Phase-encoded fMRI tracks down brainstorms of natural language processing with subsecond precision.

Natural language processing unfolds information overtime as spatially separated, multimodal, and interconnected neural processes. Existing noninvasive subtraction-based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed rapid phase-encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging data, as well as overcoming scanner noise and head-motion challenges during overt language tasks. We captured real-time information flows as coherent hemodynamic waves traveling over the cortical surface during listening, reading aloud, reciting, and oral cross-language interpreting tasks. We were able to observe the timing, location, direction, and surge of traveling waves in all language tasks, which were visualized as "brainstorms" on brain "weather" maps. The paths of hemodynamic traveling waves provide direct evidence for dual-stream models of the visual and auditory systems as well as logistics models for crossmodal and cross-language processing. Specifically, we have tracked down the step-by-step processing of written or spoken sentences first being received and processed by the visual or auditory streams, carried across language and domain-general cognitive regions, and finally delivered as overt speeches monitored through the auditory cortex, which gives a complete picture of information flows across the brain during natural language functioning. PRACTITIONER POINTS: Phase-encoded fMRI enables simultaneous imaging of high spatial and temporal resolution, capturing continuous spatiotemporal dynamics of the entire brain during real-time overt natural language tasks. Spatiotemporal traveling wave patterns provide direct evidence for constructing comprehensive and explicit models of human information processing. This study unlocks the potential of applying rapid phase-encoded fMRI to indirectly track the underlying neural information flows of sequential sensory, motor, and high-order cognitive processes.

Biological treatment evaluation in thermoradiotherapy: application in cervical cancer patients.

BACKGROUND: Hyperthermia treatment quality is usually evaluated by thermal (dose) parameters, though hyperthermic radiosensitization effects are also influenced by the time interval between the two modalities. This work applies biological modelling for clinical treatment evaluation of cervical cancer patients treated with radiotherapy plus hyperthermia by calculating the equivalent radiation dose (EQDRT, i.e., the dose needed for the same effect with radiation alone). Subsequent analyses evaluate the impact of logistics. METHODS: Biological treatment evaluation was performed for 58 patients treated with 23-28 fractions of 1.8-2 Gy plus 4-5 weekly hyperthermia sessions. Measured temperatures (T50) and recorded time intervals between the radiotherapy and hyperthermia sessions were used to calculate the EQDRT using an extended linear quadratic (LQ) model with hyperthermic LQ parameters based on extensive experimental data. Next, the impact of a 30-min time interval (optimized logistics) as well as a 4­h time interval (suboptimal logistics) was evaluated. RESULTS: Median average measured T50 and recorded time intervals were 41.2 °C (range 39.7-42.5 °C) and 79 min (range 34-125 min), respectively, resulting in a median total dose enhancement (D50) of 5.5 Gy (interquartile range [IQR] 4.0-6.6 Gy). For 30-min time intervals, the enhancement would increase by ~30% to 7.1 Gy (IQR 5.5-8.1 Gy; p < 0.001). In case of 4­h time intervals, an ~ 40% decrease in dose enhancement could be expected: 3.2 Gy (IQR 2.3-3.8 Gy; p < 0.001). Normal tissue enhancement was negligible (< 0.3 Gy), even for short time intervals. CONCLUSION: Biological treatment evaluation is a useful addition to standard thermal (dose) evaluation of hyperthermia treatments. Optimizing logistics to shorten time intervals seems worthwhile to improve treatment efficacy.

A critical time window for leukapheresis product transportation to manufacture clinical-grade dendritic cells with optimal anti-tumor activities.

