Abstract: This work presents a computational study of a hybrid plasmonic–photonic Panda-ring antenna embedded with a gold grating for dual-mode optical and terahertz (THz) transmission. The proposed structure integrates whispering gallery modes (WGMs) supported by a multi-ring resonator with surface plasmon polariton (SPP) excitation at a metal–dielectric interface, enabling strong near-field confinement and efficient far-field radiation. A systematic structural evolution—from a linear silicon waveguide to single-ring, add-drop, and Panda-ring configurations—is investigated to clarify the role of resonant coupling and power routing. Full-wave simulations using Optiwave FDTD and CST Microwave Studio are employed to analyze electric-field distributions, spectral power intensity, and radiation characteristics. The results demonstrate that the embedded gold grating facilitates effective SPP–WGM hybridization, allowing confined photonic energy to be converted into directional radiation with a peak gain exceeding 5 dBi near 1.52–1.55 µm. The proposed antenna exhibits stable dual-mode operation, making it a promising candidate for Li-Fi transmitters, THz wireless links, and integrated photonic–plasmonic communication systems.

Abstract: Shoulder girdle injuries in professional athletes often lead to prolonged recovery and decreased performance, highlighting the critical need for early and accurate diagnosis. This study aims to evaluate the effectiveness of artificial intelligence (AI) technologies in the early identification of such injuries to improve clinical outcomes and reduce reinjury rates. Employing a multicenter design, data were collected from diverse sports medicine centers involving 312 professional athletes undergoing routine screening and injury assessment. Advanced AI algorithms, including convolutional neural networks and machine learning classifiers, were applied to imaging data and biomechanical patterns for precise injury detection. Statistical analysis using receiver operating characteristic curve (ROC) and area under the curve (AUC) metrics demonstrated AI models achieved up to 92% sensitivity and 88% specificity in early injury detection. Furthermore, AI integration enabled a 23% reduction in reinjury rates compared to conventional diagnostic methods. These results confirm that AI-driven approaches provide superior diagnostic accuracy and timely intervention opportunities, facilitating individualized rehabilitation protocols. The novelty of this research lies in the successful implementation of AI across multiple centers with diverse athlete populations, validating its broad applicability. The findings support incorporating AI technology into routine sports medicine practice to enhance injury prevention and optimize athlete health. Future studies should explore real-time AI monitoring and personalized risk prediction models to further advance shoulder injury management.

Abstract: Large Language Models (LLMs) have demonstrated remarkable abilities to solve problems requiring multiple reasoning steps, yet the internal mechanisms enabling such capabilities remain elusive. Unlike existing surveys that primarily focus on engineering methods to enhance performance, this survey provides a comprehensive overview of the mechanisms underlying LLM multi-step reasoning. We organize the survey around a conceptual framework comprising seven interconnected research questions, from how LLMs execute implicit multi-hop reasoning within hidden activations to how verbalized explicit reasoning remodels the internal computation. Finally, we highlight five research directions for future mechanistic studies.

Abstract: This paper offers a comprehensive overview of academic research on sentiment analysis in urban built environments from 1999 to 2024. Based on data from the scientific database Scopus and drawing on bibliometric tools like Bibliometrix (R) and VOSviewer for performance analysis and scientific mapping, it identifies publication trends, key influential works, leading authors and institutions, funding sources, and thematic clusters. The final dataset comprises 871 English‐language documents authored by 2,068 researchers across 307 sources in 70 countries, with a total of 5,642 citations worldwide. The academic production increased after 2009, peaking in 2024. Keyword and network analyses highlight central themes (and methodological approaches?) to the study of sentiment analysis in urban built environments. These include social media platforms like Twitter/X/X, machine learning, Natural Language Processing, smart cities, and tourism. China, the USA, and India lead in publication output. Over the last twenty-five years, key publication outlets include the International Journal of Environmental Research and Public Health, Cities, and Lecture Notes in Computer Science, while the National Natural Science Foundation of China is the most common funder. The paper discusses how sentiment analysis can support urban planning and public health by linking environmental features to well-being and explores methodological emerging trends like deep learning, multimodal approaches, and context-aware models. Overall, it maps the intellectual landscape of the field and argues for future directions for human-centred, data-driven urban decision-making.

