Input Intensity Research Articles

Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs.

The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UAD) is an alternative approach that aims to identify any anomaly as an outlier from a healthy training distribution. A prevalent strategy for UAD in brain MRI involves using generative models to learn the reconstruction of healthy brain anatomy for a given input image. As these models should fail to reconstruct unhealthy structures, the reconstruction errors indicate anomalies. However, a significant challenge is to balance the accurate reconstruction of healthy anatomy and the undesired replication of abnormal structures. While diffusion models have shown promising results with detailed and accurate reconstructions, they face challenges in preserving intensity characteristics, resulting in false positives. We propose conditioning the denoising process of diffusion models with additional information derived from a latent representation of the input image. We demonstrate that this conditioning allows for accurate and local adaptation to the general input intensity distribution while avoiding the replication of unhealthy structures. We compare the novel approach to different state-of-the-art methods and for different data sets. Our results show substantial improvements in the segmentation performance, with the Dice score improved by 11.9%, 20.0%, and 44.6%, for the BraTS, ATLAS and MSLUB data sets, respectively, while maintaining competitive performance on the WMH data set. Furthermore, our results indicate effective domain adaptation across different MRI acquisitions and simulated contrasts, an important attribute for general anomaly detection methods. The code for our work is available at https://github.com/FinnBehrendt/Conditioned-Diffusion-Models-UAD.

A novel DEA-Tobit-SD assessment framework and application of provincial-level carbon emission embracing regional heterogeneity

Formulating tailored emission reduction policies for each Chinese province is crucial due to regional differences in carbon emission evolution patterns. This paper proposes a novel and comprehensive research framework that integrates data envelopment analysis (DEA), Tobit regression, and system dynamics (SD) model to analyze the influence factors and evaluate provincial emission reduction policies while considering regional differences. The DEA method assesses each province's development resource allocation and carbon emission efficiency. Based on the DEA results, each provinces’ key emission influencing factors can be derived combining with Tobit regression and sensitivity analysis of SD. Policies are then selected based on these factors to gauge their effectiveness. SD method is used to simulate carbon emissions under different policy scenarios in the future. The analysis results present obvious differences in resource allocation and regional characteristics among provinces. Qinghai's emission reduction potential has been preliminarily explored as an example. Energy structure, industry structure, energy intensity, forest coverage, and R&D input intensity are its main influencing factors for carbon emission. The forest carbon sink plays a significant role. The emission reduction of the integrated scenario is not a linear sum of all other scenarios. To ensure the completion of the neutralization goal, further adjustments to the long-term policy and extra measures are needed.

Nonlinearities and timescales in neural models of temporal interference stimulation.

In temporal interference (TI) stimulation, neuronal cells react to two interfering sinusoidal electric fields with a slightly different frequency ( , in the range of about 1-4 kHz, in the range of about 1-100 Hz). It has been previously observed that for the same input intensity, the neurons do not react to a purely sinusoidal signal at or . This study seeks a better understanding of the largely unknown mechanisms underlying TI neuromodulation. To this end, single-compartment models are used to simulate computationally the response of neurons to the sinusoidal and TI waveform. This study compares five different neuron models: Hodgkin-Huxley (HH), Frankenhaeuser-Huxley (FH), along with leaky, exponential, and adaptive-exponential integrate-and-fire (IF). It was found that IF models do not entirely reflect the experimental behavior while the HH and FH model did qualitatively replicate the observed neural responses. Changing the time constants and steady state values of the ion gates in the FH model alters the response to both the sinusoidal and TI signal, possibly reducing the firing threshold of the sinusoidal input below that of the TI input. The results show that in the modified (simplified) model, TI stimulation is not qualitatively impacted by nonlinearities in the current-voltage relation. In contrast, ion channels have a significant impact on the neuronal response. This paper offers insights into neuronal biophysics and computational models of TI stimulation.

Visual Field Asymmetries in Responses to ON and OFF Pathway Biasing Stimuli.

