Gestational hyperglycemia and autism spectrum disorder: Mechanistic pathways and emerging preventive strategies.

Tailored communication strategies according to patient characteristics for older adults with benign prostatic hyperplasia: A cross-sectional study.

Modern communication systems increasingly leverage multiple information streams—including channel observations, statistical models, and contextual knowledge—to enhance decoding reliability. However, the varying and often unpredictable quality of these sources poses a critical challenge: rigid combination rules fail when source reliability fluctuates, while manual tuning cannot adapt to dynamic operating conditions. This paper presents a neural decoder architecture that automatically learns to assess and fuse heterogeneous information sources based on their instantaneous reliability. Central to our design is a learnable gating module that dynamically weights information streams, demonstrating emergent Bayesian-like behavior—increasing reliance on statistical models under high uncertainty while transitioning to observation-dominated processing as signal confidence improves. To combat the progressive dilution of auxiliary information in deep architectures, we propose a continuous injection strategy that refreshes auxiliary features at each processing layer through dedicated encoding pathways. The underlying message-passing network adopts a heterogeneous bipartite structure with direction-dependent edge parameterization, respecting the asymmetric computational roles inherent in iterative decoding algorithms. Comprehensive experiments validate that the proposed approach not only improves nominal performance but critically maintains robustness when auxiliary information quality degrades or becomes mismatched with actual conditions.

Optical communications have emerged as a promising solution for high-speed modern communication systems and built an important infrastructure for the global information superhighway. Although recent efforts to enhance optical communications have penetrated from long-distance fiber-optic to ultra-short-reach chip-scale data transmission, "Trans-Scale" high-capacity data transmission remains great challenges. In addition to data transmission, data processing is also of great importance for flexible data management in optical communication systems. However, a "Digital Divide" (capacity gap) exists between high-capacity data transmission in fiber links and low-speed data processing at network nodes, hindering the flourishing development of optical communications. Here, we implement "Trans-Scale" high-capacity bridging between few-mode fiber and silicon multimode waveguide using a diverse hybrid integrated coupler, which includes a 3D silica fs-laser direct writing photonic chip and a 2D silicon photonic integrated circuit. On this basis, we leverage a large-scale silicon reconfigurable optical add-drop multiplexer (ROADM) with over 2000 elements to construct a multi-dimensional fiber-chip system, enabling 192-channel (3 modes, 2 polarizations, 32 wavelengths) and 20-Tbit/s trans-scale multi-dimensional data transmission and processing. This demonstration provides a superior trans-scale architecture for multi-dimensional data transmission and processing in next-generation optical communications.

Conversational alignment, the phenomenon in which interlocutors exhibit similar communicative behaviors as one another, has been documented across many levels of communication. There has been a growing recognition of the need to understand the relationship between alignment at various levels. Here, we add to the body of literature by exploring the trajectories of alignment development at different communication levels in children. Using a conversational corpus in which early school-aged children demonstrated robust lexical alignment, we examined whether they also demonstrated similar patterns of speech rate alignment. In this corpus, children (n = 45) aged 5-8 years participated in two experimental sessions. In one session, they interacted with their parents (i.e., all mothers), and in the other, they interacted with the university students. During each session, the child engaged in two 10-min conversations: a problem-solving task and a play-based task. A total of 180 conversational samples were collected. Linear mixed-effects models showed that the children did not align their speech rates across multiple contexts (i.e., different partners and tasks). Furthermore, there was no relationship between lexical and speech rate alignment. These findings suggest that alignment development in children is not a unitary phenomenon. Rather, alignment at different levels of communication may require different underlying skills and may be driven by different levels of automaticity.

Public Opinion, Faith and Mechanisms of Impact in Modern Communication

Automatic Modulation Classification (AMC) plays a pivotal role in modern wireless communication systems, supporting applications such as cognitive radio spectrum management and secure military communications. However, traditional methods based on artificial features and conventional classifiers exhibit limited robustness under low signal-to-noise ratios (SNR) and struggle to adapt to dynamic signal characteristics, particularly in fading and non-Gaussian noise environments. Among deep learning approaches, lightweight one-dimensional Residual Networks (1D-ResNet) and CNN-BiLSTM architectures have attracted considerable attention due to their complementary strengths in structural and temporal feature learning. This paper systematically compares 1D-ResNet and CNN-BiLSTM using the RadioML 2018.01A dataset, which contains 24 modulation formats. Experiments are performed at 2 dB SNR intervals across the 0-14 dB range. The results demonstrate a clear SNR-dependent performance transition. The 1D-ResNet consistently outperforms the CNN-BiLSTM model in low-SNR conditions (0-4 dB), achieving classification accuracies of 0.5242 vs. 0.4804 at 0 dB. Conversely, the CNN-BiLSTM model surpasses the 1D-ResNet at higher SNR levels, reaching 0.8755 vs. 0.8545 at 10 dB.

