Miracle-dz/UniFAR-FTUs huggingface dataset:
Mutational Slime Mould Algorithm for Gene Selection

A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.
A Review of Spatter in Laser Powder Bed Fusion Additive Manufacturing: In Situ Detection, Generation, Effects, and Countermeasures

Spatter is an inherent, unpreventable, and undesired phenomenon in laser powder bed fusion (L-PBF) additive manufacturing. Spatter behavior has an intrinsic correlation with the forming quality in L-PBF because it leads to metallurgical defects and the degradation of mechanical properties. This impact becomes more severe in the fabrication of large-sized parts during the multi-laser L-PBF process. Therefore, investigations of spatter generation and countermeasures have become more urgent. Although much research has provided insights into the melt pool, microstructure, and mechanical property, reviews of spatter in L-PBF are still limited. This work reviews the literature on the in situ detection, generation, effects, and countermeasures of spatter in L-PBF. It is expected to pave the way towards a novel generation of highly efficient and intelligent L-PBF systems.
Quantum Field Theory in Curved Spacetime: Frontmatter

This book develops quantum field theory in curved spacetime in a pedagogical style, suitable for graduate students. The authors present detailed, physically motivated, derivations of cosmological and black hole processes in which curved spacetime plays a key role. They explain how such processes in the rapidly expanding early universe leave observable consequences today, and how in the context of evaporating black holes, these processes uncover deep connections between gravitation and elementary particles. The authors also lucidly describe many other aspects of free and interacting quantized fields in curved spacetime.
Dynamic reactive power optimal allocation to decrease wind power curtailment in a large-scale wind power integration area

Autonomous voltage security regions (AVSRs) have been proposed to prevent cascading trip faults in wind farms. However, due to insufficient dynamic reactive power (DRP) support and unreasonable DRP allocation in large-scale, centralised wind power integration areas, especially for high wind power penetrations, AVSRs may be too small to guarantee secure wind farm operation, resulting in large amounts of wind power curtailment. DRP allocation is crucial for wind farms to accommodate more wind power and decrease curtailment. Therefore, a DRP allocation scheme based on the AVSR to accommodate more wind power is proposed in this work. An improved AVSR considering wind power curtailment is first derived; the function between the AVSR, DRP reserves and curtailment is then obtained, and an optimal DRP allocation scheme using Benders decomposition is finally proposed. Good performance is observed when tested on a real large-scale wind power base in Northern China.
A Robust Bayesian Dynamic Linear Model to Detect Abrupt Changes in an Economic Time Series: The Case of Puerto Rico

Economic indicators time series are usually complex with high frequency data. The traditional time series methodology requires at least a preliminary transformation of the data to get stationarity. On the other hand, the Robust Bayesian Dynamic Model (RBDM) does not assume a regular pattern and stability of the underlying system but can include points of statement breaks. In this paper, we estimate the Consumer Price Index and the Economic Activity Index of Puerto Rico using a RBDM with observational and states variances to model the outliers and structural breaks in the time series. The results show the model detects structural changes in both series, some of them just before the beginning of a recession period in Puerto Rico's economy.
Gendered experiences of Ghanaian female academics and scientists

Although women’s roles in development are vital, they are generally marginalized in all spheres of society’s development including Science, Technology, Engineering, and Mathematics (STEM). This article explored the experiences of women in STEMdomains in Ghana’s tertiary education and research institutions. Thirty participants (students, faculty, and research scientists) were selected through purposive/criterion sampling. Their experiences were elicited using semi-structured interviews. Theinterview data were thematically analysed using initial coding, axial, and selective coding under grounded theory. The findings confirmed that the Ghanaian society is still deeply patriarchal. On the STEM trajectories of the participants, gender discrimination was manifested in comments, structures, and practices. In the face of the gender discrimination experienced, the support system of the female faculty interviewed, enabled them to succeed in their professional and educational endeavours.
A Cryptosystem Analogous to LUCELG and aDigital Signature Scheme

EIGamal and LUC are examples of a public-key cryptosystem. Based on these two systems, LUCELG that depends on the strength of the two systems was constructed. The combination of EIGamal and the cubic analogue of the LUC cryptosystem (LUC~) produces a new public-key cryptosystem. Following (Smith94), a new digital signature scheme is proposed. The security aspects of the system are also looked into and although all these systems appear to depend on the intractability of factorization or of the discrete logarithm problem, the systems do not seem to be readily comparable.
Design, synthesis, and biological evaluation of tetrahydroisoquinoline stilbene derivatives as potential antitumor candidates

