(1) Background: This study investigated the effects of caffeinated chewing gum on the basketball-specific performance of trained basketball players. A double-blind, randomized crossover design was employed. (2) Methods: Fifteen participants (age: 20.9 ± 1.0 years; height: 180.9 ± 5.4 cm; mass: 77.2 ± 7.5 kg; training age: 8.2 ± 0.3 years) were recruited and divided into a caffeine trial (CAF) and placebo trial (PL). The participants in the CAF trial chewed gum containing 3 mg/kg of caffeine for 10 min, while those in the PL trial chewed a placebo gum without caffeine. Following a 15 min rest, all the participants completed basketball-specific performance tests. (3) Results: The free throw accuracy for the CAF trial was significantly higher than that for the PL trial (CAF: 79.0 ± 4.31%; PL: 73.0 ± 9.16%; p = 0.012; Cohen’s d = 0.94). Additionally, the CAF trial demonstrated significantly better performance in the 20 m segmented dash (CAF: 2.94 ± 1.12 s; PL: 3.13 ± 0.10 s; p < 0.001; Cohen’s d =1.8) and squats (p < 0.05), and exhibited lower fatigue indexes (CAF: 3.6 ± 1.6%; PL: 5.2 ± 1.6%; p = 0.009; Cohen’s d =1.0). (4) Conclusions: These findings suggest that chewing gum containing 3 mg/kg of caffeine offers moderate-to-large improvements in key performance aspects relevant to professionally trained basketball players. Full article

To reduce safety accidents caused by distracted driving and address issues such as low recognition accuracy and deployment difficulties in current algorithms for distracted behavior detection, this paper proposes an algorithm that utilizes an improved KNN for classifying driver posture features to predict distracted driving behavior. Firstly, the number of channels in the Lightweight OpenPose network is pruned to predict and output the coordinates of key points in the upper body of the driver. Secondly, based on the principles of ergonomics, driving behavior features are modeled, and a set of five-dimensional feature values are obtained through geometric calculations. Finally, considering the relationship between the distance between samples and the number of samples, this paper proposes an adjustable distance-weighted KNN algorithm (ADW-KNN), which is used for classification and prediction. The experimental results show that the proposed algorithm achieved a recognition rate of 94.04% for distracted driving behavior on the public dataset SFD3, with a speed of up to 50FPS, superior to mainstream deep learning algorithms in terms of accuracy and speed. The superiority of ADW-KNN was further verified through experiments on other public datasets. Full article

Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainability of transformer-based image classification models. Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps. We introduce two modules: the “Relationship Weighted Out” and the “Cut” modules. The “Relationship Weighted Out” module focuses on extracting class-specific information from intermediate layers, enabling us to highlight relevant features. Additionally, the “Cut” module performs fine-grained feature decomposition, taking into account factors such as position, texture, and color. By integrating these modules, we generate dense class-specific visual explainability maps. We validate our method with extensive qualitative and quantitative experiments on the ImageNet dataset. Furthermore, we conduct a large number of experiments on the LRN dataset, which is specifically designed for automatic driving danger alerts, to evaluate the explainability of our method in scenarios with complex backgrounds. The results demonstrate a significant improvement over previous methods. Moreover, we conduct ablation experiments to validate the effectiveness of each module. Through these experiments, we are able to confirm the respective contributions of each module, thus solidifying the overall effectiveness of our proposed approach. Full article

The use of by-products as a source of bioactive compounds with economic added value is one of the objectives of a circular economy. The olive oil industry is a source of olive pomace as a by-product. The olive pomace used in the present study was the exhausted olive pomace, which is the by-product generated from the air drying and subsequent hexane extraction of residual oil from the olive pomace. The objective was to extract bioactive compounds remaining in this by-product. Various types of green extraction were used in the present study: solvent extraction (water and hydroalcoholic); ultrasound-assisted extraction; Ultra-Turrax-assisted extraction; and enzyme-assisted extraction (cellulase; viscoenzyme). The phenolic profile of each extract was determined using HPLC-DAD and the total phenolic content (TPC) and antioxidant activity (ABTS, DPPH, and ORAC) were determined as well. The results showed significant differences in the yield of extraction among the different methods used, with the enzyme-assisted, with or without ultrasound, extraction presenting the highest values. The ultrasound-assisted hydroethanolic extraction (USAHE) was the method that resulted in the highest content of the identified phenolic compounds: 2.021 ± 0.29 mg hydroxytyrosol/100 mg extract, 0.987 ± 0.09 mg tyrosol/100 mg extract, and 0.121 ± 0.005 mg catechol/100 mg extract. The conventional extraction with water at 50 °C produced the best results for TPC and antioxidant activity of the extracts. The extracts from the USAHE were able to inhibit Gram-positive bacteria, especially Bacillus cereus, showing 67.2% inhibition at 3% extract concentration. Full article