Social media has become an essential component of modern communication, allowing people and organizations to immediately contact a large audience. In Bangladesh, where Islam is the most practiced religion, social media platforms have grown to be effective resources for promoting Da'wah (inviting people to Islam) and distributing Islamic information. This study explores the positive and negative effects of social media on spreading Da'wah and Islamic knowledge in Bangladesh. A qualitative approach has been followed to conduct this study. Data have been collected through document analysis. According to the findings, the positive impacts of social media in spreading Da’wah and Islamic knowledge are: spreading the Holy Quran, Sunnah, and many Islamic apps, showing the true face of Islam, the ease of spreading Da’wah, sharing authentic knowledge, etc., and the negative impacts are: false news is generated and spread through social media, lack of authenticity and verification, misinformation and misinterpretation, spread of extremist views etc. This study will be helpful for young generation to use the social media for the purpose of goodness so that they can attain success in here and hereafter. IIUC Studies, Vol.-21, Issue-1, Dec. 2024, pp. 149-168

The increasing reliance of Internet of Things (IoT) applications on low-power wide-area network technologies, particularly Long Range Wide Area Network (LoRaWAN), has amplified the need for security monitoring approaches that go beyond attack-specific signatures and generic traffic anomalies. Existing solutions are often tailored to individual threat scenarios or rely on statistical indicators, which limits their ability to systematically capture protocol-level misuse in an interpretable manner. This paper addresses this gap by proposing a protocol-aware validation methodology based on a Digital Twin abstraction of LoRaWAN communication behavior. The Over-The-Air Activation (OTAA) procedure is modeled as a finite-state machine that encodes expected message sequences, timing constraints, and specification-driven state transitions. Observed network events are continuously evaluated against this formal state model, enabling the identification of protocol-level deviations indicative of anomalous or non-conformant behavior. Illustrative examples include replay behavior, timing inconsistencies, and integrity-related anomalies, although the framework is not limited to predefined attack categories. The results demonstrate that state machine-based Digital Twin provides a structured and extensible foundation for protocol-aware security validation and Security Operation Center (SOC)-oriented telemetry enrichment. In this sense, the presented approach represents a concrete step toward protocol-aware intrusion detection for LoRaWAN networks by establishing a state-synchronized semantic validation layer upon which higher-level detection mechanisms can be built.

Mass media plays a key role in helping audiences organize facts and make sense of uncertainty, particularly during emerging medical crises when pre-existing knowledge is limited. The COVID-19 pandemic was the first major global crisis in the modern communications era in which traditional media (TV, radio, newspapers and major news sites) and social media (especially Facebook groups) both functioned as high-reach information systems, shaping public interpretation in parallel. Social media, especially closed and semi-closed Facebook groups, became a central arena for discussion, community building, and the circulation of alternative interpretations. Against this backdrop, the current study examines how anti-vaccination activists (anti-vaxxers) who are active in anti-vaccine Facebook groups perceive mainstream media coverage of COVID-19. The study employs a qualitative design based on semi-structured in-depth interviews with 70 anti-vaxxers of both genders who were active participants in anti-vaccination Facebook groups. Findings indicate that participants perceive mainstream media as advancing a biased, unidimensional narrative aligned with governmental, economic, and political interests, and as delegitimizing dissenting voices. Consistent with the hostile media effect, interviewees interpret coverage as hostile toward their community, which intensifies their tendency to avoid mainstream news and rely on Facebook group networks for validation, interpretation, and mobilization. These results highlight how crisis coverage is experienced by marginal groups and how social media group dynamics can reinforce perceptions of media hostility and deepen informational polarization.

