Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Results from the Wilkinson Microwave Anisotropy Probe(Chapter 2. Gravity and Cosmology-Four-Dimensional Phenomenology-, Part I: Invited Talks, The Next Chapter in Einstein's Legacy)

The Wilkinson Microwave Anisotropy Probe (WMAP) is currently mapping temperature and polarization anisotropies of the cosmic microwave background radiation on the full sky in 5 microwave bands. We summarize major scientific results obtained from the first year data released on February 11, 2003: (1) precision determinations of the cosmological parameters, (2) discovery of early reionization of the universe, and (3) implications for the Inflationary paradigm.
Modeling and experimental investigation for performance enhancement of a PV module using an air cooling system in semi-arid region in Libya

The main objective of this study is to experimentally and theoretically investigate the performance of a PV module with and without air cooling system PV/T under Derna City climate conditions (semi-arid region). The air collector system was fabricated and placed underneath the PV module for cooling hence a set of experimental measurements was facilitated. A mathematical model to predict electrical and thermal performance of a PV/T air collector and PV module, is developed using a MATLAB code. A comparison was made to validate the model results against their corresponding experimental data. The comparison revealed good agreement between the experimental and mathematical model results. Furthermore, the results show that electrical efficiency decreases with an increase in cell temperature. Moreover, minimum electrical efficiency is recorded at the highest cell temperature, which is found to be 9.75% based on the PV module and 10.6% for the PV/T module. As can be also observed from the results, the PV/T air collector system has higher electrical efficiency than the PV module with approximately 0.85% owing to the use of cooling system in the PV/T air collector system which reduces the cell temperature.
Comparison Of Various Mutation Operators Of Genetic Algorithm To Resolve Travelling Salesman Problem

The Travelling Salesman Problem (TSP) is a classic combinatorial optimization problem, which is simple to state but very difficult t o solve. This problem is known to be NP-hard, and cannot be solved exactly in polynomial time. Genetic algorithms have been successfully utilized for solving NP-hard problems in the last 30 years. They can reduce the running times of NPcomplete problems substantially. The main purpose of this paper is to give a brief introduction about genetic algorithm and travelling salesman problem. And we are going to discuss how genetic algorithm can be applied, especially its mutation operators to solve the TSP. In the end we will conclude that inversion mutation operator will always gives better results compared to other mutation operators. Keywords— Genetic Algorithm, Travelling Salesman problem, Mutation Operators.
Analysis and Modeling of Current Mismatch in Negative Capacitance Field-Effect Transistor

In this work, we analyze the impact of random dopant fluctuations (RDFs) on the mismatch behavior of the negative capacitance field-effect transistor (NCFET). We observe that the drain current mismatch decreases in the NCFET compared to conventional MOSFET, and it further decreases with an increase in the thickness of the ferroelectric layer (<inline-formula> <tex-math notation="LaTeX">${t}_{\text {fe}}$ </tex-math></inline-formula>). Metal–ferroelectric–metal–insulator–semiconductor (MFMIS)-type NCFET shows 24% and 22% improvement in mismatch power in comparison to baseline FET at <inline-formula> <tex-math notation="LaTeX">$\vert {V}_{\text {GS}}\vert = {0.2}$ </tex-math></inline-formula> and 1 V, respectively. We show that the mismatch in NCFET is directly proportional to channel doping concentration, similar to baseline MOSFET. For the first time, we demonstrate that mismatch power varies nonmonotonically with the variation in the Landau coefficient <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, and the other Landau coefficient <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> has an impact on mismatch at higher <inline-formula> <tex-math notation="LaTeX">${t}_{\text {fe}}$ </tex-math></inline-formula> only. We also present a compact model, under the industry standard Berkeley Short-channel IGFET Model for Bulk MOSFET (BSIM-BULK) MOS model’s framework, which can accurately capture drain current mismatch from subthreshold to strong inversion region for NCFET. The model is extensively validated with the experimentally calibrated technology computer-aided design (TCAD) simulations across different biases and temperatures.
The Innovative Behavior During Work from Home in Indonesia: The Role of Job Autonomy and Work Engagement

Covid-19 pandemic changes the way people work, with many companies implementing work from home policy as a new way of working. Employees must adapt to the new policy, which requires organizations to create an optimal working environment to enable employees to have positive behavior and performance to support the company's business. This study examines the effects of job autonomy and work engagement on innovative work behavior in Indonesia's Work from Home situation. This study is conducted through primary data collection using a quantitative research design, with questionnaires collected from 286 employees from various industries who work under the Work from Home policy. Data collected are further being analyzed using Structural Equation Modelling (SEM) to test the overall construct of the research model. This study shows that a high level of job autonomy has a positive and significant effect on work engagement and innovative work behavior. In contrast, work engagement is a significant driver of innovative work behavior and partially mediates the positive effect of job autonomy on innovative work behavior. Implementing a policy that could provide more control and freedom to employees when performing their job could increase employees' level of engagement and innovative behavior. This study has two significant contributions: first, enriching academic references in the field of human resources related to job autonomy, work engagement, and innovative work behavior variables, and secondly, providing insights for leaders and human resources department of organizations when implementing practical working policy in Work from Home situation in Indonesia.
Topics in conformal field theory

