Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a Dynamic Theory of Global Production Networks

abstract Global production networks (GPN) are organizational platforms through which actors in different regional and national economies compete and cooperate for a greater share of value creation, transformation, and capture through geographically dispersed economic activity. Existing conceptual frameworks on global value chains (GVC) and what we term GPN 1.0 tend to under-theorize the origins and dynamics of these organizational platforms and to overemphasize their governance typologies (e.g., modular, relational, and captive modes in GVC theory) or analytical categories (e.g., power and embeddedness in GPN 1.0). Building on this expanding literature, our article aims to contribute toward the reframing of existing GPN-GVC debates and the development of a more dynamic theory of global production networks that can better explain the emergence of different firm-specific activities, strategic network effects, and territorial outcomes in the global economy. It is part of a wider initiative—GPN 2.0 in short—that seeks to offer novel theoretical insights into why and how the organization and coordination of global production networks varies significantly within and across different industries, sectors, and economies. Taking an actor-centered focus toward theory development, we tackle a significant gap in existing work by systematically conceptualizing the causal drivers of global production networks in terms of their competitive dynamics (optimizing cost-capability ratios, market imperatives, and financial discipline) and risk environments. These capitalist dynamics are theorized as critical independent variables that shape the four main strategies adopted by economic actors in (re)configuring their global production networks and, ultimately, the developmental outcomes in different industries, regions, and countries.
Plant colonization across the Galápagos Islands: success of the sea dispersal syndrome

A new approach for investigating evidence for the capacity of plant colonization between islands and the success of plant morphological traits associated with seed dispersal is presented. As colonization is the result of dispersal and establishment, oceanic archipelagos provide an ideal spatio-temporal system in which to analyse plant dispersal traits related to current distributions of species across islands. The Galapagos archipelago comprises 12 islands > 10 km 2 that harbour 403 native angiosperms, of which 313 native species occupy lowland habitats that are present on all islands. We inferred the minimum number of colonization events within the archipelago for the species (289 lowland species) present on more than one island (floristic analysis). The distribution (number of islands) of species across the islands was slightly left-skewed, with 58% of all lowland species being present on one to six islands. The success of dispersal syndromes (i.e. morphological trait sets of the diaspores associated with dispersal) favourable to inter-island dispersal (medium-distance dispersal, MDD) was also analysed (syndrome analysis). In particular, the 289 lowland species were classified into four dispersal groups (syndromes): sea (thalassochory), wind (anemochory), and animal interior (endozoochory) or animal exterior (epizoochory). Most species (N = 174, 55.6%), however, displayed no traits related to MDD (unspecialized diaspores). Analyses of the distribution of syndrome traits across the 289 lowland native species and 12 islands revealed that: (1) species with one or more of the four MDD syndromes did not have broader distributions than those with unspecialized diaspores; (2) species with sea dispersal traits were the most broadly distributed; and (3) a net loss of dispersability for diaspore traits (from non-endemic natives to endemic species) was not supported for the whole flora by our analyses. In summary, our analyses showed that species with sea-drifting diaspore traits were significantly associated with the success of plant colonization across the Galapagos Islands. © 2014 The Linnean Society of London, Botanical Journal of the Linnean Society, 2014, 174, 349–358.
A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Applications

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.
BEP TI3806

As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller start-ups such as Sjauf (a ride sharing start-up) are starting to get interested in implementing Machine Learning solutions in their product. Sjauf needed a system that could automatically tell its customers how much a certain trip would cost them. Using this information multiple different models were developed and integrated into an ensemble. This ensemble as well as the models used by it were then used for price prediction. This project is a proof of concept to show that Machine Learning is capable of solving this problem in real time. After researching state of the art Machine Learning models for price recommendation, the architecture of the system was designed. The supplied data was preprocessed, after which a custom Genetic Algorithm was developed for optimising models and ensembles. After validation on real-life company data, a comparison using empirical metrics was conducted. We use these empirical metrics to show that a bagging ensemble is the most efficient and accurate model for this purpose. This bagging ensemble outperformed the currently implemented functions, whilst adhering to the set boundaries on response times. Lastly, recommendations are made to the company with an overview of potential future work in this subject.
Dynamics of coastal meta-ecosystems: the intermittent upwelling hypothesis and a test in rocky intertidal regions

