Miracle-dz/UniFAR-FTUs huggingface dataset:
Rural measurements of the chemical composition of airborne particles in the eastern United States

Quantitative measurements of particulate composition were made at three rural sites: in central South Dakota, on the Louisiana Gulf Coast, and in the Blue Ridge Mountains of Virginia. The first two sites were selected to determine background concentrations in continental polar and maritime tropical air masses, respectively, which affect the eastern United States during the summer. The Virginia site was selected as a receptor site, downwind of the midwestern source areas. The South Dakota data established the background concentrations. These concentrations were similar to the levels in Louisiana when air parcels arrived from the Gulf of Mexico, without recently passing over the United States. Levels of fine particles (diameters less than 2.5 μm) were highest in Virginia and were due chiefly to sulfate. Using trajectory and statistical analyses, it is shown that the residence time of an air parcel over the midwestern source area was the most important variable in determining the sulfate levels in the Blue Ridge Mountains.
The importance of meteorology in determining surface ozone concentrations - a neural network approach

In this paper are investigated the effects of local meteorology on surface ozone concentrations on an hourly basis for a period of at least one year in a complex coastal area where land-sea breezes influence ozone concentrations. Because ozone formation is a non-linear process, feed-forward back propagation neural network was developed to model hourly ozone concentrations from meteorological and NoX data. This method can weight relationships that are difficult to subjectively quantify and allows non-linear relationships between variables. The use of additional meteorological and nitrogen oxides dioxide (NO and N02) time series as a potential inputs of the forecasting process is also considered.
Social transformations in the Late Neolithic and the Early Chalcolithic periods in central Anatolia

Abstract This article explores the character of social transformations within Late Neolithic and Early Chalcolithic communities of central Anatolia. This comprises the demise of neighbourhood communities that formed the social basis of the Early Neolithic period and the emergence of the household as a well-defined and autonomous entity. These changes are examined by focusing mainly on settlement patterns, the organisation of space and changes in architecture. The transformations are examined on the microscale, using Çatalhöyük as a case study, and on a regional scale focused on three areas of central Anatolia: the Beyşehir-Seydişehir area, the Konya plain and the Cappadocian region. Özet Bu makalede, Orta Anadolu'nun Geç Neolitik ve Erken Kalkolitik toplumlarında yaşanan sosyal dönüşümlerin karakteri araştırılmaktadır. Bu evre, Erken Neolitik Çağın sosyal yapısının temelini oluşturan komşu ilişkilerinin çöküşünü ve sınırları belirlenmiş, özerk bir bütünlüğü simgeleyen aile biriminin ortaya çıkışını içerir. Bu değisimler daha ziyade yerleşim dokusu, alan kullanımı ve mimarideki değişimler üzerine odaklanarak incelenmiştir. Dönüşümler, Çatalhöyük örneği temel alınarak mikro ölçekte, Orta Anadolu'nun üç ayrı bölgesine (Beyşehir-Seydişehir, Konya Ovası ve Kapadokya) odaklanarak da bölgesel ölçekte ele alınmıştır.
Chromosomal Instability in Pathogenesis and Prognosis of Colorectal Cancer

Background: Approximately 50 percent of CRC patients ultimately develop metastatic disease and adjuvant chemotherapy for stage III CRC has proven to be benefi cial. For stage II colorectal cancer adjuvant chemotherapy is not standard, although this group probably harbours a subgroup of patients that will develop metastatic disease and could benefi t from adjuvant chemotherapy. The aim of this study was to investigate if certain chromosomal aberrations are related with patient’s survival. Material & Methods: Eighteen CRCs were analyzed by comparative genomic hybridization (CGH) and their DNA copy number profi les were correlated with the survival status of the patient. Results and Conclusion: Gain of 20q was signifi cantly associated with worse patient survival ( P =0.002) and therefore may serve as an indicator of poor patient outcome, and especially in patients with stage II CRC this could have clinical implications.
Portal Chromosomal Instability in Pathogenesis and Prognosis of Colorectal Cancer

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A review of the evolution, biostratigraphy, provincialism and diversity of Middle and early Late Triassic conodonts

