As part of an earlier project, a crochet machine, “CroMat”, was designed and built, and a patent was filed for it. An essential part of this crochet machine is an active thread tensioning device, which is needed to regulate the thread tension on the machine’s crochet needle. Until now, a special supplier with an integrated thread return system has been used for this purpose.

However, such commercially available supplies are very expensive and are also designed for knitting machines, whereas the newly developed crochet machine has slightly different requirements. In addition to retracting yarn, a thread tension system for the crochet machine must be able to dynamically adjust and regulate thread tension during stitch formation, which requires communication with the machine’s microcontroller. This is complicated by the fact that each type of stitch that can be formed by the crochet machine requires different lengths of yarn.

The current project focuses on developing a thread tensioner tailored specifically to the crochet machine. Key components include a wrap brake, a return spring as a thread storage device, a driven wrap wheel, and a stepper motor controlled by an Arduino via a driver board. The thread tension is measured to control the motor using a load cell with a strain gauge.

The poster shows the newly designed and built thread tensioner and provides an outlook on further improvement possibilities.

Traditional quantitative structure-activity relationship (QSAR) modeling relies solely on molecular descriptors, ignoring the biological state of target organisms. We developed TransQSAR-pf, a framework that integrates Plasmodium falciparum transcriptomic stress response signatures with classical QSAR descriptors.

Public microarray data (GSE10022) from chloroquine-resistant Plasmodium falciparum strains (treated and control) were analyzed. Differentially expressed genes were identified using limma, and Gene Set Enrichment Analysis (GSEA) revealed pathways related to conserved Plasmodium proteins, RNA-binding proteins, and PfEMP1. A transcriptomic feature matrix (764 features) combining expression signatures, pathway enrichment scores, and variability metrics was integrated with 125 triazolopyrimidine derivatives containing experimental IC50 values and molecular descriptors. Boruta feature selection was applied to

reduce the transcriptomic features to 13 critical predictors representing drug response, genotype effects, strain-specific responses, and expression variability. Machine learning models, optimized via 5-fold cross-validation, included Random Forest, SVM, and Elastic Net. The QSAR-only Random Forest baseline achieved R²=0.719 (RMSE=0.529), while integration of all transcriptomic features without selection performed poorly (R²=0.602). Critically, the Boruta-selected model achieved R²=0.762 (RMSE=0.470), a 6.1% improvement over QSAR-only prediction. Biological mapping revealed that 71.2% of predictive importance derived from conserved unknown-function genes, 17.7% from genotype-specific expression, and 11.1% from direct drug response signatures.

TransQSAR-pf demonstrates that strategic integration of pathogen transcriptomic signatures enhances antiplasmodial activity prediction. Conserved stress response pathways emerged as generalizable predictors of compound efficacy, and uncharacterized genes were identified as high-priority targets for mechanistic validation. This framework represents a shift from structure-only to biology-informed drug discovery, with direct applications for virtual screening of antiplasmodial libraries.

The selective chloroacetylation of methoxyphenol isomers (ortho-, meta-, and para-) was studied under mild conditions using catalytic amounts of various Lewis acids, including FeCl₃, FeCl₃·6H₂O, MoCl₅, WCl₆, ZnCl₂, SnCl₄, VCl₃, TAA, and TSA. The reactivity and selectivity of each isomer toward chloroacetyl chloride were evaluated in nonpolar solvents such as benzene. The influence of catalyst type, molar ratio, temperature, and reaction time on product yield and distribution was systematically investigated. Spectroscopic characterization of the reaction products was performed using IR, UV, and NMR techniques, confirming the formation of O-acylation products (methoxyphenyl chloroacetates) as well as regioisomeric C-acylation products (hydroxy-methoxyphenacyl chlorides). Notably, o-methoxyphenol gave a mixture of three main products, while m- and p-isomers primarily yielded two. Among the catalysts tested, FeCl₃ provided the highest overall yield and favored O-acylation, while stronger Lewis acids like SnCl₄ and MoCl₅ showed greater influence on C-acylation pathways. Mechanistic studies indicate that the reaction proceeds via nucleophilic substitution at the carbonyl carbon of chloroacetyl chloride rather than through a classical acylium ion intermediate, due to the aprotic solvent system and low catalyst concentration. The product distribution correlated strongly with electronic and steric effects of substituents on the aromatic ring. The findings offer valuable insights into structure–reactivity relationships in electrophilic aromatic substitution reactions of substituted phenols and demonstrate the potential of such transformations in the synthesis of bioactive intermediates.