In this work two major topics in Conformal Field Theory are discussed. First a detailed investigation of N =2 Superconformal theories is presented. The structure of the representations of the N =2 superconformal algebras is investigated and the character formulae are calculated. The general structure of N =2 superconformal theories is elucidated and the operator algebra of the minimal models is derived. The first minimal system is discussed in more detail. Second, applications of the conformal techniques are studied in the Ashkin-Teller model. The c = 1 as well as the c = 1/2 critical lines are discussed in detail.
An Integrated Model for Autonomous Speed and Lane Change Decision-Making Based on Deep Reinforcement Learning

The implementation of autonomous driving is inseparable from developing intelligent driving decision-making models, which are facing high scene complexity, poor decision-making coupling, and the inability to guarantee decision-making safety. This paper starts with the priority and logic of lane change and car-following decision-making, considering driving efficiency, safety, and comfort, then constructs a double-layer decision-making model. This paper uses two deep reinforcement learning algorithms for the upper and lower layers to process large-scale mixed state space and ensure the composite action output of lane-changing decisions and car-following decisions. In the upper layer model, we use the D3QN algorithm to distinguish the potential value of the environment and the value of selecting lane-changing actions when making lane-changing decisions. Different from the traditional mechanisms that only use negative rewards, the lane changing benefit function and dangerous action shielding mechanism are used to eliminate collisions. DDPG algorithm is adopted in the lower layer model to process car-following decisions and output continuous vehicle speed control. Besides, coupled training is taken for the two algorithms to improve the coordination of the double-layer model. This paper selected mixed standard driving cycle conditions to build a highly complex training environment and used NGSIM data to reconstruct scenes to test our model. Simulations in SUMO are presented that the double-layer model can increase the driving speed of the original data by 23.99%, which has higher effectiveness than other models.
BIM-based design decisions documentation using design episodes, explanation tags, and constraints

The process of designing a building involves producing design concepts while fulfilling various requirements and regulations. Furthermore, during the project's life-cycle, multiple experts from multiple domains collaborate in developing the different partial models, including architectural, structural, and HVAC among others. Accordingly, clearly communicating the rationale behind design decisions is crucial for developing regulatory compliant designs that also fit the owner’s needs. The developed designs are the main deliverables exchanged and handed over. However, these deliverables do not include any explanation of design intentions or documentation of design decisions. Communication among parties and reuse of knowledge are hindered by the absent explanation of existing design. To overcome this deficiency, this paper proposes a methodology for digitally documenting design decisions, incorporating their intention and rationale. Architectural concepts and evaluation criteria are represented in the form of explanation tags as well as spatial and semantic constraints, which are assigned to the individual model elements and properties. Additionally, to document how design decisions fulfill owner requirements and regulatory documents, natural language processing (NLP) is employed to facilitate querying those documents and then the individual requirements are linked to specific elements, properties, and constraints. To evaluate the proposed methodology, a prototype was implemented as a plugin inside a BIM- Authoring tool and multiple real-world use cases are discussed.
The Hanle effect Decomposition of the Stokes parameters into irreducible components

