Local Effects Research Articles

Home work: Exploring the labor market effects of subsidizing domestic services

This paper analyzes subsidies for the domestic services sector, an increasingly popular policy to create employment opportunities for low-skilled workers. Using Belgian administrative data, a differences-in-differences approach, and a shift-share instrumental variable, we estimate the local effects of the policy in targeted industries as well as overall effects on the labor market. We find that domestic service subsidies can increase female employment in the subsidized industries as well as the overall employment rate. This increase in employment is primarily driven by an increase in (formal) labor market participation and, to a lesser extent, a reduction in the rate of participation in unemployment insurance and in other social welfare programs. We also find that these subsidies can lead to an increase in the rate of work incapacity, likely due to the fact they broaden the population that can access the social safety net.

Impact analysis of digital trade on carbon emissions from the perspectives of supply and demand

Amidst the escalating challenge of global climate change, it is imperative to further explore whether digital trade, as an emerging element in the global development landscape, can reduce carbon emissions and achieve sustainable development. This study draws upon panel data encompassing 30 provinces and municipalities in China spanning the years 2013 to 2021. By establishing an index system to gauge regional digital trade development levels, the article examines the impact mechanism and spillover effects of digital trade on carbon reduction from both the supply (enterprises) and demand (residents) perspectives. The research results show that: (1) Digital trade can effectively promote regional carbon reduction, with a more pronounced effect in China's central and western regions and lower carbon emissions regions. (2) Digital trade can incentivize green innovation by enterprises and improve residents' consumption behavior, thereby reducing carbon emissions. (3) Digital trade has spillover effect on carbon emissions, and this “neighborhood effect” is greater than the “local effect”. Digital trade provides strong support for carbon reduction and sustainable development and also provides a strategic direction for government policy formulation.

Topological Learning Approach to Characterizing Biological Membranes.

Biological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order phase behavior and phenomena. Here, we present a persistent homology (PH)-based method for quantifying the structural features of individual and bulk lipids, providing local and contextual information on lipid tail organization. Our method leverages the mathematical machinery of algebraic topology and machine learning to infer temperature-dependent structural information on lipids from static coordinates. To train our model, we generated multiple molecular dynamics trajectories of dipalmitoyl-phosphatidylcholine membranes at varying temperatures. A fingerprint was then constructed for each set of lipid coordinates by PH filtration, in which interaction spheres were grown around the lipid atoms while tracking their intersections. The sphere filtration formed a simplicial complex that captures enduring key topological features of the configuration landscape using homology, yielding persistence data. Following fingerprint extraction for physiologically relevant temperatures, the persistence data were used to train an attention-based neural network for assignment of effective temperature values to selected membrane regions. Our persistence homology-based method captures the local structural effects, via effective temperature, of lipids adjacent to other membrane constituents, e.g., sterols and proteins. This topological learning approach can predict lipid effective temperatures from static coordinates across multiple spatial resolutions. The tool, called MembTDA, can be accessed at https://github.com/hyunp2/Memb-TDA.

Numerical simulation of local and upwind temperature stratification on flow and dispersion in a bi-dimensional street canyon.

The thermal effect mainly includes boundary temperature stratification and the local thermal effect. The combined effect of these factors on flow and dispersion in a bi-dimensional canyon was investigated by the RANS and LES methods to evaluate their performance. The results, including the flow field, turbulent kinetic energy, temperature, heat flux, pollutant concentration and fluxes, were compared with the data from wind tunnel experiments. The comparison results showed that the RANS method severely overestimated the impact of windward heating on the flow in the canyon because of the lack of simulated flow separation ability and the limitation of the Boussinesq model, leading to an incorrect flow field and an incorrect temperature and concentration. In contrast, LES performed better mainly because of its ability to simulate flow separation. LES regenerated the right vortexes, flow field and low wind velocity. LES slightly overestimates the overall temperature in the canyon because heat exchange is eliminated in LES but difficult to avoid in the experiment. The difference in the air exchange rate at the roof level between the LES and wind tunnel data was no more than 5%, and the pollutant concentration distribution of the LES was almost the same as that of the experiments. This work emphasizes that the RANS method has limited ability to simulate flow and dispersion when the thermal effect is considered even at a reduced-scale, while LES can simulate the combined effects of incoming flow temperature stratification and local thermal effects. It is therefore suggested that if computing resources are limited and the temperature difference is not large, a steady-state calculation RANS can be used. Otherwise, LES must be performed.

