Inventory routing with heterogeneous vehicles and hazardous material backhauling

Efficient coordination of distribution and backhauling is a critical challenge for many industries. This paper is motivated by a real-world case study at Hydro-Qu & eacute;bec, a large-scale utility company in North America, and introduces an inventory routing problem that integrates inventory management and vehicle routing under several operational constraints. The problem involves distributing multiple commodities to customer sites while backhauling hazardous materials to depots. The objective is to minimize delivery, collection, and inventory holding costs using a fleet of capacitated heterogeneous vehicles, while ensuring that hazardous materials are transported separately from regular delivery commodities. In each period, a customer's delivery and back-hauling can be split and satisfied by multiple vehicles. We propose a mathematical formulation, introduce valid inequalities, and solve the resulting model using a branch-and-cut algorithm. To tackle large-size instances, a two-phase decomposition matheuristic is developed. To highlight the value of split delivery and backhauling, we compare the solutions from our model with those when split delivery is prohibited and when backhauling is optimized independently. In addition, we investigate the order-up-to level policy and the case when stockout is allowed. An extensive numerical study is conducted on synthetic instances to evaluate the performance of the models and solution approaches. The heuristic algorithm solves the synthetic instances in less than two hours with an average optimality gap of less than 2%. Finally, a case study is conducted on the Hydro-Qu & eacute;bec network to demonstrate the real-world applicability of the model and quantify the benefits to the company. Our proposed model reduces the total routing costs by 21 % compared to the case where backhauling is not integrated and split delivery is not allowed.Publisher versio

Better reflective functioning in mothers linked to longer joint attention with infants

Joint attention is a foundational precursor to later developmental outcomes such as vocabulary, intelligence, and theory of mind. Previous research has shown that maternal sensitivity, depressive symptoms, and parent-child attachment security are associated with attention-sharing behaviors between mothers and their infants. The present study examined the relationship between mothers’ reflective functioning (the ability to recognize and interpret one’s own and one’s child’s mental states, as well as the behaviors motivated by those mental states) and joint attention. Data were collected from 72 infants aged 10–16 months and their mothers. Results indicated that mothers who reported greater difficulty in understanding and distinguishing between their own and their child's mental states (i.e., higher prementalization) tended to engage in joint attention episodes that were shorter and more frequent, and they were also more likely to terminate these interactions. In contrast, mothers expressing greater interest and curiosity about their infants’ mental states spent longer periods in joint attention, initiated these episodes less often, and were less inclined to terminate them. Additionally, mothers who felt more certain about their infants’ mental states were less likely to end joint attention episodes. After controlling for infant age and socioeconomic status, higher levels of interest and certainty continued to predict lower maternal termination, while prementalization was still linked to a higher number of joint attention episodes. These findings suggest that mothers’ perceptions of their infants’ mental states shape how they engage in shared attention during everyday play interactions

The impact of COVID-19 pandemic on tourism employees: Was it the last straw?

Tourism, as one of the most vulnerable industries, has survived numerous global crises with substantial negative impact on economies, communities, businesses, and individuals. Despite the circumvention of the industry after those experiences of mild and severe crises, COVID-19 pandemic has been the most serious case with deep global impact in every corner of the world leading to the explosion of academic research on a plethora of pandemic aspects. However, research offering insights on tourism and hospitality employees' experiences, is scarce in the relevant literature in spite of the chronic problems of employee retention, qualified and long-term labor force. Therefore, the aim of this study addresses at examining the experiences of hotel employees in T & uuml;rkiye during and after COVID-19, which caused sudden and deep changes in the lives following the severe decline in tourism employment and economic problems it ushered in. The data was collected through in-depth interviews with 21 individuals who formerly worked in city or resort hotels at various positions and departments. Two sensemaking perspectives were integrated to find out the consequences of the pandemic leading to the causes and factors to end working in the industry. Study findings offer important insights into pandemic-related dynamics and could support the development of effective tourism policy and practices leading to improve crisis management efforts in the tourism and hospitality industry

Emotional labour in online teaching for university lecturers after the February 2023 Kahramanmaraş and Hatay earthquakes: A narrative inquiry

