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Physical entropy sources that remain stable under extreme temperatures are essential for cryptography in emerging technological frontiers in deep space exploration, geothermal energy harvesting, and nuclear energy. However, conventional semiconductor platforms fail to generate stable and reliable cryptographic keys above 200 °C due to performance degradation. Here, we report a diamond-based cryptographic primitive that exploits the defect-rich sp2-bonded grain boundary network in nitrogen-incorporated ultrananocrystalline diamond (n-UNCD) film as a robust entropy source to generate cryptographic keys that remain operationally stable even after enduring extreme temperatures of 700 °C for 54 h while also surviving thermal cycling between room temperature and 700 °C for 48 h. The strength of the generated keys is assessed through several cryptographic metrics such as bit uniformity, entropy, hamming distances, and correlation coefficients, all of which are found to be near their respective ideal values. Moreover, the generated keys pass the NIST SP 800 and SP 800-90B tests and are also resilient to supply bias variations and a regression-based machine learning attack model based on the Fourier series. The robustness of the keys is attributed to the better thermal stability and chemical inertness of the n-UNCD film. This is supported by high-resolution energy-dispersive X-ray spectroscopy (EDS), which shows no significant lateral diffusion of metal atoms into the n-UNCD layer, and by Raman spectroscopy, which reveals no significant changes in the bonding configuration of the n-UNCD structure. Our findings highlight the remarkable potential of n-UNCD film for extreme environment cryptography by expanding the operational limits of conventional hardware security platforms.

Physical-layer key generation (PLKG) is a technique that produces symmetric encryption keys by exploiting the inherent characteristics of wireless channels. It offers advantages including high physical-layer security, elimination of pre-shared keys, dynamic upgradability, and resistance to quantum attacks, making PLKG a promising security solution for next-generation (6G) networks. However, satellite communication channels exhibit high dynamics and long propagation delays. Characteristics such as large Doppler shifts, short coherence times, and orbital predictability pose severe challenges to PLKG, including reciprocity degradation, low key generation rate (KGR), and susceptibility to channel-prediction attacks. This work proposes a delay-Doppler domain time-hopping key generation scheme (KE-DD-TH) based on Orthogonal Time Frequency Space (OTFS) modulation for high-speed links between Low-Earth-Orbit (LEO)/Medium-Earth-Orbit (MEO) satellites and ground terminals in Ka/Ku bands. The scheme performs non-uniform sampling on the DD domain grid of OTFS symbols using an ephemeris-driven pseudo-random time-hopping sequence generated by cascaded linear feedback shift registers (LFSRs) and a nonlinear matrix transformation. Both legitimate parties estimate the channel only at time-hopping instants and multiply two adjacent estimates to construct an "equivalent channel" matrix, yielding a random source with high entropy, high reciprocity, and low predictability. The eavesdropper's key disagreement rate (KDR) remains close to 0.5 under all signal-to-noise ratio (SNR) conditions, corresponding to the ideal random-guessing baseline. This indicates that Eve obtains negligible mutual information, i.e., I(KA;KE)≈0. By contrast, the conventional KE-DD scheme allows Eve's KDR to degrade to 0.014 at 30 dB SNR, indicating near-complete key recovery. The generated keys pass all 12 randomness tests of the NIST SP 800-22 statistical test suite.

Southeast Asia (SEA) faces persistent gaps in regional understanding and control of ticks and tick-borne diseases (TBDs) despite recent advances (2023-2025). The second international symposium on ticks and TBDs in SEA (Singapore, August 2025), following the inaugural 2023 meeting in Cambodia, served as a catalyst for regional exchange that informed this perspective. SEA's ecological and host diversity supports complex tick-host-pathogen networks, yet evidence remains fragmented due to uneven sampling that has largely focused on livestock and peri-urban environments. Key constraints include limited taxonomic resolution driven by outdated or incomplete identification keys, under-sampling of soft ticks (Argasidae), and the absence of harmonized, open-access regional reference resources (including DNA barcodes and MALDI-TOF MS spectral databases). While MALDI-TOF MS, proteomics, AI-assisted identification, and next-generation sequencing/metagenomics are increasingly applied, their broader regional uptake is limited by the absence of harmonized, open-access reference resources (including DNA barcodes and MALDI-TOF MS spectral databases). Broad ecological surveys and integrated animal and human surveillance remain limited, and vector competence studies are constrained by the scarcity of SEA-derived tick colonies and cell lines. Regional data and recent findings (2024-2026) confirm circulation of multiple TBPs (including Anaplasma, Babesia, Borrelia, Coxiella, Ehrlichia, Rickettsia, and Theileria) and highlight emerging viral findings, including southward reports of Bandavirus dabieense. Human infestations and non-communicable tick bite outcomes (e.g., tick paralysis and alpha-gal syndrome) are recognized but remain under-reported due to low clinical awareness and limited diagnostics. Importantly, the diagnostic chain is further disrupted by missed/insufficient specimen collection at the point of care, and by constrained capacity to identify (especially immature) ticks to species level-limitations compounded by the absence of harmonized, open-access regional reference resources. The symposium identified six priorities: (1) full completion and regional validation of tick identification keys for adults (in progress) and immatures (to be initiated), plus an open-access DNA barcode library anchored by curated, voucher-based collections from all SEA countries; (2) harmonization of molecular and proteomic diagnostic platforms, including expansion of regional MALDI-TOF MS and NGS protocols and reference databases; (3) development of tick colonies and cell lines from locally prevalent species to support vector competence, vaccine, and acaricide testing; (4) expansion of One Health surveillance with enhanced ecological sampling at wildlife-livestock-human interfaces; (5) establishment of open-access, region-wide data platforms for integrated tick, TBP, and ecological metadata sharing; and (6) sustained investment in human resources, training, and policy advocacy to raise research and public health visibility of ticks and TBDs.

