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التخفيف من حدة الفقر في الصين المعاصرة
استنادا إلى نظرة عامة على أوضاع الفقر، يقدم هذا الكتاب مسار التخفيف من حدة الفقر والتنمية في الصين، ويشرح نموذج التنمية والتخفي من حدة الفقر بخصائص صينية والتمسك بمباديء (سيطرة الحكومة ومشاركة المجتمع والاعتماد على الذات والتنمية الموجهة والتنمية الشاملة) كما يقدم الكتاب تلخيصا شاملا لإنجازات الصين العظيمة وخبراتها الهامة وإسهاماتها الرئيسية في قضية التخفيف من حدة الفقر في العالم، ويعرض بإيجاز نظرات وممارسات التخفيف المستهدف من الفقر في العصر الجديد من أجل توفير مراجع لكسب المعركة ضد الفقر في الصين وقضية التخفيف من حدة الفقر في العالم.
Book
The predicting role of circulating tumor DNA landscape in gastric cancer patients treated with immune checkpoint inhibitors
A more common and noninvasive predicting biomarker for programmed cell death 1 (PD-1) antibody remains to be explored. We assessed 46 patients with advanced gastric cancer who received PD-1 antibody immunotherapy and 425-genes next-generation sequencing (NGS) testing. Patients who had a > 25% decline in maximal somatic variant allelic frequency (maxVAF) had a longer progression free survival (PFS) and higher response rate than those who did not (7.3 months vs 3.6 months,
p
= 0.0011; 53.3% vs 13.3%,
p
= 0.06). The median PFS of patients with undetectable and detectable post-treatment circulating tumor DNA (ctDNA) was 7.4 months vs. 4.9 months (
p
= 0.025). Mutation status of TGFBR2, RHOA, and PREX2 in baseline ctDNA influenced the PFS of immunotherapy (
p
< 0.05). Patients with alterations in CEBPA, FGFR4, MET or KMT2B (
p
= 0.09) gene had greater likelihood of immune-related adverse events (irAEs). ctDNA can serve as a potential biomarker of the response to immunotherapy in advanced gastric cancers, and its potential role in predicting irAEs worth further exploration.
Journal Article
Strategies for Solving the Issue of Malachite Green Residues in Aquatic Products: A Review
Malachite green (MG) residue in aquatic products is a widely concerning issue, and the possible source of MG contamination includes its illegal usage and environmental pollution. A variety of strategies for solving such a problem have been proposed, and the research about them is summarized in this review. The MG contamination in aquaculture environments can be eliminated by adsorption, degraded by advanced oxidation processes (AOPs), or biodegraded by microbes or enzymes. The illegal usage of MG can be prevented by screening novel anti-Saprolegnia sp. agents from current available agricultural antibiotics, plant extracts, or antagonistic microbes. Nevertheless, deficiencies also existed in these proposed solving strategies. Therefore, further research opportunities in such areas were provided. This includes developing effective combinatorial methods (adsorption + AOPs or biodegradation) for eliminating MG from the aquaculture environment; systematically considering the impact of practical conditions on the efficiency of MG elimination; screening more efficient anti-Saprolegnia sp. agents; and systematically evaluating both the in vivo activities and safety of these agents.
Journal Article
Parallel Processing of Sobel Edge Detection on FPGA: Enhancing Real-Time Image Analysis
Detection of object boundaries and significant features within an image is one of the most important processes in image processing and computer vision, as it allows the identification of object boundaries and significant features within an image. In applications such as autonomous vehicles, surveillance systems, and medical imaging, real-time processing has become increasingly important, which requires hardware accelerators. In this paper, the improved Sobel edge detection algorithm was implemented using Verilog as an FPGA-based algorithm designed to perform real-time image processing under the Sobel edge detection algorithm for specially RGB images. The proposed design proposes an application of horizontal and vertical Sobel kernels in parallel in order to compute the gradient magnitudes for 1028 × 720 RGB images by taking the gradient magnitudes of 3 × 3 pixel windows. This work focuses on algorithmic complex reduction by using eight directional approaches, and parallel processing leads to reducing the architectural utilization.
Journal Article
A Software-Defined Directional Q-Learning Grid-Based Routing Platform and Its Two-Hop Trajectory-Based Routing Algorithm for Vehicular Ad Hoc Networks
Dealing with the packet-routing problem is challenging in the V2X (Vehicle-to-Everything) network environment, where it suffers from the high mobility of vehicles and varied vehicle density at different times. Many related studies have been proposed to apply artificial intelligence models, such as Q-learning, which is a well-known reinforcement learning model, to analyze the historical trajectory data of vehicles and to further design an efficient packet-routing algorithm for V2X. In order to reduce the number of Q-tables generated by Q-learning, grid-based routing algorithms such as the QGrid have been proposed accordingly to divide the entire network environment into equal grids. This paper focuses on improving the defects of these grid-based routing algorithms, which only consider the vehicle density of each grid in Q-learning. Hence, we propose a Software-Defined Directional QGrid (SD-QGrid) routing platform in this paper. By deploying an SDN Control Node (CN) to perform centralized control for V2X, the SD-QGrid considers the directionality from the source to the destination, real-time positions and historical trajectory records between the adjacent grids of all vehicles. The SD-QGrid further proposes the flows of the offline Q-learning training process and the online routing decision process. The two-hop trajectory-based routing (THTR) algorithm, which depends on the source–destination directionality and the movement direction of the vehicle for the next two grids, is proposed as a vehicle node to forward its packets to the best next-hop neighbor node in real time. Finally, we use the real vehicle trajectory data of Taipei City to conduct extensive simulation experiments with respect to four transmission parameters. The simulation results prove that the SD-QGrid achieved an over 10% improvement in the average packet delivery ratio and an over 25% reduction in the average end-to-end delay at the cost of less than 2% in average overhead, compared with two well-known Q-learning grid-based routing algorithms.
