Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Energy Technologies in Everyday Life: Smart Utopia?

The rise of smart energy technologies are transforming all aspects of social life. This innovative book critically interrogates the global utopian vision for smart energy and the ultimate consumer-the Resource Man-who is intended to realise its aims. Delving into domains of everyday life where this vision is largely absent, the book provides a unique conceptualisation of energy and technology in everyday practice. Through a lively analysis of energy feedback, dynamic pricing, home automation and micro-generation, the book enriches and extends the possibilities for smart energy technologies in everyday life. It proposes that prices can convey 'meanings' about energy, that automation technologies can 'act back' like the Golems of Jewish mythology and that 'home made' energies can matter to everyday practices in important ways. Crossing disciplines including sociology, human-computer interaction design and science and technology studies, the book reimagines what the Smart Utopia is and how its aims can be realised.
Supramolecular interactions between polylactide and model cyclosiloxanes with hydrogen bonding-capable functional groups

Interactions between polylactide matrix and additives bearing –COOH and –OH groups were studied for compositions of polylactide (PLA) and functionalized cyclotetrasiloxanes (CX–R, R = OH, COOH, COOMe). Inherent conformational flexibility of cyclotetrasiloxane rings enabled evaluation of supramolecular phenomena between hydrogen bonding-capable functional groups and the polylactide backbone, as well as their role in polymer crystallization. The modification of PLA with CX–R was clearly reflected in the polymer dielectric response, unobscured by interfacial polarization effects fre-quently observed for other additives. New relaxation processes appeared next to the strong α-relaxation characteristic of the PLA matrix. Addition of CX–R influenced thermally induced crystallization of amorphous matrix as well as isothermal crystallization from melt. Development of crystals with 10 3 helical chain conformation was accelerated at relatively low temperatures in the presence of CX–OH with hydrogen bond donating hydroxyl groups. A specific phase separation that hindered mobility of polymer chains was observed in samples prepared with CX–COOH of strong hydrogen bond donor/acceptor ability. The presented results may be used as a reference for other systems with nanoadditives such as carbon nanotubes (CNT), graphene oxide (GO) or carbon quantum dots, in which interactions between –COOH/–OH groups and the polymer matrix play an important role.
PERANCANGAN DESAIN RUANGAN DATA CENTER MENGGUNAKAN STANDAR TIA - 942 (STUDI KASUS : PUSLITBANG JALAN DAN JE MBATAN)

The growth of information systems development today so rapidly , it has implications for the needs of storage and processing space of data . Data center is a data storage system that ensures the existence of the dat a. Construction of the data center must meet certain standards to ensure the stored data safe and accessible . One of the standard data center which internationally recognized is TIA - 942 , this standard was issued by the Telecommunicat ions Industry Associa tion (TIA) . In conducting the data center using existing design standards TIA - 94, there are several c omponents that must be analyzed , they are determination of the location, raised floor design , electrical sys tem design, design of lighting , and fire prev ention systems. The method used to design the data center is analyze the requirements l isted in the TIA - 942 standard , and continued with the design in accord ance with the criteria required . This study aims to find a draft design for an ideal data center and meet safety standards . This study resulted in the design of the data center in accordance with the TIA - 9 42 standard to be applied in Institute of Road Engineering.
Using eye tracking data for evaluation and improvement of training process on ship’s navigational bridge simulator

The paper presents basics of the eye tracking methodology and results of preliminary test on multi-purpose marine ship’s navigation bridge simulator. The use of the eye-tracker on the navigation bridge makes it possible to objectively measure the ergonomics of the individual interfaces, as well as to evaluate the decision-making process itself, including the mental workload and stress. In the experiment, officers were faced with an unexpected and sudden situation leading to a collision. The simulated situation required the officer to act immediately and resolutely, taking into account all his/her knowledge about the ship, and interpreting the available data correctly. Outcome of this test was compared with gaze data and it was proved that experienced group with best results suffered lowest level of mental workload and was most efficient in decision making process. Based on those results a possibility of improving simulator training with the use of eye tracking data is discussed.
Learning to target advertisements at Spotify

