Miracle-dz/UniFAR-FTUs huggingface dataset:
Firm-specific antecedents affecting product diversification: evidence from India

Abstract The main purpose of this paper is to introduce what affects the firm to diversify. This paper is based on an empirical analysis of a sample of 453 manufacturing companies in India. Jacquemin-Berry Entropy-Index measure has been used to measure diversification; whereas to determine the factors affecting diversification strategy, Panel Tobit Regression Analysis has been applied. The research findings indicate that firm-specific antecedents influence the decision to diversify. Corporate managers should follow a prudent behaviour while adopting strategic planning for the growth of the company. Managers should have to conduct proper statistical analyses to find out a firm’s optimal level of corporate diversification for maximising the firm performance.
Evaluation of Physicochemical Properties of Provitamin K3 Derived Benzo[α]Phenoxazine as a Photosensitizer

Three benzo[]phenoxazine based photosensitizers known as 10-Chloro-6-methyl-5H benzo[α]phenoxazin-5-one (BPO-Cl), 6-methyl-5H-benzo[α]phenoxazin-5-one (BPO), and 6-methyl-5H-benzo[α]phenothiazin-5-one (BPT) synthesized from provitamin K3 were used as photosensitizers to fabricate a dye-sensitized solar cells (DSSC) device. The inbuilt intra-molecular charge transfer (ICT), favorable π–π interaction, planar shape, and compatible redox nature with appropriate frontier molecular orbitals (HOMO and LUMO) alignment are the significant physicochemical characteristics of benzo[α]phenoxazine moiety that rationalize to become an efficient photosensitizer. Thus, the bare benzo[]phenoxazine dyes without any efficient anchor and donor group are evaluated here with their intrinsic chemical behavior for efficient precursors in various energy transferring material. The intramolecular charge transfer occurring through aromatic rings bridged by two heteroatoms (O/S and N) is tunable with DFT analysis and single-crystal XRD data. The characteristic solar cell parameters show that chlorine substitution enhances the open-circuit voltage (Voc) from 0.53 V in BPO to 0.56 V in BPO-Cl. A noticeable enhancement in short-circuit current density (Jsc) nearly fivefold is observed for BPO dye compared to its thio-substituted BPT unit. The substitution of chlorine reduces the recombination rate in the device studied from electrochemical impedance spectroscopy. Thus BPO template is an excellent precursor to tailor various D/A groups to produce effective photosensitizer evaluated by physicochemical properties tunable with several optoelectrical, structural, morphological, and electrochemical analyses.
A matched filter based approach for high-energy estimation in calorimetry

In high-energy calorimeters, a crucial task is the reconstruction of the energy deposited by particle interactions. Standard techniques used in modern calorimeters rely on the energy estimation to select the signal with relevant information. This work presents a new approach, which performs the signal detection against noise as a first step, followed by the energy estimation task. The method is fully based on the Matched Filter (MF) theory, which is known to produce the optimum detection efficiency with respect to the signal-to-noise ratio. Furthermore, the MF output can be calibrated to estimate the signal amplitude and, thus, the energy. The proposed method is compared to different optimum filtering algorithms, which are currently being used for energy reconstruction in modern calorimeter systems. The results from simulated data show that the proposed method achieves better performance in terms of both signal detection efficiency and estimation error.
The Role of Cloud Processing for the Ice Nucleating Ability of Organic Aerosol and Coal Fly Ash Particles

