Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate management through agricultural albedo manipulation

32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 Agriculture comprises the largest managed area of the Earth’s surface and could be adversely affected by increased temperatures due to global warming. Deliberately increasing the albedo of cropland might be a method to mitigate the effects of increasing temperatures. Crops that are modified to have greater reflectance, and planted on large scales, could induce an albedo-driven cooling effect. Agricultural scientists have bred soybean varieties with a fourfold increase in pubescence (leaf hairs) that increases shortwave reflectivity by 3-5%. We used NCAR’s Community Atmosphere Model (CAM 3.0) coupled to the Community Land Model (CLM 3.0) to understand the climatic effect of such increases in shortwave albedo. Simulations indicated that agricultural regions planted with the enhanced-albedo crops would have a maximum daily temperature 1.65 oC cooler during summer than if those regions had been planted with unmodified crops. However, this cooling effect was diminished at lower latitudes because lower latent heat fluxes led to reductions in cloudiness, and allowed more solar energy to be absorbed at the surface.
An Elegant Emotion Identification Model for Crossbreed Suppressed Hole Data Fusion in Cloud Based Big Data Servers

Crossbreed suppressed hole data fusion in cloud based big data servers is a process of merging data from multiple sources into a common view. This data fusion process combines information from different sources, such as databases, cloud services, sensors, and IoT devices, to provide more insights into customer preferences and trends. The use of big data servers provides the ability to access large amounts of data from a variety of sources simultaneously, and store them in such a way that it can be manipulated and analyzed into a single, unified format. Crossbreed suppressed hole data fusion in cloud-based big data servers can enable organizations to discover precise customer preferences by correlating data from multiple sources. Moreover, this approach can help businesses to better understand customer behaviors and preferences, and uncover valuable insights that can be used to improve customer service, marketing campaigns and product offerings.
Study of neutron density fluctuation and neutron-proton correlation in Au+Au collisions using PYTHIA8/Angantyr

Utilizing the PYTHIA8 Angantyr model, which incorporates the multiple-parton interaction (MPI) based color reconnection (CR) mechanism, we study the relative neutron density fluctuation and neutron-proton correlation in Au+Au collisions at = 7.7, 11.5, 14.5, 19.6, 27, 39, 62.4, and 200 GeV. In this study, we not only delve into the dependence of these two remarkable observations on rapidity, centrality, and energy, but also analyze their interplay with the MPI and CR. Our results show that the light nuclei yield ratio of protons, deuterons, and tritons, expressed by the elegant expression , remains unchanged even as the rapidity coverage and collision centrality increase. Interestingly, we also reveal that the effect of CR is entirely dependent on the presence of the MPI; CR has no impact on the yield ratio if the MPI is off. Our findings further demonstrate that the light nuclei yield ratio experiences a slight increase with increasing collision energy, as predicted by the PYTHIA8 Angantyr model; however, it cannot describe the non-monotonic trend observed by the STAR experiment. Based on the Angantyr model simulation results, it is essential not to overlook the correlation between neutron and proton fluctuations. The Angantyr model is a good baseline for studying collisions in the absence of a quark-gluon plasma system, given its lack of flow and jet quenching.
Bridging Repairability Gaps in Shared Bus Architecture with Shared Physical Memory Implementation

Power, performance and area (PPA) are the three key differentiating factors in today's SoCs (System on-chips). To achieve best-in class PPA, complex processor cores using shared bus architecture typically use single physical memory across multiple logical memories to improve overall design efficiency. However, such an implementation imposes several limitations on enabling redundancy and repair support using on-chip memory test, redundancy analysis and repair implementation for the full range of memory space, resulting in a degraded repair coverage. This in turn could result in inferior yield and thereby increase cost margin for the product. In this paper, we propose a few novel strategies to seamlessly enable redundancy sharing across logical memories shared by a single physical memory to mitigate the limitation with conventional approaches. Additional schemes are proposed to resolve potential conflict conditions that could arise as a result of this redundancy sharing. Furthermore a comprehensive fault injection and repair verification methodology is shared to assess the efficacy of proposed methods. The experimental results by using the proposed solutions on two sample clusters on a large-scale design using shared bus architecture bridged a gap of 5-6% drop in repair coverage due to the shared physical memory implementation.
Compact Self-adaptive Coding for Spectral Compressive Sensing