Herein, a novel class of tetrahydroisoquinoline stilbene derivatives were synthesized, and their potential in vitro anticancer activities were evaluated. Most of the compounds displayed inhibitory activity against one or more representative human cancer cell lines (lung cancer A549 cells, breast cancer MCF‐7 cells, and human colorectal carcinoma HT‐29 cells), especially compound 16e, which exhibited outstanding cytotoxicity to A549 cells. The tubulin polymerization assay demonstrated that compound 16e displayed better inhibition than colchicine when tested at the same concentration. It was found that 16e arrested A549 cells in G2/M phase by downregulating the expression of cell division cycle 2 (Cdc2) and upregulating the expression of proliferating cell nuclear antigen (PCNA) and cyclin B1. Flow cytometry and Western blot analysis indicated that 16e caused apoptosis via the mitochondrial‐dependent apoptotic pathway by reducing mitochondrial membrane potential, inducing ROS accumulation, promoting the release of cytochrome C from the mitochondria into the cytoplasm, and further increasing the protein level of cleaved caspase‐3. This work may inspire new ideas for the further improvement of tubulin‐related anticancer drugs and treatments.
Computational Conformal Geometry

This new volume presents thorough introductions to the theoretical foundations - as well as to the practical algorithms - of computational conformal geometry. These have direct applications to engineering and digital geometric processing, including surface parameterization, surface matching, brain mapping, 3-D face recognition and identification, facial expression and animation, dynamic face tracking, mesh-spline conversion, and more. Computational conformal geometry is an emerging inter-disciplinary field, with applications to algebraic topology, differential geometry and Riemann surface theories applied to geometric modeling, computer graphics, computer vision, medical imaging, visualization, scientific computation, and many other engineering fields.
Hybrid network switching of power voice dispatching platform based on soft switch

With the development and progress of electric power communication technology in China, it gradually covers and improves the diversified and personalized needs of users. Different from the traditional network construction, the construction of a soft switch network is more systematic and advanced to better serve the development of network telecommunications and communication systems. This paper is based on the SIP protocol control technology as the core of the soft exchange. Let the network business media flow direct bear on the IP network through the soft exchange system and control and transfer in a business organization structure, which is to make the application layer control mainly in the business running on the server, the data, voice, video, news and other business processes. Through the experiment, the maximum number of registered users and the call processing ability of users in the domain of the design scheme are tested and verified, which proves that the scheme in this paper is superior to the traditional voice bearer method of the power grid to meet business needs. It can provide technical support for power grid production and lay a solid foundation for the sustainable and healthy development of the power grid in the future.
Model-Based Reinforcement Learning with Automated Planning for Network Management

Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores the potential of using Automated Planning (AP) to achieve this MBRL in the functional areas of network management. In addition, a comparison of several integration strategies of AP and RL is depicted. We also describe an architecture that realizes a cognitive management control loop by combining AP and RL. Our experiments evaluate on a simulated environment evidence that the combination proposed improves model-free RL but demonstrates lower performance than Deep RL regarding the reward and convergence time metrics. Nonetheless, AP-based MBRL is useful when the prediction model needs to be understood and when the high computational complexity of Deep RL can not be used.
Synthesis and evaluation of a new gel polymer electrolyte for high-performance Li-ion batteries from electrospun nanocomposite of PVDF/Ca–Al-layered double hydroxide

Poly(vinylidene fluoride) (PVDF)/Ca–Al-layered double hydroxide(CAL) (PCL) nanocomposite-based nanofabrics were electrospun for application in lithium-ion batteries as gel polymer electrolyte (GPE). The nanofabric exhibited a high β-phase content of 82.79% after the addition of CAL that was synthesized by co-precipitation method. The PCL-based GPE exhibited enhanced electrochemical properties, such as high ionic conductivity, optimal Li-ion transference number, and improved electrolyte uptake due to the presence of a highly interconnected porous structure. The PCL GPE exhibited an ionic conductivity of 3.54 × 10^–3 S cm^−1 at ambient temperature, which is much higher than that of pristine PVDF and commercial Celgard^® 2400 separators. Moreover, Li/PCL/LiCoO_2 cell showed an initial discharge capacity of 140.31 mAh g^−1, which is superior to that of PVDF and Celgard^® 2400 separators. It also exhibited high coulombic efficiency retention of 99% after 30 cycles of charging. PCL-based GPE showed superior mechanical and low thermal shrinkage properties, indicating its suitability in battery separator application. Graphical abstract
ScaFi: A Scala DSL and Toolkit for Aggregate Programming