Modern communication systems face unprecedented challenges in ensuring secure data transmission and real-time processing across distributed networks. The convergence of artificial intelligence (AI) and quantum computing (QC) fundamentally transforms communication engineering by introducing both enhanced capabilities for intelligent network management and critical threats to cryptographic security protocols that underpin global communications. These challenges are particularly acute in high-security domains such as criminal justice systems, where communication infrastructure must balance stringent security requirements with evidentiary reliability. The administration of criminal justice has historically relied on the epistemological reliability of evidence and the ontological security of information. However, the legal profession currently faces a radical discontinuity driven by the simultaneous maturation of Generative Artificial Intelligence (AI) and the accelerating development of Quantum Computing (QC). Generative AI has introduced a regime of "probabilistic truth," leading to the proliferation of hallucinated legal texts and synthetic media that threaten evidentiary standards. Parallel to this, the looming reality of QC poses a fundamental threat to the cryptographic locks securing sensitive criminal justice data, notably through strategies that target current encrypted data for future decryption. As the integration of these advanced technologies becomes an irreversible trend, there is a critical need to synthesize these divergent yet interconnected threats to understand their collective impact on judicial integrity. This review analyzes the epistemological crisis precipitated by the integration of algorithmic text generation into legal workflows and the challenges posed to digital forensics by the potential compromise of encryption standards. Furthermore, it explores the transformative potential of Quantum Machine Learning (QML) in unraveling sophisticated modern criminal schemes, particularly for identifying complex patterns in financial crimes and criminal networks, while also addressing the technical hurdles limiting the practical deployment of these models. This study underscores the critical necessity for the legal system to fortify procedural defenses against AI-generated misinformation and to accelerate the migration to quantum-resistant security infrastructures. Ultimately, this review highlights that preserving the validity of the justice system requires commitment to technological literacy and the establishment of rigorous verification frameworks to navigate the dual disruption of algorithmic probabilities and quantum insecurity.

Leadership styles play a crucial role in shaping nurses' psychological well-being and communication behaviours, especially in high-stress settings like critical care. Paternalistic leadership-characterised by benevolence, moral integrity and authority-has gained recognition for its impact on healthcare outcomes. However, its influence on organisational dissent, particularly through the lens of psychological well-being, remains underexplored. To Investigate the Mediating Role of Psychological Well-Being in the Relationship Between Paternalistic Leadership and Organisational Dissent Among Nurses in Critical Care Settings. A cross-sectional descriptive study was conducted. A convenience sample from 23 critical care units in a large educational government hospital participated. Data were collected using the Paternalistic Leadership Scale, Psychological Well-Being Scale and Organisational Dissent Scale. Statistical analyses included Pearson correlation, regression and path analysis. Among 460 nurses, paternalistic leadership was positively correlated with psychological well-being (r = 0.263, p < 0.001) and negatively correlated with organisational dissent (r = -0.278, p < 0.001). Psychological well-being also negatively correlated with dissent (r = -0.258, p = 0.001). Regression and path analysis confirmed that psychological well-being partially mediated the relationship between paternalistic leadership and organisational dissent. The mediation model showed statistically significant direct and indirect effects. Paternalistic Leadership Enhances Nurses' Psychological Well-Being and Reduces Organisational Dissent. Psychological Well-Being Acts as a Partial Mediator, Emphasising Its Importance in Translating Leadership Support Into Reduced Dissent Behaviours. Fostering paternalistic leadership and supporting nurses' psychological well-being are critical to maintaining constructive communication and reducing harmful dissent. Healthcare institutions should implement leadership development and mental health support initiatives to improve workforce morale and patient care.

Automatic modulation classification (AMC) is essential in modern wireless communication for optimizing spectrum usage and adaptive signal processing. This study explores the use of various machine learning (ML) methods for AMC, focusing on their performance in additive white Gaussian noise (AWGN) and fading channels. This study evaluates of ML classifiers such as support vector machines (SVM), K-nearest neighbors (KNN), decision trees (DT), and ensemble methods with a dataset spanning signalto-noise ratios (SNRs) from -30 dB to +30 dB. Higher-order statistical features including moments and cumulants are used to train the classifiers for AMC. Performance is measured in terms of classification accuracy and computational efficiency across different SNR levels. The findings show that linear SVM, fine KNN, and fine trees consistently achieved high classification accuracy, even at low SNRs. From the analysis, it is observed that linear SVM and fine KNN achieve over 96% accuracy at 0 dB SNR. These classifiers demonstrate significant robustness, maintaining performance in challenging noise conditions. The research highlights the promise of ML techniques in improving AMC, providing a detailed comparison of classifiers and their strengths.

Morphometric dissimilarity in association cortices linked to autism subtype with more severe symptoms.

Genetics of Autism Spectrum Disorder underscores the role of altered spontaneous neuronal activity as a catalyst for the neurodevelopmental anomalies.