Recent reports suggest the ON and OFF pathways are differentially susceptible to selective vision loss in glaucoma. Thus, perimetric assessment of ON- and OFF-pathway function may serve as a useful diagnostic. However, this necessitates a developed understanding of normal ON/OFF pathway function around the visual field and as a function of input intensity. Here, using electroencephalography, we measured ON- and OFF-pathway biased contrast response functions in the upper and lower visual fields. Using the steady-state visually evoked potential paradigm, we flickered achromatic luminance probes according to a saw-tooth waveform, the fast phase of which biased responses towards the ON or OFF pathways. Neural responses from the upper and lower visual fields were simultaneously measured using frequency tagging - probes in the upper visual field modulated at 3.75Hz, while those in the lower visual field modulated at 3Hz. We find that responses to OFF/decrements are larger than ON/increments, especially in the lower visual field. In the lower visual field, both ON and OFF responses were well described by a sigmoidal non-linearity. In the upper visual field, the ON pathway function was very similar to that of the lower, but the OFF pathway function showed reduced saturation and more cross-subject variability. Overall, this demonstrates that the relationship between the ON and OFF pathways depends on the visual field location and contrast level, potentially reflective of natural scene statistics.

Uncertainty in determining carbon dioxide removal potential of biochar

Abstract A quantitative and systematic assessment of uncertainty in life-cycle assessment is critical to informing sustainable development of carbon dioxide removal (CDR) technologies. Biochar is the most commonly sold form of CDR to date, and it can be used in applications ranging from concrete to agricultural soil amendments. Previous analyses of biochar rely on modeled or estimated life-cycle data and suggest a cradle-to-gate range of 0.20–1.3 kg CO2 net removal per kg of biomass feedstock, driven by differences in energy consumption, pyrolysis temperature, and feedstock sourcing. Herein, we quantify the distribution of CDR possible for biochar production with a compositional life-cycle inventory model paired with scenario-aware Monte Carlo simulation in a “best practice” (incorporating lower transportation distances, high pyrolysis temperatures, high energy efficiency, recapture of energy for drying and pyrolysis energy requirements, and co-generation of heat and electricity) and “poor practice” (higher transportation distances, lower pyrolysis temperatures, low energy efficiency, natural gas for energy requirements, and no energy recovery) scenarios. In the best-practice scenario, cradle-to-gate CDR (which is representative of the upper limit of removal across the entire life cycle) is highly certain, with a median removal of 1.4 kg of CO2e / kg biomass and results in net removal across the entire distribution. In contrast, the poor-practice scenario results in median net emissions of 0.090 kg CO2e / kg biomass. Whether this scenario emits (66% likelihood) or removes (34% likelihood) carbon dioxide is highly uncertain. The emission intensity of energy inputs to the pyrolysis process and whether the bio-oil co-product is used as a chemical feedstock or combusted are critical factors impacting the net carbon dioxide emissions of biochar production, together responsible for 98% of the difference between the best- and poor-practice scenarios.

Measuring Total Factor Productivity in General Technical Progress Framework

The classical Solow's total factor productivity accounting assumes that technical progress is Hicks neutral, which is a special situation in the reality of world economy. This paper expands the setting of technical progress into general technical progress framework, which can cover Hicks neutral technical progress, Harrod neutral technical progress, Solow-neutral technical progress, and various factor-biased technical changes. According to the principle of statistical index number, this paper decomposes the output index into a total factor input index and a total factor productivity index, and adopts normalized CES production function with factor-augmenting technical progress to derive the calculation formulas of the total factor input index and the total factor productivity index, and constructs a new economic growth accounting system, and finds the counteraction and compensation mechanism for diminishing marginal returns. If the factor substitution elasticity is 1 or there is no technical progress bias and factor allocation bias, then the new accounting equation degenerates into the classic Solow growth accounting equation. The new accounting system can measure the influence of total factor input and total factor productivity to economic growth, but also can measure the influences of factor input intensity and factor allocation bias in the growth rate of total factor input, and the influences of technical progress intensity and technical progress bias in the growth rate of total factor productivity. Therefore it is more precise and accurate than classical method.