The goal of this thesis work is to develop a methodology to optimize advertisement targeting inside the Spotify platform. By understanding the relevance of advertisements to users, the advertisement efficacy can be improved leading to an overall reduced advertisement load, to a better user experience, and to an increased advertisement revenue.Three types of advertisement are delivered inside the Spotify platform: house advertisements, which promote the Spotify premium subscription, label advertisements, which promote artists and albums and commercial advertisements, which promote external brands or products. For each type, machine learning models were implemented to optimize the matching of advertisements to users.The house advertisement targeting focused on maximizing the conversions of users from free to premium subscriptions. Unlike traditional advertisement targeting models, which focus on estimating the user probability of converting, the proposed approach focuses on estimating the change in probability of converting when an advertisement is delivered, so that only impressions producing a true uplift are delivered.The targeting of label advertisements optimized the number of users starting to listen to the advertised music. The implemented approach delivers relevant advertisements based on an evaluation of the affinity between the user's music and the advertised music and on the user's listening behavior.The commercial advertisement targeting optimized the number of clicks on advertisements. Given that commercial advertisements advertise external products, the main challenge faced was the lack of relevant data to inform the targeting. The implemented approach tries to deal with this problem by combining feature based methods with collaborative filtering methods.The main contribution of this thesis work is the implementation of machine learning models to improve advertisement targeting inside the Spotify platform. In particular, the proposed methodology uses uplift modeling, with a modified approach to handle bias in the training data, and also makes use of meta-data to better understand the context of the campaigns.
Parallel Algorithm of Improved FunkSVD Based on Spark

In view of the low accuracy of the traditional FunkSVD algorithm, and in order to improve the computational efficiency of the algorithm, this paper proposes a parallel algorithm of improved FunkSVD based on Spark (SP-FD). Using RMSProp algorithm to improve the traditional FunkSVD algorithm. The improved FunkSVD algorithm can not only solve the problem of decreased accuracy caused by iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, thereby achieving the effect of improving the accuracy of the algorithm. And using the Spark big data computing framework to realize the parallelization of the improved algorithm, to use RDD for iterative calculation, and to store calculation data in the iterative process in distributed memory to speed up the iteration. The Cartesian product operation in the improved FunkSVD algorithm is divided into blocks to realize parallel calculation, thereby improving the calculation speed of the algorithm. Experiments on three standard data sets in terms of accuracy, execution time, and speedup show that the SP-FD algorithm not only improves the recommendation accuracy, shortens the calculation interval compared to the traditional FunkSVD and several other algorithms but also shows good parallel performance in a cluster environment with multiple nodes. The analysis of experimental results shows that the SP-FD algorithm improves the accuracy and parallel computing capability of the algorithm, which is better than the traditional FunkSVD algorithm.
Tropical Cyclone Winds and Inflow Angle Asymmetry From SAR Imagery

This study developed a morphological model for Tropical Cyclone (TC) wind and inflow angle asymmetry based on sea surface wind fields derived from spaceborne synthetic aperture radar (SAR) images. The model extracts the standard TC morphological information (center, intensity, and radius of the maximum wind) and decomposes the SAR‐derived winds into vortex rotation winds and motion vector, making the reconstruction of the entire TC structure reliable, even in areas not mapped by SAR. The derived wind speeds and inflow angles are verified with aircraft measurements by stepped‐frequency microwave radiometer and dropsondes, obtaining root‐mean‐square errors of 4.32 m/s and 16.04°, respectively. A systematic analysis of 130 SAR TCs images, collected by RADARSAT‐2 and SENTINEL‐1, reveals that the model can capture the main asymmetrical TC structure. Both TC asymmetry and eye size decrease as TC intensity increases.
Impaired Driving Risk Assessment: A Primer for Practitioners

This primer examines the profile and characteristics of impaired drivers, including first offenders in relation to repeat offenders. It also looks at the similarities and differences in the profiles of male and female impaired drivers. Instruments available to identify those at risk for impaired driving and treatment interventions available for those at risk are outlined. The primer concludes with research needs.
FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge Computing

Federated learning (FL) involves training machine learning models over distributed edge nodes (i.e., workers) while facing three critical challenges, edge heterogeneity, Non-IID data and communication resource constraint. In the synchronous FL, the parameter server has to wait for the slowest workers, leading to significant waiting time due to edge heterogeneity. Though asynchronous FL can well tackle the edge heterogeneity, it requires frequent model transfers, resulting in massive communication resource consumption. Moreover, the different relative frequency of workers participating in asynchronous updating may seriously hurt training accuracy, especially on Non-IID data. In this paper, we propose a semi-asynchronous federated learning mechanism (FedSA), where the parameter server aggregates a certain number of local models by their arrival order in each round. We theoretically analyze the quantitative relationship between the convergence bound of FedSA and different factors, e.g., the number of participating workers in each round, the degree of data Non-IID and edge heterogeneity. Based on the convergence bound, we present an efficient algorithm to determine the number of participating workers to minimize the training completion time. To further improve the training accuracy on Non-IID data, FedSA deploys adaptive learning rates for workers by their relative participation frequency. We extend our proposed mechanism to the dynamic and multiple learning tasks scenarios. Experimental results on the testbed show that our proposed mechanism and algorithms address the three challenges more effectively than the state-of-the-art solutions.
Architecture and construction in steel

