Search Results (393)

Tire-Derived Aggregate (TDA) is a recycled fill material made by cutting scrap tires into small pieces that satisfy the gradation requirements in ASTM D 6270. Since its introduction to civil engineering applications, TDA fill and TDA backfill have been successfully implemented in many projects. However, the dynamic behavior of the TDA backfill under significant earthquakes has not been substantially addressed. The present study used nonlinear time-history Finite Element Analysis (FEA) to analyze the dynamic behavior of a retaining wall with TDA backfill captured from the full-scale shake table test. Unlike typical soil failure observed in a similar retaining wall with conventional soil backfill, significant wall sliding occurred because lightweight TDA contributed to reducing the friction resistance of the wall footing. Therefore, the analysis required modeling capability of rigid body motion and impact loading from the separation between the wall stem and the backfill. With adequate friction models and softened contact models, the FEA generated the dynamic motion of the retaining wall that matched well with the measured responses, including the wall sliding. The friction model between the wall footing and soil was most critical in accurately reproducing wall sliding motion. It was determined to use different friction coefficients for the two different earthquakes used in the study in order to simplify the rate dependence of the coefficient. Also, the softened contact model generated more reasonable impact force by allowing overclosure and finite stiffness during impact. The FEA model and modeling technique in the present study can be used for the seismic design of various field-scale retaining walls with TDA backfill. Full article

Microbially Induced Carbonate Precipitation (MICP) is a biochemical process that promotes the precipitation of calcium carbonate, mainly in the mineral form of calcite, using urease-producing bacteria. This method has numerous applications, particularly in the field of geotechnical engineering when it is adopted for soil improvement or for the consolidation of porous or cracked construction materials such as stone and concrete. One microfluidic platform made of polymethylmethacrylate (PMMA) was designed with multiple channels, and the minerals precipitated were visualized using an optical microscope. The precipitated mineral observed in all channels analyzed formed spherical mineral structures with a core and multiple external rings. The same spherical mineral structures were observed in the biocement layer precipitated on plates of the same material as that of the microfluidic platform and on limestone, following the same treatment protocol. SEM images of pieces of these layers, complemented with EDS and mineral analysis by XRD, have confirmed the existence of multiple layers of minerals with spherical structures, mainly vaterite, precipitated around a nucleation point. Overlapping minerals in both the confined microfluidic channels and the unconstrained plates indicate that overlap results from repeated injections rather than physical confinement. From the tests with the microfluidic devices, these studies revealed that crystallization depends on different factors, namely the size of the channels and the number of Sporosarcina pasteurii cells. The number of injections appeared to affect the number of rings precipitated around the inner core. Substrate effects on spatial distribution or adhesion may still exist but were not detectable in this study and require further investigation. The observation of similar mineralogical structures in both the microfluidic devices and the plates, particularly the limestone, demonstrates that microfluidic systems are effective tools for small-scale visualization of geological processes. Full article

