Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors of successful client co-production in knowledge-intensive business services

Purpose This paper aims to explore the topic of client co-production in knowledge-intensive business services (KIBS). The paper first sketches a theoretical background and reviews previous studies on factors affecting successful client co-production in such companies and then examines these factors via case study research among a small KIBS company and its five customers. Design/methodology/approach The paper is based on an in-depth analysis of literature devoted to client co-production in KIBS firms and on the results of case studies analysis. The authors explore theoretically and empirically the perception of factors behind a successful client co-production process of a KIBS company from the point of view of both customers and service provider. The examination resulted in the clarification of what a successful client-KIBS firm cooperation should look like and what kind of actions KIBS firms should undertake to provide it. Findings As the analysis shows, to perceive client-KIBS firm cooperation as successful, customers desire on hand immediate effects that would justify and compensate their time and money investments (e.g. new clients or brand recognition) and on the other hand, some of them desire positive changes in longer-term, which tangible form is associated with the newly obtained knowledge and more importantly, freshly developed and written strategy. Among the factors that influence the co-production process one can list teamwork, trust, communication and knowledge flows. Research limitations/implications Research results are limited to one KIBS company operating in Poland and its five customers. As such, they are not conclusive for the whole KIBS sector. The findings of both literature review and case study analysis indicate that there are several outcomes that are expected from the point of view of a KIBS customer when selecting the service of a KIBS company. The paper examines important aspect of service co-production and provides practical guidelines how cooperation between KIBS firms and their customers should look like. Practical implications The paper examines the relationship between a client and a KIBS company and explores the factors influencing the successful outcome of this relation. The paper provides guidelines on how this type of relation should be handled by managers or owners of KIBS firms. Originality/value The paper contributes to the literature on KIBS firms, especially in the scarce area of practical mechanics of their cooperation with customers. The paper also suggests further research possibilities in this area.
G-MultiSphere: Generalizing Massively Parallel Detection for Non-Orthogonal Signal Transmissions

The increasing demand for connectivity and throughput, despite the spectrum limitations, has triggered a paradigm shift towards non-orthogonal signal transmissions. However, the complexity requirements of near-optimal detection methods for such systems becomes impractical, due to the large number of mutually interfering streams and to the rank-deficient or ill-determined nature of the corresponding interference matrix. This work introduces g-MultiSphere; a generic massively parallel and near-optimal sphere-decoding-based approach that, in contrast to prior work, applies to both well- and ill-determined non-orthogonal systems. We show that g-MultiSphere is the first approach that can support large uplink multi-user MIMO systems with numbers of concurrently transmitting users that exceed the number of receive antennas by a factor of two or more, while attaining throughput gains of up to 60% and with reduced complexity requirements in comparison to known approaches. By eliminating the need for sparse signal transmissions for non-orthogonal multiple access (NOMA) schemes, g-MultiSphere can support more users than existing systems with better detection performance and practical complexity requirements. In comparison to state-of-the-art detectors for NOMA schemes and non-orthogonal signal waveforms (e.g., SEFDM) g-MultiSphere can be up to an order of magnitude less complex, and can provide throughput gains of up to 60%.
Thrombomodulin and catecholamines as post-mortem indicators of hypothermia