BACKGROUND AIMS: Dendritic cell (DC)-based immunotherapy is a promising approach to treat cancer. However, key aspects governing the reproducible manufacturing of high-quality DC remain incompletely defined. Here, we show that the time window between leukapheresis and DC manufacturing is critical. METHODS: Transcriptomic profiling by RNA-seq was used to unbiasedly characterize cellular states during each step of DC manufacturing process, and functional assays were used to determine the anti-tumor activities of DC. RESULTS: During preclinical development of a DC-based cytotherapy platform, CUD-002 (NCT05270720), we found that DC quality varied among different batches, even though commonly used DC maturation markers CD80, CD83 and CD86 were indistinguishable. Multivariate analysis indicated that DC quality was negatively associated with the shipping time from the leukapheresis site to the manufacturing center. To investigate the potential effect of shipping time, we stored leukapheresis materials from three donors for 0, 1, 2 or 3 days before DC manufacturing. For each step, we carried out RNA-seq analysis to unbiasedly characterize cellular states. Integrated bioinformatic analyses indicated that longer storage time reduced the expression of several transcription factors to attenuate interferon pathways. CONCLUSIONS: Consistently, we found that 3-day storage of leukapheresis materials significantly lowered the efficiency to generate DC but also impaired DC responses to inflammatory signals, resulting in inferior antigen-presentation and cytotoxic T-cell activities. Thus, we recommend using leukapheresis materials within 48 h to manufacture therapeutic DCs.

How Primary Care Clinicians Process Patient Death: Logistics, Emotions, and Opportunities for Structural Support.

BACKGROUND: Navigating the logistics and emotional processing of a patient's death is an inevitable part of many physicians' roles. While research has primarily examined how inpatient clinicians cope with patient loss, little work has explored how primary care clinicians (PCCs) handle patient death in the outpatient setting, and what support resources could help PCCs process loss. OBJECTIVE: To explore PCCs' experiences with the logistics and emotional processing of patient deaths and suggestions for supportive resources. DESIGN: Qualitative study using semi-structured interviews conducted between March and May 2023. PARTICIPANTS: Recruitment emails were sent to 136 PCCs (physicians and nurse practitioners) at three San Francisco academic primary care clinics. Twelve clinicians participated in the study. APPROACH: This study used a template analysis approach. Interview transcripts were analyzed in an iterative fashion to identify themes for how PCCs navigate patient death. RESULTS: Participants (n=12) described outpatient death notification as inconsistent, delayed, and rife with uncertainty regarding subsequent actions. They felt various emotions, notably sadness and guilt, especially with deaths of young, vulnerable patients or those from preventable illnesses. Participants identified strategies for emotional processing and recommended improvements including clear procedural guidance, peer debriefings, and formal acknowledgements of deceased patients. CONCLUSIONS: Interviewing PCCs about their experiences following a patient death revealed key themes in logistical and emotional processing, and clinic resource recommendations to better support PCCs. Given the distinct characteristics of primary care-such as enduring patient relationships, greater isolation in ambulatory settings compared to inpatient environments, and rising burnout rates-enhancing guidance and support for PCCs is crucial to mitigate administrative burdens and grief after patient loss.

Healthcare center-based cell therapy laboratories supporting off-site manufactured cell therapies: The experiences of a single academic cell therapy laboratory.

BACKGROUND: Healthcare center-based cell therapy laboratories (HC CTLs) evolved from solely processing hematopoietic stem cells for transplantation to manufacturing various advanced cellular therapies. With increasing interest in cellular therapy applications, off-site manufactured products are becoming more common. HC CTLs play a critical role in supporting these products by shipping out cellular starting material (CSM) for further manufacturing and/or receiving, storing, and distributing final products. The experiences and challenges encountered by a single academic HC CTL in supporting these products are presented. METHODS: All off-site manufacturing protocols supported before 2023 were reviewed. Collected data included protocol characteristics (treatment indication, product type), process logistics (shipping, receiving, storage, thawing, distribution, documentation), and product handling volumes (CSM shipping and final product infusions). RESULTS: Between 2012 and 2022, 15 off-site manufactured cellular therapy early-phase, single- and multicenter clinical trials were supported. Trials were sponsored by academic/research and commercial entities. The number of protocols supported annually increased each year, with few ending. Products included cancer immunotherapies and gene therapies. Autologous CSM was collected and shipped, while autologous and allogeneic final products were received, stored, thawed, and distributed. Process differences among protocols included CSM shipping conditions, laboratory analyses, final product thaw conditions and procedures, number of treatments, and documentation. DISCUSSION: HC CTLs must contend with several challenges in supporting off-site manufacturing protocols. As demand for cellular therapies increases, stakeholders should collaborate from the early phases of clinical trials to streamline processes and standardize procedures to increase value, improve safety, and reduce the burden on HC CTLs.

