Search Results (2,197)

Based on complex system theory and multi-dimensional coupling analysis paradigm, this study constructs a dynamic model covering land use, real estate development, and carbon emissions, and deeply explores the internal mechanism and evolution law of land development system in the process of moving toward a low-carbon path. Firstly, through nonlinear dynamics and bifurcation analysis, this study identifies three typical transformation paths that the system may experience: gradual, transitional, and hybrid, emphasizing the nonlinear, phased, and highly context-dependent characteristics of the transformation process. On this basis, early warning indicators and robustness analysis methods are introduced, which provide operational tools for identifying critical turning points in the system and improving the effectiveness and resilience of regulatory strategies. Furthermore, this paper proposes a multi-level regulation mechanism design framework, which combines the immediate feedback with the historical cumulative effect to achieve the refined guidance of land development patterns and carbon emission paths. The results provide a scientific basis and practical enlightenment for land use optimization, green infrastructure construction, and industrial structure adjustment under the background of realizing the “3060” dual carbon goal and the reform of territorial spatial planning in China. In the future, it is necessary to strengthen the empirical calibration of parameters, data-driven optimization, and collaborative research of multiple policy tools to further improve the applicability and decision-making reference value of the model. Full article

This paper presents a low-noise CMOS transimpedance-limiting amplifier (CTLA) for application in LiDAR sensor systems. The proposed CTLA employs a dual-feedback architecture that combines the passive and active feedback mechanisms simultaneously, thereby enabling automatic limiting operations for input photocurrents exceeding 100 µA_pp (up to 1.06 mA_pp) without introducing signal distortions. This design methodology can eliminate the need for a power-hungry multi-stage limiting amplifier, hence significantly improving the power efficiency of LiDAR sensors. The practical implementation for this purpose is to insert a simple NMOS switch between the on-chip avalanche photodiode (APD) and the active feedback amplifier, which then can provide automatic on/off switching in response to variations of the input currents. In particular, the feedback resistor in the active feedback path should be carefully optimized to guarantee the circuit’s robustness and stability. To validate its practicality, the proposed CTLA chips were fabricated in a 180 nm CMOS process, demonstrating a transimpedance gain of 88.8 dBΩ, a −3 dB bandwidth of 629 MHz, a noise current spectral density of 2.31 pA/√Hz, an input dynamic range of 56.6 dB, and a power dissipation of 23.6 mW from a single 1.8 V supply. The chip core was realized within a compact area of 180 × 50 µm². The proposed CTLA shows a potential solution that is well-suited for power-efficient LiDAR sensor systems in real-world scenarios. Full article

Virtual Machine Introspection (VMI) is a powerful technology used to detect and analyze malicious software inside Virtual Machine (VMs) from outside. Asynchronously accessing the VM’s memory can be insufficient for efficiently monitoring what is happening inside of a VM. Active VMI introduces breakpoints to intercept VM execution at relevant points. Especially for frequently visited breakpoints, and even more so for production systems, it is crucial to keep their performance overhead as low as possible. In this paper, we provide a systematization of existing VMI breakpoint implementation variants, propose workloads to quantify the different performance penalties of breakpoints, and implement them in the benchmarking application bpbench. We used this benchmark to measure that, on an Intel Core i5 7300U, SmartVMI’s breakpoints take around 81 µs to handle, and keeping the breakpoint invisible costs an additional 21 µs per read access. The availability of bpbench facilitates the comparison of disparate breakpoint mechanisms and their performance optimization with immediate feedback. Full article

AI Assess, a ChatGPT-based assessment system utilizing the ChatGPT platform by OpenAI, composed of four components, is proposed herein. The components are tested on the GPT model to determine to what extent they can grade various exam questions based on learning outcomes, generate relevant practice problems to improve content retention, identify student knowledge gaps, and provide instantaneous feedback to students. The assessment system has been explored using software engineering and computer science courses and is successful through testing and evaluation. AI Assess has demonstrated the ability to generate practice problems based on syllabus information and learning outcomes. The components have been shown to identify weak areas for students. Finally, it has been shown to provide different levels of feedback. The combined set of components, if incorporated into a complete software system and implemented in classrooms with proposed transparency mechanisms, has vast potential to reduce instructor workload, improve student understanding, and enhance the learning experience. The potential for GPT-powered chatbots in educational assessments is vast and must be embraced by the education sector. Full article

