In low- and middle-income countries, household floors made of soil remain common. Concrete floors are desirable household upgrades because they offer durability, improved hygiene, and flood resilience, and their prevalence is increasing. However, cement production is a substantial contributor to global anthropogenic carbon dioxide emissions. This study assessed the acceptability and feasibility of sustainable cement-based floors with lower embodied carbon; these floors replaced 20% of cement with fly ash, a byproduct of coal combustion. To assess whether low carbon cement floors are as acceptable as traditional cement-based floors, in-depth interviews were conducted with 30 respondents from 20 households in rural Bangladesh in which soil floors were replaced with either low carbon cement or traditional cement-based floors. Findings revealed that both flooring types were highly accepted due to ease of cleaning, health benefits, and protection from environmental hazards. Traditional cement floors were preferred in terms of repair and maintenance as the users were concerned about the availability of fly ash and concrete blocks in rural settings. Financial constraints emerged as a major barrier to cement-based floor adoption for both traditional and lower embodied carbon cement-based floors, with most respondents prioritizing roof and wall improvements over flooring. Subsidized housing programs were seen as a crucial enabler for cement-based floor installation, as self-financing was largely considered unattainable. This study demonstrates that in a rural, low-income population Bangladesh, cement-based floors with lower embodied carbon were as acceptable as traditional cement-based floors, but installation of either type of floor may require government subsidies.

Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetlin Machine (TM) offer transparency but lack semantic generalization. We propose a semantic bootstrapping framework that transfers LLM knowledge into symbolic form, combining interpretability with semantic capacity. Given a class label, an LLM generates sub-intents that guide synthetic data creation through a three-stage curriculum (seed, core, enriched), expanding semantic diversity. A Non-Negated TM (NTM) learns from these examples to extract high-confidence literals as interpretable semantic cues. Injecting these cues into real data enables a TM to align clause logic with LLM-inferred semantics. Our method requires no embeddings or runtime LLM calls, yet equips symbolic models with pretrained semantic priors. Across multiple text classification tasks, it improves interpretability and accuracy over vanilla TM, achieving performance comparable to BERT while remaining fully symbolic and efficient.

ABSTRACT Soil household floors are common in low- and middle-income countries (LMICs) and can serve as reservoirs of enteric pathogens. Cement-based floors may interrupt pathogen transmission, but little is known about pathogen survival or removal from cement-based surfaces. This study investigated the survival of Escherichia coli , an indicator of fecal contamination, on cement-based surfaces and evaluated its reduction through common household activities (mopping, sweeping, and walking). We compared E. coli fate on three mixes: (i) ordinary Portland cement (OPC) concrete (used in the United States), (ii) OPC mortar (used in Bangladesh), and (iii) OPC mortar with fly ash (a sustainable alternative to the Bangladesh mix). Additionally, we compared outcomes on cement-based surfaces with and without soil and at two temperatures representing the dry and wet seasons in Bangladesh. After 4 hours on the cement-based surfaces, E. coli decayed more than 1.1 log 10 ( C / C o ) under all conditions tested, which is significantly faster than in bulk soils. The higher temperature increased the decay rate constant ( P = 5.56 × 10 −8 ) while soil presence decreased it ( P = 2.80 × 10 −6 ). Sweeping and mopping resulted in high levels of removal for all mixes, with a mean removal of 71% and 78%, respectively, versus 22% for walking. The concrete and mortar mix designs did not impact E. coli survival or removal ( P > 0.20). Cement-based floors made with a fly ash mix performed similarly to traditional cement-based floors, supporting their potential use as a more sustainable intervention to reduce fecal contamination in rural LMIC household settings. IMPORTANCE Cement-based surfaces may serve as a health intervention to reduce the fecal-oral transmission of pathogens in household settings, but there is a critical lack of evidence about the fate of indicator organisms on these surfaces, especially in field-relevant conditions. This study provides some of the first insights into Escherichia coli survival on cement-based surfaces and the effectiveness of daily activities for removing E. coli . Additionally, this study explores the fate of E. coli on cement-based surfaces made with fly ash (which contributes fewer CO 2 emissions) versus traditional cement mixes. We found that E. coli had similar survival and removal efficiencies across all mix designs, demonstrating that fly ash mixes are feasible for use in household settings (e.g., in floors). The findings enhance understanding of fecal-oral transmission pathways and support the use of fly ash mixes in cement-based flooring in future epidemiologic studies assessing effects on enteric disease burdens.

