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This paper provides an overview of the types of inks and their formulations for digital printing of wool, silk, cotton, and polyester. The innovative artificial inks were constituted by a fusion of reactive dye, 3-chloro-2-hydroxypropyl trimethyl ammonium chloride and Tween-80 surfactant, five Perylene acid dye inks were synthesised based on Acid red 27, also polyvinyl alcohol and waterborne polyester have been utilised for disperse dye ink formulation. Likewise, sustainable inks were prepared from curcumin and tannic acid incorporating triton x-100 and sodium dodecyl sulfate, additionally dye molecules were encapsulated in a sonicator by an eco-friendly process of mini-emulsion polymerisation with styrene-co-butyl acrylate copolymers. The herbal inks constituted from bio indigo for blue ink, quebracho red for red ink, the flame of the forest for yellow ink and the mixture of three to derive black ink is included herein. Overall, greater colour values, fastness properties and print quality were acquired on the textile substrate inkjet printed with new quebracho red herbal inks. The review offers several novel ink formulations including those that are UV-curable, blue light curing or self-curable inks along with a critical analysis. The pre-treatment requirements, ink formulation, fabric composition, mechanism of printing and the desired end results are interdependent. The paper concludes by highlighting the need for more eco-friendly and simplified ink formulations for digital printing of textile substrates. [ABSTRACT FROM AUTHOR]

Imidazo[1,2-a] pyridine-based two probes with different substituents containing oxime and hydroxyl functional groups were synthesised and evaluated for detection of tabun nerve agent simulant diethylcyanophosphonate (DCNP). The nucleophile hydroxyl group present on both the probes can be rapidly phosphorylated by DCNP through a nucleophilic substitution reaction. The probes were fluorescent and on reacting with DCNP underwent fluorescence OFF, which can be visualised under a UV lamp at 365 nm wavelength. The limit of detection for probes 1 and 2 was calculated as 0.66 μM and 0.54 μM, respectively. The mechanism of detection was studied using spectroscopic techniques revealed that the hydroxyl group is involved in the reaction with DCNP. The probes were found selective for the detection of DCNP in presence of other electrophilic interfering agents. Results were obtained in aqueous media and probes can be used directly for the rapid and sensitive detection of DCNP in water without any pretreatment. [ABSTRACT FROM AUTHOR]

The article reviews three ethnographies that explore truth, doubt, and expertise in Latin America, focusing on the future of facts in the region. The books analyzed delve into topics such as economic collapse in Argentina, climate change adaptation in Guyana, and climate prediction in Brazil. The review highlights how sense-making practices around facts and futures shape perceptions of economic stability, environmental justice, and national identity in Latin America, challenging traditional notions of fact-making as linear or objective truth. The authors emphasize the importance of understanding how facts are produced, contested, and discussed in different contexts, shedding light on the complex relationships between facts, futures, and Latin America. [Extracted from the article]

This paper investigated waste management practices of small businesses in the selected townships of Gauteng province, South Africa. Surveys were conducted by means of questionnaire-administered interviews and primary data were analysed by descriptive and inferential statistics. Results indicated that most businesses generated wastes in the form of plastics (59–75%), cardboards (31–61%), and food remains (21–37%). Approximately 64–68% of small businesses relied on the municipality for waste collection and disposal. By contrast, 10–20% of businesses gave away their recyclable materials to informal waste reclaimers while only a few were recycling (7–14%) or reusing (6–12%) their wastes, respectively. The survey found a significant association between the formality of businesses and certain types of wastes that they were generating, namely, office paper, glass, metals, food items, and other waste materials. Based on these findings, some recommendations are suggested for improving existing waste management practices and maximising waste reduction efforts in the townships. [ABSTRACT FROM AUTHOR]

A good literature review that effectively synthesises current knowledge can contribute to the cumulative knowledge growth and advancement of a field. This tutorial-paper complements the existing methodological guidelines, with a particular focus on the 'literature review protocol'- which can significantly improve the quality, efficiency, and impact of a literature review endeavour. In this paper, we describe what a 'literature review protocol' is, discuss its major components, and provide essential design guidelines. In particular, we provide a comprehensive toolkit with step-by-step instructions, templates, and illustrative examples that users may adapt to their own projects. This information is appropriate for novice and expert researchers conducting a literature review for publication purposes, and also for reviewers or editors who assess literature review papers. It is also beneficial for educators engaged in research trainings. [ABSTRACT FROM AUTHOR]

This paper presents the Smart Logistics Node concept, which combines the physical infrastructure of logistics nodes with digital systems to enhance collaboration. The Smart Logistics Node benefits from data sharing, supporting infrastructure, and Connected and Automated Transport (CAT) technologies. Based on a literature review on logistics nodes and CAT, we propose a general classification of Smart Logistics Nodes distinguishing upon the node function, degree of organisational (de-)centralisation, digital integration, and infrastructure support for automated driving. Then, we classify sixteen logistics nodes and find that high digital integration is common while automation is lacking. Further automation entails mixed traffic on public roads and requires organisational changes that do not always align with current business models. Our work supports the adoption of emerging technology at logistics nodes and the comparability of business cases. Ultimately, node authorities can use our concept and classification to draw a roadmap to develop CAT capabilities. [ABSTRACT FROM AUTHOR]

Computer programming is essential nowadays but still challenging to learn due to its invisible thinking. Current programming environments are mostly designed for operating on computers directly to learn concrete programming, but this approach lacks the support for clarifying learners' thinking processes. On the other hand, using paper and pens shows usability in developing creative thinking, but it cannot help verify learners' thinking results due to its nature. To benefit from both paper and computers, we developed an interactive programming learning environment consisting of a paper board game, a computer system, and a digitalization mechanism. Learners can use the learning environment to draw down their thinking results and verify them by comparing them with the evaluation on the computer system immediately. Two experiments were conducted to compare this learning environment with paper board games and computer programming environments, respectively. The experimental results show that the subjects who learn in our environment can learn to program better than those who learn with the other two. The proposed environment is easy to prepare for learners in a classroom as their first step to programming learning. It can help learners cross over from paper board games for computational thinking to computer programming environments. [ABSTRACT FROM AUTHOR]

