Aquaculture of the giant river prawn Macrobrachium rosenbergii faces ongoing challenges associated with aberrant gonadal development, precocious puberty and inefficient sex manipulation techniques. Although microRNAs (miRNAs) serve as critical regulators of spermatogenesis across diverse taxa, their roles in modulating testicular development of M. rosenbergii remain largely uncharacterized. In this study, we performed small RNA sequencing on testicular tissues of M. rosenbergii at three representative developmental stages to profile genome-wide miRNA expression dynamics during spermatogenesis. A total of 173 miRNAs (including 12 novel miRNAs) were identified, and several key miRNAs exhibiting significant temporal expression differences were screened. GO and KEGG enrichment analyses revealed that target genes of these miRNAs were primarily involved in pathways related to sex differentiation, hormone synthesis, and energy metabolism. Further analysis indicated that miRNAs such as miR-1-3p and miR-317-3p may activate the testis developmental program during early developmental stages, while the downregulation of negative regulators like miR-124-3p and miR-210-3p may contribute to the release of developmental inhibition. A subset of miRNAs including miR-8-3p and miR-315-5p may exert regulatory effects on the IAG signaling axis through targeting genes such as ferritin and Fem-1c, thereby modulating testicular maturation. Additionally, the observed downregulation of miRNAs implicated in innate immune response and chitin metabolism points to a reconfiguration of regulatory priorities during the rapid phase of testicular growth to support reproductive development. The study contributes to a deeper understanding of miRNA-mediated regulatory mechanisms underlying crustacean development and reproduction.

Conventional radiological assessment of building materials predominantly relies on empirical hazard indices that provide screening information but do not explicitly describe photon transport within finite material geometries. This study presents an integrated experimental and deterministic modelling framework for evaluating 17 naturally occurring radioactive building materials collected in Katsina State, Nigeria. Activity concentrations of [226]Ra, [232]Th and [40]K were measured using NaI(Tl) gamma-ray spectrometry and used as source-term inputs to a deterministic photon-transport model based on the diffusion approximation of the Linear Boltzmann Transport Equation (LBTE). The governing equation was solved numerically using the Finite Element Method (FEM) for a representative slab geometry. Conventional radiological indices were also calculated for screening purposes. The measured activity concentrations were 18.80-45.50 Bq kg[-1] for [226]Ra, 14.82-93.70 Bq kg[-1] for [232]Th and 130.80-489.70 Bq kg[-1] for [40]K. The deterministic calculations produced spatial photon-flux and surface-dose estimates for the representative geometry. Comparison with conventional dose estimates gave r = 0.990 and R[2] = 0.980, with RMSE = 51.95 nGy h[-1] and MAE = 49.85 nGy h[-1]. Because both calculations use the same measured radionuclide concentrations, these statistics are interpreted as a numerical consistency assessment rather than independent validation. The study therefore demonstrates a proof-of-concept deterministic framework; independent experimental dose measurements and/or Monte Carlo benchmarking are required before quantitative predictive validity can be claimed.

Lamb wave devices have the advantage of monolithic multi-frequency integration and show great application potential in mobile communications. However, their performance is often limited by spurious modes. In this work, an arc-shaped apodization electrode structure is proposed to suppress spurious modes in Sc0.2Al0.8N Lamb wave resonators (LWRs). By shortening the overlap length of the interdigital transducer electrodes at the edges, the energy coupling in the spurious-mode regions is reduced while the main-mode region is largely preserved. Finite element method (FEM) simulations confirm the suppression of spurious responses near the anti-resonant frequency. Based on the optimized design, typical and arc-shaped apodization LWRs and filters were fabricated. The fabricated arc-shaped apodization LWR exhibits an effective electromechanical coupling coefficient (keff2) of 3.9%. The corresponding filter operates at a center frequency of 1.774 GHz, with a -3 dB bandwidth of 20 MHz and a minimum insertion loss of 4.50 dB. Compared with the typical filter fabricated on the same wafer, the insertion loss is reduced by 1.22 dB. These results demonstrate the effectiveness of the proposed electrode apodization scheme for suppressing spurious modes in Sc0.2Al0.8N Lamb wave devices.

