Search Results (119)

The coexistence of functionally similar predators offers a framework for understanding the mechanisms shaping ecological communities. Jaguars and pumas are broadly sympatric in the Neotropics, yet the processes sustaining their persistence remain unclear. Classical niche theory predicts that coexistence requires ecological differentiation, whereas modern models emphasize balancing stabilizing (reducing interspecific competition) and equalizing mechanisms (minimizing fitness differences). Although demographic components were not directly estimated, we integrated secondary dietary data compiled from 21 sympatric populations through a systematic literature review. This integration allowed us to identify trophic patterns consistent with coexistence mechanisms across taxonomic and functional prey axes. Analyses revealed strong taxonomic segregation at finer scales, with jaguars primarily consuming Artiodactyla, while pumas exploited a broader spectrum including Rodentia, Cingulata, and Pilosa. This divergence reduces direct competition, consistent with stabilizing resource partitioning. In contrast, high overlap in functional traits (e.g., body mass and locomotor habit) indicated functional convergence potentially increasing fitness equivalence, consistent with equalizing mechanisms. These dynamics suggested that jaguar–puma macroecological coexistence may be maintained by a dynamic balance between patterns consistent with stabilizing and equalizing mechanisms expressed within trophic niche axes. From a conservation perspective, strategies should move beyond species-specific approaches; preserving both taxonomic and functional prey diversity is essential to sustain the trophic requirements of predators and the ecosystems they regulate. Full article

Rehabilitation after anterior cruciate ligament reconstruction cannot be reduced to a linear, time-based sequence of protection, strength, and return to sport. Persistent asymmetries, quadriceps inhibition, and variable re-injury rates highlight that recovery is a complex adaptive process in which outcomes emerge from dynamic interactions between biological, neural, and psychological subsystems. Grounded in complexity science and chaos theory, this editorial reframes rehabilitation as the regulation of variability rather than its suppression. The Control–Chaos Continuum provides a practical structure to translate this concept into progressive exposure, where clinicians dose uncertainty as a therapeutic stimulus. Adaptive periodization replaces rigid stages with overlapping macro-blocks that respond to readiness, feedback, and context. Neuroplastic mechanisms and ecological dynamics justify the deliberate introduction of controlled “noise” to foster coordination, confidence, and resilience. Ultimately, the goal is not perfect control but stable performance under variability—the ability to function “at the edge of chaos.” This conceptual perspective articulates a clinically actionable framework—linking the Control–Chaos Continuum with adaptive periodization—to guide non-linear decision-making and safe return-to-sport. Full article

Product launch events serve as a crucial means of marketing and communication for technology brands. With the empowerment of multimodal technologies, the construction of meaning and the pathways of persuasion in these events have been reshaped. Drawing on grounded theory, this study systematically reviews and analyzes 258 smart product launch events organized by 20 leading technology brands. The findings reveal that product launch events consist of two major categories of content—namely, core information and peripheral information—which together form a reinforced persuasion mechanism, resonating with the additivity effects proposed in the Heuristic Systematic Model (HSM). Furthermore, the abundant multimodal cues embedded in these events contribute to the reinforcement mechanism through overlapping complementarity and dynamic supplementation. Finally, this study discusses the theoretical significance of the multimodality-assisted reinforced persuasion mechanism in relation to dual-process models and its appropriateness in contemporary communication contexts. By providing an in-depth investigation of smart product launch events as a novel form of content dissemination, the study conceptualizes a persuasion mechanism suitable for complex communication environments and offers practical guidance for industry marketing practice. Full article

This study aims to address the problem that traditional helical gears generate significant axial forces during transmission and innovatively proposes a design scheme of double helical face gears (DHFG). An accurate mathematical model of the tooth surface is established using spatial meshing theory and coordinate transformation. A systematic investigation using the orthogonal test method is then conducted to analyze the influence of key parameters, such as the pinion tooth number, transmission ratio, and helix angle, on gear performance. The finite element analysis results show that the overlap degree of this double helical tooth surface gear pair in actual transmission can reach 2–3, demonstrating excellent transmission smoothness. More importantly, its unique symmetrical tooth surface structure successfully achieves the self-balancing effect of axial force. Simulation verification shows that the axial force is reduced by approximately 70% compared to traditional helical tooth surface gears, significantly reducing the load on the bearing. Finally, the prototype gear is successfully trial-produced through a five-axis machining center. Experimental tests confirmed that the contact impressions are highly consistent with the simulation results, verifying the feasibility of the design theory and manufacturing process. Full article

