Loading rate-dependent mechanical properties and crack evolution characteristics of MOC-waste soil composite material

Optimized continuous bioproduction of ectoine from carbon dioxide and industrial contaminants using Guyparkeria halophila

Crushing behaviour of hollow shell granule: Effects of surface roughness and loading rate

Data driven investigation to understand the influence of total solids on biological biogas upgrading

Operational control mechanism and pilot-scale demonstration of integrated fixed-film activated sludge partial nitritation/anammox system.

Ground reaction forces are accepted as gold standard for objective assessment of weightbearing lameness but measurements at the hoof are limited to experimental devices. This study aimed to evaluate whether innovative instrumented hoof boots (IHBs) could detect and monitor lameness in horses during diagnostic anaesthesia. Twenty-six horses referred for lameness examination (15 frontlimbs, 17 hindlimbs) were equipped with IHBs and body mounted inertial sensors. Data were collected simultaneously before and after diagnostic anaesthesia until objectively considered positive. Recorded IHB data included median values of peak vertical pressure [N] (PVP), impulse [Ns] (AUC), peak loading rate [N/ms] (PLR) and further force and temporal parameters. Absolute and relative differences were compared before and after anaesthesia. Bonferroni-Holm method and Wilcoxon signed-rank test were used for statistical analysis (α=5%). Mean PVP and AUC of lame and contralateral limbs differed significantly before positive diagnostic anaesthesia (p < 0.001, p < 0.001) and not after (p = 0.074, p = 0.196) in the overall data. The PLR differed significantly before and after anaesthesia (p = 0.006, p = 0.024). Overall median PVP and AUC difference between lame and contralateral limbs differed significantly (p = 0.015, p = 0.001) between baseline lameness (PVP 3.54%, AUC 6.47%) and not after anaesthesia (PVP 0.90%, AUC 2.04%), respectively. Non-significant differences were seen in further study parameters. Main limitations were a small, mixed-breed population, unknown horse speed and different diagnoses. This study shows that the IHB was generally suitable for detection of differences between lame and contralateral limbs and to evaluate the effect of positive diagnostic anaesthesia by showing a more symmetrical distribution of PVP and AUC between contralateral limbs after improvement of lameness.

Prediction and optimization of partial denitrification and anammox performance by machine learning and key bioindicators identification.

Machine learning and explainable AI for predicting antibiotics removal in constructed Wetlands: Key factors and management implications

The ultrasoft nature and intricate geometry of the 'butterfly-shaped' spinal cord gray matter, surrounded by white matter, pose major challenges for experimental and computational characterization of tissue mechanics. With growing interest in modeling spinal cord injury, disease, and regeneration, reliable mechanical data are increasingly needed. To meet this demand, we developed a multimodal characterization pipeline, combining mesoscale spherical indentation of gray and white matter, macroscale rheometer-based compression-tension tests, nonlinear continuum mechanics modeling, and inverse finite element simulations, using Hertzian, neo-Hookean, and Ogden constitutive laws. Both testing modalities were applied sequentially on samples extracted along the cranio-caudal axis of porcine spinal cords to assess regional variations in mechanical properties. Compared with Hertzian and neo-Hookean models, Ogden models more accurately captured the nonlinear material response. For comparable loading rates, shear moduli from mesoscale indentation are consistent with those from macroscale large-strain testing, demonstrating the robustness of the multimodal approach. Based on these observations, we demonstrate the effectiveness of identifying nonlinear material parameters for spinal cord gray and white matter tissue through the simultaneous integration of indentation and large-strain data. Like this, we for the first time include information on both, tissue-type-specific properties as well as large-strain nonlinear effects. Our results demonstrate that a multimodal characterization pipeline can yield a more robust and representative material parameters with important implications for advanced treatment strategies for spinal cord injuries and disease. Statement of Significance: In the growing body of research on the mechanical characterization of brain and spinal cord tissue, discrepancies between measured properties obtained from different testing modalities remain a major challenge. In this study, we use two complementary testing methods - spherical indentation and large-strain compression-tension loading - applied to the same samples. We demonstrate that when the experimental data are analyzed using nonlinear material modeling and finite element simulations, the results from both methods agree well. We for the first time integrate both indentation and large-strain testing data to identify reliable individual material parameters for spinal cord gray and white matter tissue. Our results highlight the robustness of the presented multimodal characterization pipeline for central nervous system tissue.

• Elucidating the anaerobic digestion mechanisms for food-waste valorization • Evaluation of pre-treatments enhancing biodegradability and methane yield • Identification of key factors influencing digestion efficiency and stability • Discussion of challenges and future pathways for AD advancement The escalating global volume of food waste poses significant environmental challenges but also represents a substantial opportunity for sustainable energy generation. Anaerobic digestion (AD) is a leading technology that converts this waste into biogas, offering a dual solution for renewable energy production and waste management. This comprehensive review delves into the application of AD for food waste, providing an in-depth analysis of the underlying biochemical processes and critical operational parameters that govern biogas yield such as; temperature, pH, and organic loading rate. It further explores a range of physical, chemical, and biological pre-treatment methods to enhance the efficiency of waste breakdown. The review further investigates recent advancements, including innovative reactor designs, co-digestion strategies, and process optimization techniques, all aimed at improving system performance and scalability. While addressing key challenges like feedstock variability, process instability, and inhibition, the review proposes practical mitigation strategies. Finally, it evaluates the environmental and economic viability of AD, discussing its integral role in circular economy frameworks by transforming waste into valuable resources. This synthesis aims to guide future research and accelerate the adoption of anaerobic digestion as a cornerstone technology for sustainable waste-to-energy conversion.

Mechanistic insights into the spontaneous failure-to-success transition in pilot-scale anaerobic digestion of food waste: Roles of volatile fatty acids and microbial community dynamics.

Investigation of translaminar fracture in T700/PEEK thermoplastic laminates under tension and compression at various quasi-static loading rates

Transformation of China's petroleum pipeline industry in the renewable energy era: A review

Mechanisms of synergistic remediation of Dry anaerobic digestion stability by biochar and bioaugmentation agents.

Mechanisms of hydrogen-producing community reconstruction under organic loading control: the synergistic effects of initial organic loading rate and hydraulic retention time

Novel hybrid biochar-based constructed wetlands for contaminant of emerging concern removal in water reuse.

Control mechanism of loading rate on granite fracture: Insights from synchronous resistivity and acoustic emission monitoring

Rapid reactivation of six-month preserved anammox-HAP granules enables resilient and accelerated EGSB start-up.

The city of Boma in the Democratic Republic of the Congo is experiencing demographic and urban growth that has not been matched by adequate development of its electrical network. The current grid, dating back to 1972, suffers from critical overloading of MV/LV substations, leading to frequent load shedding and unequal electricity supply between the city center and peripheral neighborhoods. This article proposes an energy planning methodology for the 2028 horizon, based on load rate analysis, demand forecasting, and infrastructure sizing. The results indicate an average global load rate of 143.5 %, far exceeding the 80 % standard set by the International Electrotechnical Commission (IEC). To meet future demand, the installation of 42 MV/LV transformers of 630 kVA and a 15 MVA substation is recommended, representing an estimated investment of USD 14.5 million. This planning strategy aims to ensure a more reliable, equitable, and resilient electricity distribution across the entire urban area.

Physical and biological fate of protozoan (oo)cysts in slow sand filtration: A review.