The intermittent upwelling hypothesis (IUH) predicts that the strength of ecological subsidies, organismal growth responses, and species interactions will vary unimodally along a gradient of upwelling from persistent downwelling to persistent upwelling, with maximal levels at an intermediate or “intermittent” state of upwelling. To test this model, we employed the comparative-experimental method to investigate these processes at 16–44 wave-exposed rocky intertidal sites in Oregon, California, and New Zealand, varying in average upwelling and/or downwelling during spring–summer. As predicted by the IUH, ecological subsidies (phytoplankton abundance, prey recruitment rates), prey responses (barnacle colonization, mussel growth), and species interactions (competition rate, predation rate and effects) were unimodally related to upwelling. On average, unimodal relationships with upwelling magnitude explained ∼50% of the variance in the various processes, and unimodal and monotonic positive relationships agains.
Head-Heavy and Head-Light Rackets on Forehand Groundstroke Result

This research is motivated by the use of head-heavy and head-light racket on forehand groundstroke result that will get different result. The use of head-heavy racket will result in better accuracy compared to headlight racket. The objective of this research is to find out the effect of using head-heavy racket with forehand groundstroke on tennis, the influence of using head-light racket on forehand groundstroke on tennis, and to find out which racket is more influential on forehand groundstroke on tennis. The method used in this research is descriptive method with quantitative approach. The sampling technique in this research is purposive sampling with 8 tennis athletes from 25 members of UPI tennis club. The instrument used is The Hewiit Tennis Achievement Test. From the results of data processing and data analysis with homogeneity and normality test, the data are homogeneous and normal. Hypothesis calculation from equality significance test (one party) calculation results t-count 8,245 and t-table 1,761. Obtained t-count> t-table so that H0 is rejected. That is, the head-heavy racket is better than head-light. It was concluded that the head-heavy racket was more significant than the head-light racket against forehand groundstroke on tennis.
Secure Manipulation of Deduplicated Data for Cloud Storage

: Cloud storage enables to configure and use online storage devices as storage targets. The increasing demand of cloud storage requirement has led to the process of deduplication. The term data deduplication refers to the techniques that store single copy of redundant data, and provide links to that copy instead of storing other actual copies of this data. Deduplication enables to reduce cloud storage space usage and uses network bandwidth efficiently. Convergent encryption technique is used to implement deduplication in the cloud storage, but it introduces new security threats. There comes the need of secure deduplication. Security of the proposed system relies on a proxy server which applies additional encryption on the uploaded data; this protects the data against convergent encryption attacks. System also includes a deduplication manager which deals with block level deduplication. Any cloud storage provider can be easily integrated in the new architecture. The system is implemented with the help of JAVA programming language, SQL database and Amazon s3 cloud service and the results are analyzed.
Clustering Redemption-Beyond the Impossibility of Kleinberg's Axioms

Kleinberg (2002) stated three axioms that any clustering procedure should satisfy and showed there is no clustering procedure that simultaneously satisfies all three. One of these, called the consistency axiom, requires that when the data is modified in a helpful way, i.e. if points in the same cluster are made more similar and those in different ones made less similar, the algorithm should output the same clustering. To circumvent this impossibility result, research has focused on considering clustering procedures that have a clustering quality measure (or a cost) and showing that a modification of Kleinberg’s axioms that takes cost into account lead to feasible clustering procedures. In this work, we take a different approach, based on the observation that the consistency axiom fails to be satisfied when the “correct” number of clusters changes. We modify this axiom by making use of cost functions to determine the correct number of clusters, and require that consistency holds only if the number of clusters remains unchanged. We show that single linkage satisfies the modified axioms, and if the input is well-clusterable, some popular procedures such as k-means also satisfy the axioms, taking a step towards explaining the success of these objective functions for guiding the design of algorithms.
Design and Development of Efficient and Conformal Printed Antennas for Wireless Sensing and Wearable Applications