Spectral snapshot compressive imaging (SCI) has been extensively studied and applied to various fields. Although the typical coded aperture snapshot spectral imaging (CASSI) presents an effective paradigm, its fixed code designs do not sufficiently exploit flexible and optimal modulation in consideration of scene sparsity. In this paper, we present a novel Compact Self-adaptive optical Coding framework for Spectral Compressive Sensing, termed 3CS, to optimize the coded pattern adaptively for better hyper-spectral videos perception. Our framework enables extracting context high-frequency components from the compressed domain without requiring hybrid guiding camera. The specifically designed mask distribution enables higher light efficiency, and is robust against temporal correlation reduction when processing dynamic spectral videos. Extensive experiments and model discussions validate the superiority of the proposed framework over traditional end-to-end (E2E) methods in various aspects for the spectral reconstruction.
Data-Driven Resource Allocation and Group Formation for Platoon in V2X Networks With CSI Uncertainty

This paper investigates the joint resource allocation and group formation for platoon in vehicle-to-everything (V2X) networks under vehicular channel uncertainty. To achieve the high spectrum efficiency and overcome the platoon head communication range limitation, an adaptive multicast-based group cooperation communication model is developed for the platoon with dynamic topology. Considering the heterogeneous characteristics of different types of links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultra-reliability for vehicle-to-vehicle (V2V) links, we attempt to maximize the V2I capacity whilst satisfying a probability constraint for ultra-reliable V2V-supported intra-platoon communication. To handle the intractable probability constraint, a support vector clustering (SVC) based method is developed to capture the distributional geometry of massive uncertain channel samples as a sphere in high-dimensional feature space with asymmetric structure. Based on it, the probability constraint is transformed into a tractable linear convex set. After that, an exploration-selection-alternating-iterative algorithm is developed to solve the formulated problem with coupled optimization variables. Specifically, in the exploration process, a two-stage algorithm is proposed for the resource allocation problem under fixed group formation decision, which includes power control and spectrum allocation. During the selection process, a performance difference-based decision transition rate is designed to optimize group formation solution. Simulation results demonstrate the proposed data-driven approach can overcome the over-conservatism of the traditional symmetric-geometry-based uncertainty sets, and the multicast-based group cooperation communication model corresponds to a higher performance on V2I capacity than other traditional schemes.
Non-Local Transfer of Ultrafast Currents Generated by Few-Cycle Laser Pulses

Ultrafast optoelectronics promises a several-order-of-magnitude speed-up of computational devices. In recent years several experiments emerged that demonstrated the possibility of steering electrons in matter with optical fields on femtosecond timescales and this way they established building blocks for PHz devices [1], [2]. Such a typical experiment usually exploits the carrier-envelope phase (CEP) of laser pulses which causes symmetry breaking driving an oriented current in a medium. These ultrafast, CEP-dependent currents appear in a wide range of media spanning graphene, semiconductors or dielectrics. A common feature of these experiments is that currents are detected using metal electrodes and slow electrical circuit is contacted to the illuminated volume. This raises the question on what basis the current transfer from the medium to the electrodes is and whether the optical-to-electrical conversion is possible without the loss of bandwidth. A recent study [3] hints that the photocurrent transfer might be non-local, i.e. there is no need for physical transport of carriers from the volume to the electrodes. Having that in mind, we experimentally studied the relationship between the ultrafast photo current current, material, laser beam and geometry. We observed signatures that can be modelled with the Ramo-Shockley theorem that support the manifestation of non-local, i.e. quasi-instantaneous dynamics.
Exploring Investment Governance within Social Security Organization: A Key to SDG Achievement

The Employees Provident Fund is a prominent Social Security Organization in Nepal, and it aims to invest the long-term savings it accumulates in various sectors of national and infrastructure development. This investment is intended to create diverse employment opportunities for Nepali citizens, all while maintaining a robust governance mechanism to safeguard the collected contributions, ensuring there are no losses. This strategy aligns with the goal of achieving sustainable development. In order to address the pressing issue of unemployment, which has been consistently raised as a concern by the International Labor Organization, there is an imminent requirement to adapt and enhance the investment framework within the broader Investment Governance concept. This adaptation is necessary to actively generate job opportunities in the areas where unemployment is a significant concern. The main objective of this research is to assess the awareness of current contributors to the Employees Provident Fund regarding Investment Governance and Sustainable Development Goals. Additionally, it aims to validate the developed theory of Investment Governance and its impact on achieving SDGs in practical terms. Furthermore, this study intends to offer insights into the current state of the Employees Provident Fund's investment mechanism, serving as a valuable resource. The result shows that the existing contributors to the Employees Provident Fund in Nepal are highly aware of investment governance. Furthermore, the study has confirmed the accuracy of the developed theory through these tests involving the contributors of the Employees Provident Fund. This fund is a prominent Social Security Organization in Nepal and plays a vital role in focusing its long-term savings on investments in key sectors such as social (health, production, education, and tourism) and the environment. These investments are crucial for achieving Sustainable Development Goals (SDGs), particularly in generating employment, which is a significant aspect of the 8th goal among the SDGs. Beyond examining investment governance, this study also highlights the importance of further exploration into the role of the Employees Provident Fund in advancing national development.
Prevalence and risk of new-onset diabetes mellitus after COVID-19: a systematic review and meta-analysis