Context. It has been shown for the weak-field Hanle effect that the Stokes parameters I , Q , and U can be represented by a set of six cylindrically symmetrical functions. The proof relies on azimuthal Fourier expansions of the radiation field and of the Hanle phase matrix. It holds for a plane-parallel atmosphere and scattering processes that can be described by a redistribution matrix where redistribution in frequency is decoupled from angle redistribution and polarization. Aims. We give a simpler and more general proof of the Stokes parameter decomposition using powerful new tools introduced for polarimetry, in particular the Landi Degl'Innocenti spherical tensors ${\mathcal T}^K_Q(i,\mbox{\boldmath$\displaystyle\Omega$})$. Methods. The elements of the Hanle phase matrix are written as a sum of terms that depend separately on the magnetic field vector and the directions $\mbox{\boldmath$\displaystyle\Omega$}$ and $\mbox{\boldmath$\displaystyle\Omega$}'$ of the incoming and scattered beams. The dependence on $\mbox{\boldmath$\displaystyle\Omega$}$ and $\mbox{\boldmath$\displaystyle\Omega$}'$ is expressed in terms of the spherical tensors ${\mathcal T}^K_Q(i,\mbox{\boldmath$\displaystyle\Omega$})$ where i refers to the Stokes parameters ($i=0,\ldots,3$). A multipolar expansion in terms of the ${\mathcal T}^K_Q(i,\mbox{\boldmath$\displaystyle\Omega$})$ is then established for the source term in the transfer equation for the Stokes parameters. Results. We show that the Stokes parameters have a multipolar expansion that can be written as $I_i(\nu,\mbox{\boldmath$\displaystyle\Omega$})= \sum_{KQ}{\mathcal T}^K_Q(i,\mbox{\boldmath$\displaystyle\Omega$})I_Q^K(\nu,\theta)$ ($K=0,1,2$, $-K\le Q\le +K$) where the $I_Q^K$ are nine cylindrically symmetrical, irreducible tensors, θ being the inclination of $\mbox{\boldmath$\displaystyle\Omega$}$ with respect to the vertical in the atmosphere. The proof is generalized to frequency-dependent phase matrices. It is applied both to partial frequency redistribution with angle-averaged scalar frequency redistribution functions and to complete frequency redistribution with the Hanle effect in the line core and Rayleigh scattering in the wings. Non-LTE transfer equations for the $I_Q^K$ and integral equations for the associated source functions $S_Q^K$ are established. Formal vectors and matrices constructed with $I_Q^K$, $S_Q^K$, and ${\mathcal T}_Q^K$ are introduced in order to present the results in a compact matrix notation. In particular, a simple factorized form is proposed for the Hanle phase matrix.
Factors Affecting Start-up Performance

This research was conducted to identify factors contributing to start-up performance. In a knowledge-based economy, start-ups in technology can significantly contribute to the development of the economy and society. However, study about the performance of technology startup fragmented into several domains such as Information Technology, Information System, Business and Management, and another domain. Moreover, the performance of established firms and start-ups is quite different. Therefore, a study about their performance is needed. The semi-systematic literature review method was used to collect and select articles that study startup performance from leading publishers. 4,063 articles were screened and selected so that there were 42 articles analyzed in this study. With descriptive analysis, found as many as 58 factors that affect performance. The most studied factors are at the organizational level, especially strategy and capability. This research provides scientific contributions, especially in strategic entrepreneurship, and provides managerial implications for startup managers in identifying factors that affect startup performance.
Fe metal‐organic framework/pyrolyzed bacterial cellulose composite as a high‐performance anode for lithium‐ion batteries

In this research, Fe‐MOF (MIL‐53 [Fe]) was synthesized by solvothermal and applied as an anode of lithium‐ion batteries (LIBs). Carbonaceous material from pyrolyzed bacterial cellulose (pBC) was incorporated in the solvothermal synthesis of MIL‐53(Fe) to improve its morphology and electrochemical properties. The MIL‐53(Fe) with pBC addition (MIL‐53(Fe)@pBC) exhibited reduced particle size and size distribution, larger surface area and pore volume, and modified crystal shape and interior structure. The incorporation also altered the functional group of the dicarboxylic ligand and formed a thin carbon layer coating which enhanced electrical conductivity significantly. The refined microstructure of the MIL‐53(Fe)@pBC compared to the pure MIL‐53(Fe) was proved to enhance the electrochemical activities of the LIB cells. The specific capacity, rate capability, and cyclic performance were boosted with pBC addition due to the increased ion diffusion kinetics in the lithiation/delithiation process. Interestingly, the MIL‐53(Fe)@pBC anode showed a peculiar increase in the reversible capacity with LIB cycles after the initial capacity fading. The analysis after the 100th cycle suggested that the lithiation/delithiation process was mediated by phase transformation through the Li+ storage mechanism. This work has shown that the MIL‐53(Fe)@pBC is an excellent candidate for anode materials in LIBs with high efficiency at long life cycles.
SIRAJ: A Unified Framework for Aggregation of Malicious Entity Detectors

High-quality intelligence of Internet threat (e.g., malware files, malicious domains, phishing URLs and malicious IPs) are important for both security practitioners and the research community. Given the agility of attackers, the scale of the Internet, and the fast-evolving landscape of threats, one could not rely solely on a single source (such as an anti-malware engine or an IP blacklist) for obtaining accurate, up-to-date, and comprehensive threat analysis. Instead, we need to aggregate the analysis from multiple sources. However, it is non-trivial to do such aggregation effectively. A common practice is to label an indicator (malware, domains, URLs, etc.) as malicious if it is marked by a number of sources above an ad-hoc certain threshold. Often, this results in sub-optimal performance as it assumes that all sources are of similar quality/expertise, independent, and temporally stable, which unfortunately are often not true in practice. A natural alternative is to train a supervised machine learning model. However, this approach needs a sufficiently large amount of manually labeled ground truth, which is time-consuming to collect and has to be updated frequently, resulting in substantial recurring costs. In this paper, we propose SIRAJ, a novel framework for aggregating the detection output of various intelligence sources such as anti-malware engines. SIRAJ is based on the pretrain and fine-tune paradigm. Specifically, we use self-supervised learning-based approaches to learn a pre-trained embedding model that converts multi-source inputs into a high-dimensional embedding. The embeddings are learned through three carefully designed pretext tasks that imbue them with knowledge about dependencies between scanners and their temporal dynamics. The learned embeddings could be used for diverse downstream machine learning tasks. SIRAJ is designed to be general and can be used for diverse domains such as URLs, malware, and IPs. Further, SIRAJ works well even when there is limited to no labeled data available. Through extensive experiments, we show that our learned representations can produce results comparable to supervised methods while only requiring as little as 100 labeled samples. Importantly, the results show that SIRAJ accurately detects threat indicators much earlier than the baseline algorithms, a feat that is critical against short-lived indicators like Phishing URLs.
Disclosure of value-based performance measures: evidence from German listed firms