Numerical simulation of Darcy–Forchheimer flow of Casson ternary hybrid nanofluid with melting phenomena and local thermal non-equilibrium effects

The Casson fluid flow over a stretching sheet is studied in this work in the presence of porous media with melting heat transfer and chemical reaction, combining nanoparticles of Ti4Al6V,AA7072 and AA7075 with a base fluid of sodium alginate (NaAlg). The porous medium Darcy Forchheimer effect is included in the momentum equation. Through the effects of melting, the process of heat transfer is described. In order to explore the features of heat transmission in the absenteeism of LTECs (local thermal equilibrium conditions), the current study employs a mathematical model that has been simplified. For both the solid and liquid phases, the LTNE model yields two different fundamental thermal gradients. This proposed model compares the YOM (Yamada-Ota model) and XM (Xue model), two well-known trihybrid nanofluid models, in terms of performance. After applying the proper transformations, modulated non-linear PDEs (partial differential equations) are simplified in ODEs (ordinary differential equations) and the bvp4c method is used to solve them numerically. A graphic discussion of the relevant factors' significance on the pertinent fields has been presented. It is observed that the Casson ternary hybrid nanofluid temperature and velocity distributions predominate at higher melting parameter. The solid phase's heat transport rate rises with an upsurge in the interphase heat transfer parameter.

A High-Precision Earthquake Catalog for Nevada

Abstract The state of Nevada is home to one of the most seismically active regions in the world, with crustal deformation associated with the Walker Lane transitioning into Basin and Range tectonics as one traverses from west to east across the state. Despite hosting numerous prominent earthquake sequences over the past century and beyond, at present, there exists no unified research-quality earthquake catalog for the state and its surrounding region. Here, we present a newly compiled, high-precision catalog of more than 180,000 earthquakes occurring around Nevada from 2008 to 2023. The data processing workflow to create this catalog includes an absolute location step that accounts for topography and 3D variations in subsurface wavespeed, and a relative relocation step that refines event positions using differential times measured from waveform cross-correlation. We also provide an update to the local magnitude scale that better accounts for the observed distance attenuation of waveform amplitudes as well as local site effects. We describe some fundamental insights that can be derived from the new catalog, including regional variations in event depth distributions and sequence clustering statistics, and publish the catalog to the wider community to facilitate future research efforts.

Weak localization in radiative transfer of acoustic waves in a randomly-fluctuating slab

This paper concerns the derivation of radiative transfer equations for acoustic waves propagating in a randomly fluctuating slab (between two parallel planes) in the weak-scattering regime, and the study of boundary effects through an asymptotic analysis of the Wigner transform of the wave solution. These radiative transfer equations allow to model the transport of wave energy density, taking into account the scattering by random heterogeneities. The approach builds on the method of images, where the slab is extended to a full-space, with a periodic map of mechanical properties and a series of sources located along a periodic pattern. Two types of local effects, both on the (small) scale of the wavelength, are observed: one within one wavelength of the boundaries of the slab, and one inside the domain (at a distance from the boundaries large compared to the wavelength). The former impacts the entire energy density (coherent as well as incoherent) and is also observed in half-spaces. The latter, more specific to slabs, corresponds to the constructive interference of waves that have reflected at least twice on the boundaries of the slab and only impacts the coherent part of the energy density.

