Search Results (2,685)

The resource utilization of construction waste and industrial solid waste is a crucial aspect in promoting global urbanization and sustainable development. This study focuses on the preparation of mine backfill materials using construction waste in combination with various industrial solid wastes—ground granulated blast furnace slag (GGBFS), fly ash (FA), silica fume (SF), phosphorus slag (PS), fly ash–phosphorus slag–phosphogypsum composite (FA-PS-PG), and fly ash–phosphorus slag–β-phosphogypsum composite (FA-PS-βPG)—under different substitution rates (50%, 55%, 60%) as control parameters. A total of 19 mix proportions were investigated, evaluating their slump, dry density, compressive strength, uniaxial compressive stress–strain relationship, micromorphology, and phase composition. The results indicate that, compared to backfill materials prepared with pure cement, the incorporation of industrial solid wastes improves the fluidity of the backfill materials. At 56 days, the constitutive model parameter a increased to varying degrees, while parameter b decreased, indicating enhanced ductility. The compressive strength was consistently higher with PS at all substitution rates. The FA-PS-PG mixture with a 50% substitution rate achieved the highest 56-day compressive strength of 8.02 MPa. These findings can facilitate the application of construction waste and industrial solid waste in mine backfilling projects, delivering economic, environmental, and resource-related benefits. Full article

Lignin used in this work was isolated from sapele (Entandrophragma cylindricum) wood through a hybrid pulping process using soda/ethanol as pulping liquor and denoted soda-oxyethylated lignin (SOL). SOL was mixed with a polyethylene glycol (PEG)–glycerol mixture (80/20 v/v) as liquefying solvent with 98% wt. sulfur acid as catalyst, and the mixture was taken to boil at 140 °C for 2, 2.5, and 3 h. Three bio-polyols LBP1, LBP2, and LBP3 were obtained, and each of them exhibited a high proportion of -OH groups. Lignin-based polyurethane foams (LBPUFs) were prepared using the bio-polyols obtained with a toluene diisocyanate (TDI) prepolymer by the one-shot method. Gel permeation chromatography (GPC), Fourier transform infrared spectroscopy (FTIR), and carbon-13 nuclear magnetic resonance spectroscopy (¹³C NMR) were used characterize lignin in order to determine viscosity, yield, and composition and to characterize their structure. The PEG-400–glycerol mixture was found to react with the lignin bio-polyols’ phenolic -OHs. The bio-polyols’ viscosity was found to increase as the liquefaction temperature increased, while simultaneously their molecular weights decreased. All the NCO groups were eliminated from the samples, which had high thermal stability as the liquefaction temperature increased, leading to a decrease in cell size, density, and crystallinity and an improvement in mechanical performance. Based on these properties, especially the presence of some aromatic rings in the bio-polyols, the foams produced can be useful in automotive applications and for floor carpets. Full article

Foam cement, as a building insulation material, encounters a major problem in practical application, which is the difficulty in achieving a balance between its strength and insulation performance. To achieve multi-objective optimization of foamed cement mix design, this study first determined the optimal ranges of nano-silica aerogel (NSA), foaming agent, and polypropylene (PP) fiber dosage through single-factor experiments. Then, response surface methodology (RSM) was employed to construct a quadratic polynomial regression model, systematically investigating the influence of different NSA contents, foaming agent contents, and PP fibers contents on the thermal conductivity and compressive strength of foamed cement. Finally, the optimal mix ratio was further predicted and experimentally validated. The results demonstrate that the regression model developed using RSM exhibits high accuracy and reliability. The correlation coefficients R² of the regression models established by the response surface method are 0.9756 and 0.9684, respectively, indicating good prediction accuracy. The optimized mix ratio was determined as follows: NSA content, 9.548%; foaming agent content, 0.533%; and PP fiber content, 0.1%. Under this mix, the model predicted a thermal conductivity of 0.123 W/(m·K) and a 28-day compressive strength of 1.081 MPa. Experimental verification confirmed that the errors between predicted and measured values for all performance indicators were within 5%, demonstrating the high reliability of the predictive model. This study provides support for the practical application of foam cement as a thermal insulation material in construction projects and offers guidance for optimizing its mixture composition. Full article

