Search Results (26)

The globally used additive manufacturing technique called Fused Deposition Modeling plays a central role in advancing dematerialized logistics by enabling on-demand production and minimizing material waste. The integration of Artificial Intelligence (AI) into FDM processes has introduced promising avenues to improve efficiency, accuracy, and sustainability. Expressly, researchers have proved in what ways machine learning algorithms can upgrade printing parameters, initiating enhanced product quality and lower defects. In the context of dematerialized logistics, the PRISMA methodology mentioned in this review is set to maintain a structured analysis of the junction between AI and FDM. Exhaustive research of analyzed studies issued from 2009 to 2024 through databases like Scopus, Web of Science, and IEEE Xplore demonstrate an expanding reliance on AI techniques like neural networks and genetic algorithms. All these mentioned methods are used to approach challenges such as print quality inconsistencies, material overuse, and structural weaknesses. The outcome shows the prospect of AI to reshape FDM, but major obstacles remain present: many problems, such as the scalability of models and their integration into existing logistical frameworks, need further studies and research. As demonstrated, this review gives an inclusive perspective on the actual progress and highlights the main directions for what lies ahead to improve FDM processes in logistics. Full article

The increasing demand for smaller batch sizes and mass customisation in production poses considerable challenges to logistics and manufacturing efficiency. Conventional methodologies are unable to address the need for expeditious, cost-effective distribution of premium-quality products tailored to individual specifications. Additionally, the reliability and resilience of global logistics chains are increasingly under pressure. Additive manufacturing is regarded as a potentially viable solution to these problems, as it enables on-demand, on-site production, with reduced resource usage in production. Nevertheless, there are still significant challenges to be addressed, including the assurance of product quality and the optimisation of production processes with respect to time and resource efficiency. This article examines the potential of integrating digital twin methodologies to establish a fully digital and efficient process chain for on-site additive manufacturing. This study focuses on wire arc additive manufacturing (WAAM), a technology that has been successfully implemented in the on-site production of naval ship propellers and excavator parts. The proposed approach aims to enhance process planning efficiency, reduce material and energy consumption, and minimise the expertise required for operational deployment by leveraging digital twin methodologies. The present paper details the current state of research in this domain and outlines a vision for a fully virtualised process chain, highlighting the transformative potential of digital twin technologies in advancing on-site additive manufacturing. In this context, various aspects and components of a digital twin framework for wire arc additive manufacturing are examined regarding their necessity and applicability. The overarching objective of this paper is to conduct a preliminary investigation for the implementation and further development of a comprehensive DT framework for WAAM. Utilising a real-world sample, current already available process steps are validated and actual missing technical solutions are pointed out. Full article

Background: Adequate water, sanitation, and hygiene (WaSH) are critical to maintaining good health and hygiene. However, health is a function of many health determinants, and WASH services alone may not be sufficient to improve health outcomes. Objective: To identify whether the presence of WaSH services is associated with fewer children under five years of age experiencing symptoms of diarrhea in Katoma, Geita, Tanzania. Method: A cross-sectional study was conducted to collect health data, demographics, and other variables, such as WASH, food insecurity, education of the mother, vaccination data, and household income data, for 452 households with children under five. Surveys were completed in-person through interviews. Health outcome data included being sick with diarrhea or symptoms. Data analysis was performed using SAS OnDemand for Academics. Multivariate logistic regression and mixed-effects logistic regression models were employed to determine the association between the covariates and sickness of inclusion children and all the children involved in the study, respectively. Results: The findings suggest that WASH services alone do not have a significant impact on diarrhea, but other determinants of health, including the education of the mother, showed a significant impact on health outcomes among children with at least one WASH service. These demographic variables were also associated with lower food insecurity and poverty. The findings highlight the need to (1) include other covariates when analyzing WASH data to understand health outcomes; and (2) improve education attainment for women to maximize health benefits for their children. Full article