Abstract Hypothermia deaths pose a difficult challenge from the medico-legal point of view because no specific traces are left on the cadaver to be examined post-mortem. The concentrations of urinary catecholamines, adrenaline and noradrenaline increase in various stressful situations including cold stress, and high levels have been considered to be suggestive of lethal hypothermia. There is, however, a need for a better hypothermia indicator. A potential candidate could be thrombomodulin (TM), an endothelially expressed protein whose plasma concentration has been shown to elevate in response to hypothermia. TM and catecholamine levels were studied in short-term cold exposure (human subjects, n = 7), in mild and severe hypothermia with or without rewarming (rats, n = 96) and in hypothermia deaths compared with deaths from cardiovascular diseases, traumas and other causes (autopsy cases, total n = 552). Myocardial thrombomodulin transcript expression was increased in severely hypothermic rats, but was lower in hypothermia deaths than in other causes. The circulating TM level was transiently reduced in severe hypothermia. The myocardial and urinary TM protein levels were reduced in lethal hypothermia compared with other causes of death. TM and catecholamine levels correlated significantly in blood and urine both in living subjects and post-mortem examination. In severely hypothermic rats, there was an inverse relationship between plasma adrenaline concentration and myocardial thrombomodulin transcript level. The results suggest that TM expression and secretion are altered by hypothermia, possibly linked to the actions of catecholamines. Analysing the post-mortem catecholamine and TM levels provides evidence of ante-mortem cold stress in suspected hypothermia deaths. Further studies should be conducted in order to reveal the exact mechanisms behind the regulation of TM on cell level. %%%%Tiivistelma Paleltumiskuolemat ovat oikeuslaaketieteellisesti haastavia, koska vainajaan ei jaa paleltumiseen viittaavia yksiselitteisia loydoksia. Virtsan katekoliamiinien, adrenaliinin ja noradrenaliinin, pitoisuudet kasvavat stressitilanteissa kuten kylmaaltistuksessa. Korkeita pitoisuuksia on pidetty paleltumiseen viittaavana tekijana. Paremmalle paleltumista osoittavalle merkkiaineelle on kuitenkin selkea tarve. Eras mahdollinen merkkiaine voisi olla trombomoduliini (TM), joka on endoteelisolujen tuottama proteiini. Sen plasmapitoisuuden on aiemmin osoitettu nousevan alilampoisyystilassa. TM- ja katekoliamiinitasoja tutkittiin lyhyessa kylmaaltistuksessa (koehenkilot, n = 7) seka lievassa ja vaikeassa alilampoisyystilassa joko lammityksen jalkeen tai ilman lammitysta (rotat, n = 96). Lisaksi verrattiin paleltumisen, sydan- ja verisuonitautien, vammojen seka muiden syiden aiheuttamia kuolemia (ruumiinavausaineisto, n = 552). Sydanlihaksen trombomoduliini-transkriptin taso oli kohonnut vaikeasti alilampoisilla rotilla, mutta se oli matalampi paleltumiskuolemissa kuin muissa kuolemissa. Veren TM-taso oli hetkellisesti alentunut…
A Design Methodology for Cuttlefish Shaped Amphibious Robot

Most of the engineering problems can be easily solved by using biomimetic designs . Biomimetic is the process of imitating live animals to create new designs. For example, by mimicking the movements of a fish or snake, it is possible to transfer the desired swimming or crawling movements to a robot. This research is based on an amphibious robot where the propulsion system is imitated by a cuttlefish. In this study, to obtain the required sine wave motion for the cuttlefish's fin, crank-rocker mechanisms are used. Additionally, a circular slot mechanism was used to move these crank-rocker mechanism up and down as in the cuttlefish fins. Since the cuttlefish has two symmetrical wings, these crank-rocker and circular slot mechanisms are repeated symmetrically on both sides. Two separate servo motors (one on the right and one on the left) were used to control the angular position of the crankshafts in circular slots. These servo motors allow the fins to move up and down while the robot is in the water. They also serve to hold the wings at a fixed angle in terrestrial mode. In similar applied robotic researches, dozens of servo motors are used to obtain the required sine motion. This study proposes a propulsion system that can be operate with simple crank-rocker and circular slot mechanisms, instead of using too many servo motors that are expensive and constitutes control complexity. In this study, a design methodology is proposed for this new propulsion system. Various conditions have been considered in the design procedure. In the design criteria section, the required force and velocity, the capacity to overcome obstacles and the motion requirements has been considered for an amphibious robot. Furthermore, the requirements of a continuous movement for oscillating motion have been also considered. As a result of this study, minimum crank number and crank angles were obtained for the undulating motion. It has been also considered the necessary continuous balance condition, in order to make motion on land without tumbling. The calculation of the part lengths that meets the design criteria is described in the mechanism synthesis section.
Multi-Sensory Deep Learning Architectures for Slam Dunk Scene Classification