Ice nucleating particles are a minor fraction of tropospheric aerosol, yet they play a key role for cloud microphysical processes. One poorly understood process is the impact of atmospheric aging of aerosol particles on ice nucleation. Here we study the impact of cloud processing on the ice nucleation abilities of two physicochemically different aerosol particles by taking two model systems for atmospheric organic aerosol (OA), as well as coal fly ash (CFA) particles representing an inorganic aerosol type. The ice nucleation activity of the unprocessed particles is compared to aerosol particles that are first exposed to conditions mimicking trajectories though cirrus clouds (CC) and mixed‐phase clouds (MPC) prior to testing their ice nucleation activity at temperatures below 243 K. We observed that unprocessed OA do not exhibit heterogeneous ice nucleation, requiring homogeneous freezing conditions of solution droplets to form ice. However, after CC processing raffinose particles showed heterogeneous ice nucleation activity at 218 K and a water saturation ratio of 0.68–0.82, reaching activated fractions of up to 0.3. This enhancement compared to unprocessed raffinose particles results from an increase in particle viscosity upon CC processing. We also present new results of unprocessed CFA particles exhibiting strong heterogeneous ice nucleation activity at temperatures below 235 K in the deposition and/or pore condensation and freezing mode. In contrast to the OA, the CFA show a decrease in ice nucleation activity after both MPC and CC processing. Furthermore, cloud processing and generating CFA particles from aqueous suspensions do not have the same effect on their ice nucleation ability.
The comparison of numerous machine learning algorithms performance in classifying rice growth stages based on Sentinel-2 to enhance crop monitoring in national level

The rice monitoring based on Sentinel-2 (SC-S2) has been developed for over nine months. It has been observed as the first and only system which generate rice growth stages maps in 10 m spatial resolution using machine learning in Indonesia. However, the SC-S2 use Support Vector Machine to separate the rice growth stages, which may have poor performances. The objective of this study is to investigate the performance of other classifiers to increase the performance of SC-S2. We used survey data from the field campaign in 2018 and synchronized with Sentinel-2 bands. The model dataset was trained using 61 machine learning algorithms to create 61 rice growth stages models. The models were applied to the Sentinel-2 image of part of Indramayu area. The accuracy, computational time and visual inspection score were collected, and the final score was calculated. The results are the highest final score is Shrinkage Discriminant Analysis, with overall accuracy 88.1% (p<0.001) and the average accuracy of all classifiers is 76.2% (p<0.05). The implication of this study is to propose some changes in the classification process into the SC-S2 for increasing the overall performance, which will provide better information for agricultural policymakers.
Data Visualization in Splunk and Tableau: A Case Study Demonstration

This article mainly deals with data analytics and visualization techniques. Data visualization techniques helps user to understand the data in a faster and easier way. Any data which is in the form of text is difficult to analyse in a short period of time, so using some colours or visualization techniques like bar chart, scattered plots will give a better understanding of any data in a glance. Using this technique we can analyse the data in a faster way and also represent a data in a batter way. Apart from data visualization, this internship focused on handling machine generated log data. Most of the time machines will generate some kind of log data in response to our actions, which is very difficult to understand. A study says that in the present world, machines will generate large amount of data, which are difficult to understand and analysing this data can be used for the betterment of the business or to invent new things. So to understand this machine generated data there is requirement of some experience with the process and also using of the tools, we can make the machine generated data divide into events and represent it in an easy and understandable manner.
Managing eWOM for hotel performance

ABSTRACT Electronic word-of-mouth (eWOM) is an important marketing tool for achieving positive customer-related outcomes and firm performance. However, eWOM management is not well understood. This study explores how eWOM management can be utilized to optimize firm performance. Using a multiple case study approach, in-depth interviews were conducted with senior managers who were involved in the management of eWOM from nine hotels, and their websites and marketing materials were analyzed. eWoM digital platforms give even resource-constrained smaller hotels timely review analytics that can be used for the purpose of brand reputation strategy decisions. That is, those hotels that focus their limited resources on developing authentic response systems and create unique communication capabilities which are leveraged for eWOM reputation as a competitive advantage. These findings highlight that successful hotels use eWoM data analytics for decisions related long-term signaling strategies: as hotels can extend their authentic voice or an organizational character within their eWOM responses over time to complement their optimal brand position.
Tipologi Rumah Susun Sederhana Sewa (Rusunawa) di Kota Surabaya

