Miracle-dz/UniFAR-FTUs huggingface dataset:
Attribution of surface ozone to NOx and volatile organic compound sources during two different high ozone events

Abstract. Increased tropospheric ozone (O3) and high temperatures affect human health during heat waves. Here, we perform a source attribution that considers separately the formation of German surface ozone from emitted NOx and volatile organic compound (VOC) precursors during two peak ozone events that took place in 2015 and 2018 which were associated with elevated temperatures. Results showed that peak ozone concentrations can be primarily attributed to nearby emissions of anthropogenic NOx (from Germany and immediately neighboring countries) and biogenic VOC. Outside of these high ozone episodes, baseline ozone concentrations are attributed primarily to long-range transport, with ozone due to remote anthropogenic NOx emissions and methane oxidation adding to the tropospheric ozone background. We show that a significant contribution to modeled O3 coming from German NOx or VOC emissions occurs mostly in southern Germany, emphasizing that the production of ozone depends on the local interplay between NOx and VOC precursors. Shipping activities in the Baltic and North seas have a large impact on ozone predicted in coastal areas, yet a small amount of ozone from these sources can also be seen far inland, showing the importance of transported ozone on pollution levels. We have also shown that changes in circulation patterns during the peak O3 episodes observed in Germany during the 2015 and 2018 heat waves can affect the contribution of different NOx emission sources to total O3; thus, the possible influence of multiple upwind source regions should be accounted for when mitigation strategies are designed. Our study also highlights the good correlation between ozone coming from German biogenic VOC emissions and total ozone, although the diurnal variation in the ozone coming from biogenic sources is not dominated by the diurnal variation in biogenic emissions, and the peaks of ozone from biogenic sources are disconnected from local emission peaks. This suggests that the formation of O3 from local German biogenic VOC emissions is not the sole factor that influences the ozone formation, and other meteorological and chemical processes affect the diel variation of ozone with a biogenic origin. Overall, this study helps to demonstrate the importance of a source attribution method to understand the sources of O3 in Germany and can be a useful tool that will help to design effective mitigation strategies.
A method to detect abrupt shifts in river channel position using a Landsat‐derived water occurrence record

The lateral migration of river channels is an important control on the evolution of alluvial fans, deltas, and floodplains. Lateral migration consists of both gradual riverbank migration and abrupt shifts in location due to avulsions or cutoffs. Methods exist to measure bank migration, but abrupt shifts in position are rarely considered or are not emphasized. Here we describe a new method using Landsat‐derived water occurrence images that primarily focuses on detecting when a channel has abruptly shifted position, either from avulsion or cutoff. The method does not assume any a priori model of channel geometry or evolution. Within a given area of interest, binary channel images created from the fluvial water occurrence record are stacked through time. Then a channel shift intensity, Ai , is created by estimating the number of possible ending times for fluvial water voxels (a point in three‐dimensional space) in the stacked occurrence record. The number of possible end‐times for fluvial water voxels within a given region of the occurrence record reveals the likelihood that a reach of a river underwent an abrupt channel shift during the observation period. We present the results of this analysis for a 194 481 km2 region of the Amazonian basin. Follow‐up validation found three avulsions and 270 cutoffs within regions identified by this method. We show that areas above a threshold Ai contain an avulsion or cutoff with high probability. This highlights the method's potential to detect and quantify abrupt channel shifts at the basin scale. The method also successfully distinguishes between abrupt channel movement and complex braiding behaviour. As this method is applicable to any binary water‐masked time series images, future applications of this method have the potential to provide insight into the controls and spatial variance of avulsions and cutoffs across a variety of scales.
Efficient Holographic Proofs

The formalization of interaction in a complexity-theoretic setting in 1985 began a new and wildly success- ful era in computational complexity theory. This study has culminated in the development of the theory of holographic or probabilistically checkable proofs. These are proofs so robust that they may be verified (with high probability) by consideration of a randomly selected fragment of the proof text. Such machinery has profoundly recharacterized many of the classical complexity classes. Although these recharacterizations have offered new structural insight into the classes involved, the great triumph of the theory has been an unexpected connection with approximation algorithms. It provides the first general scheme for concluding that even ap- proximating certain problems is difficult. This thesis constructs probabilistically checkable proof systems for NP more efficient than any previ- ously known and discusses the ramifications of these new systems in the realm of approximation algorithms. Perhaps the most significant advance is the construction of the first O(log n)-communication probabilistic- ally checkable proof system for NP which achieves any constant error with afixed number (4) of questions. These advances are applied to improve results on the hardness of approximating SET COVER, MAX CLIQUE, CHROMATIC NUMBER, MAX 3SAT, and QUARTIC PROGRAMMING. Finally, we explore the relativized behavior of this new machinery. We show that the recharacterizations of PSPACE in this framework are lamentably unstable. These results reinforce the belief that the framework of relativization is incompatible with these new techniques. They can also be seen as a further assault of the
Development and Challenges of Diclofenac-Based Novel Therapeutics: Targeting Cancer and Complex Diseases

