Search Results (28,711)

Stormwater resource utilization is an important function of coupled grey–green infrastructure (CGGI) that has received little research focus, especially in multi-objective optimization studies. Given the complex water problems in areas with contamination-induced water shortages, it is important to incorporate more objectives into optimization systems. Therefore, this study integrated economic performance, hydrological recovery, water quality protection, and stormwater resource utilization into an optimization framework based on the non-dominant sorting genetic algorithm III (NSGA-Ⅲ). A sponge city pilot area with contamination-induced water shortages in the Yangtze River Delta was considered, optimizing four objectives under different future multi-dimensional scenarios. The results showed a time series and scenarios composed of shared socioeconomic pathways and representative concentration pathways (SSP-RCP scenarios) which, together, affected future climate change and the benefits of a CGGI. In the near and middle periods, the SSP126 scenario had the greatest influence on stormwater management, whereas, in the far period, the SSP585 scenario had the greatest influence. The far period had the greatest influence under three SSP-RCP scenarios. Under the combined influence of SSP-RCP scenarios and a time series, the SSP585-F scenario had the greatest impact. Specific costs could be used to achieve different and no stormwater-resource utilization effects through different configurations of the CGGI. This provided various construction ideas regarding CGGIs for areas with contamination-induced water shortages. Full article

We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air—free drop with no microfluidics—and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells—around 120, compared with the 10⁷ to 10⁸ needed per kilogram of body weight—which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here. Full article

Teaming draught animals with modern forest machines may offer an innovative low-impact solution to biomass harvesting in protected areas. Machine traffic only occurs on pre-designated access corridors set 50 m apart, while trees are cut with chainsaws and dragged to the corridor’s edge by draught horses. The operation presented in this study included one chainsaw operator, two draught horses with their driver, an excavator-based processor with its driver and a helper equipped with a chainsaw for knocking off forks and large branches, and a light forwarder (7 t) with his driver. Researchers assessed work productivity and harvesting cost through a time study repeated on 20 sample plots. Descriptive statistics were used to estimate productivity and cost benchmark figures, which were matched against the existing references for the traditional alternatives. The new system achieved a productivity in excess of 4 m³ over bark per scheduled hour (including delays). Harvesting cost averaged EUR 53 m⁻³, which was between 15% and 30% cheaper than the traditional alternatives. What is more, the new system increased labor and horse productivity by a factor of 2 and 7, respectively, which can effectively counteract the increasingly severe shortage of men and animals. Full article

This study aims to explore allocation strategies for idle emergency supplies in a “demander–platform–supplier” supply chain system along with government regulation during the post-disaster recovery period. Allocation of emergency supplies is a complex task that encompasses resource allocation before and after disasters. It is essential to reduce losses in disaster-stricken areas and support development during post-disaster recovery. However, there is often an excessive supply of emergency materials and a mismatch between supply and demand sides in downstream supply chains, which may lead to severe waste and difficulties in recovering surplus materials. This paper takes idle emergency resource sharing level and corporate social responsibility goodwill as endogenous variables. The allocation approaches are dynamically evaluated by incorporating random elements that influence the endogenous variables. Three stochastic differential games are introduced to examine the interactions between the players. The centralized decision-making satisfies the consistency of overall and individual rationalities at any time in the emergency material allocation process, promoting the optimal sharing levels of emergency materials and overall profits. The decentralized decision-making with cost-sharing contracts achieves local optima and increases the dual marginal effect of the emergency industry chain. This paper incorporates the sharing economy into emergency management, showing how technology-driven sharing platforms can optimize resource utilization. The results suggest introducing cost-sharing contracts between demanders and suppliers can enhance collaboration and effort, leading to better resource allocation and increased efficiency. It contributes to sustainability by promoting efficient resource utilization through idle emergency resource sharing. By optimizing allocation strategies and enhancing corporate social responsibility, the study fosters the long-term viability and resilience of the supply chain system in post-disaster management. Full article

