Xiaojian Zheng, Mohd Hazwan Mohd Puad, Habibah Ab Jalil
This study explores how Speech and Elocution Training (SET) activates language potential and fosters career-oriented development among higher vocational students through self-efficacy mechanisms. Through qualitative interviews with four vocational graduates who participated in SET 5 to 10 years ago, the research identifies three key findings. First, SET comprises curriculum content (e.g., workplace communication modules such as hosting, storytelling, and sales pitching) and classroom training using multimodal TED resources and Toastmasters International-simulated practices, which spark language potential through skill-focused, realistic exercises. Second, these pedagogies facilitate a progression where initial language potential evolves from nascent career interests into concrete job-seeking intentions and long-term career plans: completing workplace-related speech tasks boosts confidence in career choices, planning, and job competencies, enabling adaptability to professional challenges. Third, SET aligns with Bandura’s four self-efficacy determinants; these are successful experiences (including personalized and virtual skill acquisition and certified affirmation), vicarious experiences (via observation platforms and constructive peer modeling), verbal persuasion (direct instructional feedback and indirect emotional support), and the arousal of optimistic emotions (the cognitive reframing of challenges and direct desensitization to anxieties). These mechanisms collectively create a positive cycle that enhances self-efficacy, amplifies language potential, and clarifies career intentions. While highlighting SET’s efficacy, this study notes a small sample size limitation, urging future mixed-methods studies with diverse samples to validate these mechanisms across broader vocational contexts and refine understanding of language training’s role in fostering linguistic competence and career readiness.
For any local Hamiltonian H, I construct a local CPT map and stopping condition which converges to the ground state subspace of H. Like any ground state preparation algorithm, this algorithm necessarily has exponential run-time in general (otherwise BQP=QMA), even for gapped, frustration-free Hamiltonians (otherwise BQP is in NP). However, this dissipative quantum eigensolver has a number of interesting characteristics, which give advantages over previous ground state preparation algorithms. - The entire algorithm consists simply of iterating the same set of local measurements repeatedly. - The expected overlap with the ground state subspace increases monotonically with the length of time this process is allowed to run. - It converges to the ground state subspace unconditionally, without any assumptions on or prior information about the Hamiltonian. - The algorithm does not require any variational optimisation over parameters. - It is often able to find the ground state in low circuit depth in practice. - It has a simple implementation on certain types of quantum hardware, in particular photonic quantum computers. - The process is immune to errors in the initial state. - It is inherently error- and noise-resilient, i.e. to errors during execution of the algorithm and also to faulty implementation of the algorithm itself, without incurring any computational overhead: the overlap of the output with the ground state subspace degrades smoothly with the error rate, independent of the algorithm's run-time. I give rigorous proofs of the above claims, and benchmark the algorithm on some concrete examples numerically.
The human's cognitive capacity for problem solving is always limited to his/her educational background, skills, experiences, etc. Hence, it is often insufficient to bring solution to extraordinary problems especially when there is a time restriction. Nowadays this sort of personal cognitive limitations are overcome at some extend by the computational utilities (e.g. program packages, internet, etc.) where each one provides a specific background skill to the individual to solve a particular problem. Nevertheless these models are all based on already available conventional tools or knowledge and unable to solve spontaneous unique problems, except human's procedural cognitive skills. But unfortunately such low-level skills can not be modelled and stored in a conventional way like classical models and knowledge. This work aims to introduce an early stage of a modular approach to procedural skill acquisition and storage via distributed cognitive skill modules which provide unique opportunity to extend the limits of its exploitation.
We present a set-theoretic version of some basic dilation results of operator theory. The results we have considered are Wold decomposition, Halmos dilation, Sz. Nagy dilation, inter-twining lifting, commuting and non-commuting dilations, BCL theorem etc. We point out some natural generalizations and variations.
While it is easy to automate coverage data collection, it is a time consuming/difficult/expensive manual process to analyze the data so that it can be acted upon. Complexity arises from numerous sources, of which untested or poorly tested legacy code and third-party libraries are two of the most common. Differential coverage and date binning are methods of combining coverage data and project/file history to determine if goals have been met and to identify areas of code which should be prioritized. These methods can be applied to any coverage metric which can be associated with a location -- statement, function, expression, toggle, etc. -- and to any language, including both software (C++, Python, etc.) and hardware description languages (SystemVerilog, VHDL). The goal of these approaches is to reduce the cost and the barrier to entry of using coverage data analysis in large-scale projects. The approach is realized in gendiffcov, a recently released open-source tool.
