Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
5,220
result(s) for
"Wei, Zhe"
Sort by:
هذه هي الصين : قوة تسير نحو العالم
يركز كتاب \"هذه هي الصين\" على سرد قصص تنمية الصين في العصر الجديد. وقد غير الدكتور تشانغ وي وي طريقته المعروفة بالتوجيه والإرشاد في البرامج الأيديولوجية والنظرية في الماضي، وعبر عنها بلغة شعبية سائغة، وحجج منطقية صارمة، وبيانات حقيقية، وتصادمات صريحة للأفكار، وتقنيات عرض مبتكرة لمساعدة الجمهور على فهم النموذج الصيني والطريق الصيني، وتعزيز ثقتهم بمستقبل الصين.
Book
Option-C Verified Semantic Digital Twins for Decarbonized, Pressure-Reliable Central Business District Hospitals
Central business district (CBD) hospitals must sustain reliable pressure relationships in critical rooms while reducing whole-facility carbon under tight space and disruption constraints. We developed an ontology-grounded semantic digital twin that normalizes building automation system (BAS) and building management system (BMS) telemetry into a unified semantic store consistent with Brick Schema, enabling portable asset discovery via query and thereby supporting forecasting, anomaly detection, and multi-objective optimization without dependence on vendor point naming conventions. Whole-facility impacts were verified using International Performance Measurement and Verification Protocol Option C–style measurement and verification with an S0-calibrated baseline model and residual-based savings attribution. Relative to the baseline (S0), the intervention (S3) produced a step increase in the critical-room pressure-compliance pass rate, tighter room-to-corridor differential-pressure (ΔP) control across airborne infection isolation and open room strata, and intent-aligned ventilation delivery (air changes per hour ratio distribution concentrated near unity; p < 0.05 where letter groups differ). Operational-state discrimination improved (AUC 0.649→0.696) and issue-resolution times shortened (left-shifted cumulative distribution function), indicating reduced service burden. Option C verification showed energy residuals shifting negative under S3, consistent with net savings versus baseline expectations. Across progressive maturity (S0→S3), time-to-value and burden fractions decreased, carbon intensity (tCO2e m−2) decreased, long-tail exposure compressed (log-scale horizon), and composite performance indices increased (p < 0.05). These results demonstrate a verifiable pathway to pressure-reliable, decarbonized hospital operations at the whole-facility boundary while making the semantic layer’s utility explicit through query-driven, ontology-grounded asset discovery. We present an IPMVP Option-C–verifiable semantic digital-twin governance framework that links audited operational evidence (telemetry → actions → verification) to whole-facility energy and carbon outcomes while maintaining critical-room pressure-relationship reliability. Optimization benchmarking (including quantum annealing) is used as supporting decision-support evaluation, rather than as the central contribution.
Journal Article
Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms
Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices. Thereby, in this study, we proposed an offloading model for a multi-user MEC system with multi-task. In addition, a new caching concept is introduced for the computation tasks, where the application program and related code for the completed tasks are cached at the edge server. Furthermore, an efficient model of task offloading and caching integration is formulated as a nonlinear problem whose goal is to reduce the total overhead of time and energy. However, solving these types of problems is computationally prohibitive, especially for large-scale of mobile users. Thus, an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states. Afterwards, two effective Q-learning and Deep-Q-Network-based algorithms are proposed to derive the near-optimal solution for this problem. Finally, experimental evaluations verify that our proposed model can substantially minimize the mobile devices’ overhead by deploying computation offloading and task caching strategy reasonably.
Journal Article
A Combined Filtering Method for ZigBee Indoor Distance Measurement
Indoor distance measurement technology utilizing Zigbee’s Received Signal Strength Indication (RSSI) offers cost-effective and energy-efficient advantages, making it widely adopted for indoor distance measurement applications. However, challenges such as multipath effects, signal attenuation, and signal blockage often degrade the accuracy of distance measurements. Addressing these issues, this study proposes a combined filtering approach integrating Kalman filtering, Dixon’s Q-test, Gaussian filtering, and mean filtering. Initially, the method evaluates Zigbee’s transmission power, channel, and other parameters, analyzing their impact on RSSI values. Subsequently, it fits a signal propagation loss model based on actual measured data to understand the filtering algorithm’s effect on distance measurement error. Experimental results demonstrate that the proposed method effectively improves the conversion relationship between RSSI and distance. The average distance measurement error, approximately 0.46 m, substantially outperforms errors derived from raw RSSI data. Consequently, this method offers enhanced distance measurement accuracy, making it particularly suitable for indoor positioning applications.
Journal Article
Impact of Visceral Obesity and Sarcopenia on Short-Term Outcomes After Colorectal Cancer Surgery
BackgroundWith the increased prevalence of obesity and sarcopenia, those patients with both visceral obesity and sarcopenia were at higher risk of adverse outcomes.AimThe aim of this study was to ascertain the combined impact of visceral obesity and sarcopenia on short-term outcomes in patients undergoing colorectal cancer surgery.MethodsWe conducted a prospective study from July 2014 to February 2017. Patients’ demographic, clinical characteristics, physical performance, and postoperative short-term outcomes were collected. Patients were classified into four groups according to the presence of sarcopenia or visceral obesity. Clinical variables were compared. Univariate and multivariate analyses evaluating the risk factors for postoperative complications were performed.ResultsA total of 376 patients were included; 50.8 and 24.5% of the patients were identified as having “visceral obesity” and “sarcopenia,” respectively. Patients with sarcopenia and visceral obesity had the highest incidence of total, surgical, and medical complications. Patients with sarcopenia or/and visceral obesity all had longer hospital stays and higher hospitalization costs. Age ≥ 65 years, visceral obesity, and sarcopenia were independent risk factors for total complications. Rectal cancer and visceral obesity were independent risk factors for surgical complications. Age ≥ 65 years and sarcopenia were independent risk factors for medical complications. Laparoscopy-assisted operation was a protective factor for total and medical complications.ConclusionPatients with both visceral obesity and sarcopenia had a higher complication rate after colorectal cancer surgery. Age ≥ 65 years, visceral obesity, and sarcopenia were independent risk factors for total complications. Laparoscopy-assisted operation was a protective factor.
