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Development of a novel solar system incorporating a heat pipe within a porous enclosure for simulating paraffin melting
Differential contribution of working memory to auditory rhythm discrimination in stuttering and nonstuttering adults
Long-term results after modified Steel´s triple pelvic osteotomy
Abstract Long-term results of modified Steel’s triple pelvic osteotomy were evaluated to confirm if this osteotomy is indicated in the era of other redirectional osteotomies and if it can delay total hip arthroplasty (THA). In total, 317 osteotomies were performed on 225 women and 58 men between 1985 and 2012 in one institution. Thirty-four women and three men underwent bilateral surgery. Harris hip scores (HHS) and nonarthritic hip scores (nAHS) and LCE angle, Sharp-Ulmann angle and signs of acetabular version were compared. The results were statistically evaluated using a paired t-test. In total, 55 women and 21 men were contacted. Of these, 35 women and 21 men completed the questionnaires for 68 hips. The follow up ranged from 5 to 32 years (mean 18.5 years). Of the 68 hip joints, 27 had excellent or good results according to HHS and 24 for nAHS. A total of 12 patients underwent THA. Concerning the radiological evaluation, the LCE angle increased from 12° to 34° and Sharp-Ulmann angle decreased from 49° to 36°. Signs of retroversion of the acetabulum occurred in 24 patients without of necessity of surgical treatment. Non-union (five patients) represents the most frequent complication. Steel’s triple pelvic osteotomy is still indicated as a safe and effective technique for preserving hip joints and delaying THA. The disadvantage of osteotomy is a relatively long-term recovery time and lower satisfaction compared to THA.
Optical neural networks with intensity‑based projection layers as effective nonlinear activations
Abstract Optical neural networks (ONNs) offer significant advantages in speed and energy efficiency by exploiting the parallelism of light. A key challenge in their design is the implementation of nonlinear activation functions using physically realizable operations. In this work, we propose the use of optical intensity measurements—as projections from complex or quaternion-valued fields to real-valued magnitudes together with amplitude or phase encoding to next layer—as effective alternatives to traditional nonlinear activation functions in neural networks. Through systematic evaluations across image classification, image reconstruction, and physical parameter inference, we validate the effectiveness and robustness of projection layers as nonlinear activation mechanisms. The approach extends naturally to quaternion-valued representations, capturing multi-dimensional features such as amplitude, phase, and polarization. We further assess performance under noisy conditions and demonstrate its utility in a β-VAE framework for learning interpretable physical parameters. This projection-based approach not only leverages the advantages of optical computing but also establishes a scalable and hardware-efficient foundation for deep ONNs with broad applicability in image recognition, signal processing, and scientific inference.
Hamstring strength and hip mobility associated with pain and disability in lumbar instability
Individual, household and community factors associated with the double burden of malnutrition within Nigerian households
Test-time local training of neural network for tabular data
Abstract Generally, a neural network is globally trained using the dataset provided in the training phase to optimize its parameters. The trained neural network is then used to make predictions for query instances in the inference phase. This global learning approach leads to a neural network that performs well universally across various query instances. However, it may overlook local structures in some low-density data regions, potentially degrading generalization performance in these regions. Although several test-time adaptation methods have been explored in recent years, they are typically designed for vision domains and are not intended for or do not readily transfer to tabular data. In this study, we propose a test-time local training method, specifically tailored for tabular data, to make the neural network better reflect the local structure around the query instance during the inference phase. Given a query instance, the proposed method finds the nearest neighbors of that instance from the training dataset. It then localizes the globally trained neural network by fine-tuning with these nearest neighbors to better accommodate the local structure around the query instance. The localized neural network is finally used to make a prediction for the query instance. Through experiments conducted on tabular benchmark datasets for regression and classification tasks, we demonstrate that the proposed method significantly enhances the generalization ability of neural networks.
