Search Results (133)

The One Health approach is used to assess health-associated risks resulting from human exposure to antibiotic-resistant bacteria (ARB) that pose a significant public health risk. In this approach, wastewater treatment plants (WWTPs) play an important role in reducing bacteria and antibiotic-resistant genes (ARGs) in the environment. The ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) are of significant concern due to their ability to evade the effects of multiple antibiotics, including last-resort treatments such as carbapenems and glycopeptides. This study aimed to investigate the environmental surveillance of ESKAPE bacteria in wastewater and their adjacent receiving water bodies in Limpopo Province, South Africa. Methodology: Over a period of 6 months, all isolates were identified phenotypically, and genomic DNA was extracted using the QIAamp 96 DNA QIAcube^® HT Kit. Species-specific PCR was performed, followed by Sanger sequencing. The relevant sequences were compared to NCBI GenBank references using BLAST for confirmation and to assess the potential human health-associated risks. Results: ESKAPE organisms identified phenotypically were confirmed using PCR in both WWTP samples. Bacteria such as Acinetobacter baumannii and Enterobacter spp. were not detected in upstream or downstream river samples, particularly during August and September. In December and January, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa were not detected in effluent samples at both sites. Phylogenetic analysis revealed a diverse range of clinically significant genera, including Pseudomonas, Klebsiella, Enterobacter, and Staphylococcus, with strains closely related to global clinical isolates. Many of the isolates were associated with resistance to carbapenems, fluoroquinolones, and aminoglycosides. In addition, some strains clustered with both methicillin-sensitive and methicillin-resistant lineages. Conclusions: The findings emphasise the urgent need for increased genomic surveillance in environmental settings affected by wastewater discharge and highlight the importance of integrated antimicrobial resistance monitoring that connects clinical and environmental health sectors. Full article

Australia consumes approximately 29 million m³ of concrete each year with an estimated embodied carbon (EC) of 12 Mt CO_2e. High consumption of concrete makes it critical for successful decarbonization to support the achievement of ‘Net Zero 2050’ objectives of the Australian construction industry. Portland cement (PC) constitutes only 12–15% of the concrete mix but is responsible for approximately 90% of concrete’s EC. This necessitates reducing the PC in concrete with supplementary cementitious materials (SCMs) or using alternative binders such as geopolymer concrete. Concrete mixes including a combination of PC and SCMs as a binder have lower embodied carbon (EC) than those with only PC and are termed as low-carbon concrete (LCC). SCM addition to a concrete mix not only reduces EC but also enhances its mechanical and durability properties. Fly ash (FA) and granulated ground blast furnace slag (GGBFS) are the most used SCMs in Australia. It is noted that other SCMs such as limestone, metakaolin or calcinated clay, Delithiated Beta Spodumene (DBS) or lithium slag, etc., are being trialed. This technical paper presents a methodology that enables selecting LCCs with various degrees of SCMs for various elements of bridge structure without compromising their functional performance. The proposed methodology includes controls that need to be applied during the design/selection process of LCC, from material quality control to concrete mix design to EC evaluation for every element of a bridge, to minimize the overall carbon footprint of a bridge. Typical properties of LCC with FA and GGBFS as binary and ternary blends are also included for preliminary design of a fit-for-purpose LCC. An example for a bridge located in the B2 exposure classification zone (exposed to both carbonation on chloride ingress deterioration mechanisms) has also been included to test the methodology, which demonstrates that EC of the bridge may be reduced by up to 53% by use of the proposed methodology. Full article