Three-Dimensional Numerical Analysis of Seismic Response of Steel Frame–Core Wall Structure with Basement Considering Soil–Structure Interaction Effects

In recent years, numerous studies highlighted the crucial role of the soil–structure interaction (SSI) in the seismic performance of basement structures. However, there remains a limited understanding of how this interaction affects buildings with basement structures under varying site conditions. Based on the three-dimensional (3D) numerical analysis method, the influence of the SSI on the seismic response of high-rise steel frame–core wall (SFCW) structures situated on shallow-box foundations were investigated in this study. To further investigate the effects of the SSI and site conditions, three types of soil profiles—soft, medium, and hard—were considered, along with a fixed-foundation model. The results were compared in terms of the maximum lateral displacement, inter-story drift ratio (IDR), acceleration amplification coefficient, and tensile damage for the SFCW structure under different site conditions, with both fixed-base and shallow-box foundation configurations. The findings highlight that the site conditions significantly affected the seismic performance of the SFCW structure, particularly in the soft soil, which increased the lateral deflection and inter-story drift. Moreover, compared with non-pulse-like ground motion, pulse-like ground motion resulted in a higher acceleration amplification coefficient and greater structural response in the SFCW structure. The RC core wall–basement slab junction was a critical region of stress concentration that exhibited a high sensitivity to the site conditions. Additionally, the maximum IDRs showed a more significant variation at incidence angles between 20 and 30 degrees, with a more pronounced effect at a seismic input intensity of 0.3 g than at 0.2 g.

Prediction and Evaluation of Industrial Land Intensive Use: Adding Policy Impact to System Dynamics Model

Nowadays, urban development prioritizes improving quality over mere quantitative expansion, which requires a shift in land use from previous extensive use patterns to a more refined and efficient intensive use pattern. The intensive use of industrial land (IUIL) is a resource-utilization strategy for industrial land that aims at maximizing the dual growth of ecological and economic benefits by minimizing resource consumption and factor costs. In this research, IUIL is deconstructed into four aspects: input intensity, utilization intensity, economic benefits, and environmental benefits. Grounded in a qualitative analysis of the interconnected casual feedback among the various factors influencing IUIL, a system dynamics model with 36 indicators for IUIL evaluation and prediction is constructed. Policy impacts on IUIL from the supply side, utilization side, and supervision side are analyzed and embedded for a scenario simulation based on the theory of the entire life cycle of land use. The simulation shows its efficiency in reporting the variation tendency of industrial land use with varied policy impact strength. The results reveal a dynamic understanding of the evolving patterns of land intensification and present different characteristics of IUIL. This study further proposes policy recommendations to provide experience and references for the authorities to promote high-quality urbanization development in practice.

Metacognitive Involvement and Critical Thinking in Technical Students in Information Environment

Metacognitive involvement in activities and critical thinking are the soft skills students require as a basic adaptation resource in information environment at university, in life, and at work. This research explored the correlation between metacognitive involvement and critical thinking which university students of technical specialties used to navigate information environment. The study involved 58 male and 12 female fourth-year students (21.26 ± 0.47 y.o.) of the Penza State University, Penza, Russia. The list of methods employed included scientific review, survey, testing, and Spearman’s correlation analysis. The survey involved the following questionnaires: Metacognitive Involvement in Activity (G. Schraw, R. Dennison, 1994; eight-factor version by E. I. Perikova, V. M. Byzova, 2022); Critical Thinking Test (J. Kincher, 1990); author’s own questionnaire of Students and Information Environment (N. N. Krylova, 2024). The correlation analysis revealed strong relationships between nine indicators of metacognitive involvement in activities and critical thinking (p ≤ 0.001 and p ≤ 0.01). The Structure of Error Correction factor of metacognitive involvement depended on such a parameter of information environment as the input intensity. The metacognitive involvement in activities correlated with the students’ assessment of mental and information load in the curriculum, e.g., the Metacognitive Knowledge factor correlated with the mental and information load in Physics and Mathematics while the Error Correction Structure factor was associated with the load assessment in technical disciplines. In addition, some individual parameters of information environment correlated with the students’ assessment of mental and information load. In this study, metacognitive involvement in activities correlated with critical thinking in university students as they interacted with information environment because these phenomena are not isolated psychologically from each other.