Impact of far-side structures observed by Solar Orbiter on coronal and heliospheric wind simulations

Context. Solar Orbiter is a new space observatory that provides unique capabilities to understand the heliosphere. In particular, it has made several observations of the far-side of the Sun and therefore provides unique information that can greatly improve space weather monitoring. Aims. In this study, we aim to quantify how the far-side data will affect simulations of the corona and the interplanetary medium, especially in the context of space weather forecasting. Methods. To do so, we focused on a time period with a single sunspot emerging on the far-side in February 2021. We used two different input magnetic maps for our models: one that includes the far-side active region and one that does not. We used three different coronal models typical of space weather modeling: a semi-empirical model (potential field source surface or PFSS) and two different magnetohydrodynamic models (Wind Predict and Wind Predict-AW). We compared all the models with both remote sensing and in situ observations in order to quantify the impact of the far-side active region on each solution. Results. We find that the inclusion of the far-side active region in the various models has a small local impact due to the limited amount of flux of the sunspot (at most 8% of the total map flux), which leads, for example, to coronal hole changes of around 7% for all models. Interestingly, there is a more global impact on the magnetic structure seen in the current sheet, with clear changes, for example, in the coronal hole boundaries visible in extreme ultra-violet (EUV) on the western limb, which is opposite to the active region and the limb most likely to be connected to Earth. For the Wind Predict-AW model, we demonstrate that the inclusion of the far-side data improves both the structure of the streamers and the connectivity to the spacecraft. Conclusions. In conclusion, the inclusion of a single far-side active region may have a small local effect with respect to the total magnetic flux, but it has global effects on the magnetic structure, and thus it must be taken into account to accurately describe the Sun-Earth connection. The flattening of the heliospheric current sheet for all models reveals that it causes an increase of the source surface height, which in return affects the open and closed magnetic field line distributions.

Construction of robust silica-titania polydopamine composite membrane with light-absorption property for photothermal vacuum membrane distillation

A robust silica-titania polydopamine composite membrane was fabricated adding a photothermal polydopamine coating to the surface of polypropylene membrane for photothermal vacuum membrane distillation. Detailly, the surface of an inert PP membrane was modified with atomically controllable titanium and silicon to enhance the hydroxyl groups on the membrane surface, then, a photothermal effect layer of polydopamine was generated on the composite membrane surface through the self-polymerization reaction of dopamine. The polydopamine coating has significant light absorption and heating properties, and it can act as a local heater when exposed to light sources. This local heating effect significantly raises the temperature at the membrane interface, creating a thermal gradient that drives the evaporation of water molecules. Specifically, we quantitatively analyzed the three reasons (surface energy, free radicals, and photothermal effect) for the increase in evaporation rate of the pristion PP membrane caused by the membrane composite membrane. Among these reasons, the photothermal effect caused by black polydopamine is the most important factor for improving evaporation efficiency. The dark-illumination alternating experiment revealed that the PP-TiO2-SiO2-PDA composite membrane delivered a noteworthy enhancement in vacuum membrane distillation performance (approximately 40%), while the improvement of the PP membrane was only 1.7%.

Local side effects during sublingual allergen-specific immunotherapy and pathogenetically based methods for their correction (Review)

Introduction. The study of the effectiveness and safety of allergen-specific immunotherapy remains relevant due to the emergence of a personalized approach in allergology and new dosage forms. Adverse reactions during sublingual allergen-specific immunotherapy are the leading reason of treatment discontinuation. Understanding the mechanisms of side effects and knowing how to overcome them allows us to achieve better tolerability of allergen-specific immunotherapy and maintain patient compliance.The aim of the review is to combine the available literature data on the frequency, nature, and pathogenetic basis of adverse events during sublingual allergen-specific immunotherapy and to consider options for their prevention and treatment.