Understanding how tree structural traits vary across forest conditions is essential for assessing ecological integrity, detecting anthropogenic disturbances, and informing conservation strategies. This study investigates the architectural characteristics of trees within primary (PF), secondary (SF), and selectively logged forests (SLF) of the Tapajós National Forest, a representative landscape in the Brazilian Amazon. Although forest inventory datasets are increasingly available, few studies explicitly analyze the relationship between phenotypic tree traits and forest condition, or employ geometric modeling to estimate structural volume. To address this gap, we analyzed biometric data from 30 permanent plots surveyed in 2010, using a combination of statistical and geospatial techniques implemented in Python. Correlation and scatterplot analyses revealed strong associations between total tree height and maximum crown width (r > 0.8), and moderate to strong correlations among diameter at breast height (DBH), height, and crown dimensions (0.40 < r < 0.80). Comparative analyses among forest types showed that PF plots consistently exhibited greater DBH, crown depth, and total height than SF and SLF plots, reflecting structural degradation linked to anthropogenic disturbance. Distributions of wood density and crown morphology further highlighted ecological differences among forest types. To enhance structural assessment, we incorporated crown shape coefficients and directional crown radii to estimate individual tree crown volumes using ellipsoidal geometry. These volume estimates followed patterns consistent with other structural metrics and provided a scalable proxy for canopy structure, enabling spatially explicit comparisons. Overall, this integrative approach offers a robust framework for quantifying tree architecture and supports improved forest monitoring, carbon modeling, and biodiversity evaluation in tropical forest ecosystems.

INTRODUCTION

Manganese dioxide exists in several polymorphic forms, including α-MnO₂, β-MnO₂, γ-MnO₂, and δ-MnO₂. Among these, the α, β, and γ phases exhibit tunnel structures, while the δ phase possesses a layered structure. Due to its distinctive crystal framework, high surface area, rich oxygen content, and inherent catalytic properties, nanostructured MnO₂ is widely regarded as a promising gas sensing material. This study emphasizes the synthesis of MnO₂-based composites and explores their structural, optical, and morphological characteristics using XRD, UV-Visible, FT-IR, and FE-SEM, respectively.

METHOD

The α-MnO₂ phase is synthesized via a facile co-precipitate route while the β-MnO₂ phase is synthesized using the hydrothermal technique. Composites with SnO₂ areobtained via a solid state reaction method wherein α and β MnO₂ is grinded (1:1 ratio by weight), respectively, with SnO₂ using an agate mortar and pestle, followed by annealing at 400 °C for 2 hours.

RESULTS

The structural analysis reveals the formation of tetragonal α and β MnO_2. The average crystallite sizes of α-MnO₂-SnO₂ and β-MnO₂-SnO₂ are 19.33 nm and 22.58 nm, respectively. The crystallinity of α-MnO₂-SnO₂ and β-MnO₂-SnO₂, respectively, is 94.75% and 88.18%. The optical band gaps of 3.4 eV and 3.58 eV are obtained for α-MnO₂-SnO₂ and β-MnO₂-SnO₂, respectively. The morphology discloses the formation of nanorods of α-MnO₂, nanothreads of β-MnO₂, and granules of SnO₂.

CONCLUSION

Gas sensing performance can be significantly improved by developing nanocomposites that feature small crystallite sizes, high crystallinity, narrow optical band gaps, and porous structures. Such composites are well-suited for the detection of hazardous gases.

Siliceous manganese ores are the manganese ore which contain low percent of Mn, SiO₂ and Fe. Due to widespread application of manganese in the world, it is necessary to increase the production of manganese. In this work, waste acid and Raffinate are mainly used for the beneficiation process of siliceous manganese ore hydrometallurgical. The acid leaching ores performed using different solvents such as 2M HCl, waste acid and Raffinate with different ratios at 95^oC for two hours. The leached residues and filtrate are investigated with characterisation techniques. All the studies were compared with analytical grade HCl. It revealed the secondary leaching of manganese in pure acids over waste acid. Additive or catalytic activities may increase the rate of liberation of manganese. The beneficiation of siliceous manganese ore presents significant challenges due to the high silica content, which reduces the efficiency of conventional processing methods. At the same time, hydrometallurgical processes used in metal extraction generate large volumes of raffinate, a waste solution that is often acidic or alkaline and contains residual metal ions. Disposing of this raffinate poses serious environmental concerns and contributes to increased treatment costs. Hence, this study is necessary to evaluate the feasibility, effectiveness, and environmental impact of raffinate reutilization in manganese ore processing, offering a more sustainable and economical approach for the industry.

Introduction
Platinum group elements (PGEs), including platinum (Pt), rhodium (Rh), and palladium (Pd), are increasingly detected in urban soils, largely due to vehicular emissions and industrial activities. Understanding how land use influences their spatial distribution is essential for exposure risk assessment and urban planning.

Methods
A total of 137 surface soil samples were collected from urban parks (n=97), gardens (n=18), and industrial zones (n=22) in Alcalá de Henares, Spain. Samples were digested using microwave-assisted acid protocols and analysed by ICP-MS. Data were statistically evaluated by Kruskal–Wallis tests and post hoc pairwise comparisons to assess inter-group differences.

Results
Pt and Rh concentrations were significantly higher in industrial soils compared to urban parks and gardens (p < 0.05), while Pd showed no significant difference across land use categories. These patterns likely reflect the proximity of industrial zones to point sources and suggest higher vehicular density in adjacent areas. Enrichment factor (EF) analysis indicated moderate to significant anthropogenic input for Pt and Rh, with industrial areas exhibiting the highest EF values for both elements.

