Search Results (102)

The compressive strength (Fc) of cement mortar (CM) is a key parameter in ensuring the mechanical reliability and durability of cement-based materials. Traditional testing methods are labor-intensive, time-consuming, and often lack predictive flexibility. With the increasing adoption of machine learning (ML) in civil engineering, data-driven approaches offer a rapid, cost-effective alternative for forecasting material properties. This study investigates a wide range of supervised linear and nonlinear ML regression models to predict the Fc of CM. The evaluated models include linear regression, ridge regression, lasso regression, decision trees, random forests, gradient boosting, k-nearest neighbors (KNN), and twelve neural network (NN) architectures, developed by combining different optimizers (L-BFGS, Adam, and SGD) with activation functions (tanh, relu, logistic, and identity). Model performance was assessed using the root mean squared error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Among all models, NN_tanh_lbfgs achieved the best results, with an almost perfect fit in training (R² = 0.9999, RMSE = 0.0083, MAE = 0.0063) and excellent generalization in testing (R² = 0.9946, RMSE = 1.5032, MAE = 1.2545). NN_logistic_lbfgs, gradient boosting, and NN_relu_lbfgs also exhibited high predictive accuracy and robustness. The SHAP analysis revealed that curing age and nano silica/cement ratio (NS/C) positively influence Fc, while porosity has the strongest negative impact. The main novelty of this study lies in the systematic tuning of neural networks via distinct optimizer–activation combinations, and the integration of SHAP for interpretability—bridging the gap between predictive performance and explainability in cementitious materials research. These results confirm the NN_tanh_lbfgs as a highly reliable model for estimating Fc in CM, offering a robust, interpretable, and scalable solution for data-driven strength prediction. Full article

One of the important tasks in sand liquefaction assessment is to evaluate the likelihood of soil liquefaction. However, most liquefaction assessment methods are deterministic for influencing factors and fail to calculate the liquefaction probability by systematically considering the probability distributions of soil factors. Based on field liquefaction investigation cases, probability distribution fitting and a hypothesis test were carried out. For the variables that failed to pass the fitting and test, the kernel density estimation was conducted. Methods for calculating the liquefaction probability using a Monte Carlo simulation with the probability distribution were then proposed. The results indicated that for (N₁)₆₀, SM, S, and GM followed a Gaussian distribution, while CL and ML followed a lognormal distribution; for FC, SM and GM followed a lognormal distribution; and for d₅₀, ML and S followed a Gaussian and lognormal distribution, respectively. The other factors’ distribution curves can be calculated by kernel density estimation. It is feasible to calculate the liquefaction probability based on a Monte Carlo simulation of the variable distribution. The result of the liquefaction probability calculation in this case was similar to that of the existing probability model and was consistent with actual observations. Regional sample differences were considered by introducing the normal distribution error term, and the liquefaction probability accuracy could be improved to a certain extent. The liquefaction probability at a specific seismic level or the total probability within a certain period in the future can be calculated with the method proposed in this paper. It provides a data-driven basis for realistically estimating the likelihood of soil liquefaction under seismic loading and contributes to site classification, liquefaction potential zoning, and ground improvements in seismic design decisions. The practical value of seismic hazard mapping and performance-based design in earthquake-prone regions was also demonstrated. Full article

Co_0.95R_0.05Fe₂O₄ nanoparticles were synthesized using a sol-gel approach incorporating bio-based agents and were found to be single phases adopting a cubic Fd-3m structure. XPS shows the presence of Gd³⁺ and Ho³⁺ ions. The spin–orbit splitting of about 15.4 eV observed in Co 2p core-level spectra is an indication that Co is predominantly present as Co³⁺ state, while the satellite structures located at about 6 eV higher energies than the main lines confirm the existence of divalent Co in Co_0.95R_0.05Fe₂O₄. The positions of the Co 3s and Fe 3s main peaks obtained by curve fitting and the exchange splitting obtained values for Co 3s and Fe 3s levels point to the high Co³⁺/Co²⁺ and Fe³⁺/Fe²⁺ ratios in both samples. The saturation magnetizations are smaller for the doped samples compared to the pristine ones. For theoretical magnetization calculation, we have considered that the heavy rare earths are in octahedral sites and their magnetic moments are aligned antiparallelly with 3d transition magnetic moments. ZFC-FC curves shows that some nanoparticles remain superparamagnetic, while the rest are ferrimagnetic, ordered at room temperature, and showing interparticle interactions. The M_S/Ms ratio at room temperature is below 0.5, indicating the predominance of magnetostatic interactions. Full article

