Miracle-dz/UniFAR-FTUs huggingface dataset:
Inter-partner Fit Mechanisms for Sustainable Joint Ventures: Evidence from Japanese Firms in Korea

Purpose – Our study aimed to examine the roles of conflict management in maintaining a sustainable good relationship between joint venture (JV) partners. Although the concepts of relational capital, mutual commitment, and cultural adaptation have been neglected in previous research on interorganizational relationships, we treated them as inter-partner fit mechanism capabilities of affecting the level of satisfaction in a relationship with a JV partner. Design/methodology – In order to test hypotheses, we sent a questionnaire to Japanese firms operating JVs in South Korea and asked whether their levels of satisfaction with their JV partners increase when conflict management techniques are used to reduce the conflicts that usually arise between partners. Findings – The results of our study suggested that it is important for firms participating in a JV to construct and reinforce an inter-partner fit mechanism that enables them to maintain a favorable partnership. In addition, we suggested that relational capital, cultural adaptation, and mutual commitment influence conflict reduction and the level of satisfaction experienced in JVs. Moreover, the results showed that conflict management has a significant effect on developing soild partnerships and performance in JV situations. Originality/value – We advanced a new paradigm about conflict management and suggested methods of research propositions that are well grounded in the latest findings in conflict management research pertaining to JVs.
Electrochemical Double-Layer Capacitors Based on Functionalized Graphene

III ACKNOWLEDGEMENTS . VI CONTENTS. IX LIST OF FIGURES . XII LIST OF TABLES . XV LIST OF ABBREVIATIONS . XVI LIST OF PUBLICATIONS/PATENTS . XVII 1 INTRODUCTION . 18 1.1 OPERATING PRINCIPLES OF DOUBLE-LAYER CAPACITORS . 20 1.1.1 Device Configuration . 20 1.1.2 Energy Density . 22 1.1.3 Power Density . 24 1.2 THEORY OF THE ELECTROCHEMICAL DOUBLE-LAYER . 29 1.2.1 Thermodynamic Treatment . 30 1.2.2 Kinetic Theories . 33 1.3 POROUS CARBONACEOUS ELECTRODES . 44 1.3.1 Activated Carbons . 45 1.3.2 Templated Carbons . 46 1.3.3 Single-Walled Carbon Nanotubes . 48 1.3.4 Graphene-based materials . 49 1.3. 5 Review of Graphene-Based EDLCs. 59 1.4 GOALS AND LAYOUT OF THESIS WORK . 63 2 A MODEL SYSTEM FOR MEASURING THE INTRINSIC CAPACITANCE AND REDOX ACTIVITY OF FUNCTIONALIZED GRAPHENE SHEETS . 67 2.1 INTRODUCTION . 67 2.2 EXPERIMENTAL . 71 2.2.1 Production of FGSs . 71 2.2.2 FGS Characterization . 72 2.2.3 Preparation of Monolayer Electrodes . 72 2.2.4 Electrochemical Setup . 73 2.2. 5 Characterization of Electrodes . 75 x 2.3 RESULTS AND DISCUSSION . 76 2.3.1 Reduction and Annealing of FGS Powder . 76 2.3.2 Morphological Characterization of LB Films Transferred to Gold and HOPG . 78 2.3.3 Capacitance and Redox Activity . 81 2.3.4 Additional Observations: 2 D Aggregation Behavior of FGSs . 98 2.4 CONCLUSIONS . 103 3 ENHANCEMENT OF DOUBLE LAYER CAPACITANCE THROUGH DEFECT-DERIVED INTRINSIC DOPING OF GRAPHENE . 106 3.1 INTRODUCTION . 107 3.2 EXPERIMENTAL . 111 3.2.1 Exfoliation and Reduction of Graphite Oxide . 111 3.2.2 Thermal Post-Treatments . 112 3.2.3 FGS Characterization . 112 3.2.4 Preparation of Monolayer Electrodes . 114 3.2. 5 Characterization of Monolayer Electrodes. 117 3.3 RESULTS AND DISCUSSION . 122 3.3.1 Differential Capacitance of Various FGSs . 122 3.3.2 Chemical and Structural Properties of FGSs . 124 3.3.3 Evolution of Cdl with Changes in Electrode Properties . 131 3.4 CONCLUSIONS . 136 4 SPECIFIC SURFACE AREA OF GRAPHENE IN AGGREGATE FORM . 139 4.1 INTRODUCTION . 139 4.2 EXPERIMENTAL . 141 4.2.1 Production of FGSs . 141 4.2.2 Drying Techniques . 142 4.2.3 FGS Characterization . 144 4.3 RESULTS AND DISCUSSION . 146 4.3.1 Dispersion and Drying Effects . 146 4.3.2 C/O Effects . 150 4.3.3 Capillary & van der Waals Forces . 154 4.4 CONCLUSIONS . 159 5 EDLCS PRODUCED THROUGH EVAPORATIVE CONSOLIDATION OF GRAPHENE OXIDE-WATER-IONIC LIQUID GELS . 161 5.1 INTRODUCTION . 162 5.2 EXPERIMENTAL . 169 5.2.1 Synthesis of Graphene Oxide . 169 5.2.2 Electrode Fabrication . 170 xi 5.2.3 Characterization of Films . 171 5.3 RESULTS AND DISCUSSION . 173 5.3.1 Electrode consolidation . 173 5.3.2 Thermal Reduction of Graphene Oxide/IL Composites . 178 5.3.3 Electrochemical Characterization . 181 5.4 CONCLUSIONS . 187 6 CONCLUSIONS AND FUTURE WORK . 189 6.1 SUMMARY OF MAIN CONCLUSIONS . 189 6.2 FUTURE DIRECTIONS . 191 6.2.1 Improving the Electrode Assembly Strategy . 191 6.2.2 Tuning the Structure of Functionalized Graphene . 192 REFERENCES . 194
Mediating Role of Innovation on the Relationships between Market Orientation and Firm Performance in Food and Beverage Industry in southern Thailand