We present a tunable diode-laser absorption spectroscopy (TDLAS) system operating at 1.5711 µm for CO₂ gas concentration measurements. The system can operate in either a traditional direct-mode (dTDLAS) sawtooth wavelength scan or a recently demonstrated wavelength-toggled single laser differential-absorption lidar (WTSL-DIAL) mode using precompensated current pulses. The use of such precompensated pulses offsets the slow thermal constants of the diode laser, leading to fast toggling between ON and OFF-resonance wavelengths. A short measurement time is indeed pivotal for atmospheric sensing, where ambient factors, such as turbulence or mechanical vibrations, would otherwise deteriorate sensitivity, precision and accuracy. Having a system able to operate in both modes allows us to benchmark the novel experimental procedure against the well-established dTDLAS method. The theory behind the new WTSL-DIAL method is also expanded to include the periodicity of the current modulation, fundamental for the calculation of the OFF-resonance wavelength. A two-detector scheme is chosen to suppress the influence of laser intensity fluctuations in time (1/f noise), and its performance is eventually benchmarked against a one-detector approach. The main difference between dTDLAS and WTSL-DIAL, in terms of signal processing, lies in the fact that while the former requires time-consuming data processing, which limits the maximum update rate of the instrument, the latter allows for computationally simpler and faster concentration readings. To compare other performance metrics, the update rate was kept at 2 kHz for both methods. To analyze the dTDLAS data, a four-parameter Lorentzian fit was performed, where the fitting function comprised the six main neighboring absorption lines centered around 1.5711 µm. Similarly, the spectral overlap between the same lines was considered when analyzing the WTSL-DIAL data in real time. Our investigation shows that, for the studied time intervals, the WTSL-DIAL approach is 3.65 ± 0.04 times more precise; however, the dTDLAS-derived CO₂ concentration measurements are less subject to systematic errors, in particular pressure-induced ones. The experimental results are accompanied by a thorough explanation and discussion of the models used, as well as their advantages and limitations. Full article

Problem: Amid digital disruption and the cross-fertilization of RBV, DCV, and KBV, strategic management knowledge has grown fragmented with blurred boundaries. Conventional mapping (citation/co-word, LDA) lacks semantic and temporal resolution, obscuring overlaps, divergences, and turning points and hindering links to actionable indicators (e.g., the Balanced Scorecard). Hence, an integrated, semantically faithful, time-stamped map is needed to bridge research and operational metrics. Gap: Prior maps rely on citation/co-word signals, miss textual meaning, and treat RBV/DCV/KBV in isolation—lacking a theory-aligned, time-stamped, manager-oriented synthesis. Objectives: This study aims to (1) reveal how RBV, DCV, and KBV evolve and interrelate over time; (2) produce an integrated, semantically grounded map; and (3) translate selected themes into actionable managerial indicators. Method: We analyzed 25,907 WoS articles (1980–2025) with BERTopic (Sentence-BERT + UMAP + HDBSCAN + c-TF-IDF). We used an RBV/DCV/KBV lexicon to guide retrieval/interpretation (not to constrain modeling). We discovered 230 topics, retained 33 via coherence (C_V), and benchmarked them against LDA. Key findings: A concise set of 33 high-quality themes with a higher C_V than LDA on this corpus was established. A Fish-Scale view (overlapping subfields across economics, management, sociology) that clarifies RBV–DCV–KBV intersections was achieved. Era-sliced prevalence shows how themes emerge and recombine over 1980–2025. Selected themes mapped to Balanced Scorecard (BSC) indicators linking capabilities → processes → customer outcomes → financial results. Contribution: A clear, time-aware synthesis of RBV–DCV–KBV and a scalable, reproducible pipeline for structuring fragmented theory landscapes are presented in this study—bridging scholarly integration with managerial application via BSC mapping. Full article