Supported by current socio-scientific trends, programming the global behaviour of whole computational collectives makes for great opportunities, but also significant challenges. Recently, aggregate computing has emerged as a prominent paradigm for so-called collective adaptive systems programming. To shorten the gap between such research endeavours and mainstream software development and engineering, we present ScaFi , a Scala toolkit providing an internal domain-specific language, libraries, a simulation environment, and runtime support for practical aggregate computing systems development
DeepContext: Mobile Context Modeling and Prediction via HMMs and Deep Learning

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in “Location X”) are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. Most of the existing solutions rely on context obtained directly from sensors which could be hacked, noisy or insufficient, which cannot be relied upon for security applications. In this article, we take a different approach by modeling mobile context based on past context data of related users and considering its unique challenges such as missing features. To this end, we propose three models for modeling mobile context based on symbolic time series data of feature-value pairs—two stochastic models based on the theory of Hidden Markov Models (HMMs) and one model based on deep learning—personalized model (HPContext), collaborative filtering model (HCFContext), and deep learning model (DeepContext) to eventually replace HPContext. HPContext and DeepContext predict the current context using sequential history of the user's own past context observations; while HCFContext enhances the former with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in the company, gym friends, family members, etc. DeepContext models mobile context based on symbolic (i.e., categorical valued rather than continuous-valued) time series data using deep learning techniques. These models are then used to determine the context of the primary user at the current instant or some timesteps in to the future. Each of the proposed models can also be used to enhance or complement the context obtained from sensors. Finally, these models are thoroughly validated on a real-life dataset.
$p$ orbitals in 3D lattices; fermions, bosons and (exotic) models of magnetism

We demonstrate how different types of $SU(3)$ Heisenberg models can be implemented with the use of the $p$ orbitals of three dimensional optical lattices. By considering a Mott insulator with unit filling, the dynamics is well described by an effective model derived from the perturbative treatment of the tunneling elements relative to the onsite interaction terms. This yields systems with degrees of freedom that are generators of the $SU(3)$ group, which extends the Heisenberg models frequently used to analyze quantum magnetism. Due to the different character of interactions in the bosonic and fermionic cases, the choice of atom determines what type of anisotropies will appear in the couplings of the corresponding effective Hamiltonians. Experimental schemes for detection and manipulation of these systems are presented, and properties of the ground states of selected examples are discussed.
Genetic Variants Associated with Elevated Plasma Ceramides in Individuals with Metabolic Syndrome

Metabolic syndrome (MetS) is a complex condition of metabolic disorders and shows a steady onset globally. Ceramides are known as intracellular signaling molecules that influence key metabolism through various pathways such as MetS and insulin resistance. Therefore, it is important to identify novel genetic factors related to increased plasma ceramides in subjects with MetS. Here we first measured plasma ceramides levels in 37 subjects with MetS and in 38 healthy subjects by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Specifically, levels of C16 ceramide (Cer-16), C18 ceramide (Cer-18), C20 ceramide (Cer-20), C18 dihydroceramide (DhCer-18), C24 dihydroceramide (DhCer-24), and C24:1 dihydroceramide (DhCer-24:1) were significantly increased in MetS group (p < 5.0 × 10−2). We then performed single nucleotide polymorphism (SNP) genotyping to identify variants associated with elevated plasma ceramides in MetS group using Axiom® Korea Biobank Array v1.1 chip. We also performed linear regression analysis on genetic variants involved in ceramide synthesis and significantly elevated plasma ceramides and dihydroceramides. Ten variants (rs75397325, rs4246316, rs80165332, rs62106618, rs12358192, rs11006229, rs10826014, rs149162405, rs6109681, and rs3906631) across six genes (ACER1, CERS3, CERS6, SGMS1, SPTLC2, and SPTLC3) functionally involved in ceramide biosynthesis showed significant associations with the elevated levels of at least one of the ceramide species in MetS group at a statistically significant threshold of false discovery rate (FDR)-adjusted p < 5.0 × 10−2. Our findings suggest that the variants may be genetic determinants associated with increased plasma ceramides in individuals with MetS.
A Comparative Study on the Efficiency of R&D Activities of Universities in China by Region Using DEA–Malmquist