The purpose of this study showed that incremental and radical innovation mediate the relationship between market orientation and firm performance in the context of Food and Beverage (F&B) industry in southern Thailand. Resources Based View (RBV) and Strategic Innovation Theories (SIT) were applied to the study framework to reach an extensive comprehension of innovation practices in the F&B SMEs context. This study utilized a survey method to test the proposed model. A sample of 351 SMEs in the F&B industry in southern Thailand participated. The model was tested using SEMPLS. The results revealed that market orientation significantly and positively related to innovation (both incremental and radical). Consequently, innovation positively related to the performance of SMEs. The originality of innovative development demonstrated in its effort to prioritize the importance of innovation in the context of F&B industry in Thailand.
INSIDE THE BLACK BOX : UNDERSTANDING DYNAMIC RELATIONS AS A SOURCE OF SUSTAINABLE COMPETITIVE ADVANTAGE

The importance of people to organizational success has gained prominence in various fields in the past two decades, particularly in the field of strategic management. The resource-based view (RBV) proposes people-based resources are key sources of competitive advantage yet concludes their social-complexity prevents understanding their link to competitive advantage (Barney, 1996). Other approaches in strategy and strategic human resource management (SHRM) match people to strategy but do not focus on relational dynamics among people (e.g. Eisenhardt & Martin, 2000; Lepak & Snell, 2002). Although a micro analysis approach would enable a closer look and deeper understanding of social complexity and the role of people and relations as competitive resources, such an approach has not been used.
P o S ( T W E P P-1 7 ) 1 2 4 Demonstrating TTC-PON Robustness and Flexibility

In 2016, a TTC-PON (Timing, Trigger and Control system based on Passive Optical Networks) demonstrator was presented at TWEPP as an alternative to replace the TTC system, currently responsible for delivering timing, trigger and control commands in the LHC experiments. Towards a deployment foreseen for ALICE phase-1 upgrade, the system has been consolidated through flexible software implementation providing full configuration, complete calibration and extended monitoring and diagnostic tools. A new demonstrator setup was built with various FPGA platforms to test the system with an increased number of nodes and under different environmental conditions. This paper focuses on the TTC-PON system design with a discussion on its features and scaled-up tests.
Analyzing and Implementing Metaheuristic GWO and FireFly Algorithms aiming to develop a Fault-Tolerant Hybrid GWOFF Algorithm