Abstract: This study presents an outdoor modular, vertical farming system integrated into building façades to address urban food security and sustainability challenges in Singapore. The design integrates passive climate control, hydroponic and soil-based irrigation; active monitoring of vapor pressure deficit (VPD) and photosynthetically active radiation (PAR). Continuous visual imaging is used to support growth monitoring and predictive harvesting, reducing labor needs. Under experimental conditions, deployment of UCNP-coated light-conversion films improved crop yield by 30% and reduced plant heat stress. Photovoltaic arrays and battery storage enabled energy self-sufficiency and microclimate management in the modular farm. The results demonstrated that building-integrated vertical farms can enhance urban food resilience and resource efficiency, offering a scalable model for sustainable agriculture in land-constrained cities.

Abstract:

Background Physical activity (PA) is a cornerstone of child and adolescent health, with well-established benefits across physical, cognitive, and psychosocial domains. Despite these benefits, global data show persistently low levels of PA among young people. In Nigeria, anecdotal reports and empirical studies suggest a similar decline in PA among school-aged children. This raises concerns about the adequacy of school environments in fostering healthy and active lifestyles among Nigerian youth. The aim of this scoping review is to systematically explore the current evidence on school-based physical activity in Nigerian schools. Methods We will follow a five-step scoping review framework and report the review according to PRISMA-ScR guidelines. A comprehensive search of academic databases and grey literature will be conducted. A scoping review approach is appropriate given the emerging and interdisciplinary nature of research on school-based physical activity (SBPA) in Nigeria. Result We expect to map the landscape of current research on SBPA in Nigeria, including levels of participation, enablers, and barriers as well as recommendations for improvements. This review is therefore expected to highlight both the promise and the current limitations of SBPA in Nigeria. By synthesizing available evidence, we aim to provide actionable insights for policymakers, educators, and health professionals on how schools can be leveraged to promote physical activity in children and adolescents.

Abstract: R-loops, three-stranded nucleic acid structures formed by an RNA-DNA hybrid, have emerged as important regulators of transcription and genome stability. Although ad-vances in high-throughput sequencing have revealed widespread R-loop landscapes, platform-specific biases hinder the identification of conserved R-loops in specific cell types. Mouse embryonic stem cells, which are transcriptionally active, provide an ideal system for investigating the potential roles of stable R-loops in RNA biology. In this study, we integrated 13 independent R-loop profiling datasets from four experimental platforms to define 27,950 Common R-loop regions in mouse embryonic stem cells and characterized their chromatin environment and associated biological functions. Common R-loop regions were reproducibly detected across methods and were preferentially localized to pro-moter-proximal and genic regions enriched in CpG islands. Genes associated with Common R-loops were highly and stably expressed, showing strong functional en-richment in RNA metabolism process such as mRNA processing, RNA splicing, and ribonucleoprotein complex biogenesis. Chromatin state analysis revealed that Common R-loops are enriched in transcriptionally active and regulatory contexts. Transcription factor motif analyses have identified distinct regulatory environments in Common R-loop regions, including pluripotency-associated OCT4-SOX2-TCF-NANOG motifs in en-hancer, CTCF motifs in open chromatin, and YY1 motifs in promoter. Together, this study provided the first integrated analysis of conserved R-loop regions in mouse embryonic stem cells, revealing their preferential localization at regulatory loci linked to RNA metabolism and highlight R-loops as structural and functional nodes in RNA biology.

Abstract: We report the crystallization and single-crystal X-ray analysis of the monohydrate hy-drochloride salt of chloroquine, designed CQCl·H2O, an antimalarial drug (CQ) with the formula C₁₈H₂₆ClN₃. The crystal structure reveals a well-defined supramolecular architecture stabilized by an extensive hydrogen-bonding network involving CQH⁺ cations, chloride anions, and water molecules. Notably, this study provides the first crystallographic characterization of a monoprotonated chloroquine salt. Additionally, our findings demonstrate the feasibility of isolating pseudo-polymorphic forms of a commercially available CQ salt via heterogeneous crystallization.

Abstract: The transformation of Andean grains and tubers through fermentation and bioencapsulation has emerged as a key strategy to enhance their nutritional, functional, and biotechnological value, driven by advances in proteomic and metabolomic techniques. This study aimed to systematize recent evidence on the biochemical and functional modifications induced by these processes and their potential application in the development of functional foods. The methodology integrated 67 studies analyzed using tools such as R 4.5.1 with the JupyterLab interface, Scimago Graphica, and VOSviewer, incorporating data generated through LC-MS/MS, UHPLC-QTOF, Orbitrap platforms, transcriptomics, and combined omics approaches, considering original studies published between 2020 and 2025. The main findings indicate substantial increases in free amino acids (up to 64.8%), phenolic compounds (2.9–5.2%), and antioxidant activity (up to 45.0%), along with the identification of 430 polyphenols, 90 flavonoids, 14 novel oxindoleacetates, and bioactive peptides with IC50 values ranging from 0.51 to 0.78 mg/mL. Bioencapsulation showed controlled release of bioactive compounds, high-lighting nanocapsules of 133–165 nm with a maximum release of 9.86 mg GAE/g. In conclusion, the combination of fermentation and encapsulation enhances the stability, bioavailability, and functionality of Andean crops, supporting their industrial adoption for the development of sustainable nutraceutical foods that improve health and promote the valorization of traditional resources.