The use of technology in online and hybrid English classes has brought about significant changes in the role and responsibilities of English teachers. As students become increasingly reliant on technology, English teachers are now expected to provide emotional support to their students using digital tools in addition to academic guidance. This added responsibility, as one manifestation of emotional labour, can significantly impact English teachers’ well-being and job satisfaction. The current qualitative study sheds light on the experiences of 10 university lecturers teaching English after the decision to transfer the face-to-face courses online due to the devastating earthquake in Hatay and Kahramanmaraş provinces in 2023, Türkiye. The data were collected through a narrative frame and semi-structured interviews and analysed using content analysis. Findings suggested that managing learners within the hybrid classroom increased the emotional labour of lecturers, as they had to navigate the complexities of engaging both online and in-person students simultaneously. Moreover, hybrid teaching demanded enhanced emotional regulation, increased multitasking, and continuous adjustments to teaching strategies preparing for either fully online or face-to-face classes to reduce the emotional labour. The findings may inform decision-makers to take into consideration the emotional labour of educators while making decisions on transitions among face-to-face, hybrid or online teaching

Markov decision process for mixed-model assembly line design under process time uncertainty

The industry is increasingly confronted with the challenge of process duration uncertainty in production systems. These variations are particularly problematic for manufacturers that utilize Multi-Manned Mixed-Model Assembly Lines, as they can cause significant disruptions that may stop the production line. Our study explores the benefit of walking workers to dynamically adjust the workforce in response to unexpected variations in process durations at different stations, a common scenario in the automotive industry. We model the dynamic workforce assignment decision as a Markov Decision Process (MDP), and this MDP accounts for uncertainties in process times, and it incorporates dynamic task assignment and workers' movements. This MDP is subsequently translated into a linear program that we integrate into a higher-level Mixed-Integer Linear Programming model responsible for dimensioning the workforce and selecting equipment in the station. This approach results in the creation of assembly lines designed to be resilient in the face of unexpected variations in task process durations. To deal with scalability issues, we employ the Benders decomposition algorithm. The paper also presents a validation with data from a car manufacturer that reinforces the practical applicability of our methodology. Additionally, we provide managerial insights on effectively managing process time uncertainty in automotive production systems, empowering decision-makers with optimization strategies, cost-reduction approaches, and resilience-building techniques to enhance the performance and reliability of Mixed-Model Assembly Lines.European Union (EU) European Commission Joint Research Centr

Modelling nonlinear site coefficients and predominant periods for southern coasts of İstanbul by geotechnical downhole arrays

This study investigates nonlinear response spectral amplification factors (RSAF) and predominant periods (T-p) for the southern coast of the Istanbul's European Side by employing a Monte Carlo simulation-based approach. The methodology incorporates one dimensional (1D) site response analyses of simulated random shear wave velocity (V-s) profiles, along with the optimization of these profiles through least-squares error between the response spectra of recorded weak ground motions and the modelled ones. The recorded ground motions were selected from a database of near-field earthquakes with epicentral distances R-epi < 100 km and magnitudes in the range 3.9 = 4.8, a scaling procedure for bedrock PGAs was applied to model the nonlinear RSAF and T-p. In this study, the local site coefficients specified in TEBC (2018) were tested for the southern coastal soils of the Istanbul's European side for the first time, an area broadly characterized by poor soil conditions (i.e., time averaged shear wave velocity for the top 30 m, 180 < V-s30 < 360 m/s). The findings indicate that the estimated RSAFs (i.e., local site coefficients) particularly at 1.0 s period (T = 1.0 s), exceed those defined in TEBC (2018). Furthermore, the T-p values at seismic station locations under weak amplitude ground motions differ substantially from those under strong ground motions. The in-situ methods (e.g., microtremor, etc.) for determining the site T-p may be misleading, as they primarily capture linear behavior. The proposed method provides a basis for revising the short-period (T = 0.2 s) and 1.0 s period RSAFs in TEBC (2018) and highlights T-p variations for the loose granular and clayey soils of Istanbul

Optimizing election logistics: A multi-period routing problem embedding time-dependent reward functions