This contribution provides new data on Italian first language acquisition and phonological development in preschool children. In total, 104 3- to 6;4-year-old typically developing Italian children were tested with two novel nonword tasks tackling the Italian consonantal system: one for repetition (NWR) and one for discrimination (NWD). NWR data were analyzed in terms of repetition accuracy, featural characteristics, and phonological processes, while NWD was analyzed according to signal detection theory (i.e., A-prime and d-prime) and in terms of discrimination accuracy. The results show the significant role of age on children’s repetition and discrimination abilities: as the children grow older, all the scores improve and the number of errors declines. No complete overlap is found between what children can produce and what they can discriminate, which is in line with what has already been documented in other languages. The findings contribute to the state of the art on the Italian language and provide new perspectives on some methodological issues specific to this language. Full article

This paper introduces a novel generalized q-symmetric differential operator for studying a certain subclass of univalent functions with negative coefficients. We establish several significant theoretical results for this class, including sharp coefficient bounds and characterization theorems based on the generalized Hadamard product. Two significant applications demonstrate the theoretical framework’s practical utility. First, in the context of geometric modeling, we demonstrate how the function class and operator can be utilized to create and control complex, non-overlapping transformations. Second, in digital signal processing, we show that these functions serve as stable digital filter prototypes and that our operator is an effective tool for fine-tuning the filter’s frequency response. These applications bridge the gap between abstract geometric function theory and practical system design by demonstrating the operator’s versatility as a tool for analysis and synthesis. Full article

Background/Objectives: Children with Autism Spectrum Disorder (ASD) often display heightened sensitivity to simple auditory stimuli, but have difficulty discriminating and integrating multiple phonological features (segmental: consonants and vowels; suprasegmental: lexical tones) at the syllable level, which negatively impacts their communication. This study aims to investigate the neural basis of segmental, suprasegmental and combinatorial speech processing challenges in Mandarin-speaking children with ASD compared with typically developing (TD) peers. Methods: Thirty children with ASD and thirty TD peers will complete a multi-feature oddball paradigm to elicit auditory ERP during passive listening. Stimuli include syllables with single (e.g., vowel only), dual (e.g., vowel + tone), and triple (consonant + vowel + tone) phonological deviations. Neural responses will be analyzed using temporal principal component analysis (t-PCA) to isolate overlapping ERP components (early/late MMN), and representational similarity analysis (RSA) to assess group differences in neural representational structure across feature conditions. Expected Outcomes: We adopt a dual-framework approach to hypothesis generation. First, from a theory-driven perspective, we integrate three complementary models, Enhanced Perceptual Functioning (EPF), Weak Central Coherence (WCC), and the Neural Complexity Hypothesis (NCH), to account for auditory processing in ASD. Specifically, we hypothesize that ASD children will show enhanced or intact neural discriminatory responses to isolated segmental deviations (e.g., vowel), but attenuated or delayed responses to suprasegmental (e.g., tone) and multi-feature deviants, with the most severe disruptions occurring in complex, multi-feature conditions. Second, from an empirically grounded, data-driven perspective, we derive our central hypothesis directly from the mismatch negativity (MMN) literature, which suggests reduced MMN amplitudes (with the exception of vowel deviants) and prolonged latencies accompanied by a diminished left-hemisphere advantage across all speech feature types in ASD, with the most pronounced effects in complex, multi-feature conditions. Significance: By testing alternative hypotheses and predictions, this exploratory study will clarify the extent to which speech processing differences in ASD reflect cognitive biases (local vs. global, per EPF/WCC/NCH) versus speech-specific neurophysiological disruptions. Findings will advance our understanding of the sensory and integrative mechanisms underlying communication difficulties in ASD, particularly in tonal language contexts, and may inform the development of linguistically tailored interventions. Full article