The eruption of the Cumbre Vieja volcano on 19 September 2021 resulted in the deposition of over 20 million cubic meters of tephra, posing significant environmental and logistical challenges in the affected areas. This study aimed to explore the valorization of volcanic ash (VA) by evaluating its potential use in producing sustainable mortar by incorporating it as a replacement for cement or sand. Various experimental mixtures were prepared with different proportions of VA which substituted either cement or sand, and these mixes were characterized through a mechanical and microstructural campaign. Additionally, shrinkage was evaluated for the mixtures which showed good mechanical results. The results suggest that partially replacing cement with up to 15% ground VA as well as substituting sand with up to 25% VA are promising strategies for the production of sustainable mortar mixes. This research contributes to the understanding of the influence of VA in cementitious matrices and offers a novel approach for integrating locally available geomaterials into infrastructure design in volcanic active regions. Full article

Most research on domestic dog (Canis familiaris) behavior has focused on pets with restricted movement. However, free-ranging dogs exist in diverse cultural contexts globally, and their interactions with humans are less understood. Tourists can facilitate unrestricted dog movement into wilderness areas, where they may negatively impact wildlife. This study investigated which stimuli—namely, voice, touch, or food—along with inherent factors (age, sex, sociability) motivate free-ranging dogs to follow a human stranger. We measured the distance (up to 600 m) of 129 free-ranging owned and stray dogs from three villages in southern Chile as they followed an experimenter who presented them one of the above stimuli or none (control). To evaluate the effect of dog sociability (i.e., positive versus stress-related or passive behaviors), we performed a 30 s socialization test (standing near the dog without interacting) before presenting a 10 s stimulus twice. We also tracked whether the dog was in the company of other dogs. Each focus dog was video-recorded and tested up to three times over five days. Generalized linear mixed-effects models revealed that the food stimulus significantly influenced dogs’ motivation to follow a stranger, as well as a high proportion of sociable behaviors directed towards humans and the company of other dogs present during the experiment. Juveniles tended to follow a stranger more than adults or seniors, but no effects were found for the dog’s sex, whether an owner was present, the repetition of trials, the location where the study was performed, or for individuals as a random variable. This research highlights that sociability as an inherent factor shapes dog–stranger interactions in free-ranging dogs when food is given. In the context of wildlife conservation, we recommend that managers promote awareness among local communities and tourists to avoid feeding dogs, especially in the context of outdoor activities close to wilderness. Full article

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

To promote the large-scale utilization of construction and industrial solid waste in engineering, this study focuses on developing accurate prediction and optimization methods for the unconfined compressive strength (UCS) of composite recycled mortar. Innovatively incorporating three types of recycled powder (RP)—recycled clay brick powder (RCBS), recycled concrete powder (RCBP), and recycled gypsum powder (RCGP)—we systematically investigated the effects of RP type, replacement rate, and curing period on mortar UCS. The core objective and novelty lie in establishing and comparing three artificial intelligence models for high-precision UCS prediction. Furthermore, leveraging GA-BP’s functional extremum optimization theory, we determined the optimal UCS alongside its corresponding mix proportion and curing scheme, with experimental validation of the solution reliability. Key findings include the following: (1) Increasing total RP content significantly reduces mortar UCS; the maximum UCS is achieved with a 1:1 blend ratio of RCBP:RCGP, while a 20% RCBS replacement rate and extended curing periods markedly enhance strength. (2) Among the prediction models, GA-BP demonstrates superior performance, significantly outperforming BP models with both single and double hidden layer. (3) The functional extremum optimization results exhibit high consistency with experimental validation, showing a relative error below 10%, confirming the method’s effectiveness and engineering applicability. Full article

Introduction: Actinic cheilitis (AC) is a common precancerous condition affecting the lips, primarily caused by prolonged ultraviolet radiation exposure. Various treatment options are available. However, the optimal treatment approach remains a subject of debate. Objective: To summarize and compare practice-relevant interventions for AC. Materials and Methods: A pre-defined protocol was registered in PROSPERO (CRD42021225182). Systematic searches in Medline, Embase, and Central, along with manual trial register searches, identified studies reporting participant clearance rates (PCR) or recurrence rates (PRR). Quality assessment for randomized controlled trials (RCTs) was conducted using the Cochrane Risk of Bias tool 2. Uncontrolled studies were evaluated using the tool developed by the National Heart, Lung, and Blood Institute. The generalized linear mixed model was used to pool proportions for uncontrolled studies. A pairwise meta-analysis for RCTs was applied, using the odds ratio (OR) as the effect estimate and the GRADE approach to evaluate the quality of the evidence. Adverse events were analyzed qualitatively. Results: A comprehensive inclusion of 36 studies facilitated an evaluation of 614 participants for PCR, and 430 patients for PRR. Diclofenac showed the lowest PCR (0.53, 95% confidence interval (CI) [0.41; 0.66]), while CO₂ laser showed the highest PCR (0.97, 95% CI [0.90; 0.99]). For PRR, Er:YAG laser showed the highest rates (0.14, 95% CI [0.08; 0.21]), and imiquimod the lowest (0.00, 95% CI [0.00; 0.06]). In a pairwise meta-analysis, the OR indicated a lower recurrence rate for Er:YAG ablative fractional laser (AFL)-primed methyl-aminolevulinate photodynamic therapy (MAL-PDT) (Er:YAG AFL-PDT) compared to methyl-aminolevulinate photodynamic therapy (MAL-PDT) alone (OR = 0.22, 95% CI [0.06; 0.82]). The CO₂ laser showed fewer local side effects than the Er:YAG laser, while PDTs caused more skin reactions. Due to qualitative data, comparability was limited, highlighting the need for individualized treatment. Conclusions: This study provides a complete and up-to-date evidence synthesis of practice-relevant interventions for AC, identifying the CO₂ laser as the most effective treatment and regarding PCR and imiquimod as most effective concerning PRR. Full article