Controlled demolition is a critical engineering practice that enables the safe and efficient dismantling of structures while minimizing risks to the surrounding environment. This study presents, for the first time, a detailed, structured framework for understanding the fundamental principles of controlled demolition by outlining key procedures, methodologies, and directions for future research. Through original, carefully designed charts and full-scale numerical simulations, including two 23-story building scenarios with different delay and blasting sequences, this paper provides real-life insights into the effects of floor-to-floor versus axis-by-axis delays on structural collapse behavior, debris spread, and toppling control. Beyond traditional techniques, this study explores how emerging technologies, such as real-time structural monitoring via object tracking, LiDAR scanning, and Unmanned Aerial Vehicle (UAV)-based inspections, can be further advanced through the integration of artificial intelligence (AI). The potential Deep learning (DL) and Machine learning (ML)-based applications of tools like Convolutional Neural Network (CNN)-based digital twins, YOLO object detection, and XGBoost classifiers are highlighted as promising avenues for future research. These technologies could support real-time decision-making, automation, and risk assessment in demolition scenarios. Furthermore, vision-language models such as SAM and Grounding DINO are discussed as enabling technologies for real-time risk assessment, anomaly detection, and adaptive control. By sharing insights from full-scale observations and proposing a forward-looking analytical framework, this work lays a foundation for intelligent and resilient demolition practices. Full article

A novel thermal conductivity prediction model was developed to address the complex influence of pore structure in porous materials. This model incorporates pore size (d) and a pore distribution parameter (t) to calculate the material’s thermal conductivity. To validate the model’s accuracy, geopolymer foamed concrete (GFC) samples with varying pore structures were fabricated. These utilized ground granulated blast furnace slag (GGBS) as the precursor, a mixed solution of sodium hydroxide (NaOH) and sodium silicate as the alkaline activator, and sodium stearate (NaSt), hydroxypropyl methylcellulose (HPMC), and sodium carboxymethyl cellulose (CMC-Na) as foam stabilizers. Conventional pore size characterization techniques exhibit limitations; consequently, this research implements a high-fidelity machine vision-driven image analysis methodology. Pore size measurement is achieved through a combined technical approach involving equivalent diameter modeling and morphological optimization. The feasibility of the proposed theory is validated by our experimental data and data from previous literature, with the error between experimental and theoretical values maintained within 5%. The value of t increases with increasing porosity and increasing disorder in pore distribution. Based on the experimental data obtained in this study and the research data from previous scholars’ studies, the t value for porous materials can be categorized according to porosity: when porosity is approximately 30%, t ≈ 0.9; when porosity is 55~65%, t ranges from 1.2 to 1.3; and when porosity is approximately 80%, t ranges from 1.9 to 2.2. Full article

Background: Plastid RNA editing is widespread in angiosperms yet remains underexplored in the medicinal non-model species Glycyrrhiza uralensis. This study aimed to (i) comprehensively identify plastid RNA editing sites in G. uralensis, and (ii) compare the detection performance of three library construction strategies: total RNA-seq, rRNA-depleted RNA-seq, and mRNA-seq. Methods: Leaf tissue was used from three wild-sampled individual plants. Plastomes were assembled with GetOrganelle v1.7.0 and annotated using PGA. Strand-specific RNA-seq libraries were mapped to sample-matched plastomes using HISAT2 v2.2.1. Variants were identified using REDItools v2.0 under uniform thresholds. Candidate sites were visually verified in IGV v2.12.3, and read origins were confirmed by BLAST v2.13.0+; artifacts were removed via strand-specific filtering. Results: After stringent filtering, 38 high-confidence RNA editing sites were identified across 19 genes. Total RNA seq performed the best, detecting 37/38 sites consistently, whereas rRNA-depleted libraries detected fewer genuine sites and produced numerous rRNA-linked, noncanonical, noncoding-strand-dominant artifacts. Despite very low rates of plastid mapping, mRNA seq recovered a large fraction of bona fide sites under stringent, strand-aware filtering. Conclusions: We establish a set of 38 high-confidence plastid RNA editing sites in G. uralensis and suggest potential adaptive implications of editing in ndh-related genes. Methodologically, total RNA-seq is recommended for identification using de novo RNA editing due to its high sensitivity and low false-positive rate; publicly available poly(A)-selected mRNA-seq datasets can be repurposed to reliably retrieve plastid RNA editing sites when stringent strand-specific filtering is applied. Full article