Aims After the acute phase of SARS-CoV-2 infection, the onset of glycemic impairment and diabetes have been reported. Nevertheless, the exact burden of glycemic impairment and diabetes after COVID-19 has not been clearly described. Materials and methods Electronic search was run in Pubmed (MEDLINE), Web of Science, Scopus, and ClinicalTrial.org for reports published from database inception to September 2022. We included observational studies reporting quantitative data on diabetes prevalence or its onset in subjects with a history of SARS-CoV-2 infection from at least 60 days. Risk of bias was assessed by the JBI’s critical appraisal checklist. Random effect model was used to calculate pooled data. The review protocol was registered on PROSPERO (CRD42022310722). Results Among 1,630 records screened, 20 studies were included in the analysis. The mean or median age of participants ranged from ~ 35 to 64 years, with a percentage of males ranging from 28% to 80%. Only two studies were considered at low risk of bias. The estimate of diabetes prevalence, calculated on a total of 320,948 participants pooled with 38,731 cases, was 16% (95%CI: 11-22%). The estimate of proportion of incident cases of diabetes was 1.6% (95%CI: 0.8-2.7%). Subgroup analysis showed that previous hospitalization increased the prevalence of diabetes and the proportion of incident cases. Conclusion Diabetes is common in individuals who have experienced SARS-CoV-2 infection, especially if they required hospitalization. This data may be helpful to screen for diabetes and manage its complications in individuals who experienced COVID-19. Systematic review registration https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42022310722, identifier CRD42022310722.
An ECG Signal Acquisition and Analysis System Based on Machine Learning with Model Fusion

Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, which cannot address the above problems. To solve these problems, this study proposes an ECG acquisition and analysis system based on machine learning. The ECG analysis system responsible for ECG signal classification includes two parts: data preprocessing and machine learning models. Multiple types of models were built for overall classification, and model fusion was conducted. Firstly, traditional models such as logistic regression, support vector machines, and XGBoost were employed, along with feature engineering that primarily included morphological features and wavelet coefficient features. Subsequently, deep learning models, including convolutional neural networks and long short-term memory networks, were introduced and utilized for model fusion classification. The system’s classification accuracy for ECG signals reached 99.13%. Future work will focus on optimizing the model and developing a more portable instrument that can be utilized in the field.
Viral encephalitis: a clinician’s guide

The management of patients with suspected viral encephalitis has been revolutionised in recent years with improved imaging and viral diagnostics, better antiviral and immunomodulatory therapies, and enhanced neurointensive care. Despite this, disasters in patient management are sadly not uncommon. While some patients are attacked with all known antimicrobials with little thought to investigation of the cause of their illness, for others there are prolonged and inappropriate delays before treatment is started. Although viral encephalitis is relatively rare, patients with suspected central nervous system (CNS) infections, who might have viral encephalitis, are not. In addition, the increasing number of immunocompromised patients who may have viral CNS infections, plus the spread of encephalitis caused by arthropod-borne viruses, present new challenges to clinicians. This article discusses the Liverpool approach to the investigation and treatment of adults with suspected viral encephalitis, and introduces the Liverpool algorithm for investigation and treatment of immunocompetent adults with suspected viral encephalitis (available at www.liv.ac.uk/braininfections).
Green Social Prescribing in Practice: A Case Study of Walsall, UK

This paper presents a case study of Green Social Prescribing (GSP) in Walsall, a medium-sized urban area located in the West Midlands, UK. GSP is a means of enabling health professionals to refer people to a range of local non-clinical nature-based activities, e.g., community gardening and conservation volunteering. As a new practice to address multiple challenges in health and sustainability, GSP has been promoted by the UK government and the NHS in the past few years. There is as yet limited evidence and knowledge about how this approach is implemented at a local level. This paper addresses this gap of knowledge, by exploring how GSP is implemented in Walsall as a case study. Based on extensive engagement and research activities with the local partners to collect data, this paper reveals the local contexts of GSP, the referral pathways, and people’s lived experience, discussing the challenges, barriers, and opportunities in delivering GSP at the local level. This study suggests that a more collaborative and genuine place-based approach is essential, and alongside GSP, investment into infrastructure is needed to move the health paradigm further from ‘prevention’ to ‘promotion’ so that more people can benefit from what nature can offer.
Comparing field measurements and annotations as training data for UAV-based detection of standing dead trees in coniferous forest