The taxonomy, diversity, evolutionary lineages, and stratigraphical distributions of Middle and early Late Triassic conodonts are reviewed and re‐evaluated. Twenty‐five genera are recognized in the Middle and early Late Triassic, including a new genus cited in open nomenclature. Of these, 24 genera are assigned to two families and seven subfamilies. The family Gondolellidae consists of the subfamilies Cornudininae, Epigondolellinae, Neogondolellinae, Novispathodinae, Paragondolellinae and Pseudofurnishiinae. The family Gladigondolellidae is monotypic, consisting of the subfamily Gladigondolellinae. The genus Neostrachanognathus is not assigned to any family or subfamily as its origin is unclear. Conodont provincialism was low in the early Anisian, but from the late Anisian faunistic differences started to increase and became stronger during the early Ladinian, reaching a peak around the mid‐Ladinian. Provincialism remained strong until the earliest Carnian and changed to an all‐Triassic low in the early Tuvalian. The provincialism between North America and Tethys rebounded on the specific level during the late Tuvalian. Diversities on generic and specific levels have been established, and two major conodont diversity cycles are recognized: the first ranges from the Bithynian (early Anisian) to the Julian (late early Carnian), and the second is restricted to the Tuvalian (late Carnian).
Detecting Algal Bloom Using Cygnss and ERA-5 Data

Algal bloom has become a serious environmental problem caused by the overpropagation of planktons in many water-bodies, and effective remote sensing methods for monitoring it are urgently needed. Global Navigation Satellite System (GNSS)-Reectometry (GNSS-R) has been developed rapidly these years. The reflected GNSS signals received by GNSS-R satellites carry information (e.g., the surface roughness) about the specular points. When algal bloom emerges, the water surface will turn smoother, which could be detected by GNSS-R. In addition, meteorological factors also perform a key role in the formation of algal bloom. In this article, a new machine learning aided GNSS-R algal bloom detection method with the auxiliary of meteorological data is established. This work employs the Cyclone GNSS (CYGNSS) data and the fifth generation of European Reanalysis data with the application of Random Under-Sampling Boost (RUS-Boost) algorithm. During the evaluation stage, the test True Positive Rate of 80%, overall accuracy of 84.1% and the Area Under (Receiver Operating Characteristic) Curve of 0.9 were achieved when all the considered GNSS-R observables and meteorological factors are involved. Meanwhile, the contribution of each meteorological factor was also evaluated.
Unsupervised Multi-Spectral Image Super-Resolution Based on Conditional Variational Autoencoder

Unsupervised super-resolution aims to enhance the quality of images without high-resolution (HR) labels during the training stage, making it applicable to real-world scenarios. However, unsupervised super-resolution methods face the challenge of effectively learning the internal structure of images due to the absence of high-quality HR images as references. Moreover, multi-spectral remote sensing images often contain stochastic features caused by cloud and fog occlusions. These occlusions make it difficult to achieve accurate reconstruction of occluded areas through direct modeling of deep features in multi-spectral images. In this paper, we propose a method inspired by conditional variational autoencoders to address the issue of stochastic features in unsupervised multi-spectral super-resolution. Additionally, we introduce a channel attention feature fusion module to combine two types of features. We evaluated our unsupervised multi-spectral image super-resolution method using a real satellite remote sensing dataset. Experimental results demonstrate the qualitative and quantitative effectiveness of our approach.
Deep Learning for Polarimetric Radar Quantitative Precipitation Estimation: Model Interpretation and Explainability

Real-time and accurate precipitation estimation is critical for environmental protection and water resources management. Compared to traditional methods, i.e., radar reflectivity (Z) and rainfall rate (R) relations, relying on local raindrop size distributions, the deep learning model can fit the functional relationship between radar observations and rainfall rate measurements. However, the black-box nature of deep learning models makes it difficult to explain the physical mechanisms behind their results. To address this problem, this study proposes DQPENet, a deep learning model for polarimetric radar QPE utilizing dense blocks. We employ a permutation test to understand the relative importance of different radar data input variables. Additionally, we propose a regression importance value (RIV) method for the precipitation estimation task to visualize feature importance regions. Our experimental results show that radar reflectivity and specific differential phase at the lowest elevation angle are the two most important observables for the model’s precipitation estimation. Furthermore, we find that radar data closer to the rain gauge are more influential on the model’s results, indicating that the deep learning model is able to capture the underlying physical mechanism of atmospheric data.
Yields and qualities of pigeonpea varieties grown under smallholder farmers' conditions in Eastern and

) varieties on farmers’ fields in Eastern and Southern Africa where intercropping with maize is normal practice, was undertaken. The varieties were tested for accumulation of dry matter (DM), nitrogen (N) and phosphorus (P) in all above-ground organs for three years under farmers’ conditions. The study revealed that the latest introduced ICEAP 00040 outperformed all the other tested varieties (ICP 9145; ICEAP 00020, ICEAP 00053, ICEAP 00068, and a local variety called “Babati White”) under farmer-managed conditions. The harvest indices (HI), ranging from 0.08 - 0.15 on dry matter (DM) basis, were relatively low and unaffected (P>0.05) by the environmental variation. The N harvest index (NHI) was 0.28 and P harvest index (PHI) was 0.19. The better responses of ICEAP 00040 to favourable conditions could however only be realised in a minority of cases as yields generally were low. These low yields are still a major challenge in African smallholder agriculture as pulses play an important role in soil fertility maintenance as well as in the household diets. Key words:
OVERCONSOLIDATION OF ALLUVIAL CLAYS