Neural machine translation (NMT) performance is strongly influenced by tokenization strategies, particularly for morphologically rich languages such as Arabic. Despite the importance of tokenization, there is a lack of controlled, reproducible studies examining its impact under low-resource conditions, which limits our understanding of how different methods affect translation quality and training dynamics. This paper presents a controlled experimental study analyzing the impact of different tokenization methods on English → Arabic (EN → AR) translation using a Tiny Transformer model under low-resource conditions. The study aims to provide a systematic and reproducible comparison that isolates the effect of tokenization choices under fixed modeling and training constraints. Experiments are conducted with identical architecture, training steps, decoding procedure, and evaluation pipeline to ensure reproducibility. Translation quality is assessed using multiple metrics including BLEU, ChrF++, TER, and BERTScore, revealing substantial divergences and demonstrating empirically, in the context of low-resource Arabic NMT, that BLEU alone is insufficient for reliable evaluation. While the limitations of BLEU are known in general, our results provide new evidence showing that, under low-resource conditions and across different tokenization strategies, reliance on BLEU can lead to misleading conclusions about translation quality. Training dynamics are analyzed using TensorBoard, linking tokenization strategies to differences in convergence, saturation, and stability. For validation, a small-scale English → German (EN → DE) experiment confirms that the Tiny Transformer setup reproduces expected behavior. The contribution of this work lies in establishing controlled empirical evidence and practical insights, rather than absolute performance gains, for low-resource Arabic NMT. Our results provide controlled evidence that tokenization choice critically affects both translation quality and optimization dynamics, offering practical guidance for low-resource Arabic NMT research. Overall, byte-pair encoding (BPE) achieves the strongest balance across surface-level and semantic metrics under controlled low-resource conditions (BLEU: 8.57, ChrF++: 18.56, TER: 97.38, BERTScore-F1: 0.785). Character-level tokenization yields higher semantic similarity than subword-based methods, as reflected by BERTScore, but remains weaker in structural fidelity and surface-form accuracy, while SentencePiece exhibits intermediate behavior, favoring semantic adequacy over exact n-gram matching. These results confirm that tokenization choice critically influences both evaluation outcomes and optimization behavior, and that BLEU alone is insufficient for assessing Arabic translation quality. Full article

Parassi mullet (Mugil incilis) is an ecologically and economically important species that supports small-scale artisanal fisheries. However, scarce knowledge of its reproductive biology limits the development of management and conservation strategies. This research describes key milt and sperm characteristics, including milt volume, sperm concentration, motility, and ultrastructural features. Males produced an average of 40.0 ± 20 µL of milt, with sperm concentrations between 6.00 and 20.37 × 10⁹ spermatozoa mL⁻¹. Sperm motility varied between 10% and 80%, with a mean duration of 14.13 ± 4.49 min. Mature spermatozoa measured 33.79 ± 0.67 µm and exhibited a subspherical head without an acrosome, a short midpiece, and a cylindrical flagellum. The nucleus contains electron-dense heterogeneous chromatin. The centriolar complex was positioned outside the nuclear fossa consistent with Type II spermiogenesis. The flagellum comprises a main piece and tapering end piece. The axoneme had 9 + 0 arrangement at the basal body region and the typical 9 + 2 configuration along its length. These results provide the first detailed description of sperm morphology in parassi mullet and contribute to an understanding of its reproductive biology, supporting future applications in taxonomy, toxicology, conservation and aquaculture programs. Full article

Background/Objectives: An impediment to successful drug discovery is the potential for off-target liabilities to eliminate otherwise promising candidates. As the drug discovery process is time-consuming and expensive, the use of artificial intelligence (AI) methods such as machine learning (ML) has drastically increased. It is invaluable to generate models that can quickly differentiate between successful and unsuccessful small-molecule drug candidates. Previous efforts established that molecular similarity could be used with other metrics to inform predictions of potential activity against a protein target. Similar methods were pursued here to combine similarity and machine learning for a collection of models called OPLE. Methods: Models were trained with proprietary and publicly available data to predict the likelihood of a given compound to be active against targets present in existing experimental SafetyScreen panels 18 and 44. Two-dimensional (2D) Tanimoto similarity from extended-connectivity fingerprints (ECFPs) and trained ML models were combined to obtain predictions. Results: Using all training data, a relationship between similarity and activity was established by fitting a probability assignment curve. Calibrated ML label assignment likelihoods were joined with the predictions from ECFP Tanimoto similarity to known active compounds using the belief theory formula, which maintains that activity prediction increases when both pieces of evidence support it. When assessing the performance of OPLE models for SafetyScreen 18 and 44 targets with external data from ChEMBL, more than 80% of the models had recall values greater than 0.8. This indicated favorable predictive ability to identify active molecules while limiting false negative predictions. Conclusions: Predicting and experimentally verifying safety liabilities is insightful at every stage of small-molecule drug discovery. This early detection tool can help project teams save resources that could be better deployed on series with no predicted or measured off-target liabilities. Full article

