Conventional Field Research Articles

Effects of Stochastic Magnetic Noise on the SERF Co‐Magnetometer

AbstractStochastic magnetic field noise has a non‐negligible negative impact on the performance of quantum precision measurement instruments such as spin‐exchange relaxation‐free (SERF) co‐magnetometer in terms of sensitivity and stability. Conventional magnetic field error analysis and suppression methods are not applicable to stochastic magnetic field noise. In this paper, a stochastic model of the SERF co‐magnetometer is proposed to analyze the mechanism of stochastic magnetic field noise. The mean and covariance propagation model of the SERF co‐magnetometer is given based on the stochasticity model and statistical principles. The analysis of simulation experiments reveals that reducing the longitudinal electron spin polarization can somewhat reduce the standard deviation of the system output caused by stochastic magnetic field noise. And the atomic ensembles are most resistant to stochastic magnetic field noise when the electron spin polarization strongly couples with the nuclear spin polarization. Finally, the experiments verified the above conclusions. In conclusion, this paper provides theoretical and experimental support for analyzing and suppressing the effect of stochastic magnetic field noise on the SERF co‐magnetometer, which is of great significance for improving the sensitivity and stability of the measurement.

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Low-enthalpy geothermal energy can make a major contribution towards reducing CO2 emissions. However, the development of geothermal reservoirs is costly and time intensive. In particular, high capital expenditures, data acquisition costs, and long periods of time from identifying a geothermal resource to geothermal heat extraction make geothermal field developments challenging. Conventional geothermal field development planning follows a linear approach starting with numerical model calibrations of the existing subsurface data, simulations of forecasts for geothermal heat production, and cost estimations. Next, data acquisition actions are evaluated and performed, and then the models are changed by integrating the new data before being finally used for forecasting and economics. There are several challenges when using this approach and the duration of model rebuilding with the availability of new data is time consuming. Furthermore, the approach does not address sequential decision making under uncertainty as it focuses on individual data acquisition actions. An artificial intelligence (AI)-centric approach to field development planning substantially improves cycle times and the expected rewards from geothermal projects. The reason for this is that various methods such as machine learning in data conditioning and distance-based generalized sensitivity analysis assess the uncertainty and quantify its potential impact on the final value. The use of AI for sequential decision making under uncertainty results in an optimized data acquisition strategy, a recommendation of a specific development scenario, or advice against further investment. This approach is illustrated by applying AI-centric geothermal field development planning to an Austrian low-enthalpy geothermal case. The results show an increase in the expected value of over 27% and a reduction in data acquisition costs by more than 35% when compared with conventional field development planning strategies. Furthermore, the results are used in systematic trade-off assessments of various key performance indicators.

Quantum phase transition and absence of quadratic divergence in generalized quantum field theories

In ordinary thermodynamics, around first-order phase transitions, the intensive parameters such as temperature and pressure are automatically fixed to the phase transition point when one controls the extensive parameters such as total volume and total energy. From the microscopic point of view, the extensive parameters are more fundamental than the intensive parameters. Analogously, in conventional quantum field theory (QFT), coupling constants (including masses) in the path integral correspond to intensive parameters in the partition function of the canonical formulation. Therefore, it is natural to expect that, in a more fundamental formulation of QFT, coupling constants are dynamically fixed , just as the intensive parameter in the microcanonical formulation. Here, we demonstrate that the automatic tuning of the coupling constants is realized at a quantum phase transition point at zero temperature, even when the transition is of higher order, due to the Lorentzian nature of the path integral. This naturally provides a basic foundation for the multicritical point principle. As a concrete toy model for solving the Higgs hierarchy problem, we study how the mass parameter is fixed in the ϕ4 theory at the one-loop level in the microcanonical or further generalized formulation of QFT. We find that there are two critical points for the renormalized mass: zero and of the order of ultraviolet cutoff. In the former, the Higgs mass is automatically tuned to be zero and thus its fine-tuning problem is solved. We also show that the quadratic divergence is absent in a more realistic two-scalar model that realizes the dimensional transmutation. Additionally, we explore the possibility of fixing quartic coupling in ϕ4 theory and find that it can be fixed to a finite value. Published by the American Physical Society 2024

Reliability analysis and risk assessment of pile-reinforced slopes considering spatial soil variability and site investigation