With the escalating environmental crisis, electric vehicles have emerged as a key solution for emission reductions in logistics due to their low-carbon attributes, prompting significant attention and extensive research on the electric vehicle routing problem (EVRP). However, existing studies often overlook charging infrastructure (CI) capacity constraints and fail to fully exploit the synergistic potential of heterogeneous energy replenishment infrastructures (HERIs). This paper addresses the EVRP with HERIs under various capacity constraints (EVRP-HERI-CC), proposing a mixed-integer programming (MIP) model and a hybrid ant colony optimization (HACO) algorithm integrated with a variable neighborhood search (VNS) mechanism. Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. Through small-scale instance experiments, we have verified the computational complexity of the problem and demonstrated HACO’s superior performance compared to the Gurobi solver. Furthermore, comparative studies with other advanced heuristic algorithms confirm HACO’s effectiveness in solving the EVRP-HERI-CC. Sensitivity analysis reveals that appropriate CI capacity configurations achieve economic efficiency while maximizing resource utilization, further validating the engineering value of HERI networks. Full article

Human space exploration presents an unparalleled opportunity to study life in extreme environments—but it also exposes astronauts to physiological stressors that jeopardize key systems like vision. Corneal health, essential for maintaining precise visual acuity, is threatened by microgravity-induced fluid shifts, cosmic radiation, and the confined nature of spacecraft living environments. These conditions elevate the risk of corneal abrasions, infections, and structural damage. In addition, Spaceflight-Associated Neuro-Ocular Syndrome (SANS)—while primarily affecting the posterior segment—has also been potentially linked to anterior segment alterations such as corneal edema and tear film instability. This review examines these ocular challenges and assesses current mitigation strategies. Traditional approaches, such as terrestrial eye banking and corneal transplantation, are impractical for spaceflight due to the limited viability of preserved tissues, surgical complexities, anesthetic risks, infection potential, and logistical constraints. The paper explores emerging technologies like 3D bioprinting and stem cell-based tissue engineering, which offer promising solutions by enabling the on-demand production of personalized corneal constructs. Complementary advancements, including adaptive protective eyewear, bioengineered tear substitutes, telemedicine, and AI-driven diagnostic tools, also show potential in autonomously managing ocular health during long-duration missions. By addressing the complex interplay of environmental stressors and biological vulnerabilities, these innovations not only safeguard astronaut vision and mission performance but also catalyze new pathways for regenerative medicine on Earth. The evolution of space-based ophthalmic care underscores the dual impact of space medicine investments across planetary exploration and terrestrial health systems. Full article

In recent years, the rapid growth of e-commerce and on-demand delivery services has placed a significant strain on urban logistics systems. Technological advances such as unmanned aerial vehicle (UAV)-based logistics systems have thus emerged as promising solutions in urban environments and are increasingly being piloted worldwide. However, the implementation of UAV logistics risks exacerbating social inequities, particularly in marginalized communities that may disproportionately bear the noise and safety risks. To mitigate these risks, it is crucial to integrate social equity considerations into urban UAV logistics. This study explores social equity factors through a systematic literature review and social media analysis of Xiaohongshu (the Little Red Book), a popular Chinese social media platform known for its extensive user base and active discussions on social issues. This literature review involves a full-text examination, while latent Dirichlet allocation (LDA) topic modeling is used to analyze social media comment datasets. Each method identifies social equity factors and separately assesses their relative importance, resulting in the final identification of 24 key factors that provide a holistic view of public sentiment and academic discourse. The findings reveal a divide between academic concerns around systemic risks and a public focus on immediate needs. By synthesizing these insights, this study provides a social equity landscape for urban UAV logistics and actionable references for policymakers and stakeholders. Full article

Offshore logistics has a substantial economic impact in the regions where offshore activities are prevalent, and has a huge opportunity to utilize the shared and collaborative logistics approach. The collaborative and shared logistics approach usually has economic, social, and environmental impacts on several stakeholders within the entire business model. Therefore, the purpose of this paper is to explore and compare the benefits and implications of both separate and shared logistics approaches, from multi-stakeholder perspectives. A case asset is purposefully selected where two offshore installations are located near each other, and have the potential to collaborate and share logistics resources. Three scenarios are studied using a simulation modelling approach: (1) separate logistics vessels, (2) on-demand shared logistics vessels, and (3) scheduled shared logistics vessels. The simulated results show that the shared logistics concept, in this specific case, led to an enhancement in the delivery frequency, number of deliveries, and CO₂ emissions. In addition, it provides options either to enhance vessel utilization or create revenue-generating time intervals. The scheduled shared logistics scenario is more sustainable and has a higher probability of being accepted by stakeholders, as it is driven by a revenue-generating mindset. Full article

