Miracle-dz/UniFAR-FTUs huggingface dataset:
Overcoming Barriers to Renewable Energy Technology Acculturation in Indonesia ; Off-grid PV and Hybrid Case Studies

This paper discusses the use of a qualitative assessment tool called the KPDAC (knowledge, persuasion, decision, adoption, confirmation) continuum to understand the process and outcomes of renewable energy (RE) technology acculturation in off-grid villages in Indonesia. The KPDAC continuum can also be used as a tool to design strategy for RE technology deployment looking at requirements for RE acculturation and how it can be facilitated. The acculturation of renewable energy (RE) technology is used here to describe the process and outcomes of RE deployment in a community, including the extent to which RE technology acculturates or diffuses into, and becomes part of, the community’s ongoing activities. This paper presents assessments of three off-grid photovoltaic (PV) deployment case studies from Lampung, West Java and Nusa Tenggara Timur (NTT, Eastern Indonesia) provinces, using the KPDAC continuum. The assessments indicated that the communities were at different starting points in the KPDAC continuum for RE technology and required different approaches for RE technology acculturation to be successful. Facilitators need to understand how acculturation challenges vary with KPDAC continuum starting point and adopt an appropriate approach if RE technology acculturation is to be successful. Lessons from these project assessments have been used to design a long-term approach to building capacity for RE deployment through educational institutions, undertaken as part of a recently completed Australian Development Research Award (ADRA) research project that was undertaken as a collaborative AustralianIndonesian project. The purpose of capacity building is to create capable agents, who are able to facilitate the RE acculturation process in Indonesia.
SAS Methoclsfor Obtaining the Exact Hypotheses from Type I , n , m , and IV Estimable Functions in Terms of Cell Means Model

The GLM procedure produces four types of sums of squares, namely Types I, II, III and IV. To de term ine the hypotheses being tested by each type of sums of squares, GLM prints the estimable functions associated with the effects in the model. These estimable functions are difficult to interpret especially in the presence of empty cells, The cell means model (i.e., ~ . ) is sometimes used as a alternative procedurelJ of hypothesis testing in General Linear Models aod these hypotheses are easy to interpret. This paper presents SAS Macros which can be used to ob~ain the exact hypotheses being tested by the four different types of sums of squares in terms of the cell means model. An example is presented to illustrate the output from the macros.
UvA-DARE ( Digital Academic Repository ) Barriers and opportunities to oral health in Dutch-Moroccan children in the Netherlands a narrative report

Introduction Previous studies showed that 5-year-old Dutch-Moroccan children had significantly higher dmft scores compared to Dutch children of the same age, even after correction for socio-economic status. The mechanisms underlying this difference are little understood. Aim To explore cultural factors involved in poorer oral health of Dutch-Moroccan children by identifying knowledge, attitudes and behaviour of their mothers concerning their children’s oral health. Methods In 2012 mothers of Dutch-Moroccan preschool children in two cities in the Netherlands were interviewed in two focus groups (n = 16) or individual semi-structured interviews (n = 13). Semi structured interviews were also c onducted with three oral health professionals, working with Dutch-Moroccan children, and one physician from an under-five-clinic. All interviews were voice recorded, transcribed and inductively coded. MAXQDA software was used for data analysis. Results All mothers mentioned pain complaints, swelling and black front teeth as oral health problems in their children. Although mothers were aware that brushing teeth and reducing sugary snacks are effective preventative strategies, they did not sufficiently implement these measures. This was due to lack of brushing skills, insufficient awareness of the daily sugar intake of their children and their childrearing concerning these measures. Most mothers indicated they felt empowered in making dental care decisions. Conclusions This research revealed the presence of knowledge on preventive strategies regarding their children’s oral health in Dutch-Moroccan mothers, but an inadequate implementation of these measures in their daily lives. Additional qualitative research is needed to gain deeper insight for broader exposure of values, knowledge and culture.
Design of Random Number Generation Using 256 Bit LFSR in FPGA

Random numbers are used in a wide variety of applications. True random number generators are slow and expensive for many applications while pseudo random number generators (RNG) suffice for most applications. Although a majority of random number generators have been implemented in software level, increasing demand exists for hardware implementation due to the advent of faster and high density Field Programmable Gate Arrays (FPGA). FPGAs make it possible to implement complex systems, such as numerical calculations, genetic programs, simulation algorithms etc., at hardware level. This paper discusses in detail the hardware implementation of several RNGs and their characteristics. Random number generator is required extensively by many applications like cryptography, simulation, numerical analysis, text-to-speech etc. Most C libraries have a pair of library routines for initializing, and then generating random numbers. For parametric speech synthesis application, a random number generator is required to produce noise samples. Therefore, a need has been felt for the design of a dedicated hardware for random number generator that generates one random number per cycle so that text-to speech conversion is done in real time.
Provability logic meets the knower paradox