We introduce a biopolymer-bound soil composite (BSC) that utilizes lignin as a binder to solidify granular material, such as sand or soil, to create a biocomposite that can be as strong as concrete. BSCs, in general, can be made with proteins or peptides, starches and other carbohydrates, and more exotic substances such as the biological glues made by marine organisms. All of these variants of BSC require that the biopolymer be highly soluble in a suitable solvent. The final stage in the fabrication of these materials involves solvent removal, which can be accomplished by evaporation at ambient temperatures or through energy enhanced processes, such as baking in an oven. Microstructural analysis, using X-ray micro-CT scanning, reveals that the finished BSC consist of three distinct regions: the granular material (aggregate); biopolymer regions that coat the grains or the aggregate particles and can form bridges between adjacent particles; and, void spaces that develop during the drying process. Under circumstances in which the association between the biopolymer and the solvent is reversible, BSC is potentially recyclable. We have investigated the topic of recyclability of lignin-based BSC. We discovered that the re-manufactured material is stronger than virgin lignin-based BSC, by approximately a factor of two. Microstructural analysis indicates that the remanufacturing process enhances the association of lignin with the aggregate particles. Successful recycling of lignin-based BSC serves to highlight the environmentally friendly nature of this material for the construction industry, where circular materials are needed to minimize carbon emissions.

Biopolymer-bound soil composites (BSC) are a novel class of cement-free building materials using biopolymer binders, many of which are sourced from the waste streams of major industries. This study investigates the recyclability of one particular BSC that uses kraft lignin as the biopolymer. Re-manufacturing of BSC was accomplished by mechanical disruption of the virgin material, followed by re-introduction of solvent, remixing, and remolding. The compressive strength of recycled lignin-based BSC was higher than that of BSC made with virgin ingredients. To understand the microstructure of lignin-based BSC, a series of X-ray micro-CT images of the test articles were obtained. Images produced by the micro-CT method reveal differences in the microstructure of the re-manufactured specimens indicating an enhancement of the association between lignin and aggregate particles. This study demonstrates the feasibility of recycling BSC and provides insight into the importance of biopolymer-aggregate association in determining the mechanical properties of BSC.

Biopolymer-bound soil composites (BSCs) are a new material class that is being researched for construction applications, including three-dimensional (3D) printing. To use this new material, researchers must understand its flow properties, which include its thixotropic behavior. This study uses a rheometer and vane geometry to perform flow and thixotropic tests on BSC. This paper presents a first attempt to characterize BSC flow properties using two thixotropic flow models. The first model uses a non-Newtonian fluid assumption and builds on the Herschel-Bulkley flow model. The second model relies on soil mechanics and builds on the Coulomb model. Both models satisfactorily capture the flow and thixotropic behavior of the BSC. However, the thixotropic Coulombic flow model also accounts for changes in normal pressure that act on the material at varying depths.

Soil-transmitted helminths, like Ascaris lumbricoides, are significant contributors to disease burden in low- and middle-income countries (LMICs). Infections are associated with morbidity and mortality in children and are often transmitted through eggs in fecally contaminated soil. Interventions, like replacing household soil floors with cement-based alternatives, may reduce exposure to A. lumbricoides eggs, but there are currently no estimates on the removal or survival of Ascaris species eggs on cement-based surfaces. This study addresses that knowledge gap by evaluating the removal of Ascaris suum eggs from mopping and the survival of A. suum eggs on two cement-based mixes: an traditional mortar and a mortar with fly ash, which provides a more sustainable alternative to the traditional mortar mix. We assessed egg survival at two temperatures representing the dry (15°C) and wet (34°C) seasons in Bangladesh using two different egg enumeration methods. After mopping, a mean of 95.6% (SD = 4.0%) of viable eggs were removed from surfaces, with no significant differences between cement-based mixes (p = 0.51). The mean first-order decay rate constants (k) of A. suum eggs across all conditions was 0.029 day-1 (SD = 0.074 day-1). Values of k were similar between mix designs (p = 0.62) but varied significantly between temperatures (p = 4.2x10-25) and egg enumeration methods (p = 2.4x10-8). The k values were greater at 34°C compared to at 15°C, where they showed no significant inactivation. Our k values were comparable to those reported previously for different matrices, indicating comparable inactivation of Ascaris species eggs on cement-based surfaces compared to liquid and semi-solid matrices. These results provide some of the first estimates of removal efficiencies and decay rate constants in realistic environmental conditions for Ascaris species on surfaces while supporting the use of mortar mix designs with fly ash in interventions to reduce Ascaris species transmission in rural LMIC households.