This study presents a comparative assessment of emerging sustainability trends in tourist facilities, focusing on multiple hotels and resorts. By analyzing various aspects, such as energy efficiency, waste management, water conservation, and community engagement, this study evaluates the sustainability initiatives undertaken by these establishments. Through a systematic review of sustainability reports, practices, challenges, and innovations, it identifies key trends shaping the future of sustainable tourism in the hospitality industry. Initially, two case studies of hospitality developments that are awarded with sustainability prizes have been reviewed. These real estate developments are located in the Maldives and Greece, and following their detailed review, a comparison is drawn with a sample of multiple hotels in Pafos City, Cyprus. This study aims to ascertain the presence of a reciprocal relationship between sustainable tourism destinations and their immediate environs, encompassing the local community. This investigation hinges on an analysis of the sustainable practices implemented by hotels situated within these destinations. Moreover, this study explores the distance of hotels from achieving sustainable development by analyzing their environmental, social, and economic practices. This investigation encompasses a range of hotels in Cyprus and internationally. Recommendations toward a more sustainable approach for touristic real estate units has been made, and it is concluded that the development of sustainable tourist destinations requires active involvement from the local and the government authorities, supported by programs facilitating sustainable tourism. Encouraging synergies among businesses and supporting destination management partnerships are crucial. Sustainable hospitality should adhere to environmental excellence, social responsibility, and economic viability. The findings provide valuable insights for policymakers, hotel managers, and stakeholders seeking to enhance environmental performance and social responsibility within tourist accommodations. Full article

Pituitary neuroendocrine tumors (PitNET) are known to be variably infiltrated by different immune cells. Nonetheless, their role in pituitary oncogenesis has only begun to be unveiled. The immune microenvironment could determine the biological and clinical behavior of a neoplasm and may have prognostic implications. To evaluate the expression of immune-related genes and to correlate such expression with the presence of infiltrating immune cells in forty-two PitNETs of different lineages, we performed whole transcriptome analysis and RT-qPCR. Deconvolution analysis was carried out to infer the immune cell types present in each tumor and the presence of immune cells was confirmed by immunofluorescence. We found characteristic expression profiles of immune-related genes including those encoding interleukins and chemokines for each tumor lineage. Genes such as IL4-I1, IL-36A, TIRAP, IL-17REL, and CCL5 were upregulated in all PitNETS, whereas IL34, IL20RA, and IL-2RB characterize the NR5A1-, TBX19-, and POU1F1-derived tumors, respectively. Transcriptome deconvolution analysis showed that M2 macrophages, CD4+ T cells, CD8+ T cells, NK cells, and neutrophils can potentially infiltrate PitNET. Furthermore, CD4+ and CD8+ T cells and NK cells infiltration was validated by immunofluorescence. Expression of CCL18, IL-5RA, and HLA-B as well as macrophage tumor infiltration could identify patients who can potentially benefit from treatment with immune checkpoint inhibitors. Full article

With the development of biometric identification technology, finger vein identification has received more and more widespread attention for its security, efficiency, and stability. However, because of the performance of the current standard finger vein image acquisition device and the complex internal organization of the finger, the acquired images are often heavily degraded and have lost their texture characteristics. This makes the topology of the finger veins inconspicuous or even difficult to distinguish, greatly affecting the identification accuracy. Therefore, this paper proposes a finger vein image recovery and enhancement algorithm using atmospheric scattering theory. Firstly, to normalize the local over-bright and over-dark regions of finger vein images within a certain threshold, the Gamma transform method is improved in this paper to correct and measure the gray value of a given image. Then, we reconstruct the image based on atmospheric scattering theory and design a pixel mutation filter to segment the venous and non-venous contact zones. Finally, the degraded finger vein images are recovered and enhanced by global image gray value normalization. Experiments on SDUMLA-HMT and ZJ-UVM datasets show that our proposed method effectively achieves the recovery and enhancement of degraded finger vein images. The image restoration and enhancement algorithm proposed in this paper performs well in finger vein recognition using traditional methods, machine learning, and deep learning. The recognition accuracy of the processed image is improved by more than 10% compared to the original image. Full article