Oriented object detection in remote sensing images based on angle quality estimation
High Performance Multiple Inversion Layer Selective Buried Triple Gate Vertical Trench Power MOSFET
Cumulative heat exposure and mental health in older Chinese adults with social isolation mediation
Thermal desorption GC/MS on human dental calculus detected minute millet consumption in medieval Ukraine
Propolis reduced the radiation-induced bystander effect in rat liver exposed to total head irradiation
High-precision crop recommendation system with stacking ensemble classifiers for optimizing agricultural productivity
Abstract Crop productivity is crucial for farmers and economy worldwide. Factors such as fertilization, weather, and climate have a significant impact on yields. To improve crop productivity, a crop recommendation system is introduced in this paper. It provides data-driven advice on the best crops to plant, taking into account climate, weather, and soil nutrients. This research work introduces feature fusion with a stacking ensemble model comprising 18 classifiers and three novel methods to enhance crop recommendation and mitigate overfitting compared to other ensemble techniques. In this paper, we also examine two datasets for model validation; one of them is a large dataset containing nearly 28,242 records. The findings of our study reveal that feature fusion enables all ensemble classifiers to not only exceed the accuracy and precision of other established modern techniques, but also reduce overfitting, especially for the three proposed models that depend on a large dataset. In our experiments, the accuracy of ensemble models in categorizing diverse crops under different conditions ranges from 98.4% to 99.54%. Notably, the voting ensemble classifier proved to be the most effective, when applied to the first small dataset, achieving an impressive accuracy up to 99.56%. The second stacking ensemble classifier proved to be the most effective, when applied to the second large dataset, achieving an accuracy up to 85.6%.
Comprehensive metabolites characterization of Alpinia katsumadai seeds via a multiplex approach of UHPLC–MS/MS and GC–MS techniques
Abstract Alpinia katsumadai Hayata (Zingiberaceae) is an edible seed valued in traditional medicine for its remarkable therapeutic properties. The seed serves as a fragrant culinary spice, adding a unique flavor to various foods. In this study, three approaches were employed to elucidate seed metabolome including UHPLC-MS/MS-based molecular networking, HS-SPME/GC–MS, and post-silylation GC–MS. A total of 82 secondary metabolites spanning diverse chemical classes were annotated, including the report of two newly annotated catechin-guibourtinidol derivatives and deoxy phloretin-3’-C-hexoside for the first time in Alpinia genus, alongside the first hypothesis of calyxins’ class mass fragmentation pattern. HS-SPME/GC–MS analysis led to the detection of 30 aroma determinants dominated by sesquiterpenes (55.1%) and monoterpenes (36.8%). Daucene and α-phellandrene were detected at 274.41 µg/g and 161.25 µg/g, respectively, and to likely account for seed aroma. GC–MS post-silylation dissected 48 metabolites, revealing the abundance of sugars, arylheptanoids, and flavonoids, with sucrose as major sugar (23.81 mg/g), and cardamonin, as major flavonoid. Such comprehensive phytochemical mapping provides critical insights into A. katsumadai seed metabolome, offering novel evidence for its health-promoting and culinary qualities.
Distribution and status of ragweed (Ambrosia spp.) in northwestern South America and its potential health and environmental impacts
Evolutionary analysis through structural modeling of FAM222 proteins reveals a novel disordered conserved domain in vertebrates that interacts with NLK
Attribute knowledge and KBGAT for predicting the accuracy of the harmonized system code for classifying import and export commodities
Genetic response and metabolic adaptation to partial-record selection for egg production in Dokki-4 laying hens
Abstract This study aimed to evaluate the impact of partial-record egg production selection on egg production performance, serum biochemical constituents, and egg quality traits in Dokki-4 laying hens. A selection experiment was conducted over four generations involving 2,880 hens distributed across three experimental lines: high egg number line (SHEN₉₀), heavy egg weight line (SHEW₉₀), and a random-bred control line (RBCL). Selection criteria were based on performance during the first 90 days of production. Line effects were highly significant for all egg production traits, serum calcium concentrations, yolk cholesterol levels, and most egg quality traits. The SHEN₉₀ line showed superior egg number, annual egg mass production performance, and exhibited favorable metabolic profiles with reduced serum cholesterol and triglycerides. The SHEW₉₀ line achieved significantly heavier egg weights and improved shell quality traits compared to other lines. Genetic gain estimates revealed significant improvements in economically important traits. The SHEN₉₀ line showed favorable genetic gains for EN₉₀, EM₉₀, EM₃₆₅, and favorable reductions in serum cholesterol, triglycerides, and yolk cholesterol. The SHEW₉₀ line exhibited significant positive genetic gains in EW₉₀, EM₉₀, EM₃₆₅, and favorable yolk weight and yolk index percentages. Heritability estimates were moderate for selection criteria (EN₉₀: 0.22; EW₉₀: 0.17) and ranged from 0.09 to 0.31 for correlated traits. Genetic correlations revealed strong positive relationships between EN₉₀ and egg mass traits. EW₉₀ showed positive correlations with most external quality parameters. Selection for partial-record egg production represents an effective breeding strategy for simultaneously improving multiple economically important traits in Dokki-4 laying hens.