Real-time management of intra-hospital patient transport requests.

This paper addresses the management of patients' transportation requests within a hospital, a very challenging problem where requests must be scheduled among the available porters so that patients arrive at their destination timely and the resources invested in patient transport are kept as low as possible. Transportation requests arrive during the day in an unpredictable manner, so they need to be scheduled in real-time. To ensure that the requests are scheduled in the best possible manner, one should also reconsider the decisions made on pending requests that have not yet been completed, a process that will be referred to as rescheduling. This paper proposes several policies to trigger and execute the rescheduling of pending requests and three approaches (a mathematical formulation, a constructive heuristic, and a local search heuristic) to solve each rescheduling problem. A simulation tool is proposed to assess the performance of the rescheduling strategies and the proposed scheduling methods to tackle instances inspired by a real mid-size hospital. Compared to a heuristic that mimics the way requests are currently handled in our partner hospital, the best combination of scheduling method and rescheduling strategy produces an average 5.7 minutes reduction in response time and a 13% reduction in the percentage of late requests. Furthermore, since the total distance walked by porters is substantially reduced, our experiments demonstrate that it is possible to reduce the number of porters - and therefore the operating costs - without reducing the current level of service.

Integrated procurement and reprocessing planning for reusable medical devices with a limited shelf life.

We present a new model formulation for a multiproduct dynamic order quantity problem with product returns and a reprocessing option. The optimization considers the limited shelf life of sterile medical devices as well as the capacity constraints of reprocessing and sterilization resources. The time-varying demand is known in advance and must be satisfied by purchasing new medical devices or by reprocessing used and expired devices. The objective is to determine a feasible procurement and reprocessing plan that minimizes the incurred costs. The problem is solved in a heuristic manner in two steps. First, we use a Dantzig-Wolfe reformulation of the underlying problem, and a column generation approach is applied to tighten the lower bound. In the next step, the obtained lower bound is transformed into a feasible solution using CPLEX. Our numerical results illustrate the high solution quality of this approach. The comparison with a simulation based on the first-come-first-served principle shows the advantage of integrated planning.

Factors associated with a history of critical wandering among Medic-Alert subscribers.

BACKGROUND: Critical wandering occurs when an individual living with dementia leaves a location and is unaware of place or time. Critical wandering incidents are expected to increase with the growing prevalence of persons living with dementia worldwide. We investigated the association between demographic, psychopathological, and environmental factors and a history of critical wandering among Medic-Alert subscribers, both with and without dementia. METHODS: Our retrospective study included data of 25,785 Canadian Medic-Alert subscribers who were aged 40 years or older. We used multivariable logistic regression analysis to examine the associations between a history of critical wandering and dementia status as psychopathological independent variable, controlled by demographic (age, ethnic background, sex at birth, Canadian languages spoken) and environmental (living arrangement, population density) factors. RESULTS: The overall study sample comprised of mainly older adults (77.4%). Medic-Alert subscribers who were older, male sex at birth, living with dementia, of a minority ethnic group and who did not have proficiency in an official Canadian language had a higher likelihood of a history of critical wandering. Residing in an urban environment, in an institution or with a family member, were environmental factors associated with a higher likelihood of a history of critical wandering. CONCLUSIONS: People living with dementia experience a higher likelihood of a history of critical wandering compared to those without dementia. Medic-Alert and similar organizations can develop algorithms based on the associated factors that can be used to flag risks of critical wandering. This can inform preventative strategies at the individual and community levels.

The association of sedentary time with sleep disturbances among the US population, 2005 to 2014.