Background: The organic sector is often suggested as a lever with a potential for contributing to the three dimensions of sustainability: social, environmental, and economic. This study aims to investigate selected organic initiatives and organic food sectors in different locations, such as capital cities, rural areas, and the bio-district in SysOrg project consortium, in the Warsaw municipality in Poland, North Hessia region in Germany, Cilento bio-district in Italy, Kenitra province in Morocco, and Copenhagen municipality in Denmark to uncover the diverse drivers, barriers, and entry points to enable a transformation process to resilient and sustainable organic food systems. Methods: Following the methodology of the SysOrg project, this study relied on the following mixed data collection methods: quantitative (a household survey distributed among citizens) and qualitative (semi-structured interviews with organized initiatives). Results: The results demonstrate that, despite being in different stages of development in the investigated territories, the organic sector is challenged by similar barriers (e.g., undeveloped market, regulatory/budgetary constraints, and lack of knowledge and awareness) and benefits from analogous drivers (e.g., awareness and education, community support, and incentives). Conclusions: Those similarities, but also analyses of their differences and origins, allowed us to establish critical entry points for the development of a sustainable organic food system, e.g., promoting organics through a top-down approach, providing training and education, reducing information delay, popularizing negative feedback, strengthening the effectiveness of a given incentives scheme by tailored nudging mechanisms, establishing country/regional specific traditional frames, making the system more inclusive, building organic communities, and awareness-building. Full article

The article is focused on the application of neural models for state-variables estimation. The estimators are applied in the control structure (with the state speed controller) of the electric drive with an elastic shaft. The extended amount of feedback is an additional argument for the estimation of the signal. The calculations are performed for three deep neural structures based on the Convolutional Neural Network (CNN) and the long short-term memory (LSTM). The design stages and the overall concept in this case are completely different than with the applications of classical observers (e.g., the Luenberger, the Kalman filter) often used for similar objects. The direct identification of the mechanical part of the drive is not necessary. The parameters and the equations describing the plant are not used. Instead, the signals are used for training the neural networks. The results (performed for the nominal values of the two-mass system and presenting the robustness of the estimators) present the high precision of the signal estimation. The second part of the work deals with the hardware implementation of the neural estimators in the low-cost programmable device with the ARM core. The experimental transients confirm the features of the neural estimators noticed in the simulations. Full article

Traditional methods of assessing handwritten characters are often too subjective, inefficient, and lagging in feedback, which makes it difficult for educators to achieve fully objective writing assessments and for writers to receive timely suggestions for improvement. In this paper, we propose a convolutional neural network (CNN) architecture that combines the attention mechanism with multi-scale feature fusion; specifically, the features are weighted by designing a bottleneck layer that combines the Squeeze-and-Excitation (SE) attention mechanism to highlight the important information and by applying a multi-scale feature fusion method to enable the network to capture both the global structure and the local details of Chinese characters. Finally, a high-quality dataset containing 26,800 images of handwritten Chinese characters is constructed based on the application scenario of the writing grade test, covering the common Chinese characters in the writing grade exam; The experimental results show that the proposed method achieves 98.6% accuracy on the writing grade exam dataset and 97.05% on the ICDAR-2013 public dataset, significantly improving recognition accuracy. The constructed dataset and improved model are suitable for application scenarios such as writing grade exams, which helps to improve marking efficiency and accuracy. Full article