The role of the gut microbiome in disorders of gut–brain interaction

Disorders of Gut‐Brain Interaction (DGBI) are widely prevalent and commonly encountered in gastroenterology practice. While several peripheral and central mechanisms have been implicated in the pathogenesis of DGBI, a recent body of work suggests an important role for the gut microbiome. In this review, we highlight how gut microbiota and their metabolites affect physiologic changes underlying symptoms in DGBI, with a particular focus on their mechanistic influence on GI transit, visceral sensitivity, intestinal barrier function and secretion, and CNS processing. This review emphasizes the complexity of local and distant effects of microbial metabolites on physiological function, influenced by factors such as metabolite concentration, duration of metabolite exposure, receptor location, host genetics, and underlying disease state. Large‐scale in vitro work has elucidated interactions between host receptors and the microbial metabolome but there is a need for future research to integrate such preclinical findings with clinical studies. The development of novel, targeted therapeutic strategies for DGBI hinges on a deeper understanding of these metabolite‐host interactions, offering exciting possibilities for the future of treatment of DGBI.

Effects of surface nanostructure and wettability on CO2 nucleation boiling: A molecular dynamics study

Nanostructured tubes hold great potential for enhancing heat transfer in refrigeration/heat pump systems. Therefore, it is essential to study the effects of nanostructured surface characteristics on refrigerant boiling heat transfer. In this paper, the nucleation boiling behavior of CO2 on the nanostructured surface is simulated using molecular dynamics. The effect mechanism of nanostructure size and surface wettability on CO2 bubbles nucleation and growth is investigated. At first, the nucleation boiling processes of both smooth surfaces and nanostructured surfaces featuring three different wide grooves are simulated. The results show that the local thermal aggregation effect is the key for nanostructures to promote CO2 bubble nucleation. The bubble nucleation efficiency is highest on the nanostructured surface with 5 ​nm wide groove. Then, based on a 5 ​nm wide nanostructured wall surface, the wettability effect on nucleation boiling is investigated by adjusting the potential energy factor α. The results show that the hydrophilic walls enhance the solid-liquid heat transfer and the collision of atoms within the liquid, resulting in boiling heat transfer capacity improvement between CO2 and the walls. The average temperature, average heat flux and critical heat flux in the liquid phase are also improved. A significant temperature gradient between the layers of CO2 liquid is noted on hydrophilic wall, where intermolecular forces and molecular advection dominate the heat transfer mechanism. In contrast, on hydrophobic wall, intermolecular forces dominate the heat transfer process.

In-situ charge polarization effect of copper(II) ion-coordination covalent organic framework induced aerobic PET-ATRP in aqueous phase

Covalent organic frameworks (COFs) are ideal heterogeneous photocatalytic platforms offering fascinating spaces for manipulation given their clearly defined structures along with pre-customizable skeletons. Herein, an imine-based COF Im-oMe featuring N and O sites allowing the integration of CuII ions via coordination bonds in local regions of the skeleton is reported for aerobic photoinduced electron/energy transfer atom transfer radical polymerization (PET-ATRP) in the aqueous phase. CuII@Im-oMe of anchored CuII ions is capable of triggering skeleton localized polarization effects, producing greater dipole moment driving force and facilitating the charge separation and transport to enhance its photopolymerization performance. Meanwhile, the system does not require additional addition of transition metal halides, and remains highly active after 3 cycles. The present work expands the feasible design methods and strategies of heterogeneous photocatalysts available for precision production of polymers in open-air environments.

Temperature analysis and prediction for road-rail steel truss cable-stayed bridges based on the structural health monitoring

