Miracle-dz/UniFAR-FTUs huggingface dataset:
A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario

Tipping points in the Earth system describe critical thresholds beyond which a single component, part of the system, or the system as a whole changes from one stable state to another. In the present-day Southern Ocean, the Weddell Sea constitutes an important dense-water formation site, associated with efficient deep-ocean carbon and oxygen transfer and low ice-shelf basal melt rates. Here, a regime shift will occur when continental shelves are continuously flushed with warm, oxygen-poor offshore waters from intermediate depth, leading to less efficient deep-ocean carbon and oxygen transfer and higher ice-shelf basal melt rates. We use a global ocean–biogeochemistry model including ice-shelf cavities and an eddy-permitting grid in the southern Weddell Sea to address the susceptibility of this region to such a system change for four 21st-century emission scenarios. Assessing the projected changes in shelf–open ocean density gradients, bottom-water properties, and on-shelf heat transport, our results indicate that the Weddell Sea undergoes a regime shift by 2100 in the highest-emission scenario SSP5-8.5, but not yet in the lower-emission scenarios. The regime shift is imminent by 2100 in the scenarios SSP3-7.0 and SSP2-4.5, but avoidable under the lowest-emission scenario SSP1-2.6. While shelf-bottom waters freshen and acidify everywhere, bottom waters in the Filchner Trough undergo accelerated warming and deoxygenation following the system change, with implications for local ecosystems and ice-shelf basal melt. Additionally, deep-ocean carbon and oxygen transfer decline, implying that the local changes ultimately affect ocean circulation, climate, and ecosystems globally.
A comparison of multivariate and univariate models for pre-test post-test data concerning accuracy in the presence of missing data

Missing data has been posing a common problem to almost all scientific studies over the last decades. This study compares multivariate models to univariate models, the change score method and the regressor variable method, in terms of precision and accuracy over different conditions of missing data. In two simulation studies patterns of missing data were introduced, over degrees of missingness and covariance for pre-test post-test designs. The first study focuses on the MCAR pattern and its influence on the estimates of the different models. The second model makes use of a MAR pattern, where the likelihood of missing values is dependent on an observed variable. The results showed some differences between the models. The change score method appeared to have no particular advantages, the regressor variable method had the highest accuracy, while the multivariate method provided the best precision. Higher amounts of missing data had a negative impact on both accuracy and precision.
Fluidic thrust vector control based on counter-flow concept

Computational studies are conducted on the supersonic nozzle to investigate the possibility of utilizing counter-flow in fluidic thrust vector control. In this work, the design Mach number of the symmetric supersonic nozzle is set to be 2.5. For the validation of methodology, numerical results are compared with experimental data referred from the literature. Two-dimensional numerical simulations are based on well-assessed standard k–ɛ turbulence model with standard wall functions. Second-order accuracy is ensured to reveal more details of flow field. The system thrust ratio, deflection angle, and secondary mass flow ratio were studied for a wide range of nozzle pressure ratios and secondary pressure ratios. The results indicate that deflection angle and secondary mass flow ratio are found to be decreased with increasing nozzle pressure ratio as well as system thrust ratio. The secondary mass flow ratio and deflection angle decrease with the increase of secondary pressure ratio, and system thrust ratio increases with the increasing of secondary pressure ratio. The secondary mass flow rate remains under 2.4% of the primary flow to obtain efficient thrust vector control at high Mach number.
Supply-side factors and uptake of insurance products among Ghanaian households

Abstract This study examines the influence of supply-side factors on uptake of insurance, as well the moderating effect of sales agent effectiveness on the interactions. A survey research design was adopted following a quantitative approach. The target population of this study comprised Ghanaian households of income earning groups (earning less than US$190; earning between US$191 and US$1,000; as well as earning US$1,000). Using a cross-sectional survey, a structured questionnaire was administered to 520 households that participated in the survey following a convenience sampling technique in line with the ethical considerations in social science research. The formulated hypotheses were tested using hierarchical regression analyses for the direct and moderating relationships. The results show that three supply-side factors influencing insurance uptake are insurance product design, customer service, and market positioning. The relationships between insurance product design, customer service, market positioning, and insurance uptake become stronger with higher levels of sales agent effectiveness. The implications for practice point to the need for a concerted effort on development of insurance products across the income brackets, offering insurance product cost suitable for each income group and providing adequate awareness to enhance uptake of insurance products.
Mean-field Wigner function of Bose–Einstein condensates in the Thomas–Fermi limit

