Real Case Study Research Articles

Application of industry 4.0 technologies for mitigating humanitarian supply chains risks: the case of Covid-19

ABSTRACT The study aims at integrating and combining successful digital tools such as Blockchain technology, Internet of Things (IoT), and Cloud Computing (CC) to digitalise HSC. To show the enabling of these digital tools, we first conducted a real case study in collaboration with a major leader in real estate diagnostics and building technical data. Second, to mitigate the impact of various disturbances and risks of COVID-19 on supply chain (SC) performances, we modelled two logistics networks, i.e. before and after the COVID-19 pandemic. Finally, we proposed two efficient solutions. In the first solution, we proposed a risk mitigation matrix model based on the cause-effect diagram. In the second one, we developed an advanced digital model which is completely based on Industry 4.0 technologies. Results showed that the new digital model was more efficient since it enables to enhance HSC performances by mitigating risks and enabling tracking, monitoring, facilitating coordination, real-time information sharing, and flexibility during humanitarian operations.

A priority queueing-inventory approach for inventory management in multi-channel service retailing using machine learning algorithms

PurposeNowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.Design/methodology/approachThis paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.FindingsTo investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.Originality/valuePrioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

Water Quality Assessment and Decolourisation of Contaminated Ex-Mining Lake Water Using Bioreactor Dye-Eating Fungus (BioDeF) System: A Real Case Study.

The environmental conditions of a lake are influenced by its type and various environmental forces such as water temperature, nutrients content, and longitude and latitude to which it is exposed. Due to population growth and development limits, former mining lakes are being converted to more lucrative land uses like those of recreational zones, agriculture, and livestock. The fungus Ganoderma lucidum has the potential to be utilised as a substitute or to perform synergistic bacteria-coupled functions in efficient contaminated lake water treatment. The purpose of this paper is to evaluate the water quality and water quality index (WQI) of an ex-mining lake named Main Lake in the Paya Indah Wetland, Selangor. Furthermore, the current work simulates the use of a Malaysian fungus in decolourising the contaminated ex-mining lake by the BioDeF system in a 300 mL jar inoculated with 10% (v/v) of pre-grown Ganoderma lucidum pellets for 48 h. According to the results, the lake water is low in pH (5.49 ± 0.1 on average), of a highly intense dark brownish colour (average reading of 874.67 ± 3.7 TCU), and high in iron (Fe) content (3.2422 ± 0.2533 mg/L). The water quality index of the lake was between 54.59 and 57.44, with an average value of 56.45; thus, the water was categorized as Class III, i.e., under-polluted water, according to the Malaysian Department of Environment Water Quality Index (DOE-WQI, DOE 2020). The batch bioreactor BioDeF system significantly reduced more than 90% of the water's colour. The utilization of Ganoderma lucidum as an adsorbent material offers a variety of advantages, as it is easily available and cultivated, and it is not toxic.

Multidimensional subgroup discovery on event logs

Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low-structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real-life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real-life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators.

P, q-Spherical fuzzy sets and their aggregation operators with application to third-party logistic provider selection

The selection of an appropriate third-party logistics (3PL) provider has become an inescapable option for shippers in today’s business landscape, as the outsourcing of logistics activities continues to increase. Choosing the 3PL supplier that best meets their requirements is one of the most difficult difficulties that logistics consumers face. Effective decision-making (DM) is critical in dealing with such scenarios, allowing shippers to make well-informed decisions within a restricted timeframe. The importance of DM arises from the possible financial repercussions of poor decisions, which can result in significant financial losses. In this regard, we introduce p, q-spherical fuzzy set (p, q-SFS), a novel concept that extends the concept of T-spherical fuzzy sets (T-SFSs). p, q-SFS is a comprehensive representation tool for capturing imprecise information. The main contribution of this article is to define the basic operations and a series of averaging and geometric AOs under p, q-spherical fuzzy (p, q-SF) environment. In addition, we establish several fundamental properties of the proposed aggregation operators (AOs). Based on these AOs, we propose a stepwise algorithm for multi-criteria DM (MCDM) problems. Finally, a real-life case study involving the selection of a 3PL provider is shown to validate the applicability of the proposed approach.