Microfluidic Hall-effect biosensors detect superparamagnetic bead labels as they flow past a thin-film Hall element in a microchannel. Designing one couples bead magnetization, stray-field distribution, Hall transport, and channel flow across 14 parameters that finite-element solvers explore only at minutes to hours per configuration. We present a coupled analytical-numerical framework for this signal chain: Clausius-Mossotti bead magnetization with a volume fraction correction, a point-dipole stray field, a volume-averaged Hall voltage, Poiseuille transport, and a Johnson-Nyquist and Hooge 1/f noise model, evaluated across 12 sensor presets compiled from the literature, spanning graphene, III-V semiconductors, Si CMOS, bismuth, and topological insulators; any other platform can be defined from user-supplied transport parameters. Benchmarked against a companion COMSOL Multiphysics 6.0 study, the framework reproduces the Hall voltage to within 4.8% at a favorable bead-to-sensor area ratio and deviates by 22% and 15% at off-optimum geometries, consistent with the point-dipole near-field limit at h/rb=1. Three design rules follow: a signal-to-noise ridge at sensor widths comparable to the bead diameter (w*≈db; area ratios 0.4-1.0 at constant voltage, 0.5-2.6 at constant current), matching reported single-bead geometries; a material choice that must be made under an explicit electrical drive constraint; and a sampling-limited flow-velocity window. Predicted signals agree at the order-of-magnitude level with published InAs and Si CMOS experiments. We release the model as a freely accessible, no-install browser implementation with a built-in 2D axisymmetric magnetostatic finite-element (FEM) solver that maps where the dipole approximation degrades.

The trend toward high power density in compact electronic devices has intensified the demand for enhanced thermal management. Triply periodic minimal surface (TPMS) structures are attractive for heat sink (HS) applications owing to their high surface area, interconnected flow pathways, and compact design. This study presents a computational fluid dynamics investigation of porous TPMS heat sinks with gyroid, diamond, and I-WP topologies using COMSOL Multiphysics version 5.1. The effects of geometry, network morphology (sheet and solid), and relative density (RD = 15%, 30%, and 45%) were evaluated through temperature and velocity fields, pressure drop, average Nusselt number, and Darcy friction factor. The results demonstrate that, relative to the I-WP structure, the gyroid and diamond structures achieved 10.9-12.4% and 2.9-4.7% higher average Nusselt numbers, respectively. Porous gyroid structures reduced the maximum temperature by 2.2 [K] compared with the solid counterpart due to their highly interconnected pore network. At an inlet velocity of 0.3 [m/s], the average Nusselt number increased from 11 to 43 as the relative density increased from 15% to 45%, accompanied by a pressure drop increase from 1.9 to 4 [Pa]. These findings demonstrate that porous gyroid TPMS heat sinks with optimized relative density provide an effective solution for advanced thermal management in electronics.

This study evaluated the effects of dietary inclusion of stabilized, partially destoned olive cake (PDOC) on bulk milk characteristics and the physicochemical, microbiological, and sensory properties of Pecorino Siciliano PDO and Ricotta cheeses. A total of 124 Valle del Belice dairy ewes were randomly assigned to two homogeneous groups according to parity (≥3rd lambing), days in milk (60 ± 5), and milk yield (1.48 ± 0.4 kg/day). Both groups grazed the same pasture and were supplemented with 0.5 kg/head/day of concentrate, either without PDOC (CTR) or containing 17% PDOC on an as-fed basis (EXP). The concentrates were formulated to be isoenergetic. Milk was collected fortnightly from morning and evening milkings, pooled by treatment, and used for cheesemaking. Bulk milk, feeds, and PDOC were analyzed for chemical composition. Fresh Ricotta and 6-month-ripened Pecorino Siciliano PDO cheeses were evaluated for composition, fatty acid (FA) profile, microbiological characteristics, and sensory properties. Data were analyzed using the MIXED model. Dietary treatment did not affect bulk milk composition but improved milk coagulation properties. Likewise, composition, titratable acidity, color, and hardness were unaffected by dietary treatment. Total phenolic content (TPC), peroxide value, and thiobarbituric acid-reactive substances in Pecorino cheese did not differ between treatments, whereas Ricotta from the CTR group unexpectedly exhibited a higher TPC than that from the EXP group (p < 0.01). Dietary inclusion of PDOC affected only a limited number of fatty acids, resulting in lower (p < 0.05) total MUFA and MCFA contents in the EXP group. Raw milk from EXP ewes showed higher counts of total mesophilic bacteria and lactic acid bacteria (p < 0.05); however, microbial populations became comparable after contact with the wooden vat and remained similar throughout ripening (p > 0.05), with no pathogenic bacteria detected in either group. Sensory analysis revealed differences in the sensory profile of Pecorino cheeses produced from EXP milk (p < 0.05), although overall acceptability did not differ between treatments. In conclusion, dietary inclusion of PDOC in dairy ewe diets appears to preserve the composition, safety, and overall acceptability of Pecorino Siciliano PDO and Ricotta cheeses.