A growing number of road-rail steel truss bridges have been built worldwide in recent years. Due to the complex cross-sectional structure and increasing span length, these main girders are prone to developing uneven temperature fields and significant local thermal effects, which can lead to excessive temperature stresses and structural damage. An accurate assessment of the temperature distribution in road-rail steel truss girders is critical as it aids in the thermal response analysis of the bridge and the structural safety evaluation. The paper analyses the temperature distribution of the road-rail steel truss girder base on the light conditions and the structure of the main girder. A modified method for calculating the effective temperature (ET) and temperature difference (TD) is also proposed. Comparing with the conventional regional weighted averaging method, the modified method improves the accuracy of ET calculation by 20 %. A prediction method based on the long short-term memory (LSTM) network is proposed, which estimates ET and TD for road-rail steel truss girders based on the environment around the bridge. The deep learning-based method improves accuracy by 40 % compared with the conventional linear regression method for ET prediction. Meanwhile, the proposed method alleviates the current lack of prediction methods for TD of road-rail steel truss girders. A detailed analysis of the temperature of a road-rail steel truss bridge is also presented based on the structural health monitoring (SHM) system. A negative vertical temperature difference was observed in the fringe truss. The maximum transverse temperature difference (TTD) was much greater than the maximum vertical temperature difference (VTD). The above features deserve extra attention from bridge designers.

Kinetics of voids evolution during diffusion bonding of dissimilar metals on consideration of the realistic surface morphology: Modeling and experiments

A comprehensive model for analyzing the kinetics of void evolution and shrinkage during dissimilar diffusion bonding is proposed based on the sinusoidal profile. The model incorporates atom accumulation at void tip to determine the curvature radius of void neck, and integrates the local effect of surface diffusion to quantify the contribution of surface diffusion, thus avoiding the unrealistic void evolution observed in previous models. Furthermore, the model utilizes a combined diffusion coefficient for multicomponent alloys under the effect of stress and concentration gradients, to determine the interfacial diffusion coefficient for dissimilar joints. In contrast to previous models, the current model demonstrates significant improvements in predicted accuracy, efficiently depicting the realistic void evolution and interpreting the formation of various interfacial voids, which can be applied in various bonding cases, including for the dissimilar base metals with different surface conditions. Simulation results from bonding between austenitic steel and ferritic steel with different surface roughness reveal that interfacial voids predominantly form on the side of the base metal with the rougher bonding surface. Additionally, as surface roughness decreases, the prevailing surface source diffusion and interface source diffusion mechanisms promote void evolution and shrinkage, resulting in a significant reduction of the final bonding time. To achieve a high-quality bonding joint, it's suggested to place the smoother bonding surface on the higher creep-resistant base metal, which can accelerate the void closure process.

Leveraging dynamic power benchmarks and CUSUM charts for enhanced fault detection in distributed PV systems

Faults in photovoltaic (PV) systems are common during their operational lifetime. Existing PV fault detection methods, which are primarily designed for large-scale PV fields, struggle with distributed systems due to their reliance on on-site weather sensors and inability to handle complex local shading effects on PV performance in the built environment. This paper presents a fault detection scheme specifically applicable to distributed PV systems that solely relies on monitored AC output and remote weather sources. Without requiring additional equipment or detailed information on the PV configuration and local shading conditions, the proposed method consists of two steps. First, historical monitoring data is used in a bootstrap fashion to develop machine learning models that establish baselines for normal PV output and corresponding estimation uncertainty. Then, a dynamic PV power benchmark constructed based on the 3-sigma rule, and cumulative sum (CUSUM) control charts are employed simultaneously to detect system malfunctions. Through experimental verification on actual distributed PV systems, the effectiveness of the proposed method is assessed for four categories of frequently observed malfunctions. The comprehensive experimental results indicate the considerable efficiency of the proposed sensorless method in detecting both serious PV malfunctions and minor faults with a severity as small as 4.2%.