Engineered living materials (ELMs) incorporate living material to provide a gain of function over existing materials, such as self-repair. The use of bacteria in ELMs has been studied extensively in concrete, where repair can be facilitated by bacterial ureolytic calcium carbonate (CaCO₃) precipitation; however, the study of bacteria in other construction materials is limited. We examined the ability of bacterial species to survive in common latex binder chemistries, a model paint formulation, and through the film-forming process. The longest survival was by bacterial spores of Bacillus simplex str. GGC-P6A, which survive in latex emulsions, a liquid coating composition, and in a dried film for >28 days. Surprisingly, our data show that non-spore-forming Escherichia coli survive at least 15 days in liquid composition, which appear to be influenced by the composition of the outer membrane, nutrient scavenging, and the ability to metabolize toxic acrylate. Spores of GGC-P6A were shown to grow in solid paint films from sites of damage, resulting in crack filling through carbonate precipitation, demonstrating the potential for self-repair through microbially mediated CaCO₃ precipitation without directed pH modification. These data suggest that a range of bacterial species, in particular members of Bacilli, may facilitate new applications of bio-augmented, self-healing coating systems. Full article

Deep cryogenic aging (DCA) is a newly developed heat treatment technique for additive-manufactured metallic materials to reduce residual stress and improve their mechanical properties. In this study, AlSi10Mg alloy samples fabricated by selective laser melting were deep-cryogenic-treated at −160 °C and subsequently aged at 160 °C. Phase and microstructural analyses were conducted using X-ray diffraction, optical microscopy, scanning electron microscopy, and transmission electron microscopy, while the mechanical properties were evaluated through microhardness and tensile testing at room temperature. The results indicated that the DCA treatment did not have an effect on the morphology of the melt pools. However, it facilitated the formation of atomic clusters and nanoscale Si and β′ phases, as well as accelerating the coarsening of grains and the ripening of the eutectic Si phase. After DCA treatment, the mass fraction of the Si phase experienced an increase from 4.4% to 7.2%. Concurrently, the volume fraction of the precipitated secondary phases elevated to 5.1%. The microhardness was enhanced to 147 HV, and the ultimate tensile strength and yield strength achieved 495 MPa and 345 MPa, respectively, with an elongation of 7.5%. In comparison to the as-built specimen, the microhardness, ultimate tensile strength, and yield strength increased by 11.4%, 3.1%, and 19.0%, respectively. The improvement in mechanical properties is primarily attributed to the Orowan strengthening mechanism induced by the secondary phases. Full article

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into “Caciocavallo Podolico” cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds. Full article

Self-renewal and differentiation are two characteristics of hematopoietic stem cells (HSCs). Under steady physiological conditions, most primitive HSCs remain quiescent in the bone marrow (BM). They respond to different stimuli to refresh the blood system. The transition from quiescence to activation is accompanied by major changes in metabolism, a fundamental cellular process in living organisms that produces or consumes energy. Cellular metabolism is now considered to be a key regulator of HSC maintenance. Interestingly, HSCs possess a distinct metabolic profile with a preference for glycolysis rather than oxidative phosphorylation (OXPHOS) for energy production. Byproducts from the cellular metabolism can also damage DNA. To counteract such insults, mammalian cells have evolved a complex and efficient DNA damage repair (DDR) system to eliminate various DNA lesions and guard genomic stability. Given the enormous regenerative potential coupled with the lifetime persistence of HSCs, tight control of HSC genome stability is essential. The intersection of DDR and the HSC metabolism has recently emerged as an area of intense research interest, unraveling the profound connections between genomic stability and cellular energetics. In this brief review, we delve into the interplay between DDR deficiency and the metabolic reprogramming of HSCs, shedding light on the dynamic relationship that governs the fate and functionality of these remarkable stem cells. Understanding the crosstalk between DDR and the cellular metabolism will open a new avenue of research designed to target these interacting pathways for improving HSC function and treating hematologic disorders. Full article

