Search Results (182)

The recent rise in living standards has been accompanied by increased awareness and emphasis on oral health. Non-invasive assessment of gingival microcirculation and accurate evaluation of oxygen supply to oral tissues are critical for the early diagnosis of oral diseases. These factors also play a pivotal role in optimizing treatment planning and improving outcomes in dental implantology. In this study, we report the development and implementation of a novel pulse oximetry device based on reflective photoplethysmography technology, designed for non-invasive, real-time monitoring of gingival health through the measurement of oxygen saturation levels. A detailed description of the technology, including key aspects of sensor probe design, is provided, with particular emphasis on the calibration process and performance evaluation of the prototype. Furthermore, we present and discuss the first proof-of-concept gingival oxygen saturation measurements obtained in a clinical setting during oral rehabilitation consultations. Full article

The spatiotemporal regulatory mechanism underlying silicon (Si)-mediated cadmium (Cd) detoxification in rice (Oryza sativa L.) was investigated using non-invasive micro-test technology (NMT), combined with physiological and biochemical analyses. The results revealed the following: (1) Si significantly inhibited Cd²⁺ influx into rice roots, with the most pronounced reductions in ion flux observed under moderate Cd stress (Cd50, 50 μmol·L⁻¹), reaching 35.57% at 7 days and 42.30% at 14 days. Cd accumulation in roots decreased by 34.03%, more substantially than the 28.27% reduction observed in leaves. (2) Si application enhanced photosynthetic performance, as evidenced by a 14.21% increase in net photosynthetic rate (Pn), a 32.14% increase in stomatal conductance (Gs), and a marked restoration of Rubisco activity. (3) Si mitigated oxidative damage, with malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) levels reduced by 11.29–21.88%, through the upregulation of antioxidant enzyme activities (SOD, APX, CAT increased by 15.34–38.33%) and glutathione metabolism (GST activity and GSH content increased by 60.78% and 51.35%, respectively). (4) The mitigation effects of Si were found to be spatiotemporally specific, with stronger responses under Cd50 than Cd100 (100 μmol·L⁻¹), at 7 days (d) compared to 14 d, and in roots relative to leaves. Our study reveals a coordinated mechanism by which Si modulates Cd uptake, enhances photosynthetic capacity, and strengthens antioxidant defenses to alleviate Cd toxicity in rice. These findings provide a scientific basis for the application of Si in mitigating heavy metal stress in agricultural systems. Full article

Humor translation remains a significant challenge due to its reliance on wordplay, cultural context, and nuance. This study compares a Neural Machine Translation (NMT) system (hereafter referred to as MT) with a Large Language Model (GPT-based translation using three different prompts) for translating jokes from English to Thai. Results show that GPT-based models significantly outperform MT in humor retention, with the explanation-enhanced prompt (GPT-Ex) achieving the highest joke preservation rate (62.94%) compared to 50.12% in MT. Additionally, humor loss was more frequent in MT, while GPT-based models, particularly GPT-Ex, better retained jokes. A McNemar test confirmed significant differences in annotation distributions across models. Beyond evaluation, we propose using GPT-based models with optimized prompt engineering to enhance humor translation. Our refined prompts improved joke retention by guiding the model’s understanding of humor and cultural nuances. Full article

The COVID-19 pandemic caused the world to pause temporarily on an almost planetary scale. The creation and diffusion of knowledge about environmental planning and public health are now almost taken for granted. However, such processes were rather different in pre-pandemic times. It took a substantial dose of labor and resources to generate the information needed to produce useful and usable knowledge, and especially to make it available to others in a timely and effective way. As automobility has come to occupy center stage in the lives of an increasing number of suburbanized dwellers, it has taken multiple energy and public health crises, bold leadership, and the real threat of climate change to create the conditions needed to bolster sustainable Non-Motorized Transportation (NMT) as a complement to cleaner and more convenient mass transit options in cities. How does knowledge about sustainable NMT get created? How are sustainable NMT innovations diffused? How can technological and societal transitions to more sustainable realities be nurtured and augmented? This article utilizes a longitudinal and integrated knowledge creation and diffusion model with a Participatory Planning Process to analyze the adoption of measures aimed at reducing the negative consequences of too much automobility and encouraging higher levels of walking, cycling, and mass transportation. The research methods comprised autoethnographic, qualitative, and policy evaluation techniques. The study makes use of the means and ends matrix to discuss cases from five distinct realms: personal, academic, institutional, volunteering NGO, and private sector. The key findings and lessons learned promote scenarios of managed degrowth and sustainable urban transitions. Full article