Simple Summary Diclofenac is a widely used drug for its anti-inflammatory and pain alleviating properties. This review summarizes the current understanding about the drug diclofenac. The potential applications of diclofenac beyond its well-known anti-inflammatory properties for other diseases such as cancer are discussed, along with existing limitations. Abstract Diclofenac is a highly prescribed non-steroidal anti-inflammatory drug (NSAID) that relieves inflammation, pain, fever, and aches, used at different doses depending on clinical conditions. This drug inhibits cyclooxygenase-1 and cyclooxygenase-2 enzymes, which are responsible for the generation of prostaglandin synthesis. To improve current diclofenac-based therapies, we require new molecular systematic therapeutic approaches to reduce complex multifactorial effects. However, the critical challenge that appears with diclofenac and other drugs of the same class is their side effects, such as signs of stomach injuries, kidney problems, cardiovascular issues, hepatic issues, and diarrhea. In this article, we discuss why defining diclofenac-based mechanisms, pharmacological features, and its medicinal properties are needed to direct future drug development against neurodegeneration and imperfect ageing and to improve cancer therapy. In addition, we describe various advance molecular mechanisms and fundamental aspects linked with diclofenac which can strengthen and enable the better designing of new derivatives of diclofenac to overcome critical challenges and improve their applications.
Stagnation-point flows

The aim of this thesis is to consider an unsteady two-dimensional oblique stagnation-point flow travelling towards a wall. The far-field comprises an unsteady orthogonal stagnation-point flow, consisting of a mean component and an oscillatory component characterised by a relative amplitude and a dimensionless frequency parameter. Superimposed onto this, is a shear flow with constant vorticity and a time-dependent uniform stream. A similarity solution is sought, which is an exact solution of the Navier-Stokes equations. The flow is discussed in two limiting cases. At high frequency, the asymptotic structure is dependent upon the relative sizes of the mean and oscillatory components. When the mean flow is large, with a relatively small oscillatory component, the flow can be described with a Stokes layer at the wall matching an outer layer. However, when the oscillatory component dominates, a steady streaming layer is introduced to match to the far-field, which is considerably larger than that of the outer layer in the previous case. It is shown numerically in this case that the flow structure described breaks down when the relative amplitude of the oscillatory component is increased above a certain threshold, dependent upon the frequency. In the low frequency limit, at leading order, the problem is quasi-steady. Two special cases are considered, the first where the vorticity is zero in the far-field and the streamlines approach the wall orthogonally. In this case the flow is chosen to approach an oscillating wall. The second case is when the flow is steady. In certain time intervals over a single period, the flow structure becomes multi-layered. The behaviour of the flow within these intervals is investigated. Additionally, in the high frequency case, the streamline pattern is discussed in both the Stokes and steady streaming layers and the particle paths are analysed in the steady streaming layer.
Self-Supervised Association of Wi-Fi Probe Requests Under MAC Address Randomization

Wi-Fi-on devices such as smartphones search for network availability by periodically broadcasting probe requests which encapsulate MAC addresses as device identifiers. To protect identity privacy, modern devices embed random MAC addresses in probe frames, the so-called MAC address randomization. Such randomization disrupts the frame association, inadvertently frustrating identity-oblivious statistical analytic efforts such as people counting and trajectory inference. To address that, we propose <italic>Cappuccino</italic>, a novel privacy-preserving approach that captures the association of probe requests under MAC address randomization. Cappuccino first estimates pairwise frame correlation and then associates frames over time. For frame correlation, it employs a self-supervised estimator that jointly considers multiple modalities, i.e., information elements, sequence number, and received signal strength. For multiple frame association, Cappuccino formulates frames as a minimum-cost flow optimization. To the best of our knowledge, this is the first piece of work that leverages self-supervised learning to estimate frame correlation based on multiple modalities and formulates the probe request association problem as the network flow optimization. We have conducted extensive experiments in a leading and crowded shopping mall for more than three months. Cappuccino achieves remarkable performance in terms of V-measure scores (<inline-formula><tex-math notation="LaTeX">$>0.85$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>85</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="he-ieq1-3205924.gif"/></alternatives></inline-formula>).
Energy-efficient unrelated parallel machine scheduling with general position-based deterioration