Background/Objectives: Bioactive peptides derived from animal venoms, toxins, and secretions demonstrate considerable pharmacological potential for use in the management of diabetes mellitus—a highly prevalent metabolic disorder of substantial global health significance. This integrative review systematically evaluated the current evidence regarding the pharmacological mechanisms underlying the antidiabetic properties of these bioactive peptides. Methods: This study was guided by the research question “What are the mechanisms of action of peptides derived from animal venoms in modulating parameters associated with diabetes?” developed using the PECo framework. A comprehensive literature search was executed across Scopus, PubMed, and Web of Science, focusing on studies from the last five years. Out of 190 identified articles, 17 satisfied the inclusion criteria. Results: Twenty-eight distinct peptides were characterized, exhibiting structural diversity with 7–115 amino acid residues and molecular weights of 900–13,000 Da. These compounds were sourced from venomous taxa including sea anemones, marine snails, spiders, centipedes, scorpions, and snakes. Their antidiabetic mechanisms encompassed glucagon-like peptide-1 (GLP-1) receptor agonism, insulin receptor activation, potassium channel inhibition, glucose transporter type 4 (GLUT4) upregulation, and α-amylase inhibition. Sequence analyses revealed substantial homology among peptides with analogous mechanisms—notably Con-Ins and ILP-Ap04, plus SpTx1 and SsTx-4—suggesting that structural determinants underlie their functional characteristics. Toxicological evaluations of nine peptides demonstrated low-toxicity profiles despite originating from toxic venom, crucial for therapeutic development. Conclusions: These peptides exhibited exceptional pharmacological potency with effective doses in nanogram-to-nanomole per kilogram ranges. Collectively, our findings underscore the therapeutic potential of venom-derived peptides as innovative candidates for use in diabetes management. Full article

This article presents a unique study on the demolition process of a high-rise reinforced concrete building simulated using a methodology based on the Applied Element Method (AEM). Prior to the parametric analyses, the progressive collapse-based solid model was visually validated against real-world controlled demolition footage captured by both Unmanned Aerial Vehicles (UAVs) and fixed cameras, showing close agreement in building motion and debris dispersion patterns. In contrast to the Finite Element Method (FEM) model, the simulation is not blast-induced; it is instead developed on a column removal approach, which is widely adopted in progressive collapse assessments. Discussions related to the FEM model are provided as well. The parametric analysis is conducted in two stages. First, a constant removal sequence (removal of 4, 3, and 2 floors, respectively, in the first, second, and third axes) is applied to both 20- and 15-storey buildings under three time delays: 100 ms, 300 ms, and 500 ms. Based on these results, a 300 ms delay is identified as a suitable compromise for controlling debris dispersion, and this value is adopted for the subsequent analyses. In the second stage, three distinct removal sequences are examined on the 20-storey structure using the fixed 300 ms delay: Scenario 1 (4–3–2), Scenario 2 (12–8–6), and Scenario 3 (16–12–6). Among these, Scenario 3 yields the most compact horizontal debris spread. The findings indicate a strong correlation between the actual demolition behavior and the proposed model, demonstrating its capability to realistically capture complex structural failure mechanisms and provide practical guidance for optimizing controlled demolition strategies. Full article

Rock’s response time to blasting (T_min) refers to the critical time elapsed between the detonation of the explosive to the fragmentation and displacement of the rock, and it is a fundamental parameter that directly impacts blast-induced fragmentation. Although existing studies acknowledge the importance of this parameter, there are uncertainties regarding the factors determining T_min. Furthermore, existing models use complex parameters, fail to demonstrate sufficient performance in different engineering scenarios, or are not suitable for use as a practical engineering tool. To address these uncertainties and to reveal the relationship between T_min and fragmentation performance with an integrated model, a comprehensive dataset was obtained from 27 blasts conducted in 12 different quarries in Türkiye. The study followed a systematic methodology including geomechanical characterization, T_min measurement via high-speed videography, and pre- and post-blast photogrammetric fragment size analysis. The findings enabled the development of a model that predicts T_min with high accuracy (R² = 0.789, MAPE: %16.56) using parameters easily measurable in practice. More importantly, this estimation of T_min was used in an integrated model where the mean fragment size (P₅₀) could be predicted directly and successfully (R² = 0.837, MAPE: %8.37), providing a significant contribution to the literature. In light of these results, the primary engineering contribution of the study is that it has developed a practical and reliable tool applicable in the field, which treats T_min as an optimizable design variable and provides a seamless prediction framework from blasting design to the rock fragmentation. Full article