Feedback in group decision-making (GDM) is an effective procedure for eliminating preference inconsistencies among experts. As the core of GDM, feedback controls the progress and cost of the process. However, the current feedback model seldom considers interference effects caused by the interaction among experts. In addition, the stubbornness of experts to change preferences through interaction is different. This study proposes a bidirectional feedback model that considers interference effects. The model integrating quantum probability theory (QPT) into a feedback mechanism has greater flexibility and is more conducive to revealing modern cognitive psychology. First, experts were classified into concordant and stubborn discordant groups according to their personality parameters. Bidirectional feedback was proposed for a stubborn discordant group to improve the efficiency of feedback process and reduce the consensus-reaching cost. QPT was then used to describe the probability of experts modifying their preferences during the game process. Combining the interference value determined by the quantum probability with the feedback mechanism, a bidirectional feedback model driven by a minimum feedback control parameter is proposed to ensure that a certain consensus level can be achieved with minimal adjustment. The proposed feedback mechanism considers interference effects produced by experts in the interaction and can capture the feelings of conflict and compromise. Full article

As the core component of chain-less ammunition transmission system, the large long lead cylinder adopts ZL205A alloy, which has the advantages of high strength and wear resistance. However, in its main casting production process, the forming quality is mainly determined by the casting process parameters under the premise of determining a reasonable casting system. Considering that the casting process parameters are the process feedback expression of the macroscopic forming quality and comprehensive mechanical properties by controlling the coupling effect of the metal liquid flow in the microscopic flow field, the directional solidification crystallization of the alloy and the solid–liquid heat transfer and heat transfer during the filling and solidification process, the accurate and reasonable selection of casting process parameters is conducive to the stable guarantee of pouring quality. On the basis of the optimized column gap casting system, this study combined numerical simulation and data statistics. Within the rationality of each casting process parameter constructed by single-factor analysis, the response surface method was used to construct a quantitative guidance relationship of each process parameter coupling mapping casting defect, and based on this model, the optimal process parameter combination was realized as follows: compared with traditional metal mold casting and unoptimized low pressure casting, the tensile strength of non-porous casting with holding pressure 14.68 kPa, casting temperature 717.152 °C and mold preheating temperature 256.12 °C increased by 6.6% and 4.1%, respectively, hardness increased by 14.3% and 8.4% respectively, and the elongation is increased by 16.9% and 10.6%, respectively, thus efficiently and accurately improving the process quality. Full article

Carbon dioxide (CO₂) sequestration has garnered widespread attention as a key strategy for mitigating CO₂ emissions and combating the greenhouse effect. However, the mechanisms underlying the interactions between CO₂, widespread siliceous minerals and biological processes remain unclear. The present study explored the potential impacts of different CO₂ concentrations on microbial activity, environmental conditions and their feedback on the fate of CO₂. A total of 20 experimental conditions was created, with the variables including different natural and synthetic siliceous minerals (e.g., quartz sand and a type of commercial glass beads), the presence or absence of the iron-reducing microorganism Orenia metallireducens (strain Z6) and varying CO₂ concentrations (0%, 20%, 50%, 100%) in the presence of ferrihydrite and pyruvate. Geochemical, microbial and mineralogical analyses revealed that elevated CO₂ concentrations significantly inhibited microbial Fe(III) reduction and pyruvate metabolism. Interestingly, compared to cultures without mineral amendments or those with glass beads alone, the addition of quartz sand enabled strain Z6 to better withstand the environmental stress caused by elevated CO₂, promoting pyruvate fermentation and iron reduction. In addition to an increased pH, the formation of siderite, hematite and vivianite was also observed in the bioactive systems. Although both glass beads and quartz sand were primarily composed of silica, differences in the mineral structure, elemental composition and acid neutralization capacity rendered quartz sand more chemically active and unexpectedly led to greater CO₂ sequestration. Full article