Machine learning in natural language processing (NLP) analyzes datasets to make future predictions, but developing accurate models requires large, high-quality, and balanced datasets. However, collecting such datasets, especially for low-resource languages, is time-consuming and costly. As a solution, data augmentation can be used to increase the dataset size by generating synthetic samples from existing data. This study examines the effect of translation-based data augmentation on sentiment analysis using small datasets in three diverse languages: French, German, and Japanese. We use two neural machine translation (NMT) services—Google Translate and DeepL—to generate augmented datasets through intermediate language translation. Sentiment analysis models based on Support Vector Machine (SVM) are trained on both original and augmented datasets and evaluated using accuracy, precision, recall, and F1 score. Our results demonstrate that translation augmentation significantly enhances model performance in both French and Japanese. For example, using Google Translate, model accuracy improved from 62.50% to 83.55% in Japanese (+21.05%) and from 87.66% to 90.26% in French (+2.6%). In contrast, the German dataset showed a minor improvement or decline, depending on the translator used. Google-based augmentation generally outperformed DeepL, which yielded smaller or negative gains. To evaluate cross-lingual generalization, models trained on one language were tested on datasets in the other two. Notably, a model trained on augmented German data improved its accuracy on French test data from 81.17% to 85.71% and on Japanese test data from 71.71% to 79.61%. Similarly, a model trained on augmented Japanese data improved accuracy on German test data by up to 3.4%. These findings highlight that translation-based augmentation can enhance sentiment classification and cross-language adaptability, particularly in low-resource and multilingual NLP settings. Full article

Neglected tropical diseases (NTDs) constitute a group of infectious diseases that severely affect the health of impoverished populations, and the health, economies, and health systems of affected countries. Leishmaniasis and human African trypanosomiasis (HAT) are particularly notable, and malaria, despite not being neglected, is part of the “big three” (HIV, tuberculosis, and malaria) with high incidence, increasing the probability of infection by NTDs. Therefore, efforts are ongoing in the search for new drugs targeting the enzyme N-myristoyltransferase (NMT), a potential drug target that has been explored. Thus, we provide a review here that highlights the epidemiological data for these diseases and the importance of discovering new drugs against these agents. Here, the importance of NMT and its inhibitors is clear, with this study highlighting thiochromene, pyrazole, thienopyridine, oxadiazole, benzothiophene, and quinoline scaffolds, identified by computational methods followed by biological assays to validate the findings; for example, this study shows the action of the aminoacylpyrrolidine derivative 13 against Leishmania donovani NMT (IC₅₀ of 1.6 nM) and the pyrazole analog 23 against Plasmodium vivax NMT (IC₅₀ of 9.48 nM), providing several insights that can be used in drug design in further work. Furthermore, the selectivity and improvement in activity are related to interactions with the residues Val⁸¹, Phe⁹⁰, Tyr²¹⁷, Tyr³²⁶, Tyr³⁴⁵, and Met⁴²⁰ for leishmaniasis (LmNMT); Tyr²¹¹, Leu⁴¹⁰, and Ser³¹⁹ for malaria (PvNMT); and Lys²⁵ and Lys³⁸⁹ for HAT (TbNMT). We hope our work provides valuable insights that research groups worldwide can use to search for innovative drugs to combat these diseases. Full article

Curved non-motorized treadmills (cNMTs) have been demonstrated to reduce impact accelerations in comparison with motorized treadmills (MTs). Most studies have analyzed impacts in the time domain, but analysis in the frequency domain can provide useful information associated with the increase in the running risk of injury. The purpose of this study was to analyze the frequency components (low- and high-frequency bands) of impact accelerations, countermovement jump (CMJ) height, and perceived comfort during a prolonged run on different surfaces: MT, cNMT, and overground (OVG). Twenty-one recreational runners completed three randomized prolonged running tests on cNMT, MT, and OVG for 30 min (80% of the individual maximal aerobic speed). Impact accelerations were registered at minutes 5 and 30 of the test, the countermovement jump test (CMJ) was performed before (PreTest) and after (PostTest) the test, and perceived comfort was determined at the end of each test. A two-way repeated-measures analysis of variance (significance at p < 0.05) showed a reduction on cNMT in both low- and high-frequency bands of impact accelerations, such as head power (p < 0.001, ESd = 3.0) on the cNMT vs. the MT and tibia peak power (p = 0.001, ESd = 2.2) on the cNMT vs. OVG. However, cNMT was perceived as the least comfortable surface by runners. The prolonged running effect decreased impact accelerations during the treadmill running test (MT and cNMT) in the low-frequency band, while CMJ height decreased (p = 0.024, ESd = 1.4) during the PostTest vs. PreTest with the cNMT. Using a cNMT could be an interesting strategy for load reduction in long-distance runners or in return-to-play rehabilitation protocols. Full article