Previous research has documented that students' reading comprehension performance in learning English as a foreign language (EFL) differs greatly between mobile-assisted and paper-based reading. The present study extends previous work by investigating whether reading cognitive load differs between mobile-assisted and paper-based EFL reading processes and exploring whether cognitive load mediates the relationship between reading medium and EFL reading comprehension performance. The instruments used are reading comprehension tests and questionnaire surveys administered to a sample of 191 first-year college students. A repeated-measures analysis of variance (ANOVA) indicates that cognitive load is significantly greater for mobile-assisted EFL reading than for paper-based EFL reading. In addition, structural equation modeling analyses indicate that cognitive load fully mediates the relationships between medium and reading comprehension performance; these mediation effects are independent of the level of English language proficiency. These results imply that cognitive load is a pivotal factor influencing students' reading comprehension across media. Reasons for differences in reading cognitive load and its role as a mediator are discussed. Recommendations for promoting students' reading experience on mobile phones are provided. [ABSTRACT FROM AUTHOR]

Transforming Pulp and Paper (P&P) mills into Integrated Forest Biorefineries (IFBR) is a prominent solution to save Canadian P&P industry that has been facing decline of conventional paper demand. We propose a comprehensive decision model for the design of IFBR value chains by taking the uncertain demand of bioproducts into consideration. In particular, we propose a multi-stage stochastic programming model to obtain the optimal investment plan over a long-term planning horizon in the presence of various market trends. We also develop a Monte-Carlo simulation platform to validate the proposed model and to compare its performance with alternative decision models. The model is applied to a realistic case study inspired from P&P companies in Canada, where the value of incorporating the dynamic nature of uncertain demand has been estimated. Further, we elaborate on the value of considering flexibility in terms of adjusting the investment plan in response to changes in the demand. Our results indicate that the demand for bioproducts has a substantial impact on the profitability of the IFBR. We also demonstrated the significant value of explicitly incorporating the uncertainty in IFBR network design as well as adapting the investment plan to the changes in the demand. [ABSTRACT FROM AUTHOR]

Stanley Kubrick's project on the Holocaust, Aryan Papers, was dear to his heart. He worked on it for a long time, but he could not, in the end, bring himself to complete the planned film. This article canvasses some of the reasons other scholars have supplied for this film remaining unmade, including the notion that the Holocaust is unrepresentable. However, it points to a novel explanation. I argue that Kubrick's plot modifications, particularly to the conclusion, doomed the project. Specifically, Kubrick has a Jewish woman take revenge for war-time atrocities. Discussing revenge in relation to the Holocaust has until recently been as impious as representing the Holocaust itself. Jewish revenge was unfashionable in Holocaust films of all kinds when Kubrick was working on Aryan Papers in the early 1990s. Kubrick's planned film was generically ahead of its times. The vengeful Jewish woman had to wait for Inglourious Basterds in 2009. [ABSTRACT FROM AUTHOR]

This article asks why Stanley Kubrick's longstanding wish to direct the film version of Louis Begley's 1991 novel Wartime Lies never came to fruition. In Begley's novel, the protagonist Maciek recounts the experience of living in hiding under a false identity in Nazi-occupied Poland. Kubrick drafted a series of quite different plotlines and conclusions for the film version, to be called Aryan Papers, in which Maciek's aunt Tania takes a highly dramatic role in their destiny. The more Kubrick changed Aryan Papers, in response to the pressure of Hollywood expectations and the cultural valorization of active Holocaust resistance, the less it resembled the very work he had chosen to adapt. This impasse shows why the director could not ultimately bring Aryan Papers to the screen. [ABSTRACT FROM AUTHOR]

Xuan paper is one of the most famous handmade papers in China and is an important paper for conservation. However, the evenness of Xuan paper has not yet received much attention. In this study, the distribution of the in-plane grammage and mechanical properties of Xuan paper are measured. It is found that the distribution of the in-plane grammage and mechanical properties of Xuan paper is uneven. In the handmade direction, the grammage and mechanical properties of Xuan paper are overall largest in the lower area, followed by the upper and middle areas. The sheet forming process of Xuan paper has an important effect on the distribution of in-plane grammage, mechanical properties, and fiber orientation of Xuan paper. The folded state has a negative effect on the mechanical properties of the folded area of Xuan paper. This study will help raise awareness of the unevenness in physical properties of Xuan paper, which is important in both conservation and research applications. [ABSTRACT FROM AUTHOR]

This special issue of the International Journal of Production Research, focuses on the 'Carbon Neutrality' and 'SCM'. Nine articles have been selected for this special issue, and they cover various aspects to understand the manifestation of 'Carbon Neutrality' in supply chains, and to further illuminate how to achieve 'Carbon Neutrality' with supply chain innovation. This editorial provides a brief overview of the research domain, then introduces each article in the context of the state-of-the-art and highlights the contributions of selected papers to the field. Finally, the research perspectives are discussed. [ABSTRACT FROM AUTHOR]

Sea purslane (Sesuvium portulacastrum) was cultivated for two 90-day sequences at two spacings, and with various soil amendments, to evaluate its potential to arrest the increase in soil salinity and sodicity associated with irrigation using paper- and pulp-mill effluent. Its phytodesalination capacity (PDC) with respect to Na ranged from 162 to 226 kg/ha in the first sequence and 167 to 250 kg/ha in the second sequence; the PDC is reliable even under drought stress. Addition of amendments, in particular pressmud compost, increased the average Na+ uptake by 38%, leading to the highest PDC of 250 kg/ha for a single crop sequence. [ABSTRACT FROM AUTHOR]