ABSTRACT Site investigation data is of great significance for practical slope stability analyses and designs. Previous studies on the probabilistic analysis of pile-reinforced slopes, however, rarely considered the influence of site-specific information, which may lead to a biased judgment of the stability of the soil-pile system, thus resulting in unreasonable designs. This paper proposes a new integrated probabilistic framework to analyse the probabilistic characteristics and quantitative risk of the pile-reinforced slopes considering site investigation data. Particularly, with the aid of the framework, the responses of the stabilising piles under different schemes of investigation boreholes are systematically studied. The Random Finite Difference Method within the framework of Monte Carlo Simulation (MCS) and Subset simulation is employed to explore the uncertainty within the pile-reinforced slope and the associated pile responses. A soil slope is taken as an illustrative example to validate the effectiveness of the proposed framework through various parametric studies on the investigation scheme of boreholes (e.g., numbers, locations, and sampling intervals). It is found that the stability results obtained from the deterministic analysis with mean soil properties and the conventional unconditional random field MCS may be biased compared to the results by the proposed framework considering real-site situations.

Supergravity-Steered Generic Manufacturing of Nanosheets-Embedded Nanocomposite Hydrogel with Highly Oriented, Heterogeneous Architecture.

Designing nanocomposite hydrogels with oriented nanosheets has emerged as a promising toolkit to achieve preferential performances that go beyond their disordered counterparts. Although current fabrication strategies via electric/magnetic force fields have made remarkable achievements, they necessitate special properties of nanosheets and suffer from an inferior orientation degree of nanosheets. Herein, a facile and universal approach is discovered to elaborate MXene-based nanocomposite hydrogels with highly oriented, heterogeneous architecture by virtue of supergravity to replace conventional force fields. The key to such architecture is to leverage bidirectional, force-tunable attributes of supergravity containing coupled orthogonal shear and centrifugal force field for steering high-efficient movement, pre-orientation, and stacking of MXene nanosheets in the bottom. Such a synergetic effect allows for yielding heterogeneous nanocomposite hydrogels with a high-orientation MXene-rich layer (orientation degree, f = 0.83) and a polymer-rich layer. The authors demonstrate that MXene-based nanocomposite hydrogels leverage their high-orientation, heterogeneous architecture to deliver an extraordinary electromagnetic interference shielding effectiveness of 55.2dB at 12.4GHz yet using a super-low MXene of 0.3 wt%, surpassing most hydrogels-based electromagnetic shielding materials. This versatile supergravity-steered strategy can be further extended to arbitrary nanosheets including MoS2, GO, and C3N4, offering a paradigm in the development of oriented nanocomposites.

Effective Hamiltonian approach to kinetic Ising models: Application to an infinitely long-range Husimi-Temperley model.

The probability distribution (PD) of spin configurations in kinetic Ising models has been cast in the form of the canonical Boltzmann PD with a time-dependent effective Hamiltonian (EH). It has been argued that in systems with extensive energy EH depends linearly on the number of spins N leading to the exponential dependence of PD on the system size. In macroscopic systems the argument of the exponential function may reach values of the order of the Avogadro number which is impossible to deal with computationally, thus making unusable the linear master equation (ME) governing the PD evolution. To overcome the difficulty, it has been suggested to use instead the nonlinear ME (NLME) for the EH density per spin. It has been shown that in spatially homogeneous systems NLME contains only terms of order unity even in the thermodynamic limit. The approach has been illustrated with the kinetic Husimi-Temperley model (HTM) evolving under the Glauber dynamics. At finite N the known numerical results has been reproduced and extended to broader parameter ranges. In the thermodynamic limit an exact nonlinear partial differential equationof the Hamilton-Jacobi type for EH has been derived. It has been shown that the average magnetization in HTM evolves according to the conventional kinetic mean field equation.

Affine formation maneuver control of underactuated surface vessels: Guaranteed safety under moving obstacles in narrow channels

This research tackles the challenge of dynamic formation control and collision avoidance in underactuated surface vessels (USVs). Firstly, an affine transformation is employed to implement arbitrary configuration transformations within the multi-USV system. Secondly, by considering both distances and velocities between USVs and obstacles, a novel collision avoidance scheme is proposed based on conventional distance-based artificial potential field methods. This method, building on traditional distance-based artificial potential fields, offers a more precise assessment of collision risks, thereby notably shrinking the potential collision zones. Such refinement leads to reduced energy consumption and minimizes disruption to formation integrity. Thirdly, an anti-perturbation formation maneuver safety control scheme is introduced to achieve a time-varying target formation without collisions, ensuring that local position tracking errors remain bounded. The effectiveness of the proposed approaches is substantiated through comprehensive theoretical analysis and extensive numerical simulations, underscoring their practical applicability.