Morphosedimentary evolution of the Belgica Mound Drift: Controls on contourite depositional system development in association with cold-water coral mounds

Small-scale contourite drift is an important component of continental margins that can record information about complex oceanographic processes. The Belgica Mound Drift is one example of a small-scale contourite drift. It is formed under the influence of cold-water coral (CWC) mounds and represents one of the most distal contouritic expressions influenced by the Mediterranean Outflow Water (MOW) in the NE Atlantic Ocean. Three distinct evolutionary stages have been identified from new high-resolution pseudo-3D reflection seismic data, each associated with a significant change in paleoceanography, affecting both bottom-current intensity and sediment input. The pre-drift stage (Pliocene–Early Pleistocene) corresponds to the regional RD1 erosive event, which was caused by the reintroduction of the MOW in the Porcupine Seabight, creating a distinct paleotopography that will influence all ensuing sedimentary processes. The second stage (Early Pleistocene–Middle Pleistocene) is the contourite drift inception in two distinct centres of growth, strongly steered by topographic obstacles such as the CWC mounds. During the third and final stage (Middle Pleistocene–present day), the contourite drift is developed under a more stable but less dynamic environment, characterised by more continuous and mounded aggradational stratification. The final stage of the contourite drift is related to the Middle Pleistocene Transition, with a spatially variable reduction in the MOW-related bottom currents and sediment input. The spatial and temporal evolution of this drift shows that its present-day morphology is controlled by the location of initial growth. Evolving moat morphology indicates that the intensity of the bottom currents generally increases during the drift evolution.This research presents a crucial paradigm for advancing our knowledge of elucidating the complexities of smaller-sized contourite systems in diverse oceanic environments.

A 6-mode pre-amplifier for turbulence-resistant free-space optical communication

A few-mode pre-amplifier with high gain and high coupling efficiency was suitable for the turbulence-resistant free-space optical (FSO) communication. Here, we first built and experimentally demonstrated a forward- and core-pumped 6-mode (6 M) pre-amplifier. By optimizing the 6 M erbium-doped fiber (EDF) length, we achieved over 28 dB of gain and a noise figure of less than 7 dB with an input intensity of −30 dBm. Moreover, we experimentally evaluated the 6 M pre-amplifier of 2.5 Gbps On-Off Keying (OOK) signal transmission with a homemade tunable turbulence emulator. The advantages of the 6 M pre-amplifier over the single-mode (SM) pre-amplifier varied with channel turbulence conditions in terms of communication performances. Compared with the SM pre-amplifier, the coupling efficiencies increased by 4.5 dB and 5.8 dB on average, and the median bit error rates (BERs) of the communication system improved by 2 and 5 orders of magnitude, respectively, in the presence of channels with the scintillation index σI2 of 0.3978 and 1.0553 (moderate to strong turbulence).

Energy‐Water Asynchrony Principally Determines Water Available for Runoff From Snowmelt in Continental Montane Forests

ABSTRACTChanges in the volume, rate, and timing of the snowmelt water pulse have profound implications for seasonal soil moisture, evapotranspiration (ET), groundwater recharge, and downstream water availability, especially in the context of climate change. Here, we present an empirical analysis of water available for runoff using five eddy covariance towers located in continental montane forests across a regional gradient of snow depth, precipitation seasonality, and aridity. We specifically investigated how energy‐water asynchrony (i.e., snowmelt timing relative to atmospheric demand), surface water input intensity (rain and snowmelt), and observed winter ET (winter AET) impact multiple water balance metrics that determine water available for runoff (WAfR). Overall, we found that WAfR had the strongest relationship with energy‐water asynchrony (adjusted r2 = 0.52) and that winter AET was correlated to total water year evapotranspiration but not to other water balance metrics. Stepwise regression analysis demonstrated that none of the tested mechanisms were strongly related to the Budyko‐type runoff anomaly (highest adjusted r2 = 0.21). We, therefore, conclude that WAfR from continental montane forests is most sensitive to the degree of energy‐water asynchrony that occurs. The results of this empirical study identify the physical mechanisms driving variability of WAfR in continental montane forests and are thus broadly relevant to the hydrologic management and modelling communities.