Cement–Soil Mixing (CSM) Pile is an important technology for soft ground reinforcement, and its as-formed compressive strength directly affects engineering design and construction quality. To address the significant discrepancy between laboratory-tested strength and field as-formed strength arising from differing environmental conditions, this study conducted modified laboratory experiments simulating key field formation characteristics. A cement–soil preparation system considering actual immersion conditions was established, based on controlling the initial water content state of the foundation soil before pile formation and applying submerged conditions post-formation. Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. Engineering validation demonstrated that the model achieved an RMSE of 0.138, an MAE of 0.112, and an R² of 0.961. It effectively addresses the issue of large prediction deviations caused by insufficient environmental simulation in traditional mix proportion tests. The research findings establish a quantitative relationship between as-formed strength and preparation parameters, providing an effective experimental improvement and strength prediction method for the engineering design of CSM Pile. Full article

Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R² of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article

The accumulation of end-of-life tires and the rapid increase in demolition activities pose significant environmental and waste-management challenges. The redevelopment of construction materials incorporating this waste is a potentially promising strategy for minimizing environmental impact while promoting the principles of a circular economy. This study investigates the performance of self-compacting concrete (SCC) incorporating up to 20% rubber aggregates (sand and gravel) and 40% recycled concrete aggregate (RCA) for non-structural applications. A series of tests was conducted to assess fresh and hardened properties, including flowability, compressive strength, tensile strength, flexural strength, water absorption, and density. The results indicated that increasing RCA content reduced density and compressive strength, while tensile and flexural strengths were only moderately affected. Response surface methodology (RSM), utilizing a Box–Behnken design, was employed to optimize compressive, tensile, and flexural strength responses. Statistical analysis was used to identify the optimal mix proportions, which balance the mechanical performance and sustainability of SCC with recycled components. Mixtures incorporating moderate rubber content—specifically, 5–5.5% sand rubber and 0–6% coarse rubber—and 40% recycled-concrete aggregate (RCA) achieved the highest predicted performance, with compressive strength ranging from 20.00 to 28.26 MPa, tensile strength from 2.16 to 2.85 MPa, and flexural strength reaching 5.81 MPa, making them suitable for sidewalks and walkways. Conversely, mixtures containing higher rubber proportions (5.5–20% sand rubber and 20% coarse rubber) combined with the same RCA level (40%) showed the lowest mechanical performance, with compressive strength between 5.2 and 10.08 MPa, tensile strength of 1.05–1.41 MPa, and flexural strength from 2.18 to 3.54 MPa. These findings underscore the broad performance range achievable through targeted optimization. They confirm the viability of recycled materials for producing environmentally friendly SCC in non-structural applications. Full article

This study proposes a new air treatment system that integrates dehumidification, cooling, and water recovery using a Horizontal Air–Ground Heat Exchanger (HAGHE) combined with Peltier cells. The airflow generated by a fan flows through an HAGHE until it meets a septum on which Peltier cells are placed, and then separates into two distinct streams that lap the two surfaces of the Peltier cells: one stream passes through the cold surfaces, undergoing both sensible and latent cooling with dehumidification; the other stream passes through the hot surfaces, increasing its temperature. The two treated air streams may then pass through a mixing chamber, where they are combined in the appropriate proportions to achieve the desired air supply conditions and ensure thermal comfort in the indoor environment. A Computational Fluid Dynamics (CFD) analysis was carried out to simulate the thermal interaction between the HAGHE and the surrounding soil. The simulation focused on a system installed under the subtropical climate conditions of Nairobi, Africa. The simulation results demonstrate that the HAGHE system is capable of reducing the air temperature by several degrees under typical summer conditions, with enhanced performance observed when the soil is moist. Condensation phenomena were triggered when the relative humidity of the inlet air exceeded 60%, contributing additional cooling through latent heat extraction. The proposed HAGHE–Peltier system can be easily powered by renewable energy sources and configured for stand-alone operation, making it particularly suitable for off-grid applications. Full article

