Search Results (8,433)

This study proposes a standards-based Reference AI Business Model Canvas (Reference AI-BMC) that translates the use-case descriptors of ISO/IEC TR 24030 into the nine blocks of the Business Model Canvas, addressing the lack of a structured translation layer between AI standards and business-model design. Using ten selected fields of the ISO/IEC TR 24030 use-case template, a two-round Delphi process derives consensus-based mapping rules from expert judgments; Latent Dirichlet Allocation is used as a field-level semantic analysis to provide interpretive context for the Delphi-derived mappings. Primary mappings are reported as default translation references that met the 80% strict-consensus threshold, secondary mappings as context-dependent relations, and the adjudicated dual-mapping exception A5 (Threats/Challenges → Cost Structure) as a separately documented case. After converting the finalized primary mapping rules into a coding manual, three independent coders applied them to 81 AI use cases; the Layer 1 coding yielded Krippendorff’s α = 1.000, descriptively indicating no observed coder disagreement under the specified coding conditions. The Reference AI-BMC contributes a standards-based, consensus-derived translation layer for systematically organizing AI use cases in business-model terms, offering a structured starting point for early use-case workshops, preliminary portfolio screening, and standards-aware AI service design discussions. Together, these results position the Reference AI-BMC as a standards-based, consensus-derived reference layer for organizing AI use cases in BMC terms, with its applicability bounded by the ISO/IEC TR 24030 descriptor structure and the specified mapping procedure. Full article

Genetic diversity within maize inbred populations is essential for sustaining genetic gain in breeding programmes. This study evaluated 280 maize inbred lines with two checks using an augmented block design (22 × 14). At harvest, 271 lines and two checks were analysed, with nine entries excluded due to poor survival. Using both descriptive (24) and quantitative (19) traits, significant variations were observed across many traits. Descriptive traits varied among the genotypes, as revealed by graphical analysis and correlation heatmaps. The likelihood ratio test (LRT) for lines showed significant differences for several quantitative traits with moderate–high heritability, while anthesis–silking interval, tassel length, ear position, ear aspect, bad husk cover, number of plants, and number of ears per plant exhibited low heritability. High genetic advance as a percentage of the mean was observed for grain yield, plant height, grain texture, number of plants, number of kernels, and grain weight per plant. Positive associations were observed among genotypic coefficient of variation, genetic advance, and heritability. Grain yield showed significant positive correlations with yield-related traits and morphological traits, but negative correlations with flowering traits. The first 10 principal components explained 86.17% of total variation, with flowering traits contributing most to variability in PC 1. Cluster analysis grouped genotypes into 10 clusters, with substantial genetic divergence within and between cluster groups. In conclusion, the study revealed considerable genetic diversity, supporting the selection of superior parents in breeding programmes and developing improved maize varieties to enhance productivity. Full article

As a critical component of sustainable energy systems, the efficient maintenance of photovoltaic (PV) panels is essential. While deep learning is an important approach for PV panel defect detection, the high complexity of existing models and their substantial computational demand make deployment on edge platforms difficult. This paper studies an acceleration method for photovoltaic panel defect detection on the Zynq-7020 heterogeneous platform. We design DARE-YOLO, a lightweight network for photovoltaic panel defect detection, together with a Zynq-based accelerator. In DARE-YOLO, we introduce RepConv and a lightweight single-path backbone to reduce the memory bandwidth overhead caused by multi-branch structures. We further design a Dilated Context Block (DCB) and a Dual-scale Decoupled Head (DDH), which effectively improve the detection accuracy of DARE-YOLO. On the Zynq platform, we develop the accelerator through a mixed fixed-point quantization strategy, a custom convolution IP core, and pipeline unrolling. These optimizations reduce data access latency, improve computational parallelism, and increase computational throughput. Experimental results show that DARE-YOLO achieves 93.84% mAP@0.5 with only 6.4 M parameters. The accelerator has a total on-board power consumption of only 1.95 W, while delivering a throughput of 37.5 GOPS, an energy efficiency of 19.23 GOPS/W. The image inference latency is 661.3 ms. This low-power, high-efficiency co-design paradigm ensures the long-term reliability of renewable energy facilities. Full article