Evaluation of a virtual practice placement: A model to increase student capacity

AimTo describe the development and evaluation of a novel virtual practice placement. BackgroundHealth systems around the world face the challenge of recruiting and retaining sufficient nursing staff to provide high quality care. The need to train more nurses makes it hard to provide sufficient and varied high quality student placements to all students. This paper reports the result of one approach to the provision of a novel virtual placement for pre-registration student nurses. DesignOnline virtual placement evaluated by a questionnaire conducted after the placement. MethodsA total of 195 students attended the virtual practice placement between 10th October 2022 and the 10th March 2023. The survey consisted of eight questions, of which one invited a qualitative response. ResultsA total of 188 students completed the questionnaire and provided feedback. Of these 84 were adult nursing students, 67 child, 36 mental health and one learning disability student. The virtual placement required considerable resources to run, however was deemed as valuable by most students. When asked to rate the overall experience out of 5, the median scores were consistently high: adult (Mdn=5), child (Mdn=4), learning disability (Mdn=5) and mental health (Mdn=5) and mean values consistently high across fields: adult (M=4.73), child (M=5), learning disability (M=5) and mental health (M=4.67). Qualitatively, there were four main themes that emerged from the questionnaire responses: increased understanding of community healthcare and holistic approaches to care; developing interpersonal skills; a positive impact on their future career opportunities and the value of realistic case studies. ConclusionsVirtual placements are a viable addition to traditional placements. However, they require careful planning and considerable resources including experienced and dedicated facilitators. Principles for the delivery of virtual placements were produced to replicate and share best practice.

EDIT: A data inspection tool for smart contracts temporal behavior modeling and prediction

Modeling and predicting the behavior of nodes and users in blockchains provide opportunities for business strategy optimization. Indeed, the number of interactions of a node is strictly related to its balance and its prediction may be used for analytics purposes and investment strategies. However, the amount and diversity of information stored on the blockchain demand advanced tools for the modeling and analysis of blockchain data. Such tools should be able to capture the dynamicity and interaction of multiple independent actors, considering a large number of variables and dynamic interaction graph topologies. This is exacerbated by the use of smart contracts, programs stored in blockchain blocks that bring automation to blockchain’s operations and thus increasing the variability of the resulting interaction graphs. Existing modeling methodologies are unable to keep track of all these details, as they are not able to capture the temporal variability of the network.In this paper, we propose a novel framework for modeling and predicting the behavior of smart contracts on a blockchain. We propose the concept of temporal smart contracts networks, i.e., graphs representing the temporal evolution of interactions and data flow. Our framework allows the creation of temporal smart contract networks with different granularity levels by considering different interaction patterns between smart contracts, externally owned accounts, and internal transactions. Thanks to these graphs, we are able to model features such as the node in degree and amount of ether received by a smart contract, which are directly related to its behavior. We incorporate our modeling approach in Ethereum Data Inspection Tool (EDIT), a novel tool able to model interactions and predict them based on historical data. We test different machine learning models to predict features extracted by EDIT, hence allowing for the prediction of the overall behavior of the smart contract. We test EDIT on the Ethereum blockchain and model several temporal smart contracts networks, which represent the interactions and the data flow resulting from about 4 000 000 consecutive blocks. The evaluation of different real case studies shows that the proposed framework is able to predict, with a mean absolute error close to 1%, the evolution of several interesting properties (e.g., amount of received ether) related to both accounts and smart contracts.

Enhancing Photovoltaic Farm Capacity Estimation: A Comprehensive Analysis with a Novel Approach

This research paper addresses the inaccuracies in the current methods for estimating the capacity value of photovoltaic (PV) plants, which rely heavily on large‐scale data and fail to represent the actual capacity value pattern accurately. The research conducts case studies in Belgium, Texas, and California to analyze the impact of different factors on capacity value. It proposes a new metric called the Marginal Moving‐Average Limited‐Hours (MMALH) Equivalent Load‐Carring Capability (ELCC) ‐ Based capacity value. The proposed metric reduces the dependence on hourly data and better represents capacity value. The results from real case studies validate the effectiveness of the new metric, highlighting its novelty and contribution to the assessment of capacity value in PV power systems. The study emphasizes the importance of accurately assessing the capacity value of PV compared to conventional units, considering environmental factors and system parameters. The study exposes the shortcomings in current metrics and advocates for the MMALH ELCC methodology as a more precise evaluation approach. The research suggests optimizing design, employing advanced tracking systems, enhancing maintenance practices, and ensuring effective grid integration to boost solar plant efficiency. Consistent monitoring and analysis of the utilization factor are vital for pinpointing improvement areas and augmenting productivity.

