Miracle-dz/UniFAR-FTUs huggingface dataset:
Moderate nitrogen retention in temperate heath ecosystem after elevated CO2, drought and warming through 7 years

Nitrogen (N) dynamic is one of the main controlling factors of responses to climate change in N‐limited terrestrial ecosystems, which rely on nutrient recycling and retention. In this study we investigate the N partitioning in ecosystem compartments of a grassland heath, and the impact of multiple climate change factors on long‐term N retention after 15N pulse labelling. The impacts of elevated carbon dioxide (eCO2), warming and drought and the treatments in combination on ecosystem N retention were investigated in a field scale manipulation experiment. A 6‐year time‐course was assessed by pulse‐labelling with the stable N isotope 15N and by sampling after 1 day, 1 year and 6 years. After 6 years we observed that the total ecosystem retained 42% of the amended 15N across treatments (recovery of the amended 15N in the pool). The fate of the applied 15N was mainly stabilization in soil, with 36% 15N recovery in soil, while the plant compartment and microbial biomass each retained only 1%–2% of the added 15N. This suggests a moderate retention of N, for all treatments, as compared to similar long‐term studies of forest ecosystems. A decreased ammonium and vegetation N pool combined with higher 15N retention in the soil under eCO2 treatments suggests that eCO2 promoted processes that immobilize N in soil, while warming counteracted this when combined with eCO2. Drought treatments contrastingly increased the vegetation N pool. We conclude that as the organic soil layer has the main capacity for N storage in a temperate heathland‐grassland, it is important for buffering nutrient availability and maintaining a resilient ecosystem. However, the full treatment combination of drought, warming and eCO2 did not differ in 15N recovery from the control, suggesting unchanged long‐term consequences of climate change on retention of pulse added N in this ecosystem.
Adaptive fixed-time output feedback formation control for nonstrict-feedback nonlinear multi-agent systems

In this paper, an adaptive fixed-time output feedback formation control problem is investigated for nonlinear multi-agent systems with a nonstrict-feedback structure. In the controller design procedure, the neural network state observer is designed to estimate the unmeasurable state variables. Dynamic surface control (DSC) technique is applied to avoid the repeated differentiation for the virtual control signals. The dynamic surface compensation signals can realise the practical fixed-time bounded. Utilising the classified discussion method, the difficulty of controller design caused by the existence of observer error term is addressed. The technique of transformation of the index set is employed to cope with the related variables of the neighbour states, which simplifies the controller design. Under the presented control mechanism, all closed-loop signals remain bound for a fixed period of time, the formation control performance target between all followers and leader can be achieved. And the formation errors and state observers errors are both bounded such that can converge to a little domain around zero. Simulation results are provided to test the availability of the presented strategy.
A 14b 500 MS/s Single-Channel Pipelined-SAR ADC With Reference Ripple Mitigation Techniques and Adaptively Biased Floating Inverter Amplifier

This work presents a 14-bit 500 MS/s single-channel pipelined-successive-approximation-register (SAR) analog-to-digital converter (ADC) with an adaptively biased floating inverter amplifier (AB-FIA) as the residue amplifier (RA) and a hybrid reference ripple mitigation (H-RRM) technique to relax the power and area burden on the reference stabilization. Leveraging the adaptively biased architecture in the last stage FIA, the speed and open-loop gain of the proposed two-stage FIA are enhanced compared with the conventional cascode counterpart. Besides, the impact of the reference error on the pipelined-SAR conversion accuracy is alleviated by hybridizing the improved reference ripple cancellation (RRC), reference ripple neutralization (RRN), and reference buffer (RBUF). The improved RRC removes the potential noise coupled from the floating capacitor to counter the decision error during the sub-SAR conversion in the first stage. Meanwhile, the RRN facilitates a rapid reference recovery. These acts constitute the H-RRM, which assists a high-speed and high-resolution pipelined-SAR process with a relaxed integrated reference RBUF with low-power and compact area. The prototype ADC was fabricated in a 28 nm CMOS process; it consumes 6.34 mW total power at 500 MS/s, including 2.4 mW dynamic power of RBUF. It occupies an active area of 0.018 mm2, which the ADC core area of 0.0168 mm2 and the area of RBUF with a 2.3 pF decoupling capacitor is 0.00105 mm2. The measured signal to noise and distortion ratio (SNDR) and spurious free dynamic range (SFDR) are 64.2 dB and 80.55 dB with a Nyquist input, respectively, leading to a 170.2 dB Schreier figure-of-merit (FoM) (FoMS) and 9.6 fJ/conversion-step Walden FoM (FoMW).
Holistic Strategies to Control Salmonella Infantis: An Emerging Challenge in the European Broiler Sector