Abstract: The accelerating pace of artificial intelligence research and deployment makes both extraordinary opportunity and profound peril increasingly apparent. This paper discusses the innovative proposition that AI can be marsharded, paradoxically, as a proactive guardian of human cognition against the harmful applications of the very technology on which it relies. The heuristic of “brain hacking”—an intentional deployment of AI-driven interventions that systematically augment mental capacities while fortifying neural substrates against adversarial incursions—emerges as a promising trajectory for both theoretical and practical inquiry. Central to the inquiry is the acknowledgment that the human brain, as a highly interactive and non-linear complex adaptive system, is susceptible to perturbations from sophisticated external agents. Nevertheless, leveraging the quasi-infinite adaptiveness of advanced AI algorithms may permit the engineering of defensive architectures that preserve both the integrity and the adaptive plasticity of neural circuits. This paper systematically reviews emergent scholarship across deep neural network design, reinforcement learning paradigms, and convergent advances in cognitive neuroscience, converging to identify convergent leverage points for human neural fortification. The research objective is to fabricate a multilayered AI-mediated cognitive firewall that autonomously surveys the brain’s operational state, diagnostically distinguishes anomalous patterns of activity, and pre-emptively desensitizes or reroutes them before they achieve disruptive penetration. Through rigorous simulation and empirical validation, the framework aspires to safeguard the epistemic domain of the human mind without impairing its intrinsic generative capacities. This study further addresses the essential ethical dimensions inherent in deploying artificial intelligence for the safeguarding of neural integrity, advocating for transparency, systematic safety, and the preservation of personal autonomy. Confronting these issues explicitly allows us to construct a future in which AI operates not only as a catalyst for remarkable technological advance, but also as a vigilant guardian of human cognition and psychological health.

Abstract: Chiplet technology enables the integration of heterogeneous dies into a single system, of-fering improved performance, scalability, and design flexibility. To support chiplet-based architectures, advanced packaging methods such as System-in-Package (SiP), 2.5D inter-posers, and 3D stacking are essential. A key enabler in these technologies is the fine-pitch Redistribution Layer (RDL), which ensures high interconnect density, signal integrity, and thermal efficiency. This study presents the development and optimization of fine-pitch RDL for two inter-poser types—2.5D RDL interposers and Embedded Bridge Die interposers—fabricated using fan-out wafer-level packaging (FOWLP). A newly developed positive photoresist was used in the photolithography process to define sub-micron RDL features. Process pa-rameters such as exposure energy and focus settings were systematically optimized to improve pattern resolution and structural integrity. Experimental results demonstrated that optimized lithographic conditions significantly enhanced the fidelity and uniformity of fine-pitch RDLs, enabling reliable signal trans-mission and manufacturability in multi-die systems. The findings confirm that fine-pitch RDL is a foundational technology for next-generation interposer solutions, supporting tighter die spacing and improved system performance. This technology can be imple-mented across various chiplet-based packaging platforms, such as those used in next generation artificial intelligence (AI) processors and high-performance computing (HPC) architectures.

Abstract: Wetland restoration plays a crucial role in biodiversity conservation, particularly in semi-arid landscapes. The present study documents the avifaunal diversity of Kanwas Pakshi Vihar Wetland (Gopalpura Pakshi vihar), Kota district, Rajasthan, which represents a successfully restored wetland ecosystem. Post-restoration surveys recorded 91 bird species dominated by wetland-dependent taxa, indicating improved habitat quality.