An archaeometric approach was applied to the study of the hoard found in the Samshvilde fortress, one of the richest areas of archaeological artefacts in Caucasian Georgia, since it is representative of the historical events from the Neolithic period until the most recent epochs. In this context, four coins dated back to the 12th–13th centuries AD to the reign of King Giorgi III, Queen Tamar, and King Giorgi IV of Georgia underwent different analytical methods to collect information about the technological production process, the probable forge location, and their conservation state. Optical microscope observations provided details about the decorations, the stylistic aspects, and a preliminary evaluation of the conservation state. Portable X-ray fluorescence and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy analysis revealed a composition consisting almost exclusively of copper with a lower amount of tin, lead, and silver. Moreover, degradation products (copper chlorides and sulphates) and cuprite patina were identified. The metal-supplying areas could be related to the nearby Bolnisi Mining District, and the forge location could coincide with the hoard location in Samshvilde, but further data and analyses are necessary. Full article

High-quality eye-tracking data are crucial in behavioral sciences and medicine. Even with a solid understanding of the literature, selecting the most suitable algorithm for a specific research project poses a challenge. Empowering applied researchers to choose the best-fitting detector for their research needs is the primary contribution of this paper. We developed a framework to systematically assess and compare the effectiveness of 13 state-of-the-art algorithms through a unified application interface. Hence, we more than double the number of algorithms that are currently usable within a single software package and allow researchers to identify the best-suited algorithm for a given scientific setup. Our framework validation on retrospective data underscores its suitability for algorithm selection. Through a detailed and reproducible step-by-step workflow, we hope to contribute towards significantly improved data quality in scientific experiments. Full article

We consider a nonlinear Dirichlet problem driven by the $(p(z),q)$-Laplacian and with a logistic reaction of the equidiffusive type. Under a nonlinearity condition on a quotient map, we show existence and uniqueness of positive solutions and the result is global in parameter $\lambda$. If the monotonicity condition on the quotient map is not true, we can no longer guarantee uniqueness, but we can show the existence of a minimal solution $u_{\lambda }^{*}$ and establish the monotonicity of the map $\lambda ⟼u_{\lambda }^{*}$ and its asymptotic behaviour as the parameter $\lambda$ decreases to the critical value ${\widehat{\lambda }}_1(q)>0$ (the principal eigenvalue of $(-{\Delta }_q,W_0^{1,q}(\Omega ))$). Full article

We examine some of the consequences of the Exochronous (timeless) metric and the associated $\Sigma$GR cosmological model for the formation of galaxies, and, in particular, their characteristic rotation curves. We show how the cumulative curvature from the multiple spatial hypersurfaces in this model leads to a modified version of the Poisson equation, in which the gravitational potential is computed over 4D space. Using this new form of the Poisson equation, we derive an analytic expression for gravitational potential as a function of radial distance for a uniform gas cloud undergoing gravitational collapse. We show that this results in a radial velocity profile that provides an excellent fit with commonly observed galaxy rotation curves, and hence fully accounts for the effects previously ascribed to dark matter. An expression can be derived for the equivalent matter density profile corresponding to the $\Sigma$GR gravitational potential, from which it is evident that this is very similar in form to the well-known Navarro–Frenk–White profile. As a further illustration of the consequences of adopting the Exochronous metric, we show how the principle can readily be incorporated into particle-mesh N-body simulations of large-scale structure evolution, using a relaxation solver for the solution to the Poisson equation and the evolution of the gravitational potential. Examples of the use of this simulation model are shown for the following cases: (a) the initial evolution of a large-scale structure, and (b) galaxy formation from a gravitationally collapsing gas cloud. In both cases, it is possible to directly visualise the build-up of the gravitational potential in 3D space as the simulation evolves and note how this corresponds to what is currently assumed to be dark matter. Full article