Deep eutectic solvents (DES) represent an innovative and environmentally friendly approach for chitin isolation. Chitin is a natural nitrogenous polysaccharide, characterized by its abundance of amino and hydroxyl groups. The hydrogen bond network in DES can disrupt the crystalline structure of chitin, facilitating its isolation from bioresources by dissolving or degrading other components. DES are known for their low cost, natural chemical constituents, and recyclability. Natural deep eutectic solvents (NADES), a subclass of DES made from natural compounds, offer higher biocompatibility, biodegradability, and the lowest biotoxicity, making them highly promising for the production of eco-friendly chitin products. This review summarized studies on chitin isolation by DES, including reviews of biomass resources, isolation conditions (raw materials, DES compositions, solid–liquid ratios, temperature, and time), and the physicochemical properties of chitin products. Consequently, we have concluded that tailoring an appropriate DES-based process on the specific composition of the raw material can notably improve isolation efficiency. Acidic DES are particularly effective for extracting chitin from materials with high mineral content, such as crustacean bio-waste; for instance, the choline chloride-lactic acid DES achieved purity levels comparable to those of commercial chemical methods. By contrast, alkaline DES are better suited for chitin isolation from protein-rich sources, such as squid pens. DES facilitate calcium carbonate removal through H⁺ ion release and leverage unique hydrogen bonding interactions for efficient deproteination. Among these, potassium carbonate-glycerol DES have demonstrated optimal efficacy. Nonetheless, further comprehensive research is essential to evaluate the environmental impact, economic feasibility, and safety of DES application in chitin production. Full article

In Japan, telepharmacy is becoming increasingly popular due to deregulation triggered by the outbreak of COVID-19. The purpose of this study was to gain an understanding of the actual state of telepharmacy in Japan by interviewing pharmacists who have experience with telepharmacy and describing its advantages and disadvantages, as well as their outlook for its use going forward. The interviews were conducted online using Zoom. Each interview lasted approximately 30 min. Eleven people were interviewed. The advantages mentioned by the pharmacists were classified into three main categories: “Better communication”, “Time savings”, and “Improved safety”. The disadvantages were classified into the following nine categories: “Drug delivery problems”, “Communication failures”, “Ease of use for patients”, “Emotional reactions”, “Pharmacy system”, “Communication issues”, “Healthcare system issues”, “App system issues”, and “Cost”. Many of these factors correspond to the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs presented by Venkatesh, et al. Many of the pharmacists mentioned that the use of telepharmacy is likely to expand further in the future, but that this will require further development of communication technology and the widespread use of systems such as electronic prescriptions. Full article

To achieve carbon neutrality in China’s fossil energy sector, saline aquifer CO₂ geological storage has become a critical strategy. As research into carbon reduction and storage potential evaluation advances across various geological scales, the need arises for consolidating key CO₂ storage cases and establishing a standardized classification system and evaluation methodology. This paper provides a comprehensive review of notable CO₂ storage projects in saline aquifers, covering aspects such as project overviews, structural and reservoir characteristics, caprock integrity, and seismic monitoring protocols. Drawing on insights from mineral and oil and gas exploration, as well as international methods, this paper outlines the stages and potential levels of saline aquifer storage in China. It proposes an evaluation framework with formulas and reference values for key coefficients. The study includes successful global projects, such as Sleipner and Snøhvit in Norway, In Salah in Algeria, and Shenhua in China’s Ordos Basin, which provide valuable insights for long-term carbon capture and storage (CCS). By examining geological characteristics, injection, and monitoring protocols in these projects, this paper analyzes how geological features impact CO₂ storage outcomes. For example, the Sleipner project’s success is linked to its straightforward structure, favorable reservoir properties, and stable caprock, while Snøhvit illustrates diverse structural suitability, and In Salah demonstrates the influence of fractures on storage efficacy. CO₂ storage activities are segmented into four stages—survey, investigation, exploration, and injection—and are further categorized by storage potential: geological, technical, techno-economic, and engineering capacities. This study also presents evaluation levels (prediction, control, technically recoverable, and engineering) that support effective reservoir selection, potential classification, and calculations considering factors like reservoir stability and sealing efficacy. Depending on application needs, volumetric or mechanistic methods are recommended, with precise determination of geological, displacement, and cost coefficients. For China, a dynamic evaluation mechanism characterized by multi-scale, tiered approaches and increasing precision over time is essential for robust storage potential assessment. The levels and methods outlined here serve as a scientific foundation for regional and stage-based comparisons, guiding engineering approvals and underground space management. To align with practical engineering demands, ongoing innovation through laboratory experiments, simulations, and field practice is crucial, supporting continual refinement of formulas and key parameter determinations. Full article