Introduction: Resection of primary tumor and liver metastases is the gold standard for colorectal cancer with liver-only metastases (CRLM). Although treatment options have expanded to enable conversion of unresectable to resectable CRLM, about 40% of patients will have definitively unresectable disease. Major advances in surgical techniques, immunosuppressive protocols and patient selection criteria for liver transplantation have resulted in improved outcomes. Areas covered: A literature search has been conducted in Pubmed for articles published between 2014 and 2024. This review paper comments on current liver-directed treatment options for CRLM: resection, percutaneous ablation, conversion-chemotherapy, TACE, SIRT, and SABR. We explore evidence for liver transplantation in patients with unresectable CRLM, comment on possible limitations for implementation in clinical practice and give an overview of the current guidelines on liver transplantation in the USA, Europe, the United Kingdom, and Australia/New Zealand. Expert opinion: The recent randomized TRANSMET trial, investigating liver transplantation versus chemotherapy in unresectable CRLM, shows promising 5-year OS reaching similar values as for other accepted liver transplantation indications. Further investigations with RCTs to investigate reproducibility and feasibility in clinical practice are needed. Before liver transplantation can be implemented as a standard treatment option, reorganizations at federal, regional and hospital levels would be required. [ABSTRACT FROM AUTHOR]

Fluorotelomer-based acrylate polymers and surfactants used to grease-proof food contact paper (FCP) are potential sources of dietary exposure to perfluoroalkyl substances (PFAS). Food contact substances (FCS) containing polyfluorinated long-chains (≥C8) were voluntarily removed by their manufacturers from the U.S. market in 2011 due to health concerns and largely replaced with FCSs containing short-chain (≤C7) PFAS. In 2020, FDA findings of potential biopersistence of 6:2 FTOH (CF3(CF2)5CH2CH2OH) similarly prompted an additional voluntarily phase-out of FCSs containing 6:2 FTOH by their manufacturers that was completed by the end of 2023. To monitor the phase-out process, a screening method was developed to detect FCPs containing ester-linked polyfluorinated pendant chains. Direct Analysis in Real Time-Isotope Dilution-High Resolution Mass Spectrometry (DART-ID-HRMS) enabled rapid semi-quantitative detection of 6:2 FTOH in FCP saponification reaction headspace without requiring sample concentration or chromatography. 19F-NMR analysis confirmed 6:2 FTOH pendant chain identity and detection dependence on saponification. The speed and specificity of this approach arise from ester saponification in the presence of stable isotopically labeled 6:2 FTOH; high FTOH differential volatility relative to nonfluorinated matrix, and the facile production of FTOH gas-phase anions (e.g., [M + O2]·−, [M-H + CO2]−) under ambient ionization conditions. The efficiency of this simple workflow makes it well-suited for monitoring the phase-out of FCS containing ester-linked polyfluorinated chains from the U.S. marketplace. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic had an exceptionally long and consequential rally effect in China. Drawing on an eight-wave nationwide survey, this article shows that the Wuhan lockdown boosted public confidence in the central government. The persistence of the pandemic and the initial success of the zero-COVID policy sustained the enhanced trust in the central government for over two years. However, the rally effect did not dissipate as quietly as usual. As trust in the central government returned to the pre-pandemic level following the Shanghai lockdown, defiant protests collectively known as the White Paper Movement broke out. Angry protesters demanded the termination of the zero-COVID policy and called for accountability from the ruling party and its top leader. This article argues that while the rally effect lasted exceptionally long in the authoritarian country, it could not be prolonged indefinitely. Ultimately, the dissonance between the declining trust in the central government and the persisting restrictive measures provoked the protests. [ABSTRACT FROM AUTHOR]

Smith is the precursor of neither the orthodox nor the heterodox view of money. For him, money arose neither as medium of exchange out of barter, as in the orthodox view, nor as unit of account established by the state, as in the heterodox view. Instead, money emerges in that agreement on the valuation of mutual services in which exchange based on the desire for deserved credit consists. Money emerges in exchange, but, unlike the orthodox view, it emerges as measure of value. Unlike the heterodox perspective, paper money is a possible consequence of a historical process, not its beginning. [ABSTRACT FROM AUTHOR]

The article "Women's Rights After War on Paper: An Analysis of Legal Discourse" published in the Journal of Genocide Research discusses the introduction of laws in Rwanda and other countries to advance women's rights after conflict. The laws aim to bolster the economic, social, and political standing of women, with a focus on gender parity for enduring democratic peace. The article critically examines the language and implementation of these laws, arguing that they may entrench existing inequalities and create exclusionary circumstances for women. The analysis is based on original datasets from the Women's Rights After War project, highlighting the limitations of legal frameworks in providing transformative change and justice for women affected by conflict. [Extracted from the article]

Anthocyanins, a significant subclass of polyphenols, are prevalent across various plant species. Known for their vibrant colors, which make them desirable as natural pigments, anthocyanins have also garnered attention for their diverse biological activities, leading to their widespread use in food processing and supplement development. However, their structural instability poses a challenge, as it directly impacts their bioavailability and, consequently, their nutritional efficacy. This review paper delves into the chemical structure and food sources of anthocyanins and examines strategies to enhance their stability and bioavailability, particularly through improved understanding of their digestion and absorption in the human body. Additionally, it explores the broad spectrum of anthocyanins' biological activities, including their antioxidant, hypoglycemic, anticancer, cardiovascular, anti-inflammatory, and neuroprotective effects, analyzing these properties from a mechanistic perspective. Factors such as pH, temperature, light exposure, solvent interactions, and metal ions are known to negatively affect anthocyanin stability. Thus, future research should prioritize processing techniques that can preserve their structure more effectively. [ABSTRACT FROM AUTHOR]