Soil organic matter thermal pools as influenced by depth, tillage, and soil texture – A Rock-Eval® analysis study on the cropland soils of the Swiss Plateau

This study investigated the relationships between the soil organic matter content (SOM), SOM thermal pools, soil properties, and tillage practices, on cropland soils of the central plateau of Switzerland. Soil samples were collected in 45 no-till and conventional tillage fields in five layers from 0 to 40 cm depth. Soil organic carbon content (SOC) and hydrocarbon compound (HC) pools were analysed with Rock-Eval® thermal analysis. In addition, the clay content was determined by sedimentation.The SOM contents were highest in 0–5 cm of no-till soils and the plough pan of tilled soils. SOM was less oxygenated and showed lower degradation under conventional tillage then no-tillage. The proportion of the thermo-stable pool was mainly explained by the SOC:clay ratio, regardless of tillage practices. Below a SOC:clay ratio of 0.08, all pools were decreasing nearly equally with decreasing SOC. Above this ratio, thermo-stable pools increased only slightly, while thermo-labile pools increased linearly with SOC:clay on the full SOC:clay range. The 0.08 SOC:clay threshold in the thermo-stable pools content corresponded to the lower threshold of structure stability determined for Swiss and UK soils, below which, on average, severely degraded structures are observed in the field. These findings suggest that a large proportion of thermo-labile pool is necessary for acceptable to high soil structure quality, to protect the thermo-stable forms from degradation, and to reach the carbon sequestration potential of the soil.

Pesticide Residues in Organic and Conventional Agricultural Soils across Europe: Measured and Predicted Concentrations.

During the growing season of 2021, 201 soil samples from conventionally and organically managed fields from 10 European countries and 8 cropping systems were taken, and 192 residues of synthetic pesticides were analyzed. Pesticide residues were found in 97% of the samples, and 88% of the samples contained mixtures of at least 2 substances. A maximum of 21 substances were found in conventionally managed fields, and a maximum of 12 were found in organically managed fields. The number and concentration of pesticide residues varied significantly between conventional and organic fields in 70 and 50% of the case study sites, respectively. Application records were available for a selected number of fields (n = 82), and these records were compared to the detected substances. Residues from 52% of the applied pesticides were detected in the soils. Only 21% of the pesticide residues detected in the soil samples were applied during the 2021 growing season. From the application data, predicted environmental concentrations of residues in soil were calculated and compared to the measured concentrations. These estimates turned out not to be accurate. The results of this study show that most European agricultural soils contain mixtures of pesticide residues and that current calculation methods may not reliably estimate their presence.

Low-dose radiation therapy for COVID-19 pneumonia: Comparison of dosimetry and life-time attributable risk of cancer with conventional AP-PA fields and bone marrow sparing VMAT.

Low-dose radiation therapy (LDRT) to lungs did show encouraging results in COVID-19 patients in some clinical trials. However, there has been some concern regarding the long-term risk of radiation-induced cancer (RIC). Compared to the conventional AP-PA field technique, volumetric modulated arc therapy (VMAT) can potentially reduce the dose to the marrow and other organs at risk (OARs) and thus minimize the risk of cancer. We designed a dosimetry study to study if VMAT can reduce the exposure to the marrow and other OAR doses and curtail the estimated life-time attributable risk (LAR) of cancer. We retrieved the computed tomography scan data of 10 patients (aged 40-60 years, median 48 years) who have been already treated for any malignancy in the region of the thorax. A dose of 1.0 Gy in single fraction was prescribed to both lungs. All the organs were delineated as per the established guidelines. The dosimetry achieved by the two plans was compared to find the difference. Mean OAR doses were used to estimate the LAR for both plans and compared. Planning target volume coverage parameters like conformity index and homogeneity index were significantly better with VMAT (P value < 0.05 for all). The mean dose to most OARs was significantly lower with VMAT (P value < 0.05 for all). The mean dose to the marrow was significantly lower with VMAT (59.05 vs 81.9 cGy with P value < 0.05). The overall LAR was significantly lower with VMAT as compared to the conventional plan (0.357% vs 0.398%, P value < 0.05). Compared to the conventional technique, VMAT provides better OAR dosimetry for lung irradiation (a prescription dose of 1.0 Gy or more) in COVID-19 pneumonia. VMAT significantly reduces the risk of RIC. We therefore suggest if lung LDRT is used for COVID-19 patients, VMAT is the preferred technique for a prescription dose of ≥1.0 Gy.