This study models two alternative downstream processes based on magnetic separation with the objective of understanding the economic feasibility of these processes compared to the traditional mAb process. The key focus lies in the economic understanding of the cell harvest and capture steps in the models. Here, the models revealed that integrating cell removal and product capture in a single operation is the main factor driving the unified productivity between USP and the magnetic bead-based processes. This results in significant economic benefits, such as savings in both the cost of goods per gram of mAb and fixed costs, as well as increasing annual facility output. The predicted savings potential approaches 38% for COGs, 17% for capital investment, and 40% for annual facility output. For mammalian cell-based manufacturing, the magnetic separation-based DSP provides a highly valuable option due to its integration of several individual unit operations compared to the traditional process both in reducing process time and cost and accommodating higher demands. Full article

Predictive maintenance can help infrastructure managers to reduce costs and improve railway availability while ensuring safety. However, its accuracy depends on reliable data from various sources, especially track measurement data. When analysing track data over time, historical maintenance actions must be considered, as otherwise the interpretation of the data would be misleading. This research aims to address inconsistencies in recorded maintenance data by detecting unrecorded track works through track geometry evaluations. The main goal is to provide the foundations for accurate descriptions of track behaviour, supporting the implementation of effective predictive maintenance regimes. As part of the research, three different approaches are analysed and evaluated, whereby two of them are based on cross-sectional analyses and the third one detects track works in longitudinal track dimension. The results show that the CRAB algorithm produces the most statistically significant results. Conversely, the cumulative track geometry-based algorithm provides a homogeneous representation of past maintenance work and a result that is statistically only marginally inferior. Consequently, these two methods are best suited to build the foundation for making accurate cross-sectional conclusions about track geometry behaviour. This allows for the verification and enhancement of existing maintenance databases. Full article

The efficient, economical, and sustainable production of biodiesel from waste cooking oils (WCOs) depends on the availability of simple, rapid, and low-cost methods to test the quality of potential feedstocks. The aim of this study was to establish the applicability of stochastic modeling of e-nose profiles in the evaluation of recycled WCO characteristics. Olfactory profiles of 10 WCOs were determined using a Sensigent Cyranose^® 320 chemical vapor-sensing device with a 32 sensor-array, and a stepwise multiple linear regression (MLR) analysis was performed to select stochastic parameters (explanatory variables) for inclusion in the final predictive models of the physicochemical properties of the WCOs. The most important model parameters for the characterization of WCOs were those relating to the time of inception of the e-nose signal “plateau” and to the concentration of volatile organic compounds (VOCs) in the sensor region. A comparison of acid values, peroxide values, water contents, and kinematic viscosities predicted by the MLR models with those determined by conventional laboratory methods revealed that goodness of fit and predictor accuracy varied from good to excellent, with all metric values >90%. Combining e-nose profiling with stochastic modeling was successful in predicting the physicochemical characteristics of WCOs and could be used to select suitable raw materials for efficient and sustainable biodiesel production. Full article

In recent years, accessibility to services and activities using sustainable transport modes has become an important goal for land use and transport planning policies. Traditional approaches rely on objective measures, such as travel time, distance, costs, and other spatial data, overlooking personal preferences, experiences, demographic features, and socio-cultural and economic dynamics. Attributes that affect perceptions of accessibility require attention but our understanding of the factors influencing perceived accessibility is inadequate, particularly in Australia’s expanding suburbs. Through a literature review and questionnaire surveys, this study aims to take into account various travel characteristics and enhance the understanding of how perceived safety and service quality impact mobility behaviour and perceived accessibility within the Australian context. The study found that perceived safety and service quality have significant impacts on perceived accessibility and hence influence the use of public transport. We have identified critical factors that require proper consideration in transport and land use planning and policies to enable a better contribution from public transport to the liveability and well-being of residents in Australian suburbs. Full article

