Miracle-dz/UniFAR-FTUs huggingface dataset:
Back-Illuminated Double-Avalanche-Region Single-Photon Avalanche Diode

The key features of a single-photon avalanche diode (SPAD) are its ability to detect a single photon and provide a digital signal output. The avalanche multiplication process, which generates a detectable electrical signal that can be amplified up to a high voltage without the need for additional circuits, allows SPADs to detect individual photons. Specifically, a SPAD fabricated in CMOS technology can detect near-infrared (NIR) signals, which is a crucial requirement in many applications such as light detection and ranging (LiDAR), time-of-flight (ToF) imaging, and NIR optical tomography. These applications require specific performance characteristics, for example, high photon detection probability (PDP). In this article, we propose an optimized SPAD developed based on 40 nm backside illuminated (BSI) CMOS image sensor (CIS) technology. The SPAD is designed and fabricated using a heavily doped p-type (P+) and a retrograde doped deep n-well (DNW) junction. The doping-optimized guard-ring (GR) for the expansion of the avalanche multiplication region maximizes PDP, while maintaining its original capability, premature edge breakdown (PEB) prevention at the edge of the junction. We demonstrate the effectiveness of GR optimization by comparing the electrical and optical experimental results with the conventional SPAD. The proposed double-avalanche-region (DAR) SPAD achieves a peak PDP of about 89% at the wavelength of 700 nm and a PDP of 45% at 940 nm, which are the highest values among SPADs reported so far at the excess bias voltage of 2.5 V. The dark count rate (DCR) is 27 cps/μm2 and the full width at half-maximum (FWHM) of the timing jitter is 89 ps at the same operating condition.
Synthesis of Directional Modulation LFM Radar Waveform for Sidelobe Jamming Suppression

This study proposes a directional modulation linear frequency modulated (DM-LFM) waveform synthesis approach to enhance the sidelobe jamming suppression capacity in radar systems. The DM-LFM waveform expression is modeled with a transmit phase matrix in the phased array antenna. The DM-LFM waveform synthesis criterion is formulated to reduce the phase distortion and power loss in the desired beam direction and simultaneously maximize the mismatch between the transmit waveforms in the mainlobe and sidelobe jammer regions. Thereby, the DM-LFM waveform provides the beam with the freedom to scramble the radar transmit waveform toward the sidelobe. The transmit phase matrix is iteratively solved using a combination of the least ${P}$ -norm, the alternating direction method of multipliers (ADMM), and the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithms. Simulation results demonstrate that the DM-LFM has a moderate power loss of 2.7 dB and a minor phase error of 0.37° when using an eight-element phased array. In the jammer region, the peak level (PL) of the matched filtered output of DM-LFM is one order of magnitude lower than that of the traditional phased array (TPA). The sidelobe jamming suppression performance of DM-LFM improves, as the jammer region narrows and the number of antenna elements increases. The DM-LFM shows an excellent sidelobe jamming-resistant performance.
Pregnancy Complications and Feto-Maternal Monitoring in Rabbits