Basketball teams at all levels of the game invest a considerable amount of time and effort into collecting, segmenting, and analysing footage from their upcoming opponents previous games. This analysis helps teams identify and exploit the potential weaknesses of their opponents and is commonly cited as one of the key elements required to achieve success in the modern game. The growing importance of this type of analysis has prompted research into the application of computer vision and audio classification techniques to help teams classify scoring sequences and key events using game footage. However, this research tends to focus on classifying scenes based on information from a single sensory source (visual or audio), and fails to analyse the wealth of multi-sensory information available within the footage. This dissertation aims to demonstrate that by analysing the full range of audio and visual features contained in broadcast game footage through a multi-sensory deep learning architecture one can create a more effective key scene classification system when compared to a single sense model. Additionally, this dissertation explores the performance impact of training the audio component of a multi-sensory architecture using different representations of the audio features.
Factors Affecting on the Digital Piracy Behavior: An Empirical Study in Vietnam

Digital products are illegally used in various areas in the world. This behavior causes bad effects on e-commerce activities. This study aimed to find out factors influencing digital piracy behavior in Vietnam, where digital piracy rate is fairly high. In previous research, the approach focused on separate aspects and the scope focused mainly on students. Therefore, an integrated approach could return a better result. Based on the theory of planned behavior and related researches, this study proposed an integrated model to explore the factors affecting digital piracy behavior in Vietnam. Some viewpoints that have been integrated into the proposed model include a psychological, moral, legal, economic and technological viewpoint. The sample consists of 264 people in Ho Chi Minh City, Vietnam. The results showed that the perceived behavioral control strongly influences the intention, and then on the digital piracy behavior. Besides, perceived behavioral control is influenced by technology development and perceived risk. This research confirmed most of the proposed hypotheses and showed the important role of technology in predicting this kind of behavior in Vietnam. Based on the findings of this study, several suggestions have been made to mitigate the digital piracy situation in Vietnam.
—EmerGen(e)tic— Exploring the use of genetic algorithms in emergent distributed systems

Adaptive and emergent systems exist to attempt to answer the deﬁciencies inherent to distributed systems, and the necessarily ﬁnite ability of any programmer to predict all possible eventualities in which his software may one day ﬁnd itself. This paper argues that these systems fail to go far enough, and then proposes a further development—genetic systems—which utilises genetic programming to extend the versatility of a given system massively, if not inﬁnitely. This paper then proceeds to detail the EmerGen(e)tic framework for rapidly testing genetic algorithm modules within emergent systems, as well as an example module pertaining to the cache updating behaviour of a web server. This paper concludes by proposing further avenues of potentially-fruitful research based upon these programs and its ﬁndings.
Multiple Security Certification System between Blockchain Based Terminal and Internet of Things Device: Implication for Open Innovation

As the number of Internet of Things (IoT) devices increases, services expand and illegal hacking and infringement methods become more sophisticated, an effective solution for blockchain technology is required as a fundamental solution to security threats. In this paper, we develop the security module of an IoT device based on blockchain technology that blocks hacking and information infringement and forms a multi-security blockchain system between the IoT device and the user device and we develop a user application. We contribute to addressing the security threats faced by IoT application services by developing a new method. In particular, we present some schemes for the development of a multi-security certification system based on blockchain for IoT security.
Construction of the Remote Welding System based on Power Line Communication