This work engages with Karen Barad's philosophy and theory of agential realism through research practices of critical autoethnography and arts-based methods. The work explores how knowledge, memory, language, and experience remain alive within practitioners and inform who we become and how we inherit the stories involved in being educators and therapists. This paper presents two entangled research processes involving engagement with material artefacts related to the author's professional identity development and practice as an arts therapist in Aotearoa, New Zealand. Through Barad's theory of diffraction, engagement with and reassembling research artefacts supports a consideration of what can happen when we map and remember stories from different times and spaces in our lives. Elucidating and exploring the contours of memories and experiences can be helpful practices to engage in as narrative is the most potent mechanism for memory and teaching, and as therapists and educators, we are living through and practicing from the tangle of these narratives. [ABSTRACT FROM AUTHOR]

The effect of polyvinyl alcohol, a green water-soluble polymer, usually is not satisfied as a secondary-fiber reinforcing agent in pulp and paper-making industry due to the same negative charges of PVA and pulp fiber. In this study, 3-chloro-2-hydroxypropyltrimethylammonium chloride was used as an etherifying agent to prepare cationic modification of polyvinyl alcohol (CPVA) with degree of etherification of 0.059 under a reaction time of 4 hours and n(PVA : etherifying agent) = 1 : 0.5. Compared with PVA, CPVA exhibits positive charge and smaller particle size, enabling it to effectively combine with negatively charged fibers and get in between the fibers. As a result, CPVA demonstrates a remarkable ability to improve the paper's physical properties. With a dosage of only 0.2% CPVA (based on the absolute dry pulp), which is merely 1/10 of the dosage of PVA, CPVA increases the paper's tensile index by 54.09%, burst index by 25.96%, and tear index by 21.09%. CPVA as a reinforcing agent for secondary fiber pulps, which can be attributed to the alteration in charge, to enhance CPVA's affinity for the electrostatic interaction of the fibers. Consequently, CPVA emerges as a viable and eco-friendly enhancement solution, demonstrating significant potential in secondary fiber reuse. [ABSTRACT FROM AUTHOR]

This document is a special issue of the International Journal of Production Research focused on service-oriented manufacturing supply chain management in the context of Industry 4.0. Service-oriented manufacturing (SOM) is an emerging paradigm where manufacturers transform their products into platforms using Industry 4.0 technologies, allowing for communication between customers and manufacturers. This shift enhances the flow of information through the supply chain and creates new operational challenges. The special issue includes 16 articles covering various sub-areas such as mechanism design, supply chain contracts, behavioural modelling, emerging business models, optimisation techniques, product design, and smart manufacturing. The papers provide insights and practical applications to advance service-oriented manufacturing supply chain management in the Industry 4.0 era. [Extracted from the article]

This study evaluates the impact of sales promotions that are featured in store flyers distributed by prominent retailers and their capacity to attract consumers to their establishments. A distinctive conceptual framework is introduced that scrutinizes the influence of ethically-minded consumer behavior, digital citizenship/literacy, and a retailer's orientation toward environmental sustainability and explicit ecological concerns. A quasi-experimental design was used to formulate four distinct scenarios from a possible set of eight, stemming from the combinations of three variables: 2 Retailer Positioning (green vs. without explicit ecological claims) × 2 Flyer Mediums (paper vs. digital) × 2 Discount Levels (5% discount vs. 30% discount). Through an online survey, a sample of 269 responses was garnered to gauge consumer reactions to these store flyers, evaluating their attitudes and receptiveness. The findings suggest that attitudes toward paper flyers were more favorable when presented by non-green retailers compared to their green counterparts. This can be attributed to a perceived inconsistency between a green brand positioning and the use of paper flyers. Furthermore, the propensity to transition to digital flyers was notably stronger for green retailers compared to non-green retailers, but this inclination was most pronounced when higher discounts were offered. [ABSTRACT FROM AUTHOR]

During COVID-19, blood demand exceeded pre-pandemic levels due to reduced donations, causing shortages. Given the severe shortage, it's crucial to optimise blood use, prevent shortages, minimise wastage, and reduce unnecessary transfusions in all hospitalised patients. Designing a reliable blood supply chain network (BSCN) is an effective solution, especially for COVID-19 patients. This strategic decision significantly impacts emergency management performance. An efficient and reliable blood supply chain requires the consideration of multiple factors, including scarceness and perishability of blood, simultaneously. However, existing studies have not addressed all relevant factors in an integrated blood supply chain, and this paper aims to bridge this gap. Furthermore, an efficient Benders Decomposition based heuristic approach is proposed to solve the model. The solution approach has been compared with a set of commonly used meta-heuristic algorithms, including the red deer algorithm (RDA), tree growth algorithm (TGA), and genetic algorithm (GA). The outcomes illustrate that the proposed heuristic approach can solve small and large-size problems in significantly less CPU time than the other proposed solution approaches. For large-size problems, it can reduce the average CPU time by about 80% compared to TGA, about 80% compared to GA, and about 83% compared to RDA. A real case study has been implemented to validate the proposed mathematical model and solution method. The sensitivity analysis has been conducted to validate the significance of the model's parameters; consequently, several managerial insights have been derived. [ABSTRACT FROM AUTHOR]

A key advantage of social media is the real-time exchange of views with large communities. In disaster situations, such bidirectional information exchange is most useful to victims and support teams, especially in communications with authorities, volunteers, and the public. This paper addresses challenges faced by the healthcare supply chain during the COVID-19 pandemic with analyses of Twitter data using an Artificial Intelligence-driven multi-step approach. We investigate tweets for information about healthcare supply chains, such as the scarcity of testing kits, oxygen cylinders, and hospital beds during the pandemic. We deployed machine learning to classify such tweets into imperative and non-imperative categories based on need severity. The study sought to predict the location of victims requesting help based on their imperative tweets if geo-tag information was missing. The proposed approach used four steps: (1) keyword-based informative tweet search, (2) raw tweet pre-processing, (3) content analysis to identify tweet trends, public sentiment, topics related to healthcare supply chain challenges, and crisis classification to label imperative and non-imperative tweets, (4) locating the point-of-crisis from imperative tweets to facilitate coordination of help operations. The pre-processing of tweets, trend analysis, and sentiment analysis relied on natural language processing and machine learning for topic modelling (K-mean clustering), crisis classification (random forest), and point-of-crisis detection (Markov chain). Results demonstrate the potential to capture significant, timely, and actionable information on healthcare supply chain challenges to respond quickly and appropriately in a pandemic. [ABSTRACT FROM AUTHOR]