Journal Article
Comparison and Optimization of Generalized Stamping Machine Fault Diagnosis Models Using Various Transfer Learning Methodologies
The integration of artificial intelligence (AI) with stamping technology has become increasingly critical in smart manufacturing, driven by advancements in both fields. Total clearance, a crucial determinant of both process and product quality in stamping operations, significantly impacts cutting precision, material deformation, and the longevity of stamping equipment. Consequently, real-time monitoring and prediction of total clearance are essential for effective process control and fault diagnosis. However, the heterogeneity of stamping machine designs necessitates the development of numerous machine-specific models, posing a significant challenge for practical implementation. This research addresses this challenge by developing a generalized fault diagnosis model applicable across multiple stamping machine types. Specifically, the model is designed to monitor four distinct machine models: OCP-110, G2-110, G2-160, and ST1-110. Vibration data, acquired using accelerometers strategically placed at two distinct sensor locations on each machine, serve as the primary input for the model. Four prominent deep learning architectures—a 10-layer convolutional neural network (CNN), a CNN with residual connections (CNN-Res), VGG16, and ResNet50—were rigorously evaluated in conjunction with fine-tuning strategies to determine the optimal model architecture. The resulting generalized fault diagnosis model achieved an average accuracy, recall rate, and F1 score exceeding 99%, demonstrating its efficacy and reliability for real-world applications. This proposed approach offers the potential for scalability to additional stamping machine types and operational conditions, thereby streamlining the deployment of predictive maintenance systems by equipment manufacturers.
Journal Article
Comparison of supraglottic airway device and endotracheal tube in former preterm infants receiving general anesthesia: a randomized controlled trial
To date, endotracheal tube (ETT) remains the mainstream for preterm infants receiving general anesthesia. We aim to compare the perioperative respiratory adverse events between using supraglottic airway device (SAD) and ETT in former preterm infants receiving general anesthesia. Former preterm infants below 52 weeks of postmenstrual age scheduled for herniorrhaphy were randomized to receive SAD or ETT for general anesthesia. Infants with severe congenital cardiopulmonary disease, prolonged oxygen or mechanical ventilation dependence, and recent respiratory tract infection were excluded. Muscle relaxant agents and opioids were avoided in this study. 40 infants were assigned into SAD or ETT groups. Infants in the SAD group had a much lower rate of intraoperative desaturation than those in the ETT group (21.1% vs. 73.7%,
p
= 0.003). Incidences of other intraoperative and postoperative 24-h respiratory adverse events were similar between groups, including laryngospasm/bronchospasm, cough and stridor during anesthesia, and postoperative apnea, bradycardia, and supplemental oxygen use. All participants were extubated successfully in the operation room. SAD is recommended in former preterm infants receiving general anesthesia for herniorrhaphy in their early infancy as it much decreases the incidence of intraoperative desaturation compared to ETT.
Journal Article
Rapidity and Energy Dependencies of Temperatures and Volume Extracted from Identified Charged Hadron Spectra in Proton–Proton Collisions at a Super Proton Synchrotron (SPS)
The standard (Bose–Einstein/Fermi–Dirac, or Maxwell–Boltzmann) distribution from the relativistic ideal gas model is used to study the transverse momentum (pT) spectra of identified charged hadrons (π−, π+, K−, K+, p¯, and p) with different rapidities produced in inelastic proton–proton (pp) collisions at a Super Proton Synchrotron (SPS). The experimental data measured using the NA61/SHINE Collaboration at the center-of-mass (c.m.) energies s=6.3, 7.7, 8.8, 12.3, and 17.3 GeV are fitted well with the distribution. It is shown that the effective temperature (Teff or T), kinetic freeze-out temperature (T0), and initial temperature (Ti) decrease with the increase in rapidity and increase with the increase in c.m. energy. The kinetic freeze-out volume (V) extracted from the π−, π+, K−, K+, and p¯ spectra decreases with the rapidity and increase with the c.m. energy. The opposite tendency of V, extracted from the p spectra, is observed to be increasing with the rapidity and decreasing with the c.m. energy due to the effect of leading protons.
Journal Article
MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model
With the continuous development of deep learning, more and more domains use deep learning technique to solve key problems. The security issues of deep learning models have also received more and more attention. Nowadays, malware has become a huge security threat in cyberspace. Traditional signature-based malware detection methods are not adaptable to the current large-scale malware detection. Thus many deep learning-based malware detection models are widely used in real malware detection scenarios. Therefore, we need to secure the deep learning-based malware detection models. However, model testing currently focuses on image and natural language processing models. There is no related work to test deep learning-based malware detection models specifically. Therefore, to fill this gap, we propose MalFuzz. MalFuzz uses the idea of coverage-guided fuzzing to test deep learning-based malware detection models. To solve the model state representation problem, MalFuzz uses the first and last layer neuron values to approximately represent the model state. To solve the new coverage calculation problem, MalFuzz uses the fast approximate nearest neighbor algorithm to compute the new coverage. The mutation strategy and seed selection strategy in image model or natural language processing model testing is not appropriate in deep learning-based malware detection model testing. Hence MalFuzz designs the seed selection strategy and seed mutation strategy for malware detection model testing. We performed extensive experiments to demonstrate the effectiveness of MalFuzz. Based on MalConv, Convnet, and CNN 2-d, we compared the modified TensorFuzz and MAB-malware with MalFuzz. Experiment results show that MalFuzz can detect more model classification errors. Likewise, the mutation operation of MalFuzz can retain the original functionality of malware with high probability. Moreover, the seed selection strategy of MalFuzz can help us explore the model state space quickly.
Journal Article