Simple Summary Rabbit farming plays a significant role in the global production of animal-derived protein, with hundreds of millions of rabbits being produced every year. However, research on feto-maternal monitoring in rabbits remains limited. This review aims to explore intricate factors affecting maternal and fetal health, providing valuable insights for farmers and researchers alike. Standardizing monitoring approaches, such as ultrasonography and other screening methods, and establishing reference parameters would enable more effective tracking of fetal well-being. This, in turn, would facilitate differentiation between healthy and unhealthy rabbits, offer guidance for treatments, and enhance rabbit welfare and reproductive efficiency, thereby holding substantial promise for the rabbit farming industry. Abstract Rabbit production holds significant relevance in modern agriculture due to its potential as a sustainable source of high-quality protein and efficient feed conversion, contributing to food security and economic diversification. Nevertheless, studies incorporating feto-maternal monitoring in this species are uncommon. This review gathers research on the monitoring and evaluation of factors affecting rabbit gestation, providing a better understanding of the causes of prenatal development abnormalities. These include studies regarding how chronic maternal hypertension, gestational diabetes, maternal stress, ectopic gestation, maternal uterine ischemia and fetal hypoxia, intrauterine growth restriction, superfetation, maternal age, maternal nutritional status, maternal physical condition, maternal and embryonic genotype, and the intrauterine location of rabbit fetuses can potentially impact rabbits’ reproduction and maternal and fetal health. Among other monitoring techniques, ultrasonography, considered one of the best tools for diagnosing pregnancy and conducting follow-up, is also reviewed. Details on measurable fetal-development parameters in rabbits and precautions to be considered before and during the examination are also provided. Additional studies are required to understand why some events occur and their consequences throughout gestation, allowing the determination of new biomarkers or cut-offs that can be helpful for early diagnosis and improve reproductive efficiency.
Characteristics of Metabolites in the Development of Atherosclerosis in Tibetan Minipigs Determined Using Untargeted Metabolomics

Background: Atherosclerosis (AS) is a chronic progressive disease caused by lipometabolic disorder. However, the pathological characteristics and mechanism of AS have not been fully clarified. Through high-fat and high-cholesterol diet induction, Tibetan minipigs can be used as the AS model animals, as they have a very similar AS pathogenesis to humans. Methods: In this study, we built an AS model of Tibetan minipigs and identified the differential abundance metabolites in the development of AS based on untargeted metabolomics. Results: We found that sphingolipid metabolism and glucose oxidation were obviously higher in the AS group and phenylalanine metabolism was reduced in the AS group. Moreover, in the development of AS, gluconolactone was enriched in the late stage of AS whereas biopterin was enriched in the early stage of AS. Conclusions: Our research provides novel clues to investigate the metabolic mechanism of AS from the perspective of metabolomics.
Impact of Behavioural Traits on Reasons for Commencement of Business Venture by New Age Indian Entrepreneurs

Entrepreneurs play a very important role in a country’s economy by creating jobs and providing the much-needed employment, which is an essential aspect in a vastly populated country such as India. They are often said to be the backbone of an economy. In this study, entrepreneurs who have established their businesses in India after 1991, have been named as new-age entrepreneurs, and they are the area of interest of this study. 1991 has been considered as a benchmark year for this type of classification, since it gave rise to significant changes in the Indian economy with the opening up of the economy. As per GEM (Global Entrepreneurship Monitor) 2021 data, four out of five Indians (adults) or greater perceived the availability of good opportunities to commence a business in the country and also according to them it is easy to begin a business. However, more than 50% of these adults did not begin a new business, according to GEM 2022 data. Hence, it is very alarming and one needs to investigate the reasons for new venture creation by the entrepreneurs, which will in turn motivate others to do the same. This study has examined the behavioural traits of new-age Indian entrepreneurs and how these traits have an impact on commencement of their businesses, and see whether any pattern arises.
Conserving land: population and sustainable food production

This report considers the impact of population size growth and distribution as well as consumption patterns and agricultural technology on the amount of arable land and sustainable food production (to feed present generations without jeopardizing food for the future) worldwide. The report opens with a brief overview of the current security of the global food supply and then discusses the factors that could limit the expansion of the food supply to match future population growth such as 1) the closing of the agricultural frontier 2) the 1985 peak in per capital global crop production 3) the fact that farming and other human activities may have siphoned off enough fresh water to account for 2 inches of the 6-inch increase in the global sea level 4) the reduction in plant-growing potential of land equal in area to China and India combined since World War II and the degradation beyond restoration of land equal in size to Hungary 5) deteriorating soil quality and 6) the increasing resistance of crop pests to pesticides. The ways in which population growth affects the availability of arable land are analyzed and the implications of this relationship are considered. Finally suggestions are made about how the development strategies of increasing agricultural production conserving natural resources and stabilizing population can be used together to ensure lasting food security. This report also considers social and economic issues such as the fact that women provide most of the work in subsistence farming and need productive and dignified livelihoods. Data provided in the report quantifies the average availability of arable land to people in 125 countries with populations exceeding 1 million people for the time periods 1960-61 1990 and 2025 under the UNs most recent low medium and high population projections and with the assumption that the amount of arable land will remain constant. In addition to the text information is presented in "boxes" on the earths arable land and population definitions of key terms and concepts soil as a critical natural resource Chinas land "super rice" and population growth the special case of Africa arable land and conflict breadbasket countries the qualified success in Machakos and women and the land.
Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product