BACKGROUND: Sleep problems increase the risk of premature illness and death. We evaluated the association between sedentary time and sleep disturbances. METHODS: A cross-sectional analysis of the US nationally representative data of 21,414 adults (aged > = 18 years) from National Health and Nutrition Examination Survey (NHANES) (2005-2014) was performed. The data of sleep disturbances were assessed using NHANES questionnaire results, which included the question, "{Have you/has sp} ever been told by a doctor or other health professional that {you have/s/he has} a sleep disorder?". All participants were stratified by quartiles of sedentary behavior distribution, which was the explanatory variable (sedentary time quartile cut points: Q1, 0 < = Q1 < 3 h; Q2, 3 < = Q2 < 5 h; Q3, 5 < = Q3 < 8 h; Q4, 8 < = Q4 < 20 h). We used multivariable logistic regression and the restricted cubic splines (RCS) model to assess the relationship between sedentary time and sleep disturbances. RESULTS: In the unadjusted multivariable logistic regression model (crude model), there was a demonstrated tendency for the odds of sleep disturbances to increase with the sedentary time (Q1 as reference, Q2: OR, 1.31 [95% CI 1.09-1.58] P = 0.005; Q3: OR, 1.62 [95% CI 1.39-1.88] P < 0.001; Q4: OR, 1.75 [95% CI 1.48-2.06] P < 0.001; P for trend < 0.001). In the adjusted model 4, adjustment for gender, age, marital type, education type, race, family poverty index ratio, waist circumference, recreational type, smoke status, drink status, diabetes mellitus status, cardiovascular disease status, sleep duration type, body mass index, the OR in Q2 subgroup didn't significantly increase (Q1 as reference. Q2: OR, 1.18 [95% CI 0.96-1.44] P = 0.1). However, the ORs in Q3 and Q4 (Q3: OR, 1.35 [95% CI 1.14-1.59] P < 0.001; Q4: OR, 1.45 [95% CI 1.21-1.75] P < 0.001) both revealed that the risk of sleep disturbances increased with increasing sedentary time, P for trend < 0.001. The unadjusted RCS model revealed that the risk of sleep disturbances increased non-linearly with increasing sedentary time for total participants (P for non-linearity < 0.001). After adjusting for all covariates, the RCS results revealed that the risk of sleep disturbances increased non-linearly with increasing sedentary time for total participants (P for non-linearity = 0.012). CONCLUSIONS: This study suggested that the longer sedentary time was strongly associated with the sleep disturbances. The protective effect of recreational activities on sleep disturbance, has not been significantly demonstrated.

The 2024 Noto Peninsula Earthquake and the Strategy of Medical Assistance from the Tohoku University Hospital.

A moment magnitude (Mw) 7.5 earthquake (the Global IDentifire (GLIDE) number: # Q-2024-000001-JPN) struck the Noto Peninsula of Ishikawa Prefecture on 1 January 2024 at 16:10 (Japan Standard Time). The reversed fault, 150 km in length and subducting beneath the peninsula, resulted in maximum seismic intensity 7 shaking, triggered the tsunami, destroyed over 43 thousand buildings, and disrupted roads and lifelines. The disaster claimed 236 deaths, including 15 indirect disaster deaths as of Jan. 28, 2024. There were Disaster Base Hospitals (DBHs) in the region, which survived structurally but suffered from impaired functions and the surge of medical needs of affected people. The disaster medical system of Japan immediately responded and coordinated the hundreds of emergency medical teams (EMTs), i.e., the Japan Disaster Medical Assistance Team (DMAT), from all over the country. Tohoku University Hospital, which had the experience of the 2011 Great East Japan Earthquake (GEJE), joined the coordinated response, dispatching a chain of DMATs, which helped the medical and public health coordination in Wajima City. The medical and public health needs included injuries, non-communicable diseases, infectious diseases, mental health issues, and maternal and child health issues, which were similar in the affected communities in GEJE. Although the actual damage far exceeded expectations, the structural retrofitting and business continuity plans of DBHs and the coordinated response of the national disaster medical system enhanced the effectiveness of medical and public health response.

Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study.