Future wireless technologies would require flexibility from electronics that will enable the electronic components to adapt according to the everyday use environment. Flexible electronics has been used in many wireless sensing and wearable applications. One of the fastest growing wireless technologies of this decade is Radio Frequency Identification (RFID) which is an automatic identification technology that uses electromagnetic interaction to identify, sense and track people or objects with transponders known as tags. RFID is rapidly replacing the bar code technology in supply chain applications and huge amount of tags are needed to be produced in order to meet the needs of this application. The production method and material selection are few of the key parameters which are under study for the cost-effective and efficient fabrication of RFID tags and wearable antennas. The latest manufacturing technologies such as inkjet, thermal and three dimensional (3D) printing have shown good potential in improving the fabrication process, however they need to be optimized and explored further to get the best possible results. This thesis reports the use of novel manufacturing methods for the development of passive Ultra High Frequency (UHF) RFID tags and wearable antennas on versatile substrates. Commercially available as well as 3D printed flexible substrates along with different conductive inks/pastes are used for the improvement in the fabrication process. The first part of the research compares inkjet and thermal printing for the RFID fabrication in detail and suggests suitable optimizing parameters for the materials under study. The second part of the research focuses on 3D printing of the substrates and then utilizing brush painting, 3D dispensing and embroidery process to improve the overall fabrication. In addition, the fabricated antennas are tested for humidity, bending and stretching for specific applications. The results indicate that the approach and methodologies used have great potential in improving the fabrication of RFID tags and antennas. The fabricated tags show excellent results and achieve the required performance for modern RFID applications such as supply chain, wearable biomedical sensing and environment monitoring. This detailed study will be very helpful to find out appropriate materials for fabricating wireless components with the best possible results, i.e. easy to fabricate, reliable and better wireless performance, for future applications such as Internet of Things (IoT) and smart RFID packaging.
Adaptive reinforcement learning with active state-specific exploration for engagement maximization during simulated child-robot interaction

Abstract Using assistive robots for educational applications requires robots to be able to adapt their behavior specifically for each child with whom they interact.Among relevant signals, non-verbal cues such as the child’s gaze can provide the robot with important information about the child’s current engagement in the task, and whether the robot should continue its current behavior or not. Here we propose a reinforcement learning algorithm extended with active state-specific exploration and show its applicability to child engagement maximization as well as more classical tasks such as maze navigation. We first demonstrate its adaptive nature on a continuous maze problem as an enhancement of the classic grid world. There, parameterized actions enable the agent to learn single moves until the end of a corridor, similarly to “options” but without explicit hierarchical representations.We then apply the algorithm to a series of simulated scenarios, such as an extended Tower of Hanoi where the robot should find the appropriate speed of movement for the interacting child, and to a pointing task where the robot should find the child-specific appropriate level of expressivity of action. We show that the algorithm enables to cope with both global and local non-stationarities in the state space while preserving a stable behavior in other stationary portions of the state space. Altogether, these results suggest a promising way to enable robot learning based on non-verbal cues and the high degree of non-stationarities that can occur during interaction with children.
Using trophic models to assess the impact of fishing in the Bay of Biscay and the Celtic Sea

Using the Bay of Biscay and Celtic Sea area as a case study, we showed how stock-assessments and trophic models can be useful and complementary tools to quantify the fishing impacts on the whole food web and to draw related diagnoses at the scale of marine ecosystems. First, an integrated synthesis of the status and trends in fish stocks, derived from ICES assessments, was consolidated at the ecosystem level. Then, using the well-known Ecopath and Ecosim and the more recently developed EcoTroph approach, we built advice-oriented ecosystem models structured around the stocks assessed by ICES. We especially analysed trends over the last three decades and investigated the potential ecosystem effects of the recent decrease observed in the overall fishing pressure. The Celtic/Biscay ecosystem appeared heavily fished during the 1980–2015 period. Some stocks would have started to recover recently, but changes in species composition seem to lead to more rapid and less efficient transfers within the food web. This could explain why the biomass of intermediate and high trophic levels increased at lower rates than anticipated from the decrease in the fishing pressure. We conclude that, in the frame of the Ecosystem approach to fisheries management, trophic models are key tools to expand stock assessment results at the scale of the whole ecosystem, and to reveal changes occurring in the global parameters of the trophic functioning of ecosystems.
A reliability evaluation methodology of complex systems based on dynamic object oriented Bayesian networks

A novel reliability evaluation methodology of complex systems is proposed by using Dynamic Object Oriented Bayesian Networks (DOOBNS). This modeling methodology consists of two main phases: one construction phase for Object Oriented Bayesian Networks (OOBNs) and another construction phase for DOOBNs. In the first phase, the network fragments that have the same structures and parameters are divided into classes, then the classes are encapsulated. OOBN construction is completed according to the relationship among the encapsulated classes. In the second phase, every fragment of DBNs which was constructed by the last phase is encapsulated as a class which is called DOOBN. DOOBN construction is completed according to the relationship between time fragments. The correctness of this methodology is validated by using an all series system, an all voting system, a voting after series system, a series after voting system, a parallel after series system and a series after parallel system. This methodology can model the system from global to local levels, effectively reducing the modeling difficulty, and can take more efficient arithmetic reasoning algorithms.
Light/Dye Microvascular Injury Eliminates Pial Arteriolar Dilation in Hypotensive Piglets

