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This paper studies the mineralization process and the ore-forming setting of the Kantian fluorite deposit in South China through the zircon U-Pb chronology, quartz Rb-Sr dating, whole rock geochemistry, H-O isotope and fluid inclusions. A mineralization model for the Kantian fluorite deposit is proposed as follows: The subduction of the Pacific plate during the late mid-Jurassic and late Jurassic led to the formation of several fault zones. As the subduction gradually decelerated and eventually ceased, continental extension and lithosphere thinning occurred, accompanied by upwelling of the asthenosphere. This asthenosphere upwelling facilitated melting of the lithospheric mantle, resulting in the ascent of magma into the lower crust. Consequently, the intrusion of magma into the lower crust caused substantial heat, triggering partial melting of the lower crust. The molten material then ascended along faults, generating a significant quantity of granites. During the Early Cretaceous period, the Yanshan cycle witnessed another notable phase of magmatic activity. The residual magmatic hydrothermal fluid from this period served as a source of heating, while the pre-existing fault structures provided favorable pathways and storage space. Atmospheric precipitation infiltrated into the depth along these faults, and the deep-seated residual magmatic hydrothermal fluid heated the infiltrated atmospheric water, inducing cyclic upwelling. The upward migration of hydrothermal fluid continuously extracted Ca and F elements from the granites. Ongoing interaction between the fluid and the surrounding rocks led to changes in pH, coupled with alterations in temperature and pressure, ultimately resulting in the precipitation of fluorite with associated alteration zones in the surrounding rocks. [Display omitted] • Ca and F element in the fluorite were derived through the surrounding rocks. • The ore-forming fluids belong to the NaCl-H 2 O system and exhibit characteristics of medium–low temperature, low salinity, and low density, and were derived from meteoric water. • The intrusion age of surrounding rock in Kantian fluorite deposit was 159.4 ± 2.2 Ma, and the ore-forming of fluorite was 135.6 ± 0.7 Ma. • The tectonic setting responsible for the diagenesis and mineralization processes is believed to be an intracontinental stretching environment associated with the subduction of the Pacific plate. The Yanshanian events have led to the development of South China as a unique granite province globally. Within South Jiangxi, numerous metallic and non-metallic deposits have a close association with Mesozoic granite formations. The Kantian fluorite deposit, a newly discovered non-metallic deposit in southern Jiangxi Province, is one such deposit that is linked to Mesozoic granites. The orebody at the Kantian fluorite deposit is identified as occurring in veined structures within the silicification fracture zone of Mesozoic granites, with strict control exerted by fault systems. Fluorite ore can be classified into two categories based on the amount of quartz and fluorite. The first category is the fluorite quartz type, which has a higher amount of quartz than fluorite. The second category is quartz fluorite type, which has more fluorite than quartz. Among these two categories, the quartz fluorite type is precipitated during the primary mineralization stage. Through the analysis of fluid inclusions within the fluorite, it was determined that the homogenization temperature is relatively low, ranging from 133 to 236℃. The fluid inclusions also display low salinity (0.18–3.39 wt% NaCl eqv.) and low density (0.75–0.96 g/cm3). Laser Raman spectroscopy revealed that the primary components of the fluid inclusions are H 2 O, with minor C 3 H 6. Isotope analysis indicated δD VSMOW values of fluorite ranging from −61.9 ‰ to −54.8 ‰ (average −59.8 ‰), while δ18O VSMOW values varied from 1.3 ‰ to 3.1 ‰ (average 2.0 ‰). This study presents chronological, petrological and geochemical data that suggest several processes. The metallogenic age is 135.6 ± 0.7 Ma. The mineralizing fluids were identified to the NaCl-H 2 O system, with heated meteoric water identified as the primary source; The ore-forming materials originated from the granite, and water–rock interactions are proposed as the primary mechanism for fluorite precipitation. Based on a tectonic setting characterized by stretching, which is closely linked to the subduction of the Pacific plate, we proposed a model for the Kantian fluorite deposit. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hydrochemical characteristics of the initial geothermal fluid were reconstructed. • Inverse models were used to identify the initial geothermal fluid formation. • Initial geothermal fluids originate from hydrothermal sulfide oxidation. • Ductile deformation and metamorphic domes provided energy and channel. Analyzing the genesis and resource potential of orogenic geothermal systems poses significant challenges due to their complex geological backgrounds and evolutionary histories. This paper presents a novel synthesis of hydrogeochemical and isotope geochemical data, coupled with geological and geophysical results, to comprehensively understand the origins of deep, high-temperature orogenic geothermal systems. This understanding is achieved through the utilization of advanced geochemical modeling techniques to analyze the geochemical attributes of initial geothermal fluids. The hydrochemical and H/O/S isotopic composition of a total of 67 geothermal and non-geothermal water samples from the Dabie orogenic belt (DOB) were analyzed. Integrated multicomponent geothermometry (IMG) was employed to reconstruct the characteristics of the initial geothermal fluids, and PHREEQC inverse simulations creatively determined the most likely pathways of their genesis and evolution. The findings indicate that all geothermal waters from the three major geothermal fields of Luotian, Yingshan, and Yuexi are alkali-sulfate type waters. The initial geothermal fluids are acidic sulfate type with reservoir temperatures of 156–222 °C, 115–179 °C, and 100–189 °C, respectively. The primary processes responsible for the formation of these initial geothermal fluids are the dissolution of anhydrite from sulfide oxidation and, to a lesser extent, direct sulfide oxidation. Generally, the formation of geothermal systems in the investigated geothermal fields is associated with the extensional collapse and lithospheric thinning of the Dabie orogen since the Early Cretaceous Period. The spatial distribution of the three geothermal fields in this study corresponds to the tectonic-magmatic framework of magmatic intrusion-induced domes (Luotian and Yuexi) in the DOB. The reactivation of the Tan-Lu Fault during the Cenozoic Era suggests the presence of a deep heat source comprising magmatic and metamorphic components. This heat source, combined with the infiltration of paleo-atmospheric and modern atmospheric precipitation water along regional fault systems, results in the formation and gradual dilution of initial geothermal fluids. Neutralization occurs through interactions with calcite and fine-grained sodium aluminosilicates, ultimately leading to the formation of the collected geothermal water. The conceptual model established in this study elucidates the distinctive genesis and formation mechanism of the orogenic geothermal system exemplified by the DOB. [ABSTRACT FROM AUTHOR]

In the present paper, we made an investigation of extreme rainfall events for 1860―1909 in the Korean Peninsula, and the possible atmospheric circulation state by comparing recent rainfall variability with atmospheric circulation indices. In the present study, very significant historical rainfall records and flood damage records since the mid-19th century, modern rainfall records and global climate reanalysis data were applied. Results show: (1) Pentad extreme rainfall events had undergone a clear decrease, with an abrupt falling after shifting point of 1879―1880. (2) The monthly frequencies of extreme rainfall events revealed remarkable descent trends for June–October, which changed from 29 time/pentad to 7.3 time/pentad on average, especially for July–September. (3) Almost all climate zones except for northern land displayed significant decreasing trends, nevertheless no clear incidence of extreme rainfall event was found over the typical pluvial regions. (4) Through an analogue method by comparing the extreme precipitation weather and the atmospheric mechanism for recent decades (1961―2016), we suggested that West Pacific Subtropical High had experienced a strengthen change for the past period and the typhoon-related torrential rain processes had been suppressed for the study area, and the upper air jet stream might have been displaced poleward during 1860―1909. This study made a first investigation of the extreme rainfall events since the mid-19th century applying both instrumental and documental records in the study area, and some significant findings were obtained, which will help in understanding extreme climate variability and its mechanism of late 19th century and early 20th century in the Korean Peninsula. • Several main climate variables which are related to SR-NPKP were investigated. • 13 indices having a close connection with the variability of SR-NPKP were obtained. • Neural network model was proposed for 1-yr lag prediction of SR-NPKP. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proposed MSDI can capture the occurrence of drought. • There was a tendency of aridity with a mutation point of 1983. • The drought severity was the heaviest in the DXJB, with an average MSDI of –0.75. • The most severe drought event occurred from September 1988 to October 1990. • ENSO had the largest influence on the drought variation in the XJB. The reasonable and appropriate drought indicator is a crucial instrument for drought investigation and assessment, which can objectively compare the severity, duration, and frequency of drought events, so as to achieve the purpose of accurately monitoring and evaluating drought. However, a univariate drought index is often difficult to precisely describe the complex drought situation, and it is urgent to establish a multivariate comprehensive drought index to identify and reveal the drought characteristics. Based on Copula models, combined with meteorological factors precipitation and hydrological factors runoff, this paper constructed a Multivariate Standardized Drought Index (MSDI), which can characterize meteorological drought and hydrological drought simultaneously and evaluate the spatial–temporal evolution characteristics of drought over the Xijiang River Basin (XJB) in South China. The results indicated that: (1) the proposed MSDI can capture the occurrence of drought as sensitively as the Standardized Precipitation Index (SPI), and can represent the termination of drought as effectively as the Standardized Runoff Index (SRI), which can comprehensively depict the drought situation in the XJB, China; (2) since the 1980 s, there was a tendency of aridity with a mutation point of 1983 in the downstream of Xijiang River (DXJB), and the worst comprehensive drought occurred in 1989; (3) among the tributaries, the comprehensive drought severity was the heaviest in the DXJB, with an average MSDI value of –0.75; (4) based on the run theory and Copula model, the most serious drought event occurred from September 1988 to October 1990, with a drought severity of 33.36, a drought duration of 27 months, and a drought return period exceeding 50 years; and (5) the drought evolution was significantly influenced by abnormal atmospheric circulation factors, while ENSO had the largest influence on the drought variation in the XJB, followed by sunspot. [ABSTRACT FROM AUTHOR]

• This paper presents a unique dataset that brings together data from meteorological sources on weather anomalies and night light emissions in 0.5 by 0.5-degree grid cells for the entire globe for the period 1992-2013. • This dataset allows researchers to investigate the economic impact of weather anomalies at an unprecedented geographical resolution. • We find that storms, excessive precipitation and cold spells indeed reduce the growth of economic activity visibly in the emission of night light at the local level. • We also show these negative effects do not tend to last long and generally cause positive spillovers in neighboring areas. How do weather anomalies affect the economy at the local level? This paper presents a new data set that links weather data to annual average night-light emission data for 24.000 0.5° × 0.5° grid-cells around the globe for the period 1992–2013. Interpreting night-light emission as a proxy for economic activity, these data allow one to investigate how weather anomalies affect economic activity. Global coverage avoids selection bias, while high spatial resolution avoids averaging out heterogeneity in local impacts at higher aggregation levels. Our data show significant effects on the local growth of night-light for storms, excessive precipitation, droughts, and cold spells. Moreover, we find evidence for significant spatial spillovers to neighboring areas. Our results suggest that these offsetting spillovers are typically local. As positive and negative effects average out in larger areas, our results call for the analysis of economic effects of weather anomalies at a high geographical resolution. Finally, our results are driven by events in lower income regions. As climate change is expected to make weather patterns more erratic, our new data can inform emerging debates on how this will affect the economy in both science and politics. [ABSTRACT FROM AUTHOR]

• A method of analyzing ground snow loads based on a snowmelt model is proposed. • The method solves problems of insufficient sample data for observed snow loads. • Probabilistic properties of ground snow loads are analyzed. Ground snow loads, the study of which relies on the snowpack data from historical records of meteorological stations, are the basis of calculating the roof snow loads. In the absence of observation data, the snow accumulation and melt model, also called the snowmelt model, can be used to simulate the annual extreme samples of ground snow loads in each locality, and then the ground snow loads under a given recurrence interval can be obtained through probability analysis. In this paper, a multi-layer snowmelt model is used to input meteorological data such as precipitation, air temperature, wind speed, and relative humidity to obtain detailed information on the long-term snowpack, including snow density, snow depth, and snow water equivalent (SWE), which overcomes the problem of insufficient sample data for observed snow loads. Firstly, the precision of multi-layer snowmelt is checked through data of snow depth and SWE. Then, based on the model, meteorological stations in thirty-seven localities of six Chinese regions are selected to simulate the ground snow loads for nearly 60 years. Subsequently, the maximum snow depth and its occurrence time are compared, and the relationship between snow depth, snow density, and SWE in each region is analyzed. Finally, a variety of probabilistic models are used to calculate the ground snow