Background: The on-demand warehousing (ODW) model is emerging as a platform-enabled logistics solution based on resource sharing for businesses needing storage space. It is based on a business-to-business marketplace where companies can provide (or request) storage services with an elevated level of flexibility. The literature focusing on this topic is still scarce, and while the potential advantages of such a model seem quite clear, challenges and criticalities need to be further explored. Methods: Starting from a state-of-the-art analysis of ODW, a two-step methodology was adopted: first, a SWOT analysis was conducted to help summarize the challenges related to this emerging model. Then, an exploratory analysis of multiple case studies was employed to provide a first discussion on the advantages and criticalities of this model, highlighting its latest evolution. Results: The ODW model is still evolving, as several former pure ODW platforms have been changing their business model to become on-demand 4PLs (defined as “mixed ODW-4PLs”), adapting their core activities to manage the criticalities of on-demand services. Conclusions: This study represents the first attempt to investigate benefits and criticalities of ODW models, outlining the latest trend of ODW and identifying two distinct types of ODW model currently present on the market. Full article

The sharing economy is beneficial for sustainable development. It effectively utilizes underused resources and reduces unnecessary production, consumption, and waste through resource sharing. This study investigates the factors that can increase consumers’ perceived sustainability of a sharing economy service and examines the impact of perceived sustainability on their intentions to continue using the service. Furthermore, the study considers the moderating effect of perceived green transparency. Internet surveys are conducted to collect responses from users of a transportation service (Uber) and an on-demand logistics service (Uber Eats). This study provides suggestions for service providers in the sharing economy to develop sustainability strategies. Full article

Reasonable matching of capacity resources and transported cargoes is the key to realizing intelligent scheduling of less-than-truck-load (LTL) logistics. In practice, there are many types and numbers of participating objects involved in LTL logistics, such as customers, orders, trucks, unitized implements, etc. This results in a complex and large number of matching schemes where truck assignments interact with customer order service sequencing. For the truck–cargo online matching problem under real-time demand, it is necessary to comprehensively consider the online matching process of multi-node orders and the scheduling of multi-types of trucks. Combined with the actual operation scenario, a mixed-integer nonlinear programming model is introduced, and an online matching algorithm with a double-layer nested time window is designed to solve it. By solving the model in a small numerical case using Gurobi and the online matching algorithm, the validity of the model and the effectiveness of the algorithm are verified. The results indicate that the online matching algorithm can obtain optimization results with a lower gap while outperforming in terms of computation time. Relying on the realistic large-scale case for empirical analysis, the optimization results in a significant reduction in the number of trips for smaller types of trucks, and the average truck loading efficiency has reached close to 95%. The experimental results demonstrate the general applicability and effectiveness of the algorithm. Thus, it helps to realize the on-demand allocation of capacity resources and the timely response of transportation scheduling of LTL logistics hubs. Full article

In recent years, global warming and environmental problems have become more serious due to greenhouse gas (GHG) emissions. Harvesting solar energy for production and logistic activities in supply chains, including factories and distribution centers, has been promoted as an effective means to reduce GHG emissions. However, it is difficult to balance the supply and demand of solar energy, owing to its intermittent nature, i.e., the output depends on the daylight and season. Moreover, the use of large-capacity solar power generation systems and batteries incurs higher installation costs. In order to maintain low costs, demand-to-supply management of solar energy, based on appropriate seasonal analysis of power generation and consumption and the capacity planning for power generation and the storage battery, is necessary. In this study, the on-demand cumulative control method is applied to actual power consumption data and solar power generation data estimated at a distribution center. Moreover, the monthly, seasonal, and temporal characteristics of power generation and consumption at the distribution center are analyzed. Additionally, the total amount of power purchased is investigated for solar energy demand-to-supply management. Full article