Carbon emissions from forest fires release large amounts of carbon and have important implications for the global and regional carbon cycle and atmospheric carbon concentrations. Considering the significant spatial and temporal variations in different forest fires, this study explores the relationship between different forests and carbon emissions from forest fires. This study developed a high-resolution (0.05° × 0.05°) daily global inventory of carbon emissions from biomass burning during 2016–2022. The inventory estimates of carbon emissions from biomass burning are based on the newly released FY-3 data product, satellite and observational data of biomass density, and spatial and temporal variable combustion factors. Forest fire carbon emissions were assessed using active fire data from FY-3 series satellites from 2016 to 2022, and it was linearly compared with GFED, FEER, and GFAS data on time and spatial scales with R2 of 0.7, 0.73, and 0.69, respectively. The results show spatial patterns of forest cover and carbon emissions, with South America, Africa, South-East Asia, and northern Asia as high-emission zones. The analysis shows an overall upward trend in global forest fire carbon emissions over the study period. Different types of forests exhibited specific emission patterns and temporal variations. For example, most needleleaf forest fires occur in areas with low tree cover, while broadleaf forest fires tend to occur in areas with high tree cover. The study showed that there was a relationship between inter-annual trends in forest fire carbon emissions and land cover, with biomass burning occurring mainly in the range of 60–70% tree cover. However, there were also differences between evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, deciduous needleleaf forest, and mixed forest indicating the importance of considering differences in forest types when estimating emissions. This study identifies the main sources of carbon emissions from forest fires globally, which will help policymakers to take more targeted measures to reduce carbon emissions and provide a reliable basis for appropriate measures and directions in future carbon mitigation actions.
Development of a multivalent adjuvanted inactivated vaccine against variant arthrotropic avian reoviruses

Variant avian reoviruses (ARVs) are economically important emerging pathogens of poultry, which mainly affect young broiler chickens and cause significant production losses. Currently, there are no effective commercial vaccines available for control and prevention of emerging variant ARVs. In this study, monovalent inactivated adjuvated (20% Emulsigen D) broiler breeder vaccines containing antigens from ARV genotype cluster (C) group -2, -4, -5, or -6, and a multivalent vaccine containing antigens from all the four indicated genotypic cluster groups were developed and evaluated for their efficacy in protecting broiler progenies against homologous or heterologous ARV challenge. The use of monovalent or multivalent inactivated vaccines in a prime-boost immunization strategy induced the production of ARV specific antibodies in broiler breeders. The maternal antibodies were effectively transferred to broiler progenies. Broiler progenies obtained from immunized breeders demonstrated milder clinical symptoms and reduced gross and histopathological lesions after homologous ARV challenge. More severe gross and histological lesions were observed in challenged progenies from unvaccinated broiler breeders. However, cross protection was not observed when either of the monovalent-vaccine groups were challenged with a heterologous virus. In addition, the progenies from the unvaccinated ARV challenged control or heterologous ARV challenged vaccinated groups had significantly reduced body weight gain (p < 0.01) than the unchallenged-control, challenged-multivalent, or homologous ARV-challenged monovalent vaccine groups. However, homologous ARV challenged progenies in the multivalent or monovalent vaccine groups had similar body weight gain as the control unchallenged group with significantly reduced viral load (p < 0.01) in the gastrocnemius tendon tissue. This study indicates that broad-spectrum protection of broiler progenies from variant ARV infections is feasible through the development of multivalent vaccines after proper characterization, selection and incorporation of multiple antigens based on circulating ARV genotypes in targeted regions.
Karakteristik Sistem Awan Konvektif Cumulonimbus Berbasis Radar Cuaca C-Band di Sekitar Bandara Internasional Sultan Hasanuddin Makassar

