Search Results (327)

Existing scholarship on Sino-Western hybrid architecture (yanglou) has often treated Chinese elements as marginal, overlooking the agency of indigenous spatial logic. This study examines how traditional Chinese feng shui mediated the localization of Western architecture in the late Qing Dynasty through the case of the Tianjin Postal Museum. The research has three objectives: to distinguish Western architectural features from Chinese spatial rationales, to analyze the mediating mechanisms of feng shui, and to interpret the implications of this case for indigenous knowledge systems in the process of modernization. Using spatial semantic analysis based on UAV mapping and field surveys, the study finds that although the museum displays Western structural systems and proportional canons, its underlying spatial organization follows Chinese logic. This organization includes an enclosed courtyard, a north–south axis that structures dynamic and static zones, and re-signified elements such as the octagonal tower and parapet, which were repurposed to regulate qi and mitigate sha. The findings suggest that feng shui functioned as a pragmatic indigenous framework that enabled the creative appropriation of Western forms and challenged passive diffusion models of architectural modernization. Full article

This paper presents a 2-GS/s voltage–time hybrid pipelined analog-to-digital converter (ADC) with a 14-bit digital output, implemented in a 28-nm CMOS process. To alleviate the gain–bandwidth–power trade-off in deeply scaled technologies, the proposed architecture employs a SHA-less front-end and a low-gain inverter-based push–pull RA for energy-efficient coarse quantization. The residue is then transferred to the time domain via a highly linear constant-current voltage-to-time converter (CC-VTC) and digitized by a four-channel time-interleaved gated-ring-oscillator (GRO) TDC. To recover dynamic linearity degraded by low-gain amplification and interleaving mismatches, a multiplier-less digital background calibration engine is implemented. Leveraging mean absolute value (MAV) statistics and dither-injected least-mean-squares (LMS) algorithms, it effectively compensates for inter-channel and interstage errors with minimal hardware overhead. The prototype occupies an active area of 0.16 mm². At 2 GS/s, the ADC achieves a Nyquist SNDR of 63.42 dB and an SFDR of 73.71 dB, corresponding to an ENOB of 10.24 bits. Consuming 86.9 mW from a 1-V supply, it achieves a Walden FoM of 35.9 fJ/conv.-step. Measurement results from multiple chips under a wide range of operating conditions verify the robustness of the proposed ADC. Full article

In this study, a hybrid multi-layer scheme for reversible color image encryption is proposed, ensuring lossless reconstruction and strong cryptographic security concurrently. This method consists of three main stages. First, session-specific keys are generated using HKDF-SHA256 along with a timestamp-based mechanism to prevent replay attacks and support dynamic key management. Second, a four-layer confusion–diffusion structure is applied. It uses Gram–Schmidt orthogonal matrices, integer-based PWLCM chaotic mapping, the Hill cipher, and dynamically created S-Boxes. These operations rely on integer modular arithmetic ${\mathbb{Z}}_{256}$ and Q16.16 fixed-point precision. Finally, ChaCha20 stream encryption with HMAC-SHA256 authentication is used to secure data transmission in distributed environments. Experimental tests conducted on standard images show strong cryptographic performance, including near-ideal entropy (7.9993 bits), a significant avalanche effect ($NPCR99.6\mathrm{\%}$, $UACI33.4\mathrm{\%}$), and very low pixel correlation. The method achieves perfect lossless reconstruction and provides an effective key space $\ge 2¹²⁸$. These results confirm the suitability of the proposed scheme for secure image protection in applications requiring bit-exact recovery, such as medical imaging, digital forensics, and satellite communications. Full article

