Abstract: The surface properties and electrical behavior of carbon-based materials can be effectively tailored by energetic ion irradiation. In this study, graphene oxide (GO), cyclic olefin copolymer foils (COC, Topas 112 and 011, respectively) were irradiated with 1 MeV Au ions using a 3 MV Tandetron accelerator at fluences of 1 × 10¹⁴, 1 × 10¹⁵, and 2.5 × 10¹⁵ ions/cm². The irradiation induced systematic modifications in surface chemistry, morphology, wettability, and electrical properties. Compositional changes before and after irradiation were investigated using Rutherford backscattering spectrometry (RBS) and elastic recoil detection analysis (ERDA), while surface morphology and roughness were characterized by atomic force microscopy (AFM), revealing a clear fluence-dependent evolution of nanoscale topography. The vibrational characteristics will be assessed through Raman spectroscopy. Surface wettability was evaluated by static contact angle measurements, and surface free energy was determined using the Owens–Wendt–Rabel–Kaelble (OWRK) method, showing a consistent decrease in water contact angle and an increase in surface free energy with increasing ion fluence in Topas 112/011 but not in GO. Electrical characterization demonstrated a pronounced fluence-dependent decrease in sheet resistivity across all investigated substrates. The results show that 1 MeV Au-ion irradiation enables controlled modification of both surface and electrical properties of carbon-based foils.

Abstract: In today’s digitally connected world, social media has become central to culture, shaping how we interact, see ourselves, and feel. Platforms like Facebook, Instagram, and TikTok are promoted as ways to connect, but growing evidence shows they can also cause anxiety, social comparison, and emotional strain. Many studies explore these positive and negative effects, but fewer examine changes in academic discussion about social media and well-being over time. To address this issue, the present study employs BERTopic, a dynamic topic model, to analyze 7,254 journal articles indexed in the Web of Science between 2010 and 2025. The analysis identifies 110 distinct research topics and reveals that the most prominent themes converge around anxiety-related outcomes, social connection and support, as well as contextual and methodological developments such as COVID-19 communication and AI-based depression detection. Temporal trend analysis indicates a clear shift in scholarly focus. Research published between 2010 and 2016 adopted a relatively balanced perspective, addressing both the connective potential and the psychological risks associated with social media use. However, since 2017—coinciding with the rapid rise of visually oriented platforms—academic attention has increasingly centered on anxiety-related issues, particularly fear of missing out and body image concerns. By mapping the shift from connection to anxiety focus, the study shows how academic research tracks social change. The results also suggest that future research should explore platform-specific mechanisms, identify protective factors against digital stress, and contribute to the creation of healthier online spaces.

Abstract: We present a high-dimensional quantum key distribution protocol by using N-qudits quantum light states, that is, product states with N photons, each of them in a quantum superposition of dimension d which provides a high dimension d^N and accordingly a very high security. We present the implementation of this protocol in different types of optical fibers where the mentioned states undergo perturbations under propagation in optical fibers; such perturbations can be notably reduced in a passive (autocompensation) or active way and importantly the N-qubits present a great robustness against such optical perturbations. Likewise, quantum states also undergo attenuation, that is, some photons are lost under propagation in the optical fibers and then effective N′ (< N)-qudits are obtained which also are used to generate secret keys. In fact, the detection of states combines standard projective measurements along with photon coincidences. Besides, we analyze the security of this high-dimensional protocol under an intercept and resend attack realized by Eve, and the resulting secure key rates are calculated showing a significative increasing with the dimension provided by the number N of photons.

Abstract: This paper presents a comparative analysis of natural gas and electric power prices using visibility graph methodology, a technique from complex network theory that transforms temporal sequences into network representations. We analyze 1,826 daily observations from the Italian energy market (2019-2023), implementing a three-stage preprocessing pipeline (logarithmic transformation, LOESS detrending, and first differencing) before constructing visibility graphs. Our topological analysis reveals striking differences: gas exhibits substantially higher connectivity (6,202 versus 5,354 edges), heavier-tailed degree distributions (maximum degree 117 versus 54), and dramatically longer-range connections (average temporal distance 26.4 versus 11.0 days). Paradoxically, despite power displaying twice the raw volatility, gas generates more structured long-range correlations due to storage-enabled intertemporal linkages. Both series exhibit small-world properties with high clustering (≈0.76), short path lengths (4.59 and 5.36), and positive assortativity (≈0.17). Correlation analysis reveals moderate contemporaneous return correlation (Pearson r = 0.456) with substantial time variation (range 0.173– 0.696), no lead-lag relationships, and partial synchronization of topological properties. Node-level degree and clustering show positive correlations between markets, while closeness centrality exhibits strong negative correlation (r = −0.719), indicating fundamentally different global network organization. Structural similarity (Jaccard coefficient 0.404) confirms 40% shared visibility connections with 60% commodity-specific structure. These findings demonstrate that physical storability fundamentally shapes temporal correlation structure, with direct implications for risk management, forecasting model selection, and portfolio construction in energy markets.