With the 2024 US Presidential Election now concluded, the growing complexity of designing effective election campaigns has become clearer. Motivated by the logistical challenges associated with US election campaigns, we introduce the Reward-driven Multi-period Politician Routing Problem. It involves diverse politicians planning their campaigns over multiple days, considering constraints such as clustered locations, time-and location-dependent rewards, budget limits, mandatory rest days, and flexible daily routes that can be either open or closed, with starting and ending locations not known in advance. We model the problem as a mixed-integer linear program, complemented with several valid inequalities, and innovate by designing new subtour elimination techniques that jointly deal with open and closed paths. We developed 36 new benchmark instances tailored to the US presidential elections. To tackle large-sized instances, we develop a Sequential Route Construction Matheuristic that exploits the multi-period structure of the problem to provide efficient and effective solutions. We incorporate time-dependent reward profiles (concave, convex, linearly decreasing, linearly increasing, and periodic) into the objective function to capture diverse decision-making perspectives. Experimental results show interesting computational issues on the different tested models and the impact of the chosen reward profile on their performance.Publisher versio

Metaverse acceptance in younger and older cohorts: Testing technology acceptance model

The monthly users of Metaverse, a multi-user virtual reality platform, are over 400 million around the world (Nikolovska, 2023). However, research about individual differences in acceptance of the metaverse is still limited. In the present study, we examined individuals’ cognitive responses, attitudes toward using, and willingness to engage in metaverse based on the Technology Acceptance Model (Davis, 1987). Hence, we developed the Metaverse Acceptance Scale (MAS) and explored how young adults and older adults differ in subscales of attitude, intention to use, perceived usefulness, perceived ease of use, and eagerness to know more about metaverse. The participants (N = 721) filled out a demographic questionnaire and MAS online. MAS demonstrated a 4-factor structure with adequate validity and reliability: Attitude, behavioral intention, perceived usefulness, and perceived ease of use. Not only did the subscales and items show variations between younger and older individuals, but also the associations between components of MAS. The associations between perceived ease of use and attitude, perceived usefulness and intention to obtain further information, and attitude and intention to acquire further information showed variations in the two cohorts. The acceptance, engagement, and intention to adopt metaverse can vary based on age. Thus, different age groups may be active in different domains of the metaverse. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026

Optimizing collection processes using conservative Q-learning

This study proposes a reinforcement learning framework based on Conservative Q-Learning (CQL) to optimize debt collection strategies while mitigating customer churn. Traditional rule-based approaches often fail to adapt to individual customer profiles or evolving behaviors. To address this limitation, our framework dynamically recommends actions tailored to each customer's characteristics. Using customer datasets, we evaluate the performance of the proposed model across various reward coefficient settings (representing potential future profit loss in case of churn). The results show that while standard Q-Learning generally underperforms the rule-based strategy, CQL achieves overall performance comparable to rule-based approaches. Notably, product-level analysis shows statistically significant improvements for general-purpose installment loan (GPL) customers, while outcomes for credit card (CC) and overdraft (OD) customers are weaker. This likely reflects reinforcement learning's tendency to prioritize higher-value cases, suggesting that product-specific models may further enhance performance across loan types

SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks

Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or specificity required for early intervention. Prior studies rarely demonstrate clinical-grade performance on blood culture samples or in independent external cohorts. We address these gaps with a surface-enhanced Raman spectroscopy and deep learning workflow (SERS-DL) that performs sepsis instance recognition and causative pathogen identification directly from target-analyte enriched blood. We assembled a primary dataset of SERS spectra acquired from 653 analyte-enriched blood samples collected at a tertiary hospital in Qatar and an external blind cohort of 70 independent samples. After rigorous preprocessing and class-weighted augmentation of SERS spectra, we trained SuperRamanNet, a lightweight one-dimensional classifier based on super operational neural networks. In five-fold, sample-contained cross-validation, the system achieved 99.67 % accuracy for binary sepsis recognition and 98.84 % accuracy for six-class pathogen identification. On the external cohort, performance remained high at 98.28 % for pathogen typing, indicating robust generalizability. Comparative benchmarks and ablation studies confirmed consistent gains over convolutional and operational baselines and quantified the impact of augmentation and architectural choices. Residual confusions were concentrated between control and Escherichia coli and among certain Gram-negative classes, underscoring the need for improved raw class balance during blood sample collection. Overall, this rapid, culture-free, and portable SERS-DL pipeline delivers near clinical-grade accuracy for sepsis detection and pathogen identification directly from blood. The compact model and streamlined workflow support point-of-care translation, with potential to accelerate triage, guide early therapy, and reduce the global sepsis burden. © 2025 The Authors.Qatar Research, Development and Innovation Council (QRDI) ; Qatar National Research Fund (QNRF)Publisher versio