ABSTRACT Deadwood provides a habitat for a large number of species and acts as a significant carbon storage. Thus, deadwood mapping allows planning various actions related to biodiversity conservation and forest management. Airborne laser scanning (ALS) provides an efficient means for monitoring large forested areas and is thus a potential method for deadwood mapping. Machine learning (ML) can be used for automating deadwood detection from ALS data. However, ML requires training data, collecting of which is laborious using field measurements. This study inspected the feasibility of training data manually annotated from aerial images for the mapping of individual standing dead trees. The standing dead trees were mapped from an ALS dataset collected with an unmanned aerial vehicle (UAV). The study was carried out by performing ML-based standing dead tree detection using annotated training data and field-measured training data, and comparing the performances of the models trained on these two datasets. The study found that using annotated training data improves the performance of standing dead tree detection due to its higher availability compared to field-measured data. For large trees (height > 14 m), the precision, recall, Cohen’s kappa score, and Matthews correlation coefficient achieved by the best classifier trained on annotated data were 0.23, 0.48, 0.17, and 0.20, respectively. In comparison, the corresponding metrics for the best classifier trained on field-measured data were 0.14, 0.52, 0.04, and 0.13. Annotated standing dead tree data is not a representative sample of the true standing dead tree population, as small trees and trees without crowns (snags) can often not be identified from aerial images. However, the study found that identifying such trees is challenging even when using field-measured training data, and thus using annotated training data does not bias results. In general, the results of the study showed that ALS-based standing dead tree detection should focus on the detection of large trees.
Automatic MTPA Control Method for Magnetic-Geared Motor Using Sensorless Control Based on Extended Electromotive Force Model

This paper describes an automatic MTPA (Maximum Torque Per Ampere) control method for a magnetic-geared motor using sensorless control based on the extended electromotive force model. The magnetic flux due to the permanent magnet appears in the q axis as well as in the d axis in a magnetic-geared motor when a load is applied to the low-speed rotor. Therefore, it is difficult to apply a MTPA control method to a magnetic-geared motir. In this paper, an automatic MTPA control method for a magnetic-geared motor using sensorless control is proposed. The electric circuit equation of a magnetic-geared motor is shown, and mathematical sensorless control model based on the extended electromotive force model is described. Finally, the effectiveness of the proposed MTPA control method is investigated by the comparison of the mathematical simulation, finite element analysis, and measurements on a prototype.
Active volcanoes of the world

About 500 active volcanoes presently exist on the Earth’s surface, of which around 50 erupt each year. Volcanoes played a crucial role in the evolution of the planet and early life, and are constantly reshaping the morphology of Planet Earth. Many active volcanoes are located in dense settlement areas, with over 500 million people living in close proximity of still active or dormant volcanoes. On one side, volcanoes provide valuable soil and rock basis for agriculture, but often the "mountains of fire" cause disastrous societal and economical disasters caused by ash clouds, lahars, lava flows, and pyroclastic flows. Eruptions are still difficult to predict, although volcanologists around the world are constantly working on new ways to understand the character and behavior of volcanoes. Active Volcanoes of the World is an official book series of the International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI). The series aims to be a scientific library of monographs that provide authoritative and detailed reviews of state-of-the art research on individual volcanoes or a volcanic area that has been active in the last 10.000 years, e.g. the Teide Volcano or the Chiapas Region. The books in the series cover the geology, eruptive history, petrology and geochemistry, volcano monitoring, risk assessment and mitigation, volcano and society, and specific aspects related to the nature of each described volcano. The A ctive Volcanoes of the World series contains single and multi-authored books as well as edited volumes. The Series Editors, Dr. Corrado Cimarelli (LMU Munich) and Dr. Sebastian Müller (Universität Mainz) are currently accepting proposals and a proposal document can be obtained from the Publisher, Dr. Johanna Schwarz (johanna.schwarz@springer.com).
High-performance Type-y Spin-orbit Torque MRAM Devices

We demonstrated in-plane field-free-switching spin-orbit torque (SOT) magnetic tunnel junction (MTJ) devices capable of low switching current density, fast speed, high reliability and most importantly, manufactured uniformly by the 200 mm-wafer platform. The SOT-MTJ devices are integrated on 200 m-wafers using two-metal-layers back-end-of-line (BEOL) process. The performances of the devices are systematically studied, including magnetic properties, endurance, data retention and switching dynamic of the devices. Specifically, the devices exhibit low critical switching current density JC~160 MA/cm2 at 0.3 ns, high tunneling magnetoresistance (TMR) ratios exceeding 100%, ultra-high endurance over 1012 cycles, and excellent thermal stability Eb > 100 kBT. The research provides flexible solutions for high-speed and energy efficient memory applications. As a prospective, it is expected to obtain excellent performance of the devices through further optimizing the MTJ film stacks and corresponding fabrication processes.