Abstract: α-Mangostin, a natural product from Garcinia mangostana L, presents most antibacterial activity in plant flavonoids against Staphylococcus aureus so far. Recently, it was reported that the quinone pool is a key target of α-mangostin against Gram-positive bacteria. To further confirm this and investigate the detail of α-mangostin killing S. aureus, the interactions between α-mangostin and a key enzyme as type II NADH:quinone oxidoreductase (NDH-2), together with possible non-enzymatic mechanisms, were explored. Through the enzyme kinetic inhibition experiments, it was found that α-mangostin mainly competes with the menaquinone-binding sites of NDH-2, and the half-maximal inhibitory concentration (IC₅₀) of α-mangostin on NDH-2 is 4.95 μM. Fluorescence analyses indicated that α-mangostin can spontaneously bind to NDH-2 to form an α-mangostin–NDH-2 complex. Subsequently, molecular simulation further indicated that α-mangostin can dock to the menaquinone-binding sites of NDH-2. Another, non-enzymatic mechanism showed that α-mangostin can cause membrane potential depolarization and disrupt the proton motive force balance, thereby promoting the cell-membrane destruction of S. aureus. These results suggest that α-mangostin mainly can interact with the amino acid residues at the menaquinone-binding pocket of NDH-2 to block the electron transfer at the quinone pool in the respiratory chain of S. aureus, and which will hinder the energy supply and promote its incidental effect on membrane disruption, ultimately leading to the death of S. aureus. This once again proves that the quinone pool is a key target of plant flavonoids against Gram-positive bacteria.

Abstract: The compelling study by Liu et al delivers a critical verdict: The primary tumor site is not merely an anatomical detail, but a fundamental prognostic imperative in the surgical management of colorectal liver metastases. Their analysis of 178 patients definitively establishes right-sided colonic origin as an independent harbinger of aggressive disease, characterized by significantly higher recurrence rates and inferior survival outcomes compared to left-sided and rectal cancers. This biological dichotomy is further elucidated by the strong association of right-sided tumors with an adverse prognostic profile, including rampant lymph node metastasis, elevated D-dimer (reflecting a pro-thrombotic, pro-metastatic state), hypoalbuminemia, and resistance to neoadjuvant therapy. These findings necessitate an immediate paradigm shift in clinical practice. We can no longer treat colorectal cancer as a monolith. Preoperative risk stratification, surgical decision-making, and adjuvant therapy plans must be tailored according to the primary tumor location. For patients with right-sided primaries, these data suggest a more aggressive multimodal approach and vigilant, personalized surveillance to improve upon the discouraging outcomes this study clearly exposes.

Abstract: This paper investigates a receiver-centric decoding framework for unit-rate transmission in which no redundancy is conveyed through the physical channel. Only k information bits are transmitted over an additive white Gaussian noise (AWGN) channel, while reliability is pursued by structured hypothesis testing and increased receiver-side computational complexity. The receiver embeds each candidate information hypothesis into a higher-dimensional (k, n) linear block code and evaluates all 2^k hypotheses in parallel. For each hypothesis, a single message-passing iteration on the Tanner graph is employed as a soft refinement operator, and the final decision is obtained via an orthogonality-based constraint metric that measures the consistency of the refined estimate with the hypothesis-induced code structure. The parity-related terms used within this metric are not modeled as stochastic channel observations and do not introduce additional mutual information beyond the channel output; instead, they act as deterministic, hypothesis-conditioned constraint weights that control how strongly code consistency is enforced within the decision rule. The relationship between metric weighting, apparent horizontal shifts in bit-error-rate (BER) curves, and information-theoretic limits is explicitly clarified. Simulations for a short (8, 24) code demonstrate that near maximum-likelihood decision behavior can be approached by trading receiver complexity for reliability in a finite-hypothesis regime, without altering the physical channel model or violating established channel-capacity principles.