Early detection of autism spectrum disorder (ASD) requires integrating clinical observation with developmental history and everyday functioning, yet care pathways are often strained by high demand and limited specialist resources. In this context, parents' narratives provide ecologically valid signals about social communication, language, and repetitive behaviors; however, free-form accounts are difficult to analyze systematically in routine practice. This article reviews a special study, which automatically analyzed caregivers' openended responses to 12 questions inspired by the ADI-R in 51 families (children aged 2-8 years; 25 ASD and 26 controls). The study aimed to automatically detect ASD in children through computational analysis of parents' speech. The best subject-level classification strategy was achieved by converting text into semantic representations (using OpenAI's text-ada-embedding-large-v3), training models per question, including the question in the input, and aggregating decisions via majority voting (84% accuracy; ROC-AUC 1.0). The clinical goal of this pilot study was to demonstrate the potential to support screening, prioritization, and referral, and potentially reduce waiting times and professional burden, thereby improving timely access to evaluation and early intervention. We discuss limitations (small sample, selected population, possible influence of prior intervention) and ethical risks (sociolinguistic bias, privacy, stigmatization, false positives/negatives), and we propose steps toward responsible clinical translation.

Children readily respond to others' bids for communicative interactions from early childhood and actively initiate these themselves. However, the extent and variety of early child-initiated communicative intentions is poorly understood, with theoretically derived intentions lacking systematic empirical support from naturalistic observations. This study, using a cross-sectional data set, provides a fine-grained characterization of communicative behaviors across three time points in the second year of life (13, 18, and 23months, N=47). We coded one-hour-long video recordings of home observations using a novel coding scheme to document the type of interactions toddlers initiated using four deictic gestures (reach, point, give, hold out) to meet a range of communicative goals, such as sharing interest, attention, or emotion, requesting an object or an action, seeking information or help, and giving information. Expressive interactions accounted for 49.9% of events, followed by requestive (40%), information/help seeking (8.3%), and information giving intentions (1.7%). These findings characterize early communicative toddler-caregiver interactions and provide insights into the age-related patterns of toddlers' propensity to seek and transmit information which emerge increasingly as part of toddlers' communicative repertoire.

Eye contact is critical in face-to-face social interactions. Prior research has shown that dialog partners primarily focus on the eyes of the speaker both when the speaker is speaking and, particularly, when the speaker is not. These findings are compatible with the communicative but specifically the social function of eye contact. This is in line with animal cognition literature showing that animals with more eye sclera tend to be more social. Several human studies have reported a left perceptual bias in eye contact, with the listener focusing on the right side of the speaker's face. Some studies attributed this bias to hemispheric specialization. Here two eye-tracking experiments using human and virtual human speakers confirmed a systematic bias towards the right eye of the speaker. Findings, however, are modulated by the amount of sclera and communicative events. Larger eyes (more sclera visibility) attracted more fixations, so that faces with larger left eyes do not systematically induce the left perceptual bias to the right side of the face. Moreover, a difference in fixations on the left or right eye of the speaker was found depending on whether somebody was speaking or not. Our results are consistent with the left perceptual bias, but suggest the bias is not solely perceptual. Instead, our findings suggest the social function in eye contact modulates the bias towards the right eye. Face-scanning behavior emerges as an unfolding dynamic shaped by a flow of social and communicative action ladders. These findings shed light on the most fundamental aspects of human communicative behavior.

Sequential communication patterns reflecting building blocks of conversation: Associations with toddler temperament and mother and father sensitivity.

Visual communication in modern Indian paintings relies heavily on symbols to convey meanings that go beyond simple or literal representation. These symbols come from various sources, such as cultural memories, mythology, personal gender experiences, social and political conditions, and the personal stories of artists. Together, these elements create a strong visual language through which modern Indian art shares ideas, emotions, and social issues. This paper examines semiotics as a tool for interpreting symbols in visual communication, specifically in modern Indian paintings. The study builds on the semiotic theories of Ferdinand de Saussure and Charles Sanders Peirce, focusing on how visual signs create meaning through representation and interpretation. Using a qualitative research approach, the study conducts semiotic analysis on selected modern Indian artworks to explore the symbolic roles of colour, form, line, space, and imagery. Artists like S. H. Raza, V. S. Gaitonde and F. N. Souza use abstraction and expressive distortion to express themes of spirituality, identity, and existential struggle. Meanwhile, women artists such as Arpita Singh, Gogi Saroj Pal, and Nalini Malani use symbolic imagery to tackle issues of gender, memory, mythology, and social critique. Their artworks show how personal experiences and shared cultural symbols intersect, creating layered and complex visual meanings within modern Indian visual communication. The study highlights the viewer's active role in meaning-making, where interpretation is shaped by cultural background, visual awareness, and personal experiences. The findings indicate that semiotics provides a clear and effective framework for understanding the intricate symbolic systems found in modern Indian paintings. By placing modern Indian art within a semiotic and visual communication context, this research contributes to interdisciplinary discussions and underscores the continuing importance of symbolic interpretation in understanding contemporary visual expression at both national and global levels.