We examine the determinants of the disclosure of value-based (VB) performance measures in Germany. We argue that firms are more likely to disclose VB performance measures when information asymmetry is greater, as greater information asymmetry means firms have a greater need to credibly signal a shareholder value orientation. Using a hand-collected dataset of German listed firms covering 1,528 firm-years from 2004 to 2011, we demonstrate that firms are more likely to disclose a VB performance measure if the free float is larger than the blocking minority and also, when firms are large, if they have high foreign sales to total sales ratios and are not cross-listed internationally. Our results indicate that German firms use VB performance measures to improve investor communication and to substantiate their shareholder value orientation. Our results should be interpreted against a background of increased shareholder value orientation and sophisticated cost accounting in German firms.
Cartooning and sexism in the time of Covid-19: Metaphors and metonymies in the Arab mind

Using a large-scale corpus of 706 coronavirus cartoons by male and female Arab artists, this study takes a fresh and more cognitive look at sexism in multimodal discourse. Specifically, it examines the role of salience and grammar (and hence of metaphor and metonymy) in gender bias and/or in discrimination against women. It argues that both men and women are vulnerable to the influence of stereotypical and outdated beliefs that create unconscious bias. But this raises the crucial issue of whether we can speak of ‘overt’ sexism in images. Issues around terminology and conceptualization are thus also investigated. Importantly, this paper makes the following contributions to feminist and cross-cultural pragmatics: (i) it brings a distinctly Arabic perspective to gender and language; (ii) it expands socio-cognitive pragmatics beyond spoken and written communication; (iii) it shows a close coupling between an Arabic grammar and other aspects of culture; and (iv) it has the potential for impact beyond academia, specifically in the sphere of coronavirus care or of health communication.
CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK KLASIFIKASI CITRA PENYAKIT DIABETES RETINOPATHY

Diabetic Retinopathy (DR) merupakan suatu komplikasi yang terjadi karena adanya kerusakan pada pembuluh darah retina. DR melalui citra retina mata sudah pernah diteliti menggunakan proses peningkatan kualitas citra maupun teknik filtering. Citra DR, memiliki garis tebal dan tipis pada citra fundus dimana tebal tipisnya digunakan untuk menentukan apakah citra fundus tersebut terkategori sebagai citra DR. Biasanya, teknik filtering diperlukan dalam mempertajam garis pada citra fundus. Pada penelitian ini, optimasi dalam klasifikasi citra retina mata yang terdiagnosa retinopati telah dilakukan menggunakan algoritma Convolutional Neural Network (CNN) dengan tujuan pengenalan pembuluh darah pada retina mata terdiagnosa DR. Urgensi dari penelitian ini yaitu melakukan uji performa CNN dalam proses klasifikasi citra DR pada jumlah data yang cukup besar tanpa menggunakan preprosesing apapun sehingga dapat disimpulkan bahwa CNN saja sudah mampu mengklasfikasi objek dengan baik. Citra berasal dari Kaggle database dengan total 88702 citra yang di sortir menjadi 88000 data. Hasil klasifikasi citra benar yaitu 82445 dengan prosentse 93,68% sedangkan citra salah klasifikasi yaitu 5555 citra dengan prosentase kegagalan yaitu 6,32%.
A magnetometer suitable for measurement of the Earth's field based on transient atomic response

We describe the development of a simple atomic magnetometer using 87Rb vapour suitable for monitoring Earth's magnetic field. The magnetometer is based on time-domain determination of the transient precession frequency of the atomic alignment around the measured field. A sensitivity of 1.5 nT Hz−1/2 is demonstrated by the measurement of the Earth's magnetic field in the laboratory, while for a simulated Earth field inside a magnetic shield a 130 pT Hz−1/2 sensitivity was attained. We discuss the various parameters determining the magnetometer precision and accuracy and predict a sensitivity of 30 pT Hz−1/2.