loads at 50-year recurrence intervals. The goodness of fit of each probabilistic model for the snow loads sample is examined, and the calculated values of ground snow loads are compared with recommended values by the Chinese code. The results indicate that the snowmelt model can accurately reproduce the detailed process of ground snow events, which can be used to obtain the samples of ground snow load extreme. Due to gravity compaction, ground snow loads corresponding to the maximum depth in a certain year differ from the maximum ground snow loads in that year, which requires more attention. The goodness of fit is higher and the results are larger than the values of the code when generalized extreme distribution and logarithmic normal distribution are adopted to estimate extreme ground snow loads. [ABSTRACT FROM AUTHOR]

• Temperature and precipitation anomalies are key drivers of within-country inequality. • Agriculture is a fundamental channel in the climate change-inequality nexus. • Warmer temperatures have a stronger impact on income disparities in rural countries. • Urban areas are more exposed to income disparities induced by extreme precipitations. • Inward FDI diversification enhances resilience, moderating climate-induced inequality. This paper investigates the impact of climate change on within-country income inequality for more than 150 nations over the period 2003–2017. Specifically, we control for a large number of determinants of income disparities detected by extant literature and focus on agriculture as one of the most crucial channels in the climate change-inequality nexus. We find that temperature increases and precipitation anomalies have significant adverse effects on within-country inequality, especially in the presence of larger shares of population in rural areas and of workers in agriculture. We also provide evidence on the lower distributional impact of climate change in economies that are more diversified across value chain activities. [ABSTRACT FROM AUTHOR]

• A spatial mode-based calibration model is developed to post-process short-term forecasts of precipitation fields. • The calibration model directly embeds spatial structure into calibrated ensemble forecasts. • Calibrated ensemble forecasts are shown to be of high quality at both grid-cell and basin scales. Statistical calibration of precipitation forecasts from numerical weather prediction (NWP) models is routinely performed grid-cell by grid-cell, aiming to produce accurate and reliable ensemble forecasts for precipitation fields. Calibrated ensemble members from different grid-cells are then connected using ensemble reordering to form spatially structured ensemble forecasts. However, ensemble reordering approaches, such as the well-known Schaake shuffle method, are often criticized for not considering real physical atmospheric states of precipitation events. In this paper, we propose a spatial mode-based calibration (SMoC) model for post-processing forecast precipitation fields and producing ensemble forecasts with an inbuilt spatial structure, so that ensemble reordering is not required. The SMoC model is developed based on spatial modes derived from empirical orthogonal function (EOF) analyses of precipitation fields and linear regressions of derived EOF expansion coefficients of the first few dominant modes. Unlike conventional calibration models that are applied to forecast grid-cells individually, SMoC is applied to the whole forecast fields, and the spatial structure is inherently present in calibrated ensemble forecasts. There is therefore no need to rely on ensemble reordering for spatial reconstruction. The performance of SMoC is evaluated by applying it to NWP forecasts of substantive precipitation events over the Brisbane drainage basin in eastern Australia. Cross-validated results show that SMoC calibrated forecasts are of high quality at both grid-cell and basin scales. The spatial structure of precipitation is found to be well embedded in the ensemble members of the calibrated forecasts for the basin. [ABSTRACT FROM AUTHOR]

• Propose a directed graph deep learning method for streamflow forecasting. • The method uses two processes to express the runoff and confluence process. • Hidden Markov regression is used to estimate forecast uncertainty. • The proposed method shows its excellent performance compared with other models. Reliable and accurate multi-step streamflow forecasting is of vital importance for the utilization of water resources and hydropower energy system. In this paper, a spatial deep learning model, directed graph deep neural network, is proposed for multi-step streamflow forecasting. The proposed model uses spatial information capture process and feature aggregation process to exploit multi-site hydrological and meteorological information. The spatial information capture process consists of multiple convolutional layers to extract the precipitation information of meteorological stations. And the feature aggregation process uses the multi-layer perceptron to aggregate the precipitation information and the streamflow information. The proposed model is applied in a real-world case study in the upstream of Yangtze River basin. Experimental results demonstrate that the proposed model significantly outperforms artificial neural network, Long Short-Term Memory Network, Gated recurrent unit and Convolutional Neural Network in terms of forecasting accuracy. In addition to the forecast accuracy, the hidden Markov regression is employed to quantify the forecasting uncertainty given by the directed graph deep neural network. The uncertainty estimation result demonstrates that the hidden Markov regression is able to handle the heteroscedastic and non-normal forecasting uncertainty given by directed graph deep neural network. [ABSTRACT FROM AUTHOR]