(1) Background: Mechanical thrombectomy (MT) successfully treats ischemic strokes by extracting the thrombus, or clot, using a stent retriever to pull it through the blood vessel. However, clot slippage and/or fragmentation can occur. Real-time feedback to a clinician about attachment between the stent and clot could enable more complete removal. We propose a system whereby antibody-targeted magnetic nanoparticles (NPs) are injected via a microcatheter to coat the clot, oscillating magnetic fields excite the particles, and a small coil attached to the catheter picks up a signal that determines the proximity of the clot to the stent. (2) Methods: We used existing simulation code to model the signal from NPs distributed on a hemispherical clot with three orthogonally applied magnetic fields. An in vitro apparatus was built that applied fields and read out signals from a 1.5 mm pickup coil at a variable distance and orientation angle from a sample of 100 nm iron oxide core/shell NPs. (3) Results: Our simulations suggest that the sum of the voltages induced in the pickup coil from three orthogonal applied fields could localize a clot to within 180 µm, regardless of the exact orientation of the pickup coil, with further precision added via rotation-correction formulae. Our experimental system validated simulations; we estimated an in vitro distance recovery precision of 41 µm with a pickup coil 1 mm from the clot. (4) Conclusions: Magnetic NP sensing could be a safe and real-time method to estimate whether a clot is attached to the stent retriever during MT. Full article

Background: Antiphospholipid syndrome (APS) is a rare systemic autoimmune disease characterized by persistent antiphospholipid antibodies (aPL) in combination with recurrent thrombosis in the veins and/or arteries, obstetric morbidity, and various non-thrombotic associated complications. APS can be primary, as an isolated condition, or secondary in the context of another autoimmune disease, especially systemic lupus erythematosus. This comprehensive clinical review aims to summarize the current understanding of APS pathogenesis, diagnostic approaches, and treatment strategies for this unique clinical entity. Methods: A comprehensive review of the existing literature on APS was conducted, focusing on pathophysiological mechanisms, current diagnostic criteria, and therapeutic approaches. Results: APS pathogenesis involves complex interactions between aPL, phospholipid-binding proteins, and the coagulation cascade. Apart from the cardinal features of thrombosis and APS-related obstetric morbidity, APS is associated with a wide spectrum of clinical manifestations. Diagnosis remains challenging due to overlapping symptoms with other conditions, and clinicians should maintain a high index of suspicion in order to set the diagnosis. The recently published 2023 ACR/EULAR criteria although not definitive for clinical decision-making, these criteria offer clinicians a valuable tool to aid in determining whether further investigation for APS is warranted. Continued refinement of these criteria through ongoing feedback and updates is anticipated. Treatment strategies center on anticoagulation, but individualized approaches are necessary. Conclusions: Early diagnosis and multidisciplinary management of APS are critical to reducing morbidity and improving outcomes. Moreover, familiarization with the 2023 ACR/EULAR criteria is encouraged, recognizing that ongoing feedback and updates will contribute to their ongoing refinement and improvement. While VKAs remain the mainstay of treatment for most APS patients further research is needed to optimize treatment strategies and deepen our understanding of APS’s underlying disease mechanisms. Full article

Migraines are a frequently occurring neurological condition that affects up to 16% of the global population. The precise pathomechanism of the disease remains unknown, but from animal and human observations, it appears that calcium/calmodulin-dependent protein kinase II alpha (CamKIIα), pituitary adenylate cyclase-activating polypeptide (PACAP), and vasoactive intestinal polypeptide (VIP) are involved in its pathogenesis. One of the animal models of migraines uses the systemic administration of nitroglycerin (NTG), which, as a nitric oxide (NO) donor, initiates a self-amplifying process in the trigeminal system, leading to central sensitization. Endocannabinoids, such as anandamide (AEA), are thought to play a modulatory role in trigeminal activation and sensitization phenomena. In the present experiment, we aimed to investigate the effect of NTG and AEA on CamKIIα, PACAP/VIP, and vasoactive intestinal polypeptide type 1 receptor (VPAC1) expression levels in the upper cervical spinal cord (C1-C2) of rats, where trigeminal nociceptive afferents are clustered. Four groups of animals were formed: in the first group, the rats received only the vehicle; in the second group, they were treated with an intraperitoneal injection of NTG (10 mg/kg); animals in the third and fourth groups received AEA (2 × 5 mg/kg) half an hour before and one hour after the placebo or treatment with NTG. Four hours after the placebo/NTG injection, the animals were transcardially perfused, and the cervical spinal cords were removed for Western blot. Our results show that both NTG and AEA alone can increase the expression of CamKIIα and VPAC1 in the C1-C2 segments. Interestingly, the combination of NTG and AEA had no such effect on these markers, possibly due to various negative feedback mechanisms. Full article