As dogs live longer, they face an increased risk of functional age-related changes, including physiological and behavioral alterations. However, data on these age-related processes in domestic dogs remain limited. In this study, we assessed serum concentrations of cytokines (TNF-α, IL-6, IL-10), personality and cognition using validated scales (Dog Reinforcement Sensitivity Theory Personality Questionnaire (RSTQ-D), Dog Impulsivity Assessment Scale (DIAS), and Canine Cognitive Assessment Scale (CCAS)) in 44 healthy pet dogs from two non-overlapping age groups (1–4 years old and ≥11 years old). Generalized linear models revealed small but significant (p = 0.001) negative effects of age-by-cytokine interactions in predicting sensitivity to RST personality traits related to fear and anxiety in older dogs. Specifically, higher TNF-α and IL-10 levels predicted lower scores on the Fight/Flight/Freeze System (FFFS) and the Behavioral Inhibition System (BIS). These findings provide novel insights into previously unrecognized associations between the immune system and personality in healthy older dogs, possibly contributing to broader profiles of vulnerability or, conversely, resilience. Further research is needed to confirm these associations and clarify their functional meaning. Such knowledge could facilitate the early identification of complex aging-related phenotypes and inform individualized preventive strategies to support the health and well-being of aging companion dogs. Full article

Attachment styles, rooted in Bowlby’s Attachment Theory, significantly influence our romantic relationships, workplace behavior, and decision-making processes. Traditional methods like self-report questionnaires often have biases, so we aimed to develop a predictive model using objective physiological data. In our study, participants engaged in the Secretary problem, a sequential decision-making task, while their brain activity was recorded with a 16-electrode EEG device. We transformed this data into coherence graphs and used Node2Vec and PCA to convert these graphs into feature vectors. These vectors were then used to train a machine learning model, XGBoost, to predict attachment styles. Using participant-level nested 5-fold cross-validation, our first model achieved 80% accuracy for Secure and 88% for Fearful-avoidant styles but had difficulty distinguishing between Avoidant and Anxious styles. Analysis of the first three principal components showed these two groups overlapped in coherence space, explaining the confusion. To address this, we created a second model that categorized participants as Secure, Insecure, or Extremely Insecure, improving the overall accuracy to about 92%. Together, the results highlight (i) large-scale EEG connectivity as a viable biomarker of attachment, and (ii) the empirical similarity between Anxious and Avoidant profiles when measured electrophysiologically. This method shows promise in using EEG data and machine learning to understand attachment styles. Our findings suggest that future research should include larger and more diverse samples to refine these models. If validated in multi-site cohorts, such graph-based EEG markers could guide personalised interventions by objectively assessing attachment-related vulnerabilities. This study demonstrates the potential for using EEG data to classify attachment styles, which could have important implications for both research and therapeutic practices. Full article

With the growth of the urban population and economic level, the issue of urban parking space shortages (UPSSs) has assumed growing prominence. This persistent issue not only exacerbates traffic congestion but also contributes to environmental pollution, highlighting the need for system-oriented mitigation strategies. First, an algorithm for mitigating UPSSs based on nudge theory was constructed, in order to determine how the nudge strategies work. Second, nudge tools, including gain disclosure, salience, and outcome notification, were integrated to construct a mitigation model for UPSSs, which synthesizes nudge theory, the model of self-regulatory processes involved in behavioral change, and system dynamics (NT-SPBC-SD theory). Finally, four scenarios of natural development, guide adjustment, balanced regulation, and enhanced change were simulated. The findings of this study are as follows: (1) The UPSS mitigation had multiple overlapping effects and critical point effects, and the nudge strategy gradually decayed or even rebounded over time. (2) Under the enhanced change scenario, the degree of UPSSs, the amount of illegal parking, and CO₂ emissions from civil vehicles decreased by 21.2%, 6.93%, and 14.54%, respectively. (3) After quantitative comparisons, the balanced regulation scenario with lower implementation costs instead demonstrated superior overall performance. The results support subsequent research and guide the enhancement of urban parking management policies to advance urban sustainability. Full article

