Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategische Gruppen in der deutschen Brauwirtschaft

The prevailing existence of differences in corporate success between companies of a certain industrial sector can be explained by the concept of strategic groups. This study applies the concept of strategic groups to the German brewing industry, a sector captured in a decade-long crisis. Against this background, a mail-survey of all German brewing companies (N=1260) has been conducted, showing a response rate of 21.9% (n=281). The questionnaire focused on aspects of strategic management in German brewing companies. The empirical results have been carefully tested by applying uni-, bi- and multivariate analyses. The results show that the surveyed businesses vary in their perception of the intensity of competition in the sector. Furthermore, the study provides an outline of the German brewers corporate, competitive and cooperative strategies.By applying a factor analysis, the multitude of strategic variables has been reduced to the major influencing factors. Afterwards, strategic groups have been identified by using hierarchical cluster analysis. The strategic dimensions identified by factor analysis have been used as cluster-forming variables in the cluster analysis. An overall of six strategic groups could be identified. A next step was to find out the impact of membership in a strategic group on corporate success. Results show that there are significant differences in corporate success between the strategic groups.This study reveals that the concept of strategic groups can be used as an appropriate tool for analyzing the German brewing industry. Furthermore, the assumptions inherent in this concept (i.e. the existence of a limited number of strategic groups and differences in corporate success between companies in different groups) could be verified in this particular industrial sector. The German breweries can utilize this study s results to determine their own position in the struggle of competition and compare themselves with more successful competitors. Additionally, ways and means to close strategic gaps and to overcome mobility barriers are presented.
The role of the 1.5 order formalism and the gauging of spacetime groups in the development of gravity and supergravity theories

The 1.5 formalism played a key role in the discovery of supergravity and it has been used to prove the invariance of essentially all supergravity theories under local supersymmetry. It emerged from the gauging of the super Poincaré group to find supergravity. We review both of these developments as well as the auxiliary fields for simple supergravity and its most general coupling to matter using the tensor calculus.
PADA PEMBANGKIT LISTRIK TENAGA MIKRO-HIDRO

A renewable energy source for mini / micro hydro usually exploits the potential energy of water flow that have certain head and the specific discharge which is converted into electrical energy through a turbine and generator. However, in many rivers, the average of water resource potential has a large discharge and low head. Thus, the development of a low head turbine or a very low head is very suitable to be developed in Indonesia. Screw turbine uses low head and high flow rate of water to generate torque and rotation as mechanic energy. Screw turbine has many advantages such as simple in construction and installation, low cost in operating and maintenance, and fish-friendly. The influencing parameters to mechanics performance of screw turbine are pitch distance, water discharge and the shaft slope. The objective of this research is to investigate the effect of shaft slope angle and discharge variations to mechanics performance of a 2-blades screw turbine in laboratory scale. A model used in the research was a 2-blades screw turbine, pitch (distance of a screw wave) is 1.6 Ro, external radius (Ro) is 0.1419 m, and internal radius (Ri) is 0.0762 m. The screw turbine was tested in Hydraulic Laboratory of the Vocational School of Civil Engineering, Gadjah Mada University, with various average discharges of water was about 0.00364 m 3 /s to 0.00684 m 3 /s, and the angle of turbine shaft slope was from 25°to50°. The result of the testing showed that the discharge and turbine shaft slope affected the turbine power and efficiency. The highest turbine power and efficiency were produced at average discharge was 0.00684 m 3 /s. The optimum screw turbine shaft slope was 35
Development and Locomotion Control of a Horizontal Ledge-Climbing Robot

Ledge-like design is the most common architectural element for the outer walls of the buildings in intensive population. However, how to overcome these obstacles and continue climbing tasks is also one of the greatest design issues for conventional climbing robots. Instead of treating these ledges as obstacles, this study proposes an innovative climbing robot which mimics the unique locomotion styles of the human climbers via the ingenuous usage of the ledges on the wall. A central pattern generator based locomotion controller with five oscillators is adopted to provide desired robot joint commands for horizontal ledge climbing. An optimization procedure is applied to obtain the values relevant to phase coupling parameters because even a small mismatch of synchronization of robot joint motion may seriously affect the success of succeeding climbing process. Experiments demonstrate that the proposed climbing robot is able to perform a continuous ledge climbing task without falling on the ground.
Mitigating oscillator pulling due to magnetic coupling in monolithic mixed-signal radio-frequency integrated circuits