Urban logistics encompass transportation and delivery operations within densely populated urban areas. It faces significant challenges from the evolving dynamic and stochastic nature of on-demand and conventional logistics services. Further challenges arise with application doctrines shifting towards crowd-sourced platforms. As a result, “traditional” deterministic approaches do not adequately fulfil constantly evolving customer expectations. To maintain competitiveness, logistic service providers must adopt proactive and anticipatory systems that dynamically model and evaluate probable (future) events, i.e., stochastic information. These events manifest in problem characteristics such as customer requests, demands, travel times, parking availability, etc. The Stochastic Dynamic Vehicle Routing Problem (SDVRP) addresses the dynamic and stochastic information inherent in urban logistics. This paper aims to analyse the key concepts, challenges, and recent advancements and opportunities in the evolving urban logistics landscape and assess the evolution from classical VRPs, via DVRPs, to state-of-art SDVRPs. Further, coupled with non-reactive techniques, this paper provides an in-depth overview of cutting-edge model-based and model-free reactive solution approaches. Although potent, these approaches become restrictive due to the “curse of dimensionality”. Sacrificing granularity for scalability, researchers have opted for aggregation and decomposition techniques to overcome this problem and recent approaches explore solutions using deep learning. In the scope of this research, we observed that addressing real-world SDVRPs with a comprehensive resolution encounters a set of challenges, emphasising a substantial gap in the research field that warrants further exploration. Full article

In recent years, space agencies, such as the National Aeronautics and Space Administration (NASA) and European Space Agency (ESA), have expanded their research activities in the field of manufacturing in space. These measures serve to reduce limitations and costs through fairing size, launch mass capabilities or logistic missions. The objective, in turn, is to develop technologies and processes that enable on-demand manufacturing for long-term space missions and on other celestial bodies. Within these research activities, in-situ resource utilization (ISRU) and recycling are major topics to exploit local resources and save transport capacity and, therefore, costs. On the other hand, it is important to carefully consider which items can be brought and which must be manufactured on the Moon. Consequently, on-demand needs in future space missions are considered regarding frequency, raw material and required manufacturing processes according to investigations by ESA and NASA. In conclusion, manufacturing in space state-of-the-art shows a strong focus on additive processes, primarily considering semicrystalline or amorphous plastics. The subtractive processing of metallic or ceramic materials, in turn, currently represents a research gap. Consequently, an approach for in-situ resource utilization-based subtractive manufacturing in space is presented to supplement the existing processes. The latter uses a high-pressure jet of water, with regolith simulate as abrasive in suspension, being directed at the workpiece, which is moved to separate metal and glass. Proof-of-concept results are presented, including suitable process windows, achieved cutting geometries, as well as the effects of parameter variations on the system technology and consumables used. The focus of the investigations supplements the general requirements for the design of machine tools for space applications with inertial process-specific boundary conditions as a step towards higher technology maturity. Full article

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics. Full article

All mortality risk factors are higher in non-breastfed infants compared to infants under five months of age who receive Exclusive Breastfeeding (EBF). Examining the predicting role of maternal ideation in EBF practices can help to direct and strengthen the cooperation between multidisciplinary healthcare providers to formulate multidisciplinary breastfeeding enhancement strategies. Methods: This correlational cross-sectional study investigates the relationship between maternal ideation and EBF practice among Saudi nursing mothers at Maternal and Children’s Hospital (MCH) in Najran, Saudi Arabia. The study incorporated 403 Saudi nursing mothers aged 6–12 months with healthy infants. The data collected using a questionnaire comprises demographic characteristics and obstetric history, the EBF Practice scale, and a maternal ideation scale. The data was collected from the beginning of November 2022 to the end of January 2023 and analyzed using I.B.M. version 22. Results: Breastfeeding initiation within one hour occurred among 85.1% of women, while 39.2% fed their newborn only colostrum during the first three days. EBF until six months was practiced by 40.9% of the participants day and night and on-demand (38.7%). Furthermore, 60.8% of the study participants had satisfactory overall EBF practices. The cognitive part of maternal ideation shows that 68.2% of the participants had adequate knowledge and 63.5% had positive beliefs regarding EBF practice. The maternal psychological ideation dimensions show that 81.4% had high EBF self-efficacy. The maternal social ideation dimensions showed that high injunctive and descriptive norms were present among 40.9% and 37.5%, respectively. In addition, healthcare providers (39.2%) had the most significant social influence, followed by husbands (30.5%). Binary logistic regression shows that the mother’s age, occupation, and education are the significant demographic predictors of satisfactory EBF practices (p < 0.05). All maternal ideation constructs were positive predictors of satisfactory EBF practices (p < 0.05). Conclusion: Maternal ideation constructs are positive predictors of satisfactory EBF practice and can be used to predict high-risk groups and plan for further intervention. Full article