Waveforms define the shape, structure, and frequency characteristics of signals, whereas modulation schemes determine how information symbols are mapped onto these waveforms for transmission. Their appropriate selection plays a critical role in determining the efficiency, robustness, and reliability of data transmission. In wireless communications, the choice of waveform influences key factors, such as network capacity, coverage, performance, power consumption, battery life, spectral efficiency (SE), bandwidth utilization, and the system’s resistance to noise and electromagnetic interference. This paper provides a comprehensive analysis of the waveforms and modulation schemes used across successive generations of mobile cellular networks, exploring their fundamental differences, structural characteristics, and trade-offs for various communication scenarios. It also situates this analysis within the historical evolution of mobile standards, highlighting how advances in modulation and waveform technologies have shaped the development and proliferation of cellular networks. It further examines criteria for waveform selection—such as SE, bit error rate (BER), throughput, and latency—and discusses methods for assessing waveform performance. Finally, this study presents a comparative evaluation of modulation schemes across multiple mobile generations, focusing on key performance metrics, with the BER analysis conducted through MATLAB simulations. Full article

Background/objectives: Multiple systemic treatments are available for metastatic castration-resistant prostate cancer (mCRPC), with unclear safety profiles. This study seeks to describe the safety determined in randomized clinical trials of a systemic treatment for mCRPC and whether safety differs by age. Methods: We utilized individual patient data from industry-funded phase 2/3 trials in mCRPC on abiraterone acetate (AA). Vivli, a clinical trial repository site, was used. One investigator independently performed screening. Relative effects of treatment were assessed with frequencies and odds of serious adverse events (SAEs). The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was used. Subgroup analysis measured odds of SAEs as modified by age. Results: We identified 14 trials with 4296 patients. The median age of participants was 69 years. Nearly all participants experienced at least one adverse event (98.4% abiraterone, 97.3% standard of care [SOC]). More serious adverse events (grade 3 or 4) and deaths (grade 5) occurred in those receiving SOC (71.8%) compared to abiraterone (64.1%). The most frequent adverse event category was “Musculoskeletal and Connective Tissue Disorders”. The most frequent event types included anemia, back pain, hypertension, fatigue, hypokalemia, and bone pain. The odds of all events were lower in those receiving abiraterone compared to SOC. Odds of a serious musculoskeletal event were lower in older subjects by 22% (OR 0.78; 95% CI 0.63, 0.96). Conclusions: In this IPD meta-analysis, abiraterone acetate provides no greater risk of SAE in those receiving abiraterone than those receiving SOCs. Patients in the RCTs are younger and healthier than those in the general population; consequently, the results of RCTS might not be applied to the general population, especially those under-represented in the RCTs. There is a need to further evaluate abiraterone-related fractures and neuromuscular toxicities (NMTs) as key outcomes to gain insight into risk factors related to these adverse events. A real-world prospective study is warranted to examine the overall risks and benefits associated with treatment. Full article

This study presents a hybrid artificial intelligence model designed to enhance translation quality for low-resource languages, specifically targeting the Hakka language. The proposed model integrates phrase-based machine translation (PBMT) and neural machine translation (NMT) within a recursive learning framework. The methodology consists of three key stages: (1) initial translation using PBMT, where Hakka corpus data is structured into a parallel dataset; (2) NMT training with Transformers, leveraging the generated parallel corpus to train deep learning models; and (3) recursive translation refinement, where iterative translations further enhance model accuracy by expanding the training dataset. This study employs preprocessing techniques to clean and optimize the dataset, reducing noise and improving sentence segmentation. A BLEU score evaluation is conducted to compare the effectiveness of PBMT and NMT across various corpus sizes, demonstrating that while PBMT performs well with limited data, the Transformer-based NMT achieves superior results as training data increases. The findings highlight the advantages of a hybrid approach in overcoming data scarcity challenges for minority languages. This research contributes to machine translation methodologies by proposing a scalable framework for improving linguistic accessibility in under-resourced languages. Full article

Neural machine translation (NMT) models combining textual and visual inputs generate more accurate translations compared with unimodal models. Moreover, translation models with an under-resourced target language benefit from multisource inputs (source sentences are provided in different languages). Building MultiModal MutliSource NMT (M³S-NMT) systems require significant efforts to curate datasets suitable for such a multifaceted task. This work uses image caption translation as an example of multimodal translation and presents a novel public dataset for translating captions from multiple European languages (viz., English, German, French, and Czech) into the distant and under-resourced Arabic language. Moreover, it presents multitask learning models trained and tested on this dataset to serve as solid baselines to help further research in this area. These models involve two parts: one for learning the visual representations of the input images, and the other for translating the textual input based on these representations. The translations are produced from a framework of attention-based encoder–decoder architectures. The visual features are learned from a pretrained convolutional neural network (CNN). These features are then integrated with textual features learned through the very basic yet well-known recurrent neural networks (RNNs) with GloVe or BERT word embeddings. Despite the challenges associated with the task at hand, the results of these systems are very promising, reaching 34.57 and 42.52 METEOR scores. Full article