20-220 km dan panjang rata-rata awan 65 km dominan membentuk sistem linier. Morfologi yang paling banyak terbentuk adalah Trailing Stratiform . Pada musim peralihan, inisiasi Cumulonimbus dominan di dataran tinggi dan pada musim hujan inisiasi dominan di laut pesisir. Secara umum Cumulonimbus matang di laut pesisir pada siang hari. Cluster of Cells dan Bow Echo adalah sistem yang dominan terjadi di darat, dengan windshear yang relatif lebih kuat dari semua sistem konvektif. Sistem sebagian besar berpropagasi lambat / slow-moving (< 3 m/s). Vertical wind shear kategori kuat rata-rata terjadi pada lapisan 0,3 – 2 km.
Spin-Polarized 2D Electron Gas at the EuTiO3/EuO Interface

Perovskite oxide heterostructures have provided opportunities for new technologies and materials with novel properties.In particular, researchers have been interested in the magnetism and two-dimensional electron gases that form at the interface of many of these heterostructures. Often, these properties are due to polar discontinuities, but here we use first-principles calculations to examine a heterostructure whose elements, EuO and EuTiO3, are both non-polar, yet are still conductive due to oxygen vacancies. Furthermore, the free electrons at the interface of this heterostructure are predicted to be spin-polarized, opening up possibilities for future research and devices.
Methodological issues of modelling farm and landscape scale indicators for sustainable land systems

Abstract Geografisk Tidsskrift, Danish Journal of Geography 106(2): 35–43, 2006 This paper presents results from the strategic EU research project MEA-scope (www.MEA-scope.org), which aims at the development of a GIS-based framework to model farm and landscape scale indicators for rural sustainability. Problems in scaling information between the farm- and the landscape levels in particular are addressed. Both disaggregation and aggregation techniques are needed to convey information between the two levels. This is demonstrated in a case study of (1) disaggregating landscape level soil maps for farm level modelling, and (2) subsequent landscape level aggregation of the farm model results. This interaction between farm- and landscape level information sources gives the opportunity for integrated modelling of farm level indicators (e.g. nutrient balances, energy use, farmers age, employment etc.) and landscape level indicators (e.g. groundwater quality, corridors between habitats and population density). The case studies illustrate the modelling and scaling of Nitrogen surplus as one indicator for water pollution in one of the MEA-scope landscapes, but the case studies has general application for the modelling of other indicators in other landscapes, and in other projects. In this context, the MEA-scope framework is being discussed in comparison to the conceptual framework of the Global Land Project (www.globalland-project.org), which reveals different shortcomings in the MEA-scope approach and lessons for future research work on the interface between land use systems, ecological systems and social systems.
Performance Prediction in UAV-Terrestrial Networks With Hardware Noise

To enhance the service quality of the unmanned aerial vehicle (UAV), the UAV-aided Internet of Things (IoT) systems could deploy a Deep Neural Network (DNN) for performance prediction for the users. Non-orthogonal multiple access (NOMA) is applied to such networks in order to improve spectrum efficiency, and results in improved quality of service at the ground users under the mobility of UAV. The outage and ergodic capacity requirements of the IoT users may not be satisfied due to some imperfect system parameters such as hardware noise. A DNN-based algorithm for performance prediction and the design of multiple antennas at the UAV under hardware noise is proposed. In this DNN-based UAV-NOMA, the central controller (server) collects system parameters periodically based on observing the state of IoT system and performs adjustments to the dynamic environment. The closed-form expressions for the outage probability and the ergodic capacity are derived to evaluate the performance of a group of IoT users. Our numerical results demonstrate that: i) In contrast to the traditional UAV-NOMA system, the UAV cannot know the performance at each IoT user in order to adjust the parameters (i.e. power allocation factors) before transmitting the signals to the devices; while the proposed DNN-based IoT system is capable of predicting the performance; ii) The performance of the IoT users can be significantly improved by integrating more antennas at the UAV and limiting levels of hardware noise; iii) By designing NOMA, the UAV-NOMA-based IoT system can increase the throughput to the tune of 40% compared with the benchmark (the orthogonal multiple access (OMA)-based IoT).
Role of aggressive locoregional surgery in treatment strategies for ipsilateral supraclavicular lymph node metastasis of breast cancer: a real-world cohort study