Preface. Contributors. Introduction. Part 1: History of iron and steel construction. The nineteenth century. The twentieth century. Part 2: Materials. Properties of steel. Structural steel components for buildings. Steel sheet and strip. Stainless and related steels. Nature of corrosion. Anti-corrosion measures. Fire protection. Part 3: Principles of steel framing. The architecture of steel. Basic theory of framing. Multiple bay single-storey buildings. Floor framing and services above and below floors. Multi-storey frames. Tall structures. Composite floors and structures. Transfer structures. Foundation structures. Atria. Tensile structures. Part 4: Steel construction. Structural connections for steel work. Fabrication and erection. Tolerances and movements in building frames. Insertion and strengthening of steel frames and upgrading facades. Part 5: Secondary steel elements. Principles of cladding. Heavy and lightweight cladding. Window walls and rain screen facades. Decking and built up roofing. Fastenings. Metal stud work and lath. Metal windows and louvres, cills and lintels. Metal door frames, screens and security. Staircases and balustrades. Gutters, downpipes and overflows. Decorative iron and steel. Part 6: Outstanding contemporary steel architecture. The last 25 years. Structural steel design awards. Futures. Appendices: Relevant codes, standards and general publications. Advisory services for the steel construction industry. Index of architects. Index of buildings. Subject index.
Optimizing a scaffoldless approach for cartilage tissue engineering

Optimizing a Scaffoldless Approach for Cartilage Tissue Engineering By Benjamin D. Elder Articular cartilage has a poor intrinsic healing response, so tissue engineering provides a promising approach for cartilage regeneration. The major objective of this proposal was to enhance the self-assembling process, used in articular cartilage tissue engineering, by investigating the effects of construct confinement, hydrostatic pressure application, and growth factor addition. First, the effects of construct confinement in different directions and at different times were investigated. It was demonstrated that construct confinement resulted in enhanced biomechanical properties in the direction orthogonal to the confinement surface, either by enhancing collagen organization or by increasing collagen production. Next, the effects of hydrostatic pressure at different timepoints, magnitudes, and frequencies on the biomechanical and biochemical properties of self-assembled constructs were determined. It was demonstrated that the application of static hydrostatic pressure, at 10 MPa, for 1 h/day, from days 10-14 days led to significant increases in compressive and tensile properties, accompanied by significant increases in GAG and collagen content, respectively. To our knowledge, this was the first study to demonstrate increases in the biomechanical properties of tissue from pure HP application. Furthermore, the effects of exogenous application of growth factors, at varying concentrations, dosages, and combinations, with and without hydrostatic pressure, were assessed on the biochemical and biomechanical properties of engineered constructs. A systematic approach was used to determine the effects of BMP-2, IGF-I, and TGF-pi, alone and in combination, on the functional properties of engineered constructs. This was the first study to demonstrate significant increases in both compressive and tensile biomechanical properties as a result of growth factor treatment. Also, for the first time, synergistic and additive effects on construct biomechanical and biochemical properties were found when combining growth factor treatment with hydrostatic pressure application. Finally, the effects of various decellularization treatments were examined, and it was determined that it was possible to remove cells while maintaining construct functional properties. The results presented in this thesis are exciting, as they have allowed for a better understanding of the self-assembling process, and have allowed the self-assembled constructs to mature into functional articular cartilage, as evidenced by biomechanical and biochemical properties spanning native tissue values.
GiveMe Views: uma ferramenta de suporte a evolução de software baseada na análise de dados históricos [GiveMe Views: a support tool to software evolution based on historical data analysis]

This paper presents Give Me Views tool that can indicate possible modules and components that may be impacted when a corrective maintenance, adaptive or evolutionary is performed. Provides views that allow user to obtain information about the relationships between modules and components based on statistical analyzes performed by an SAE (Statistical Analysis Engine) on historical software data. A proof of concept was performed in order to verify the feasibility of the tool in software development activities in a real context of use. The tool deployment results in a software development company are presented.
Traumatized muscle-derived multipotent progenitor cells: Pro-angiogenic activity, promotion of nerve growth, and osteogenic differentiation