ABSTRACTS: Cerebral vasodilation in response to hypotension is necessary to maintain adequate cerebral blood flow. This study in newborn pigs examines the hypothesis that endothelial injury in vivo inhibits cerebral vasodilation in response to hypotension in newborn pigs, thus suggesting that this response is endothelium dependent. Chloralose-anesthetized piglets with closed cranial windows were studied before and after injury caused by light/dye or before and after dye only sham control. Light/dye injury was produced by injecting sodium fluorescein i.v. and passing filtered light from a mercury arc lamp though the cranial window. Measurements of pial arteries and arterioles were made during normotensive and hypotensive periods. Hemorrhagic hypotension (to 50% of the mean arterial control value) caused pial arterial and arteriolar diameters to increase 49 ± 8% and 66 ± 8%, respectively. After the light/dye injury, dilation in response to hypotension was absent, whereas dilations in response to isoproterenol and constriction in response to hypertension (3.33 to 4.0 kPa increase in arterial pressure) and hypocapnia were retained. These findings are consistent with the hypothesis that hypotension-induced cerebral arteriolar vasodilation is dependent on endothelial signals influencing adjacent smooth muscle.
Optimization of the DET curve in speaker verification

Speaker verification systems are, in essence, statistical pattern detectors which can trade off false rejections for false acceptances. Any operating point characterized by a specific tradeoff between false rejections and false acceptances may be chosen. Training paradigms in speaker verification systems however either learn the parameters of the classifier employed without actually considering this tradeoff, or optimize the parameters for a particular operating point exemplified by the ratio of positive and negative training instances supplied. In this paper we investigate the optimization of training paradigms to explicitly consider the tradeoff between false rejections and false acceptances, by minimizing the area under the curve of the detection error tradeoff curve. To optimize the parameters, we explicitly minimize a mathematical characterization of the area under the detection error tradeoff curve, through generalized probabilistic descent. Experiments on the NIST 2008 database show that for clean signals the proposed optimization approach is at least as effective as conventional learning. On noisy data, verification performance obtained with the proposed approach is considerably better than that obtained with conventional learning methods.
Aerosol composition and sources during the Chinese Spring Festival: fireworks, secondary aerosol, and holiday effects

Abstract. Aerosol particles were characterized by an Aerodyne aerosol chemical speciation monitor along with various collocated instruments in Beijing, China, to investigate the role of fireworks (FW) and secondary aerosol in particulate pollution during the Chinese Spring Festival of 2013. Three FW events, exerting significant and short-term impacts on fine particles (PM2.5), were observed on the days of Lunar New Year, Lunar Fifth Day, and Lantern Festival. The FW were shown to have a large impact on non-refractory potassium, chloride, sulfate, and organics in submicron aerosol (PM1), of which FW organics appeared to be emitted mainly in secondary, with its mass spectrum resembling that of secondary organic aerosol (SOA). Pollution events (PEs) and clean periods (CPs) alternated routinely throughout the study. Secondary particulate matter (SPM = SOA + sulfate + nitrate + ammonium) dominated the total PM1 mass on average, accounting for 63–82% during nine PEs in this study. The elevated contributions of secondary species during PEs resulted in a higher mass extinction efficiency of PM1 (6.4 m2 g-1) than during CPs (4.4 m2 g-1). The Chinese Spring Festival also provides a unique opportunity to study the impact of reduced anthropogenic emissions on aerosol chemistry in the city. Primary species showed ubiquitous reductions during the holiday period with the largest reduction being in cooking organic aerosol (OA; 69%), in nitrogen monoxide (54%), and in coal combustion OA (28%). Secondary sulfate, however, remained only slightly changed, and the SOA and the total PM2.5 even slightly increased. Our results have significant implications for controlling local primary source emissions during PEs, e.g., cooking and traffic activities. Controlling these factors might have a limited effect on improving air quality in the megacity of Beijing, due to the dominance of SPM from regional transport in aerosol particle composition.
Parental Sleep and Employment: Evidence from a British Cohort Study

We show that sleep deprivation exerts a strong negative effect on labour market performance. We exploit variations in child sleep quality to instrument for parental sleep quality. A one-hour reduction in sleep duration significantly decreases labour force participation, the number of hour’s worked and household income. In addition, we find that low-skilled mothers are more likely to opt out of the labour market and work less hours than high-skilled mothers when exposed to sleep deprivation. We argue that sleep is a major determinant of employment outcomes that needs more attention in designing economic models of time allocation and employment policies.