Sequence-to-sequence neural machine translation (NMT) has achieved great success with many language pairs. However, its performance remains constrained in low-resource settings such as Mongolian–Chinese translation due to its strong reliance on large-scale parallel corpora. To address this issue, we propose ILFDN-Transformer, a Mongolian–Chinese NMT model that integrates implicit language features and a deliberation network to improve translation quality under limited-resource conditions. Specifically, we leverage the BART pre-trained language model to capture deep semantic representations of source sentences and apply knowledge distillation to integrate the resulting implicit linguistic features into the Transformer encoder to provide enhanced semantic support. During decoding, we introduce a deliberation mechanism that guides the generation process by referencing linguistic knowledge encoded in a multilingual pre-trained model, therefore improving the fluency and coherence of target translations. Furthermore, considering the flexible word order characteristics of the Mongolian language, we propose a Mixed Positional Encoding (MPE) method that combines absolute positional encoding with LSTM-based dynamic encoding, enabling the model to better adapt to complex syntactic variations. Experimental results show that ILFDN-Transformer achieves a BLEU score improvement of 3.53 compared to the baseline Transformer model, fully demonstrating the effectiveness of our proposed method. Full article

The worldwide increase in antifungal resistance, particularly in Candida sp., requires the exploration of novel therapeutic agents. Natural compounds have been a rich source of antimicrobial molecules, where peptides constitute the class of the most bioactive components. Therefore, this study looks into the target-specific binding efficacy of insect-derived antifungal peptides (n = 37) as possible alternatives to traditional antifungal treatments. Using computational methods, namely the HPEPDOCK and HDOCK platforms, molecular docking was performed to evaluate the interactions between selected key fungal targets, lanosterol 14-demethylase, or LDM (PDB ID: 5V5Z), secreted aspartic proteinase-5, or Sap-5 (PDB ID: 2QZX), N-myristoyl transferase, or NMT (PDB ID: 1NMT), and dihydrofolate reductase, or DHFR, of C. albicans. The three-dimensional peptide structure was modelled through the PEP-FOLD 3.5 tool. Further, we predicted the physicochemical properties of these peptides through the ProtParam and PEPTIDE 2.0 tools to assess their drug-likeness and potential for therapeutic applications. In silico results show that Blap-6 from Blaps rhynchopeter and Gomesin from Acanthoscurria gomesiana have the most antifungal potential against all four targeted proteins in Candida sp. Additionally, a molecular dynamics simulation study of LDM-Blap-6 was carried out at 100 nanoseconds. The overall predictions showed that both have strong binding abilities and are good candidates for drug development. In in vitro studies, Gomesin achieved complete biofilm eradication in three out of four Candida species, while Blap-6 showed moderate but consistent reduction across all species. C. tropicalis demonstrated relative resistance to complete eradication by both peptides. The present study provides evidence to support the antifungal activity of certain insect peptides, with potential to be used as alternative drugs or as a template for a new synthetic or modified peptide in pursuit of effective therapies against Candida spp. Full article

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. Full article

To advance evidence-based urban design prioritizing non-motorized mobility, this study proposes a street view-enabled explainable machine learning framework that systematically links built environment semantics to non-motorized transportation vitality optimization. By integrating Baidu Street View images with deep learning-based object detection (Faster R-CNN), we quantify fine-grained human-powered and mechanically assisted mobility vitality. These features are fused with multi-source geospatial data encompassing 23 built environment variables into an interpretable machine learning pipeline using SHAP-optimized random forest models. The key findings reveal distinct nonlinear response patterns between HP and MA modes to built environment factors; for instance, a notable promotion in mechanically assisted NMT vitality is observed as enterprise density increases beyond 0.2 facilities per ha. Emergent synergistic and threshold effects are evident from variable interactions requiring multidimensional planning consideration, as demonstrated in phenomena such as the peaking of human-powered NMT vitality occurring at public facility densities of 0.2–0.8 facilities per ha, enterprise densities of 0.6–1 facilities per ha, and spatial heterogeneity patterns identified through Bivariate Local Moran’s I clustering. This research contributes an innovative technical framework combining street view image recognition with explainable AI, while practically informing urban planning through evidence-based mobility zone classification and targeted strategy formulation, enabling more precise optimization of pedestrian-/cyclist-oriented urban spaces. Full article