Enhancing Global Simulation of Smoke Injection Height for Intense Pyro‐Convection Through Coupling an Improved One‐Dimensional Plume Rise Model in CAM‐chem

AbstractThe impact of wildfire smoke is largely determined by the height where they are injected into the atmosphere. Current plume rise models tend to underestimate the high smoke injection heights because the previous models and configurations were mainly constrained and validated by the plume height observation from Multi‐angle Imaging SpectroRadiometer (MISR), of which most cases inject low within the planetary boundary layer (PBL). Here we retrieve smoke injection heights from intense pyro‐convections based on pyrocumulonimbus satellite images in PYROCAST data set alongside meteorological reanalysis. It largely augments the MISR data set with smoke injection heights up to the upper troposphere and lower stratosphere (UTLS). Constrained by both MISR and PYROCAST, we show that a scaling down of factor 0.2 to the entrainment efficiency parameterized in the one‐dimensional plume‐rise model (1‐D PRM, Freitas et al. (2010, https://doi.org/10.5194/acp‐10‐585‐2010)) significantly improves the model performance for high injection cases without compromising the accuracy of low injection cases. We also found that the fire intensity input can be obtained through a simplified dependence on the biome and biomass burning emission flux. While being unable to represent high cases before, the improved 1‐D PRM model predicts similarly well in injection heights both low near the PBL height and high into the UTLS. The improved 1‐D PRM is then coupled into Community Atmosphere Model with Chemistry (CAM‐chem). The coupled CAM‐chem‐PRM, when predicting injection heights in tests imitating real BB emission, exhibited consistent predictive capabilities with the standalone 1‐D PRM while saw a mere 15% increase of computation time.

Research on the Impact of Agricultural Production Outsourcing on Farmers’ Fertilizer Application Intensity: An Inverse U-Shaped Relationship

Agricultural production outsourcing services encourage a shift in the way crops are grown in developing countries and make it easier for small farmers to join the social division of labor in agriculture. This makes production more efficient and has a big effect on the inputs used in agriculture, especially fertilizer. This paper empirically tests the impact of production outsourcing on farmers’ fertilizer intensity using the instrumental variables method with non-planar panel data from the 2020–2022 China Land Economy Survey (CLES) of farm plots. The results showed that there was a significant inverted U-shaped relationship between the degree of agricultural production outsourcing and the intensity of fertilization on farmers’ plots. Mechanistic analysis shows that agricultural production outsourcing affects the fertilizer intensity by changing the labor allocation of farmers. Especially as the degree of agricultural production outsourcing increases, the intensity of farm labor inputs by farmers gradually decreases, and the impact of fertilizer intensity on the plots showed a tendency to be promoted first and then suppressed. The moderating effect showed that plot size was a major moderating factor. This means that the bigger the plot, the flatter the inverted U-shaped curve became, and the same level of outsourcing could lead to less fertilizer application. This happened by moving the inflection point of the inverted U-shaped curve to the left, which stopped the fertilizer application at a lower level of outsourcing. Heterogeneity analysis showed that participation in technology-intensive production outsourcing was beneficial in terms of reducing fertilizer intensity, and that an increased degree of agriculture production outsourcing was beneficial for farmers with large plot sizes and younger heads of household.