Military combat can result in the need for comprehensive care related to both physical and psychological trauma, most commonly chronic pain and post-traumatic stress disorder (PTSD). These conditions tend to co-occur and result in high levels of distress and interference in everyday life. Thus, it is imperative to develop effective, time-efficient treatments for these conditions before they become chronic and resistant to change. We developed and pilot-tested the Pain and Trauma Intensive Outpatient Treatment (PATRIOT) Program, a brief, intensive (3 weeks, six sessions) integrated chronic pain and PTSD treatment. An overview and session-by-session outline of the PATRIOT Program is provided, followed by results from the first pilot evaluation of the PATRIOT Program’s feasibility, acceptability, and preliminary efficacy in a sample of eight participating Veterans with comorbid chronic pain and PTSD. There were no treatment dropouts. At post-treatment, there were significant reductions in PTSD symptoms based on the Clinician-Administered Assessment of PTSD (CAPS). Pain and catastrophic thinking also decreased from pre- to post-treatment. With continued investigations and support, the PATRIOT Program may offer a brief, cost-effective, and more easily accessible treatment option for individuals who could benefit from learning skills to manage pain and PTSD more effectively. Full article

Association Rule Mining (ARM) relies on concept lattices as an effective knowledge representation structure. However, classical ARM methods face significant limitations, including the generation of misleading rules during data-to-formal-context mapping and poor handling of heterogeneous data types such as linguistic, continuous, and imprecise data. This study aims to address these limitations by introducing a novel fuzzy data structure called the “fuzzy iceberg lattice” and its corresponding construction algorithm. The primary objectives of this study are to enhance the efficiency of extracting and visualizing frequent fuzzy closed item sets and to optimize both execution time and storage requirements. The necessity of this research stems from the high computational cost and redundancy associated with traditional fuzzy approaches, which, while capable of managing quantitative and imprecise data, are often impractical for large-scale applications in real scenarios. The proposed approach incorporates a ‘fuzzy min-max basis algorithm’ to derive exact and approximate rule bases from the extracted fuzzy closed item sets, eliminating redundancy while preserving valuable insights. Experimental results on benchmark datasets demonstrate that the proposed fuzzy iceberg lattice outperforms traditional fuzzy concept lattices, achieving an average reduction of 74.75% in execution time and 70.53% in memory usage. This efficiency gain, coupled with the lattice’s ability to handle crisp, quantitative, fuzzy, and heterogeneous data types, underscores its potential to advance ARM by yielding a manageable number of high-quality fuzzy concepts and rules. Full article

The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications. Full article

With the acceleration of urbanization, urban bus scheduling systems are facing unprecedented challenges. Traditional bus scheduling provides the original schedule time and the planned time of arrival at the destination, where the schedule time is the departure time of the bus. However, various factors encountered during the drive result in significant differences in the driving time of the bus. To ensure timely arrivals, the bus scheduling system has to rely on manual adjustments to optimize the schedule time to determine the actual departure time. In order to reduce the scheduling cost and align the schedule time closer to the actual departure time, this paper proposes a dynamic scheduling model, LSTM-SVR, which leverages the advantages of LSTM in capturing the time series features and the ability of SVR in dealing with nonlinear problems, especially its generalization ability in small datasets. Firstly, LSTM is used to efficiently capture features of multidimensional time series data and convert them into one-dimensional effective feature outputs. Secondly, SVR is used to train the nonlinear relationship between these one-dimensional features and the target variables. Thirdly, the one-dimensional time series features extracted from the test set are put into the generated nonlinear model for prediction to obtain the predicted schedule time. Finally, we validate the model using real data from an urban bus scheduling system. The experimental results show that the proposed hybrid LSTM-SVR model outperforms LSTM-BOA, SVR-BOA, and BiLSTM-SOA models in the accuracy of predicting bus schedule time, thus confirming the effectiveness and superior prediction performance of the model. Full article