Salmonella spp. has been globally recognized as one of the leading causes of acute human bacterial gastroenteritis resulting from the consumption of animal-derived products. Salmonella Enteritidis, S. Typhimurium, and its monophasic variant are the main serovars responsible for human disease. However, a serovar known as S. Infantis has emerged as the fourth most prevalent serovar associated with human disease. A total of 95% of isolated S. Infantis serovars originate from broilers and their derived products. This serovar is strongly associated with an elevated antimicrobial (AMR) and multidrug resistance, a resistance to disinfectants, an increased tolerance to environmental mercury, a heightened virulence, and an enhanced ability to form biofilms and attach to host cells. Furthermore, this serovar harbors genes that confer resistance to colistin, a last-resort antibiotic in human medicine, and it has the potential to acquire additional transferable AMR against other critically important antimicrobials, posing a new and significant challenge to global public health. This review provides an overview of the current status of the S. Infantis serovar in the poultry sector, focusing on its key virulence factors, including its virulence genes, antimicrobial resistance, and biofilm formation. Additionally, novel holistic strategies for controlling S. Infantis along the entire food chain are presented in this review.
Applying prospect theory to multiattribute problems with independence assumptions

We discuss a descriptive theory of decision making which has received much attention in recent decades: prospect theory. We specifically focus on applying the theory to problems with two attributes, assisted by different independence assumptions. We discuss a process for solving decision problems using the theory before applying it to a real life example of purchasing breakdown cover.
Advances for Pulmonary Functional Imaging: Dual-Energy Computed Tomography for Pulmonary Functional Imaging

Dual-energy computed tomography (DECT) can improve the differentiation of material by using two different X-ray energy spectra, and may provide new imaging techniques to diagnostic radiology to overcome the limitations of conventional CT in characterizing tissue. Some techniques have used dual-energy imaging, which mainly includes dual-sourced, rapid kVp switching, dual-layer detectors, and split-filter imaging. In iodine images, images of the lung’s perfused blood volume (PBV) based on DECT have been applied in patients with pulmonary embolism to obtain both images of the PE occluding the pulmonary artery and the consequent perfusion defects in the lung’s parenchyma. PBV images of the lung also have the potential to indicate the severity of PE, including chronic thromboembolic pulmonary hypertension. Virtual monochromatic imaging can improve the accuracy of diagnosing pulmonary vascular diseases by optimizing kiloelectronvolt settings for various purposes. Iodine images also could provide a new approach in the area of thoracic oncology, for example, for the characterization of pulmonary nodules and mediastinal lymph nodes. DECT-based lung ventilation imaging is also available with noble gases with high atomic numbers, such as xenon, which is similar to iodine. A ventilation map of the lung can be used to image various pulmonary diseases such as chronic obstructive pulmonary disease.
Triangulated Categories and Matrix Factorizations