Hybrid electrostatic waves in linearized gravity

Linearized gravity around a rotating black hole or compact object introduces the concept of a gravitomagnetic field, which originates from the matter–current in the rotating object. Plasma in proximity to this object is subsequently subjected to motion guided by this gravitomagnetic term (where mass serves as the effective charge) in addition to the conventional magnetic field. Such an interplay of fields complicates the accessible plasma waves of the system and thus merits exploration to delineate this interplay, to identify any observable signatures of the gravitomagnetic field in weakly magnetized systems, and to motivate future numerical work in a fully relativistic setting where the effects may be stronger. In this work we analyse the dispersion of the upper and lower hybrid electrostatic waves in a plasma immersed in both magnetic and gravitomagnetic fields. In particular, we discuss the effective augmentation or cancellation of the two fields under the right conditions for the upper hybrid wave. In contrast, the lower hybrid wave experiences a frequency up-shift from the gravitomagnetic field regardless of whether it is parallel or antiparallel to the magnetic field for the studied field strengths.

Exploring carbonate reservoirs potential, north Egypt

Carbonate reservoirs contain a significant portion of the world's oil reserves. The Middle East is home to many of these reservoirs. Carbonate reservoirs can be found in a few very large oil fields, including Egypt's Zohr field, the largest conventional gas field in Egypt. In this article, we present a couple of successful tertiary carbonate reservoirs that are bearing hydrocarbon in the Egyptian Western Desert and Gulf of Suez provinces, they are distributed across Egypt's subsurface, and they are characterized by heterogeneous porosity and permeability. These heterogeneities are caused by the wide spectrum of tectonostratigraphic environments in which carbonates are deposited and by subsequent diagenetic alteration of the original rock fabric. The structural analysis of the study areas based on the interpretation of both geophysical and geological data, in JD, Abu Sennan, and the North Amer areas in the Western Desert and offshore Gulf of Suez respectively showed that tectonostratigraphic history during tertiary time affected a great extent carbonate reservoir quality. Major fault trends formed secondary fracture porosity possibly allowing hydrothermal solutions to pass through the reservoirs and form secondary vuggy porosity. These characteristics are considered important factors of promising carbonate reservoirs. In this study, several carbonate reservoirs have been outlined. The study workflow helped us better identify carbonate prospects with high fractured density.

Workflow for Window Composition Detection to Aid Energy-Efficient Renovation in Low-Income Housing in Korea

Enhancing the efficiency of windows is important for improving the energy efficiency of buildings. The Korean government has performed numerous building renovation projects to reduce greenhouse gas emissions and mitigate energy poverty. To reduce the costs and manpower requirements of conventional field surveys, this study presents a deep-learning model to examine the insulation performance of windows using photographs taken in low-income housing. A smartphone application using crowdsourcing was developed for data collection. The insulation performance of windows was determined based on U-value, derived considering the frame-material type, number of panes, and area of windows. An image-labeling tool was designed to identify and annotate window components within photographs. Furthermore, software utilizing open-source computer vision was developed to estimate the window area. After training on a dataset with ResNet and EfficientNet, an accuracy of approximately 80% was achieved. Thus, this study introduces a novel workflow to evaluate the insulation performance of windows, which can support the energy-efficient renovation of low-income housing.

A Comparative Study of Soil Fertility in Organic, Semi-Organic, and Conventional Rice Field Farming Systems (Case Study: Nguntoronadi District, Wonogiri, Indonesia)

The soil fertility of rice fields is closely related to rice crop production. The research aims to identify soil fertility under different rice field farming systems, find the key factor of soil fertility, and recommend strategies to improve soil fertility based on the key factor. The research was conducted in Nguntoronadi District, Indonesia, on conventional, semi-organic, and organic rice fields. The research was an exploratory descriptive survey through a field survey approach and soil chemistry and physics analysis. Soil sampling was conducted in 12 Land Map Units (LMUs) with three replicates using purposive sampling methods. Observation indicators include soil pH, organic C, total N, C/N ratio, available P, available K, exchangeable Ca, exchangeable Mg, Cation Exchangeable Capacity (CEC), Base Saturation (BS), Aluminum saturation, soil texture, and worm population density representing soil chemical, physical, and biological properties. Soil fertility is determined using Principal Component Analysis (PCA) and scoring based on the category. The research results show that the level of soil fertility under various rice field farming systems was included in the moderate with ranges of 0.53-0.70, and organic farming has the highest soil fertility. The key factors of soil fertility include pH, organic C, available P, available K, Ca-dd, CEC, and Aluminum saturation. The appropriate management direction is the addition of organic fertilizer in the planting period.