This study investigates the pyrolysis of construction, renovation, and demolition (CRD) wood waste to produce biochar, with a focus on its robustness, scalability, and characterization for energy and environmental applications. Pyrolysis conditions, including the temperature, biomass residence time (BRT), and feedstock mass, were varied to evaluate their effects on biochar properties. High-temperature biochars (B800) showed the highest fixed carbon (FC) (87%) and thermostable fraction (TSF) (96%) and the lowest volatile carbon (VC) (9%), with a high carbon content (92%), a large BET surface area (300 m²/g), and a high micropore volume (0.146 cm³/g). However, the hydrogen (0.9%) and oxygen (2.2%) content, Van-Krevelen parameters (H/C: 0.1; O/C: 0.02), and biochar yield (21%) decreased with increasing temperature. Moderate-temperature biochars (B600) have balanced physicochemical properties and yields, making them suitable for adsorption applications. Methyl orange dye removal exceeded 90% under the optimal conditions, with B600 fitting well with the Freundlich isotherm model (R² = 0.97; 1/n = 0.5) and pseudo-second-order kinetic model (R² = 1). The study highlights biochar’s suitability for varied applications, emphasizing the need for scalability in CRD wood pyrolysis. Full article

With the emergence of AI technology, its adoption in higher education has become an interesting field for researchers. The present study explores the acceptance of AI for learning the English language by Pakistani EFL students using the UTAUT-2 and Metacognition theory. The UTAUT-2 questionnaire was adapted with minor changes to make it suitable for the EFL context. Data were collected from the English departments of the top ten general universities in Pakistan to make the findings generalizable. Another step taken to ensure generalizability was the sampling of 611 students randomly from both undergraduate (BS and ADP) and postgraduate (MPhil and PhD) programs studying in different semesters. PLS-SEM was employed for data analysis. In the first step, the PLS algorithm was run for the measurement model, which confirmed the reliability, validity, and fitness of the model. Second, the bootstrapping method was used for hypothesis testing. The findings reveal that six of the ten hypotheses for direct relationships are supported. Habit (0.489) was found to be the strongest contributor to BI, followed by PE (0.141), SI (0.100), and FC (0.093). Moreover, actual use behaviour was predicted by habit (0.325) instead of BI and FC. These findings are supported by metacognition theory, as the habit of AI seems to shape the metacognitive knowledge of EFL learners in place of traditional learning methods, and other factors seem to reinforce the metacognitive experience of using AI language. The study suggests implications for EFL experts, academia, and policymakers to strategically integrate AI into language learning by informing them of its potential benefits and risks. Full article

This study investigates the primary technological and socio-environmental factors influencing the adoption intentions of AI-powered technology at the corporate level within higher education institutions. A conceptual model based on the Diffusion of Innovation Theory (DOI), the Technology–Organization–Environment (TOE), and the Technology Acceptance Model (TAM) combined framework were proposed and tested using data collected from 367 higher education students, faculty members, and employees. SPSS Amos 24 was used for CB-SEM to choose the best-fitting model, which proved more efficient than traditional multiple regression analysis to examine the relationships among the proposed constructs, ensuring model fit and statistical robustness. The findings reveal that Compatibility “C”, Complexity “CX”, User Interface “UX”, Perceived Ease of Use “PEOU”, User Satisfaction “US”, Performance Expectation “PE”, Artificial intelligence “AI” introducing new tools “AINT”, AI Strategic Alignment “AIS”, Availability of Resources “AVR”, Technological Support “TS”, and Facilitating Conditions “FC” significantly impact AI adoption intentions. At the same time, Competitive Pressure “COP” and Government Regulations “GOR” do not. Demographic factors, including major and years of experience, moderated these associations, and there were large differences across educational backgrounds and experience. Full article

The proteolytic processing of the SARS-CoV-2 spike glycoprotein by host cell membrane-associated proteases is a key step in both the entry of the invading virus into the cell and the release of the newly generated viral particles from the infected cell. Because of the critical importance of this step for the viral infectivity cycle, it has been a target of extensive efforts aimed at identifying highly specific protease inhibitors as potential antiviral agents. An alternative strategy to disrupt the pre-fusioviden processing of the SARS-CoV-2 S glycoprotein aims to protect the substrate rather than directly inhibit the proteases. In this work, we focused on furin, a serine protease located primarily in the Golgi apparatus, but also present on the cell membrane. Its cleavage site within the S glycoprotein is located within the stalk region of the latter and comprises an arginine-rich segment (SPRRARS), which fits the definition of the Cardin–Weintraub glycosaminoglycan recognition motif. Native mass spectrometry (MS) measurements confirmed the binding of a hexadecameric peptide representing the loop region at the S1/S2 interface and incorporating the furin cleavage site (FCS) to heparin fragments of various lengths, as well as unfractionated heparin (UFH), although at the physiological ionic strength, only UFH remains tightly bound to the FCS. The direct LC/MS monitoring of FCS digestion with furin revealed a significant impact of both heparin fragments and UFH on the proteolysis kinetics, although only the latter had IC50 values that could be considered physiologically relevant (0.6 ± 0.1 mg/mL). The results of this work highlight the importance of the long-range and relatively non-specific electrostatic interactions in modulating physiological and pathological processes and emphasize the multi-faceted role played by heparin in managing coronavirus infections. Full article