In this thesis we study triangulated categories and look at one specific example, the homotopy category of matrix factorizations. First we define categories and functors. Then we introduce additive and triangulated categories and see that the octahedral axiom can be replaced by Neeman’s mapping cone axiom. After this we look at matrix factorizations and the homotopy category of matrix factorizations, HMF(S, x), which leads us to one of our main results, i.e. that HMF(S, x) is triangulated. We prove this with both the octahedral axiom and Neeman’s mapping cone theorem. Lastly we look at the homotopy category of totally acyclic complexes over a local, regular ring and see that this is equivalent to HMF(S, x). Sammendrag I denne oppgaven ser vi på triangulerte kategorier og trekker frem ett spesifikt eksempel: homotopikategorien av matrisefaktoriseringer. Vi begynner med å definere kategorier og funktorer for så å introdusere additive og triangulerte kategorier. Her viser vi at oktaederaksiomet kan erstattes med Neemans "mapping cone"-aksiom. Deretter ser vi på matrisefaktoriseringer og homotopikategorien av matrisefaktoriseringer, HMF(S, x), som vi viser at er triangulert. Til dette bruker vi både oktaederaksiomet og Neemans aksiom. Til slutt ser vi på homotopikategorien til totalt asykliske komplekser og viser at over en lokal, regulær ring, er denne ekvivalent med HMF(S, x).
Wake Character in the Wind Turbine Array: (Dis-)Organization, Spatial and Dynamic Evolution and Low-dimensional Modeling

To maximize the effectiveness of the rapidly increasing capacity of installed wind energy resources, new models must be developed that are capable of more nuanced control of each wind turbine so that each device is more responsive to inflow events. Models used to plan wind turbine arrays and control behavior of devices within the farm currently make questionable estimates of the incoming atmospheric flow and update turbine configurations infrequently. As a result, wind turbines often operate at diminished capacities, especially in arrays where wind turbine wakes interact and inflow conditions are far from ideal. New turbine control and wake prediction models must be developed to tune individual devices and make accurate power predictions. To that end, wind tunnel experiments are conducted detailing the turbulent flow in the wake of a wind turbine in a model-scale array. The proper orthogonal decomposition (POD) is applied to characterize the spatial evolution of structures in the wake. Mode bases from distinct downstream locations are reconciled through a secondary decomposition, called double proper orthogonal decomposition (DPOD), indicating that modes of common rank in the wake share an ordered set of sub-modal projections whose organization delineates underlying wake structures and spatial evolution. The doubly truncated basis of sub-modal structures represents a reduction to 0.015% of the total degrees of freedom of the wind turbine wake. Low-order representations of the Reynolds stress tensor are made using only the most dominant DPOD modes, corrected to account for energy excluded from the truncated basis with a tensor of constant coefficients defined to rescale the low-order representation of the stresses
Electroreflectance of thin-film solar cells: Simulation and experiment

Electromodulated reflectance (ER) is a standard characterization method to determine critical points such as the band gap in the band structure of semiconductors. These critical points show up as spectrally narrow features in ER and are typically evaluated using Aspnes's third-derivative functional form. ER spectra of stratified semiconductor systems such as thin-film solar cells, however, are significantly distorted by optical interference due to their layered structure. Furthermore, strong built-in electric fields result in a deviation from the typically assumed low-field conditions. We present here simulations of ER spectra from stratified systems based on transfer matrices using the Franz-Keldysh theory in its general form. For realistic thin-film solar cell conditions, distortions of ER line shapes due to the above-mentioned interferences and strong electric fields appear in the simulations. Furthermore, the results show good agreement with measured ER spectra of a structurally well-characterized $\mathrm{Cu}(\mathrm{In},\mathrm{Ga}){\mathrm{Se}}_{2}$ (CIGS) solar cell. Our analysis points out the restrictions on the determination of energetic position and number of critical points from ER spectra of stratified systems.
Long COVID, the Brain, Nerves, and Cognitive Function

