Search Results (244)

Background: Rhodiola rosea (RR) is marketed as an adaptogen; however, evidence for acute/short-term effects—and especially dose–response effects—in trained adults across performance and cognition is limited. Objective: Test whether RR improves resistance performance (strength, power) and executive function in resistance-trained adults using a randomized crossover with placebo and a capsule-free baseline. Methods: In a randomized, double-blind, crossover trial with four conditions—no-capsule control (CON), placebo (PL), low-dose RR (LDRR), and high-dose RR (HDRR). Twenty-seven resistance-trained adults completed the conditions. Day-7 testing included bench press (BP) and leg press (LP) 1-repetition maximum (1RM); a third set to failure at 60% 1RM with set-3 volume; Tendo mean/peak power; a 30 s Wingate; and the Stroop Color–Word Test. Secondary endpoints were Rating of Perceived Exertion (RPE), Readiness to perform using a Visual Analog Scale (VAS), and hemodynamics. Results: Versus CON, LDRR increased BP 1RM (+5.59 kg; p = 0.003), set-3 repetitions (+4.30; p < 0.001), set-3 volume (+168.6 kg; p < 0.001), and mean power (+29.7 W; p = 0.026). HDRR increased set-3 repetitions (+2.78; p = 0.005) and peak power (+34.2 W; p = 0.026), with a trend for set-3 volume (p = 0.086). LP 1RM exceeded CON with LDRR (+35.7 kg; p < 0.001) and HDRR (+47.7 kg; p < 0.001); contrasts vs. PL were significant. Wingate outcomes showed no consistent effects. Stroop improved vs. CON across all sections: Word +10.5 to +17.4 counts (p < 0.05), Color +6.1 to +12.0 (p ≤ 0.03), and Color–Word +10.2 to +18.9 (p < 0.001). Conclusions: Short-term RR consumption, regardless of dose or gender, improved resistance performance and significantly enhanced Stroop outcomes, with minimal changes in anaerobic cycling and RPE, and no consistent acute hemodynamic effects. Full article

This study examined lexical complexity patterns in elementary reading textbooks across four pivotal decades (1957, 1974, 1995, 2014) to understand how educational reforms have influenced developmental progressions in reading materials. The study analyzed a corpus of 320,000 words from one continuously published core reading program across grades 1–4 for four copyrights. The corpus consisted of a 20,000-word sample for each grade and year, analyzed for type-token ratio, percentage of complex words, and percentage of single-appearing words. Results revealed three major shifts: (a) systematic within-grade complexity increases in earlier programs (1957, 1974) were replaced by flat progression in later programs (1995, 2014), (b) steep across-grade differentiation collapsed with grade-to-grade increases in lexical diversity declining from greater than 100% to under 10%, and (c) first-grade expectations accelerated dramatically, whereas third- and fourth-grade texts remained remarkably stable across all six decades. By 2014, first graders encountered lexical complexity levels that characterized fourth-grade texts in 1957. These findings challenge narratives of declining text complexity and reveal that contemporary elementary readers experience compressed developmental progressions with elevated starting points but minimal growth trajectories. The implications suggest the need for reconceptualizing text design to balance appropriate challenges with systematic scaffolding, particularly for students dependent on school-based literacy instruction. Full article

Some generative linguists report that in formal settings, learners of English as a foreign language often strive to acquire morphemes such as the third-person singular –s and produce utterances such as *he play. This study reviews generative linguistics, psychology, neuroscience, and biolinguistics, examining how speech and other forms of action involve hierarchically organised groups (chunks) of words or acts that are invariably produced in linear order. Chunks contribute to brain efficiency, facilitating acquisition and enabling brain automaticity. A study was conducted to improve the accuracy rates of sentence segments featuring the third-person singular –s (e.g., “he VERB+s”) by orally rehearsing chunk-based sentences (e.g., [He plays] [a lot]). Sixty-four children from three Spanish schools, learning English as a foreign language and aged 8–11, participated in this study. The participants, divided into a control group and two experimental groups, completed an oral sentence transformation task following a pre-test–post-test design. The Wilcoxon test showed statistically significant results for the experimental groups after the administration of oral chunk-based training. Quartiles and deciles demonstrated improvement in these groups. The findings suggest that oral chunk-based training could foster chunk and morpheme acquisition. This pedagogy might enhance brain efficiency in learning and promote automatic speech. Full article