This study numerically investigates a D-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor integrated with an Au-TiO2 micro-grating metastructure for SARS-CoV-2-related biomolecular interaction analysis. The grating modulates phase matching between the core-guided and surface plasmon polariton modes, enhancing the refractive index (RI) response. The finite element method (FEM) was used to optimize the PCF air-hole diameters and Au-TiO2 grating parameters. The sensor shows a broad RI response range of 1.19-1.43, with a maximum local wavelength sensitivity (WS) of 18 500 nm/RIU, minimum full width at half maximum (FWHM) of 10 nm, and maximum figure of merit (FOM) of 240 RIU[-1]. For modeled spike RBD and anti-RBD monoclonal IgG binding, maximum WS values of 5000 and 4782 nm/RIU were obtained. These results support compact fiber-integrated SPR platforms for label-free monitoring of SARS-CoV-2-related RI variations, while specificity and reliability require experimental validation.

This study developed and systematically evaluated a computational framework combining finite element modeling (FEM) and machine learning for the synthetic recovery of tissue-specific elastic properties from high-dimensional displacement fields generated within a fixed finite element model. A heterogeneous three-dimensional breast model comprising skin, adipose, and glandular tissues was reconstructed from medical images and used to generate 1000 finite element simulations with varying tissue elastic moduli and prescribed compression displacements. Random Forest regression models were trained to estimate the elastic modulus of each tissue from the nodal displacement field and compression displacement. The framework was evaluated through input-configuration and prediction-strategy comparisons, low-dimensional baselines, learning curve analysis, boundary-condition-free surface observations, repeated noise and spatial-subsampling experiments, structured observation degradation, marginal extrapolation, and joint parameter-space distribution-shift experiments. The tissue-specific Random Forest models achieved mean absolute percentage errors of 2.93%, 1.86%, and 1.45% for skin, adipose tissue, and glandular tissue, respectively, under five-fold cross-validation. The apparent influence of additional non-target tissue moduli was sensitive to the treatment of scalar predictors within the high-dimensional feature space, while target-wise standardization substantially improved adipose and glandular performance in the simultaneous multi-output model. Comparison with lower-dimensional baselines showed that supervised partial least squares regression outperformed the full-field Random Forest for adipose and glandular tissue, whereas the Random Forest retained lower percentage error for skin. Marginal leave-one-range-out experiments further showed that the severe extrapolation deterioration observed with Random Forest was strongly algorithm dependent: PLS substantially reduced prediction errors in all held-out target intervals. Complementary four-dimensional block holdout experiments showed model- and tissue-dependent performance under structured joint parameter-space distribution shift, with PLS providing lower errors for adipose and glandular tissue and Random Forest showing lower aggregate error for skin. Prediction accuracy also improved progressively with increasing training set size. Removing displacement components prescribed directly by the boundary conditions produced negligible changes in performance, while useful predictive information was retained using only external skin-surface observations. Repeated random and spatially structured degradation experiments, including spatially correlated noise and distributed surface-observation patterns, showed progressive, tissue-dependent performance losses with increasing noise and decreasing observation density. Overall, the results support the feasibility of machine learning-based synthetic recovery of tissue-specific elastic properties within the controlled computational setting considered in this study, while showing that model representation, target scaling, and the geometry of distribution shift substantially affect predictive behavior. These results characterize surrogate parameter recovery within the represented simulation framework and should not be interpreted as demonstrating unrestricted global identifiability or universal extrapolation capability.