We study the mean-field phase-space distribution of the ground state of an anisotropic, harmonically trapped Bose–Einstein condensate at zero temperature. First, we derive the Fourier transform of the ground-state wave function in the Thomas–Fermi limit in d dimensions. Second, we obtain analytical approximations for the Thomas–Fermi Wigner function in one and three dimensions with a factorization ansatz of the coherence function. These approximate expressions are compared with exact numerical simulations of the Gross–Pitaevskii equation.
The track inspection vehicle data is utilized for fault feature extraction of jointless track circuit compensation capacitor using CEEMDAN

In order to keep pace with the advancement of track inspection vehicle operation and maintenance technology, this paper proposes a fault feature extraction technique for compensation capacitor based on the induced voltage data acquired by the track inspection vehicle. Firstly, a finite element model is developed to simulate the induced voltage acquisition process, utilizing the principles of high-speed track inspection vehicle and electromagnetic field theory. Next, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is chosen to extract the fault features by comparing the effectiveness and orthogonalization of three signal decomposition methods, namely Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and CEEMDAN. The decomposed modal components are used to calculate the energy value, which is subsequently used as feature information for fault classification using a neural network. Finally, the proposed method is tested on actual railway field data, and statistical analysis shows an accuracy of 87%.
Invariant Kalman Filter for Correlated Wide Band Noises

Kalman filtering is a powerful estimation method. One of its weaknesses is related to the white or coloured nature of the disturbing noises in the Kalman filtering model. At the same time, real noises are rarely white or coloured. They are mostly wide band. In this regard, white or coloured noise Kalman filtering makes concessions on adequacy. This pushes system scientists to develop mathematical methods of estimation for systems corrupted by wide band noises. In applications, wide band noises are detected by their autocovariance and cross‐covariance functions, which do not allow to model them uniquely. Therefore, it becomes important to develop estimation methods which are independent of a class of wide band noises, but dependent on the unique autocovariance and cross‐covariance functions. Such results are called invariant results. In this paper, we prove a complete set of invariant equations for Kalman type filter for a linear signal‐observation system corrupted by correlated wide band noises. This filter has a ready form to be used in applications, just respective numerical methods must be developed.
Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression

Gaussian mixture regression (GMR) is an effective tool in developing soft sensors for online estimating difficult-to-measure variables in industrial processes with multiple operating modes. However, the GMR usually requires a sufficient amount of labeled samples to guarantee accurate probability density function (PDF) estimations because of its supervised learning process. Unfortunately, in soft-sensor applications, labeled samples could be very infrequent due to technical or economic limitations, which may lead the GMR-based soft sensors to unreliable parameter estimation and model selection, resulting in poor prediction performance. To tackle this problem, a semisupervised GMR (S2GMR) was proposed, where both labeled and unlabeled samples were effective. In the S2GMR, the PDFs of Gaussian components in input space and the functional dependence between input and output variables were learned simultaneously based on the expectation–maximization algorithm. Moreover, the Bayesian information criterion was employed to automatically determine the number of Gaussians for the S2GMR. The S2GMR was first investigated by a numerical example, and then applied to a real-life ammonia synthesis process for estimating the oxygen concentration at the top of the primary reformer. The two case studies verified the effectiveness of the proposed method.
Biological networks : a thermodynamical approach

Biological networks: a thermodynamical approach Yohann Grondin Many real systems can be represented by networks, that is a set of nodes connected to each other. The study of these systems as such has proven extremely useful as it gives access to a series of parameters that characterise their non-trivial architecture. This ar­ chitecture is the product of many factors from the evolutionary mechanisms that shape the system during its growth to the functional dynamics on a shorter time scale. Gain­ ing knowledge on the architecture is then of importance but faces many challenges in particular in the study of biological networks. The first challenge is in terms of the method used to generate networks as we need to adopt an approach that, we expect, would allow us to understand those constraints and forces that shape the network. The second challenge is that of understanding the relationship between the architec­ ture of the system and its dynamics and functionality. The third challenge is to get access using suitable techniques to the network archi­ tecture from expression data, such as mRNA abundances, for example. We first show in this thesis that it is possible to generate networks from a thermody­ namical viewpoint. This approach allows us to relate the architecture of network to some constraints. Furthermore, we show that some information on the structure resides in the non-randomness of the links between nodes. If we were to draw an analogy with tradi­ tional thermodynamics, networks could be modelled in a first approximation as perfect gases. On a dynamical network of our design, we show a dependence of the architecture on the distribution of the level of expression of the nodes. Surprisingly, the distribution of the periods of those networks is a power-law and independent of the underlying archi­ tecture of the system. By comparing the data obtained from our model to experimental mRNA data we found a correlation between the degree of connectivity of genes and their level of abundance. Finally, we show how we can apply a method used traditionally in image reconstruc­ tion to inference of networks.
Unintentional Programs: An Interproject Orientation for Aligning IT with Organizational Strategy