An analysis of frequency pulling in a varactor-tuned LC VCO under coupling from an on-chip PA is presented. The large-signal behavior of the VCO’s inversion-mode MOS varactors is outlined, and the susceptibility of the VCO to frequency pulling from PA aggressor signals with various modulation schemes is discussed. We show that if the aggressor signal is aperiodic, band-limited, or amplitude-modulated, the varactor-tuned LC VCO will experience frequency pulling due to time-modulation of the varactor capacitance. However, if the aggressor signal has constant-envelope phase modulation, VCO pulling can be eliminated, even in the presence of coupling, through careful choice of VCO frequency and divider ratio. Additional mitigation strategies, including new inductor topologies and system-level architectural choices, are also examined. The analysis is then applied to improve a fully-integrated half-duplex UHF microtransceiver in which signal coupling between the LO and PA caused frequency pulling that prevented the use of QPSK signaling at certain data rates. We determine that a VCO operating at 4× transmit frequency will be naturally insensitive to pulling from QPSK signals. To validate the proposed solution, a prototype IC containing a pair of QPSK transmitters with integrated 100 mW Class-C PAs was designed and fabricated in 0.18um SOI. The transmitters—one utilizing a 2× VCO, one utilizing a 4× VCO— were designed to closely match the performance of the original microtransceiver when transmitting QPSK data. The transmitter with the 2× VCO experienced frequency pulling from the PA while transmitting QPSK data, but the transmitter with the 4× VCO did not, thereby confirming the analysis in this work. A revision of the microtransceiver was designed in 0. 5μm SOS utilizing an offchip PA inductor to reduce signal coupling with the VCO. A second revision of the microtransceiver with two prototype transmitters was designed in 0. 25μm SOS utilizing 4× VCOs and figure-8 VCO inductors for maximum insensitivity to pulling from QPSK and band-limited modulation, as well as other design improvements that leverage the higher ft of the smaller process. Both revisions also include a hardware FSK modulator, a new charge pump, and a redesigned fractional-N synthesizer to attenuate a divided-reference spur in the IF output. These revisions of the radio will enable future researchers to focus on system-level applications where highly-integrated medium-power transceivers with fully-functioning IQ modulation are needed.
District heating networks in the framework of spatial planning

The way to a more sustainable energy future is running over a transition process that drives change in our views and actions on renewables, emissions and efficiencies. The interaction between energy and space evolves in this transition, with district heating taking up a growing role. Distributed (renewable) energy generation, the reuse of waste and excess heat significantly contributes to the sustainable energy system, but more than the former it is constrained by spatial dependencies. In contrast to North and East European countries, Flanders has a highly centralised energy production profile. When focusing on thermal energy, decentralised supply such as district heating and cooling is rare. Adding to the poor transportability of heat and the high investment cost of network infrastructure, spatial factors such as proximity and density of heat demand play an important role. Meanwhile acknowledging the need for an integral approach, this paper explores the relationship between district heating and spatial planning. Via heat mapping, areas with sufficient heat demand are located and investigated for their spatial potential. First a linear heat density map is drawn and subsequently the preliminary district heating potential is represented in a heat tariff map. The latter proves to be prospective for screening district heating projects by network administrators.
Efficiency of Bio-Sourced Composites in Confining Recycled Aggregates Concrete

This research investigates the efficiency of using Flax Fibers reinforced bio-sourced polymer by comparison to traditional system based on Carbone Fiber Reinforced Epoxy Polymer in order to confine recycled aggregates concrete. Four concrete formulations have been formulated by incorporating recycled aggregates from demolition waste (0%, 30%, 50% and 100%). An air-entraining agent was added to the formulations to achieve the level of 4% occluded air. The main objective is to discuss and to evaluate the effectiveness of confining them using bio-sourced composite by comparison to traditional ones. To hit this target, the developed approaches are both experimental and analytical. The first part is experimental and aimed to characterize the mechanical behavior of the materials: the composites used in the confining process the unconfined concrete (effect of incorporating recycled aggregates on the overall mechanical characteristics). We establish that bio-sourced composites are efficient in strengthening recycled aggregates concrete especially if they are air-entrained. The second part of this work is dedicated to analytical modeling of mechanical behavior of confined concrete with composite under compression based on Mander’s model. The input parameters of the model were modified to consider the rate of recycled aggregates incorporation. Comparison between experimental results and the modified Mandel’s Model is satisfactory.
Direct Determination of Diethanolamine in High Salinity Dehydrogenation Reaction Solutions by Ion Chromatography (IC)