Conventional DNA encryption methods often require additional noncoding strands as physical keys or covering media, leading to a density decrease from redundancy. Here, we present a nanopore-based photoresponsive DNA information steganography system (NAPDISS) that encodes 26 encrypted English letters by using only five isomerizable azobenzene-modified coding DNAs, thereby eliminating the need for extra synthesis. Encoding was implemented through two complementary schemes comprising a letter code with sequence-defined photoresponsive DNAs representing letters and an address code with poly(dA)3 (A3) concentrations defining the letters' positions. Utilizing light as secret keys, NAPDISS conceals messages by obscuring the nanopore readouts, allowing recovery only through combined pre- and post-irradiation analyses. This approach achieves a logical storage density of 0.2-1.0 bits per nucleotide-about one order of magnitude higher than those of existing DNA-structure-based methods. Moreover, simplified sample processing reduces the readout time from hours to <10 minutes. Collectively, this work provides fresh insights into balancing density, security and efficiency, advancing DNA steganography toward secure and instantaneous messaging applications.

Body area networks (BANs) require secure intra-body communication, yet sensor nodes are too resource-constrained for conventional public-key cryptography, and pre-shared key schemes conflict with plug-and-play clinical workflows. This paper introduces PhysioKey, a TinyML-based key agreement framework that derives symmetric session keys from physiological signals without pre-shared secrets or trusted third parties. A lightweight 1D-CNN (6320 parameters, INT8-quantized, 31.2 KB flash) extracts embeddings from ECG and PPG windows on ARM Cortex-M4 class devices, which are reconciled through fuzzy commitment with BCH error-correcting codes. Patient-level 5-fold cross-validation on PTB-XL (500 patients, dual-ECG) achieves EER of 7.8%&#xb1;0.8% with ROC AUC 0.978&#xb1;0.004; on BIDMC (53 patients, ECG + PPG), a dual-encoder architecture reduces cross-modal EER to 30.6%&#xb1;1.2%. Since standalone PhysioKey yields only 7-24 effective key bits, the recommended deployment mode is a hybrid PhysioKey + ECDH protocol providing 128-bit security while PhysioKey adds physical on-body authentication; standalone operation suits energy-constrained scenarios with its 27&#xd7; advantage over ECDH. HKDF-SHA-256 post-processing yields session keys passing all six NIST SP 800-22 tests (&#x2265;96% at the 1024-bit level).

The Vision Transformer (ViT) has achieved notable success in computer vision, with its variants widely validated across various downstream tasks, including semantic segmentation. However, as general-purpose visual encoders, ViT backbones often do not fully address the specific requirements of task decoders, highlighting opportunities for designing decoders optimized for efficient semantic segmentation. This paper proposes Strip Cross-Attention (SCASeg), an innovative decoder head specifically designed for semantic segmentation. Instead of relying on the conventional skip connections, we utilize lateral connections between encoder and decoder stages, leveraging encoder features as Queries in cross-attention modules. Additionally, we introduce a Cross-Layer Block (CLB) that integrates hierarchical feature maps from various encoder and decoder stages to form a unified representation for Keys and Values. The CLB also incorporates the local perceptual strengths of convolution, enabling SCASeg to capture both global and local context dependencies across multiple layers, thus enhancing feature interaction at different scales and improving overall efficiency. To further optimize computational efficiency, SCASeg compresses the channels of queries and keys into one dimension, creating strip-like patterns that reduce memory usage and increase inference speed compared to traditional vanilla cross-attention. Experiments show that SCASeg's adaptable decoder delivers competitive performance across various setups, outperforming leading segmentation architectures on benchmark datasets, including ADE20K, Cityscapes, COCO-Stuff 164k, and Pascal VOC2012, even under diverse computational constraints.