Private sector engagement in infrastructure procurement and ownership can have benefits for both investors and society. However, a significant challenge for authorities is determining when private sector participation provides value for money. Private investors may demand high returns on their investments even in projects with seemingly low risks. Governments with good credit ratings and access to lower cost capital may view private investors overprice in comparison to the level of risk they take. This paper examines whether the financing premiums (in addition to the cost of state financing) of four public-private partnership road projects in Finland are reasonable relative to the risks borne by private partners, using long time series of actual data. To the best of our knowledge, this is the first study to mostly use actual ex-post data on project costs over an extended period to evaluate whether the government overpays private investors. Our analysis indicates that the financing cost of the four projects is on average 201 basis points higher than the financing cost the government pays on its debt. We conclude that this premium is reasonable compensation for the risk the private investors bear in the projects. This finding has implications for selecting the most effective procurement policy for road projects.

Developing sustainable construction materials is important to help reduce the anthropogenic impacts of the construction industry. Currently, the production of concrete accounts for 8 % of global carbon emissions. Therefore, alternatives to concrete must be developed, to reduce its use in the future. New construction materials will help to facilitate a green transition as envisioned in global climate initiatives. Materials such as lignin are ideal, as they can be implemented with little additional cost to manufacture construction materials. We introduce a novel material, lignin-based biopolymer-bound soil composite (BSC), which is similar to other BSCs using other types of biopolymers. In addition, a design methodology is presented, which allows the manufacture of lignin-based BSCs with tailored characteristics. Two kinds of lignin — hydrolysis lignin and alkali lignin — were investigated, with five mix designs developed for each type of lignin. The lignin-based BSCs were found to have compressive strength ranging from 1.6 - 8.1 MPa, which allows them to be implemented in non-structural construction applications. Ultimate compressive strength, density, and other parameters were measured, leading to the development of design relationships for lignin-based BSC. The design relationships presented in this study will help introduce lignin-based BSC as a sustainable form of construction.

Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an innovative joint phase traffic signal cycle control method that operates effectively with varying control intervals. Our method features an adjust all phases action design, enabling simultaneous phase changes within the signal cycle, which fosters both immediate stability and sustained TSC effectiveness, especially at lower frequencies. The approach also integrates decentralized actors to handle the complexity of the action space, with a centralized critic to ensure coordinated phase adjusting. Extensive testing on both synthetic and real-world data across different intersection types and signal setups shows that our method significantly outperforms other popular techniques, particularly at high control intervals. Case studies of policies derived from traffic data further illustrate the robustness and reliability of our proposed method.

Biopolymer-bound soil composites (BSC) are a novel class of cement-free building materials utilizing starch, protein, and lignin binders. While BSC are sustainable composite materials suitable for a wide range of construction applications, their manufacture is complicated, as quality issues (internal defects, improper mixing, improper compaction, etc.) may occur during manufacture. Even though conventional vision-based or acoustic-based quality control methods might be able to detect quality issues during the manufacture of BSC, they are unable to easily monitor the unique strength gain process of BSC that occurs when the wet material dries out. Conventional quality control methods are usually tailored to a single quality issue such as crack formation, requiring the use of multiple methods to completely verify material quality, which is inefficient. To address these issues we propose BioSys, a multi-functional quality control system to enable large-scale manufacture of BSC through non-destructive vibration-based testing. BioSys is multi-functional in the sense that it is used to: (1) identify internal defects (crack formation and improper mixing); (2) detect improper compaction; and (3) monitor desiccation. Unlike current methods, BioSys performs these tests in an efficient, non-intrusive manner, by generating signals from an impulse hammer tapping at different locations on a BSC specimen and measurement of response signals from an accelerometer. BioSys contains two different machine learning pipelines trained on the resulting time-series data with accuracy of up to 99% for defect detection and up to 100% for detecting improper compaction. BioSys reaches a MAPE of 5% for monitoring the strength gain of BSC during desiccation.

Lignin-based biopolymer-bound soil composites (BSCs) are a new class of sustainable construction materials that utilize a bio-based biopolymer - lignin - as a binder. Prior use of lignin suggests that lignin is a promising candidate for the development of bio-based construction materials. Inspired by these applications, lignin-based BSCs were developed using lignoboost lignin, lignoforce lignin, alkali lignin, and hydrolysis lignin. Uni-axial compressive testing of lignin-based BSC shows that the compressive strength for these BSCs range from 1.6-8.1 MPa, which makes them appropriate for low compressive strength construction applications. We performed a life cycle assessment (LCA) of lignin-based BSC, with the functional unit being a CMU-sized block (V V = 6423 cm(3) ). The major advantage of BSC lies in the elimination of ordinary portland cement, which is common to many construction materials, including many forms of concrete. Furthermore, the use of lignin in lignin-based BSC results in carbon sequestration (lignin approximate to 60 wt% carbon), potentially making construction materials made from lignin-based BSC carbon negative. Additionally, a design guide for estimating the life cycle carbon footprint of lignin-based BSC for a required compressive strength was developed. By utilizing the results from material tests and the LCA, designers are now able to use lignin effectively in construction applications, as they can now design lignin-based BSC for a target compressive strength with a full understanding of the life cycle carbon footprint implications.

