Search Results (811)

Background/Objectives: Correction surgery for adult spinal deformity (ASD) reduces disability but may lead to spinal stiffness. Cultural diversity may also influence how this stiffness affects daily life. We aimed to evaluate the impact of correction surgery on Japanese patients with ASD using a newly developed questionnaire and to clarify how these patients adapt to their living environment postoperatively in response to spinal stiffness. Methods: This retrospective study included 74 Japanese patients with operative ASD (mean age: 68.2 ± 7.5 years; fusion involving >5 levels) with a minimum follow-up of 1 year. Difficulties in performing various activities of daily living (ADLs) were assessed using a novel 20-item questionnaire tailored to the Oriental lifestyle. The questionnaire also evaluated lifestyle and environmental changes after surgery. Sagittal and coronal spinal parameters were measured using whole-spine radiographs, and clinical outcomes were assessed using the ODI and SRS-22 scores. Results: Coronal and sagittal alignment significantly improved postoperatively. Although the total ADL score remained unchanged, four trunk-bending activities showed significant deterioration. The lower instrumented vertebrae level and pelvic fusion were associated with lower scores in 11 items closely related to trunk bending or the Oriental lifestyle. After surgery, 61% of patients switched from a Japanese-style mattress to a bed, and 72% swapped their low dining table for one with chairs. Both the ODI and SRS-22 scores showed significant postoperative improvements. Conclusions: Trunk-bending activities worsened postoperatively in Japanese patients with ASD, especially those who underwent pelvic fusion. Additionally, patients often modified their living environment after surgery to accommodate spinal stiffness. Full article

In today’s volatile supply chain environment, organizations require flexible and collaborative procurement strategies. Swap contracts, originally developed as financial instruments, have recently been adopted to address inventory imbalances—such as the 2021 COVID-19 vaccine swap between South Korea and Israel. Despite its increasing adoption in the real world, theoretical studies on swap-based procurement remain limited. This study proposes an integrated model that combines buyer-to-buyer swap agreements with long-term wholesale contracts under demand uncertainty. The model quantifies the expected swap quantity between parties and embeds it into the profit function to derive optimal order quantities. Numerical experiments are conducted to compare the performance of the proposed strategy with that of a baseline wholesale contract. Sensitivity analyses are performed on key parameters, including demand asymmetry and swap prices. The numerical analysis indicates that the swap-integrated procurement strategy consistently outperforms procurement based on long-term wholesale contracts. Moreover, the results reveal that under the swap-integrated strategy, the optimal order quantity must be adjusted—either increased or decreased—depending on the demand scale of the counterpart and the specified swap price, deviating from the optimal quantity under traditional long-term contracts. These findings highlight the potential of swap-integrated procurement strategies as practical coordination mechanisms across both private and public sectors, offering strategic value in contexts such as vaccine distribution, fresh produce, and other critical products. Full article

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). These models are integrated into two architectures and evaluated across various product interpretation tasks, including image-grounded question answering, brand recognition and visual retrieval based on natural language prompts. ColQwen2, built on the Qwen2-VL backbone with LoRA-based adapter hot-swapping, demonstrates strong performance, allowing end-to-end image querying and text response synthesis. It excels at identifying attributes such as brand, color or usage based solely on product images and responds fluently to user questions. In contrast, ColPali, which utilizes the PaliGemma backbone, is optimized for explainability. It delivers detailed visual-token alignment maps that reveal how specific regions of an image contribute to retrieval decisions, offering transparency ideal for diagnostics or educational applications. Through comparative experiments using footwear imagery, it is demonstrated that ColQwen2 is highly effective in generating accurate responses to product-related questions, while ColPali provides fine-grained visual explanations that reinforce trust and model accountability. Full article