This study proposes program orientation as an organizational culture that motivates organizations to manage interrelated projects like a program without a formal structure or program team. We hold that collaboration, with cooperation and coordination as its main elements, plays a critical role in aligning interrelated projects dynamically. The empirical results support our hypotheses that program orientation indirectly affects information systems alignment with interproject cooperation, coordination, and adaptability mediating its effects. This study thus confirms the benefit of treating interrelated projects as a program, even when they are not governed by a formal program structure.
Modeling of Hash Functions on the Basis of Irreducible Polynomials in a Finite Fields

—In this paper the method for constructing hash functions on the basis of irreducible polynomials in finite fields has been considered. The problem of searching for irreducible polynomials was considered. Computer modeling of hash functions using irreducible polynomials was performed. The results of the use of various irreducible polynomials and their analysis was presented.
Discrete-Time SO(n)-Constrained Kalman Filtering

The estimation of the orientation of a rigid body is important in the aerospace and robotics fields. The attitude of a rigid body is naturally described by a rotation matrix, which belongs to the special orthogonal group of rigid-body rotations, denoted by SO(3). Because of the orthonormality and determinant constraints, parameterizations of SO(3) are often used. However, parameterizations may suffer from singularities or nonuniqueness of representation. Recently, there has been significant interest in working directly with SO(3) and more generally with SO(n). This paper presents a discrete-time Kalman-like filter that performs estimation directly on SO(n). The filter presented honors the orthonormality and determinant constraints associated with SO(n). The developed filter is applied to a spacecraft attitude estimation problem that involves estimation on SO(3)×R3. Its effectiveness is demonstrated in a numerical example, comparing its performance with the well-established multiplicative extended Kalman f.
Asymptotically Optimal Control of Make-to-Stock Systems

We consider multiclass make-to-stock production/inventory systems in which the manager makes three decisions, including pricing, outsourcing, and scheduling, to maximize the long-run average profit. For a sequence of systems in the heavy-traffic regime, with linear or strictly convex holding/waiting cost functions, we propose a sequence of policies and establish its asymptotic optimality. Our proof combines the lower bound approach and a thorough steady-state analysis of the systems. We also establish general results on the existence and tightness of the stationary distributions of the state processes under a more general family of policies. Funding: X. Gao’s research is supported in part by the Hong Kong Research Grants Council [Grants 14201520, 14201421, and 14212522]. J. Huang’s research is supported in part by the Hong Kong Research Grants Council [Grants 14500819 and 14505820] and the National Natural Science Foundation of China [Grant 72222023].
Determinants of dairy farmers’ choice of marketing channels in Bihar, India

In the past one and half decade, milk production in Bihar has increased substantially, from 2.7 million tons in 2001–02 to 8.7 million tons in 2016–17 lifting the per capita daily milk availability in the state from 88g to 228g. However, dairy sector is dominated by small farmers, and because of their small scale they are forced to sell milk to informal buyers who often exploit them by paying less than the market price. This paper analyses farmers’ sale pattern of milk and identifies factors influencing their choice of market channels. Results indicate that small farmers prefer selling milk directly to consumers because of the higher offer prices and nearness to the production centres. Large and educated farmers sell to formal buyers. However, if the payments from formal buyers are delayed, farmers tend to switch over to informal buyers. The likelihood of selling to informal buyers increases if farmers find it difficult to sell directly to the consumers located far away from the production centres.
Surgical outcomes of resected cystic neoplasms of pancreas: Experience from a tertiary care centre in India.

Objectives Cystic neoplasms of the pancreas form a rare heterogeneous group of pancreatic tumors with variable clinical and diagnostic characteristics. Its incidence has increased in recent years due to improvements in cross-sectional imaging methods and awareness amongst surgeons. Material and Methods This study aimed to study the demographic, clinical, imaging, and histopathologic characteristics, incidence of malignancy and outcome of surgical resection of pancreatic cystic neoplasms. Retrospective analysis of 91 patients who underwent surgical resection for cystic neoplasm of the pancreas between 2006 to 2017 at a tertiary care institute was done. Results There was a female preponderance in the study with a mean age of 47.2 years. Abdominal pain (46.1%) and jaundice (23.1%) were the most common symptoms. Computed tomography and endoultrasound (EUS) were the most commonly used imaging methods in the study and demonstrated good surgical correlation. Pancreaticoduodenectomy (37.1%) was the most commonly performed procedure followed by distal pancreaticosplenectomy (31.8%). Of the lesions, 9.8% were found malignant. Solid pseudopapillary epithelial neoplasm (SPEN) (37.3%) was the most common neoplasm followed by serous (21.9%), intraductal papillary mucinous neoplasm (IPMN) (15.3%) and mucinous neoplasm (14.3%). Preoperative radiological diagnostic correlation was found to be 75-100% implying the importance of imaging in cystic neoplasms of the pancreas. Morbidity and mortality in the study group were 28.5% and 2.1%, respectively. Conclusion Pancreatic cystic neoplasms were mostly benign with female preponderance and presented in the younger age group with prevalence of SPEN higher than IPMN in our subcontinent. These can be reliably diagnosed on preoperative cross-sectional imaging, and surgical resection is associated with favourable outcome and acceptable morbidity.
A Comparison of Two Quantile Models With Endogeneity