The secure management of classified documents containing sensitive information is critical for governments, military organizations, and the industry. Traditional data loss prevention (DLP) systems lack robustness against insider threats, particularly regarding access log integrity and tamper-proof auditing. To address log security, the previous literature has proposed multiple solutions, including private and hybrid blockchain models (e.g., Ethereum + MultiChain) to ensure audit trail integrity. However, hybrid architectures often face challenges such as unpredictable transaction costs (gas fees) and potential privacy risks when scaled for enterprise DLP logs. Conversely, private architectures may require higher resources, potentially causing bottlenecks on endpoints. In this paper, we propose an optimized Blockchain-Based Classified Document Management System (B2CDMS) utilizing a permissioned architecture. Our work demonstrates the challenges, advantages, and weak points of current solutions. We optimized a permissioned blockchain (BC) (Hyperledger Fabric v2.5) with an External Chaincode Builder using the Chaincode-as-a-Service (CCaaS) pattern. We compared our proposed private architecture with a hybrid architecture (Ethereum + MultiChain) and a public solution (Ethereum). We conducted a comprehensive analysis using pseudo Trellix ePolicy Orchestrator (ePO) Data Loss Prevention (DLP) logs. Experimental results on an Apple Silicon M4 (Apple Inc., Cupertino, CA, USA) testbed show that the proposed architecture achieves a throughput of 845.8 Transactions Per Second (TPS) with a sub-second latency of 55 ms, aiming to eliminate the bottlenecks of public blockchains. Furthermore, the system introduces a privacy-preserving hashing mechanism (i.e., committing only deterministic Secure Hash -bit (SHA-256) digests to the immutable ledger while keeping the actual sensitive Personally Identifiable Information (PII) strictly in off-chain databases) compliant with General Data Protection Regulation (GDPR). It ensures that classified document metadata remains immutable and secure against rogue access benefiting from admin privileges. This study concludes that permissioned blockchain architectures offer a scalable and resource-efficient solution for forensic evidence preservation throughout the classified document lifecycle. Full article

This paper presents a hardware-accelerated signal processing architecture for OFDM-based vehicular networks that integrates crypto-agile adaptive encryption on a Xilinx Kintex-7 FPGA. The encryption layer is tightly coupled to the OFDM modulation/demodulation pipeline, enabling secure real-time signal processing for V2X communications without disrupting the baseband chain. A context-aware pre-selection unit dynamically selects among hardware cipher primitives based on latency constraints, security requirements, and channel conditions. The current prototype implements and synthesizes AES-128 as the primary block cipher, while ASCON (NIST lightweight AEAD) and Keccak (SHA-3 foundation) are validated through RTL simulation and architectural integration, demonstrating crypto-agility across block, AEAD, and sponge-based primitives. DES is retained solely as a legacy reference for backward-compatibility evaluation and is not recommended for secure V2X deployment. The design adopts a modular decoupling strategy in which cryptographic engines interface with a unified buffering and interleaving subsystem, enabling hardware-based single-cycle cipher switching without partial reconfiguration. FPGA results demonstrate sub-microsecond cryptographic processing latencies with moderate resource utilization, preserving the timing budget of latency-sensitive vehicular services. AES-128 provides standard-strength encryption, while ASCON and Keccak offer lightweight and sponge-based alternatives suited to constrained IoV platforms. Specifically, the implemented AES-128 core achieves a throughput of 1.02 Gbps with a switching latency of 86 ns, verified across 10 randomized transitions with a 99.99% success rate and zero data corruption. The ASCON and Keccak cores attain throughput-to-area efficiencies of 2.01 and 1.47 Mbps/LUT, respectively, at a unified clock frequency of 50 MHz. All acronyms are defined at first use and a complete list of abbreviations is provided prior to the reference section. Full article

This Data Descriptor presents an anonymized, shuffled dataset of creatinine-normalized urinary metabolite measurements from 73 Bulgarian children with autism spectrum disorder (ASD), released to support reuse in secondary analyses and cross-cohort comparisons. The public release represents a pathway-oriented 24-marker subset from a broader urinary diagnostic panel, assembled as a self-contained resource for investigators working in these metabolic domains. Spot urine results are provided as individual-level values after creatinine normalization; for trimethylamine, values below the limit of quantification (LOQ) were replaced with LOQ/2. The deposit contains measurements for 24 urinary markers grouped into three functional classes (neurotransmitters and aromatic amino acid precursors; one-carbon/methylation and vitamin-related metabolites; and energy metabolism/organic acids with microbiome-related amines). The underlying cohort comprised children aged 3–13 years, and no contemporaneous neurotypical control group was enrolled. Second-morning, midstream, acid-stabilized spot urine samples were collected within the provider’s workflow; metabolites were measured by LC–MS/MS, and spot urinary creatinine was measured enzymatically for normalization. The release includes the results table in both XLSX and CSV formats, a reference limits and units file for contextual interpretation, a data dictionary, a README, a changelog, and SHA-256 checksums for integrity verification. The public files contain de-identified analytical variables only and omit individual-level demographics, dates, standalone urinary creatinine, and richer clinical metadata to preserve anonymity. Full article