The electronic band structure of two-dimensional lithium is calculated using the Dirac equation. Lithium is modeled as a two-dimensional square lattice in which the two strongly bound inner electrons and the fixed nucleus are treated as a positively charged ion (+e), while the outer electron is assumed to be uniformly distributed within the cell. The electronic potential is obtained by considering Coulomb-type interactions between the charges inside the unit cell and those in the surrounding cells. A numerical method that divides the unit cell into small pieces is employed to calculate the potential and then the Fourier coefficients are obtained. The Bloch method is used to determine the energy bands, leading to an eigenvalue matrix equation (in momentum space) of infinite dimension, which is truncated and solved using standard matrix diagonalization techniques. Convergence is analyzed with respect to the key parameters influencing the calculation: the lattice period, the dimension of the eigenvalue matrix, the unit-cell partition used to compute the potential’s Fourier coefficients, and the number of neighboring cells that contribute to the electronic interaction. Full article

We develop a unified likelihood-based framework for limited outcomes built on the two-piece normal family. The framework includes a censored specification that accommodates boundary inflation, a doubly truncated specification on $(0,1)$ for rates and proportions, and a survival formulation with a log-two-piece normal baseline and Gamma frailty to account for unobserved heterogeneity. We derive closed-form building blocks (pdf, cdf, survival, hazard, and cumulative hazard), full log-likelihoods with score functions and observed information, and stable reparameterizations that enable routine optimization. Monte Carlo experiments show a small bias and declining RMSE with increasing sample size; censoring primarily inflates the variability of regression coefficients; the scale parameter remains comparatively stable, and the shape parameter is most sensitive under heavy censoring. Applications to HIV-1 RNA with a detection limit, household food expenditure on $(0,1)$, labor-supply hours with a corner solution, and childhood cancer times to hospitalization demonstrate improved fit over Gaussian, skew-normal, and beta benchmarks according to AIC/BIC/CAIC and goodness-of-fit diagnostics, with model-implied censoring closely matching the observed fraction. The proposed formulations are tractable, flexible, and readily implementable with standard software. Full article

The article presents a method that supports the evaluation of design manufacturability in the area of input material selection, enabling the reduction in material diversity under single-piece and small-batch production conditions. The proposed approach combines the analysis of alternative materials with activity-based costing (ABC) and data concerning actual and planned material requirements. The method enables the assessment of the impact of semi-finished product substitution on material costs, processing costs, production organisation, and material-management costs before order execution is launched. In the conducted case study, it was demonstrated that effective management of material diversity can significantly reduce the range of materials and decrease total manufacturing costs. For the analysed period, the number of material items was reduced from 32 to 19 (a 41% reduction), resulting in cost savings of approximately 11,000 PLN. In addition to total cost, the approach supports the assessment of operational benefits associated with reduced material diversity, such as a lower number of material items, fewer suppliers, reduced inbound inspection and receipt operations, and decreased inventory levels and capital tied up in stock. Material substitution may decrease or increase direct material costs and may increase machining time when larger dimensions are used; therefore, the method jointly evaluates cost and lead-time impacts prior to order release. The results confirm that integrating design, technological, and logistics data is an effective approach to rationalising material management in machinery manufacturing enterprises. Full article

Cryopreservation of bovine ovarian cortical tissue offers a promising strategy for preserving female fertility and genetic resources, yet outcomes remain variable and influenced by both protocol and tissue size. This study investigated how slow freezing-thawing (SFT) and two vitrification-warming procedures (VW1 and VW2) affect preantral follicle morphology and granulosa cell proliferation in bovine ovarian cortex fragments of two dimensions (1 × 10 × 5 mm and 1 × 10 × 10 mm). Tissue from six cows was processed for histological evaluation and Ki67 immunostaining. Small fragments subjected to SFT showed no significant reduction in the proportion of morphologically normal follicles compared with fresh controls, representing the best overall preservation. In contrast, vitrification decreased morphological integrity, with VW2 performing better than VW1 in both fragment sizes. Small SFT pieces contained more morphologically normal follicles than large ones. Granulosa cell proliferation capacity was largely maintained across cryopreservation protocols, increasing with follicular stage; a size-related difference only appeared on VW2, where small fragments displayed higher Ki67 positivity. These findings underscore the relevance of jointly evaluating cryopreservation protocol and fragment size to optimize bovine ovarian tissue preservation, strengthening the evidence supporting SFT of small fragments as a robust option for safeguarding cortical integrity and improving tissue-based fertility preservation strategies. Full article