Background: Breast cancer patients with synchronous ipsilateral supraclavicular lymph node metastases (ISLNM) have unfavorable prognoses. The role of supraclavicular lymph node dissection (SLND) as a surgical intervention in the treatment of this condition remains controversial. In this study, we aimed to evaluate the prognostic factors associated with breast cancer with ISLNM and to assess the potential impact of aggressive locoregional surgical management on patient outcomes. Methods: We conducted a retrospective analysis of 250 breast cancer patients with ISLNM who were treated with curative intent at our institution between 2000 and 2020. The cohort was stratified into groups based on the extent of axillary surgery. The first group, comprising 185 patients, underwent level I/II axillary dissection. The second group, consisting of 65 patients, underwent aggressive locoregional surgery, including levels I/II/III (infraclavicular) dissection in 37 patients and levels I/II/III + SLND in 28 patients. Our study evaluated overall survival (OS) and disease-free survival (DFS) as primary endpoints, and locoregional recurrence-free survival (LRRFS) and distant metastasis-free survival (DMFS) as secondary endpoints. Results: The median follow-up time among all patients was 5.92 years (1.05–15.36 years). The 5-year OS rate was 71.89%, while the DFS rate, LRRFS rate, and DMFS rates were 59.25%, 66.38%, and 64.98%, respectively. A significant difference in OS, DFS, LRRFS, and DMFS was observed between the second group and the first group (p < 0.01). No beneficial impact on recurrence, metastasis, or survival outcomes was observed in the levels I/II/III + SLND group compared to the levels I/II/III dissection group. Multivariate logistic regression analysis revealed that levels I/II/III ± SLND surgery and T stage were associated with OS (p = 0.006 and p = 0.026), while levels I/II/III ± SLND surgery, ER+/HER2-, and histologic grade were associated with DFS (p = 0.032, p = 0.001, p = 0.032). Conclusion: Breast cancer with ISLNM may be considered a locoregional disease, requiring a combination of systemic and local therapies. Aggressive locoregional surgery has been shown to positively impact recurrence, metastasis, and survival outcomes. This approach may provide improved management of the ISLNM for breast cancer patients.
Predicting DNN classification performance on degraded imagery

The use of deep neural networks (DNNs) is the dominant approach for image classification, as it achieves state-of-the-art performance when sufficiently large training datasets are available. The best DNN performance is reached when test data conditions are similar to the training data conditions. However, if the test conditions differ, there will usually be a loss of classification performance, for instance when the test targets are more distant, blurry or occluded than those observed in the training data. It is desirable to have an estimate of the expected classification performance prior to using a DNN in practice. A low expected performance may deem the DNN unsuitable for the operational task at hand. While the effect on classification performance of a single changed test condition has been investigated before, this paper studies the combined effect of multiple changed test conditions. In particular, we will compare two prediction models for the estimation of the expected performance compared to the DNN performance on the development data. Our approach allows performance estimation in operation based on knowledge of the expected operational conditions, but without having access to operational data itself. We investigate the aforementioned steps for image classification on the MARVEL vessel dataset and the Stanford Cars dataset. The changing test conditions consist of several common image degradations that are imposed on the original images. We find that the prediction models produce acceptable results in case of small degradations, and when degradations show a constant accuracy falloff over their range.