Vehicle-to-everything (V2X) technology is pivotal for enhancing road safety, traffic efficiency, and energy conservation through the communication of vehicles with their surrounding entities such as other vehicles, pedestrians, roadside infrastructure, and networks. Among these, traffic signal control (TSC) plays a significant role in roadside infrastructure for V2X. However, most existing works on TSC design assume that real-time traffic flow information is accessible, which does not hold in real-world deployment. This study proposes a two-stage framework to address this issue. In the first stage, a scene prediction module and a scene context encoder are utilized to process historical and current traffic data to generate preliminary traffic signal actions. In the second stage, an action refinement module, informed by human-defined traffic rules and real-time traffic metrics, adjusts the preliminary actions to account for the latency in observations. This modular design allows device deployment with varying computational resources while facilitating system customization, ensuring both adaptability and scalability, particularly in edge-computing environments. Through extensive simulations on the SUMO platform, the proposed framework demonstrates robustness and superior performance in diverse traffic scenarios under varying communication delays. The related code is available at https://github.com/Traffic-Alpha/TSC-DelayLight .

Urban health management predominantly relies on reactive care, missing the preventative potential offered by 'Digital Twin' technologies. ‘DigitalMe’ extends precision health principles to urban environments, creating a digital twin that integrates physiological data with personal digital footprints such as social media interactions. This fusion of personalized medicine with public health is demonstrated in pilot implementations, offering insights for city planners, and enhancing healthcare delivery. However, challenges including data privacy, consent, and ethical concerns persist. Addressing these requires collaborative strategies like robust Public-Private Partnerships, comprehensive cybersecurity measures, and strong community engagement. Ultimately, DigitalMe aims to transform urban healthcare into a more proactive, personalized system integrated with city residents’ daily lives.

This paper explores the mapping from specific digitalization practices to specific resilient performance against a great shock. Based on an adapted structure-conduct-performance framework, this paper hypothesizes by theoretically analyzing how the pre-shock establishment of digital retailing practices could trigger physical retailers' bounce-back and bounce-forward performance against the COVID-19 crisis. Using a difference-in-differences strategy, the hypotheses are examined with a sample including 549 observations of 50 Chinese listed retailers from the first quarter of 2018 to the third quarter of 2020. The empirical results mainly indicate the following binary findings. First, regardless of the differences in triggering bounce-back performance, different digital retailing practices are found to be effective in triggering physical retailers' bounce-forward performance against the COVID-19 crisis. This somewhat addresses the concern about the temporality of digitalization-enabled resilience by revealing the generality across digital retailing practices in the sense of triggering resilient performance. Second, it is shown that physical retailers' bounce-back performance at a specific stage of the COVID-19 crisis could only be triggered by digital retailing practices that coincidentally apply to the shock-induced market structure changes at the stage. The results emphasize each digitalization practice's individuality in triggering resilient performance. This justifies the non-negligibility of the direct mapping from specific digitalization practices to specific resilient performance in digitalization-enabled resilience evaluation.

Accurate traffic forecasting is vital to intelligent transportation systems, which are widely adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling spatial-temporal dynamics in traffic data, among which the graph convolution network (GCN) is at the center for exploiting the spatial dependency embedded in the road network graphs. However, these GCN-based methods operate intrinsically on the node level (e.g., road and intersection) only while overlooking the spatial hierarchy of the whole city. Nodes such as intersections and road segments can form clusters (e.g., regions), which could also have interactions with each other and share similarities at a higher level. In this work, we propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting by exploiting the spatial hierarchy and modeling multi-scale spatial correlations. Apart from the node-level spatiotemporal blocks, AHSTN introduces the adaptive spatiotemporal downsampling module to infer the spatial hierarchy for spatiotemporal modeling at the cluster level. Then, an adaptive spatiotemporal upsampling module is proposed to upsample the cluster-level representations to the node-level and obtain the multi-scale representations for final predictions. Experiments on two real-world datasets show AHSTN achieves better performance over several strong baselines.