Background: Loneliness is both a social and a health-related problem, and among LGBT people, feelings of loneliness are often exacerbated. According to the minority stress theory, stress and loneliness can be directly related to the discrimination and stigma that are experienced over time in a heteronormative society. Exposure to social stigma causes mental health problems, stress, and loneliness, coupled with increased social and economic vulnerability. Method: The aim of this study was to conduct an exploratory analysis of homosexual men’s feelings of loneliness and their relationship with experiences of discrimination throughout their life. A qualitative methodology involving focus groups and individual interviews was utilized. Results: The results show the double or overlapping discriminations that gay men experience as a result of their age and sexual orientation and, in some cases, their HIV status, and the relationship between said discrimination and the feeling of unwanted loneliness. Exposure to such intolerance is more common among those with a reduced social network, which limits their social participation and has a negative impact on their well-being. Conclusion. The aging process of LGBT people implies a new context of intervention and research that must be addressed to prevent episodes of unwanted loneliness that negatively affect the quality of life among this demographic. Full article

Recently, interest in optimizing judging schemes for large-scale innovation competitions has grown as the complexities in evaluation processes continue to escalate. Although numerous methods have been developed to improve scoring fairness and precision, challenges such as evaluator subjectivity, workload imbalance, and the inherent uncertainty of scoring systems remain inadequately addressed. This study introduces a novel framework that integrates a genetic algorithm-based work cross-distribution model, advanced Z-score adjustment methods, and a BP neural network-enhanced score correction approach to tackle these issues. First, we propose a work crossover distribution model based on the concept of information entropy. The model employs a genetic algorithm to maximize the overlap between experts while ensuring a balanced distribution of evaluation tasks, thus reducing the entropy generated by imbalances in the process. By optimizing the distribution of submissions across experts, our model significantly mitigates inconsistencies arising from diverse scoring tendencies. Second, we developed modified Z-score and Z-score Pro scoring adjustment models aimed at eliminating the scoring discrepancies between judges, thereby enhancing the overall reliability of the normalization process and evaluation results. Additionally, evaluation metrics were proposed based on information theory. Finally, we incorporate a BP neural network-based score adjustment technique to further refine the assessment accuracy by capturing latent biases and uncertainties inherent in large-scale evaluations. Experimental results conducted on datasets from national-scale innovation competitions demonstrate that the proposed methods not only improve the fairness and robustness of the evaluation process but also contribute to a more scientific and objective assessment framework. This research advances the state of the art by providing a comprehensive and scalable solution for addressing the unique challenges of large-scale innovative competition judging. Full article

Background/Objectives: Rare diseases are predominantly genetic in etiology and characterized by a prolonged ‘diagnostic odyssey’. Advances in genetic testing (GT) have helped shorten the time to diagnosis for rare/undiagnosed conditions. We aimed to synthesize the evidence on psychosocial factors related to GT for rare diseases to inform more person-centered approaches to care. Methods: We conducted a systematic literature search in six databases using structured terms (September 2024). Retrieved articles underwent independent dual review. Data were extracted and collated in tables for analysis. Thematic analysis was used to identify promoters/barriers to GT for patients and families. Findings were validated by a patient advocate and were reported using PRISMA-ScR guidelines. Synthesized findings were mapped to the Theory of Planned Behavior to inform intervention development. Results: Of 1730 retrieved articles, 32 were included for data extraction/synthesis. Studies employed qualitative (n = 19), quantitative (n = 10), and mixed-methods (n = 3) approaches. Nearly all (29/32, 91%) were non-interventional, reporting on decision-making cognitions/processes (19/32, 59%), attitudes/preferences (15/32, 47%), psychosocial impact (6/32, 19%), and knowledge/awareness (4/32, 8%) of pre-conception/prenatal/diagnostic GT and carrier screening. Promoters included understanding GT, ending the diagnostic odyssey, actionable outcomes, personal/family history, altruism, and reproductive decision-making. Barriers included logistical (e.g., distance, cost), psychological burden, perceived lack of benefit, and discrimination/social stigma concerns. Conclusions: Some psychosocial factors related to GT for rare diseases overlap with those in literature on GT for common conditions. Identified factors represent targets for theory-informed, person-centered interventions to support high-quality GT decisions that are informed and aligned with patient/family values and preferences. Full article