Today, supply chains (SCs) have been struggling with a new type of disruption known as outbreaks such as epidemics or pandemics. This type of disruption has features such as long-term and uncertain lifespan and leads to severe fluctuations in product demand. This paper elaborates on a hybrid approach based on artificial neural networks (ANN) and mathematical programming techniques to efficiently deal with this type of disruption in the production planning of SCs. In the first phase of the hybrid approach, a multi-layer perceptron ANN model with an optimised structure is developed to efficiently predict the demand and its peak points. In the second phase, the predicted demands are considered as input in a new multiobjective agile recovery production planning model. The proposed model minimises total costs and delivery times and maximises responsiveness. A real case study in Iran is conducted to verify and validate the proposed hybrid approach. The prediction error of the ANN method is about 1 percent. According to the predicted demand, optimal decisions are determined by the proposed model. The impact of under-estimation and over-estimation of demand is evaluated in terms of total costs, delivery time, responsiveness and shortage costs in the SCs. [ABSTRACT FROM AUTHOR]

This paper aims to optimise healthcare resource pre-positioning, patient scheduling, and patient transferring under uncertain demands and stochastic resource consumption. We propose a two-stage stochastic programming model that formulates the patient scheduling problem as a Markov decision process. To address complexities and uncertainties, we use Artificial Intelligence (AI) techniques to improve both model formulation and algorithmic performance. To tackle the limited data challenge, we introduce a Wasserstein distance-based ambiguity set and propose a two-stage distributionally robust optimisation (DRO) model, which derives a deterministic equivalent using the Lagrangian dual of non-anticipativity constraints. The solution process is accelerated with a scenario decomposition approach and the K-means clustering method. Both theoretical and numerical results demonstrate the consistency of the two-stage DRO model with the sample average approximation (SAA) method. The potential of AI to improve the model's performance is evident through the significant reduction in computation time achieved with the K-means clustering approach without compromising solution quality. Compared to the SAA method, the DRO model exhibits a considerable reduction in the waiting penalty cost for out-of-sample cases, ranging from $ 43.06\% $ 43.06 % to $ 81.23\% $ 81.23 %. Numerical results show that the proposed algorithm outperforms SAA methods and several benchmark policies in terms of computational efficiency and solutions. [ABSTRACT FROM AUTHOR]

This paper suggests control strategies for ordering various COVID-19 vaccines and assigning vaccine recipients to immunisation stations in order to minimise shortages. To determine the optimal quantity of multiple vaccines to order, a fuzzy periodic review model is proposed. Furthermore, vaccine recipients are prioritised into different groups based on their occupation (e.g. essential workers), age cohort, co-morbidities, and pre-existing diseases. To model vaccine recipients' waiting and improve vaccination effectiveness by reducing congestion in immunisation stations, a queuing framework is utilised. Due to the suppliers' lack of commitment to the mass production of vaccines during the COVID-19 pandemic, the number of orders delivered to the cross-docking facility is considered uncertain. A rolling planning horizon approach is implemented using an iterative method to prevent vaccine shortages. To validate the proposed model, a case study is conducted using data from Arak City in Iran, and sensitivity analysis is performed on the model parameters. The analysis of the results indicates that the rolling planning horizon approach and the possibilistic chance-constrained programming improve network performance against operational risks, including the COVID-19 pandemic. Moreover, implementing this method reduces costs and vaccine shortages in the network compared to the current situation. [ABSTRACT FROM AUTHOR]

A defining feature of the response to the COVID-19 pandemic was a pervasive and palpable collapse of sense-making in individuals and organisations. A key factor underlying this chaos was the inability of humans to make sense of the big and noisy data environment. Such big data environments are especially amenable to interpretation by Artificial intelligence. In this study, a theoretical framework has been developed for the use of AI for sense-making in survival supply chains. The proposed framework, 'The Cognitive Model of Survival Supply Chains', is articulated as consisting of scan, store, interpret, execute, and learn as its purposive components. This framework is presented as a scaffolding to organise extant knowledge, identify gaps, synthesise new knowledge, and guide future research and practice. Research questions have been identified to develop a systematic research agenda. Implications for research, policy and practice for managing pandemics have been drawn out for researchers, policy makers, and practitioners, respectively. The paper also presents the design and architecture of an AI based solution for sense-making in survival supply chains. Such a solution is urgently needed for organising the efforts of diverse stakeholders in presenting a concerted and effective response to disasters that may unfold in the future. [ABSTRACT FROM AUTHOR]

It is necessary to control patient congestion in medical centers during pandemics where medical demand grows rapidly. Also, managing generated medical waste is critical since pandemic waste can be a source of disease spread. Although some researchers have studied healthcare optimization in medical systems, there is still a lack of models simultaneously managing congestion in medical centers integrated with waste management using a new application of queueing systems. The model is also the first to use a data-driven method to develop a mathematical model of healthcare and waste management. To fill these gaps, this paper develops a multi-shift mathematical model to manage the congestion in the medical center and medical waste during the pandemic. To this aim, patients are categorized using machine learning algorithms at first. Then, the number of outpatients and inpatients, as well as medical waste, is modeled as a Markovian healthcare waste queueing-inventory system (HWQIS) using a bulk service queueing model. A case study based on the Covid-19 pandemic is applied after the model has been validated using twelve test problems. By determining the optimal size of waste packages, vehicle capacity, and the number of servers, we minimized the patients waiting time and reduced waste accumulation. [ABSTRACT FROM AUTHOR]