The progress made in several fields after 2023 is rather significant. Attenuation achieved by the best HCFs was reduced to 0.05–0.10 dB/km at 1550 nm, while the lowest attenuation achieved in a single-mode fiber with a pure silica core equals 0.14 dB/km. Polarization mode dispersion (PMD) has been reduced to a level typical of SMFs, through fiber spinning. In November 2024, Microsoft announced a 2-year plan to install 15,000 km of HCF cables between and within data centers processing data for Microsoft Azure cloud services. Furthermore, several HCF manufacturers have emerged: UK-based Microsoft Azure Fiber and two Microsoft subcontractors, namely Corning Inc. and Heraeus Covantics, plus two major HCF manufacturers in China, YOFC and Linfiber. Additionally, extensive work was carried out on optical amplifiers to enable new transmission bands in HCFs, both at short wavelengths (≈1300–1500 nm), with bismuth-doped active fibers, and long wavelengths (≈1700–2100 nm), with thulium- and holmium-doped fibers. On the other hand, progress in HCF standardization, splicing and elimination of loss bands introduced by contaminants, has been marginal. Standardization is blocked by multiple fiber designs being tried, with no clear winner emerging yet. Despite this, hollow-core fibers have been successfully debuted in large-scale commercial data centers and are also used in low-latency data links. Full article

NanoArduSiPM represents a paradigm shift in the ArduSiPM (Architected Detection Unit for Silicon Photomultipliers) roadmap, evolving from a standalone instrument into a high-density modular building block (36 mm × 42 mm × 3 mm, 7 g). This revision does not merely pursue miniaturization; it re-engineers the signal-processing chain to maintain high performance within a scaled-down footprint, enabling the transition from single-unit detection to scalable, distributed multi-detector systems. NanoArduSiPM is based on a three-layer architecture comprising an external scintillator and Silicon Photomultiplier (SiPM) detection module, a dedicated high-speed discrete analog front-end, and a System-on-Chip (SoC) for embedded acquisition and processing. The physical implementation adopts high-integrity PCB routing and rigorous isolation techniques designed to suppress digital–analog coupling, a critical requirement in such a compact form factor. This deterministic layout strategy provides the architectural foundation for time-tagging capabilities, currently under quantitative characterization, by addressing the fundamental sources of signal interference at the hardware level. Beyond hardware integration, NanoArduSiPM introduces the capability for extended firmware functionality, including event tagging via external inputs and the implementation of coincidence and veto logic. This framework supports the acquisition of multiple correlated histograms and allows multiple units to be interconnected on a shared SPI bus. By shifting from standalone operation to a coordinated, hierarchical architecture, NanoArduSiPM enables distributed detection schemes where event selection and correlation are handled natively within the system, reducing the dependency on external data acquisition electronics. The compact modular architecture, together with the high-performance discrete analog front-end and embedded data handling, makes NanoArduSiPM suitable for applications where low mass and low power consumption are critical, targeting applications such as space-based payloads, laboratory instrumentation, remote sensing, and large-scale distributed multi-channel detection systems. While no radiation-tolerance qualification of the complete system has been performed in this work, the microcontroller family used in the design is also available in radiation-tolerant variants, which may support future implementations targeting more demanding radiation environments. Full article

To overcome the limitations of existing UAV object detection algorithms—particularly missed detections, false alarms, and the progressive loss of fine-grained features for small objects—this paper proposes SDP-YOLOv8, a lightweight and parameter-efficient enhancement of YOLOv8. The design aims to improve small-object detection accuracy while maintaining a lightweight architecture suitable for deployment on memory-constrained UAV platforms. Four lightweight-oriented modifications are introduced: (1) SCFS, which combines SPD-Conv for low-information-loss downsampling with a C2f block and SimAM attention; (2) DCSPPF, expanding the receptive field via parallel dilated convolutions; (3) a GhostConv-infused Patch Merging upsampling layer for local context enhancement; and (4) an extra small-scale detection head to preserve fine details. On VisDrone2019, experimental results show that SDP-YOLOv8 improved mAP@0.5 by 3.90% and mAP@0.5:0.95 by 2.60%, with a 14.4% reduction in parameters. The model maintains real-time performance (53.5 FPS on an RTX 3090 at FP32 with batch size 1, 38.7 FPS on a Jetson Orin Nano with TensorRT FP16 at batch size 1) and offers a favorable trade-off between detection accuracy, parameter efficiency, and memory footprint, making it a potential candidate for onboard deployment on resource-limited UAVs in aerial monitoring scenarios, pending further validation on diverse datasets and hardware platforms. Full article