Perylenetetracarboxylic dianhydride (PTCDA) derivatives have received significant attention as molecule photocatalysts. However, the poor recyclability of molecule-type photocatalysts hinders their widespread applications. Herein, immobilization of PTCDA on Al₂O₃ was achieved by simply physical mixing, which not only dramatically improved their recyclability, but also surprisingly improved the reactivity. A mechanism study suggested that the photo-exited state (PTCDA*) of PTCDA could promote the oxidation of thioanisole to generate PTCDA^•−, which sequentially reduces oxygen to furnish superoxide radicals to achieve the catalytic cycle. Herein, the immobilization support Al₂O₃ was able to facilitate the strong adsorption of thioanisole, thereby boosting the photocatalytic activity. This work provides a new insight that the immobilization of organic molecular photocatalysts on those supports with proper adsorption sites could furnish highly efficient, stable, and recyclable molecular-based heterogeneous photocatalysts. Full article

In contributing towards the discourse on developing teachers’ capabilities for Education for Sustainable Development (ESD), this study examines the relationships between sustainability knowledge, readiness, and self-efficacy for teaching sustainability concepts among vocational teachers in Malaysian colleges. Grounded in Bandura’s self-efficacy theory, the research assesses the combined effect of teachers’ sustainability knowledge and readiness on their ability to teach sustainability effectively. Using a cross-sectional survey design, a sample of three hundred and seventy-five (375) vocational college teachers and structural equation modeling (SEM), the results indicate no significant link between teachers’ sustainability knowledge and their readiness for ESD. However, a positive relationship between teachers’ readiness and their self-efficacy was found. The study shows that while sustainability knowledge does not directly enhance readiness for ESD, it is a strong predictor of self-efficacy in teaching sustainability. Moreover, readiness has a greater effect on self-efficacy than sustainability knowledge alone, highlighting the importance of conceptual understanding in building teachers’ confidence and competence in sustainability education. Despite focusing specifically on Malaysia and using self-reported data, which to some extent limits the study’s findings, the outcomes offer practical insights for educational policymakers, vocational institutions, and educators. They underscore the need for a comprehensive educational approach beyond just knowledge transfer. This research contributes to the sustainability education discourse and suggests areas for future studies, including exploring contextual differences and adopting longitudinal study designs to better understand the dynamics between sustainability knowledge, readiness, and teaching self-efficacy in vocational education. Full article

As constituents of one of the vital agricultural ecosystems, paddy fields exert significant influence on the global carbon cycle. Therefore, conducting observations and simulations of CO₂ flux in rice paddy is of significant importance for gaining deeper insights into the functionality of agricultural ecosystems. This study utilized an eddy covariance system to observe and analyze the CO₂ flux in a rice paddy field in Eastern China and also introduced and parameterized the Jarvis multiplicative model to predict the CO₂ flux. Results indicate that throughout the observation period, the range of CO₂ flux in the paddy field was −0.1 to −38.4 μmol/(m²·s), with a mean of −12.9 μmol/(m²·s). The highest CO₂ flux occurred during the rice flowering period with peak photosynthetic activity and maximum CO₂ absorption. Diurnal variation in CO₂ flux exhibited a “U”-shaped curve, with flux reaching its peak absorption at 11:30. The CO₂ flux was notably higher in the morning than in the afternoon. The nocturnal CO₂ flux remained relatively stable, primarily originating from respiratory CO₂ emissions. The rice canopy CO₂ flux model was revised using boundary line analysis, elucidating that photosynthetically active radiation, temperature, vapor pressure deficit, phenological stage, time, and concentration are pivotal factors influencing CO₂ flux. The simulation of CO₂ flux using the parameterized model, compared with measured values, reveals the efficacy of the established parameter model in simulating rice CO₂ flux. This study holds significant importance in comprehending the carbon cycling process within paddy ecosystems, furnishing scientific grounds for future climate change and environmental management endeavors. Full article

This article presents a coherent and comprehensive proposal of a renewed contemporary Aristotelian–Thomistic approach to the evolutionary view of reality and the position of theistic evolution. Beginning with a proposal of a hylomorphically–grounded essentialist definition of species—framed within a broader revival of biological essentialism—a constructive model of the Aristotelian–Thomistic metaphysics of evolution is being offered, together with a reflection on the alleged violation of the principle of proportionate causation in evolutionary transitions and the role of teleology and chance in evolution. The theological part of the article addresses a number of questions concerning the Thomistic school of theology in its encounter with the evolutionary worldview, including the question of whether God creates through evolution, the query concerning the concurrence of divine and created causes in evolutionary transitions, and the question regarding evolutionary and theological notions of anthropogenesis. A list of ten postulates grounding a contemporary Thomistic version of theistic evolution is offered as a conclusion to the research presented in the text. Full article