Abstract: Background: The management of asymptomatic cryptococcal antigenemia in pregnant women with advanced human immunodeficiency virus (HIV) disease presents a therapeutic dilemma. Clinicians must balance the risks of vertical transmission, immune reconstitution inflammatory syndrome (IRIS), and antifungal teratogenicity. Case Summary: We report a case of a 28-year-old HIV-positive woman in Kenya who presented at 34 weeks of gestation with symptoms suggestive of meningitis. She had self-discontinued her antiretroviral therapy (ART) 18 months prior. Laboratory investigations confirmed a positive serum cryptococcal antigen (CrAg) with a high HIV viral load (41200 copies/mL). Lumbar puncture ruled out meningeal involvement. A multidisciplinary team initiated preemptive therapy with high-dose fluconazole (800 mg daily). Faced with her advanced gestation and the imperative to prevent perinatal transmission, a calculated risk was taken to initiate ART (tenofovir/lamivudine/dolutegravir) after only 7 days, a significant deviation from standard guidelines. At 36 weeks, she had a spontaneous vaginal delivery complicated by uterine inversion and postpartum hemorrhage, which was managed successfully. She did not develop cryptococcal IRIS. At 3-month follow-up, her viral load was suppressed (51 copies/mL), and her infant was HIV-negative with normal development at 6 months. Conclusions: This case highlights the importance of routine CrAg screening in pregnant women with advanced HIV. Preemptive fluconazole in the third trimester is feasible. The timing of ART initiation may need individualization to prevent vertical transmission in late gestation, particularly in the context of isolated antigenemia, where the IRIS risk profile may differ from cryptococcal meningitis. These decisions require multidisciplinary input and close monitoring.

Abstract:

Waterlogging stress, particularly during flowering severely constrains wheat production, yet the optimal timing and frequency of waterlogging stress memory and its linkage to post-stress nitrogen acquisition remain unclear. We conducted pot experiments under glasshouse over two consecutive growing seasons (2022/23 and 2023/24) using the Japanese bread wheat cultivar Norin 61 to evaluate eight treatment combinations of waterlogging stress memory applied at the tillering, stem elongation, and booting stages, followed by waterlogging during flowering stage. Leaf greenness (SPAD), chlorophyll fluorescence (Fv′/Fm′), photosynthetic rate, yield and its components, and nitrogen dynamics were assessed. To quantify post-stress nitrogen uptake, ¹⁵N-labeled ammonium sulfate was applied immediately after waterlogging termination at flowering, and ¹⁵N uptake and allocation to plant organs and grains were determined during grain filling and at harvest. Treatments that included tillering-stage stress memory consistently delayed leaf senescence, maintained higher photosynthetic performance, increased thousand-grain weight, and improved grain yield relative to the non-primed treatment, with reproducible effects across both seasons. These treatments also showed higher post-stress ¹⁵N uptake and greater ¹⁵N allocation to grains. Overall, tillering-stage waterlogging stress memory was associated with improved tolerance to flowering-stage waterlogging in wheat through maintenance of post-stress nitrogen uptake capacity and nitrogen allocation to grains.

Abstract: Purpose: Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory condition often accompanied by various comorbidities that significantly affect patient outcomes. High resolution computed tomography has emerged as a valuable tool for diagnosing and managing COPD-related comorbidities. This study aims to explore the impact of chest CT imaging in identifying and characterizing comorbidities in COPD patients. Methods: The study was conducted on 99 patients with COPD, with an average age of 67,8 (37-88), 86% were men (85), and 14% were women (14). The patients underwent chest HRCT to identify the presence of comorbidities. Results: According to the GOLD classification, ABE type, 3% were type A, 27% were type B, and 69% were type E. The prevalence of comorbidities identified on chest HRCT was reported as 66% for coronary artery calcification (CAC), 83% for osteoporosis, 36% for pulmonary artery enlargement (PAE), 31% for emphysema, 19% for bronchiectasis, 17% for hiatal hernia, 14% for lung cancer, 12% pulmonary infections and 3% for interstitial abnormalities. In 4% there were no comorbidities, one comorbidity was found in 11%, two comorbidities in 17%, and three comorbidities and more in 68% of cases. Conclusion: Chest HRCT imaging serves as a valuable tool for identifying and assessing comorbidities in patients with COPD. Incorporating chest CT imaging into the routine evaluation of COPD patients can contribute to a more comprehensive understanding of their condition and lead to better clinical outcomes.

Abstract: Additive manufacturing is increasingly used in construction, yet reliable quality assurance for 3D-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, voxel-based methods showed superior detection of volumetric and subsurface defects, while mesh-based methods achieved more precise localization of surface irregularities with substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a construction-oriented benchmark for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing.