Engineering programmes have been giving more weight to experiential learning, largely because many students still find it difficult to see how classroom theory connects to the work that engineers handle on the ground. With this in mind, a robotics-centred Project-based Learning (PBL) module was introduced to first-year general engineering students as part of the faculty’s engineering spine. The module asks students to design, build, and program small autonomous robots capable of navigating and competing in a set arena. Even a simple task of this kind draws together multiple strands of engineering. Students shift between sketching mechanical layouts, wiring basic circuits, writing code, testing prototypes, and negotiating the usual challenges that arise when several people share responsibility for the same piece of hardware. To explore how students learned through the module, a mixed-methods evaluation was carried out using survey responses alongside reflective pieces written by the students themselves. Certain patterns appeared repeatedly. Many students felt that their technical skills had grown, particularly in breaking down a messy problem into smaller, more workable components. Teamwork also surfaced as a prominent theme. Groups often had to sort out issues such as a robot veering off course due to a misaligned sensor or a block of code producing unpredictable behaviour. These issues were undoubtedly challenging for the students, but they also had a certain pedagogical flavour, with many students describing them as a source of frustration as well as a learning opportunity. Later iterations of the module may benefit from more targeted support at key stages. Despite the many challenges, robotics has been shown to be an attractive way for students to step into engineering practice. The project helped them build technical capability, but it also encouraged habits that matter just as much in real work, such as planning, communicating clearly, and returning to a problem until it behaves as expected. Taken together, the experience offers useful guidance for curriculum designers seeking to create early learning environments that feel authentic and manageable and for motivating students who are just beginning their engineering journey. Full article

Additive manufacturing technologies for metals are developing rapidly. Among them, wire arc additive manufacturing (WAAM) has become widespread due to its accessibility. However, parts produced using WAAM require surface post-processing; therefore, hybrid technologies have emerged that combine additive and subtractive processes within a single compact manufacturing complex. Such systems make it possible to organize single-piece and small-batch production, including for the repair and restoration of equipment in remote areas. For this purpose, hybrid equipment must be lightweight, compact for transportation, provide sufficient workspace, and be capable of folding for transport. This paper proposes the concept of a multifunctional metal 3D printer based on hybrid technology, where WAAM is used for printing, and mechanical post-processing is applied to obtain finished parts. To ensure both rigidity and low mass, a 3-UPU parallel manipulator and a worktable with two rotational degrees of freedom are employed, enabling five-axis printing and machining. The printer housing is foldable for convenient transportation. The kinematics of the proposed 3D printer are investigated as an integrated system. Forward and inverse kinematics problems are solved, the velocities and accelerations of the moving platform center are calculated, singular configurations are analyzed, and the workspace of the printer is determined. Full article

Convex decomposition plays a central role in computational geometry and is a key preprocessing step in applications such as robotic motion planning, 2D packing, pattern recognition, and manufacturing. This work revisits the minimum convex decomposition problem and proposes both an exact mathematical model and an efficient heuristic algorithm capable of handling simple polygons as well as polygons with holes. The methodology incorporates a visibility-preserving bridge transformation that converts holed polygons into equivalent simple instances, enabling the extension of classical decomposition schemes to more general topologies. In addition, a convex-union post-processing phase is implemented to reduce the number of convex parts obtained by either method. The performance of the proposed approach is evaluated on benchmark instances from the literature and on a new dataset of polygons with holes introduced in this work. The exact model consistently produces optimal decompositions for small and medium instances, while the heuristic achieves near-optimal solutions with significantly reduced computation times. The union phase further decreases the number of resulting convex pieces in most cases. All codes, datasets, and results are publicly released to facilitate reproducibility and comparison with future methods. Full article