Macro-algae is an umbrella term for seaweed, which is an important ingredient in many novel food products in New Zealand and other Australasian countries. While attitudes, consumption motivation, knowledge, and socio-demographic consumer profiles have been investigated in specific countries in the region, consumer behavior such as willingness to eat and factors driving this behavior have not yet been explored. Therefore, the present study fills this research gap in a New Zealand context and explores predictors of New Zealand consumers’ willingness to eat macro-algae and their perceived trust towards the countries of origin of these products. The symbolic value of food, health importance, food safety concerns, and food fussiness were the factors under investigation. The work builds on an online questionnaire and a sample of 437 consumers mirroring the New Zealand population in terms of gender, age, and annual household income. Data were collected through an opt-in panel provider in November 2023. The data analysis consisted of descriptive statistics and partial least square structural equation modeling. Results show that health importance and food fussiness tendencies are the strongest predictors of willingness to eat and trustworthiness of the two countries of origin. Best practice recommendations for marketing managers in New Zealand food retail are provided. Full article

Background/objective: Osteosynthesis is an alternative treatment for stabilizing femur-bone traumas. The initial stability of the fixation systems is one of the biomechanical parameters affecting implant failure and bone union, especially in surgeries of intercalary reconstructions after the removal of bone tumors. This study aimed to investigate the initial biomechanical effect of using one or two osteosynthesis plate configurations for femoral fixation and the effect of fastening the allograft to the osteosynthesis plate in the case of femoral allograft reconstructions. Methods: Three finite-element models of a femur with three different fixation conditions for a transverse osteotomy in the middle of the diaphysis, i.e., using one and two osteosynthesis plates and an intercalary allograft, were constructed. An eight-hole compression plate and a six-hole second plate were used to simulate osteosynthesis plates. The plate screws were tightened previously to the loading, and the tightening sequences simulate the bolt-tightening procedure in a surgical environment. The models were imported into the ADINA System for nonlinear analysis, using compression loads applied over the femur head. Results: Models with the dual fixation systems had the most outstanding compression stiffness. The femur head movement in the dual plate system was 24.8% smaller than in the single plate system. A statistical analysis of a region of interest (VOI) placed in the femur diaphysis showed that the biomechanical effect of using the dual plate system is smaller in the osteotomy region than at the femur head, e.g., a displacement average decrease of only 5% between the two systems, while the maximum value decreases by 26.8%. The allograft fixation to the second osteosynthesis plate leads to an improvement in the system stability. Conclusions: The results presented in this work show that including the bolt analysis in the femoral diaphysis osteotomy fixation will allow for capturing the nonlinear behavior of the osteotomy region more realistically. The stability of the intercalary reconstruction of the femoral diaphysis was higher when the allograft was fastened to the second osteosynthesis plate. Full article

Research on the combined substitution of supplementary cementitious materials (SCMs) has already demonstrated that it might be one of the few viable options to produce low-carbon concrete at scale. This paper presents an experimental investigation on the performance and durability of rice husk ash (RHA) and calcined clay (CC) in ternary blended concrete exposed to chloride attacks under wet/dry cycles. Portland cement (PC) was replaced by RHA and CC up to 50% by weight to produce low-carbon concrete. Samples were subjected to wet/dry cycles in 3.5% NaCl water, with mineralogical composition and microstructure development before and after exposure analysed by TGA-DSC, MIP, XRD, and SEM. The durability of the concrete against wet/dry cycles was investigated in terms of compressive strength, water absorption, open porosity, density, thermal conductivity, and electrical resistivity. The results showed that concrete mixes with CC and RHA up to 60% exhibited an increase of 33% in compressive strength, followed by minimal changes in water absorption. While a decrease in electrical resistivity was measured in all samples with RHA and CC, increasing the CC content to 50% resulted in improved resistance to chloride penetration. Increasing the CC content resulted in a more refined microstructure, with an overall decrease in porosity of up to 32% compared to the control series. While RHA alone did not contribute to significant improvements after wet/dry cycles, the combined substitution of RHA and CC at SCM replacement levels of 60% showed an overall improvement in hardened properties and durability. This investigation provides valuable insights into the long-term performance and strength of innovative low-carbon concrete. Full article