We present here the Calar Alto Schmidt-Lemaitre Telescope (CASTLE) concept, a technology demonstrator for curved detectors, that will be installed at the Calar Alto Observatory (Spain). This telescope has a wide field of view (2.36x1.56 deg^2) and a design, optimised to generate a Point Spread Function with very low level wings and reduced ghost features, which makes it considerably less susceptible to several systematic effects usually affecting similar systems. These characteristics are particularly suited to study the low surface brightness Universe. CASTLE will be able to reach surface brightness orders of magnitude fainter than the sky background level and observe the extremely extended and faint features around galaxies such as tidal features, stellar halos, intra-cluster light, etc. CASTLE will also be used to search and detect astrophysical transients such as gamma ray bursts (GRB), gravitational wave optical counterparts, neutrino counterparts, etc. This will increase the number of precisely localized GRBs from 20% to 60% (in the case of Fermi/GMB GRBs).
A coupled mech-hydro-chemical model for rock geometry alteration of fractures under water-rock interaction (WRI) and geostress is developed. Processes including WRI, asperity deformation, mineral chemical dissolution and pressure dissolution etc., are taken into account. A feature of this model lies in its approximate linearization to the non-linear pressure dissolution process, which makes this model compatible with existing numerical solute transport models. Case study shows that although usually only a thin layer of rock surface is invaded by WRI, the mechanical weakening of this thin layer tend to induce significant increase in rock surface deformation. Thus, the distributed flow field, mineral dissolution rates, and surface alteration increments etc., are all affected. This indicates that when fracture flow related issues are concerned, we should focus on the top thin layer of rock surface rather than the matrix. Results also show that fluid flow enhances the dissolution of rocks and development of flowing channels; however, at places where stress induces high pressure-dissolution but fluid flows slowly, minerals precipitate and fill up fracture voids. This suggests the hydraulic condition plays a key role in the development of fracture flow channels and the evolution of fracture geometry.
Liam Gannon, Lilia Boeri, Christopher A. Howard
et al.
The lattice dynamics of members of the M2Mo6Se6 family of materials with guest ions M = K, Rb, Cs, In, and Tl has been studied using inelastic x-ray scattering and Raman spectroscopy at room temperature, as well as by ab-initio calculations. We find a good match between calculations and experiment, both for structure factors (Eigenvectors) and for the calculated phonon frequencies. The observed lattice dynamics for Tl2Mo6Se6 show no signs of anharmonicity or absence of avoided dispersion crossings, thus ruling out previously hypothesised rattling phonon modes. The reduced mode energies for In2Mo6Se6 are identified as only partially responsible for the lower superconducting transition temperature Tc in this material when compared to Tl2Mo6Se6.
The formal weight enumerators were first introduced by M. Ozeki, and it was shown in the author's previous paper that there are various families of divisible formal weight enumerators. Among them, three families are dealt with in this paper and their properties are investigated: they are analogs of the Mallows-Sloane bound, the extremal property, the Riemann hypothesis, etc. In the course of the investigation, some generalizations of the theory of invariant differential operators developed by I. Duursma and T. Okuda are deduced.
Cognitive Network is a technique which is used to improve the spectrum utilization. Current network scenario is experiencing the huge spectrum scarcity problem due to the fixed assignment policy so in this method great amount of spectrum remain unused. To overcome this limitation the spectrum allocation must be in dynamic manner. In this paper the spectrum allocation is discussed thoroughly. Interference is the most important factor that needs to be considered. It is caused by the environment (noise) or by other radio users. It limits the possibility of spectrum reuse. Channel assignment is one of the techniques used to control interference in the network. There exist a trade-off between network capacity and level of contention. In cognitive radio networks spectrum assignment or spectrum allocation or frequency assignment is used to avoid interference. It is the process of simultaneous selection of operating central frequency and bandwidth. In doing so, the process of sensing the spectrum becomes very crucial; it must be reliable, accurate and efficient. The accuracy of sensing affects the overall operation of cognitive networks. Accurate results not only lead to higher utilization of the spectrum but also preserve the privacy of primary user. Accuracy of sensing is highly affected by the natural causes like noise, shadowing, fading etc. There are many other challenges as well, like, hardware requirements, hidden node problem, security, sensing frequency and duration, decision fusion etc.