Alluvial clay, widely distributed throughout the world, is generally in an overconsolidated state. The phenomenon occurs in spite of the fact that the clay has not undergone stress release. This is attributed to development of an additional structure induced by aging. In this paper, the overconsolidated state of three alluvial clays found in Tokyo Bay, Singapore, and the Arabian Gulf are presented as well as a discussion of the causes of overconsolidation and the formation process of each clay.Characteristics of overconsolidation caused by aging such as secondary compression, chemical bonding, and desiccation stress are first discussed. Effective overburden stress, σ^-υo, consolidation critical stress, σ^-c, and overconsolidation ratio, OCR, of the three clays are presented. Causes of overconsolidation and the formation process of each clay are evaluated from 1) the σ^-υo-σ^-c-OCR relationship, and 2) the changes in sea level from deposition to the present. It is concluded that 1) chemical bonding, 2) secondary compression, 3) cementation, 4) oxidation, and 5) desiccation stress are the causes of overconsolidation in the three clays.
Tree Species Classification of Point Clouds from Different Laser Sensors Using the PointNet++ Deep Learning Method

Tree species information is a crucial factor in forest resource inventory. Light detection and ranging (LiDAR), as an emerging active remote sensing technology, has unique advantages in extracting three-dimensional (3-D) vegetation structure information, and its application in forest resource assessment and research is gaining increasing attention. Airborne laser scanning (ALS), unmanned aerial vehicle laser scanning (UAVLS) and terrestrial laser scanning (TLS) are important means to acquire 3-D forest data. The challenge of traditional machine learning based tree classification lies in extracting and selecting numerous key diagnostic features from large amounts of LiDAR data, requiring extensive feature extraction expertise, which limits its scalability. The use of deep learning methods for fast and accurate classification and identification of tree species in individual tree point clouds represents a new development direction of LiDAR technology in forest resource inventory applications. In this study, PointNet++ was used to classify tree species by point cloud data obtained from TLS, ALS and UAVLS, respectively. The research results show that high accuracy in tree species classification can be achieved by using point cloud deep learning methods.
Regolith Thermophysical Features of Mare Smythii in Vertical Direction Revealed by CE-2 MRM Data

Mare Smythii is a special basin, which is one of the oldest mare basins with the considerably young mare basalts and the high density of floor-fractured craters. In this paper, the Chang’E-2 (CE) lunar microwave radiometer (MRM) data was used to evaluate the thermophysical features of the floor deposits in mare Smythii. Based on the theoretical simulation and the previous geological results, several special findings are noted as follows. (1) The substrate temperature in mare Smythii is likely fairly high. (2) The regolith thermophysical parameters change greatly with depth in the northeast unit, and there probably exists a special material in the shallow layer of the lunar regolith with strong thermal absorption ability. These special findings will be of fundamental significance to improve understanding the thermal evolution of the Moon.
Innovation clusters in European regions Preliminary version

This paper investigates on the presence of innovation clusters in European regions. The analysis is based on an original statistical databank set up by CRENoS on regional patenting at the European Patent Office spanning from 1978 to 2001 and classified by ISIC sectors (2 digit). We consider 175 regions of 17 countries in Europe, the 15 members of the European Union plus Switzerland and Norway. Firstly, an analysis of the spatial distribution of innovation activities in Europe is performed. Some global and local indicators for spatial association are presented, summarising the presence of a general dependence process in the distribution of the phenomena under examination. The analysis is implemented for 23 manufacturing sectors to assess for the presence of significant differences in the their spatial features. Moreover, the extent and strength of spatial externalities are evaluated for three different periods: 1988-90, 1994-96 and 1999-01. Secondly, this paper contributes to the analysis of the process of spatial agglomeration of innovative activities by investigating directly its determinants. Our main purpose is to identify the extent to which the degree of specialisation or diversity in a region may affect the innovative output in a particular local industry. Other local externalities are also tested such as home market effect and other agglomeration phenomena. Moreover the geographical extent of such effects is measured by means of the usual tests of spatial econometrics.
HAHNet: A convolutional neural network for HER2 status classi�cation of breast cancer