In impedance imaging, the incompatibility and nonlinearity of the inverse problem lead to problems such as blurred boundaries and severe artifacts in the reconstructed images, making it difficult to meet the requirements for precise identification of multi-layer tissue structures in the legs. To this end, this paper proposes a post-processing algorithm for leg EIT that integrates the boundary attention mechanism, with a Wasserstein generative adversarial network as the training framework, cyclic residual U-Net as the generator, and the boundary attention module embedded in the RecurrentBlock. This leads to adaptive enhancement of the ability to extract organizational boundary features through a three-path fusion of spatial attention, channel attention, and learnable Laplacian edge enhancement. A leg anatomy prior constraint loss function was designed, integrating six constraints—pixel loss, edge loss, hierarchical tissue constraint, total variation regularization, structural similarity loss, and histogram matching—to guide the reconstruction results to conform to the multi-layered tissue structure features of the leg. A simulation dataset of leg sections containing multiple tissues such as skin, fat, muscle, bone, blood vessels, and nerves was constructed, and the pre-reconstructed images were obtained using the hybrid total variation regularization algorithm as the network input. The simulation results show that, under noise-free and different signal-to-noise ratio conditions, the proposed BAM-R2UNet algorithm achieves the best performance in RMSE, SSIM and PSNR metrics compared with HTV, DnCNN and standard U-Net algorithms, can remove artifacts, accurately restore the boundary and conductivity distribution of leg tissues, and has stronger anti-noise robustness. Full article

Edge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and packet corruption can inject invalid payloads into the edge processing pipeline and trigger unnecessary buffering, parsing, and, most critically, computationally expensive cryptographic operations such as digital signature verification. This leads to wasted computation, increased latency, and reduced energy efficiency at the edge, particularly under dense IoT traffic. This paper presents an energy-aware and fault-tolerant Quantum-Dot Cellular Automata (QCA)-based integrity pre-filter for IoT-to-edge blockchain transaction ingestion. At the circuit level, we adapt and modify a previously reported fault-tolerant five-input majority gate (MV5) structure and use it as a robust primitive for nanoscale integrity-screening circuits. Building on this modified MV5, we design a set of QCA integrity blocks, including a parity checker, a compact XNOR gate circuit, a parity-bit generation circuit, and a sender-to-channel/receiver nano-communication integrity workflow suitable for early screening of corrupted payloads. Compared with the best previously reported baseline considered in this study, the modified MV5 achieves 76.47% tolerance to single-cell omission defects, corresponding to a 17.47 percentage-point increase and an approximately 29.61% relative improvement over the prior 59% omission-tolerance result, while preserving 100% tolerance against extra-cell deposition defects. At the system level, the proposed circuit is discussed as a potential early screening stage for edge-IoT blockchain transaction ingestion. A bounded analytical model is used to estimate the possible reduction in unnecessary signature-verification workload under assumed corruption and detection conditions. This analysis is not intended as a deployment-level validation; full edge-node implementation, throughput measurement, queueing-delay evaluation, real traffic traces, retransmission behavior, and empirical signature-verification profiling remain future work. The proposed parity/chunk-parity pre-filter is designed for low-cost detection of random transmission-induced corruption and does not replace cryptographic authentication, hashing, digital signatures, CRC-based detection, or blockchain validation. All proposed designs are validated using QCADesigner tools. Full article

The increasing resolution of global ocean circulation models has made data output an important constraint on runtime efficiency and operational timeliness. The current dedicated-process asynchronous I/O scheme in the Mass Conservation Ocean Model (MaCOM) sends output data from compute processes to a group of reserved I/O processes. Although this design separates part of the writing work from the main time-stepping loop, it still introduces centralized data aggregation, additional I/O process management, and high memory pressure on the I/O side at large process counts. This paper presents MaCOM–PAIO, a PAIO-enabled asynchronous I/O optimization for MaCOM. Built on the existing PAIO/PAIOM asynchronous I/O stack, MaCOM–PAIO implements a thread-based asynchronous output path, adapts the PnetCDF execution path used by MaCOM to route selected collective writes to PAIO, and uses PAIOM asynchronous zones to submit history and restart output operations as background tasks. The implementation keeps the numerical solver unchanged and preserves the PnetCDF-style calling path at the application level, while replacing the dedicated I/O process path with I/O–thread-based asynchronous execution on the allocated HPC nodes. Experiments were conducted on a 1/12${}^{\circ }$ global MaCOM configuration. Strong-scaling tests show that, at 1646 compute processes, MaCOM–PAIO reduces the total runtime from 1167.45 s to 276.53 s and lowers the compute-side I/O blocking ratio from 67.2% to 4.9% under the tested configuration. In an independent bandwidth test at 1080 compute processes, the measured write bandwidth increases from approximately 0.10 GiB/s to 0.90 GiB/s for output volumes of about 82 GiB. The maximum memory footprint of the I/O entities is also reduced from approximately 18.2 GiB in the legacy dedicated-I/O scheme to approximately 1.9 GiB in MaCOM–PAIO. These results demonstrate that PAIO-based integration is a practical approach for improving MaCOM I/O performance under the evaluated hardware/software environment and workload. Full article