The approach adopted to handle hard problems is known as metaheuristics. The problem is considered as hard if discovering the optimal solution for it may not be always possible within the stip-ulated time. Discovering a single solution to a problem is easy and can be accomplished extremely fast, but ﬁnding the best possible solution to the same problem is very long. Optimization algorithms are intended to bridge this gap. The research paper aims at solving the problems for ﬁnding the optimal solution for two popular metaheuristic algorithms, GWO (Grey Wolf Optimization) and FF (Fireﬂy) algorithms. Both the metaheuristics algorithms are studied and implemented. The two technical features comprised of metaheuristic algorithms are exploration and exploitation. The optimal solution has been evaluated alongside Makespan and Utilization Rate for both GWO (Grey Wolf Optimization) and FF (Fireﬂy) algorithms. The lower value of the Makespan and higher Utilization Rate is always desirable. Both the algorithms have been modiﬁed via using mathematical functions to enhance the readings concerning performance evaluation parameters. The GWO is been modiﬁed via developing a hybrid version comprising GWO and PSO (Particle Swarm Optimization) algorithms denoted as the Hybrid Modiﬁed GWOPSO al-gorithm. The Fireﬂy algorithm too has been modiﬁed and is denoted as a Modiﬁed FF algorithm. The conducted modiﬁcations have been measured via different performance evaluation parameters. Finally, the fault tolerance factor is considered and the modiﬁed versions like Modiﬁed GWOPSO and Modiﬁed FF are hybridized to develop a new proposed algorithm Hybrid GWOFF (Hybrid Grey Wolf optimizer Fireﬂy) algorithm and its performance have been evaluated with and without fault tolerance by using different performance parameters.
Producing Regulatory T Cells - Role in Protecting Murine Granzyme B Serine Protease Inhibitor 6 Plays a Critical

Regulatory T cells (Tregs) play a pivotal role in the maintenance of immune tolerance and hold great promise as cell therapy for a variety of immune-mediated diseases. However, the cellular mechanisms that regulate Treg maintenance and homeostasis have yet to be fully explored. Although Tregs express granzyme-B (GrB) to suppress effector T cells via direct killing, the mechanisms by which they protect themselves from GrB-mediated self-inﬂicted damage are unknown. To our knowledge, we show for the ﬁrst time that both induced Tregs and natural Tregs (nTregs) increase their intracellular expression of GrB and its endogenous inhibitor, serine protease inhibitor 6 (Spi6) upon activation. Subcellular fractionation and measurement of GrB activity in the cytoplasm of Tregs show that activated Spi6 2 / 2 Tregs had signiﬁcantly higher cytoplasmic GrB activity. We observed an increase in GrB-mediated apoptosis in Spi6 2 / 2 nTregs and impaired suppression of alloreactive T cells in vitro. Spi6 2 / 2 Tregs were rescued from apoptosis by the addition of a GrB inhibitor (Z-AAD-CMK) in vitro. Furthermore, adoptive transfer experiments showed that Spi6 2 / 2 nTregs were less effective than wild type nTregs in suppressing graft-versus-host disease because of their impaired survival, as shown in our in vivo bioluminescence imaging. Finally, Spi6-deﬁcient recipients rejected MHC class II-mismatch heart allografts at a much faster rate and showed a higher rate of apoptosis Tregs GrB-mediated injury. have signiﬁcant clinical implications for Treg-based therapy in immune-mediated
Periodic Density Functional Theory Study of VOn Species Supported on the CeO2(111) Surface

We model monomeric vanadia adspecies on the CeO2(111) surface of composition VOn·Ce12O24 (n = −1, 0, ., 4) using the DFT+U approach and statistical thermodynamics. At low oxygen pressure (10−9 atm), VO4 is the most stable species below 400 K; in the 400−900 K range, VO2 is stable; and above 900 K, VO becomes stable. In all of these systems, vanadium is stabilized in the +5 oxidation state. Using the energies of hydrogenation and oxygen vacancy formation as reactivity descriptors, we predict an enhanced reactivity of the vanadia/ceria system in Mars−van Krevelen-type oxidation reactions. At the origin of this support effect is the ability of ceria to stabilize reduced states by accommodating electrons in localized f states. We also calculate the frequencies of the normal vibrational modes of the supported VOn species and their infrared intensity.
Automatic Punjabi Caption Generation For Sports Images