Background: Breast cancer is a major health problem for women. Human epidermal growth factor receptor-2 (HER2) is a very important diagnostic and prognostic factor for breast cancer, and HER2 status classiﬁcation is essential for the development of treatment plans for breast cancer. Generally speaking, pathologists will adopt immunohistochemistry (IHC) to assess HER2 status, which requires additional economic costs. Furthermore, the manual assessment of HER2 status is time-consuming and error-prone. In recent years, deep learning has been widely used in medical ﬁeld and has attained great achievements. However, the existing deep learning methods for HER2 status classiﬁcation of conventional hematoxylin and eosin (H & E) stained images are not accurate enough. Results: To address these problems, a neural network model named HAHNet is proposed in this paper. HAHNet combines multi-scale features with attention mechanisms, which is able to directly classify HER2 status of H & E stained histological images of breast cancer. Typically, the HAHNet network mainly includes convolution preprocessing, attention mechanism, downsampling, and multi-scale feature extraction. The experimental results show that HAHNet outperforms other existing methods with regard to six metrics of Accuracy, Sensitivity, Precision, F-score, MCC, and AUC. Conclusions: Collectively, the above experiments demonstrate that our proposed HAHNet achieves high performance in classifying the HER2 status of breast cancer using only H & E stained samples, which can be used in case classiﬁcation and helps to reduce the cost required for diagnosis.
Explorer Phylogeographic analysis and identification of factors impacting the diffusion of Foot-and-Mouth disease virus in Africa

Foot and mouth disease (FMD) is endemic in sub-Saharan Africa. Due to the complexity of the disease epidemiology and the lack of data available, there is a need to use modeling approaches to ﬁll the gaps in our understanding of the virus circulation on this continent. Using a phylogeographic approach
Spatial and temporal variability of windborne dust in the Sahel-Sahara zone in relation with synoptic environment

Windborne dusts have been recognised as climatic indicators in the Sahel region, because of their ability to cause reduction in horizontal visibility. These phenomena have intensified after the exceptionally drought periods in the 1970s over the Sahel region. In this work, we have analysed the spatial and temporal evolution of visibility reduction in the Sahel-Sahara zone between 1957 and 1987. The results showed that windborne dusts were more frequent and severe over the western coast of West Africa. The zone characterised by the maximum variability included largely, the Sahel region (15° to 20°N). Stations in this region were characterised by a progressive reinforcement of suspended windborne dust between 1970 and 1987. Dust emissions in the Sahelian region increased during dry years and were associated with weak monsoon flux, African Easterly Jet (AEJ) reinforcement and weak Tropical Easterly Jet (TEJ).
A Fuzzy Q-learning-based Approach for Auto-scaling and Computation O�oading in Edge/Cloud Computing

The fast growth of under developing mobile applications in recent years has emerged a diversity of delay-sensitive applications such as multimedia streaming, virtual reality, augmented reality, and online gaming applications to facilitate daily activities in different aspects of human life. Edge computing has been raised as an Internet-based distributed computing model to enable mobile devices to offload tasks to nearby edge servers rather than transfer them to remote cloud servers. A joint auto-scaling and task offloading approach in edge/cloud computing is proposed in this paper. Due to dynamic changes in usage and access to mobile applications over time, it requires addressing their workload fluctuations as challenging issues. The future workload is predicted using long short-term memory (LSTM) network, supported with the differential evolution (DE) algorithm for selecting the LSTM hyperparameters. A fuzzy Q-learning technique is also utilized to make scaling decisions at runtime, and a learning automata-based technique is used to make decisions on offloading tasks of mobile devices to edge/cloud layers. The proposed approach is validated using the iFogSim simulator under synthetic and real-world patterns. The results show that it achieves better performance in terms of execution time, energy consumption, and delay violation compared to the baseline approaches.
A Two-Step Method for Winter Wheat Leaf Chlorophyll Estimation from UAV Hyperspectral Imagery

Leaf chlorophyll content (LCC) is a critical indicator for precision agriculture. Accurately estimating winter wheat LCC based on remote sensing at high spatial resolution is of great significance for agricultural management and decision. In this study, a two-step method was used to retrieve wheat LCC from UAV hyperspectral imagery. The first step is converting canopy reflectance to leaf reflectance using Look-up tables (LUTs) generated from the unified model of bidirectional reflectance distribution function (BRDF). The second step is retrieving wheat LCC from derived leaf reflectance using the PROSPECT-PRO model. Retrieved wheat LAI, leaf reflectance, and LCC were validated against field measurements. The results indicate a good agreement between retrieved and measured LAI with RMSE of 0.092 and R2 of 0.605. Leaf reflectance retrieved from UAV canopy reflectance exhibit good consistency with measured leaf reflectance with RMSE of 0.017 and R2 of 0.962. Retrieved wheat LCC is of good quality compared to measured LCC, with R2 of 0.7675 and RMSE of 4.33 μg/cm2. In conclusion, this study holds potential in estimating wheat LCC from UAV hyperspectral imagery.