Conclusions
Land use type plays a key role in PGE distribution. Industrial zones act as hotspots for Pt and Rh accumulation, emphasising the need for targeted environmental monitoring and potential remediation strategies. Future studies should explore temporal changes and linkages with human biomonitoring data to assess health implications.

Triply periodic minimal surface (TPMS) structures, particularly the gyroid topology, have been considered as appropriate next-generation lightweight structures due to their high stiffness-to-weight ratio, geometric continuity, and tunable mechanical behavior. In the present study, the mechanical performance of gyroid-based sandwich structures under three-point bending is examined, with a focus on the influence of unit cell resolution and wall thickness on the flexural response.

Three gyroid TPMS core structures were designed using nTop software, featuring a uniform porosity of 70%, and were cast between two solid plates that are each 1 mm thick (20 × 120 mm²) to create sandwich beams with overall dimensions of 20 mm × 20 mm × 160 mm. The core configurations were as follows: (i) 2×2×10 unit cells (10 mm³ each, wall thickness ≈ 1.54 mm), (ii) 4×4×32 unit cells (each 5 mm³, wall thickness ≈ 0.77 mm), and (iii) 6×6×36 unit cells (≈ 3.33̅ mm³ each, wall thickness ≈ 0.52 mm).

The specimens were manufactured employing the stereolithography (SLA) 3D printing technique using a photosensitive polymer resin. The three-point bending tests were performed using a standard fixture according to ASTM C393 standard to measure parameters such as core shear modulus, facing bending stiffness, and failure modes. Pre- and post-testing by Computed Tomography (CT) was carried out to inspect internal flaws, structural integrity, and damage evolution. Results are expressed in the context of unit cell optimisation and its effects on load transfer, stiffness, and energy absorption. This work contributes to the design optimisation of TPMS-based sandwich cores for structural applications with optimised mechanical performance.

Introduction: This study investigates advanced deep learning methods to improve the detection of periportal fibrosis (PPF) in medical imaging. Schistosoma mansoni infection affects over 54 million individuals globally, predominantly in sub-Saharan Africa, with around 20 million experiencing chronic complications. PPF, present in up to 42% of these cases, is a leading outcome of chronic liver disease, significantly contributing to morbidity and mortality. Early and accurate detection is critical for timely intervention, yet conventional ultrasound diagnosis remains highly operator-dependent. We developed a convolutional neural network (CNN) model trained on non-invasive ultrasound images to automatically identify and classify PPF severity.

Methods: This research leveraged a CNN for automated detection of PPF. The model was trained and evaluated on a curated subset of 200 ultrasound images from a total pool of 371 images, evenly split between cases and controls, and sourced from the U-SMRC study, which investigates risk factors associated with advanced schistosomiasis morbidity in Lake Albert and Lake Victoria. Images were annotated according to the Niamey protocol, where a pattern score of ≥2 denoted the presence of PPF. The dataset was randomly split into training (80%) and validation (20%) sets to optimize performance.

Results: The approach achieved a diagnostic accuracy of 80%, with a sensitivity and specificity of 80% and 84%, respectively.

Conclusion: These findings highlight the potential of deep learning to reduce diagnostic subjectivity and support scalable screening programs. Future work will focus on validation with larger datasets and multi-class fibrosis grading to enhance clinical utility.

Introduction:
Biodiesel, primarily composed of fatty acid methyl esters (FAMEs), offers a renewable alternative to conventional diesel fuel. However, its widespread application is limited by its poor oxidative stability, largely due to the high content of unsaturated FAMEs, particularly polyunsaturated compounds. Partial hydrogenation can improve stability but often degrades cold flow properties by increasing the proportion of trans-monounsaturated and saturated FAMEs. Achieving selective formation of cis-monounsaturated FAMEs is essential for balancing oxidative stability and cold flow performance.

Methods:
This study reviews conventional and unconventional catalytic systems for selective partial hydrogenation of biodiesel FAMEs. Conventional methods involve hydrogen gas with metal-supported catalysts, while alternatives include catalytic transfer hydrogenation (CTH), simultaneous transesterification–hydrogenation, and biphasic aqueous/organic systems. Experimentally, biodiesel derived from waste cooking oil (WCO) was upgraded using a Ru-TPPTS biphasic catalytic system to enhance oxidative stability.

Results:
Conventional hydrogenation showed limited selectivity and often compromised cold flow properties. Unconventional methods, particularly CTH and biphasic systems, demonstrated improved selectivity and efficiency. The Ru-TPPTS biphasic system significantly modified the FAME profile of WCO biodiesel, reducing polyunsaturated compounds and increasing the proportion of saturated components. This led to an improvement in oxidative stability of over 100%.

Conclusions:
Selective partial hydrogenation is a promising route for enhancing biodiesel quality. Biphasic systems and CTH methods offer effective, efficient, and scalable solutions. Continued optimization of these approaches could support the broader adoption of biodiesel as a sustainable fuel.