With advancements in cloud computing and machine learning algorithms, an increasing number of cropland datasets have been developed, including the China land-cover dataset (CLCD) and GlobeLand30 (GLC). The unique climatic conditions of the Tibetan Plateau (TP) introduce significant differences and uncertainties to these datasets. Here, we used a quantitative and visual integrated assessment approach to assess the accuracy and spatial consistency of five cropland datasets around 2020 in the TP, namely the CLCD, GLC30, land-use remote sensing monitoring dataset in China (CNLUCC), Global Land Analysis and Discovery (GLAD), and global land-cover product with a fine classification system (GLC_FCS). We analyzed the impact of terrain, climate, population, and vegetation indices on cropland spatial consistency using structural equation modeling (SEM). In this study, the GLAD cropland area had the highest fit with the national land survey (R² = 0.88). County-level analysis revealed that the CLCD and GLC_FCS underestimated cropland areas in high-elevation counties, whereas the GLC and CNLUCC tended to overestimate cropland areas on the TP. Considering overall accuracy, GLC and GLAD performed the best with scores of 0.76 and 0.75, respectively. In contrast, CLCD (0.640), GLC_FCS (0.640), and CNLUCC (0.620) exhibited poor overall accuracy. This study highlights the significantly low spatial consistency of croplands on the TP, with only 10.60% consistency in high and complete agreement. The results showed substantial differences in spatial accuracy among zones, with relatively higher consistency observed in low-altitude zones and notably poorer accuracy in zones with sparse or fragmented cropland. The SEM results indicated that elevation and slope directly influenced cropland consistency, whereas temperature and precipitation indirectly affected cropland consistency by influencing vegetation indices. This study provides a valuable reference for implementing cropland datasets and future cropland mapping studies on the TP region. Full article

Background/Objectives: Chronic attention problems occur in approximately 25% of children after acquired brain injury (ABI). When delivered daily, transcranial direct current stimulation (tDCS) may improve attention; however, access to daily in-clinic tDCS treatment can be limited by other commitments, including concurrent therapy, school commitments, and caregiver schedules. Treatment access can be improved through home-based interventions, though these require several practical and safety considerations in a pediatric ABI population. This study evaluated the safety, feasibility, and tolerability of remotely monitored at-home tDCS during online gamified attention training in pediatric ABI. Methods: We conducted a randomized, single-blind, dose-controlled clinical trial of at home tDCS in Brisbane, Australia (10 tDCS sessions; 20 min; 1 mA or 2 mA; bilateral dorsolateral prefrontal cortex). Participants attended our clinic at baseline for clinical assessments, fitting of the personalized tDCS headband, and training in how to use tDCS at home. All sessions were remotely supervised using live videoconferencing. We assessed the feasibility and tolerability of at-home tDCS and our customized, personalized at-home tDCS headband as primary outcomes. As secondary outcomes, we evaluated changes in functional connectivity (fc) and reaction time (RT). Results: Seventy-three participants were contacted over six months (January-June 2023) and ten were enrolled (5 males; mean age: 12.10 y [SD: 2.9]), satisfying a priori recruitment timelines (CONSORT reporting). All families successfully set up tDCS and completed attention training with excellent protocol adherence. There were no serious adverse events over the 100 total sessions. Nine participants completed all stimulation sessions (1 mA: n = 5, 2 mA: n = 4). Participants in the 2 mA group reported greater tingling, itching, and discomfort (all p < 0.05). One participant in the 1 mA group was unable to complete all sessions due to tolerability challenges; however, these challenges were resolved in the second half of the intervention by gradually increasing the stimulation duration across the 10 days alongside additional coaching and support. Conclusions: Overall, daily remotely supervised at-home tDCS in patients with pediatric ABI is safe, feasible, and tolerable. Our results support larger, sham-controlled efficacy trials and provide a foundation for the development of safe and effective at-home stimulation therapeutics that may offer targeted improvement of neurocognitive symptoms in children. Full article