A soil health assessment tool for vegetable cropping systems in tropical soils based on beta-glucosidase, arylsulfatase, and soil organic carbon

Vegetable production is characterized by a strong dependence on external inputs and intense tillage, which can harm soil health (SH) by reducing organic matter levels, soil biota diversity, and overall ecosystem functions. Tools to assess SH affected by organic and conventional vegetable production systems are crucial and still scarce. This study aimed to develop a practical and reliable SH assessment tool for vegetable production systems in tropical soils by integrating Cornell's Comprehensive Assessment of Soil Health (CASH) with the four-quadrant model (4QM). Furthermore, the study evaluated the relationship between management practices and SH status. Arylsulfatase (ARYL) and β-glucosidase (GLU) activities, and soil organic carbon (SOC) were analyzed in 90 soil samples (0 to 10 cm depth) collected from 53 organic and conventional vegetable farms in the Federal District, Brazil. The adoption of practices aimed at improving or maintaining SH was significantly greater on organic farms than on conventionally managed farms, resulting in a SOC content and ARYL activity 27 % and 86 % higher in organic farms than in conventional systems, respectively (P < 0.05). ARYL proved to be more accurate in separating organic and conventional fields than GLU, highlighting its potential for SH evaluation in vegetable production systems. The integration of CASH and the 4QM, along with ARYL, GLU, and SOC measurements, effectively addressed the complexity of both organic and conventional vegetable production systems. This combined approach facilitated differentiation between systems based on varying levels of adoption of best management practices (BMPs). Further research is needed to assess the performance of this approach across different soil types and regions where vegetables are cultivated.

An Accuracy Assessment of Field and Airborne Laser Scanning–Derived Individual Tree Inventories using Felled Tree Measurements and Log Scaling Data in a Mixed Conifer Forest

Abstract On-the-ground sample-based forest inventory methods have been the standard practice for more than a century, however, remote sensing technologies such as airborne laser scanning (ALS) are providing wall-to-wall inventories based on individual tree measurements. In this study, we assess the accuracy of individual tree height, diameter, and volume derived from field-cruising measurements and three ALS data-derived methods in a 1.1 ha stand using direct measurements acquired on felled trees and log-scale volume measurements. Results show that although height derived from indirect conventional field measurements and ALS were statistically equivalent to felled tree height measurements, ALS measured heights had lower root mean square error (RMSE) and bias. Individual tree diameters modeled using a height-to-diameter-at-breast-height model derived from local forest inventory data and the software ForestView had moderate RMSE (8.3–8.5 cm) and bias (-3.0 – -0.3 cm). The ALS-based methods underdetected trees but accounted for 78%–91% of the field reference harvested merchantable volume and 71%–99% of the merchantable volume scaled at the mill. The results also illustrate challenges of using mill-scaled volume estimates as validation data and highlight the need for more research in this area. Overall, the results provide key insights to forest managers on accuracies associated with conventional field-derived and ALS-derived individual tree inventories. Study Implications: Forest inventory data provide critical information for operational decisions and forest product supply chain planning. Traditionally, forest inventories have used field sampling of stand conditions, which is time-intensive and cost-prohibitive to conduct at large spatial scales. Remote sensing technologies such as airborne laser scanning (ALS) provide wall-to-wall inventories based on individual tree measurements. This study advances our understanding of the accuracy of conventional field-derived and ALS-derived individual tree inventories by evaluating these inventories with felled tree and log scaling data. The results provide key insights to forest managers on errors associated with conventional field and ALS-derived individual tree measurements.

Reverse Mapping of Coarse Grained Polyglutamine Conformations from PRIME20 Sampling.