The knower paradox states that the statement `We know that this statement is false' leads to inconsistency. This article presents a fresh look at this paradox and some well-known solutions from the literature. Paul Egre discusses three possible solutions that modal provability logic provides for the paradox by surveying and comparing three different provability interpretations of modality, including one by Solovay. To check whether this solution is satisfactory, we use the criteria for solutions to paradoxes defined by Susan Haack. This extended abstract aims to describe to what extent the knower paradox can be solved using provability logic and to what extent a solution proposed in the literature satisfies Haack's criteria. Finally, the extended offers some reflections on the relation between knowledge, proof, and provability, as inspired by the knower paradox.
Process strategy for food enzyme purification using expanded bed adsorption in laboratory and pilot scale

Downstream processing of food enzymes can be improved by integrating solid-liquid separation and primary capture into the single unit operation of expanded bed adsorption. Ion exchange adsorption of the target enzyme directly from unclarified broth has the potential to increase product yield and reduce process times while lowering capital investment and consumable requirements. Lysed fermentation broth was diluted and applied to anion-exchange expanded bed adsorption. The targeted intracellular enzyme was eluted purified and concentrated. Residence time distribution analysis as a function of linear velocity and viscosity measured hydrodynamic behavior of resin inside the column. Dynamic binding capacity over ten consecutive purification cycles averaged 5249 Enzyme specific units/mL_media (± 17%) and no negative trend was observed for up to ten consecutive purifications. Contrarily, a change in hydrodynamic behavior was observed, as resin expansion necessitated higher linear velocities with increasing number of purification cycles. Efficiency of resin regeneration was established via total ionic capacity and dynamic binding capacity of BSA. Scaling up the process strategy to pilot scale (i.d. 10 cm) produced results consistent with those from lab scale, processing 9.5 L of broth in about 100 minutes.
Structural graph learning in real-world digital forensics

This thesis dives into the role structural graph learning can play in digital forensics by using real-world data collected by Web-IQ. Using real-world forensic data provides challenges for machine learning that not many fields offer, as forensics concerns itself with possibly incriminating data that owners often intentionally obscure. We compare different approaches used in graph learning and pattern recognition on graphs, listing strengths and weaknesses for this project. We find that many approaches do not have the scalability to perform on the large graphs used in practice. Modern graphs often are entity graphs, often containing millions of nodes with different types of vertices and edges to more expressively visualize different relations in the graph. We find that many approaches in graph learning make the assumption that all vertices and edges are of the same type, not exploiting the semantic information gained by using different types. A system was built that solves all these problems by using representation learning. This is done with node embeddings created by random walks guided by metapaths. Representation learning makes it so there is no explicit feature engineering required, reducing the problem of intentionally obscured data. Random walks are chosen for their efficiency, to ensure scalability to the large graphs used in practice. Finally metapaths restrict the choices of a random walk in each step by forcing it to choose a specific type of edge or vertex, resulting in a walk that honors the higher level relations in the graph. The pipeline shows similarities to the wellknown word2vec, but adapted to graphs. We test this system with a supervised classification task on a dataset containing albums of images, predicting the category of the album. The dataset contains around 1.35 million nodes, of which 41291 are albums. We compare embeddings generated by walks created by different combinations of metapaths, and find a significant improvement in classification results in all cases using metapaths over cases not using metapaths.
A digital fountain retrospective

We introduced the concept of a digital fountain as a scalable approach to reliable multicast, realized with fast and practical erasure codes, in a paper published in ACM SIGCOMM '98. This invited editorial, on the occasion of the 50th anniversary of the SIG, reflects on the trajectory of work leading up to our approach, and the numerous developments in the field in the subsequent 21 years. We discuss advances in rateless codes, efficient implementations, applications of digital fountains in distributed storage systems, and connections to invertible Bloom lookup tables.
Adipose tissue mitchondrial function is modulated by antioxidants