The Internet of Drones (IoD) has emerged as a critical extension of the Internet of Things, enabling unmanned aerial vehicles to support diverse applications, including precision agriculture, logistics, disaster monitoring, and security surveillance. Despite its rapid growth, securing IoD communications remains a significant challenge due to the open wireless environment, high drone mobility, and strict computational and energy constraints. Existing authentication mechanisms either rely on computationally expensive cryptographic operations or remain validated only at the protocol or simulation level, leaving a critical gap in practical, hardware-validated solutions suitable for resource-constrained drone platforms. This gap motivates the need for a lightweight, privacy-preserving authentication scheme that is both theoretically sound and experimentally deployable on real hardware. To address this, we propose a Physically Unclonable Functions (PUF)-assisted lightweight authentication scheme for IoD environments that binds cryptographic keys to each drone’s intrinsic hardware characteristics via PUFs. The scheme employs dynamically generated pseudo-identities to conceal permanent drone identities and prevent tracking, while authentication and key agreement are achieved using efficient symmetric cryptographic primitives, including SHA-256 for key derivation and updates, AES-256 for secure communication, and lightweight XOR operations to minimize overhead. Forward secrecy is ensured through rolling key updates, and periodic renewal of PUF challenges enhances resistance to replay and modeling attacks. To validate practicality, both software-based and hardware-based implementations were developed and evaluated. The software evaluation demonstrates a low communication overhead of 708.5 bytes and an average computation time of 18.87 ms. The hardware implementation on a Nexys A7-100T FPGA operates at 100 MHz with only 12.49% LUT utilization and low dynamic power consumption of approximately 182.5 mW. These results confirm that the proposed framework achieves an effective balance between security, privacy, and efficiency. The significance of this work lies in providing a fully hardware-validated, PUF-based authentication framework specifically tailored to the real-world constraints of IoD environments, offering a practical foundation for securing next-generation drone networks. Full article

Background/Objectives: At the international level, harmonized networks of dementia clinical studies are available, but Italian participation remains limited. This systematic review aims to define harmonization rules to facilitate the inclusion of Italian clinical studies in existing networks and to propose standardized data collection methods to enable comparison of the study results. Methods: A systematic review was conducted (January 2019–December 2024) to identify Italian clinical studies evaluating Alzheimer’s disease and other dementias as outcomes. Eight modifiable risk factors were extracted: BMI, arterial hypertension, diabetes, dietary patterns, alcohol consumption, smoking habits, depressive symptomatology, and physical activity. WHO definitions and internationally accepted criteria were used as reference standards. Variable harmonization potential was assessed using the DataSHaPER methodology and classified as complete, partial, or impossible, considering information loss across studies. Results: Of 365 records identified, 18 studies met the inclusion criteria. Obesity assessed via BMI showed the highest harmonization potential (44% complete, 33% partial), along with dietary habits measured by food frequency questionnaires (44% complete). Diabetes and physical inactivity followed (33% complete), assessed through fasting glucose or pharmacological treatment and the IPAQ, respectively. Smoking habits classified as current, former, or never smokers were reported in 28% of studies. Depression (assessed by GDS or CES-D) and hypertension (blood pressure measurement or antihypertensive treatment) showed complete harmonization in only 22% of studies. Conclusions: Italian studies show substantial limitations in the harmonization of modifiable risk factor data for Alzheimer’s disease, mainly due to heterogeneous and non-standardized data collection methods, highlighting the need for uniform research protocols. Full article