Abstract A simple and rapid method is reported for the determination of diethanolamine (DEA) in dehydrogenation reaction solutions. Based on ion chromatography with conductivity detection, the eluent and its concentration, column temperature, and flow rate were optimized. The analysis was done with a cation column and 4.0 mM methanesulfonic acid (MSA) as the eluent at a flow rate of 1.0 mL/min and column temperature of 313 K. Under the optimal conditions, satisfactory linearity was exhibited with a correlation coefficient of 0.9993. The separation of diethanolamine in a high salinity matrix (Na+) was completed within 17 minutes. Satisfactory recoveries from 98.01 to 101.64% were obtained for spiked samples with relative standard deviations from 1.20% to 1.58% (n = 5). The detection limit for DEA was 14.37 μg/L. The method was suitable for the determination of diethanolamine (DEA) in the dehydrogenation reaction solutions and may be modified for other alkanolamines in industrial processes.
Weak Target Detection Based on Message Passing Network Fusion with Deep Neural Network

This paper addresses the issue of weak target detection in sea clutter using distributed radar network. Bayesian track-before-detect filter is applied to local radar and the result of local detection is fused by a new scheme based on message passing, which can enhance the correlation between target signals while suppressing that between interference. By reasonably setting parameters, it can reduce false alarm probability. Deep neural network is adopted to realize the scheme, which is robust and can achieve high efficiency. Simulation results demonstrate that the proposed detector can not only increase detection probability but also decrease the false alarm rate.
Comparison of the proportional hazard model and the accelerated failure model in the mixed cure model

Traditional survival analysis models such as the Cox model and the accelerated failure time model (AFT) assume that all individuals will eventually experience specified endpoint events, such as recurrence or death. However, in recent years, with the Advancement of science and technology and the improvement of medical standards, in many clinical trials, there are some individuals who will not experience terminal events after treatment, that is, they will not relapse or die. The researchers believe that these individuals have been cured and call them long-term survivors. In this case, using the traditional Cox model and the AFT model will cause large errors and affect the judgment. Therefore, we consider applying a mixed healing model to the data. In the previous period, we have compared the model of proportional risk function and proportional risk mixed healing model and accelerated failure function model with accelerated failure mixed healing model. In this paper, we want to compare the predicted effects of the PHMC model and the AFTMC model. Methods: We use Monte Carlo simulations to generate data that satisfy and do not satisfy proportional assumptions. Using the consistency probability, the average square error of regression coefficient and 95% confidence interval to cover the original parameter as the evaluation index, the discriminant precision and fitting effect of the same data are compared. Result: For the survival data based on the assumption of proportional risk, the fitting effect of PHMC model is more accurate than that of AFTMC model. For the survival data based on the assumption that the proportional risk is not satisfied, the fitting effect of AFTMC model is better than that of PHMC model. Conclusion: The PHMC model is recommended for survival data based on the assumption of proportional risk assumptions. The AFTMC model is recommended for survival data based on the assumption that the proportional risk is not met.
CARE: learning convolutional attentional recurrent embedding for sequential recommendation

Top-N sequential recommendation is to predict the next few items based on user's sequential interactions with past items. This paper aims at boosting the performance of top-N sequential recommendation based on a state-of-the-art model, Caser. We point out three insufficiencies of Caser - do not model variant-sized sequential patterns, treating the impact of each past time step equally, and cannot learn cumulative features. Then we propose a novel Convolutional Attentional Recurrent Embedding (CARE) learning model. Experiments conducted on a large-scale user-location check-in dataset exhibit promising performance, comparing to Caser.
The voice of silence: interpreting silence in truth discovery on social media

This paper enhances the interpretation of silence for purposes of truth discovery on social media. Most solutions to fact-finding problems from social media data focus on what users explicitly post. Absence of a post, however, also plays a key role in interpreting veracity of information. In this paper, we focus on (absent links in) the retweet graph. A user might abstain from propagating content for many potential reasons. For example, they might not be aware of the original post; they might find the content uninteresting; or they might doubt content veracity and refrain from propagation (among other reasons). This paper formulates a joint fact-finding and silence interpretation problem, and shows that the joint formulation significantly improves our ability to distinguish true and false claims. An unsupervised algorithm, Joint Network Embedding and Maximum Likelihood (JNEML) framework, is developed to solve this problem. We show that the joint algorithm outperforms other unsupervised baselines significantly on truth discovery tasks on three empirical data sets collected using the Twitter API.
Image-based Malware Classification using Deep Convolutional Neural Network and Transfer Learning

Malware classification is a major challenge as they have multiple families and its type has been ever increasing. With the involvement of deep learning and the availability of massive data, neural networks can easily address this problem. This experimental work focuses on classifying the malware that are in the form of grayscale images into their respective families with high accuracy and low loss. We used transfer learning in a pretrained VGG16 model obtaining an accuracy of 88.40% of accuracy. Additionally, upon experimenting with the ResNet-18, InceptionV3 model to classify the malware images into their families didn't give better results as compared to our custom model. The custom model based on convolution neural network achieved better accuracy and was able to classify malware with 98.7% validation accuracy. Upon comparing our custom model with VGG16, ResNet-18, InceptionV3 the custom model gave better accuracy with a better f1 score of 0.99. But improper tuning of VGG16 yielded low accuracy and high parameter loss. In overall the approach of classifying malware by converting them into images and classifying thus obtained images makes the malware classification problem easier.
A Robust Multi-Sphere SVC Algorithm Based on Parameter Estimation