The adherents of various religions have during times of prayer and worship oriented themselves toward a fixed, sacred direction or location. Since ancient times, followers of Judaism have turned in prayer to Jerusalem. Traditionally, Zoroastrians have prayed facing a source of light—typically the sun or a fire—representing divine truth and presence. By the second and third centuries of the common era, many Christian communities prayed facing the east when offering the Lord’s prayer and other supplications. Initially, Muḥammad and his followers prayed toward Jerusalem, called the “Qiblih” (a technical word first used in the Qur’án for the direction of ṣalát, the Islamic obligatory prayer), but near the midpoint of Muḥammad’s ministry, the Qiblih was changed to the Kaabah in Mecca. In the mid-nineteenth century, the Báb, founder of the Bábí religion, redefined the Qiblih as “Him Whom God shall make manifest,” a figure whose imminent appearance the Báb anticipated. Years later, Bahá’u’lláh, founder of the Bahá’í Faith, confirmed the Báb’s designation of the Qiblih and claimed to be the figure promised by the Báb—and, thus, the Qiblih. Since Bahá’u’lláh’s passing in 1892, Bahá’ís have regarded the Shrine of Bahá’u’lláh near ‘Akká as their Qiblih. This paper considers three issues related to the concept of the Qiblih. First, it briefly surveys the concept in Judaism, Zoroastrianism, Christianity, Islam, and other traditions. Second, it examines the significance and implications of the Qiblih in Bahá’í texts and their antecedents in Bábí texts. In this regard, it argues that in Bahá’í theology, the Qiblih symbolizes the role, station, and authority of the Manifestation of God, the figure who, in Bahá’í thought, serves as the intermediary between God and humanity from age to age. Moreover, Bahá’u’lláh’s designation of a new Qiblih signaled the independence of the Bahá’í religion. Third, this study explores how from a Bahá’í perspective, Quranic verses concerning the Qiblih may be viewed. These include how Muḥammad’s alteration of the Qiblih to the Kaabah reflected his authority as the Manifestation of God to change a prior law. Further, attention is given to Qur’án 2:143 (“And thus We have made you a middle community…”), which occurs in the midst of the only verses in the Qur’án that decree a change in the Qiblih. Whereas Quranic commentators and scholars of Islam, influenced by the doctrine of Islam’s finality, interpreted the word “middle” (vasaṭ) in this verse as meaning just, moderate, or exemplary, Bahá’u’lláh affirmed the word’s more basic meaning and regarded the Muslim community as a religious community between other communities that preceded it and that will come after it, thus anticipating the emergence of a new religious community, which could potentially have its own Qiblih. Full article

Live streaming sales rely heavily on streamers, with both genuine and AI-generated virtual streamers gaining popularity. However, these streamer types possess contrasting capabilities. Genuine streamers are superior at building trust and reducing product valuation uncertainty but have limited reach, while virtual streamers excel at broad audience engagement but are less effective at mitigating uncertainty, often leading to higher product return rates. This trade-off creates a critical strategic gap; that is, brand firms lack clear guidance on whether to invest in genuine or virtual streamers or adopt a hybrid approach for their live channels. This study addresses this gap by developing a theoretical analytical model to determine a monopolistic brand firm’s optimal streamer strategy among three options: using only a genuine streamer, only a virtual streamer, or a combination of the two (hybrid approach). The researchers model consumer utility, factoring in uncertainty and the streamers’ differential impact on reach, to derive optimal decisions on pricing and streamer selection. The analysis yields several key findings with direct managerial implications. First, while a hybrid strategy leverages the complementary strengths of both streamer types, its success depends on employing high-quality streamers; in other words, this strategy does not justify settling for inferior talent of either type. Second, employing a virtual streamer requires a moderate price reduction to compensate for higher consumer uncertainty and prevent high profit-eroding return rates. Third, a pure strategy (only genuine or only virtual) is optimal only when that streamer type has a significant cost advantage. Otherwise, the hybrid strategy tends to be the most profitable. Moreover, higher product return costs directly diminish the viability of virtual streamers, making a genuine or hybrid strategy more attractive for products with expensive return processes. The results provide a clear framework for brand firms—that is, the choice of streamer is a strategic decision intertwined with pricing and product return costs. Firms should pursue a hybrid strategy not as a compromise but as a premium approach, use targeted pricing to mitigate the risk of virtual streamers, and avoid virtual options altogether for products with high return costs. Full article