Optimizing COVID-19 medical waste management using goal and robust possibilistic programming

During the global Coronavirus Disease (COVID-19) pandemic, the exponential rise in Hazardous Medical Waste (HMW) due to increased demand for personal protective equipment and heightened medical requirements posed significant threats to public health. This study proposes an innovative approach using a reverse logistics supply chain network that comprehensively integrates sustainability factors (e.g., cost, working conditions, exposure risks, and environmental impact) to manage the risks associated with medical waste effectively amid the pandemic. This research focuses on employing a guideline-based allocation of medical waste to specific technologies, leveraging the Torabi-Hassini (TH), Lp-metric (Lebesgue metric), and Goal Attainment (GA) approaches and robust possibilistic programming to address uncertainties. A real-case study validates the proposed model, demonstrating its ability to balance multiple objectives by optimizing the flow among treatment centers and introducing new Temporary Treatment Centers (TTCs). Also, we analyze broad sensitivity through weights assigned to the objective functions to obtain Pareto solutions. The convexity of the Pareto front confirms the conflict among the objective functions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach specifies that the Lp-metric approach outperforms the others, and the TH approach is regarded as the second rank. The study's findings highlight the model's efficacy and provide crucial managerial insights for health organization administrators in efficiently managing the HMW supply chain network.

Unveiling the Endowment Effect: Understanding Its Significance and Applications

The endowment effect is a widespread prevalent cognitive bias that affects peoples decision-making process in various aspects. This phenomenon is mainly because of peoples cognitive biases towards certain things. The primary manifestation of this cognitive bias is that when a person owns an item or asset, his/her evaluation of the value of the item or asset is more significant than when he does not own the item or asset. Thus, this research paper aims to unveil the significance and applications of the endowment effect and its relationship with risk aversion. Meanwhile, the paper will also explore how individuals tend to assign higher value to items they own. To conduct this research, a comprehensive literature review was performed to examine existing studies on the endowment effect, loss aversion and related behavioural economics theories. Real-life examples and case studies were also analyzed to illustrate the practical implications of these cognitive biases. The findings of this research paper demonstrate that the endowment effect and loss aversion are deeply ingrained in peoples decision-making process, leading individuals to make emotional rather than rational choices.Understanding the endowment effect is crucial for making informed decisions, as it allows individuals to distinguish between subjective and objective thinking. While it may not eliminate these biases from decision-making processes, strategies such as seeking outside perspectives, considering alternatives, and conducting thorough analyses may mitigate the endowment effects influence.

Adjusting for Time-Varying Treatment Switches in Randomized Clinical Trials: The Danger of Extrapolation and How to Address It

When choosing estimands and estimators in randomized clinical trials, caution is warranted, as intercurrent events, such as, due to patients who switch treatment after disease progression, are often strongly associated with patients’ time-varying prognostic factors. Consequently, for patients who did experience intercurrent events, there are typically no comparable patients who did not. Statistical analyses may then easily lure one into making large implicit extrapolations, which often go unnoticed. We will illustrate this problem of implicit extrapolations using a real oncology case study, with a right-censored time-to-event endpoint, in which patients can cross over from the control to the experimental treatment after disease progression, for ethical reasons. We address this by developing an estimator for the survival risk ratio contrasting the survival probabilities at each time t if all patients would take experimental treatment with the survival probabilities at those times t if all patients would take control treatment up to time t, using randomization as an instrumental variable to avoid reliance on no unmeasured confounders assumptions. This doubly robust estimator can handle time-varying treatment switches and right-censored survival times. Insight into the rationale behind the estimator is provided and the approach is demonstrated by reanalyzing the oncology trial.

A novel data-driven triangular-type staircase layout design in personnel safety evacuation in high-rise buildings

When an accident or crisis occurs in a high-rise building, evacuation by stairs is the most recognized safe evacuation method, where the design of stair layouts greatly impacts the economy and safety of high-rise buildings. Therefore, an economical and reasonable staircase layout design is especially important. Most previous studies tend to reduce the number of evacuation routes due to economic considerations alone, which is an unreasonable unilateral consideration. In this sense, a reasonable staircase layout should consider economic and evacuation efficiency. To address this issue, this study proposes a new evacuation time calculation model, which is then combined with an economic model to develop a bi-objective optimization model. After that, a set of solutions that achieve different trade-offs between economy and safety are obtained, allowing for a staircase arrangement that meets safety requirements while minimizing economic investment. Finally, the feasibility of the proposed model is verified by a real case study, where the Pareto front surface is obtained to facilitate decision-making for stakeholders with different risk preferences. The numerical results also allow decision-makers to make informed decisions considering both economics and safety in stairway settings.