To improve the robustness to noise, outliers and arbitrary cluster boundaries, a robust multi-sphere support vector clustering (SVC) algorithm is proposed in this paper. The proposed algorithm can automatically estimate a suitable kernel parameter, and determine the cluster number. The Gaussian kernel parameter is firstly estimated through a kernel parameter estimation algorithm which is based on support vector domain description (SVDD) and original local variance (LV) algorithm. Based on the estimated kernel parameter, the SVC algorithm classifies the given data points into different clusters and then the SVDD algorithm is performed several times for each cluster. At last, the membership is computed and the final clustering result is obtained based on these computed memberships. Several simulations verify the effectiveness of the proposed algorithm.
Analyzing online opinions and influence campaigns on blogs using BlogTracker

Blogging has become an essential part of the new print media of the 21st century despite the emergence of social media platforms like Twitter and Facebook, with many news agencies, media outlets, journalists and users using this medium to write without any restriction on topics of choice or events that happen over the world. Although social networking sites have also become a hotbed where users share their views, it suffers from distraction when users try to air their views on topics that affect them due to character limitation, real-time toxic behavior, and content ownership rights. Social networking sites like Facebook, Twitter and Reddit are sometimes used to drive traffic to blogs sites. The blogosphere, defined as the network of blogs, is growing at an exponential rate. Medium.com and WordPress.com are among the top blogging platforms, with WordPress leading the way as a top blogging platform and followed by other platforms like Medium, Hashnode, Tumblr, and blogger. Analyzing blog data helps understand the pulse of a society, know what resonates with a community, and recognize the grievances of a group, among other reasons. Since there is no character limit in blogs, unlike Twitter, blogs allow much depth in discourse, allowing it to be an effective platform for setting narratives. Blogs also provide a convenient platform to develop situational awareness during a socio-political crisis or humanitarian crisis in a conflict-torn region or a disaster-struck area. To address the difficulty of having a publicly accessible blog data analytical solution since solutions like Blogdex, among others, were either discontinued or made proprietary, we present BlogTracker. This tool helps users analyze public discussions with real-time data update capability and analyze narratives and emotion distribution on associated blog posts and trackers. This demonstration shows how the BlogTracker application analyses blog data with a case study about COVID-19.
Modelling social capital: the structural hole connections game

Social Capital is considered as the value that an actor draws from the network. It is measured as an ability to bond with others (bonding capital) as well as an ability to form a bridge that connects otherwise disconnected actors or groups in the network (bridging capital). In order to model the social capital, the strategic nature of forming links needs to be considered and baked within the network formation models. In this paper, we develop a strategic network formation game named Structural Hole Connections Game (shc) and define an associated allocation function that distributes the total network value to the actors in the network in such a way that captures their bonding and bridging social capital. Our proposed shc game generalizes the utility function that models both the bonding and bridging capabilities of an actor with high social capital. We first analytically deduct the efficient and stable networks of the shc game. Finally, we analyse a real-world social network of employees of an IT company and identify individuals with binding and bridging social profiles.
Fast colonization algorithm for seed selection in complex networks based on community detection

An ongoing challenge in network science is influence maximization (IM), which sets out to define those nodes which maximize the dissemination of influence. Most of the recent research proposals on the IM problem offer solutions that are still highly time consuming for usage in the context of real-world complex networks. This article develops a novel seed selection framework based on the principle of maximizing influence at the community level with an emphasis on global homogeneous seed spacing. Our proposed framework, called Colonise, consists of the following stages: (i) community tuning, (ii) node centrality computation, and (iii) seed assignment. Particularly, phase (i) iteratively breaks down the network into communities, using the Louvain method, based on the number of desired seeds; phase (ii) measures a target node centrality on each community to reduce the number of seed candidates; phase (iii) assigns nodes as seeds from the highest centrality nodes found in each community. In contrast to global centrality-based seed selection, we exploit the structure of communities and circumvent overlapped assignment, such that we select efficiently the number of seed nodes to boost information diffusion. The simulation results---based on 12 diverse synthetic and real-world networks, and employing the SIR epidemic model---prove that our proposed Colonise algorithm surpasses state-of-the-art selection methods in all simulated scenarios, with an increased diffusion efficiency ranging between +0.15% up to +173.53% (22.36% on average), without compromising either diffusion coverage or speed.