Twitter is now an established and a widely popular news medium. Be it normal banter or a discussion on high impact events like Boston marathon blasts, February 2014 US Icestorm, etc., people use Twitter to get updates. Twitter bots have today become very common and acceptable. People are using them to get updates about emergencies like natural disasters, terrorist strikes, etc. Twitter bots provide these users a means to perform certain tasks on Twitter that are both simple and structurally repetitive. During high impact events these Twitter bots tend to provide time critical and comprehensive information. We present how bots participate in discussions and augment them during high impact events. We identify bots in high impact events for 2013: Boston blasts, February 2014 US Icestorm, Washington Navy Yard Shooting, Oklahoma tornado, and Cyclone Phailin. We identify bots among top tweeters by getting all such accounts manually annotated. We then study their activity and present many important insights. We determine the impact bots have on information diffusion during these events and how they tend to aggregate and broker information from various sources to different users. We also analyzed their tweets, list down important differentiating features between bots and non bots (normal or human accounts) during high impact events. We also show how bots are slowly moving away from traditional API based posts towards web automation platforms like IFTTT, dlvr.it, etc. Using standard machine learning, we proposed a methodology to identify bots/non bots in real time during high impact events. This study also looks into how the bot scenario has changed by comparing data from high impact events from 2013 with data from similar type of events from 2011. Lastly, we also go through an in-depth analysis of Twitter bots who were active during 2013 Boston Marathon Blast.
Tárcio A. Vieira, Michel Zamboni-Rached, Marcos R. R. Gesualdi
In this paper we implement experimentally the spatial shape modelling of nondiffracting optical beams via computer generated holograms. The results reported here are the experimental confirmation of the so called Frozen Wave method, developed few years ago. Optical beams of this type can possess potential applications in optical tweezers, medicine, atom guiding, remote sensing, etc..
In this paper a model incorporating chiral symmetry breaking and dynamical soft-wall AdS/QCD is established. The AdS/QCD background is introduced dynamically as suggested by Wayne de Paula etc al and chiral symmetry breaking is discussed by using a bulk scalar field including a cubic term. The mass spectrum of scalar, vector and axial vector mesons are obtained and a comparison with experimental data is presented.
Advances in technology has given rise to new computing models where any individual/organization (Cloud Service Consumers here by denoted as CSC's) can outsource their computational intensive tasks on their data to a remote Cloud Service Provider (CSP) for many advantages like lower costs, scalability etc. But such advantages come for a bigger cost "Security and Privacy of data" for this very reason many CSC's are skeptical to move towards cloud computing models. While the advances in cryptography research are promising, there are no practical solutions yet for performing any operations on encrypted data [1]. For this very reason there is strong need for finding alternative viable solutions for us to benefit from Cloud Computing. A technique to provide confidentiality without encryption was proposed in the past namely "Chaffing and Winnowing: Confidentiality without Encryption" by Ronald L. Rivest [2]. While this technique has been proposed for packet based communication system, its not adaptable in all cloud service models like Software-as-Service, Platform-as-Service or Infrastructure-as-Service [3]. In this paper we propose an adaptation of this technique in a cloud computational setup where CSC's outsource computational intensive tasks like web log parsing, DNA Sequencing etc to a MapReduce like CSP service.
There is no single definition of complexity (Edmonds 1999; Gershenson 2008; Mitchell 2009; De Domenico, et al., 2019), as it acquires different meanings in different contexts. A general notion is the amount of information required to describe a phenomenon (Prokopenko, et al. 2008) , but it can also be understood as the length of the shortest program required to compute that description, as the time required to compute that description, as the minimal model to statistically describe a phenomenon, etc.
The systematic investigation of hyperdeformation (HD) at high spin in the $Z=40-58$ part of the nuclear chart has been performed in the framework of the cranked relativistic mean field theory. The properties of the moments of inertia of the HD bands, the role of the single-particle and necking degrees of freedom at HD, the spins at which the HD bands become yrast, the possibility to observe discrete HD bands etc. are discussed in detail.