An inverse coarse-graining protocol is presented for generating and validating atomistic structures of large (bio-) molecules from conformations obtained via a coarse-grained sampling method. Specifically, the protocol is implemented and tested based on the (coarse-grained) PRIME20 protein model (P20/SAMC), and the resulting all-atom conformations are simulated using conventional biomolecular force fields. The phase space sampling at the coarse-grained level is performed with a stochastical approximation Monte Carlo approach. The method is applied to a series of polypeptides, specifically dimers of polyglutamine with varying chain length in aqueous solution. The majority (>70 %) of the conformations obtained from the coarse-grained peptide model can successfully be mapped back to atomistic structures that remain conformationally stable during 10 ns of molecular dynamics simulations. This work can be seen as the first step towards the overarching goal of improving our understanding of protein aggregation phenomena through simulation methods.

Dosimetric Comparison of The Effects of Different Treatment Plan Techniques on Reduction in Critical Organs in Whole Brain Radiotherapy Application

Different radiotherapy treatment techniques can be used in whole-brain radiotherapy (WBRT). This study aims to investigate the dosimetric advantages of the anterior isocenteric (AI) technique which is produced as an alternative to the central isocentric (CI) technique. 25 whole brain patients were included in this retrospective study. Plans were made with two treatment techniques for each patient. One central isocenter (CI) was made using a conventional helmet field (HF) to center the whole brain, which is the isocenter of the target volume. An automatic margin of 5 mm was given to the planning target volume (PTV) with multileaf collimators (MLC) for both plans. For CI and AI techniques, a total dose of 30 Gy was given in 10 fractions with 6 MV photon energy. The two planning techniques were compared dosimetrically. The dose homogeneity index (DHI) had lower values in the AI plan according to CI plans significantly (p=0.049). There was a 6,57% difference between CI and AI planning techniques for the maximum dose of the right lens. For the minimum dose and mean dose AI plans significantly had lower values according to the CI plan (p=0.001 and p=0.028 respectively). In this dosimetric study, we found that the AI treatment technique for WBRT was superior to the CI technique for DHI and organs at risk. We recommended to use the AI technique, especially to better protect organs at risk in WBRT.

Evaluation of soil fertility index in organic, semi-organic, and conventional rice field management systems

Rice farming in Madiun Regency implements three different management systems, that is organic, semi-organic, and conventional. The different implementation of these rice field management systems impacts soil fertility index and rice productivity. This purpose of this research was to know the effect of rice field management systems on soil fertility index and rice productivity in the Madiun Regency. The research uses an explorative descriptive qualitative method with a survey approach. Soil samples were taken using a random sampling method and 3 types of soil management systems (conventional, semi-organic and organic), and rice production samples were taken using an estimation method. The research results show that the soil fertility index ranges from 0.78 to 0.82, which is high. The highest soil fertility index is in the semi-organic management system and the lowest is in the organic management. The management system of semi-organic gave a response to the highest rice production of 6.89 tons/ha. Management system of semi-organic is a better management for increasing and maintaining soil fertility and crop production than conventional or organic. Farming activities results show that organic management systems increase the highest income, increasing 115.58% and 53.30% in semi-organic compared to conventional. The rice field management system has the effect of increasing the worm population density with the highest in the organic system, namely 4.19 individuals m-2. The Pontoscolex worm type dominates the three management systems. There is a correlation between organic matter content and earthworms.

Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image Generation

A 360-degree (omni-directional) image provides an all-encompassing spherical view of a scene. Recently, there has been an increasing interest in synthesising 360-degree images from conventional narrow field of view (NFoV) images captured by digital cameras and smartphones, for providing immersive experiences in various scenarios such as virtual reality. Yet, existing methods typically fall short in synthesizing intricate visual details or ensure the generated images align consistently with user-provided prompts. In this study, autoregressive omni-aware generative network (AOG-Net) is proposed for 360-degree image generation by outpainting an incomplete 360-degree image progressively with NFoV and text guidances joinly or individually. This autoregressive scheme not only allows for deriving finer-grained and text-consistent patterns by dynamically generating and adjusting the process but also offers users greater flexibility to edit their conditions throughout the generation process. A global-local conditioning mechanism is devised to comprehensively formulate the outpainting guidance in each autoregressive step. Text guidances, omni-visual cues, NFoV inputs and omni-geometry are encoded and further formulated with cross-attention based transformers into a global stream and a local stream into a conditioned generative backbone model. As AOG-Net is compatible to leverage large-scale models for the conditional encoder and the generative prior, it enables the generation to use extensive open-vocabulary text guidances. Comprehensive experiments on two commonly used 360-degree image datasets for both indoor and outdoor settings demonstrate the state-of-the-art performance of our proposed method. Our code is available at https://github.com/zhuqiangLu/AOG-NET-360.