As businesses undergo digital transformation, supply chain fraud poses an increasing threat, necessitating more sophisticated detection and prevention methods. This paper explores the application of machine learning (ML) and artificial intelligence (AI) in detecting and preventing supply chain fraud. The research design involves analyzing a dataset of supply chain operations and employing various ML algorithms to detect consumer-based fraud within the supply chain, which occurs when consumers partake in deceptive practices during the order process of e-commerce transactions. We analyzed 180,000 transactions from an international company recorded between 2015 and 2018. This study emphasises the necessity of human oversight in interpreting the results generated by these technologies. The implications of supply chain fraud on financial stability, legal standing, and reputation are discussed, along with the potential for ML technology to identify irregularities indicative of fraud. Descriptive findings highlight the prevalence of fraudulent transactions in specific payment types. The AI sequential and the CatBoost classifiers were the top-performing algorithms across all performance metrics. The top features to detect unusual orders are delivery status, payment type, and late delivery risks. The discussion emphasises the promising predictive capabilities of the ML and AI models and their implications for detecting supply chain fraud. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of supply chain (SC) risk mitigation and recovery strategies during long-term disruptions caused by the COVID-19 outbreak, which affect both suppliers and plants. Consequently, concurrent disruptions in supply and production are observed, which vary in duration and result in time-varying reductions in supply and production capacity. To cope with long-term disruptions, a modified multi-portfolio approach that integrates simulation and predictions is proposed to develop efficient mitigation and recovery plans. This approach involves selecting primary and recovery supply and production portfolios concurrently. To achieve this objective, time-dependent mixed integer programming (MIP) models that incorporate preparedness and recovery measures are developed to optimise SC operations. A prediction-based decomposition optimisation method is proposed to solve MIP problems and coordinate supply and production portfolios under disruptions and uncertainties. Furthermore, a heuristic approach is established to provide a comprehensive solution process. Finally, computational experiments and comparative analysis are conducted on a real-life case study. The results demonstrate that the proposed modelling and optimisation methods can effectively address disruptions and improve SC resilience. In addition, the developed models and approaches have the potential to serve as decision-making tools in SC management during disruptions. [ABSTRACT FROM AUTHOR]

In most European member states, employees stay with a company for over ten years. During this time, people gain valuable experience and build up expertise that is lost to production companies when employees leave. Despite increasing digitalisation, there will still be processes, especially in individual production, that require experts. Ontologies are an established method for formalising knowledge. Standards are needed to ensure interoperability. However, not all reference models are based on the standardised basic formal ontology. To the best of our knowledge, there are also no references for modelling human-bound production steps. We have developed the freely accessible ROXANA ontology based on actual use cases from special-purpose machines. This ontology is based on the basic formal ontology and uses references from the common core ontologies and the industrial ontology foundry. The difficulty with expert-bound process steps in production is individuality and complexity. The design of special-purpose machines is individual, as are the machine-specific documents and the associated workflows. This paper presents the ontology, explains it using practical examples, and shows its implementation in a real software-based expert system. [ABSTRACT FROM AUTHOR]

Fierce marketing competition and varied customer requirements on products make a lot of companies transfer to the customisation strategy. However, customers may be overwhelmed by the vast assortment of products and options. This paper proposes a methodology aimed at optimising the range of customised product varieties considering synergy effects of modules and preferences of experts. The methodology outlines key steps required for a successful customised product configuration. Firstly, a questionnaire based on the Kano model is designed to identify customer preferences for product module customisation. The potential customers can be segmented based on the distance among their multidimensional preferences. Secondly, within each segmented market, the customer satisfaction function that considers module synergies is established using the quantified Kano model and coefficient matrix. Thirdly, the cost function that considers experts' preferences is established based on the prospect theory. Finally, a bi-objective optimisation model is formulated to maximise customer satisfaction and minimise cost under technical constraints. Pareto solutions are obtained by solving the model with the non-dominated sorting genetic algorithm II (NSGA-II). Modules and attributes available to customers can be determined by the solutions. An illustrative example of modular laptop computers confirms the methodology's effectiveness in optimising product configuration. [ABSTRACT FROM AUTHOR]

Current manufacturing environments have to face a competitive and evolving framework characterised by high-mix and low-volume demands. To properly deal with new requests coming from the market, Reconfigurable Assembly Systems (RASs) have to be designed. In this paper, we propose a methodology for the design of the architecture of a RAS, featuring both human and mobile robotic agents, and dealing with multi-product assembly of medium-light-weight products, originally assembled manually. In particular, a systematic methodology is proposed to define the number of workstations, specifying which set of operations must be performed in each one, and to design the system layout guaranteeing reconfigurability. To this end, the concepts of operation, datasheet of an operation, assembly tree, and macro-operation are introduced and formalised. Guidelines to automatise the required operations in a flexible way are also proposed. The method is applied to a use case where we define the proper number of workstations of a RAS to assemble several types of motorbike brakes, showing the reconfigurability of the system. [ABSTRACT FROM AUTHOR]

In many settings, fleets of assets must perform series of missions with in-between finite breaks. For such fleets, a widely used maintenance strategy is the fleet selective maintenance (FSM). Under resource constraints, the FSM problem selects an optimal subset of feasible maintenance actions to be performed on a subset of components to minimise the maintenance cost while ensuring high system reliability during the upcoming mission. The majority of extant FSMP models are focussed on traditional physics-based reliability models. With recent advances in Machine Learning (ML) and Deep Learning (DL) algorithms, data-driven methods have shown accuracy in predicting remaining useful life (RUL). This paper proposes a predictive FSM strategy for fleets of complex and large multicomponent systems. It relies on a concurrent ML/DL and optimisation approach where a clustering algorithm is used to determine the health states of components and a probabilistic RUL prognostics model is used for component reliability assessment. An optimisation model is developed to solve the predictive FSM problem to ensure high reliability of all systems within the fleet. An efficient two-phase solution approach is developed to solve this complex optimisation problem. Numerical experiments show the validity of the approach and highlight the improved maintenance plans achieved. [ABSTRACT FROM AUTHOR]