Setiap Rumah Susun Sederhana Sewa (Rusunawa) memiliki karakteristik yang berbeda-beda baik dari segi penentuan lokasi, profil penghuni, penentuan tarif sewa, dan berbagai faktor lainnya. Penelitian ini bertujuan untuk mengidentifikasi tipologi Rusunawa di Kota Surabaya berdasarkan karakteristik spasial dan pengelolaannya. Metode analisis tumpang susun (overlay) dan vector buffering dipilih untuk mengidentifikasi karakteristik spasial Rusunawa dan metode klaster hierarki untuk menentukan tipologi Rusunawa berdasarkan karakteristik pengelolaannya. Hasil penelitian ini menunjukkan bahwa secara spasial, mayoritas lokasi Rusunawa berada di area yang diarahkan oleh rencana tata ruang sebagai kawasan dengan fungsi campuran. Lokasi Rusunawa juga menyebar di area pusat dan pinggiran kota di mana pola persebaran ini dipengaruhi oleh faktor tujuan penyediaan dan target sasaran penghuninya. Sedangkan dari sisi pengelolaan, Rusunawa di Kota Surabaya terbagi menjadi dua tipologi yaitu Klaster Rusunawa Provinsi dan Klaster Rusunawa Kota. Kedua klaster memiliki perbedaan dari sisi status pengelola dan kebijakan tarif sewa.
Estimation of surface roughness and cutting speed in CNC WEDM by artificial neural network that employed trainable activation function

Activation functions are the most significant properties of artificial neural networks (ANN) because these functions are directly related with the ability of ANN in learning or modelling a system or a function. Furthermore, another reason for the significance of the fact that determination of optimal activation function in ANN is its relationship with success level. In this experimental study, the effects of different types of wire electrodes, cooling techniques and workpiece materials on surface roughness (Ra) and cutting speed (Vc) in wire electrical discharge machining (WEDM) were investigated by using trainable activation functions (AFt) and modelling them in ANNs. So far, a number of methods have been performed according to the data set in order to optimally predict Ra and Vc results. Among these methods, randomized ANN with AFt was found to be the best one for robust prediction according to RMSE values. While the value was 0.280 for Vc, it was 0.2104 for Ra. Optimum activation functions in Ra and Vc were found at first and third degree trainable functions, respectively.
Porous Characteristics of Three-Dimensional Disordered Graphene Networks

The porous characteristics of disordered carbons are critical factors to their performance on hydrogen storage and electrochemical capacitors. Even though the porous information can be estimated indirectly by gas adsorption experiments, it is still hard to directly characterize the porous morphology considering the complex 3D connectivity. To this end, we construct full-atom disordered graphene networks (DGNs) by mimicking the chlorination process of carbide-derived carbons using annealing-MD simulations, which could model the structure of disordered carbons at the atomic scale. The porous characteristics, including pore volume, pore size distribution (PSD), and specific surface area (SSA), were then computed from the coordinates of carbon atoms. From the evolution of structural features, pores grow dramatically during the formation of polyaromatic fragments and sequent disordered framework. Then structure is further graphitized while the PSD shows little change. For the obtained DGNs, the porosity, pore size, and SSA increase with decreasing density. Furthermore, SSA tends to saturate in the low-density range. The DGNs annealed at low temperatures exhibit larger SSA than high-temperature DGNs because of the abundant free edges.
Optimization of Dynamic Task Location within a Manipulator’s Workspace for the Utilization of the Minimum Required Joint Torques

The determination of the optimal position of a robotic task within a manipulator’s workspace is crucial for the manipulator to achieve high performance regarding selected aspects of its operation. In this paper, a method for determining the optimal task placement for a serial manipulator is presented, so that the required joint torques are minimized. The task considered comprises the exercise of a given force in a given direction along a 3D path followed by the end effector. Given that many such tasks are usually conducted by human workers and as such the utilized trajectories are quite complex to model, a Human Robot Interaction (HRI) approach was chosen to define the task, where the robot is taught the task trajectory by a human operator. Furthermore, the presented method considers the singular free paths of the manipulator’s end-effector motion in the configuration space. Simulation results are utilized to set up a physical execution of the task in the optimal derived position within a UR-3 manipulator’s workspace. For reference the task is also placed at an arbitrary “bad” location in order to validate the simulation results. Experimental results verify that the positioning of the task at the optimal location derived by the presented method allows for the task execution with minimum joint torques as opposed to the arbitrary position.
Plant associated fungal endophytes as a source of natural bioactive compounds