Image understanding and language generation have always been a diﬀicult task in the field of Artificial Intelligence. Automatic Image Caption Generation is concerned with the task of understanding the image and generating a caption for it. In this paper, we represented our research work that uses the Deep Learning technique to create Punjabi captions for a given image and its associated news document. High-level features of the images are extracted using the pre-trained VGG-19 (Visual Geometry Group) model. These image features are merged with features of news text which are extracted using LSTM (Long Short Term Memory). The proposed model augments keywords from associated news text to generate suitable captions. Using both BLEU scores and human evaluations, we show that the proposed method is successful in generating intelligible and suitable captions.
Measuring daily-life fear perception change: A computational study in the context of COVID-19

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people’s daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders (“nightmare” and “insomnia”) take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.
Pressurized hot water extraction (PHWE) ofn-alkanes and polyaromatic hydrocarbons (PAHs): Comparison for PAHs with supercritical fluid extraction

High-temperature liquid water and steam were used to extract n alkanes and polyaromatic hydrocarbons (PAHs) from spiked sea sand at different temperatures. Pressurized hot water extraction (PHWE) was followed by solid phase collection. Tenax as the trapping material with n-heptane as the eluent gave recoveries larger than 95% for diesel hydrocarbons extracted with steam at 250°C. Recoveries from Tenax of hydrocarbons extracted with liquid water under the same conditions were lower. Good (>90%) recoveries were also obtained for low molecular weight PAHs, extracted with steam at 250°C and with n-heptane as eluent for the Tenax trap. n-Heptane containing 10% ethyl acetate was more efficient, eluting high as well as low molecular weight PAHs with good recovery. PHWE was also tested in the extraction of a real sediment sample certified for PAHs, and the results were compared with those obtained with supercritical fluid extraction (SFE) using neat CO2 and modified CO2. Steam at 250°C and 300°C gave similar recoveries as those obtained by SFE with use of a modifier. © 2000 John Wiley & Sons, Inc. J Micro Sep 12: 412–418, 2000
High-dimensional near-critical percolation and the torus plateau

We consider percolation on $\mathbb{Z}^d$ and on the $d$-dimensional discrete torus, in dimensions $d \ge 11$ for the nearest-neighbour model and in dimensions $d>6$ for spread-out models. For $\mathbb{Z}^d$, we employ a wide range of techniques and previous results to prove that there exist positive constants $c$ and $C$ such that the slightly subcritical two-point function and one-arm probabilities satisfy \[ \mathbb{P}_{p_c-\varepsilon}(0 \leftrightarrow x) \leq \frac{C}{\|x\|^{d-2}} e^{-c\varepsilon^{1/2} \|x\|} \quad \text{ and } \quad \frac{c}{r^{2}} e^{-C \varepsilon^{1/2}r} \leq \mathbb{P}_{p_c-\varepsilon}\Bigl(0 \leftrightarrow \partial [-r,r]^d \Bigr) \leq \frac{C}{r^2} e^{-c \varepsilon^{1/2}r}. \] Using this, we prove that throughout the critical window the torus two-point function has a"plateau,"meaning that it decays for small $x$ as $\|x\|^{-(d-2)}$ but for large $x$ is essentially constant and of order $V^{-2/3}$ where $V$ is the volume of the torus. The plateau for the two-point function leads immediately to a proof of the torus triangle condition, which is known to have many implications for the critical behaviour on the torus, and also leads to a proof that the critical values on the torus and on $\mathbb{Z}^d$ are separated by a multiple of $V^{-1/3}$. The torus triangle condition and the size of the separation of critical points have been proved previously, but our proofs are different and are direct consequences of the bound on the $\mathbb{Z}^d$ two-point function. In particular, we use results derived from the lace expansion on $\mathbb{Z}^d$, but in contrast to previous work on high-dimensional torus percolation we do not need or use a separate torus lace expansion.
Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection

Computationally efficient, accurate, and privacy-preserving data storage and retrieval are among the key challenges faced by practical deployments of biometric identification systems worldwide. In this work, a method of protected indexing of biometric data is presented. By utilising feature-level fusion of intelligently paired templates, a multi-stage search structure is created. During retrieval, the list of potential candidate identities is successively pre-filtered, thereby reducing the number of template comparisons necessary for a biometric identification transaction. Protection of the biometric probe templates, as well as the stored reference templates and the created index is carried out using homomorphic encryption. The proposed method is extensively evaluated in closed-set and open-set identification scenarios on publicly available databases using two state-of-the-art open-source face recognition systems. With respect to a typical baseline algorithm utilising an exhaustive search-based retrieval algorithm, the proposed method enables a reduction of the computational workload associated with a biometric identification transaction by 90%, while simultaneously suffering no degradation of the biometric performance. Furthermore, by facilitating a seamless integration of template protection with open-source homomorphic encryption libraries, the proposed method guarantees unlinkability, irreversibility, and renewability of the protected biometric data.
Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation

In the classic setting of unsupervised domain adaptation (UDA), the labeled source data are available in the training phase. However, in many real-world scenarios, owing to some reasons such as privacy protection and information security, the source data is inaccessible, and only a model trained on the source domain is available. This paper proposes a novel deep clustering method for this challenging task. Aiming at the dynamical clustering at feature-level, we introduce extra constraints hidden in the geometric structure between data to assist the process. Concretely, we propose a geometry-based constraint, named semantic consistency on the nearest neighborhood (SCNNH), and use it to encourage robust clustering. To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry. Also, we develop a more SCNNH-compliant structure with an additional semantic credibility constraint, named semantic hyper-nearest neighborhood (SHNNH). After that, we extend our method to this new geometry. Extensive experiments on three challenging UDA datasets indicate that our method achieves state-of-the-art results. The proposed method has significant improvement on all datasets (as we adopt SHNNH, the average accuracy increases by over 3.0% on the large-scaled dataset). Code is available at https://github.com/tntek/N2DCX.
Polarization properties of FRB 20201124A from detections with the 100-m Effelsberg Radio Telescope

The repeating FRB source, FRB 20201124A, was found to be highly active in March and April 2021. We observed the source with the Effelsberg 100-m radio telescope at 1.36 GHz on 9 April 2021 and detected 20 bursts. A downward drift in frequency over time is clearly seen from the majority of bursts in our sample. A structure-maximizing dispersion measure (DM) search on the multi-component bursts in our sample yields a DM of 411.6±0.6 pc cm−3. We find that the rotation measure (RM) of the bursts varies around their weighted mean value of -601 rad m−2 with a standard deviation of 11.1 rad m−2. This RM magnitude is 10 times larger than the expected Galactic contribution along this line of sight (LoS). We estimate a LoS magnetic field strength of 4–6 μG, assuming that the entire host galaxy DM contributes to the RM. Further polarization measurements will help determine FRB 20201124A’s RM stability. The bursts are highly linearly polarized, with some showing signs of circular polarization, the first for a repeating FRB. Their polarization position angles (PAs) are flat across the burst envelopes and vary between bursts. We argue that the varying polarization fractions and PAs of FRB 20201124A are similar to known magnetospheric emission from pulsars, while the observed circular polarization, combined with the RM variability, is hard to explain with Faraday conversion. The high linear polarization fractions, flat PAs, and downward drift from FRB 20201124A bursts are similar to previous repeating sources, while the observed circular polarization is a newly seen behaviour among repeaters.
Probing neural networks with t-SNE, class-specific projections and a guided tour

We use graphical methods to probe neural nets that classify images. Plots of t-SNE outputs at successive layers in a network reveal increasingly organized arrangement of the data points. They can also reveal how a network can diminish or even forget about within-class structure as the data proceeds through layers. We use class-specific analogues of principal components to visualize how succeeding layers separate the classes. These allow us to sort images from a given class from most typical to least typical (in the data) and they also serve as very useful projection coordinates for data visualization. We find them especially useful when defining versions guided tours for animated data visualization.
Dust grain growth at high redshift: starburst-driven CMB-dark supershells

We present a novel scenario for the growth of dust grains in galaxies at high-redshift (z ∼ 6). In our model, the mechanical feedback from massive star clusters evolving within high-density pre-enriched media allows to pile-up a large amount of matter into massive supershells. If the gas metallicity (≥ Z ), number density (≥ 10 cm−3) and dust-to-gas mass ratio (∼ 1/150 × Z) within the supershell are sufficiently large, such supershells may become optically thick to the starlight emerging from their host star clusters and even to radiation from the Cosmic Microwave Background (CMB). Based on semi-analytic models, we argue that this mechanism, occurring in the case of massive (≥ 10 M ) molecular clouds hosting ≥ 10 M star clusters, allows a large mass of gas and dust to acquire a temperature below that of the CMB, whereupon dust grain growth may occur with ease. In galaxies with total stellar mass M∗, grain growth within supershells may increase the dust mass by ∼ 10 M (M∗/10 M ).