Background: In Staphylococcus aureus, antimicrobial resistance (AMR) imposes significant fitness costs (FCs), including reduced growth rate, interbacterial competitiveness, and virulence. However, the FC molecular basis remains poorly understood. This study investigated the FC omic basis and compensatory adaptations in high-risk HA-, LA-, and CA-MRSA, acquiring mono- or cross-resistance to second-line daptomycin (DAP) and dalbavancin (DAL), as well as reduced susceptibility (RS) to first-line glycopeptides, i.e., vancomycin and teicoplanin (GLYs, i.e., VAN, TEC), related to the specific mechanism of action (MOA)-related AMR-mechanisms and genomic backgrounds, paying increasing FCs. Methods: The FC omic basis associated with mono- or cross- DAP-/DAL-R and GLY-RS were investigated by integrated omics. This study focused on core-genome essential (EG) and accessory virulence gene (VG) SNPomics and transcriptomics by Illumina MiSeq whole-genome sequencing, RNA-seq, and bioinformatic analysis. Results: Moderate impact nsSNPs were identified in EGs related to vital cellular functions and VGs. Comparative EG transcriptomics revealed differential expressions and key dysregulations—via asRNAs—prevalently affecting the protein synthesis and cell-envelope EG clusters, as well as the VG cluster. Conclusions: Our data, firstly, underlined the EG and VG mutation- and transcription-driven omic-based FC burden and the compensatory adaptations associated with the emergence of mono-DAP-R, cross-DAP-R/hGISA, and DAP-R/DAL-R/GISA, linked to specific MOA-related AMR-mechanisms and genomic backgrounds in high-risk HA-, LA-, and CA-MRSA. Full article

Background/Objectives: Physiologically based pharmacokinetic (PBPK) modeling is an important tool in biologic drug development. However, a standardized modeling strategy is currently missing. A cross-industry collaboration developed PBPK models for seven case studies, including monoclonal antibodies, antibody–drug conjugates, and bispecific T-cell engagers, to identify key parameters and establish a workflow to simulate biologic drugs in monkeys and in humans. Methods: PBPK models were developed in the monkey with limited data, including the molecular weight, the binding affinity to FcRn, and the additional systemic clearance of IgG, which is 20% of the total clearance. The binding affinity was only available for human FcRn and corrected for the known species-dependent differences in IgG binding. The strategy of monkey simulations was evaluated with an additional 14 studies published in the literature. Three different scenarios were simulated in humans afterwards: without, with allometrically scaled, and with optimized additional systemic clearance. Results: The plasma peak concentration and the area under the curve were predicted within 50% of the observed data for all studied case examples in the monkey, which demonstrates that sparse input parameters are sufficient for successful predictions in the monkey. Simulations in humans demonstrated the need for additional systemic clearance, because drug exposure was highly overpredicted without an additional systemic clearance term. Allometric scaling improved the predictions, but optimization led to the best fit, which is currently a limitation in the translation from animals to humans. Conclusions: This work highlights the importance of understanding the general mechanisms of drug uptake in different tissue types and cells in both target-dependent and -independent processes. Full article

The ammonia adsorption capacity of lignin-rich biomass solids was tested for the first time at low partial pressures (<1.5 kPa) and 20 °C. The biomass samples included untreated tree barks, husks, and peats, as well as the biochars produced by their slow pyrolysis. Proximate and ultimate analyses, lignin content, and metal content are also presented. The untreated biosolids had higher VM/FC ratios, molar H/C, and O/C than the treated biosolids (biochars and treated biochars). A novel methodology is described for the safe generation of gaseous ammonia at predictable low partial pressures from tabletop-scale batch reaction experiments of NaOH with (NH₄)₂SO₄ in aqueous solution, leading to the determination of ammonia adsorption capacities from low-cost experiments. Statistically significantly larger NH₃ adsorption capacities were obtained for the untreated biosolids than for their biochars (p < 0.001). In contrast, the biochars were found to be poor NH₃ adsorbers without further treatment. The NH₃ adsorption capacities from this study’s biosolids were compared with those of common adsorbent types in the same conditions using the existing literature through equilibrium model interpolation (Dubinin–Astakhov, Toth, and Freundlich) or cubic spline fit from graphical isotherms. Controls consisting of commercially sourced activated carbons (AC) had low adsorption capacities, close to those derived from the literature in the same conditions for similar materials, confirming the methodology’s robustness. The untreated biosolids’ NH₃ adsorption capacities were in the same range as those reported for silica, gamma-alumina, and some of the treated or doped ACs. They also performed better than the undoped, untreated ACs. The work suggests lignin-rich untreated biosolids such as barks and peats are competent low-cost ammonia adsorbents. Full article