SARS-CoV-2, a single-stranded RNA coronavirus, causes an illness known as coronavirus disease 2019 (COVID-19). Long-term complications are an increasing issue in patients who have been infected with COVID-19 and may be a result of viral-associated systemic and central nervous system inflammation or may arise from a virus-induced hypercoagulable state. COVID-19 may incite changes in brain function with a wide range of lingering symptoms. Patients often experience fatigue and may note brain fog, sensorimotor symptoms, and sleep disturbances. Prolonged neurological and neuropsychiatric symptoms are prevalent and can interfere substantially in everyday life, leading to a massive public health concern. The mechanistic pathways by which SARS-CoV-2 infection causes neurological sequelae are an important subject of ongoing research. Inflammation- induced blood-brain barrier permeability or viral neuro-invasion and direct nerve damage may be involved. Though the mechanisms are uncertain, the resulting symptoms have been documented from numerous patient reports and studies. This review examines the constellation and spectrum of nervous system symptoms seen in long COVID and incorporates information on the prevalence of these symptoms, contributing factors, and typical course. Although treatment options are generally lacking, potential therapeutic approaches for alleviating symptoms and improving quality of life are explored.
Constructing Smartness and Intelligence: A Content Analysis of Postsecondary Disability Literature

Abstract Expectations of smartness are woven into the foundation of postsecondary education (e.g. admissions, grading). This content analysis examines current postsecondary dis/ability literature through the theoretical frame of DisCrit to identify how concepts of smartness are treated within the field. The findings call for greater interrogation of the ways smartness operates as a parallel ideology to whiteness, and how together they influence how students experience and are known in postsecondary education.
Coevolutionary Fuzzy Attribute Order Reduction With Complete Attribute-Value Space Tree

Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant and irrelevant information, the selection, evaluation, and combination of those large-scale attributes pose huge challenges to traditional methods. Fuzzy rough sets have emerged as a powerful vehicle to deal with uncertain and fuzzy attributes in big data problems that involve a very large number of variables to be analyzed in a very short time. In order to further overcome the inefficiency of traditional algorithms in the uncertain and fuzzy big data, in this paper we present a new coevolutionary fuzzy attribute order reduction algorithm (CFAOR) based on a complete attribute-value space tree. A complete attribute-value space tree model of decision table is designed in the attribute space to adaptively prune and optimize the attribute order tree. The fuzzy similarity of multimodality attributes can be extracted to satisfy the needs of users with the better convergence speed and classification performance. Then, the decision rule sets generate a series of rule chains to form an efficient cascade attribute order reduction and classification with a rough entropy threshold. Finally, the performance of CFAOR is assessed with a set of benchmark problems that contain complex high dimensional datasets with noise. The experimental results demonstrate that CFAOR can achieve the higher average computational efficiency and classification accuracy, compared with the state-of-the-art methods. Furthermore, CFAOR is applied to extract different tissues surfaces of dynamical changing infant cerebral cortex and it achieves a satisfying consistency with those of medical experts, which shows its potential significance for the disorder prediction of infant cerebrum.
The Impact of COVID-19-Related Psychological Stress on Fetoplacental Circulation: A Doppler Scan of Umbilical Vessels in Third-Trimester Antenatal Women

Introduction: The placenta is an important organ of pregnancy. A multitude of physiological and pathological factors influence blood flow in the placenta during pregnancy. However, the fetal effects of maternal psychological stress were inconclusive. The recent COVID-19 pandemic had unprecedented economic, social, and psychological effects. The effect of COVID-19-induced psychological stress in antenatal women and its resultant fetal impact were studied by observing the Doppler waveforms of the uterine and umbilical arteries. Methods: The cross-sectional study was conducted on 26 healthy third-trimester antenatal women who satisfied pre-set inclusion and exclusion criteria. A pandemic-related pregnancy stress scale (PREPS) was used to evaluate the stress in pregnant women and categorize it into mild, moderate, and severe levels. The Doppler ultrasound of the uterine and umbilical vessels was done along with a routine growth scan in the third trimester of pregnancy. The arterial waveforms, Pulsatility index (PI) of uterine and umbilical arteries, umbilical vein blood flow, and biometric parameters of the fetus were recorded and analyzed. Results: Seventeen of the 26 participants were found to be moderately stressed. Among the three dimensions of the PREPS tool, the perinatal infection stress dimension was expressed predominantly. A strong expression of the positive affirmation dimension was seen in antenatal women. The mean Pulsatility index in the mild, moderate, and severe groups was 0.74, 0.93, and 0.63, respectively. The association between the PREPS score and the Pulsatility index of the umbilical artery alone was found to be significant at p=0.02. Conclusion: The COVID-19 pandemic caused moderate to severe levels of psychological stress in pregnant women. The statistically significant association between the PREPS score and the umbilical artery PI indicates possible fetoplacental compromise, suggesting the need for cognitive therapy to manage psychological stress in antenatal women.
A new economic scheme for CCC charts with run rules based on average number of inspected items