Abstract: Maize silage is widely used due to its high fermentability but requires protein supplementation, commonly from soybean meal (SBM). Rising costs have driven interest in alternative protein sources, while microbial inoculants are used to improve silage fermentation and stability. This study evalu-ated the effects of partially replacing SBM with marula oilcake (MOC), with or without bacterial inoculants, on maize silage fermentation characteristics, nutrient preservation, aerobic stability, and in vitro digestibility. Whole-crop maize (< 38% dry matter) was supplemented with SBM or MOC, treated with either Lalsil Fresh or Sil-All 4x4®, and ensiled anaerobically for 90 days. Post-ensiling analyses included chemical composition, fermentation end-products, microbial populations, aerobic stability, volatile fatty acid profiles, gas production, and in vitro digestibility. Fermentation quality, nutrient composition, and aerobic stability differed significantly among treatments (P < 0.05). SBM-based silages exhibited greater lactic acid production, higher crude protein and digestibility, but also elevated butyric and branched-chain volatile fatty acids, indicating increased proteolysis. In contrast, MOC-based silages showed lower lactic acid concentrations and digestibility but reduced butyric fermentation, suggesting improved protein preservation. Microbial inoculants enhanced fermentation parameters more effectively in SBM than in MOC silages. These results indicate that protein source and inoculation strategy markedly influence maize silage fermentation outcomes, highlighting the need for further processing of alternative protein supplements to optimize silage quality.

Abstract: In this study, we employed the 5-meter Accuracy Digital Elevation Model (DEM) developed by the Geospatial Information Authority of Japan, to analyze the spatial distribution of Yayoi-period archaeological sites. Rather than relying on conventional regional cross-tabulations—such as prefecture-level classifications—this approach adopts a Geographic Information System (GIS)–based analysis that enables higher spatial precision as well as more intuitive and visually accessible interpretation. Through this methodology, we aim to reconstruct the geographical conditions of ancient Japan at the end of the Yayoi period, approximately 1,800 years ago, and to offer a new perspective on the long-standing debate concerning the location of Yamatai (Yamataikoku). The results of analyses using the 5m DEM substantially increase the likelihood that Yamatai was located in northern Kyushu. Furthermore, northern Kyushu exhibits highly distinctive patterns of land use that vary markedly by region. The areas surrounding present-day Asakura City and Ogori City appear to have been specialized primarily for military purposes. In contrast, the Yoshinogari site—one of the largest Yayoi-period settlements in Japan—shows a pronounced specialization in agriculture, particularly large-scale wet-rice cultivation. The area corresponding to modern Fukuoka City, meanwhile, functioned as a major urban center in which both military and agricultural functions were concentrated. By introducing a GIS-based approach that has been relatively underutilized in previous research, this study serves as a pilot project while simultaneously representing an ambitious attempt to expand the horizons of visualization in ancient Japanese historical studies.

Abstract: Mangroves represent a key component of coastal ecosystems. From 1897 to 2024, Taiwan’s southwest coast experienced marked climatic shifts, including a 2.0 °C increase in average annual temperature and a 56.5 mm reduction in annual rainfall. Among 18 coastal towns in western Taiwan, Taixi Township in Yunlin County exhibited a cumulative land subsidence of −283.0 cm from 1975 to 2023. The grey/white mangrove (Avicennia marina) in regions with severe subsidence exhibited slow growth or mortality. In the present study, mangrove area (MA) was estimated using a quadratic polynomial trend equation. The total MA at Tougang Ditch was −0.0084(t − 21.0)2 + 2.8, with t = 21.0 in 1995, and that at Budai Lagoon was −0.0468(t − 12.3)2 + 26.1, with t = 12.3 in 1986, supported by high coefficients of determination (R² > 0.85), respectively. SPOT-6 satellite images from February 22, 2025, were used to assess the coastal landscapes of Chiayi County and Tainan City. The total MA and windbreak forest area were 281.9 and 896.3 ha, respectively. The long-term assessment method introduced in this study may help predict mangrove health and carbon sink stocks and refine carbon sequestration estimates in subsidence or sea-level-rise regions.

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.