Zinnia (Zinnia elegans) is a widely cultivated ornamental plant whose growth and floral traits can be compromised by abiotic stresses, especially water deficit. Melatonin (MEL) has stood out as a plant growth regulator with antioxidant potential, capable of mitigating the adverse effects of water stress. This study aimed to evaluate the effects of foliar MEL application on the growth and floral characteristics of Z. elegans under different water regimes. The experiment was carried out in a greenhouse using a randomized block design in a 4 × 2 factorial scheme with five replications. The first factor consisted of four water conditions: 80% of field capacity (FC) (no stress), 20% of field capacity (severe stress), early water restriction (20% of FC followed by 80% of FC), and late water restriction (80% of FC followed by 20% of FC). The second factor corresponded to the foliar application of MEL at two concentrations (0.0 and 1.0 mM). Growth variables (plant height, stem diameter, number of leaves, leaf area, and dry mass of different organs) and floral characteristics (number of petals, area, perimeter, and diameter) were evaluated. Water deficit, especially under severe stress (20% FC), significantly reduced plant growth and floral traits, decreasing the total dry mass by 60.27% and total floral area by 47.57% compared to the control. However, the application of 1.0 mM MEL attenuated the deleterious effects of water deficit, increasing total dry mass by 50.26% and total floral area by 25.56% under severe stress (20% FC) compared to untreated plants, making it a promising strategy for zinnia production in environments with limited water availability. Full article

As the inner driving factor of space vitality, environmental perception can be expressed in many ways. Given the current lack of in-depth research on related perceptions, the study integrated theoretical origin and empirical study methods to clarify the role that preference played as the common foundation of different expression ways of environmental perception. The study also explored the interaction mechanism of different preference expression ways in the “quality-to-vitality” pathway and significant environmental characteristics of them, so as to realize the transformation from landscape design to urban vitality. Key findings indicate that: (1) Three environmental preference expressions—emotion, satisfaction, and behavioral preference—collectively lend credence to a significant chain mediation pathway (“emotion → satisfaction → behavioral preference”) in the quality-to-vitality process; (2) Pedestrian safety infrastructure (e.g., traffic barricades, well-maintained pavements) could ensure perceived security and walking activities; (3) Cultural/recreational facilities mean complementary legibility-enhancing elements (appropriate spatial enclosure, pleasant color schemes, architectural coherence) to evoke positive affect; (4) Streetscape diversity and visual interest might mitigate monotony induced by excessive block length, serving as vital vitality catalysts in some degree. Full article

The Controller Area Network (CAN) protocol used in in-vehicle networks is vulnerable to external attacks because it lacks authentication and encryption mechanisms. Accordingly, CAN Intrusion Detection Systems (IDSs) have been studied. However, existing IDSs remain difficult to deploy in practical vehicles because of their limited real-time capability, complex preprocessing, and high computational cost. To overcome these limitations, this paper proposes an ultra-lightweight Convolutional Neural Network (CNN)-based IDS that significantly reduces parameters and computational complexity while maintaining high detection performance. The proposed IDS improves area efficiency through a streaming pipeline, computation-block reuse, and constrained Processing Element (PE) parallelism. In addition, its lightweighting effect was quantitatively evaluated against an RTL baseline implemented under identical platform and design constraints. When an attack is detected, an Intrusion Prevention System (IPS) integrated with the CAN controller generates an error frame to block it in real time. The proposed IDS achieved over 99.97% detection performance for known frame-level message-injection scenarios on the Car-Hacking Dataset. It also achieved branch-wise real-time feasibility with an 11.46 µs ID-branch precomputation latency and a 5.68 µs DATA-complete-to-decision latency at 50 MHz. In TSMC 28 nm ASIC synthesis, the proposed IDS required 70,592 gates, with an estimated ASIC power of 2.0231 mW and an active inference energy of 34.68 nJ. Full article