The Global climate reorganisation in the late Pliocene linked to enhancement of the Atlantic Ocean Thermohaline Circulation (AOTC), instigated a transition to glacial-interglacial cyclicity in the Quaternary. Enhancement of the AOTC amplified atmospheric precipitation over the Eurasian interior strengthening Northern Hemisphere Glaciation. Increased rainfall on the vast Russian Plain drained into the endorheic Caspian Sea, which makes the Caspian geological record highly potential for tracing atmospheric precipitation changes. Two major palaeohydrological events in the Caspian Sea, the Akchagylian transgression and the Akchagylian marine incursion, led to a five-fold enlargement of the Caspian Sea surface area and transformed the basin palaeoecology, enabling active interregional faunal dispersals. The Akchagylian Stage still lacks an unequivocal age model with two age constraints – the "long Akchagylian" (3.6–1.8 Ma) and the "short Akchagylian" (2.7–2.1 Ma) standing on magnetostratigraphic studies of geological records in Turkmenistan and the Kura Basin, respectively. The age discrepancies also exist within the Kura Basin, where the fossil mammal-bearing Kvabebi locality with Akchagylian marine fauna was magnetostratigraphically dated at 3.2 Ma. In this paper, we try to resolve the age contradictions for the Akchagylian Stage. We revisit the Kvabebi (Georgia) and Kushkuna (Azerbaijan) sections of the western Kura Basin and provide new magnetostratigraphic and 40Ar/39Ar age constraints on these marginal Akchagylian deposits. Moreover, we revise the magnetostratigraphy of 25 geological records from Turkmenistan and the Kura Basin and propose a new unified age model for the Akchagylian Stage: 1. Intrabasinal Akchagylian freshwater-mesohaline transgression at 2.95 ± 0.02 Ma; 2. Akchagylian marine incursion through establishment of a Caspian-Arctic connection (2.75–2.45 Ma); 3. Akchagylian–Apsheronian boundary highlighting a Caspian-Black Sea connection at 2.13 Ma. The sudden expansion of the Caspian Sea at 2.95 ± 0.02 Ma potentially correlates to the interglacial intensification of the AOTC between 2.95 and 2.82 Ma. The new ages constrain a much shorter (2.95–2.1 Ma) Akchagylian than in previously mentioned regional geological time scales (3.6–1.8 Ma) and strongly appeal to reconsider the ages of numerous archaeological and mammalian sites in the Caspian region. • Akchagylian transgression at ~2.95 Ma led to a five-fold expansion of the Caspian Sea. • The Caspian Sea level rose due to the switch to a positive water budget. • The Caspian Sea rise correlates with enhanced Atlantic Ocean Thermohaline Circulation. • Interregional correlation of the Akchagylian transgression to the upper Gauss chron. • New integrated age model places Akchagylian between 2.95 Ma and 2.13 Ma. [ABSTRACT FROM AUTHOR]

The paper presents the monitoring results of the temporal variability of daily 7Be activity concentration in the atmospheric surface layer at Sevastopol in 2011–2020. The 7Be activity concentration in individual samples of atmospheric aerosols varies from 0.1 to 13.3 mBq m−3 and averages 4.0 ± 2.0 Bq m−3. Higher 7Be activity concentrations have been observed during the period from May to August while lower concentrations have been measured from December to January. Quantitative estimates of the influence of precipitation (amount, duration, intensity) on the temporal variability of 7Be activity concentration have been obtained. It has been found that daily 7Be activity concentration decrease by 2–82% on the first day with precipitation. It has been shown that an increase in precipitation duration and a decrease in its intensity lead to a more significant decrease in the daily 7Be activity concentration. The estimates of the scavenging coefficient have been obtained; the average value is 0.6 ± 1.0 h−1. An increase in the precipitation intensity or amount is accompanied by a decrease in the 7Be scavenging coefficient. Mean 0.5-folding and residence times of 7Be activity concentration in the atmosphere during a moderate rain event are 2.9 ± 2.2 and 15.4 ± 13.6 h, respectively. The recovery of 7Be activity concentration in the atmosphere after precipitation has been investigated. It has been found that this process takes 1–2 days. The mean value of the reload coefficient is 0.94 ± 0.34 d−1. The relationship between the value of the reload coefficient and local