Antioxidants are widely used as reactive oxygen species (ROS) scavenging agents in experimental research and in conditions where oxidative stress plays a primary role. However, the effect of antioxidant supplementation on white and brown adipose tissue functionality is understudied, and the role of ROS and/or antioxidant treatment during adipose tissue browning, a process in which the adipocytes’ mitochondrial density and activity increase, is largely unknown. In paper I, by using antioxidants and ROS-sensitive fluorescent probes in cultured β3-AR-stimulated adipocytes, we observed that 24-48-hour antioxidant treatment increases the mitochondrial ROS production associated with reduced respiration and increased glycolysis. Moreover, treatment of mice with the antioxidant N-acetylcysteine (NAC) blunted the β3-AR agonist-induced browning response of white adipose tissue and reduced the mitochondrial activity in brown adipose tissue even in the absence of β3-AR stimulation. Previous studies have shown positive effects of prolonged NAC treatment on whole-body metabolism in mice. In light of these seemingly contradictory results, we hypothesize that chronic antioxidant exposure, in a dose-dependent manner, can lead to so-called mitohormesis. Indeed, in paper II, by treating mice with a set of different NAC doses across a defined time course, we found that prolonged supplementation with a high dose of NAC leads to increased mitochondrial function of white adipose tissue, reduced fat mass and improved insulin sensitivity. In summary, this thesis demonstrates that the adipose tissue response to antioxidant treatment in mice is biphasic and tightly connected to the adipose tissue type, the dosage and the treatment duration. This thesis also provides an alternative explanation for previously reported controversial findings where antioxidants (such as NAC) have exerted deleterious effects on health. Finally, the results of this thesis provide new insights into the appropriate design of antioxidant treatment studies: optimizing treatment dosage and duration may be the key to achieve success with antioxidant therapy.
Walkrnn: Reading Stories from Property Graphs

WalkRNN, the approach described herein, leverages research in learning continuous representations for nodes in networks, layers in features captured in property graph attributes and labels, and uses Deep Learning language modeling via Recurrent Neural Networks to read the grammar of an enriched property graph. We then demonstrate translating this learned graph literacy into actionable knowledge through graph classification tasks.
Engineering enhanced enzymes for suicide gene therapy of cancer

Herpes simplex virus type 1 (HSV) thymidine kinase (TK) has been widely used in suicide gene therapy for the treatment of cancer due to its broad substrate specificity and the inability of the endogenous human TK to phosphorylate guanosine analogs, such as ganciclovir (GCV) and acyclovir (ACV). The basis of suicide gene therapy is the introduction of a gene that encodes a prodrug-activating enzyme into tumor cells. After administration, the prodrug is selectively converted to a toxic drug by the suicide gene product thereby bringing about the eradication of the cancer cells. A drawback with this therapy is the low activity of the enzyme towards the prodrugs, requiring high doses of prodrug that result in adverse side effects. Earlier studies revealed two HSV TK variants derived by random mutagenesis with enhanced activities towards ACV and GCV in vivo. While these mutants contain multiple amino acid substitutions, molecular modeling suggests that the alanine 168 (A168) substitutions to phenylalanine (SR39) or tyrosine (mutant 30) may be responsible for the observed increase in prodrug sensitivity. To evaluate this, site-directed mutagenesis was used to individually substitute A168 with phenylalanine or tyrosine to reflect the mutations found in mutant SR39 and 30, respectively. While both mutants display increased prodrug specificity, the most noteworthy alteration is the 763-fold greater relative specificity A168F displays towards ACV, compared to wild-type TK, making the use of ACV feasible in gene therapy applications. 1 Data in this chapter were submitted to Protein Science in the following manuscript: Willmon, C.L. , Sussman, D. , and Black, M.E. (2006) Role of herpes simplex virus-1 thymidine kinase alanine 168 in prodrug activation.
Uncovering Data Landscapes through Data Reconnaissance and Task Wrangling

Domain experts are inundated with new and heterogeneous types of data and require better and more specific types of data visualization systems to help them. In this paper, we consider the data landscape that domain experts seek to understand, namely the set of datasets that are either currently available or could be obtained. Experts need to understand this landscape to triage which data analysis projects might be viable, out of the many possible research questions that they could pursue. We identify data reconnaissance and task wrangling as processes that experts undertake to discover and identify sources of data that could be valuable for some specific analysis goal. These processes have thus far not been formally named or defined by the research community. We provide formal definitions of data reconnaissance and task wrangling and describe how they relate to the data landscape that domain experts must uncover. We propose a conceptual framework with a four-phase cycle of acquire, view, assess, and pursue that occurs within three distinct chronological stages, which we call fog and friction, informed data ideation, and demarcation of final data. Collectively, these four phases embedded within three temporal stages delineate an expert’s progressively evolving understanding of the data landscape. We describe and provide concrete examples of these processes within the visualization community through an initial systematic analysis of previous design studies, identifying situations where there is evidence that they were at play. We also comment on the response of domain experts to this framework, and suggest design implications stemming from these processes to motivate future research directions. As technological changes will only keep adding unknown terrain to the data landscape, data reconnaissance and task wrangling are important processes that need to be more widely understood and supported by the data visualization tools. By articulating a concrete understanding of this challenge and its implications, our work impacts the design and evaluation of data visualization systems.
Learning Environmental States via Communication