RUL prediction for two-phase degrading systems considering physical damage observations

This paper focuses on a specific type of two-phase degrading system commonly encountered in industrial practice. The first phase is moderate with a low degradation rate while the second is rapid with a high rate. Current studies usually rely solely on sensor measurements to divide phases and predict the remaining useful life (RUL), ignoring the utilization of actual physical damage observations, such as wear depth and crack length. These observations, available during system shutdown periods, directly reflect system states and phase changes. To this end, we propose a novel RUL prediction framework consisting of offline training and online prediction processes. In the offline training process, the physical damage observations and sensor measurements are utilized to estimate the parameters of a two-phase Wiener process and a bijective function matrix. In the online prediction process, real-time sensor measurements are transformed into virtual damage observations for RUL prediction. To enhance the accuracy of phase change point detection, a change point detection algorithm is proposed for both processes. The effectiveness is demonstrated using a simulation and a real case study.

Efficient management of HVAC systems through coordinated operation of parallel chiller units: An economic predictive control approach

In developed countries, air conditioning systems have become major contributors to energy consumption in buildings. Cooling installations made up of independent chiller units connected in parallel pose a challenge in finding the most energy-efficient management strategy. This article proposes a novel economic model predictive control strategy to optimize the operation of multiple-chiller HVAC systems according to an economic cost comprising energy consumption as well as a thermal comfort index. Provided that the gradient of the economic cost function can be calculated or estimated, the proposed controller only entails the solution of a single quadratic programming (QP) problem at each sampling period, reducing computational requirements and thus facilitating the deployment on commercial embedded HVAC management platforms. The proposed controller improves economic performance. As our numerical analysis shows, optimal sequencing is achieved for the case of dual-chiller plants. Moreover, the controller adapts to possible variations in the economic criteria, enabling the system to respond to changes in electricity price and user preferences. A realistic case study, using a high fidelity model of a building simulated in TRNSYS, demonstrates the effectiveness of the proposed methodology. In particular, the proposed controller demonstrates to be more efficient than state-of-the-art QP-based economic predictive controllers in achieving a reduction of the energy consumption up to 5.19%, which is in line with the targeted reduction of 7.9% by 2030 in the EU Green Deal's ‘Fit for 55’ package.

Two-stage stochastic programming approach for fleet sizing and allocating rail wagon under uncertain demand

In this paper, a scenario-based two-stage stochastic programming model has been developed to determine the optimal number of wagons and allocation of full and empty wagons during different time periods to optimize profit. In allocating wagons, when the wagon arrives at a station, it is not possible to reallocate the wagon at the moment of arrival, this issue has not been addressed before. In addition, the possibility of renting wagons to/from other companies is concerned in the railway planning. These two actions have not been addressed before. In this paper, firstly, the research problem is formulated in the form of an integer programming model with deterministic data. Due to the inherent uncertainty in the transportation demands of the real case study, the deterministic model has been transformed into a scenario-based two-stage stochastic programming model with recourse. Then the L-shaped method was proposed to solve the stochastic model and by using it for 34 instances, its better efficiency than CPLEX software was shown. The stochastic model was solved using the L-shaped method and the results show that the studied company should rent 749 wagons to meet the demands, and on average, the stochastic model is expected to have about 1.1% added value in the company's profit compared to the deterministic model.

DNA barcoding reference libraries of Italian Plecoptera: a gap analysis

Plecoptera is a small order of aquatic insects that is considered one of the most endangered groups of insects due to increasingly altered freshwater ecosystems. Plecoptera nymphs can be challenging to identify at the species level because diagnostic characters for most species are lacking, while adults can be difficult to collect routinely given their short life span. Species identification by DNA barcoding is increasingly used as an alternative to morphological identification, but gaps and inaccuracies in reference databases needed for taxonomic assignment can undermine the utility of barcoding in real case studies. Here we aim to: i) quantify the number of Italian species of Plecoptera with barcodes from specimens collected worldwide and from Italy; ii) perform a regional assessment of DNA barcoding coverage; iii) calculate the intraspecific distance among available sequences to evaluate the potential presence of errors and cryptic species. As reference databases, we used both a non-curated database (BOLD) and a curated database (MIDORI2) to test the effect of sequence selection on the availability of reference sequences. We found that 67.6% and 51.8% of the Italian Plecoptera species were represented in BOLD and MIDORI2. Most of the available sequences were obtained from specimens collected outside Italy, with only 21.8% and 13.5% of the Italian species having sequences from specimens collected in Italy. Endemisms were poorly represented, and intraspecific distances for some species were high, which suggest cryptic diversity or erroneous assignments. Our results support the growing need to increase international cooperation among barcode initiatives and to promote the integration between molecular biologists and zoologists to exploit the full potential of DNA barcoding to protect biodiversity.

Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications

For complex and multi-layered clastic oil reservoir formations, modeling lithofacies and petrophysical parameters is essential for reservoir characterization, history matching, and uncertainty quantification. This study introduces a real oilfield case study that conducted high-resolution geostatistical modeling of 3D lithofacies and petrophysical properties for rapid and reliable history matching of the Luhais oil reservoir in southern Iraq. For capturing the reservoir's tidal depositional setting using data collected from 47 wells, the lithofacies distribution (sand, shaly sand, and shale) of a 3D geomodel was constructed using sequential indicator simulation (SISIM). Based on the lithofacies modeling results, 50 sets of porosity and permeability distributions were generated using sequential Gaussian simulation (SGSIM) to provide insight into the spatial geological uncertainty and stochastic history matching. For each rock type, distinct variograms were created in the 0° azimuth direction, representing the shoreface line. The standard deviation between every pair of spatial realizations justified the number of variograms employed. An upscaled version of the geomodel, incorporating the lithofacies, permeability, and porosity, was used to construct a reservoir-flow model capable of providing rapid, accurate, and reliable production history matching, including well and field production rates.

A new air gap flux‐based technique to detect loss of field in synchronous generators

AbstractA new technique is proposed for loss of field (LOF) detection in synchronous generators (SGs). The proposed scheme employs the variations of SG angular velocity ∆ωφ and load angle ∆δφ to reach this goal, while the mentioned parameters are estimated through air gap flux estimation. The proposed scheme performance is evaluated using realistic simulation case studies based on IEEE C. 37. 102 standard compared to the conventional impedance‐based one. The obtained results show that the presented scheme excels the conventional scheme from speed and security viewpoints. Moreover, the estimation procedure of ∆ωφ and ∆δφ is experimentally validated by using a 10 kVA SG and the results demonstrate that the proposed technique can be easily set and implemented.

Comparing the accuracy of real-time transit arrival estimations with a Kalman Filter motion model in different transit networks models

Estimation of arrival time at transit stops is an active research field. Arrival time can be estimated with forecasting, stochastic, or learning methods. Regardless of the arrival-time estimation model, the transit lines must be modeled to identify the line segments where vehicles move. Hence, different network models can be created to identify when a transit vehicle is moving. The presented paper uses the Kalman Filter motion model to estimate transit arrivals on different network models. It is shown that the Kalman Filter motion model estimations vary significantly depending on the network model. The paper estimates arrival time in a real-life case study, using eight network models of transit lines: dwell, dwell/intersection, interval, stop, intersection barrier, interval/barrier, and adaptive based. The adaptive and interval/barrier-based network model leads to the most accurate results when the Kalman Filter motion model estimates arrivals.

Implementing team-based learning in a large environmental chemistry course and its impact on student learning and perceptions

Team-based learning (TBL) is an instructional strategy where students participate in a set of activities including, applying course concepts to real-life case studies in instructor-selected teams. Here, we describe how TBL has been incorporated into a 3rd year, large, environmental chemistry course and investigate the benefits of using this strategy. A combination of pre/post survey and coursework data were analyzed to understand: (1) What were student perceptions of TBL? (2) How did using TBL to deliver content influence student learning, measured by exam performance? (3) How did students’ team skills evolve? Post-survey results indicate that students perceived TBL as enhancing their interest in course content, creating real-world connections, and most helpful for achieving practical critical thinking skills. Student performance on TBL-related final exam items was significantly better (Mean = 73%, SD = 21%) than non TBL-related final exam items, (Mean = 65%, SD = 21%), despite the level of complexity being similar between the two categories. The pre/post survey results indicate that, as compared to the start of term, students reported being significantly more comfortable expressing opinions in group meetings (t(78) = 4.25, p < 0.001, Cohen's d = 0.48), and leading group discussions (t(78) = 3.11, p = 0.003, Cohen's d = 0.35), by the end of the term. The one-minute reflections (completed following the first and fifth TBL activities) indicated that there was a 14% increase (77% vs. 91%) in the number of students reporting on collective team decision making. This study demonstrates the wide-ranging positive impacts of TBL to student learning in a large Environmental Chemistry course all while enhancing active learning and applying chemistry concepts to relevant and real-life case studies.