ABSTRACT Endophytes are a potent source of bioactive compounds that mimic plant-based metabolites. The relationship of host plant and endophyte is significantly associated with alteration in fungal colonisation and the extraction of endophyte-derived bioactive compounds. Screening of fungal endophytes and their relationship with host plants is essential for the isolation of bioactive compounds. Numerous bioactive compounds with antioxidant, antimicrobial, anticancer, and immunomodulatory properties are known to be derived from fungal endophytes. Bioinformatics tools along with the latest techniques such as metabolomics, next-generation sequencing, and metagenomics multilocus sequence typing can potentially fill the gaps in fungal endophyte research. The current review article focuses on bioactive compounds derived from plant-associated fungal endophytes and their pharmacological importance. We conclude with the challenges and opportunities in the research area of fungal endophytes.
Quality Assessment of Doughnut Prepared from Wheat Flour Enriched with Bambara Nut Flour

ABSTRACT The proximate composition, pasting, and functional properties of flour blends from wheat and bambara nut at 10%, 20%, and 30% were carried out. The codes used were: DEL (90% wheat flour and 10% bambara nut flour), PIE (80% wheat flour and 20% bambara nut flour), ROW (70% wheat flour and 30% bambara nut flour), and ALE (100% wheat flour as control). Doughnuts were prepared using these blends and their sensory properties assessed. Results obtained revealed that the protein content of the flour increased with increase in bambara nut addition from 10.88–11.78%. Significant differences (p < 0.05) were observed in the fat, ash, and crude fiber (1.88–2.23%, 1.22–1.88%, and 3.96–4.02%) respectively. Doughnuts with 10% bambara nut flour (DEL) were most acceptable amongst the samples because there was no significant difference (p < 0.05) between this sample and the control in most parameters assessed.
The Effectiveness of the Learning Cycle Model (5E and 7E) in Learning to Build Flat Side Sides Viewed From Student Self-Eficacy

The purpose of this study was to test the effectiveness of mathematics learning using the 5E learning cycle model and the 7E learning cycle model; as well as distinguishing more effective learning between the learning cycle models 5E and 7E in terms of the self-efficacy of class VIII students in learning to build flat side space. This research is a quasi-experimental study with a pretest-posttest non-equivalent comparison-group design. The study population included all eighth grade students of state junior high schools in Yogyakarta City consisting of high and medium level schools. With stratified random sampling technique, 2 schools were selected as research samples. The research instrument used student self-efficacy questionnaires. To test the effectiveness of the LC 5E model and the LC 7E model, whether there is an interaction between the learning model and the school level, as well as whether there is a difference in student self-efficacy in high school and medium school, the two way ANOVA test is used. Furthermore, to compare the self-efficacy of students who use the LC 5E model and the LC 7E model in high school and high school level students are using an independent sample t test, while to test whether there is a difference in effectiveness between the LC 5E model and LC 7E model against self-efficacy students used the N-Gain effectiveness formula. Each analysis was carried out at a significance level of 5%. The results showed that: 1) there was no difference in self-efficacy between students whose learning used the LC 5E model and students whose learning used the LC 7E model; 2) the self-efficacy of students who use the LC 5E model is not higher compared to those who use the LC 7E model in high and medium level schools; 3) there is no interaction between the learning cycle model with the school level on learning to build flat side space; 4) there is no difference in self-efficacy between students in high schools and students in medium schools; and 5) there is no difference in effectiveness between LC 5E and LC 7E models on student self-efficacy.
Training of Scientific Literation and Self Efficacy Students Using Scientific Critical Thinking (SCT) Models