Remote welding technology plays an important role in the construction of national key projects. Instead of manual welding and full-autonomous welding, it is the best way to apply remote welding technology to practical engineering by employing a special robot. This study uses the remote welding power supply cables as the carrier to construct the power line communication network. The key technical problems of the power line communication for complicated electromagnetic environment are proposed in this paper. On this basis, the welding parameters and the environment parameters are transferred to the local control unit, by the power line communication network. The influence rule on weld formation, microstructure, mechanical properties, and welding defects can be revealed by analyzing the above factors. This study explores a feasible way for the robot remote welding by the way of integration innovation. It will lay a foundation for the application of the robot remote welding technology in the construction of national key engineering.
Improving Convolutional Neural Network (CNN) Architecture (miniVGGNet) with Batch Normalization and Learning Rate Decay Factor for Image Classification

The image classification is a classical problem of image processing, computer vision, and machine learning. This paper presents an analysis of the performance using Convolutional Neural Network (CNN) for image classifying using deep learning. MiniVGGNet is CNN architecture used in this paper to train a network for image classification, and CIFAR-10 is selected dataset used for this purpose. The performance of the network was improved by hyper parameter tuning techniques using batch normalization and learning rate decay factor. This paper compares the performance of the trained network by adding batch normalization layer and adjusting the value of learning rate decay factor for the network architecture. Based on the experimental results, adding batch normalization layer allow the networks to improve classification accuracy from 80% to 82%. Applying learning rate decay factor will improve classification accuracy to 83% and reduce the effects of overfitting in learning plot. Performance analysis shows that applying hyper parameter tuning can improve the performance of the network and increasing the ability of the model to generalize.
Description and validation of the ice-sheet model Yelmo (version 1.0)

Abstract. We describe the physics and features of the ice-sheet model Yelmo, an open-source project intended for collaborative development. Yelmo is a thermomechanical model, solving for the coupled velocity and temperature solutions of an ice sheet simultaneously. The ice dynamics are currently treated via a “hybrid” approach combining the shallow-ice and shallow-shelf/shelfy-stream approximations, which makes Yelmo an apt choice for studying a wide variety of problems. Yelmo's main innovations lie in its flexible and user-friendly infrastructure, which promotes portability and facilitates long-term development. In particular, all physics subroutines have been designed to be self-contained, so that they can be easily ported from Yelmo to other models, or easily replaced by improved or alternate methods in the future. Furthermore, hard-coded model choices are eschewed, replaced instead with convenient parameter options that allow the model to be adapted easily to different contexts. We show results for different ice-sheet benchmark tests, and we illustrate Yelmo's performance for the Antarctic ice sheet.
Étude de l'autoimmunité contre le foie induite par mimétisme moléculaire

Autoimmune hepatitis (AIR) is a disease of unknown aetiology characterised by a progressive destruction of the hepatic parenchyma. In order to acquire a better understanding of this auto immune process, the mechanisms governing the immunological balance between tolerance and immunity in the liver have to be identified. To do so, we generated a recombinant adenovirus expressing the two major autoantigens found in type 2 AIR, cytochrome P450 2D6 (CYP2D6) and formiminotransferase cyclodeaminase (FTCD). The effects of viral load and injection site on the immune response were studied. Both the site of injection and amount of recombinant adenovirus were found to influence the phenotype (Th lITh2) and magnitude of the immune response. In a previous murine model of AIR, three consecutive DNA vaccinations with a plasmid encoding for CYP2D6 and FTCD resulted in the onset of an AIR. In the present study, we observed that this autoimmune process can be prevented by an intravenous injection of Ad-CTLA-4-2D6FTCD before DNA vaccination. It seems that the expression of the autoantigens in an inflammatory environment in combination with a constant stimulation of T -ce Il clones would yield to a tolerization toward these autoantigens. Moreover, this tolerization is antigen specific, since animais that received the control adenovirus (control Ad + DNA) developed AIR. In summary, this animal model allowed the characterisation of immune mechanisms involved in the development of liver autoimmunity.
Hardware, Software, and Statistical Tools for Cognitive Neuroscience

of the Thesis. viii Chapter 1: A Novel Paradigm for Social Neuroscience Research. 1 1.1 Background. 1 1.2 Materials and Methods. 4 1.3 Results. 11 1.4 Discussion. 12 1.5 Conclusion. 14 Chapter 2: Statistical Pipeline for Assessing Spike-Phase Relationships. 16 2.1 Background. 16 2.2 Materials and Methods. 20 2.3 Simulation. 21 2.4 Results. 25 2.5 Discussion. 26 2.6 Conclusion. 28 Appendix. 29 References. 30
Multi-Project Localization Translation Memory System

