which quality characteristics are significantly affected by function-valued covariates such as electrocardiogram (ECG) signals; however, previous surveillance methods with covariate adjustment mainly use scalar-valued covariates to construct control charts to investigate the stability of nonindustrial processes, such as in the medical and public health fields. Few existing approaches address the infinite-dimensional regression encountered with function-valued covariates. Thus, an applicable scheme needs to be developed. Here, by relaxing the assumption of normality, a novel surveillance strategy for functional logistic regression (FLR) is proposed. In this approach, the response is a binary variable, and the covariate information is obtained from functional data. To address the above problems, functional principal component analysis (FPCA) is used to extend the score test to situations with functional covariates. Moreover, the score statistic is integrated into an exponentially weighted moving average (EWMA) charting scheme to detect abnormal changes in the location and scale parameters based on additional information. The simulation results show that the proposed approach is more efficient than previous approaches in detecting small to moderate shifts. Finally, the proposed charting scheme is applied to a real case study to demonstrate its utility and applicability.

Supply chain network design

Goal: This study aims to minimize the total cost of a supply chain network and determine the optimal product flow under demand uncertainty. Design / Methodology / Approach: A mathematical model is presented to minimize the total supply chain cost by identifying the optimal facility locations and product flows. The applicability of the proposed model is evaluated through a real-life case study of a multinational sporting goods retailer with sensitivity analysis. Moreover, Monte Carlo simulation is used to capture the demand uncertainty and test the robustness of the model. Results: The minimized cost is achieved with optimal facility locations and product flows. The optimal result shows a 3% reduction in the total cost, making it the most robust solution under demand uncertainty. Limitations of the investigation: The proposed model is only applicable to a single-commodity supply chain network. In addition, the cost components of the network are limited to facility costs and transportation costs, disregarding the other cost components. Practical implications: This research demonstrates a methodology that can be used as a decision support system by managers to make strategic and tactical decisions in a supply chain network when demand is uncertain. Originality / Value: The MILP and simulation techniques used together to construct a three-tiered supply chain under uncertainty receive little attention in the literature. In addition to developing a novel three-echelon MILP model, this research makes use of a real-world case study to illustrate the methodology's performance in the context of demand uncertainty through simulation.

Model and solution approach to coordinate production-inventory strategies considering nonlinear price-sensitive demand: application to Canadian pulp and paper industry

This study addresses a practical problem within a multi-level supply chain where a wide range of customers can be served through different strategies such as make-to-stock, make-to-order, or vendor-managed inventory. The customer demand is stochastic, and sensitive to pricing associated with different production-inventory strategies. We propose a two-stage stochastic mixed-integer non-linear programming model. In the first stage, decisions are made regarding the selection of production-inventory strategies and pricing to maximise the expected profit. The second stage involves decisions related to production, inventory, and distribution, which are used to evaluate the first-stage decisions under various scenarios with different levels of accuracy. To solve the model, a metaheuristic approach based on the Simulated Annealing algorithm is developed. To showcase the practical applicability of our model and solution approach, we use a real case study in a Canadian pulp and paper supply chain. The results revealed that both the production-inventory strategy assigned to customers and the sales price underwent changes across scenarios. Furthermore, we demonstrated that by implementing the SA algorithm, we could improve the initial profit by up to 1.43% through slight adjustments in the sales price and assigned strategies for customers.

Experimental Characterization of Screw-Extruded Carbon Fibre-Reinforced Polyamide: Design for Aeronautical Mould Preforms with Multiphysics Computational Guidance

In this research work, the suitability of short carbon fibre-reinforced polyamide 6 in pellet form for printing an aeronautical mould preform with specific thermomechanical requirements is investigated. This research study is based on an extensive experimental characterization campaign, in which the principal mechanical properties of the printed material are determined. Furthermore, the temperature dependency of the material properties is characterized by testing samples at different temperatures for bead printing and stacking directions. Additionally, the thermal properties of the material are characterized, including the coefficient of thermal expansion. Moreover, the influence of printing machine parameters is evaluated by comparing the obtained tensile moduli and strengths of several manufactured samples at room temperature. The results show that the moduli and strengths can vary from 78% to 112% and from 55% to 87%, respectively. Based on a real case study of its aeronautical use and on the experimental data from the characterization stage, a new mould design is iteratively developed with multiphysics computational guidance, considering 3D printing features and limitations. Specific design drivers are identified from the observed material’s thermomechanical performance. The designed mould, whose mass is reduced around 90% in comparison to that of the original invar design, is numerically proven to fulfil thermal and mechanical requirements with a high performance.

Space efficiency and throughput performance in AVS/RS under variant lane depths

An automated vehicle storage and retrieval system (AVS/RS) is a widespread automated warehouse solution that hosts hundreds of stock-keeping units (SKU) and counts thousands of incoming and outgoing unit loads corresponding to a sequence of time-dependent storage and retrieval transactions. AVS/RS ensures high storage density, reduced cycle time, and high productivity. This study introduces and applies an original data-driven comparative and competitive multi-scenario methodology to measure and control the performance of a multi-deep tier-captive AVS/RS. This original methodology measures and controls the impact of lane depth (1), assignment strategy (2), opening strategy (3), and dispatching strategy (4) on the storage capacity, system throughput, and space efficiency in the design and configuration of an AVS/RS. The proposed methodology was applied to a real case study, demonstrating that the combination of the four leverages significantly affects system performance.