The advent of fault-tolerant quantum computers poses an existential threat to the current blockchain technology, which relies on cryptographic primitives like elliptic-curve cryptography and SHA-256 hashing. This manuscript surveys the emerging field of quantum-secure blockchains, categorizing the main research directions into two paradigms. The first, post-quantum blockchain, seeks to replace classical cryptographic elements with quantum-resistant algorithms. The second, more radical approach aims to construct an intrinsically quantum blockchain, where the ledger’s security and state are encoded directly in quantum mechanical principles. We delve into three promising intrinsic schemes: those based on Greenberger–Horne–Zeilinger (GHZ) states and entanglement in time, those leveraging multi-time states and pseudo-density matrices, and hypergraph-based approaches. As the principal original contribution of this work, we present a comprehensive theoretical framework for a topological quantum blockchain based on non-Abelian anyons, providing the first detailed encoding scheme mapping classical blockchain data to braiding sequences. We further develop the connection to Chern–Simons theory, establishing a field-theoretic foundation where the blockchain’s history is encoded in Wilson loops, and its immutability follows from topological and gauge invariance. Extending this framework, we introduce a holographic AdS/CFT interpretation, revealing that the topological blockchain can be understood as a dual description of a black hole analog in anti-de Sitter space, where the blockchain’s history is encoded in the microstates of a black hole and linking braids between blocks correspond to wormholes. We provide a detailed physical and mathematical analysis of each scheme, comparing their security assumptions, resource requirements, and feasibility in the near and long terms. The topological approach, in particular, offers a compelling new path toward a blockchain with inherent fault tolerance, where the chain’s history is encoded in the topology of anyon worldlines, making it naturally resistant to decoherence and local tampering. Full article

Background/Objectives: Patients with severe hemophilia A on prophylaxis with emicizumab exhibit a mild/moderate bleeding phenotype that requires the use of either recombinant FVIII (rFVIII) or bypassing agents (BPAs) in patients with inhibitors, in the case of breakthrough bleeding or surgery. Since factor IX (FIX) limits the formation of the FIXa–emicizumab–FX complex, exogenously added FIX might enhance complex formation and thrombin generation. This study aimed to compare the procoagulant effects of various FIX concentrates with recombinant activated FVII (rFVIIa), activated prothrombin complex concentrate (aPCC), and rFVIII in SHA patients with and without inhibitors under emicizumab prophylaxis. Methods: Hemostatic changes were monitored using two optimized global coagulation assays: rotational thromboelastometry and calibrated automated thrombin generation. Tubes containing corn trypsin inhibitor (CTI) were used during blood collection to prevent activation. Low concentrations of tissue factor (TF) were used to trigger coagulation in both assays. Results: Ex vivo addition of recombinant FIX concentrates significantly increased the procoagulant activity of emicizumab, achieving levels comparable to therapeutic doses of rFVIIa or rFVIII, and the proportion of active FIXa within the concentrates is a major contributor to their procoagulant function. We assessed the influence of FVIII inhibitors on the hemostatic efficacy of rFIX concentrates and BPAs, finding that rFIX-induced thrombin generation increased in the presence of inhibitors, and no significant differences were observed with BPAs. Conclusions: These findings suggest that FIX concentrates could be an effective alternative to BPAs for emicizumab-treated patients, particularly those with inhibitors. Further studies are needed to confirm their in vivo efficacy and to evaluate thrombotic risk. Full article

In contemporary communication systems, digital images occupy an irreplaceable role; however, the privacy-related risks attendant to their prevalent application have grown increasingly salient. This paper presents an image encryption scheme integrating a novel two-dimensional Ackley-Sine chaotic map (2D-ASM) with dynamic DNA operations. First, a two-dimensional Ackley-Sine chaotic map, constructed based on the Ackley function and sine function, is designed and validated through a series of chaotic indicators. Results demonstrate that 2D-ASM exhibits superior chaotic properties compared to several existing state-of-the-art chaotic maps, with its maximum Lyapunov exponent (LE) exceeding 23, Permutation Entropy (PE) close to 1 in the full parameter range, and correlation dimension (CD) significantly higher than comparative chaotic systems. The proposed 2D-ASM-based image encryption scheme leverages the SHA-256 hash value of the plaintext image and four external keys to jointly generate the initial conditions and parameters of the 2D-ASM chaotic system, thereby ensuring a sufficiently large key space of 2²⁵⁶. Subsequently, chaotic sequences generated by 2D-ASM are employed to permute and diffuse the plaintext image, followed by dynamic DNA coding, operations, and decoding to obtain the encrypted image. Security analyses and comparisons with several existing representative algorithms confirm that the proposed encryption scheme achieves excellent encryption performance: the Number of Pixels Change Rate (NPCR) is above 99.6%, the Unified Average Changing Intensity (UACI) approaches 33.4%, and the information entropy of ciphertext images reaches 7.999 or higher. The scheme can effectively resist various potential attacks, including statistical and differential attacks, and outperforms representative algorithms in pixel correlation reduction and anti-interference performance. Full article