This thesis takes a general look at localization both as a concept and as a process. As a concept, it tries to give a general description, including how it is seen from academic sources and from companies that develop localized products. As a process, this thesis suggests a way of conceptualizing it as a flow of smaller sub-process, which can be duplicated, rearranged and made to run in parallel if needed. This concept has the potential to make the entire process easier to understand and reason about and easier to implement in practice. Finally, this thesis gives a description for a translation memory system. Translation is one of the most important sub-processes of localization. The translation memory system is one of the many tools that are used to make it more time and cost efficient. The system described in this thesis works for a situation that deals with multiple projects, both related and unrelated.
Quality of Life of Long-Term Survivors of Leukemia and Lymphoma

Abstract This study examined quality of life among long-term survivors of leukemia and lymphoma. Analyses of data collected from a standardized instrument designed specifically for cancer survivors and from in-depth face-to-face interviews suggest that long-term survivors experience positive outcomes as a result of their cancer experience along with long-term physical and psychosocial sequelae. The author concludes that quality-of-life outcomes partially may be a function of the cognitive frame or meaning that survivors attribute to their experience. Ensuring adequate care and attention to the needs of long-term survivors requires an understanding of the effects of disease on quality of life as well as the varied ways in which long-term survivors adapt and live the remainder of their lives.
Analytical Expression of Capon Spectrum for Two Uncorrelated Signals Using the Inner Product of Mode Vectors

SUMMARY An analytic expression of the Capon spectrum is derived for two uncorrelated incident signals. On the basis of this theoretical for mulation, we discuss the effect of a factor arising from the inner product of mode vectors with respect to the incident angles, which compromises the resolution. We show numerical examples to demonstrate the effect that the inner product of mode vectors has on the shape of the Capon spectrum.
A novel adaptive second-order sliding mode controller for autonomous underwater vehicles

Navigating, directing, and controlling autonomous underwater vehicles (AUVs) are demanding and considered complicated compared to the autonomous surface-level performance. In such vehicles, the motion can be controlled depending on the estimation of indefinite hydrodynamic forces and the disturbances that occurs in this vehicle in the underwater background. In this article, Gray Wolf optimization (GWO) is performed along with the second-order sliding mode control (GW-SoSMC) approach for controlling the yaw angle in AUV. The main purpose of the article is to reduce the error that occurs in the system between the controlled signal and desired signal corresponding to yaw angle. Using this proposed model, both the occurrence of chattering and the controlling performance of AUV system can be diminished. Moreover, the proposed model is compared with the existing approaches like, FireFly-SoSMC (FF-SoSMC), Genetic Algorithm-SoSMC (GA-SoSMC), Gray Wolf-SMC (GW-SMC), Group search optimization-SoSMC (GSO-SoSMC) and Artificial Bee Colony-SoSMC (ABC-SoSMC). From the simulation results, it shows that the performance of the implemented technique in terms of steady-state response, error analysis, yaw angle analysis, and controller response is enhanced while comparing with the existing approaches.
Sampled-Data Decentralized Output Feedback Control for a Class of Switched Large-Scale Stochastic Nonlinear Systems

In this article, a decentralized output feedback control problem for switched large-scale stochastic nonlinear systems subject to asynchronous switching is considered via sampled-data control. We design a state observer by only using the sampled information. Utilizing the average dwell time method, a sampled-data controller is constructed to ensure that the closed-loop system is globally asymptotically stable in mean square. Two examples are provided to demonstrate the developed strategy’s effectiveness.