Abstract: Childhood and adolescence are critical periods for the development of Executive Functions (EF), which underpin self-control, planning, and social adaptation, and are often compromised in children growing up in psychosocially vulnerable contexts. This study examined the effects of STap2Go, a fully digital, strategy-based EF training, on EF performance and self-perceived maladjustment in 36 at-risk children and adolescents compared with 32 controls. Participants completed pre- and post-intervention assessments using the Neuropsychological Assessment Battery of Executive Functions (BANFE-3) and the Multifactorial Self-Evaluative Test for Child Adaptation (TAMAI). Results showed a significant effect of training on global EF and on General Maladjustment, with improvements only in the intervention group. These findings support the inclusion of scalable, avatar-guided EF stimulation programs such as STap2Go within social inclusion pathways for youth in vulnerable situations.

Abstract: Neural oscillations are fundamental to the integration of sensory, affective, and cognitive processes that contribute to pain perception. Transcranial alternating current stimulation (tACS) provides a valuable tool for investigating and modulating these oscillatory dynamics. In this review, we examine the effects of tACS on pain perception and pain-related oscillations in both healthy participants and individuals with chronic pain, highlighting methodological variability and mechanistic uncertainties that may contribute to mixed findings. We identified 14 studies, including 9 studies of experimental pain in healthy individuals and 5 of clinical pain disorders, comparing tACS to sham. Somatosensory alpha was the most frequently targeted oscillatory feature. Results varied considerably. Several studies reported reductions in pain, increases in alpha power, or changes in sensorimotor and prefrontal connectivity, but others showed no meaningful neural or behavioral effects. Out of the 14 studies, 6 demonstrated analgesic benefits and 2 showed improvements only under specific conditions or within subgroups, for a total of 8/14 studies with positive findings. Possible sources of heterogeneity include variation in stimulation duration, electrode montage, frequency alignment with individual rhythms, contextual state, and anatomical and neurophysiological differences across individuals. Pre-registered studies with sufficient power are needed to replicate effects within the most promising intervention protocols to establish a foundation in the field. We also recommend inclusion of brain imaging or electrophysiological recordings to verify whether stimulation effectively modulates the targeted neural oscillations. Finally, recent methodological advances, including phase-specific tACS, amplitude-modulated tACS, and individualized electric-field modeling, offer new opportunities to enhance mechanistic precision and clinical applicability. We argue that by integrating these approaches, future research can move beyond fixed, one-size-fits-all protocols, toward personalized, state-dependent, closed-loop tACS approaches. Exploring these frontiers will transform tACS from an exploratory tool into a reliable intervention for pain.

Abstract: The term “pristine interface” is used for differentiating emulsions that consist of only water and oil with no surfactant from the Pickering emulsions, which are also surfactant-free but stabilized with colloidal particles. We review 23 papers dedicated to such emulsions prepared from a wide variety of liquids. We studied here the evolution of one of such emulsion, hexadecane-in-water at 4% vl, over a long period of time, from days to weeks. We discovered that the droplet size is growing with time with the rate that depends on mixing conditions, which supports a coalescence hypothesis. However, this coalescence is unusual because the size reaches a certain constant value, which contradicts typical coalescence behavior. In order to explain this peculiarity, we employ a theoretical model that was developed for pristine nano-bubbles stability. We hypothesize the existence of a layer of structured water molecules at the interface, following Eastoe and Ellis (Adv in Colloid and Interface Sci., 134-135, 89-95, 2007) and many other prominent scientists. Then we point out that the Electric Double Layer exerts a force on the water dipole moments in this layer (dielectrostatic force) that compensates Kelvin’s pressure. The droplet size calculated using this model is close to the measured sizes. The second factor associated with this layer is the repulsion of the water dipole moments, which we show can compensate for surface tension parallel to the interface. After ruling out alternative hypotheses with our data, we conclude that the model suggested for explaining the stability of nano-bubbles is also consistent with our results for these “pristine emulsions”.

Abstract: Agriculture is pivotal for the economy of a country as it is a major source of food, em-ployment and raw materials. However, challenges such as diseases, soil degradation, and water scarcity persist. Technology adoption can address these issues, improving production and quality. Machine learning enables prediction in agriculture. It opti-mizes irrigation, fertilization, and crop selection, aiding decision-making for food se-curity and crop management. This study proposes multi-model machine learning models for eleven staple (Bananas, Maize, Wheat, Cassava, Rice , Soybeans, Barleys, Potatoes, Beans dry, Peas dry and Cocoa beans ) crop yield prediction. The compara-tive results show that the prediction results of the proposed multi-model algorithm are significantly better than linear model. The error trend seasonality-artificial neural network (ETS-ANN) achieved 80% R² for Cassava crop yield prediction whereas Ba-nanas achieved lowest R² (20%).