As the Pamir Plateau is known as the “Water Tower of Central Asia”, accurate precipitation dataset is essential for the study of climate and hydrology in this region. Based on the monthly precipitation observations from 268 meteorological stations in the Eastern Pamir Plateau (EPP) during the April-to-September warm season of 2010–2024, this paper comprehensively evaluates the applicability of eight multi-source precipitation datasets in complex terrains by using statistical indicators, constructs a skill-weighted ensemble mean dataset (Skill-Ens), and analyzes the elevation-dependent characteristics of precipitation in the EPP. The research findings are as follows: (1) The warm-season precipitation in the EPP shows a significant elevation-dependent feature, with the maximum precipitation altitude (MPA) in the range of 2400–2800 m. Precipitation is reduced above this elevation range, but a second MPA may appear in the glacier area above 4000 m. (2) Among the studied eight datasets, the first-generation Chinese Global Land-surface Reanalysis (CRA40/Land) performs the best overall. A long-term (1979–2020) high-resolution (1/30°) precipitation dataset for the Third Pole region (TPHiPr) can most accurately capture the elevation-dependent characteristics of precipitation, while the satellite datasets are relatively poor in this respect. (3) The skill-weighted ensemble mean dataset (Skill-Ens) constructed in this study can significantly improve precipitation estimation (DISO = 0.35), especially in the MPA region, and can accurately depict the elevation-dependent characteristics of precipitation as well (CC = 0.92). In a word, this paper provides the applicable options for precipitation data in complex terrain areas. With the Skill-Ens, the limitation of the individual dataset has been compensated for, which is of significant application value in improving the accuracy of hydrological simulations in high-elevation mountainous areas. Full article

Background: The microphone & receiver-in-ear (M&RIE) integrates two traditional hearing aid microphones, while an additional in-ear microphone helps preserve natural sound perception. However, the impact of this third microphone on hearing aid amplification remains unclear in patients with moderate hearing loss. Methods: In this prospective crossover study, thirty-nine participants with moderate hearing loss and no prior hearing-aid use were randomly assigned to be sequentially fitted with both traditional and M&RIE receivers. The abbreviated profile of hearing aid benefit (APHAB) and word recognition score (WRS) were assessed. Audiological amplification was evaluated using real-ear measurements to determine whether a third in-ear microphone limits real-ear gain. Results: WRSs and APHAB scores showed no significant differences between the standard and M&RIE receivers. In addition, real-ear measurements across all frequencies and earplug types revealed no significant differences in real-ear insertion gain between the two receivers at a conversational level (65 dB SPL). Conclusions: The M&RIE provides amplification comparable to that of the standard receiver while preserving natural sound cues without significant audiological disadvantages. Full article

Part-of-speech (POS) tagging in low-resource, morphologically rich languages (LRLs/MRLs) remains challenging due to extensive affixation, high out-of-vocabulary (OOV) rates, and pervasive polysemy. We propose MRL-POS, a unified Transformer-CRF framework that dynamically selects informative affix features and integrates them with deep contextual embeddings via a novel dual-gating co-attention mechanism. First, a Dynamic Affix Selector adaptively adjusts n-gram ranges and frequency thresholds based on word length to ensure high-precision affix segmentation. Second, the Affix–Context Co-Attention Module employs two gating functions that conditionally amplify contextual dimensions with affix cues and vice versa, enabling robust disambiguation of complex and ambiguous forms. Third, Layer-Wise Attention Pooling aggregates multi-layer XLM-RoBERTa representations, emphasizing those most relevant for morphological and syntactic tagging. Evaluations on Uyghur, Kyrgyz, and Uzbek show that MRL-POS achieves an average F₁ of 84.10%, OOV accuracy of 84.24%, and Poly-F₁ of 72.14%, outperforming strong baselines by up to 8 F₁ points. By explicitly modeling the symmetry between morphological affix cues and sentence-level context through a dual-gating co-attention mechanism, MRL-POS achieves a balanced fusion that both preserves local structure and captures global dependencies. Interpretability analyses confirm that 89.1% of the selected affixes align with linguistic expectations. This symmetric design not only enhances robustness in low-resource and agglutinative settings but also offers a general paradigm for symmetry-aware sequence labeling tasks. Full article