ABSTRACT New statistical techniques and procedures have been developed to control high-yield processes along with looking for process improvement opportunities and minimizing production cost. Cumulative count of conforming control chart is generally a technique for high-quality processes, when nonconforming items are rarely produced. The objective of this study is to design control chart based on cumulative count of conforming items and run rules that develops an economic model based on the average number of inspected items to design m-of-m CCC chart in order to facilitate minimum average cost per item produced. The optimal design parameters for different values of nonconforming fraction and different cost parameters in each scenario are determined. Finally, to analyze the behavior of optimal economic solutions, sensitivity analysis of the model parameters is performed.
Clinical pharmacy services for tuberculosis management: a systematic review

Objective: This study aims to systematically review the content and potential effects of clinical pharmacy services in tuberculosis (TB) care management. Methods: Searches were performed in PubMed, Embase, Cochrane, Scopus, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Study characteristics and outcomes were extracted, and clinical pharmacy service components were characterized using the Descriptive Elements of Pharmacist Intervention Characterization Tool. Results: Twenty articles were included for full-text assessment, of which 10 fulfilled inclusion criteria, comprising 1,168 patients (N = 39 to 258 per study). These articles included five prospective cohort studies, two case–control studies, two quasi-experimental studies, and one cross-sectional study. Intervention foci within clinical pharmacy services were medication adherence (50%), medication safety (40%), education to patients/caregivers regarding needs/beliefs (30%), optimizing medication/therapy effectiveness (30%), emphasizing HRQoL (10%), and drug selections (10%). The three most frequently applied interventions were drug information/patient counseling (80%), adverse drug reaction monitoring (50%), and drug use evaluation (20%). Based on the World Health Organization (WHO) outcome classification, treatment success ranged from 72% to 93%, with higher cure outcomes (53%–86%) than treatment completion (7%–19%). Other outcomes, including isoniazid metabolites, medication counts, sputum conversion, adherence/compliance, knowledge, and quality of life, were better in the intervention group than those in comparator groups, and/or they improved over time. Risk of bias analysis indicated that the included studies were not comparable to a randomized clinical trial. Conclusion: Clinical pharmacy services as single or composite interventions potentially improve TB outcomes, but its evidence is still inconsistent and limited due to the lack of randomized controlled studies using the WHO outcome classification. Systematic review registration: https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=199028, identifier CRD42020199028.
Gauge/Gravity Duality and Field Theories at Strong Coupling

In this thesis, we apply the gauge/string duality in its supergravity limit to infer some properties of field theories at strong coupling. Experiments at RHIC and at the LHC indeed suggest that the quark–gluon plasma behaves as one of the most perfect fluid ever achieved in any controlled experimental setup. Perturbative approaches fail at accounting for its properties, whereas lattice QCD methods face technical as well as conceptual difficulties in computing dynamical aspects of this new state of matter. As a result, the AdS/CFT correspondence currently is the best tool at our disposal for analytically modelling this phase of QCD. One of the contributions of this thesis amounts to deriving a stochastic Langevin equation for a heavy quark moving across a maximally supersymmetric Yang–Mills plasma at strong coupling. Even though this theory seems to describe in a surprisingly satisfactory way the high–energy, deconfined phase of QCD, it is also of much interest to try and search for a string theory dual making closer contact with QCD at lower energies. As such, the other main focus of this thesis deals with supergravity solutions of lesser supersymmetry, without conformal invariance and exhibiting confinement. We build for the first time the gravity dual to metastable states of such theories. In particular, we find the contribution from anti–branes to the inflation potential in some general scenario of string cosmology.