Journal Article
An imbalance data quality monitoring based on SMOTE-XGBOOST supported by edge computing
Product assembly involves extensive production data that is characterized by high dimensionality, multiple samples, and data imbalance. The article proposes an edge computing-based framework for monitoring product assembly quality in industrial Internet of Things. Edge computing technology relieves the pressure of aggregating enormous amounts of data to cloud center for processing. To address the problem of data imbalance, we compared five sampling methods: Borderline SMOTE, Random Downsampling, Random Upsampling, SMOTE, and ADASYN. Finally, the quality monitoring model SMOTE-XGBoost is proposed, and the hyperparameters of the model are optimized by using the Grid Search method. The proposed framework and quality control methodology were applied to an assembly line of IGBT modules for the traction system, and the validity of the model was experimentally verified.
Journal Article
Single-Layer-Graphene-Coated and Gold-Film-Based Surface Plasmon Resonance Prism Coupler Sensor for Immunoglobulin G Detection
A graphene-based surface plasmon resonance (SPR) prism coupler sensor is proposed for the rapid detection of immunoglobulin G (IgG) antibodies. The feasibility of the proposed sensor is demonstrated by measuring the IgG concentration in phantom mouse and human serum solutions over the range of 0–250 ng/mL. The results show that the circular dichroism and principal fast axis angle of linear birefringence increase in line with increases in IgG concentration over the considered range. Moreover, the proposed device has a resolution of 5–10 ng/mL and a response time of less than three minutes. In general, the sensor provides a promising approach for IgG detection and has significant potential for rapid infectious viral disease testing applications.
Journal Article
Edge computing-based proactive control method for industrial product manufacturing quality prediction
With the emergence of intelligent manufacturing, new-generation information technologies such as big data and artificial intelligence are rapidly integrating with the manufacturing industry. One of the primary applications is to assist manufacturing plants in predicting product quality. Traditional predictive models primarily focus on establishing high-precision classification or regression models, with less emphasis on imbalanced data. This is a specific but common scenario in practical industrial environments concerning quality prediction. A SMOTE-XGboost quality prediction active control method based on joint optimization hyperparameters is proposed to address the problem of imbalanced data classification in product quality prediction. In addition, edge computing technology is introduced to address issues in industrial manufacturing, such as the large bandwidth load and resource limitations associated with traditional cloud computing models. Finally, the practicality and effectiveness of the proposed method are validated through a case study of the brake disc production line. Experimental results indicate that the proposed method outperforms other classification methods in brake disc quality prediction.
Journal Article
Shaping the Future of Higher Education: A Technology Usage Study on Generative AI Innovations
Generative Artificial Intelligence (GAI) is rapidly reshaping the landscape of higher education, offering innovative solutions to enhance student engagement, personalize learning experiences, and improve academic performance prediction. This study provides an in-depth exploration of GAI applications in educational contexts, drawing insights from 67 case studies meticulously selected from over 300 papers presented at the AIED 2024 conference. The research focuses on eight key themes from student engagement and behavior analysis to the integration of generative models into educational tools. These case studies illustrate the potential of GAI to optimize teaching practices, enhance student support systems, and provide tailored interventions that address individual learning needs. However, this study also highlights challenges such as scalability, the need for balanced and diverse datasets, and ethical concerns regarding data privacy and bias. Further, it emphasizes the importance of improving model accuracy, transparency, and real-world applicability in educational settings. The findings underscore the need for continued research to refine GAI technologies, ensuring they are scalable, adaptable, and equitable, ultimately enhancing the effectiveness and inclusivity of AI-driven educational tools across diverse higher education environments. It should be noted that this study primarily draws from papers presented at the AIED 2024 conference, which may limit global representativeness and introduce thematic biases. Future studies are encouraged to include broader datasets from diverse conferences and journals to ensure a more comprehensive understanding of GAI applications in higher education.
Journal Article
The Role of Necroptosis in Cardiovascular Disease
A newly discovered mechanism of cell death, programmed necrosis (necroptosis), combines features of both necrosis and apoptosis. Necroptosis is tightly modulated by a series of characteristic signaling pathways. Activating necroptosis by ligands of death receptors requires the kinase activity of receptor-interacting protein 1 (RIP1), which mediates the activation of receptor-interacting protein 3 (RIP3) and mixed lineage kinase domain-like (MLKL) two critical downstream mediators of necroptosis. Recently, different cytokines have been found participating in this mechanism of cell death. Necroptosis has been proposed as an important component to the pathophysiology of heart disease such as vascular atherosclerosis, ischemia-reperfusion injury, myocardial infarction and cardiac remodeling. Targeting necroptosis signaling pathways may provide therapeutic benefit in the treatment of cardiovascular diseases.
Journal Article