Multiple-choice questions (MCQs) are a cornerstone of medical education, but generating high-quality items automatically remains challenging. We present KiMED, an LLM-based platform for automated MCQ generation in German, enhanced through Knowledge Graph (KG)-assisted retrieval. Biochemistry course materials - including book chapters, staff-written sections, and slide transcripts - were structured into subtopics, from which entities, properties, and relationships were extracted to construct a KG. The KG supports multi-agent MCQ generation by providing precise, contextually relevant information for stems, keys, and distractors - the question, correct and wrong answers. Evaluation of selected subtopics showed that 87% of entities and 82% of relationships were accurately represented, and 45% of KG-based MCQs were deemed usable by experts, compared to 23% from unstructured text. These results indicate that structured context significantly improves MCQ quality, though reliable question generation still requires optimization across context, agent workflows, and post-processing.

This study aimed to evaluate and compare the performance of eight contemporary LLMs on the endodontics section of the DUS, assessing their accuracy in both theoretical knowledge and simulated clinical scenarios from historical exam data. The performance of eight different large language models (Claude 4, DeepSeek V3, Gemini 2.5 Pro, ChatGPT-4o, ChatGPT-5, Grok 4, LLaMA 4, and Perplexity) was evaluated using 127 multiple-choice endodontics questions from the Specialization Exam in Dentistry (DUS) administered by the Student Selection and Placement Center (&#xd6;SYM) between 2012 and 2021. The models' responses were compared against the official answer keys. Statistical analyses were performed using Pearson's chi-square and McNemar tests, with a significance level of &#x3b1;&#x2009;=&#x2009;0.05. Significant differences existed among LLMs in overall accuracy (p&#x2009;<&#x2009;0.001). Gemini 2.5 Pro achieved the highest accuracy (90.6%), outperforming ChatGPT-4o (61.4%) and LLaMA 4 (71.7%). In Clinical Practice Questions (CPQ), Gemini 2.5 Pro (93.9%) surpassed ChatGPT-4o (57.6%; p&#x2009;=&#x2009;0.019). For General Knowledge and Concept Questions (GKCQ), Gemini 2.5 Pro (89.4%), Grok 4 (85.1%), and DeepSeek V3 (84.0%) exceeded ChatGPT-4o (62.8%; p&#x2009;<&#x2009;0.001). No significant intra-model differences emerged between CPQ and GKCQ performance (p&#x2009;>&#x2009;0.05). Contemporary LLMs demonstrate substantial competence in endodontic knowledge, with Gemini 2.5 Pro excelling in both theoretical and clinical queries. However, significant performance variability across models (61.4%-90.6%) and the complexity of retrieving and resolving clinical exam queries necessitate domain-specific optimization and expert oversight for reliable integration into dental education and practice.

Black flies are a group of hematophagous insects with high species diversity, driven by a combination of ecological, evolutionary, and life-history factors. By integrating DNA barcoding with traditional morphological method, a new black fly species was discovered. Simulium (Gomphostilbia) wiangpingense sp. nov. is described based on females, males, pupae, and mature larvae collected from Chiang Mai province, Thailand. This new species is assigned to the S. burtoni subgroup of the S. varicorne species-group within the subgenus Gomphostilbia Enderlein, 1921. It is characterized in the female and male adults by the antennae with eight flagellomeres, and pleural membrane bare; in the female by the sensory vesicle elongate, 0.62 to 0.70 times length of the third palpal segment, mandible without teeth on the outer margin, and wing with the subcosta haired; in the male by the sensory vesicle ellipsoidal, 0.42 to 0.43 times length of the third palpal segment; in the pupa by the gill with eight filaments arranged as 3&#x2009;+&#x2009;3 + 2 from dorsal to ventral; and in the larva by the long postgenal cleft. Morphologically and genetically, the new species is most closely related to S. burtoni Takaoka & Davies, 1995. Phylogenetic analysis and species delimitation clearly distinguish the new species from its congeners, providing additional support for its recognition as a distinct species. Taxonomic notes for distinguishing the new species from related species within the S. burtoni subgroup from Thailand and other countries, and identification keys to all the 16 species of the S. varicorne species-group are provided.