A new type of household/therapeutic air freshener is proposed, in which beneficial components are released from compositions containing plant raw materials through induction heating under an alternating-frequency electromagnetic field. The composition is heated by metal elements (e.g., pieces of metal foil) included in its composition. The advantage of the proposed air freshener is the possibility of using a wide range of plant raw materials without prior additional processing. Another advantage of this air freshener is the possibility of controlling it via a smartphone, which is in line with the concept of a ‘smart home.’ The proposed approach bypasses labour-intensive production of simple oils, reduces the product cost, and expands the number of plants (helpful for small-scale cultivation in countries such as Kazakhstan). At the same time, however, it remains possible to use traditional compositions based on industrially produced aromatic oils. Specific examples of the implementation of the proposed method are presented. Prospects include programme-controlled emission of odours from polymer matrices, Internet of Things applications, implementation of sound–visual effects (colour–aroma–music), and even integration with neuromorphic materials. Full article

Background/Objectives: In this study, three antimicrobial peptides (1–3) were conjugated onto bare aluminum nanoparticles (NP) to produce peptide-conjugated nanoparticles (NP1–NP3) in order to evaluate their biological effects. Methods: The peptide-functionalized Al₂O₃ nanoparticles were characterized and subsequently analyzed for their antimicrobial activity against selected bacterial strains. The findings were compared with those of bare Al₂O₃ nanoparticles and free antimicrobial peptides. Through this comparison, the enhanced impact of combining nanoparticles with peptides in addressing antimicrobial resistance was demonstrated. Additionally, biofilm inhibition, microbial cell viability inhibition, DNA cleavage, antioxidant, and amylolytic activity assays were performed to comprehensively evaluate the biological functionality of the synthesized nanoparticles. Results: Although all tested samples exhibited significant antimicrobial activity, peptide-conjugated nanoparticles NP1, NP2, and NP3 provided superior activity with an MIC value of 16 mg/L. The highest biofilm inhibition activities were observed for NP2 as 53% and 70% against S. aureus and P. aeruginosa, respectively. Additionally, NP1–NP3 inhibited microbial cell viability by 100% at a concentration of 6.25 mg/L and free peptide 3 displayed E. coli inhibition as 100% at a concentration of 12.5 mg/L. Furthermore, we evaluated the biological potential of antimicrobial peptide-functionalized Al₂O₃ nanoparticles through antibiofilm, antioxidant, antidiabetic activities, and DNA cleavage assays. Peptide-conjugated nanoparticles NP1, NP2, and NP3 exhibited the highest antioxidant activities as 43.70%, 45.22%, and 59.57%, respectively. Except for NP3, the compounds were observed to act as α-amylase enzyme activators. NP and NP1–NP3 completely degraded the supercoiled circular form into small pieces. Conclusions: Our findings suggest that peptide–aluminum nanoparticle conjugation may be a promising formulation for enhancing biological activity. Further in vitro and in vivo tests may help clarify the therapeutic potential of this novel nanoformulation. Full article

Upper tract urothelial carcinoma (UTUC) is a rare malignancy that accounts for a small minority of all urothelial cancers. Historically, treatment recommendations for UTUC have been extrapolated from bladder cancer trials due to limited high-quality, UTUC-specific evidence. However, emerging data has shown how UTUC exhibits distinct biological, molecular, and clinical features compared to bladder cancer. In this piece, we provide an analysis of the current evidence supporting adjuvant chemotherapy and immunotherapy for UTUC. We discuss landmark trials such as the POUT trial for adjuvant chemotherapy, as well as pivotal trials such as CheckMate 274, IMvigor 010 and AMBASSADOR that examine the role of adjuvant immunotherapy for UTUC. Additionally, we briefly highlight advances in cancer genetics and the emerging use of circulating tumor DNA as a potential biomarker. While there has been significant progress made in adjuvant treatments for UTUC, substantial knowledge gaps remain. Clinical trials using UTUC-specific populations will be critical in improving outcomes and personalizing care for this patient population. Full article