ABSTRACT This paper investigates an energy-efficient scheduling problem on unrelated parallel machines considering general position-based deterioration which arises from the labour-intensive textile industry. The actual processing time of a job is not only associated with the job and the machine but also with its position in the processing sequence. The objective is to minimise the total energy consumption with a bounded makespan. To address this problem, we first establish a mixed-integer linear programming (MILP) model. Afterwards, the initial model is improved by deriving lower and upper bounds on the makespan, and an upper bound on the number of jobs processed on each machine. We also develop an iterative heuristic embedded with a variable neighbourhood search procedure (IHVNS). The algorithm obtains initial solutions iteratively by solving assignment problems and then repairs and improves them with the VNS procedure. Computational results demonstrate that the improved model is up to 230 times faster than the original one. Moreover, the proposed heuristic yields excellent solutions with average gaps of less than 0.73% for large-scale instances. Especially, the results reveal that the IHVNS algorithm is more suitable than MILP models for solving large-scale problems with tight makespan restrictions.
LETTER: Radial current in a tokamak caused by a biased electrode

It is demonstrated that measurements of radial electric currents excited from biased electrodes in TUMAN 3 and other tokamaks provide a crucial test in validating different models for L-H transitions. The results are assessed from the viewpoint of a previously developed theory, which is briefly described. There is evidence from the voltage-current characteristics in TUMAN 3 which corroborates features of this theory. A spontaneously occurring Ohmic H-mode is switched off when a substantial positive biasing voltage is applied to the electrode
Design of Dual-Port Pattern-Reconfigurable MIMO Antenna With Four Operating States Using the TCM

Based on the theory of characteristic modes (TCM), this letter presents a novel pattern-reconfigurable dual-port multiple-input–multiple-output (MIMO) antenna with four operating states at the 2.4 GHz ISM band. The proposed antenna consists of the rectangular platform (RP), excitation units, and reconfigurable feeding network. Four elaborated characteristic modes (CMs) of the RP can be stimulated effectively by six groups of balanced inductive exciters. By using a reconfigurable feeding network controlled by four switches, two orthogonal CMs associated with the two ports are switched, and four possible operating states for the reconfigurable MIMO antenna are generated. In order to demonstrate the proposed TCM-based conceptual design, a prototype is fabricated and the measured results agree well with the simulated ones. Furthermore, the port isolations of the proposed MIMO antenna are higher than 36 dB.
An Optimized Approach for Predicting Water Quality Features Based on Machine Learning

Traditionally, water quality is assessed using costly laboratory and statistical methods, rendering real-time monitoring useless. Poor water quality requires a more practical and cost-effective solution. The machine learning classification approach appears promising for rapid detection and prediction of water quality. Machine learning has been used successfully to predict water quality. However, research on machine learning for water quality index (WQI) prediction is generally lacking. Therefore, this research aims to identify the important features for the WQI, which necessitated the classification of numerous indicators. This study develops four machine learning models (Artificial Neural Network, Support Vector Machine, Random Forest, and Naïve Bayes) based on the WQI and chemical parameters. The Langat Basin in Selangor dataset from the Department of Environment of Malaysia trains and validates each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by several feature selection strategies such as information gain, correlation, and symmetrical uncertainty. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized Random Forest classifier with the WQI parameter selected by the information gain feature selection method achieved the highest performance. The experimental results show that the WQI parameters are more relevant in predicting the WQI than the other variables. Consequently, this result shows that parameter oxygen (DO) and biochemical oxygen demand (BOD) are important features for predicting WQI. The proposed model achieved reasonable accuracy with minimal parameters, indicating that it could be used in real-time water quality detection systems.
Estimating the number of true null hypotheses and the false discovery rate from multiple discrete non-uniform permutation p-values

vi CHAPTER 1. General Introduction . 1 1.
Standoff Polarimetric Aerosol Detection (SPADE) for Biodefense