Blast mitigation of structures is an important research topic due to increasing intentional and accidental human-induced threats and hazards. This research area is essential to building capabilities in sustaining structural protection, site planning, protective design efficiency, occupant safety, and response and recovery plans. This paper investigates experimental tests and finite element analysis (FEM) of thin A36 steel sheets subjected to blast. Six field blast tests were performed at standoff distances of 300 mm and 500 mm. The explosive charges comprised 334 g of bare Composition B, and the steel sheets were 2 mm thick. The experimental results, derived from the analysis of high-speed camera recordings of the blast events, were compared with FEM simulations conducted using Abaqus^®/Explicit version 6.10. Three constitutive material models were considered in these simulations. First, the FEM simulation results were compared with experimental results. It was shown that the FEM analysis provided reliable results and was proven to be robust and cost-effective. Second, an extensive set of 460 additional numerical simulations was carried out as a parametric study involving varying standoff distances and steel sheet thicknesses. The results and methodologies presented in this paper offer valuable and original insights for engineers and researchers aiming to predict damage to steel structures during real detonation events and to design blast-resistant structures. Full article

Blasting operations in surface mining pose a risk of flyrock, which is a critical safety concern for both personnel and infrastructure. This study presents the use of unmanned aerial vehicles (UAVs) and photogrammetric techniques to improve the accuracy of blast design, particularly in relation to controlling burden values and reducing flyrock. The research was conducted in a basalt quarry in Lower Silesia, where high rock fracturing complicated conventional blast planning. A DJI Mavic 3 Enterprise UAV was used to capture high-resolution aerial imagery, and 3D models were created using Strayos software. These models enabled precise analysis of bench face geometry and burden distribution with centimeter-level accuracy. The results showed a significant improvement in identifying zones with improper burden values and allowed for real-time corrections in blasthole design. Despite a ten-fold reduction in the number of images used, no loss in model quality was observed. UAV-based surveys followed software-recommended flight paths, and the application of this methodology reduced the flyrock range by an average of 42% near sensitive areas. This approach demonstrates the operational benefits and enhanced safety potential of integrating UAV-based photogrammetry into blasting design workflows. Full article

This study presents a discrete event simulation model to minimize operating costs in quarry mining processes by determining the optimal allocation of backhoes and dump trucks, which are the primary mining equipment. The modeling focuses on four principal vehicle types (24-ton dump truck, 2.0 m³ backhoe, 41-ton dump truck, 4.64 m³ backhoe) commonly deployed in quarry mining. The simulation replicates the sequential mining stages involving soil removal, rock ripping (weathered rock or weathered soil), and blasting operations. This methodology is applied to a case study of mining process planning under resource constraints, incorporating real-world quarry conditions in South Korea. Results demonstrate that optimizing the number of equipment units reduces construction costs and shortens the construction period by decreasing dump truck waiting times. When the number of backhoes is limited to 10 during operations, findings indicate an increase in costs and a gradual decline in net profit. Additionally, the interaction between the 24-ton and 41-ton dump trucks is shown to influence the optimal allocation strategy. The simulation-based optimization executes iterative experiments for each scenario, yielding statistically robust results within a 95% confidence interval, thereby supporting informed decision-making for managers. Full article

This study focuses on the monitoring and early warning of landslide hazards induced by blasting caving in the Shizhuyuan polymetallic mine. A 30-channel microseismic monitoring system was deployed to capture the spatiotemporal characteristics of rock mass fracturing during a large-scale directional stratified blasting operation (419 tons) conducted on 21 June 2012. A total of 85 microseismic events were recorded, revealing two distinct zones of intense rock failure: Zone I (below 630 m elevation, P1–P3, C6–C8) and Zone II (above 630 m elevation, P4–P5, C1–C6). The upper slope collapse occurred within 5 min post-blasting, as documented by real-time monitoring and video recordings. Principal component analysis (PCA) was applied to 54 microseismic events in Zone II to determine the kinematic characteristics of the slip surface, yielding a dip direction of 324.6° and a dip angle of 73.2°. Complementary moment tensor analysis further revealed that shear failure dominated the slope instability, with pronounced shear fracturing observed in the 645–700 m height range. This study innovatively integrates spatial microseismic event distribution with geomechanical mechanisms, elucidating the dynamic evolution of blasting-induced landslides. The proposed methodology provides a novel approach for monitoring and forecasting slope instability triggered by underground mining, offering significant implications for disaster prevention in similar mining contexts. Full article