Discovering unknown states of the environment is a problem of interest in many fields. This paper investigates how an agent can refine existing states and learn new states in its environment by communicating with another agent. We simulate an environment consisting of a learning agent, a mentor agent, and a set of objects. The learning agent, implemented in a predictive coding framework, communicates with the mentor agent to resolve ambiguity regarding causes generating an observation. Both agents are modeled using human body skeletons from the UTD-MHAD dataset. Communication takes place using body gestures and movements. Our experiments show that communication helps the agent to learn: (1) environmental states that are beyond its perceptual field, and (2) the action classes related to each object without supervision or reinforcement. Over time, the latter leads to learning appropriate body movements for each state and more efficient communication.
Development of Aerial Robot Swarm for Interaction with People and Other Robots

In this paper we present Penn Aerial Robotics’s solution to the 2019 IARC Mission 8. In this challenge a human player works together with several autonomous helper robots in a scavenger hunt, while under attack from sentry robots. The helper robots work together to find four storage bins and read four parts of a QR code. The QR code is reconstructed and decoded to provide a 3 digit code to open pad locks on the storage bins and retrieve objects from inside them. The human player can only issue vocal or gesture commands. Thus, the challenge requires integration of different machine perception systems for obstacle avoidance, object detection and speech recognition. We propose a solution which combines statistical and machine learning system. We use semi-direct visual odometry for indoor localization, which is fused with IMU data. A forward facing Intel RealSense camera provides a depth map which is used for obstacle avoidance. Finally a downward facing camera allows tracking of the player and bin localization.
Residential Energy Management Systems Based on Multi-Agent Systems : A Survey 1

Residential sectors all around the world use a significant amount of energy. The incorporation of automation technologies within the smart home (SH) is motivated by economic and comfort-based advantages to house owners. The transfer to demand response (DR) application and dynamic electricity pricing for residential clients indicates that the conventional home power strategies are neither flexible nor sufficient. An intelligent, efficient, and more flexible SH energy management system (EMS) is desired. A multi-agent system (MAS), due to its intelligence and its autonomous and distributed characteristics, is capable of solving such dynamic and complex problems. This paper reviews studies of home energy management systems (HEMSs) based on MASs, including HEMSs with the objective of minimizing residents’ energy bills while maximizing their comfort, as well as those that only work to reduce the energy consumption in the user’s house. Also, the pricing schemes, algorithms, and simulation platforms used are discussed as different characteristics of the surveyed HEMSs. Keyword: Home Energy Management System, Multi-Agent System, Demand Response, Smart Home, Energy, Comfort.
M ay 2 01 6 SOME APPLICATIONS OF WEIGHTED NORM INEQUALITIES TO THE ERROR ANALYSIS OF PDE CONSTRAINED OPTIMIZATION PROBLEMS

The purpose of this work is to illustrate how the theory of Muckenhoupt weights, Muckenhoupt weighted Sobolev spaces and the corresponding weighted norm inequalities can be used in the analysis and discretization of PDE constrained optimization problems. We consider: a linear quadratic constrained optimization problem where the state solves a nonuniformly elliptic equation; a problem where the cost involves pointwise observations of the state and one where the state has singular sources, e.g. point masses. For all three examples we propose and analyze numerical schemes and provide error estimates in two and three dimensions. While some of these problems might have been considered before in the literature, our approach allows for a simpler, Hilbert space-based, analysis and discretization and further generalizations.
The Anticompetitive Effect of Minority Share Acquisitions: Evidence from the Introduction of National Leniency Programs

We address the growing concern that minority shareholding (MS) in rival firms may lessen competition, using the introduction of national leniency programs (LPs) as a shock that destabilizes collusive agreements. Based on data from 63 countries, we find a large and significant immediate increase in domestic horizontal MS acquisitions once an LP is introduced but only in countries where the LP is deemed to be effective. There is no effect on non-horizontal or cross-border MS acquisitions. Our findings suggest that firms may use MS acquisitions to either stabilize collusive agreements or soften competition in the event that collusion breaks down. (JEL G34, K21, L12, L41)