In many industrial processes, it may not be practical to obtain sample sizes larger than one due to various reasons. As a result, practitioners must rely on control charts based on individual observations for statistical process monitoring and control. The sequential probability ratio test (SPRT) chart is an appropriate solution for such situations. Existing literature predominantly delves into the statistical aspects of designing or optimising an SPRT chart, neglecting the economic and associated design aspects. However, past experience suggests that statistical design may not always be optimal from an economic perspective. This paper aims to bridge this research gap by proposing a two-stage Markov chain approach to develop an economic model for the optimal design of the SPRT chart with individual observations. Following the principles of economic-statistical designs, optimal parameters are determined by solving a mixed-integer non-linear programming problem that minimises the long-run cost per item while ensuring compliance with practical constraints. Through an extensive study, we find that the economic performance of the SPRT chart surpasses that of competing individual control charts in many cases. This indicates that the economic-statistical design of the SPRT chart can yield significant economic benefits while maintaining desirable statistical properties. Moreover, enhancements have been introduced to refine the optimal design of the SPRT chart, retaining excellent overall performance across a wide range of shifts. Finally, we provide two industrial examples to illustrate the effectiveness of the proposed SPRT chart. [ABSTRACT FROM AUTHOR]

This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products. [ABSTRACT FROM AUTHOR]

The rise of mass customisation drives manufacturers to adopt leasing agreements for their machinery rather than outright their ownership. This industrial trend results in large-scale leased manufacturing systems, where the asset conditions and maintenance requirements for a fleet of machines and/or production lines from several manufacturers are continuously monitored and managed by a lessor. In this paper, we propose an operations and maintenance planning model that explicitly models (i) dynamic real-time failure rate predictions for machines based on sensor-driven degradation data, (ii) optimal routes for maintenance teams across a number of geographically-distributed manufacturing sites, and (iii) operational outcomes. The model can handle complex maintenance and operational outcome interdependencies between multiple machines, and incorporate demand for different products, heterogeneous machine capacities and capabilities, factory topology, and the resulting production flow capacities of products. To demonstrate the model's practicality, it is applied to three experimental case studies with various numbers of machines, operating schedules, product types, and flow capacities. The results show that the proposed model significantly outperforms the traditional reliability-based maintenance model in key metrics such as the number of unexpected failures, the cost of preventive maintenance actions, machine downtime, the percentage of unsatisfied demand, and total cost. [ABSTRACT FROM AUTHOR]

Firms painstakingly assemble vast supply networks of cyber and physical interdependence. Wars, pandemics, and cyber-attacks have disrupted and rippled recently through these networks. The ripple effect in interdependent networks can be caused by functional and structural failures simultaneously, known as mixed ripple effects, which have been studied less in the past. To help managers enhance network resilience against mixed ripple effects, we propose a design and protection strategy from a network structure perspective. This paper is the first to propose a growth-maturity-decline (GMD) model for designing an interdependent network structure. Then, a protection strategy based on node collaboration and redundancy is proposed. Considering that a single resilience metric makes it difficult to distinguish the performance of different strategies, we develop a three-dimensional quantitative framework to evaluate network resilience by conducting case studies on synthetic and real-life supply chain networks. The simulation results indicate that (i) the GMD model enhances network resilience at different phases; (ii) the threshold of the upper capacity limit parameter for cyber-supply networks is 2.7; and (iii) the more uniform the role distribution of firms, the more resilient the network is. This study can provide more informed management decision support for improving network resilience. [ABSTRACT FROM AUTHOR]

Liquified Petroleum Gas (LPG) is an oil refinery product that must adhere to quality specifications with respect to certain impurities. Refineries apply an LPG purification process that consists of a flow network with several process units (PUs). Current methods focus on optimising the performance of each PU separately; there exists no known approach for identifying the whole process optimum. In this paper, we present an approach for optimising the LPG purification process as a whole. We utilise operational scenarios to model the non-linear transformations of each PU. These scenarios enable us to devise a Mixed Integer Linear Program (MILP) that minimises energy consumption. The obtained solution is an approximation of the optimum but offers actionable support to refinery engineers. To enable the applicability of our approach at large scale, we propose two filters based on Pareto dominance and Data Envelopment Analysis (DEA) to identify the Pareto optimal and the Best Practice set of scenarios, respectively. By filtering out the rest, we reduce the solution space of the corresponding MILP. Further, we provide computational evidence that the application of these filters vastly improves solution time and enables applicability under real production conditions. [ABSTRACT FROM AUTHOR]

With the prevalence of low-carbon transmission, traditional energy power generation enterprises (TGEs) are facing increasing pressure. By comparison, renewable energy power generation (REPG) holds the advantage of low carbon. To promote the development of REPG, various regulation policies have been implemented. This paper aims at investigating the interplays between renewable energy power generation enterprise (RGE) and TGE's production decisions under different regulation policies from the social welfare perspective. We consider the feed-in tariff (FIT), renewable portfolio standard (RPS), and carbon tax (CT) policies. Our analyses reveal several major findings. First, the influence of the regulation policy on RGE and TGE's power generation quantities depends on the gap between REPG cost and traditional energy power generation (TEPG) cost. Second, the regulator's optimal regulation policy hinges on his emphasis on the economic benefit and the environmental protection. Third, as the REPG cost decreases and the regulator's emphasis on environmental protection increases, the regulator's optimal policy from the social welfare perspective exhibits a trend of 'FIT-CT-RPS.' Fourth, TGE's preferred policy focuses on FIT or CT, and RGE's preferred policy is always RPS regardless of how the regulator's attention on environmental protection changes. On this basis, managerial implications for promoting REPG are provided. [ABSTRACT FROM AUTHOR]