Abstract This article studies the relationship between the two most-used quantile models with endogeneity: the instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen 2005) and the local quantile treatment effects (LQTE) model (Abadie, Angrist, and Imbens 2002). The key condition of the IVQR model is the rank similarity assumption, a restriction on the evolution of individual ranks across treatment states, under which population quantile treatment effects (QTE) are identified. By contrast, the LQTE model achieves identification through a monotonicity assumption on the selection equation but only identifies QTE for the subpopulation of compliers. This article shows that, despite these differences, there is a close connection between both models: (i) the IVQR estimands correspond to QTE for the compliers at transformed quantile levels and (ii) the IVQR estimand of the average treatment effect is equal to a convex combination of the local average treatment effect and a weighted average of integrated QTE for the compliers. These results do not rely on the rank similarity assumption and therefore provide a characterization of IVQR in settings where this key condition is violated. Underpinning the analysis are novel closed-form representations of the IVQR estimands. I illustrate the theoretical results with two empirical applications.
Partition functions for supersymmetric gauge theories on spheres

In this paper we briefly review the main idea of the localization technique and its extension suitable in supersymmetric gauge field theory. We analyze the partition function of the vector multiplets with supercharges and its blocks on the even- and odd-dimensional spheres and squashed spheres. We exploit the so-called Faà di Bruno’s formula and show that multipartite partition functions can be written in the form of expansion series of the Bell polynomials. Applying the restricted specialization argument we show that q-infinite-product representation of partition functions admits presentation in terms of the Patterson–Selberg (or the Ruelle-type) spectral functions.
Random forest robustness, variable importance, and tree aggregation

Random forest methodology is a nonparametric, machine learning approach capable of strong performance in regression and classification problems involving complex datasets. In addition to making predictions, random forests can be used to assess the relative importance of explanatory variables. In this dissertation, we explore three topics related to random forests: tree aggregation, variable importance, and robustness. In Chapter 2, we show that the method of tree aggregation used in one popular random forest implementation can lead to biased class probability estimates and that it is often beneficial to combine the tree partitioning algorithm used in one implementation with the aggregation scheme used in another. In Chapter 3, we show that imputing missing values proir to assessing variable importance often leads to inaccurate variable importance measures. Using simulation studies, we investigate the impact on variable importance of six random-forest-based imputation techniques and find that some techniques are prone to overestimating the importance of variables whose values have been imputed, while other techniques tend to underestimate the importance of such variables. In Chapter 4, we propose a new robust approach for random forest regression. Adapted from a popular approach used in polynomial regression, our method uses residual analysis to modify the weights associated with training cases in random forest predictions, so that outlying training cases have less impact. We show, using simulation studies, that this approach outperforms existing robust techniques on noisy, contaminated datasets.
Experimental study of gradual/abrupt dynamics of HfO2-based memristive devices

The resistance switching dynamics of TiN/HfO2/Pt devices is analyzed in this paper. When biased with a voltage ramp of appropriate polarity, the devices experience SET transitions from high to low resistance states in an abrupt manner, which allows identifying a threshold voltage. However, we find that the stimulation with trains of identical pulses at voltages near the threshold results in a gradual SET transition, whereby the resistive state visits a continuum of intermediate levels as it approaches some low resistance state limit. On the contrary, RESET transitions from low to high resistance states proceed in a gradual way under voltage ramp stimulation, while gradual resistance changes driven by trains of identical spikes cover only a limited resistance window. The results are discussed in terms of the relations among the thermo-electrochemical effects of Joule heating, ion mobility, and resistance change, which provide positive and negative closed loop processes in SET and RESET, respectively. Furthermore, the effect of the competition between opposite tendencies of filament dissolution and formation at opposite metal/HfO2 interfaces is discussed as an additional ingredient affecting the switching dynamics.