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Background: Protein kinase R (PKR) inhibits general mRNA translation by phosphorylating the alpha subunit of eukaryotic translation initiation factor 2 (eIF2). PKR also modulates NF-κB signaling during viral infections, but comparative studies of PKR-mediated NF-κB responses across mammalian species and their regulation by viral inhibitors remain largely unexplored. This study aimed to characterize the conserved antiviral and inflammatory roles of mammalian PKR orthologs and investigate their modulation by poxviral inhibitors. Methods: Using reporter gene assays and quantitative RT-PCR, we assessed the impact of 17 mammalian PKR orthologs on general translation inhibition, stress-responsive translation, and NF-κB-dependent induction of target genes. Congenic human and rabbit cell lines infected with a myxoma virus strain lacking PKR inhibitors were used to compare the effects of human and rabbit PKR on viral replication and inflammatory responses. Site-directed mutagenesis was employed to determine key residues responsible for differential sensitivity to the viral inhibitor M156. Results: All 17 mammalian PKR orthologs significantly inhibited general translation, strongly activated stress-responsive ATF4 translation, and robustly induced NF-κB target genes. Inhibition of these responses was specifically mediated by poxviral K3 orthologs that effectively suppressed PKR activation. Comparative analyses showed human and rabbit PKRs similarly inhibited virus replication and induced cytokine transcripts. Amino acid swaps between rabbit PKRs reversed their sensitivity to viral inhibitor M156 and NF-κB activation. Conclusions: Our data show that the tested PKR orthologs exhibit conserved dual antiviral and inflammatory regulatory roles, which can be antagonized by poxviral K3 orthologs that exploit eIF2α mimicry to modulate the PKR-NF-κB axis. Full article

Private array equality comparison (PAEC) aims to evaluate whether two arrays are equal while maintaining the confidentiality of their elements. Current private comparison protocols predominantly focus on determining the relationships of secret integers, lacking exploration of array comparisons. To address this issue, we propose a swap test-based quantum protocol for PAEC, which satisfies both functionality and security requirements using the principles of quantum mechanics. This protocol introduces a semi-honest third party (TP) that acts as a medium for generating Bell states as quantum resources and distributes the first and second qubits of these Bell states to the respective participants. They encode their array elements into the received qubits by performing rotation operations. These encoded qubits are sent to TP to derive the comparison results. To verify the feasibility of the proposed protocol, we construct a quantum circuit and conduct simulations on the IBM quantum platform. Security analysis further indicates that our protocol is resistant to various quantum attacks from outsider eavesdroppers and attempts by curious participants. Full article

Federated learning (FL) enables collaborative model building among a large number of participants without sharing sensitive data to the central server. Because of its distributed nature, FL has limited control over local data and the corresponding training process. Therefore, it is susceptible to data poisoning attacks where malicious workers use malicious training data to train the model. Furthermore, attackers on the worker side can easily manipulate local data by swapping the labels of training instances, adding noise to training instances, and adding out-of-distribution training instances in the local data to initiate data poisoning attacks. And local workers under such attacks carry incorrect information to the server, poison the global model, and cause misclassifications. So, the prevention and detection of such data poisoning attacks is crucial to build a robust federated training framework. To address this, we propose a prevention strategy in federated learning, namely confident federated learning, to protect workers from such data poisoning attacks. Our proposed prevention strategy at first validates the label quality of local training samples by characterizing and identifying label errors in the local training data, and then excludes the detected mislabeled samples from the local training. To this aim, we experiment with our proposed approach on both the image and audio domains, and our experimental results validated the robustness of our proposed confident federated learning in preventing the data poisoning attacks. Our proposed method can successfully detect the mislabeled training samples with above 85% accuracy and exclude those detected samples from the training set to prevent data poisoning attacks on the local workers. However, our prevention strategy can successfully prevent the attack locally in the presence of a certain percentage of poisonous samples. Beyond that percentage, the prevention strategy may not be effective in preventing attacks. In such cases, detection of the attacked workers is needed. So, in addition to the prevention strategy, we propose a novel detection strategy in the federated learning framework to detect the malicious workers under attack. We propose to create a class-wise cluster representation for every participating worker by utilizing the neuron activation maps of local models and analyze the resulting clusters to filter out the workers under attack before model aggregation. We experimentally demonstrated the efficacy of our proposed detection strategy in detecting workers affected by data poisoning attacks, along with the attack types, e.g., label-flipping or dirty labeling. In addition, our experimental results suggest that the global model could not converge even after a large number of training rounds in the presence of malicious workers, whereas after detecting the malicious workers with our proposed detection method and discarding them from model aggregation, we ensured that the global model achieved convergence within very few training rounds. Furthermore, our proposed approach stays robust under different data distributions and model sizes and does not require prior knowledge about the number of attackers in the system. Full article