meteorological parameters (air temperature, relative humidity, atmospheric pressure, wind speed, surface net solar radiation flux) has been investigated. No statistically significant correlations at a 95% confidence level between the reload coefficient and the considered meteorological parameters have been revealed. Parameterization describing a decrease in the daily 7Be activity concentration in the atmosphere due to precipitation and its recovery during the precipitation-free period has been introduced. • Atmospheric precipitation reduced the daily 7Be activity concentration by 2–82%. • Variation in the daily precipitation amount did not affect the 7Be scavenging ratio. • The 7Be scavenging coefficient anticorrelated with precipitation intensity/amount. • It takes 1.1 ± 0.6 days to recover 7Be activity concentration after precipitation. • Local meteorological conditions did not affect the 7Be recovery rate. [ABSTRACT FROM AUTHOR]

• Dynamic yield prediction with the integral regression model at any time intervals. • Remote sensing-based TVDI replaces precipitation to represent the actual amount of water available to crops. • We couple the three types of factors (sunshine hours, temperature, and precipitation/TVDI) into a comprehensive factor for yield prediction. • The combination of sunshine hours, temperature, and TVDI has better dynamic yield prediction results. This paper describes integrating remote sensing and meteorological data for dynamic winter wheat yield prediction at fixed date intervals with an integral regression model. In most cases, both rainfall and irrigation supply the water needs of crops. We postulated that meteorological precipitation could not fully represent the actual amount of water available to crops, whereas the temperature vegetation dryness index (TVDI) can represent actual water, which monitors soil moisture. According to this, meteorological and remote-sensing data based on sunshine hours, temperature, and TVDI formed a light–temperature–water data combination, which are the three most fundamental and critical factors related to crop growth. The new data combination was used as the input variables of the integral regression model, which could predict the crop yield at a fixed date interval of any length. The dynamic results were then compared with that of the meteorology data combination (sunshine hours, temperature, and precipitation). We selected a total of seven county-level areas in semi-humid and arid regions in China. The proposed dynamic prediction approach achieved good results at the county level from March 21 to maturity at 16-day intervals with predicted R2 > 0.94 and root mean squared error range of 15.54–121.01 kg ha−1 between predicted and actual yield with leave-one-year-out cross-validation for each prediction date. The trend of dynamic prediction accuracy had a relatively stable state. In addition, TVDI contained more crop water information, and prediction results using TVDI and meteorology were generally superior to that using only meteorology for semi-humid and arid areas, with the best results for arid regions. Thus, integrating meteorology and TVDI could effectively dynamically predict yield at county level for most agroclimatic regions, and particularly for arid regions. The work has implications for dynamic yield prediction and improving prediction accuracy and can be extended to other areas. [ABSTRACT FROM AUTHOR]

Amino acids in rain samples offer important information on their origin and roles in biogeochemical cycling. This paper describes the development and validation of an alternative analytical procedure for amino acids in atmospheric precipitation using gas chromatography coupled with mass spectrometry (GC–MS). The method detection limits (MDLs) of these amino acids ranged from 1.46 ng L−1 (Proline) to 135 ng L−1 (Arginine), which were much lower than previous methods like high-performance liquid chromatography (HPLC) and comparable to those with liquid chromatography-tandem mass spectrometry (LC-MS/MS). None of the target analytes were detected in blank samples, which was attributed to the optimized pretreatment protocol we adopted. We conclude that the GC–MS based technique developed in this study possesses advantage due to low detection limits. We further demonstrate the applicability of this method by analyzing rain samples from the background atmosphere of the Tibetan Plateau (TP). Similar relative abundances of amino acids were found in the TP rains, marine rains and Arctic ice. The correlations between FAAs and water- soluble ions in the Tibetan Plateau rainwaters showed that the FAAs were most likely originated from biomass burning. Given the similarity of snow and rain samples, our method also provides potential for the future analysis of amino acids in glacier ice cores. • A GC–MS analytical method for amino acids in precipitation was performed and optimized. • The developed method was characterized with high sensitivity and selectivity, while low blank value. • This is the first time that free amino acids were determined in the Tibetan Plateau rain. [ABSTRACT FROM AUTHOR]