This study aims to enhance the performance of an innovative steel slag–ceramsite foam concrete (SSCFC) to advance sustainable green building materials. An eco-friendly composite construction material was developed by integrating industrial by-product steel slag (SS) with lightweight ceramsite. Employing a three-factor, three-level orthogonal experimental design at a fixed density of 800 kg/m³, 12 mix proportions (including a control group) were investigated with the variables of water-to-cement (W/C) ratio, steel slag replacement ratio, and ceramsite replacement ratio. The governing mechanisms of the W/C ratio, steel slag replacement level, and ceramsite replacement proportion on the SSCFC’s fluidity and compressive strength (CS) were elucidated. The synergistic application of range analysis and analysis of variance (ANOVA) quantified the significance of factors on target properties, and partial least squares regression (PLSR)-based prediction models were established. The test results indicated the following significance hierarchy: steel slag replacement > W/C ratio > ceramsite replacement for fluidity. In contrast, W/C ratio > ceramsite replacement > steel slag replacement governed the compressive strength. Verification showed R² values exceeding 65% for both fluidity and CS predictions versus experimental data, confirming model reliability. Multi-criteria optimization yielded optimal compressive performance and suitable fluidity at a W/C ratio of 0.4, 10% steel slag replacement, and 25% ceramsite replacement. Full article

Objective: This study aimed to develop a performance evaluation index system for a district-level public hospital in Chongqing, China, based on Diagnosis-Related Groups (DRGs), to provide a benchmark for performance assessment in similar hospitals. The system was constructed using a literature analysis, the Delphi method, and the Analytic Hierarchy Process (AHP) to identify and weight relevant indicators. Results: The evaluation system consists of three primary indicators and eighteen secondary indicators. Key secondary indicators include the Case Mix Index (CMI), cost consumption index, low-risk group mortality rate, the proportion of patients with three- or four-level surgeries at discharge, and the proportion of medical service revenue to medical income. In 2020, significant improvements were observed in several indicators, such as a decrease in the low-risk group mortality rate to 0% and increases in the proportion of patients with three- or four-level surgeries and CMI by nearly 10% and 13%, respectively. Conclusions: This study successfully developed a comprehensive and scientifically sound performance evaluation index system for a district-level public hospital in Chongqing. The system has proven effective in objectively assessing inpatient medical care performance and providing valuable guidance for improving healthcare services in similar settings. Full article

Social media platforms like Instagram significantly shape destination images and influence tourist behavior. Understanding how different cities are represented and perceived on these platforms is crucial for effective tourism marketing. This study provides a comparative analysis of Instagram content and engagement patterns in Seoul and Tokyo, two major Asian metropolises, to derive actionable marketing insights. We collected and analyzed 59,944 public Instagram posts geotagged or location-tagged within Seoul (n = 29,985) and Tokyo (n = 29,959). We employed a mixed-methods approach involving content categorization using a fine-tuned convolutional neural network (CNN) model, engagement metric analysis (likes, comments), Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis and thematic classification of comments, geospatial analysis (Kernel Density Estimation [KDE], Moran’s I), and predictive modeling (Gradient Boosting with SHapley Additive exPlanations [SHAP] value analysis). A validation analysis using balanced samples (n = 2000 each) was conducted to address Tokyo’s lower geotagged data proportion. While both cities showed ‘Person’ as the dominant content category, notable differences emerged. Tokyo exhibited higher like-based engagement across categories, particularly for ‘Animal’ and ‘Food’ content, while Seoul generated slightly more comments, often expressing stronger sentiment. Qualitative comment analysis revealed Seoul comments focused more on emotional reactions, whereas Tokyo comments were often shorter, appreciative remarks. Geospatial analysis identified distinct hotspots. The validation analysis confirmed these spatial patterns despite Tokyo’s data limitations. Predictive modeling highlighted hashtag counts as the key engagement driver in Seoul and the presence of people in Tokyo. Seoul and Tokyo project distinct visual narratives and elicit different engagement patterns on Instagram. These findings offer practical implications for destination marketers, suggesting tailored content strategies and location-based campaigns targeting identified hotspots and specific content themes. This study underscores the value of integrating quantitative and qualitative analyses of social media data for nuanced destination marketing insights. Full article