Mycoviruses are viruses that infect fungi, including plant pathogens. The infection of these mycoviruses is sometimes associated with impaired phenotypes of their fungal hosts, a phenomenon known as hypovirulence. Thus, using mycoviruses as biological control agents has emerged as a promising tool to combat forest diseases. The invasive ascomycete fungus Fusarium circinatum, which causes pine pitch canker (PPC) disease in Pinus tree species and other coniferous trees, is infected by the mycovirus Fusarium circinatum mitovirus 1 (FcMV1), FcMV2-1, and FcMV2-2. However, its impact on pathogen fitness remains unclear. The most accurate method used to identify the effect of a mycovirus on its host is the generation of isogenic lines with and without the mycovirus. The present study aimed to cure F. circinatum isolates infected by FcMV1 using different approaches. For this purpose, three replicates of each isolate were exposed to thermal treatment (38 °C) and antibiotic treatment (ribavirin, cycloheximide, kanamycin, and rifampicin mixed with cAMP)(cyclic adenosine monophosphate) for five successive passages. The viral titer of FcMV1 was then assessed using qPCR (quantitative polymerase chain reaction) after the first week and after the fifth week of the treatment. The results revealed differences in treatment efficacy among F. circinatum isolates, with some showing very low virus titers at the end of the experiment. Both thermal and antibiotic treatment effectively reduced the viral load in all isolates. In addition, the antibiotic cycloheximide and rifampicin +cAMP reduced the viral titer more than ribavirin and kanamycin. The isolate Fc179 was found to be more prone to antibiotic treatment than the other two isolates (001 and Va221). This study demonstrated the possibility of using some isolates of F. circinatum for fine-tuning cures for mitovirus, in order to create virus-free strains for biological control in the future. Full article

In order to improve the physical and mechanical properties and the ability to perform in practical applications of prefabricated monolithic composite columns, high-performance fiber-reinforced cementitious composites (HPFRCC) material was prefabricated into mold shells to form HPFRCC precast monolithic composite columns. Through the axial compression test, the axial compression failure form, failure mechanism, bearing capacity, deformation ability, and influencing factors were studied. The results showed that compared with RC precast monolithic composite column, the HPFRCC specimens showed better deformation performance. HPFRCC prefabricated shells provided additional restraint beyond stirrups. The HPFRCC composite columns’ yield compressive strain increased by 11.59% on average compared with the RC composite column, and the peak compressive strain increased by 10.92%. The larger the ρ_v of stirrups was, the larger the compressive strain of the key point of the columns was. Compared with the FC-P-01 (ρ_v was 1.05%), the yield compressive strain of FC-P-02 (ρ_v was 1.48%) increased by 21.63%, and the yield compressive strain of FC-P-03 (ρ_v was 0.74%) decreased by 11.20%. The calculation model of the axial bearing capacity of the HPFRCC composite column was established through theoretical mechanical analysis, and the calculated values of the model fit with the experimental values. Full article

Background: Over the last two decades, therapy for benign esophageal strictures has shifted from empirical dilatations and surgery to evidence-based and complex endoscopic and surgical procedures, aiming to achieve long-term esophageal patency. Aim: The purpose of our study is to provide descriptive evidence regarding the appropriate tailored medical, endoscopic, and surgical management of benign esophageal strictures. Methods: This retrospective study includes patients with benign esophageal strictures; the data collected encompass the complete patient profiles, detailed etiologic and anatomic workups of the strictures, comprehensive imaging, as well as management and follow-up details. Technical and clinical success rates, adverse events, stricture patency, and the need for additional therapy have been evaluated. Results: Most of the strictures (80.2%) were complex, requiring advanced techniques for management. The primary treatment involved endoscopic dilation, performed with Savary-Gillard bougie dilators in 76.7% of cases and pneumatic balloon dilators in 23.3% of cases. Clinical success was achieved in 95.3% of patients, with a significant improvement in the Ogilvie dysphagia score. Patients with caustic strictures required repeated dilations over the years, compared to shorter intervals for peptic strictures. Adverse events were minimal (e.g., perforation 2.3% and bleeding 4.7%) and managed predominantly endoscopically. Refractory strictures (16.3%) required advanced interventions, including fully covered self-expandable metallic stents (fc-SEMS) and corticosteroid injections. Conclusions: Both our data and the current literature support the use of tailored endoscopic strategies as the first-choice options for managing benign esophageal strictures. Our results strongly suggest against one-size-fits-all therapeutic alternatives. Full article