Abstract : The polarization of laser light elastically scattered by small particles is investigated for the purpose of remotely detecting bio-aerosol presence in typical ambient background aerosols. The information source is small-particle scattering code computations, not actual measurements. Two separate types of particles - natural background aerosols vs. bio-aerosols - are combined with a concentration ratio of 100-to-1. The objective is to assess the feasibility of detecting the introduction of the bio-aerosol into the typical background aerosol by analyzing the polarization state of the laser light scattered by the combination of particles. The incident laser light is assigned two elementary linear polarizations, oriented parallel to or at + 45 deg relative to the scattering plane. The state of polarization of the light redirected through various scattering angles is specified by polarimetric quantities developed from the scattered light Stokes parameters. Incoherent laser and receiver specifications are employed to evaluate signal-to-noise for three scenarios: outdoor stadium, indoor auditorium, and building heating-ventilating-air-conditioning (HVAC) system. Of these, the HVAC application is very feasible with conventional laser and detector technology. The indoor application is feasible. Coherent laser and advanced photo-diode detector technologies substantially enhance signal-to-noise especially for the outdoor application.
SLC7A1 Overexpression Is Involved in Energy Metabolism Reprogramming to Induce Tumor Progression in Epithelial Ovarian Cancer and Is Associated with Immune-Infiltrating Cells

Cationic amino acid transporters (SLC7A1/CAT1) are highly expressed in human ovarian cancer (OC) tissues. However, the specific biological functions and mechanisms involved remain unclear. We used bioinformatics analysis to explore SLC7A1 expression level, prognostic value, and tumor mutation burden (TMB) in ovarian cancer (OC) tissues. We performed in vitro experiments to identify the expression and biological function of SLC7A1 in epithelial ovarian cancer (EOC) tissues and cells. An amino acid autoanalyzer was used to detect the effect of SLC7A1 on amino acid metabolism in EOC cells. Finally, SLC7A1 in OC was evaluated for cell-to-cell signalling and immune infiltration using online databases. We found that increased SLC7A1 expression in EOC cells and tissues was associated with poorer survival outcomes (P < 0.05) but not with tumor stage or grade of OC (P > 0.05). SLC7A1 is involved in the transport of phenylalanine and arginine in EOC cells, and its knockdown reduced the proliferation and migration of EOC cells and the resistance of cells to cisplatin. Furthermore, the TIMER database indicated that SLC7A1 overexpression was significantly positively correlated with levels of CD4+ memory resting cells, CD8+ effector memory cells, M0 macrophages, and cancer-associated fibroblasts (CAFs) in OC (P < 0.05) and significantly negatively correlated with CD4+ memory-activated cells (P < 0.05). Cell immunofluorescence indicated that SLC7A1 overexpression may affect the distribution of immune-infiltrating lymphocytes in tumors by inhibiting the expression of CCL4. Therefore, we concluded that SLC7A1 is involved in the metabolic remodelling of amino acids in EOC to promote tumor development and cisplatin resistance and is related to the tumor-infiltrating immune microenvironment of OC. SLC7A1 is a biomarker for predicting EOC progression and cisplatin resistance and represents a promising target for EOC treatment.
The impact of wind direction on wind farm power output calculation considering the wake effects of wind turbines

The wind farm is a collection of all wind turbine generators situated at a particular distance. The wind speed and wind direction play an important role in wind farm power calculation. The power curve of the wind farm is not simply the summation of the wind turbine’s power curve. It is complex due to the intermittent nature of wind speed and its direction. The power curve is obtained from the data taking the wind speed and wind power at that speed. There are many logistic functions used in the literature to analyze the wind power curve that helps to calculate wind farm power output and energy generated. In this paper, the 3-parameter deterministic process (3P-DP) method is used for wind power curve calculation. The wake effect is analyzed by Jensen’s model with wind speed and wind direction. The wind farm power is obtained from the new proposed formula and compared with the already existing one. The results are verified from real data obtained from the literature.
The Effects of Extinction on the Galactic Nova Rate

Although many efforts have been made to correctly predict the total galactic nova rate, inconsistencies in the literature of the last two decades have shown that this issue remains largely unresolved. Since 1993, predicted global rates have varied from 20yr−1 [Della Valle and Duerbeck, 1993], and 41 ± 20yr−1 [Hatano et al., 1997], to 30 ± 10yr−1 [Shafter, 2002]. Comparisons with observed quantities at around 5-10yr−1 within the past 50years show evidence that one of the largest physical inhibitors of nova observability within the Milky Way is extinction. Here, a dust extinction calculator was developed based on an axisymmetric double exponential dust distribution. The objective of this project was to develop a nova distribution model that would ultimately reproduce the observed nova rate when extinction is accounted for. The resulting product of these models showed that, with a simple double exponential distribution of novae, for which only 16 nova are ultimately observable in any given year and 10 observed, the number of novae erupting within the galaxy must be approximately 48yr−1. This information is then compared with the CBAT list of data from all novae that have been recorded since 1612. The concluding remarks of this work are such that, although this model is rudimentary, adding the effects of a galactic bulge and spiral arms into the dust and novae distribution would improve upon it and serve as a good starting point for research regarding the relationship between galactic structure and observations of novae and other phenomena.