In emerging environments such as cloud computing and the Internet of Things (IoT), secure authentication and key negotiation play a crucial role in protecting data transmitted over public networks. However, many existing authentication protocols are still designed based on classical public-key cryptography primitives, and quantum computing may threaten their security. To address this challenge, we propose a post-quantum authentication and key agreement protocol that uses the lattice-based Kyber key encapsulation mechanism (KEM). Our proposed protocol integrates cryptographic authentication, smart card protection, and post-quantum key encapsulation mechanisms, enabling mutual authentication between users and servers and securely establishing session keys. The security of the protocol is formally analyzed in the Real-or-Random (ROR) model under the random oracle assumption and the IND-CCA security of the underlying KEM scheme. Furthermore, through informal security analysis, we have further demonstrated that the protocol possesses important security properties, including anonymity, untraceability, perfect forward confidentiality, and resistance to known attacks. In addition, the computational cost and communication overhead of the proposed scheme are evaluated and compared with several representative authentication protocols. The results show that the proposed protocol can provide strong security while maintaining low computational cost and communication overhead.

Classic ideomotor theory proposed that actions can be automatically triggered by internally evoked representations of action-related features. This study examined whether motor execution in ideomotor action is more closely linked to lexical-semantic labels or to perceptually based evaluative content. In Experiments 1 and 2, participants responded to the Korean words "short" and "long" by pressing left or right keys. Semantic labels alone did not modulate keypress response duration (RD). However, in Experiment 2, task-irrelevant auditory tones of varying durations produced a graded increase in RD. Experiment 3 tested whether this modulation reflected physical duration itself or the evaluative processes involved in distinguishing and categorizing stimulus durations. Participants categorized six auditory stimuli as "short" or "long," with categorization difficulty manipulated by varying the distance between boundary stimuli. RD increased gradually across stimulus durations, but this effect was not stronger in the easy condition despite the larger physical spacing between tones. In addition, RD showed a category-related increase beyond what could be explained by physical duration alone. Together, these findings suggest that motor execution is influenced less by lexical-semantic labels than by perceptually based evaluative and categorical processing, helping to clarify the level of mental content that serves as an ideomotor cue.

This work presents a secure data transmission process in the Internet of Things (IoT). Initially, the required data are collected and given to the Adaptive and Sparse Attention-based Dense Long Short-Term Memory (ASA-DLSTM) network for intrusion detection. The adaptive nature of the model allows for optimizing the parameters using the Sorted Fitness-based Addax Optimization Algorithm (SF-AOA). Once intrusions are detected, the data is used for the data transmission phase. It is performed using Optimal Key-based Elliptic Galois Cryptography (OK-EGC). By combining Elliptic with Galois fields and an optimal key management strategy, the proposed OK-EGC method enhances both encryption efficiency and security. Moreover, the integration of optimal key-based management using the same SF-AOA ensures that cryptographic keys are dynamically optimized based on the network's security requirements. Then, the effectiveness of the model is compared with existing systems. The accuracy of the implemented SF-AOA-ASA-DLSTM technique is 95.97%, which is higher than the conventional techniques, such as DNN (83.77%), SVM (83.19%), 1DCNN (90.26%), and ASA-DLSTM (93.6%) for the batch size value 64. Thus, the results display that the designed model addresses the critical challenges of IoT data security by providing both robust intrusion detection and secure communication.

Ocean monitoring is essential for understanding climate change and marine ecosystem dynamics, yet achieving comprehensive global coverage remains a challenge in oceanography. Current technologies face limitations in cost, power, hardware, and depth capacity that restrict widespread monitoring capabilities. Here we show that biohybrid robotic jellyfish (Aurelia aurita) can serve as autonomous vertical ocean profilers by integrating microcontrollers with positively buoyant sensor payloads, achieving controlled vertical-profiling capabilities. Laboratory experiments demonstrated repeatable up-down trajectories, quantified force balance limits, and identified predictable, size-dependent descent swimming speeds. Field deployments in Massachusetts coastal waters and the open ocean off the Florida Keys demonstrated field operation to ocean depths >25 m with successful in situ temperature and depth measurements. To our knowledge, this represents the first biohybrid jellyfish platform to combine autonomous, pressure-triggered vertical profiling with onboard oceanographic sensing in natural marine environments. This approach leverages the global distribution and remarkable swimming efficiency of living jellyfish while eliminating propulsion power requirements by utilizing the animal's natural swimming capabilities. While further development is required for long-term ocean deployment, this study lays the groundwork for a new class of biohybrid ocean-sensing platforms with advantages in cost, power, and mission flexibility, providing a pathway toward dense sensor networks and increased ocean monitoring observations.