The Impact of Introducing Generic Therapy Assistants to an Acute Occupational Therapy Stroke Service

Abstract Background It has been recommended that patients post-Stroke should receive 45 minutes of Occupational Therapy (OT) input on at least five days per week to evoke meaningful and significant improvements in patient-centred outcomes. However, service capacity cannot always meet recommended levels. Generic Therapy Assistants (GTAs) offer opportunities to bolster staffing and increase intensity and quality of OT input. This pilot aims to investigate the impact of the recruitment of two GTAs upon OT services in an acute Stroke unit. Methods Two GTAs were recruited to the service and underwent tailored training, followed by in-service training and supervision support. Data was collected prior to their recruitment for a four-week period (Timepoint 1) and again following their appointment (Timepoint 2). Duration of OT sessions were categorised as High (>36 minutes), Moderate (23-36 minutes) and Low Intensity (<23 Minutes). Key metrics were the number of sessions that took place, the proportion of sessions deemed low, moderate and high intensity and a functional measure of included patients using the Functional Independence Measure (FIM). Results Total sessions increased from 113 to 184 from Timepoint 1 to 2, and sessions deprioritized reduced from 116 to 71, with greater contact time per day during Timepoint 2. The proportion of sessions described as High Intensity increased from 45% to 63%. Twelve patients from each cohort had functional changes measured, with scores recorded on initial assessment and on discharge. Average increases of 3.75 points were observed during Timepoint 1, and 8 points at Timepoint 2. Conclusion The addition of two GTAs to the Stroke service in this acute hospital facilitated positive changes to the OT service capacity and delivery. Increased numbers of sessions and session duration were observed, alongside a greater proportion of sessions being of High Intensity, and greater functional improvements when compared to similar data collection prior to their recruitment.

Unveiling energy transition strategy: A deep dive into China's ambitious renewable energy policy and its impact on carbon emission dynamics

Over the past 70 years, China has issued approximately 20,000 renewable energy (RE) policies. How to interpret China's ambitious RE strategy through these policy documents and assess its effectiveness has been a question of interest. Therefore, based on 15,603 Chinese RE policy documents from 1975 to 2022, this study employs text mining, Latent Dirichlet Allocation (LDA), Policy Modeling Consistency-Text Encoder (PMC-TE), and System-Generalized Method of Moments (SYS-GMM). It assesses the policy input intensity (PII) for different energy types, regions, and time periods, while also investigating the impact of PII about RE on carbon emissions. The study findings are as follows: (1)The PII is not uniform, with hydropower having the highest PII at 80,591, followed by solar (25,839) and biomass energy (18,903). The PII of wind (11,377) and geothermal energy (11,340) is similar, while hydrogen energy exhibits the weakest PII at 3488. (2)There are regional disparities in PII, with solar and wind energy being most prominent in northern regions, solar and hydropower being most significant in eastern regions, and wind and biomass energy aligning with local resources in southern regions. (3)RE policies have driven China's energy transition through four distinct phases. (4)Policy effectiveness varies, with solar, wind, and hydropower policies having the most significant impact on carbon emissions reduction, followed by biomass and geothermal energy. (5)China's government role in the RE strategy has shifted from a dominant role to a regulatory one.

A simple, robust, cost-effective, and low-input ChIP-seq method for profiling histone modifications and Pol II in plants.

Chromatin immunoprecipitation and sequencing (vs ChIP-seq) is an essential tool for epigenetic and molecular genetic studies. Although being routinely used, ChIP-seq is expensive, requires grams of plant materials, and is challenging for samples that enrich fatty acids such as seeds. Here, we developed an Ultrasensitive Plant ChIP-seq (UP-ChIP) method based on native ChIP-seq combined with Tn5 tagmentation-based library construction strategy. UP-ChIP is generally applicable for profiling both histone modification and Pol II in a wide range of plant samples, such as a single Arabidopsis seedling, a few Arabidopsis seeds, and sorted nuclei. Compared with conventional ChIP-seq, UP-ChIP is much less labor intensive and only consumes 1 μg of antibody and 10 μl of Protein-A/G conjugated beads for each IP and can work effectively with the amount of starting material down to a few milligrams. By performing UP-ChIP in various conditions and genotypes, we showed that UP-ChIP is highly reliable, sensitive, and quantitative for studying histone modifications. Detailed UP-ChIP protocol is provided. We recommend UP-ChIP as an alternative to traditional ChIP-seq for profiling histone modifications and Pol II, offering the advantages of reduced labor intensity, decreased costs, and low-sample input.