In the Karaganda coal basin, deteriorating geomechanical conditions have been observed, including seam disturbances, diminished strength of argillite–aleurolite strata, water ingress, and pronounced floor heave, all of which markedly increase the labor intensity of maintaining developmental headings. The maintenance and operation of these entries for a reference coal yield of 1000 t necessitate 72–75 man-shifts, of which 90–95% are expended on mitigating ground pressure effects and restoring support integrity. Conventional heave control measures—such as relief drifts, slotting, drainage, secondary blasting, and the application of concrete or rock–bolt systems—deliver either transient efficacy or incur prohibitive labor and material expenditures while lacking unified methodologies for predictive forecasting and support parameter design. This study therefore advocates for an integrated framework that synergizes geomechanical characterization, deformation prognosis, and the tailored selection of reinforcement schemes (incorporating both sidewall and floor-anchoring systems with directed preloading), calibrated to seam depth, geometry, and lithological properties. Employing deformation state assessments to optimize reinforcement layouts for floor stabilization in coal mine workings is projected to curtail repair volumes by 30–40% whilst significantly enhancing operational safety, efficiency, and the punctuality of face preparation. Full article

The cement industry is responsible for approximately 7–8% of global CO₂ emissions, primarily due to the energy-intensive production of clinker. In response to growing environmental concerns and the pressure to decarbonize the construction sector, the composition of cement has been evolving toward more sustainable alternatives. This article presents a review of the recent literature and EPD reports concerning changes in cement composition and their environmental impact, as assessed through Life Cycle Assessment (LCA) methodologies. This paper reviews the literature of recent LCA studies on cement with alternative materials. For a thorough analysis, VOSviewer_1.6.18 was used to find the research gap in this field. The companies’ EPD reports were analyzed to compare the most relevant information. The data that was extracted from the reports concerns carbon footprint, energy consumption, and system boundaries. The analysis reveals a clear trend toward reducing clinker content by incorporating supplementary cementitious materials (SCMs) such as fly ash, ground granulated blast furnace slag, natural pozzolans, and limestone. These modifications significantly lower key LCA indicators, particularly Global Warming Potential (GWP). Despite the growing number of studies on individual SCMs, there is a lack of integrated reviews comparing their environmental performance within a standardized LCA framework. This study addresses this gap by systematically comparing the environmental profiles of various low-clinker cement types and highlighting the critical role of supplementary cementitious materials selection. The findings confirm that changes in cement formulation are not only occurring but are essential for reducing the environmental footprint of construction materials. Full article

Coal gangue (CG), a major solid waste generated during coal development, presents critical environmental challenges due to its large-scale accumulation and associated ecological impacts, thereby necessitating the development of efficient utilization strategies. This investigation developed a composite geopolymer system through the alkali-activated co-utilization of uncalcined CG and blast furnace slag (BFS), demonstrating an environmentally sustainable approach for industrial byproduct value addition. The effects of key parameters, including BFS content, liquid-to-solid ratio, alkali activator dosage, waterglass modulus, and curing regime, on the strength development were first investigated through single-factor experiments. Based on these results, response surface methodology was applied to optimize the preparation parameters and develop a quadratic regression model describing the relationship between compressive strength and the influencing factors. The optimal conditions (a waterglass modulus of 1.06, an alkali activator dosage of 13.81%, and an initial 24 h curing temperature of 30 °C) were determined to maximize compressive strength. The reaction mechanisms were further explored using XRD and SEM-EDS, which confirmed the existence of calcium silicate hydrate, calcium aluminum silicate hydrate, and geopolymer gel in the composite geopolymer matrix. Full article