Diagnosis of clinical obesity has been highlighted by the recent publication from a Commission Report in The Lancet, suggesting the addition of a new diagnostic category, “Preclinical Obesity,” to the already existing ones. Diagnostic criteria for obesity began in the first half of the 20th century, when life insurance companies provided information tables of ideal body weight levels and/or desirable body weight levels based on actuarial associations with mortality. This was replaced by the body mass index or BMI in the third quarter of the 20th century. This tool documented the epidemic of obesity in the US in the last three decades of the 20th century. The recognition of the importance of fat distribution, pioneered by the work of Jean Vague in France, provided a new understanding of obesity. The limitations of BMI and the availability of effective new treatments have heightened the need for new diagnostic guidelines. Obesity represents an increase in body fat and an alteration in its distribution and function. But at the same time, obesity is a stigmatized word and a pejorative term. This communication discusses ways to better diagnose the increase in body fat and its abnormal distribution. We ask whether there is an alternative word to replace obesity and suggest that adiposity or healthy weight could be options. Full article

This study demonstrates that John Eliot’s mission to Native Americans was fundamentally driven by his millenarian understanding of the Kingdom of God. In contrast with interpretations that portray his ministry as motivated by cultural imperialism or economic interest, this paper argues that Eliot’s theological vision—particularly his belief in the imminent establishment of Christ’s earthly reign—was the primary impetus for his work. Through a close analysis of Eliot’s writings, including post-Restoration texts, this research reveals his consistent adherence to millenarianism across time. The findings are summarized in three key points: First, Eliot’s millenarianism is evident in his references to the kingdom of God, which he understood as opposed to earthly powers, governed by the Word of God, and advanced through human responsibility. Second, millenarianism not only initiated but also sustained his Indian mission beyond 1660. Third, Eliot’s millennial beliefs shaped his ethical posture toward Native Americans, contributing both to the disruption of traditional tribal structures and to his advocacy for Native protection during Metacom’s War. Full article

Sentiment analysis methods for interactive services such as Danmu in online videos are challenged by their colloquial style and diverse sentiment expressions. For instance, the existing methods cannot easily distinguish between similar sentiments. To address these limitations, this paper proposes a dual-channel model integrated with attention mechanisms for multi-dimensional sentiment analysis of Danmu. First, we replace word embeddings with character embeddings to better capture the colloquial nature of Danmu text. Second, the dual-channel multi-dimensional sentiment encoder extracts both the high-level semantic and raw contextual information. Channel I of the encoder learns the sentiment features from different perspectives through a mixed model that combines the benefits of self-Attention and Dilated CNN (ADCMix) and performs contextual modeling through bidirectional long short-term memory (BiLSTM) with attention mechanisms. Channel II mitigates potential biases and omissions in the sentiment features. The model combines the two channels to erase the fuzzy boundaries between similar sentiments. Third, a multi-dimensional sentiment decoder is designed to handle the diversity in sentiment expressions. The superior performance of the proposed model is experimentally demonstrated on two datasets. Our model outperformed the state-of-the-art methods on both datasets, with improvements of at least 2.05% in accuracy and 3.28% in F1-score. Full article