Objective: This study aimed to evaluate the relationship between craniocervical posture and skeletal malocclusion patterns in adolescents. Methods: This cross-sectional study included 80 adolescents aged 10–15 years diagnosed with skeletal Class I, Class II Division 1, Class II Division 2, or Class III malocclusion. Postural parameters—Sagittal Head Angle (SHA), Craniocervical Angle (CA), and Shoulder Angle (SA)—were assessed using standardized sagittal-plane digital photographs obtained in Natural Head Position. Skeletal classification and cephalometric measurements (SNA°, SNB°, ANB°, GoGn/SN°, and Occlusal Plane/SN°) were determined from lateral cephalometric radiographs. Intergroup comparisons were performed using the Kruskal–Wallis test, and posture–skeletal relationships were evaluated using Pearson and Spearman correlation analyses (p < 0.05). Results: No significant differences were observed in postural parameters among skeletal malocclusion classes (p > 0.05). In the overall sample, SHA showed weak negative correlations with SNA° (r = −0.284, p < 0.01) and SNB° (r = −0.381, p < 0.01), and a weak positive correlation with Occlusal Plane/SN° (r = 0.235, p < 0.05). No significant associations were identified for CA or SA. Subgroup analysis demonstrated that these associations were present exclusively in the Class II Division 2 group, where SHA showed strong negative correlations with both SNA° (r = −0.653, p < 0.01) and SNB° (r = −0.605, p < 0.01). Conclusions: Sagittal head posture may show phenotype-specific associations during adolescence, particularly in Class II Division 2 malocclusion. Incorporating postural assessment into orthodontic evaluation may enhance diagnostic understanding during growth. Full article

To achieve high capacity, high speed, and secure image transmission, we propose a multi-image computational ghost imaging (CGI)-based encryption scheme that integrates compressed sensing (CS), block scrambling, and dynamic-salt-driven bidirectional XOR diffusion. First, multiple images are partitioned into 8 × 8 pixel blocks, and their spatial structure is disrupted through random scrambling. The scrambled composite image then undergoes pixel-level encryption via two-round bidirectional XOR diffusion, using session-unique keys derived from SHA-256-based dynamic salt, eliminating the statistical characteristics of the original images. Subsequently, each pixel block is subjected to both Gaussian CS and Hadamard-based CGI measurements in parallel, achieving dual-mode compressive encryption and enhancing robustness through measurement redundancy. Finally, only the scrambling key, the XOR-diffusion key, and the compressed measurements are stored; the original image information is thus transformed into unrecognizable measurement data. During the decryption process, the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) with a Discrete Cosine Transform (DCT) sparse basis is employed for dual-sparse reconstruction from the compressed measurements, recovering the encrypted composite image. An inverse XOR operation is then applied to remove the pixel-level diffusion, followed by block reordering using the scrambling key to restore the original images. Experimental results demonstrate that the proposed scheme enables efficient and secure multi-image transmission while maintaining high decrypted image quality. Security analysis indicates that the scheme possesses high key sensitivity, effectively resisting chosen-plaintext attacks. Histogram uniformity analysis and cropping attack resistance experiments further confirm its excellent statistical security and robustness. Full article

Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a “MasterChain” virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevner’s design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash Algorithm 3 (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit Rivest–Shamir–Adleman (RSA)’s 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article