Leveraging explainable AI for informed building retrofit decisions: Insights from a survey

Accurate predictions of building energy consumption are essential for reducing the energy performance gap. While data-driven energy quantification methods based on machine learning deliver promising results, the lack of Explainability prevents their widespread application. To overcome this, Explainable Artificial Intelligence (XAI) was introduced. However, to this point, no research has examined how effective these explanations are concerning decision-makers, i.e., property owners. To address this, we implement three transparent models (Linear Regression, Decision Tree, QLattice) and apply four XAI methods (Partial Dependency Plots, Accumulated Local Effects, Local Interpretable Model-Agnostic Explanations, Shapley Additive Explanations) to an Artificial Neural Network using a real-world dataset of 25,000 residential buildings. We evaluate their Prediction Accuracy and Explainability through a survey with 137 participants considering the human-centered dimensions of explanation satisfaction and perceived fidelity. The results quantify the Explainability-Accuracy trade-off in building energy consumption forecasting and how it can be counteracted by choosing the right XAI method to foster informed retrofit decisions. For research, we set the foundation for further increasing the Explainability of data-driven energy quantification methods and their human-centered evaluation. For practice, we encourage using XAI to reduce the acceptance gap of data-driven methods, whereby the XAI method should be selected carefully, as the Explainability within the methods varies by up to 10 %.

Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study

Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.

Machine learning prediction of bio-polyol yields and hydroxyl values from acid-catalyzed liquefaction of lignocellulosic biomass

Amid the escalating demand for alternatives to petroleum resources and the imperative to decrease carbon emissions, there is an increasing interest in transforming lignocellulosic biomass into valuable chemicals. This study utilized machine learning models to analyze the acid-catalyzed liquefaction process of lignocellulosic biomass and to predict and characterize the yield and hydroxyl value (HV) of bio-polyols. A total of 612 yield data samples and 229 HV data samples were collected and analyzed. From these data, four machine learning models were constructed: Random Forest (RF), Gradient Boosting Regression (GBR), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). The Bayesian Optimization Algorithm (BOA) was applied to refine the hyperparameters of the models, thereby enhancing their predictive accuracy. To clarify the effects of input features on predictive outcomes and to understand their interactive dynamics, one-dimensional and two-dimensional Accumulated Local Effects (ALE) analyses were performed. The results show that the GBR model was particularly precise in forecasting the yield and HV of bio-polyols, with corresponding test set determination coefficients (R2) of 0.82 and 0.91. The most influential factors on yield were reaction time (15.1 %), lignin content (13.1 %), and glycerol mass (12 %), while for HV, they were glycerol mass (28.9 %), particle size (15.1 %), and liquid-to-solid ratio (11.1 %). The ALE analysis also exposed the intricate interaction mechanisms between feedstock composition and liquefaction conditions. The verification results confirmed that the optimal model displayed exceptional generalization capabilities, with a Mean Absolute Percentage Error (MAPE) of 9.18 %. Utilizing this model, a user-friendly software package has been developed to enable rapid and precise prediction of the lignocellulosic biomass conversion process. This research not only delivers strategies to cut down on experimental costs and time but also offers a novel perspective on the industrial utilization of biomass-derived polyols.

Ligand Driven Control and F‐Doping of Surface Characteristics in FASnI3 Lead‐Free Perovskites: Relation Between Molecular Dipoles, Surface Dipoles, Work Function Shifts, and Surface Strain

AbstractThe performance and operational stability of perovskite‐based devices heavily rely on the interfacial properties between the photoactive perovskite layer and charge transport layers. Understanding the theoretical relationship between surface/interface dipoles and surface energetics is crucial for scientific understanding and practical applications. In this study, a method is applied that bridges classical electromagnetism and modern atomistic approaches. The impact of dipolar ligand molecules functionalizing the FASnI3 perovskite surface is investigated, with inspection of the interplay between surface dipole, charge transfer, and local strain effect, and corresponding shifts in the valence level. The results reveal that the contribution of individual molecular entities to surface dipoles and electric susceptibilities follows an essentially additive behavior. The influence of F doping usually used to mitigate Sn oxidation in Sn‐based layer for photovoltaic applications, is also discussed. Furthermore, the findings are compared with predictions from classical approaches employing a capacitor model that links the induced vacuum level shift and the molecular dipole moment. The insights gained from the study provide theoretical guidelines for fine‐tuning the work functions of materials, thus enabling effective interfacial engineering in this class of semiconductors.