This paper analyzes the efficiency of the input and output of R&D activity and the status of its development and management in universities in each region of China and proposes suggestions for improvement. The DEA–Malmquist model was used to analyze the static and dynamic science and technology statistics for universities in each region during 2006–2019 to reveal changes in the input and output of R&D activity. The overall efficiency of the R&D activity of universities in all regions of China was low. Among the 27 regions studied, DEA revealed effective efficiency in 20 regions in 2006, accounting for 74.07%, and in 19 regions in 2019, accounting for 70.37%. The Malmquist index was greater than 1 in 17 regions in 2006–2019, with an average value of 1.023 during 2006–2019. The technological progress of R&D activity in universities in each region plays a major role in the improvement of the overall efficiency. Conclusions: The efficiency of the R&D activity of Chinese universities in all regions is low in general, with large disparities between regions. The R&D activity of Chinese universities lacks scientific management. It is necessary to optimize the allocation of research resources, construct an evaluation system for the efficiency of R&D activity, and offer incentives for research to improve the output and promote the transformation of results in Chinese universities.
LAMONT DOHERTY GEOLOGICAL OBSERVATORY OF COLUMBIA UNIVERSITY

Reinsch (1967) introduced the cubic smoothing spline as the solu¬ tion to a certain variational problem in the theory of data smoothing. This smoothing operator has good transient response, a minimal curva¬ ture property, and compensation for end effects so that all the data is used. In addition, the data can have arbitrary spacing and weighting. In this paper we represent Reinsch1s equations in terms of convolu¬ tions for the special case of equally-spaced and equally-weighted data. We then use a Fourier transform to obtain a frequency domain represen¬ tation, solve the characteristic polynomial of the system, and apply an inverse transform to the frequency response function to obtain the impulse response function. The Lagrangian multiplier of the variational problem parameterizes the family of digital filters derived in this man¬ ner. This representation which is exact for finite data sets extends the range of application of the smoothing spline and, incidentally, simplifies the algebra and reduces the amount of computer time and storage required, by allowing one to select the degree of smoothing on the basis of frequency domain considerations. One can do time domain filtering without end effects in a computationally efficient manner. Smoothings of four tree-ring width sequences from forest interior sites are presented to illustrate the particular suitability of these filters for data exhibiting both non-stationary and episodic behavior.
Instabilities and transport in magnetized plasmas

In a magnetized plasma, naturally occurring pressure anisotropies facilitate instabilities that are expected to modify the transport properties of the system. In this thesis we examine two such instabilities and, where appropriate, their effects on transport. First we consider the collisional (fluid) magnetized magnetorotational instability (MRI) in the presence of the Braginskii viscosity. We conduct a global linear analysis of the instability in a galactic rotation profile for three magnetic field configurations: purely azimuthal, purely vertical and slightly pitched. Our analysis, numerical and asymptotic, shows that the first two represent singular configurations where the Braginskii viscosity’s primary role is dissipative and the maximum growth rate is proportional to the Reynolds number when this is small. For a weak pitched field, the Braginskii viscosity is destabilising and when its effects dominate over the Lorentz force, the growth rate of the MRI can be up to 2 √ 2 times faster than the inviscid limit. If the field is strong, an over-stability develops and both the real and imaginary parts of the frequency increase with the coefficient of the viscosity. Second, in the context of the ICM of galaxy clusters, we consider the pressureanisotropy-driven firehose instability. The linear instability is fast (∼ ion cyclotron period) and small-scale (ion Larmor radius ρi) and so fluid theory is inapplicable. We determine its nonlinear evolution in an ab initio kinetic calculation (for parallel gradients only). We use a particular physical asymptotic ordering to derive a closed nonlinear equation for the firehose turbulence, which we solve. We find secular (∝ t) growth of magnetic fluctuations and a k−3 ‖ spectrum, starting at scales & ρi. When a parallel ion heat flux is present, the parallel firehose instability mutates into the new gyrothermal instability. Its nonlinear evolution also involves secular magnetic energy growth, but its spectrum is eventually dominated by modes with a maximal scale ∼ ρilT/λmfp, (lT is the parallel temperature gradient scale). Throughout we discuss implications for modelling, transport and other areas of magnetized plasma physics.