BACKGROUND: The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth. OBJECTIVE: This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic. METHODS: Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases. RESULTS: After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11%). The overall SAR of SARS-CoV-2 was 23.04% (95% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4% (95% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3% was observed in April 2020 (95% CI 31.6-45), while the lowest SAR of 15.6% was observed in September 2020 (95% CI 13.9-17.3). It consistently decreased from 32% to 21.1% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95% CI 2.58-2.74). CONCLUSIONS: Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants.

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

A Multi-Sensing IoT System for MiC Module Monitoring during Logistics and Operation Phases.

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module's structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module's structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module's structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module's structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations.

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes' priority for task allocation and scheduling. The AHP decides the fog nodes' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

Digital Twin Approach for Operation and Maintenance of Transportation System-Systematic Review.

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain company operational capacity at the required level. A comprehensive review of the existing literature is conducted to address this, offering an up-to-date analysis of relevant content in this field. The methodology employed is a systematic literature review using the Primo multi-search tool, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The selection criteria focused on English studies published between 2012 and 2024, resulting in 201 highly relevant papers. These papers were categorized into seven groups: (a) air transportation, (b) railway transportation, (c) land transportation (road), (d) in-house logistics, (e) water and intermodal transportation, (f) supply chain operation, and (g) other applications. A notable strength of this study is its use of diverse scientific databases facilitated by the multi-search tool. Additionally, a bibliometric analysis was performed, revealing the evolution of DT applications over the past decade and identifying key areas such as predictive maintenance, condition monitoring, and decision-making processes. This study highlights the varied levels of adoption across different transport sectors and underscores promising areas for future development, particularly in underrepresented domains like supply chains and water transport. Additionally, this paper identifies significant research gaps, including integration challenges, real-time data processing, and standardization needs. Future research directions are proposed, focusing on enhancing predictive diagnostics, automating maintenance processes, and optimizing inventory management. This study also outlines a framework for DT in transportation systems, detailing key components and functionalities essential for effective maintenance management. The findings provide a roadmap for future innovations and improvements in DT applications within the transportation industry. This study ends with conclusions and future research directions.

Towards a circular economy: Development of a support tool for designing reverse logistics systems.

Reverse Logistics (RL) of end-of-use/end-of-life products is a key approach for supporting the transition to a circular economy. However, lack of knowledge and experience in designing RL is one of the barriers for companies in implementing successful RL. This research proposes an RL support tool (RLST) for designing RL systems, developed through iterative cycles of theoretical development and empirical testing/feedback from potential users. The RLST builds upon the principles of configuration systems to adapt the various aspects of RL design into a knowledge base and, subsequently, into an Excel-based support tool - in addition to allowing companies to assess their motivation/driver and set the context (e.g., product characteristics, the existence and nature of Extended Producer Responsibility (EPR) legislation), it supports the design of the RL network/channel and other aspects such as stakeholder collaboration, legislation, consumer behaviour and incentives, use of digital technologies, key performance indicators and factors around governance/programme management. Such a tool can be helpful for practitioners in addressing the knowledge gaps, stimulating discussions among stakeholders for scenario building and for analysing how different scenarios might work. The research advances the knowledge on RL systems design for the circular economy along with, for the first time, building knowledge and application of configuration systems in the field of RL.

Spatial impact of the digital economy on low-carbon logistics efficiency in RCEP countries.

A thorough study of the spatial impact of the digital economy on low-carbon logistics efficiency would be greatly significant for Regional Comprehensive Economic Partnership (RCEP) countries to improve low-carbon logistics efficiency and achieve sustainable cooperation. This study constructs a theoretical framework from the perspective of spatial effects on the impact of the digital economy on low-carbon logistics efficiency in RCEP countries. The entropy method was used to measure the level of digital economic development. The super-efficiency SBM model was used to measure low-carbon logistics efficiency. Spatial feature analysis was conducted using kernel density estimation and Moran's index, followed by empirical analysis using spatial econometric models to examine the spatial impact of the digital economy on low-carbon logistics efficiency in RCEP countries. The results indicate that in RCEP countries, both low-carbon logistics efficiency and the level of digital economic development exhibit significant spatial positive correlation. Furthermore, the digital economy can promote low-carbon logistics efficiency in economically neighboring countries through spatial spillover effects. The improvement of domestic low-carbon logistics efficiency can also promote low-carbon logistics efficiency in neighboring countries. This conclusion was supported by endogeneity tests and a convergence analysis. Additionally, the mechanism analysis revealed that improving the level of green energy can enhance the spatial spillover effects of the digital economy and promote low-carbon logistics efficiency. Finally, countermeasures and suggestions was proposed to improve the low-carbon logistics efficiency of RCEP countries through the digital economy.