Following trauma from high-energy blasts, a population of multipotent progenitor cells (MPCs) can be derived from the debrided muscle tissue. Given the non-intrusive nature of MPC isolation, MPCs represent an attractive clinical alternative to the more widely used mesenchymal stem cells derived from bone marrow and other tissue sources. This dissertation presents work which further defines the utility and limitations of MPCs in applications relevant to the repair of extremity injuries, including angiogenesis, peripheral nerve repair, and bone formation. The secretome of MPCs enhanced in vitro angiogenesis, in a manner dependent on MPC production of vascular endothelial growth factor-A (VEGF). Encapsulated within mechanically tunable injectable hydrogel constructs, MPCs retained strong pro-angiogenic activity when implanted in vivo, supporting potential clinical use when enhanced vessel recruitment is desired. Neurotrophically-induced MPCs, in combination with endothelial cells (ECs) co-cultured on aligned, nanofibrous scaffolds or via secretome interactions, supported neurite outgrowth and extension of chick embryonic dorsal root ganglia. These findings suggest that products of induced MPCs may be useful to enhance nerve guide conduit-based repair of peripheral nerves. ECs influenced earlier and stronger MPC osteogenic gene expression, and IL-1β was associated with increased mineralization. The use of MPCs in bone replacement applications may thus result in mineralization without functional bone formation, depending on the level of inflammation at the site of construct implantation. Taken together, this work extends the potential utility of MPCs for limb regenerative applications, especially for enhanced vessel or nerve recruitment. Caution must be exercised as MPCs may be influenced towards a mineralizing phenotype by the tissue environment, likely contributing to the heterotopic ossification pathology commonly seen following blast trauma.
Intelligent Matching of the Control Voltage of Delay Line Interferometers for Differential Phase Shift Keying-Modulated Optical Signals

In optical communications, differential phase shift keying (DPSK) provides a desired modulation format that offers high tolerance to nonlinear effects in high-speed transmissions. A DPSK demodulator converts the phase-coded signal into an intensity-coded signal at receivers. One demodulation scheme is called balanced detection and is based on a tunable delay line interferometer (DLI). Demodulation performances are determined by the phase delay generated by the DLI, while the phase delay is controlled by a tunable driving voltage on the DLI device. However, a problem in the dynamic adjustment of the control voltage prevents the application of DPSK demodulators. The receivers need to scan the whole control voltage range of the DLI and find the control voltage that maximizes the demodulation performance, but the scan-based method needs to undergo a very long searching time. In our work, we found that the relation between DLI control voltages and demodulation performance can be predicted rapidly by a feedforward neural network (FNN). In this paper, we propose a new method to quickly locate the best DLI control voltage based on an FNN. We also verify the proposed method in simulations and telecommunication systems, and the results show that the proposed method can significantly improve the efficiency of resolving the best demodulation voltages.
Knowledge management in family business succession: current trends and future directions

Purpose This paper aims to review the literature on knowledge management in the family business, addressing the research question as follows: “How is knowledge managed across generations in the family business? ” This study synthesizes the literature, highlighting the role of multiple stakeholders who affect knowledge management along with the phases of the succession process. Stemming from these findings and embracing a practice-based view, this paper offers research directions to guide future contributions on knowledge construction in the family business. The purpose of this study is not only to conclude the previous research but also to provide insights for future research directions and to provide practical implications. Design/methodology/approach Through a systematic integrative literature review, this paper collects map and analytically examine 63 published peer-reviewed articles related to knowledge management in the family business from 39 academic journals. This paper applies a rigorous approach to identify the sample of articles, map descriptive information of the reviewed literature and map theoretical contributions according to a multi-stakeholder and multi-stage framework. Findings The sampled articles are analyzed according to a multi-stakeholder and multi-stage framework inspired by Daspit et al. (2016). This study identifies critical gaps emerging from the analysis, thus opening the way to future research directions. In particular, this paper prompts scholars to advance the understanding of family-related knowledge and to clarify the assumptions on knowledge in their research. Research limitations/implications This paper contributes to the debate on knowledge management in the family business, by systematizing the current literature. In addition, this study embraces a “knowledge from practice perspective” and offers research directions to guide future contributions on knowledge construction in family business succession and potential areas to further management research investigating the role of family-related knowledge. Practical implications are also provided to benefit family businesses, consultants and policymakers. Originality/value This study provides a systematic integrative literature review of the articles published on knowledge management in family business according to a multi-stakeholder and multi-stage framework. Moreover, it draws an agenda for future research advancing a “knowledge from a practice perspective” in the family business literature.