A patient-specific, minimally invasive temporomandibular joint prosthesis was developed using an integrated workflow combining medical imaging, semi-automated CAD modeling, finite element analysis, and additive manufacturing. A minipig anatomical model was used to assess geometric feasibility and preliminary structural behavior. Three condylar component sizes were evaluated under simplified loading conditions, with additional simulations examining loading orientation. Miniaturization maintained stresses below the assumed material yield strengths, while peak stresses remained localized near the inferior bone-contacting edge. Physical fitting confirmed geometric conformity and surgical feasibility. This proof-of-concept study demonstrates the feasibility of an anatomy-specific workflow for designing and manufacturing miniaturized TMJ prostheses.

Dielectrophoretic (DEP) enrichment is a powerful strategy to enhance bacterial capture efficiency and accelerate the response of electrochemical biosensors by actively concentrating target bioparticles at the sensing interface. In this study, a combined numerical-experimental framework is developed to rationally design a DEP-assisted electrochemical biosensor with improved bacterial capture and detection performance. A finite-element modeling (FEM) approach was used to model the coupled electric field, dielectrophoretic force, and particle-transport phenomena, providing a quantitative basis for comparing bacterial trapping efficiency across different interdigitated electrode geometries. The modeling reveals that a wave-shaped interdigitated electrode (W_IDE) generates extended high-field regions and an enlarged effective capture area, resulting in an improved bacterial capture efficiency of 19% compared to 12% for the conventional rectangular interdigitated electrodes (R_IDE) under positive DEP (pDEP) conditions. Based on these insights, the W_IDE was fabricated on a printed circuit board (PCB) substrate, modified with platinum-black (Pt-black) to increase electroactive surface area, and interfaced with a custom-built 16-channel portable potentiostat unit, enabling sequential impedance measurements. The developed biosensor was applied to pDEP-assisted impedance detection of Staphylococcus aureus and Micrococcus luteus using vancomycin as the capture probe. The pDEP-assisted operation enabled rapid and highly sensitive detection down to 10 CFU/mL within 30 min with a wide linear range (10-10[5] CFU/mL) in 0.1× PBS, outperforming passive detection (10[2]-10[5] CFU/mL) for both Staphylococcus aureus and Micrococcus luteus. In skim milk, however, the linear detection ranges shifted to 10[2]-10[5] CFU/mL with pDEP-assisted detection and 10[3]-10[5] CFU/mL under passive detection conditions. Overall, this work highlights the significance of combining FEM-optimized electrodes with DEP-driven enrichment to achieve improved bacterial capture, sensitivity, and robustness in electrochemical biosensors.

Objective.Integrating medical linear accelerators and magnetic resonance imaging (MRI) scanners for MRI-guided radiotherapy (MRgRT) is complicated by mutual magnetic field coupling, making efficient field mapping crucial. This study proposes a computationally efficient methodology to characterize an MR scanner's magnetic fringe field by solving a boundary value problem (BVP) from sparsely sampled data. This approach accurately maps fields in free space and near ferromagnetic materials without requiring proprietary internal MR coil models or full-scale room simulations.Approach.A full-scale finite element method (FEM) model of a proximity-type MRgRT system, validated against experimental measurements, was used as the reference. A hybrid BVP methodology was developed where Laplace's equation was first solved using sparsely sampled magnetic field boundary conditions. To account for the linac, this free-space solution was subsequently used as an initial condition for a refined model that incorporated the ferromagnetic properties of the linac head and was solved using Maxwell's equations. Accuracy was assessed by comparing the BVP solutions to the full-scale FEM reference across various sampling resolutions.Main results.The BVP approach reconstructed the magnetic field to within 1 G of the reference using a coarse 40 cm boundary sampling. The hybrid method successfully characterized field distortions from the linac; ignoring the ferromagnetic structure led to errors of -7 G to 2 G near the component, whereas including it reduced the discrepancy to within 1 G. The methodology was efficient, reducing computation time from 2 h for the full-scale model to under 10 min, while using a fraction of the memory.Significance.This study presents a validated, efficient, and accessible BVP-based methodology for magnetic field characterization in MRgRT systems. By enabling accurate field mapping from sparse data, accounting for ferromagnetic components, and drastically reducing computational demands, this approach offers a robust tool for rapid engineering assessments and serves as a practical alternative to complex full-scale simulations.