Designing a sustainable reverse logistics network for used cell phones based on offline and online trading systems.

Unsustainable production and consumption are driving a significant increase in global electronic waste, posing substantial environmental and human health risks. Even in more developed nations, there is the challenge of low collection rates. In response, we integrate offline and online trading systems and design a material efficiency strategy for used cell phones. We propose a new multi-objective optimization framework to maximize profit, carbon emissions reduction, and circularity in the process of recycling and treatment. Considering multi-period, multi-product, multi-echelon features, as well as price sensitive demand, incentives, and qualities, we established a new multi-objective mixed-integer nonlinear programming optimization model. An enhanced, Fast, Non-Dominated Solution Sorting Genetic Algorithm (ASDNSGA-II) is developed for the solution. We used operational data from a leading Chinese Internet platform to validate the proposed optimization framework. The results demonstrate that the reverse logistics network designed achieves a win-win situation regarding profit and carbon emission reduction. This significantly boosts confidence and motivation for engaging in recycling efforts. Online recycling shows robust profitability and carbon reduction capabilities. An effective coordination mechanism for pricing in both online and offline channels should be established, retaining offline methods while gradually transitioning towards online methods. To increase the collection rate, it is essential to jointly implement a transitional strategy, including recycling incentives and subsidy policies. Additionally, elevating customer environmental awareness should be viewed as a long-term strategy, mitigating the cost of increasing collection rates during the market maturity stage (high collection rates).

Analysis on the spatial correlation network and driving factors of carbon emissions in China's logistics industry.

Promoting the green development of the logistics industry has become a focal point of attention in China. This study combines an improved gravity model and social network analysis method, focusing on the nineteen provinces of the Yangtze Economic Belt and Yellow River Basin. It constructs a spatial correlation network of carbon emissions in the logistics industry from 2010 to 2021, exploring its formation mechanism and spatial evolution characteristics. The study utilizes a quadratic assignment procedure model to investigate internal driving factors. Building upon this, the study employs an improved STIRPAT model to predict the emission reduction path of the future logistics industry. Results are as follows: (1) Carbon consumption is most pronounced in Shandong, Jiangsu, and Shanghai. The carbon emission shows the characteristics of larger downstream; (2) The bridges of carbon emission-related networks gradually shift from Shandong to Shanxi, Anhui, and Sichuan. Carbon emissions in each sector and spatial spillover effects exhibit dynamic correlations and interactive influences; (3) Energy intensity, freight volume, and the spatial correlation network of the logistics industry are highly correlated; (4) The overall carbon emissions from the logistics industry show a decreasing trend in the future. Anhui and Shaanxi provinces will have high carbon emissions in 2035. The conclusions aim to provide policy suggestions for the region's low-carbon transformation.

Human performance in the rail freight yard.

Human performance in the rail freight yard has been identified as a source of risk for rail freight operations. This is both within the yard itself, and also with train preparation issues leading to incidents on the network. The rail freight yard is an area that has received limited research attention. Over 30 hours of observations were conducted at five major freight yards in Great Britain, along with 30 interviews of rail freight ground staff. Task models, human performance factors and potential solutions that were further explored in a workshop with freight personnel. This analysis led to an understanding of freight yard activities, the impact of freight yard design and environment, and the role external pressures on freight yard performance including upstream planning. The implications are discussed for both current freight operations, and for future technology and process change within the rail freight sector.

Human performance in the rail freight yard is critical to safety and performance, but receives little research attention. A structured study included observations in the yard, interviews with ground staff, and a validation workshop. Results include task models, influencing factors, potential solutions and implications for future technology and process change.