The emergence of renewable energy microgrids (REM)-supported logistics parks reflects the urgent social requirement for sustainability facing energy crisis and climate change. Focusing on the emergency REM capacity planning and dynamic inventory operations problem for a REM-supported cold warehouse, this paper constructs a novel two-stage stochastic optimization method to comprehensively minimise long-term investment and short-term emergency operation costs of the system, while improving the resilience of energy-constrained supply chain management of perishable products. In particular, we formulate the second stage emergency operation problem as a Markov decision process that considers the interaction between energy and operational decisions constrained by intermittent of renewable energy, the randomness of the occurrence time, and the balance between power supply and temperature changes of the cold warehouse. We propose a novel two-phase PSO-based MPC algorithm to solve the entire problem. We also examine the effectiveness of our algorithm via an empirical study based on the data in East China. Nine typical scenarios clustered from the real data suggest different optimal capacity planning and operational policies. The empirical results show that it can reduce an overall 9% of operating costs on average compared to other heuristic policies. Moreover, installing REM can improve the resilience of power systems by 23% (risk impact area reduction ratio) and improve the resilience of inventory systems by 49%. [ABSTRACT FROM AUTHOR]

Over the last decades, scheduling theory has been put into practice so as to tackle production process as a combinatorial optimisation problem and specifically as a PFSP (Permutation Flowshop Scheduling Problem). Often, NP-completeness of PFSP leads numerous research towards suggesting heuristic algorithms. The objective is to propose an optimal or near-to-optimal order of n jobs processing on m machines with a minimum completion time of all jobs. In this paper, a two-phase heuristic algorithm will be presented, named IFRS (Improved FRS). FRS, within the first phase, is going to be used to find a superior order of jobs out of Taillard's instances; then the order of jobs will be improved again in phase 2. While running IFRS, a completely new idea, named Pixelation of time matrices, will be used for the very first time and provide a final pattern, which can be used in any heuristic algorithm with makespan criterion. After using the hard benchmark instances of Taillard, the superiority of IFRS over 20 algorithms is going to be shown by tables and statistical graphics. Due to its low makespan values, IFRS benefits flow production, as does NEH. [ABSTRACT FROM AUTHOR]

The laser powder bed fusion (L-PBF) additive manufacturing process offers many advantages. However, its industrial integration still needs to be improved due to the certification constraints of the produced parts. Thus, this paper proposes a phenomenological causal chain of process failures to ensure the control of the L-PBF process and further certify the quality of the parts produced. Consequences of unsuitable thermal regimes are analysed using a failure mode and effects analysis (FMEA) approach, allowing a more global understanding of all the causal chain-related data. The proposed approach is based on standard acquisition equipment available on industrial machines and proves relevant for identifying process failures. Experimental validation of the proposed monitoring method is implemented on a use case using dedicated software. Finally, it is shown that the method is relevant in this confrontation and compatible with the time scale of the process for future NC integration and real-time monitoring. [ABSTRACT FROM AUTHOR]

Based on the policy incentives for equity financing and firms' awareness of managing supply disruption risk, this paper examines a two-tier supply chain. A large manufacturer can purchase products either from a capital-constrained unreliable supplier who has disruption risk or a reliable backup supplier with a higher cost. We investigate the value and interactive use of the backup supplier and equity financing on firms' performances. The results show both the backup supplier and equity financing alone can always benefit the manufacturer, but the unreliable supplier can become worse off. However, when the backup supplier and equity financing interact, their impact changes dramatically. Under equity financing, the backup supplier can make the unreliable supplier better, and the effect of the backup supplier on disruption risk management differs with different wholesale prices. Moreover, with a backup supplier, equity financing can benefit both the manufacturer and the unreliable supplier. Specifically, with the backup supplier, the unreliable supplier can lose all autonomy on whether or not to raise equity financing. Then, the manufacturer and unreliable supplier can achieve Pareto optimality due to 'bidirectional free riding'. These insights provide strategic guidance for product ordering, enabling firms to manage disruption risk better and achieve high profitability. [ABSTRACT FROM AUTHOR]

This paper studies the constrained two-dimensional non-guillotine cutting problem with defects, in which a set of items of a specific size is cut from a large rectangular sheet with defective areas, with the number of each type of cut item cannot exceed a specified quantity. The objective is to maximise the total value of the cut items. We propose a decomposition approach to address the problem. The process involves decomposing the original problem into a master problem and a subproblem. The master problem is formulated as a one-dimensional contiguous bin packing problem, while the subproblem is an x-Check problem to identify a two-dimensional packing that does not lead to any overlap. The x-Check problem is effectively addressed by using an integer linear programming model. When the x-Check problem proves infeasible, cuts are added to the master problem, and the iteration is repeated until the x-Check finds a feasible solution. Furthermore, we introduce several novel techniques, including valid inequalities, preprocessing techniques, and lifting the cut methods to improve the performance of the algorithm. Extensive computational results show that our method can quickly find the optimal solution for the 5450 instances in the literature. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers. [ABSTRACT FROM AUTHOR]

The editorial discusses the review process at the Journal of Personal Selling & Sales Management (JPSSM), highlighting the roles of authors, reviewers, and editors. It outlines the steps involved in submitting a manuscript, the responsibilities of reviewers, and the benefits of engaging in the peer review process. The editorial emphasizes the importance of timely, thorough, transparent, and transformative reviews, providing guidance on how to write a good review and respond to reviewer comments. Authors are encouraged to view the revision process as collaborative and to address reviewer feedback respectfully and professionally. [Extracted from the article]