The physicochemical mechanisms and numerical characterization of amine-ether gemini surfactant emulsion flooding remain insufficient, limiting its field application in low-permeability reservoirs. This study developed a refined numerical simulation method that integrates full-process emulsion kinetics, including generation, coalescence, dispersion-assisted oil displacement, and demulsification, with graded emulsion characterization using the differentiated inaccessible pore volume (IPV) and residual resistance factor (RRF). Core-flooding validation demonstrated that the model accurately reproduced the key dynamic responses of water cut reduction and oil production increase, with a relative error of about 3.0%. Mechanistic analysis showed that the enhanced oil recovery performance arose from the combined effects of ultralow interfacial tension and emulsion-induced profile control. Relative to conventional surfactant flooding, emulsion flooding increased oil recovery by an additional 4.8–5.0% and lowered water cut by about 12 percentage points. For the Shengli Oilfield pilot block, the optimized injection design involved a surfactant concentration of 1.2 wt.%, an injection rate of 60 m³/d, a slug size of 0.01 PV, an injection–production ratio of 0.95, and a stepwise concentration-decline strategy. The field pilot further confirmed the applicability of the method: daily oil production of the well group increased by 46.5%, while comprehensive water cut decreased by 8.6 percentage points. These results demonstrate the value of the proposed method for both mechanistic characterization and field design of amine-ether gemini surfactant emulsion flooding in heterogeneous low-permeability reservoirs. Full article

Yaks (Bos grunniens) on the Qinghai–Tibetan Plateau face severe nutritional limitations during the dry season due to dependence on highly lignified, low-quality roughage. Identifying safe and effective rumen regulators capable of enhancing fiber utilization in this species is therefore of great practical importance. This study employed a two-pronged approach integrating in vitro mechanistic investigation and in vivo validation to evaluate the effects of the amphoteric surfactant cocamidopropyl betaine (CAPB) on rumen fermentation, the micro-spatial distribution of digestive enzymes, apparent total tract digestibility, and the macroscopic growth performance of yaks. In the in vitro fermentation trial (Experiment 1), a randomized block design was employed where a straw-based high-forage diet was used as the substrate and supplemented with 0, 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0% CAPB (based on substrate dry matter, DM) for a 48 h batch culture. The results showed that as the CAPB supplementation level increased, cumulative gas production, the degradation rates of DM and neutral detergent fiber (NDF), and the yields of total volatile fatty acids and microbial protein all exhibited significant quadratic responses (p < 0.05), peaking at the 0.5–1.0% supplementation levels. Concurrently, CAPB significantly promoted the transfer and release of carboxymethyl cellulase and xylanase into the free liquid phase (p < 0.01). In the in vivo validation trial (Experiment 2), 24 healthy growing male yaks (initial body weight 131.2 ± 8.4 kg) were allocated in a completely randomized design to four groups and fed a basal diet supplemented with 0, 0.5, 1.0, or 2.0% CAPB for 44 days. The results indicated that, while maintaining a stable DM intake, the addition of 0.5% CAPB significantly increased the average daily gain (ADG) of yaks (p < 0.05), improved the feed-to-gain ratio, and significantly enhanced the apparent total tract digestibility of NDF and ether extract (p < 0.05). However, when the supplementation dose exceeded the safety threshold (≥2.5% in vitro and ≥2.0% in vivo), both fermentation parameters and growth advantages declined. In conclusion, under the present experimental conditions, 0.5% CAPB improved roughage fermentation efficiency, putatively through an ‘enzyme elution’ mechanism, and was associated with macroscopic improvements in NDF and EE apparent digestibility and ADG in growing yaks. These findings identify 0.5% CAPB as a promising candidate rumen regulator for improving roughage utilization in growing yaks; broader generalization will require larger-scale and longer-duration trials. Full article

Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized ‘Yotsuboshi’ strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model’s classifications of ‘NormalN’ and ‘LowN’ statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions. Full article