Acute lymphoblastic leukemia (ALL) is the most common cancer affecting children, making up about 80% of all acute leukemia cases in the pediatric population. While treatment with L-asparaginase (ASNase) has greatly improved survival rates, its bacterial origin often causes immune reactions in some patients, which can reduce how well the therapy works. To overcome this challenge, previous in silico studies designed a humanized chimeric ASNase by swapping out the predicted immunogenic parts of the bacterial enzyme with similar, less immunogenic segments from the human version—while keeping the enzyme’s active site intact. In this study, the chimeric L-asparaginase designed was successfully cloned, expressed, and purified using the Escherichia coli Rosetta strain. The production conditions (37 °C, 0.01 mM IPTG, 2–4 h) were optimized, and we purified the enzyme in a single step with nickel-affinity chromatography. The enzyme’s activity was confirmed in vitro, showing that it is possible to produce a functional humanized variant in a bacterial system. These results lay important groundwork for future research to assess the immune response and therapeutic potential of this novel chimeric enzyme. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

In this study, the impact of the genomic scaffold on the properties of bacteriophages was investigated by swapping the genomic scaffolds surrounding the tailspike genes between two Przondovirus phages, KP192 and KP195, which infect Klebsiella pneumoniae with different capsular types. A yeast-based transformation-associated recombination cloning technique and subsequent “rebooting” of synthetic phage genomes in bacteria were used to construct the phages. Using Klebsiella strains with K2, K64, and KL111 capsular types, it was shown that the capsular specificity of the synthetic phages is fully consistent with that of the tailspike proteins (tsp). However, the efficiency of plating and the lytic efficiency of these phages strongly depended on the genomic scaffold used and the Klebsiella strain used. Synthetic phages with swapped genomic scaffolds demonstrated superior reproduction efficiency using a number of strains compared to wild-type phages, indicating that some elements of the swapped genomic scaffold enhance phage replication efficiency, presumably by blocking some of the host anti-phage defense systems. Our findings demonstrate that even in the case of closely related phages, the selection of the genomic scaffold used for tsp gene transplantation can have a profound impact on the efficiency of phage propagation on target bacterial strains. Full article

This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements–compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system’s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s. Full article

The Domain Name System (DNS) is a key part of the Internet, and it is used for global domain name resolution. However, it has security risks due to its centralized or semi-centralized design and reliance on a few root servers. To improve DNS security and long-term stability, this study proposes the consensus roots system, a blockchain-based distributed domain architecture. The system uses a 1 + N master-subchain structure to solve the problem of trust and data synchronization across blockchains. The master chain acts as a relay and uses Hyperledger Fabric, a consortium blockchain platform, to support semi-centralized cross-chain communication. Subchains are local blockchains that need to connect with the master chain. To ensure safe and reliable transactions, the system uses a staged-proposal atomic swap method on the master chain. Compared to prior approaches, this work introduces a cross-chain architecture that enables more efficient trust synchronization, reducing latency and improving scalability without compromising security. Full article

This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be drawn from the grid when costs or equivalent ${\mathrm{CO}}_2$ emissions are low. An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. Battery tests were conducted to assess charging time variability, and traffic density measurements were collected in the city of Valencia across multiple days to provide a realistic scenario, while real-time data of the electricity cost is integrated into the control proposal. The results show that incorporating traffic and electricity price forecasts into the control algorithm can reduce electricity costs by up to 11% and decrease associated ${\mathrm{CO}}_2$ emissions by more than 26%. Full article

Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents a non-iterative real-time Feature Sub-image Based Stripmap Phase Gradient Autofocus (FSI-SPGA) algorithm. The FSI-SPGA algorithm combines 2D Constant False Alarm Rate (CFAR) for coarse point selection and spatial decorrelation for refined point selection. This approach enables the accurate extraction of high-quality scattering points. Using these points, the algorithm constructs a feature sub-image containing comprehensive phase error information and performs a non-iterative phase error estimation based on this sub-image. To address the multifunctional, low-power, and real-time requirements of small UAV SAR, we designed a highly efficient hybrid architecture. This architecture integrates dataflow reconfigurability and dynamic partial reconfiguration and is based on an ARM + FPGA platform. It is specifically tailored to the computational characteristics of the FSI-SPGA algorithm. The proposed scheme was assessed using data from a 6 kg small SAR system equipped with centimeter-level INS/GPS. For SAR images of size 4096 × 12,288, the FSI-SPGA algorithm demonstrated a 6 times improvement in processing efficiency compared to traditional methods while maintaining the same level of precision. The high-efficiency reconfigurable ARM + FPGA architecture processed the algorithm in 6.02 s, achieving 12 times the processing speed and three times the energy efficiency of a single low-power ARM platform. These results confirm the effectiveness of the proposed solution for enabling high-quality real-time SAR imaging under stringent SwaP constraints. Full article