A Study of Degenerate Four‐Wave Mixing and Phase Conjugation in Metallic Nanohybrids

AbstractA theory of degenerate four‐wave mixing (DFWM) and phase conjugation is developed for metallic nanohybrids, which consist of an ensemble of interacting metallic nanoshells and noninteracting quantum dots (QDs). It is considered that three incident waves are applied to the metallic nanohybrid, and they produce a fourth output mixed wave. These waves induce dipoles in metallic nanoshells, generating surface plasmon polaritons, and interact with each other via the dipole–dipole interactions (DDI). In DFWM, the input and output waves travel in opposite directions, and this retroreflective nature is responsible for the phenomenon of phase conjugation. The analytical expressions for the input transmitted and output reflected wave intensities are calculated in the presence of the surface plasmon polaritons (SPPs) and the DDI polaritons. It is demonstrated that the phase conjugation, the phase coherent phenomena, and the intensities of both input transmitted and output reflected waves are enhanced due to SPPs and DDI polaritons. These findings indicate that the nanohybrid acts as a phase conjugate device and a phase coherent optical amplifier, which can be applied to fabricate optical nanoamplifiers, phase conjugate mirrors, and nanosensors by measuring the intensity of the output reflected wave.

Impacts and threshold effects of urban–rural integration on the transition of arable land use functions

This study examines the impact of urban–rural integration (URI) on the transition of arable land use functions (TALUF), providing both theoretical and empirical insights into how URI can promote TALUF. Utilizing panel data from 41 cities in the Yangtze River Delta (YRD) from 2003 to 2022, this research employs the entropy method, Durbin model, and spatial threshold model to assess the levels of URI and TALUF in the region and examine the direct impact, spatial spillover effects, and threshold effects of URI on TALUF. The findings are: (1) During the study period, both TALUF and URI levels in the YRD showed continuous improvement, although significant spatial imbalances persisted, with regional differentiation gradually diminishing. (2) URI exerts a significant inhibitory effect on TALUF and produces a negative spatial spillover effect. (3) URI exerts a single threshold effect on TALUF: below the economic development threshold of 0.733, URI and its spatial term inhibit TALUF, whereas above this threshold, they promote it. Consequently, fostering high-quality regional economic development, enhancing intercity cooperation, driving agricultural technological innovation, and increasing agricultural input intensity are essential strategies for arable land use policies in the context of URI.

Power-law localization in one-dimensional systems with nonlinear disorder under fixed input conditions

We conduct a numerical investigation into wave propagation and localization in one-dimensional lattices subject to nonlinear disorder, focusing on cases with fixed input conditions. Utilizing a discrete nonlinear Schrödinger equation with Kerr-type nonlinearity and a random coefficient, we compute the averages and variances of the transmittance, T, and its logarithm, as functions of the system size L, while maintaining constant intensity for the incident wave. In cases of purely nonlinear disorder, we observe power-law localization characterized by 〈T〉∝L−γa and 〈lnT〉≈−γglnL for sufficiently large L. At low input intensities, a transition from exponential to power-law decay in 〈T〉 occurs as L increases. The exponents γa and γg are nearly identical, converging to approximately 0.5 as the strength of the nonlinear disorder, β, increases. Additionally, the variance of T decays according to a power law with an exponent close to 1, and the variance of lnT approaches a small constant as L increases. These findings are consistent with an underlying log-normal distribution of T and suggest that wave propagation behavior becomes nearly deterministic as the system size increases. When both linear and nonlinear disorders are present, we observe a transition from power-law to exponential decay in transmittance with increasing L when the strength of linear disorder, V, is less than β. As V increases, the region exhibiting power-law localization diminishes and eventually disappears when V exceeds β, leading to standard Anderson localization.