The ability of scientific literacy and self-efficacy becomes an essential part of learning chemistry in the framework of preparing students to enter the industrial era 4.0 and the era of society 5.0. The Scientific Critical Thinking (SCT) model is one of the models to practice student’s scientific literacy skills and self-efficacy in learning the buffer solution concept. The research conducted during COVID 19, so learning carried out online using the Google Classroom and Zoom Meeting applications. The research design uses the type of pre-experiment with one group pretest-posttest design. The sample of this study were students of class XI MIPA 3 of SMAN 6 Banjarmasin in 2019/2020. The independent variable is the SCT, and the dependent variable is the ability of scientific literacy and self-efficacy. Data collection uses test and non-test techniques. Data analysis techniques used descriptive and inferential analysis techniques. The results of this study indicate (1) there is a significant difference in scientific literacy ability between the results of the pretest and posttest with tcount> ttable ie 13.2> 2, 00, (2) there is a significant difference in students’ self efficacy with tcount 2, 00, (3) students respond with criteria both for learning with the SCT model. Thus, the Scientific Critical Thinking model can train the scientific literacy ability and self-efficacy of students of XI MIPA 3 students at SMAN 6 Banjarmasin.
Reaction Mechanisms in the OO Chemistry

The work presented in this paper continues the work presented in the previous Alife (Bersini, 1999) conference by improving the Connections between the computerized object-oriented chemistry and the real natural chemistry. This improvement is the result of a clearer definition of the type of computational structure which codes for a molecule, of the different reaction mechanisms between two molecules to obtain one or several new ones, and of the way the dynamics (i.e. the molecular concentration change in time) and the metadynamics (i.e. the appearance of new molecules) simultaneously make the whole chemical system to evolve in time. What makes a molecule computationally unique. in terms of a strictly ordered tree, will be deeply described. Simulation results are presented for a simple chemical reactor composed of four atoms with different valence, and allowing molecules to deterministically or randomly interact according to two mechanisms: the chemical single-link-crossover and the open-bond reaction. Flow the chemical kinetics is called into play will be shown for very elementary reactions
Advances in Metal Additive Manufacturing: A Review of Common Processes, Industrial Applications, and Current Challenges

In recent years, Additive Manufacturing (AM), also called 3D printing, has been expanding into several industrial sectors due to the technology providing opportunities in terms of improved functionality, productivity, and competitiveness. While metal AM technologies have almost unlimited potential, and the range of applications has increased in recent years, industries have faced challenges in the adoption of these technologies and coping with a turbulent market. Despite the extensive work that has been completed on the properties of metal AM materials, there is still a need of a robust understanding of processes, challenges, application-specific needs, and considerations associated with these technologies. Therefore, the goal of this study is to present a comprehensive review of the most common metal AM technologies, an exploration of metal AM advancements, and industrial applications for the different AM technologies across various industry sectors. This study also outlines current limitations and challenges, which prevent industries to fully benefit from the metal AM opportunities, including production volume, standards compliance, post processing, product quality, maintenance, and materials range. Overall, this paper provides a survey as the benchmark for future industrial applications and research and development projects, in order to assist industries in selecting a suitable AM technology for their application.
Signature Texture Features Extraction Using GLCM Approach in Android Studio

Signature Features Extraction is a method for deriving informative values of image signature for indexing and signature identification. It is a dimensionality reduction of image data to be manageable for processing. Image texture is a key spatial attribute used for feature extraction and image coding. However, Android environment lack of efficient algorithm for automatic extracting texture features which may cause serious security issues and unreliability problem in the Android application (app). The challenge in Signature Features Extraction of a mobile app is to be as robust and stable as possible. For that, the present paper presents an efficient algorithm for extracting signature texture features using a gray level co-occurrence matrix (GLCM). The image signature is quantized into five texture features of energy feature, entropy feature, contrast feature, dissimilarity feature, and homogeneity feature. The processes developed in the android studio environment to be used in mobile phone cell applications as a tool for signature detection. The results show that the present method provides significant results due to obtained average values and acceptable computational time.