Investigating the spatiotemporal coupling and coordinated evolution of economic and ecological resilience in Africa provides theoretical support and scientific foundation for the continent’s green and high-quality development. From the perspective of evolutionary resilience, this study constructs an evaluation model for Africa’s economic resilience and ecological resilience. Using kernel density models, namely the “economic-ecological” resilience zoning method, the coupling coordination degree model, and the Haken model, this study explores the spatiotemporal alignment, coupling, and synergistic evolution of economic and ecological resilience in Africa in a step-by-step manner. The results show that (1) the overall level of economic resilience in Africa is relatively low, with increasing regional disparities. Spatially, economic resilience exhibits a distribution pattern of “low values widely spread, high values concentrated”; the level of ecological resilience, in contrast, shows a more pronounced dispersion, with a spatial distribution of “low values concentrated, high values dispersed”; (2) based on the “economic-ecological” resilience zoning method, most African countries and regions fall into the low economic resilience category, with weak economic resilience and prominent issues related to economic instability. The seven major high economic resilience zones largely overlap with the high economic resilience-high ecological resilience areas, demonstrating good spatiotemporal alignment between economic and ecological resilience; (3) in terms of the spatiotemporal coupling relationship between economic resilience and ecological resilience, most of Africa falls into the disordered category, with an increasingly obvious polarization trend in the coupling coordination degree; (4) from the perspective of the synergistic relationship between economic resilience and ecological resilience, ecological resilience dominates the symbiotic system formed by economic resilience and ecological resilience. The development of ecological resilience and economic resilience is mutually inhibitive, with prominent contradictions between the economy and the environment. Ecological and economic resilience have formed an internal mechanism of positive feedback in the synergistic system. The regional differences in the synergistic value have expanded, while the differences within regions have narrowed, indicating an emerging trend of spatial differentiation. Full article

Amyotrophic lateral sclerosis (ALS) has an interactive, multifactorial etiology that makes treatment success elusive. This study evaluates how regulatory dynamics impact disease progression and treatment. Computational models of wild-type (WT) and transgenic SOD1-G93A mouse physiology dynamics were built using the first-principles-based first-order feedback framework of dynamic meta-analysis with parameter optimization. Two in silico models were developed: a WT mouse model to simulate normal homeostasis and a SOD1-G93A ALS model to simulate ALS pathology dynamics and their response to in silico treatments. The model simulates functional molecular mechanisms for apoptosis, metal chelation, energetics, excitotoxicity, inflammation, oxidative stress, and proteomics using curated data from published SOD1-G93A mouse experiments. Temporal disease progression measures (rotarod, grip strength, body weight) were used for validation. Results illustrate that untreated SOD1-G93A ALS dynamics cannot maintain homeostasis due to a mathematical oscillating instability as determined by eigenvalue analysis. The onset and magnitude of homeostatic instability corresponded to disease onset and progression. Oscillations were associated with high feedback gain due to hypervigilant regulation. Multiple combination treatments stabilized the SOD1-G93A ALS mouse dynamics to near-normal WT homeostasis. However, treatment timing and effect size were critical to stabilization corresponding to therapeutic success. The dynamics-based approach redefines therapeutic strategies by emphasizing the restoration of homeostasis through precisely timed and stabilizing combination therapies, presenting a promising framework for application to other multifactorial neurodegenerative diseases. Full article