Introduction: Gestational diabetes mellitus (GDM) is a form of hyperglycemia that develops during pregnancy and poses risks to both the mother and fetus. In other words, it is a glucose intolerance disorder first recognized during pregnancy, specifically in the second and third trimesters, with approximately 7–14% of pregnancies worldwide being affected. Methodology: A systematic literature search was conducted across three major well-established databases; PubMed, Scopus, and ScienceDirect. The search was conducted with the aim of identifying the most suitable studies for the evaluation of fetal cardiac function using Doppler ultrasound techniques in pregnancies affected by GDM. Results: Following a comprehensive full-text assessment, 186 papers were excluded, mainly due to discrepancies in the population, unsuitable study design, publishing type, or unavailability of full-text access. Ultimately, 12 studies met all the inclusion criteria and were incorporated into the scoping review. From the studies included it was found that the conventional pulsed-wave Doppler was the most frequently used modality, assessing parameters such as the E/A ratios, myocardial performance index (MPI), and the isovolumic relaxation time (IVRT). The advanced techniques of choice included tissue Doppler imaging (TDI), speckle-tracking echocardiography (STE), dual-gate Doppler, and automated MPI. Conclusions: Doppler ultrasound techniques, particularly the advanced modalities like TDI and STE, provide valuable insights into fetal cardiac function in GDM pregnancies. Their integration into routine prenatal surveillance may enhance the early detection of cardiac dysfunction and inform timely clinical interventions. Full article

In this paper, we investigate the approximate string matching problem when the allowed edit operations are non-overlapping unbalanced translocations of adjacent factors. This kind of edit operation takes place when two adjacent substrings of the text swap, resulting in a modified string. The two involved substrings are allowed to be of different lengths. Such large-scale modifications of strings have various applications, notably in fields such as computational biology and genomics, where structural rearrangements play a key role. However, despite their central role in many fields of text processing, little attention has been devoted to the problem of matching strings allowing for this kind of edit operation. In this paper, we present three algorithms for solving the problem, all of them with an $\mathcal{O}\left(n{m}^3\right)$ worst-case and an $\mathcal{O}\left(m^2\right)$-space complexity, where m and n are the length of the pattern and of the text, respectively. Specifically, our first algorithm is based on the dynamic programming approach. Our second solution improves the previous one by making use of the Directed Acyclic Word Graph of the pattern. Finally, our third algorithm is based on an alignment procedure. We also show that under the assumptions of equiprobability and independence of characters, our second algorithm has an $\mathcal{O}\left(n{log}_{\sigma }^{2}m\right)$ average time complexity for an alphabet of size $\sigma \ge 4$. Full article

Word problem posing is a critical component of student mathematics learning. This study examined the effects of a problem-posing intervention designed to improve mathematics performance and sentence-writing conventions. Using a multiple baseline across participants design, three third-grade students with mathematics and writing difficulties received one-on-one intervention delivered after school at a university reading center. Data were collected from baseline, intervention, and maintenance phases. Visual analysis and Tau-U statistical analysis indicated that all three students showed improvements in problem solving, problem posing, total words written, words spelled correctly, and correct writing sequence. Post-intervention data suggested that students maintained the improvement over baseline. Discussion and implications for future practice and research were provided. Full article

The rise in social media has improved communication but also amplified the spread of hate speech, creating serious societal risks. Automated detection remains difficult due to subjectivity, linguistic diversity, and implicit language. While prior research focuses on high-resource languages, this study addresses the underexplored multilingual challenges of Arabic and Urdu hate speech through a comprehensive approach. To achieve this objective, this study makes four different key contributions. First, we have created a unique multi-lingual, manually annotated binary and multi-class dataset (UA-HSD-2025) sourced from X, which contains the five most important multi-class categories of hate speech. Secondly, we created detailed annotation guidelines to make a robust and perfect hate speech dataset. Third, we explore two strategies to address the challenges of multilingual data: a joint multilingual and translation-based approach. The translation-based approach involves converting all input text into a single target language before applying a classifier. In contrast, the joint multilingual approach employs a unified model trained to handle multiple languages simultaneously, enabling it to classify text across different languages without translation. Finally, we have employed state-of-the-art 54 different experiments using different machine learning using TF-IDF, deep learning using advanced pre-trained word embeddings such as FastText and Glove, and pre-trained language-based models using advanced contextual embeddings. Based on the analysis of the results, our language-based model (XLM-R) outperformed traditional supervised learning approaches, achieving 0.99 accuracy in binary classification for Arabic, Urdu, and joint-multilingual datasets, and 0.95, 0.94, and 0.94 accuracy in multi-class classification for joint-multilingual, Arabic, and Urdu datasets, respectively. Full article