Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Prices and Congestion for Transmission Grid Operation

This project focuses on the co-development of forecasting tools for nodal prices and grid congestion in restructured wholesale power markets, exploiting the existence of common causal factors. The objective is to design tools suitable for market operators and for market traders, taking into account the distinct purposes, data availability, and time horizons of these distinct users. Moreover, ways are sought to combine the strengths of time-series methods and structural agent-based test-bed modeling to produce tools suitable for medium-term scenario-conditioned forecasting as well as shorter-term inferential forecasting. The MISO is used as our primary case study. Approximately 50% of U.S. generating capacity is now traded through restructured wholesale power markets under the centralized direction of market operators [1]. Grid congestion in these markets is managed by nodal pricing, 1 the pricing of power in accordance with the location and timing of its injection into, or withdrawal from, the transmission grid. Grid congestion causes nodal prices to separate across the grid [2], a reflection of the need to commit generation out of least-cost (" merit ") order. This project focuses on the co-development of nodal price and grid congestion forecasting tools for restructured wholesale power markets. Co-development is undertaken in recognition that nodal prices and grid congestion in these markets are co-determined through solutions of optimal power flow problems for day-ahead markets. The electricity price at a particular node is the shadow price solution corresponding to the balance constraint for this node. The congestion on a branch is measured by the shadow price solution corresponding to the operating capacity constraint on this branch. 2 We are particularly interested in the design of nodal price and grid congestion forecasting tools for two categories of participants, market operators and market traders, taking careful account of their distinct purposes, data availability, and time horizons. Moreover, we are seeking ways to combine the strengths of time-series methods with structural modeling tools that reflect a fundamental understanding of actual power system operations. Our ultimate objective is to produce tools suitable for medium-term scenario-conditioned forecasting as well as shorter-term inferential forecasting. By " medium-term " forecasting we mean forecasting at least one month forward in time. Our project work builds in part on prior collaborative studies by project participants. Some of these studies [3-6] propose new time-series tools for price forecasting. Others [7-11] focus on the development and application of agent-based test beds for studying the general dynamic …
Reclocking for high-level synthesis

Describes a powerful post-synthesis approach called reclocking, for performance improvement by minimizing the total execution time. By back annotating the wire delays of designs created by a high level synthesis system, and then finding an optimal clockwidth, we resynthesize the controller to improve performance without altering the datapath. Reclocking is versatile and can be applied not only for wire delay consideration, but also for bit-width migration, library migration and for feature size migration supporting the philosophy of design reuse. Experimental results show that with reclocking, the performance of the input designs can be improved by as much as 34%.
DReX

We present DReX, a declarative language that can express all regular string-to string transformations, and can still be efficiently evaluated. The class of regular string transformations has a robust theoretical foundation including multiple characterizations, closure properties, and decidable analysis questions, and admits a number of string operations such as insertion, deletion, substring swap, and reversal. Recent research has led to a characterization of regular string transformations using a primitive set of function combinators analogous to the definition of regular languages using regular expressions. While these combinators form the basis for the language DReX proposed in this paper, our main technical focus is on the complexity of evaluating the output of a DReX program on a given input string. It turns out that the natural evaluation algorithm involves dynamic programming, leading to complexity that is cubic in the length of the input string. Our main contribution is identifying a consistency restriction on the use of combinators in DReX programs, and a single-pass evaluation algorithm for consistent programs with time complexity that is linear in the length of the input string and polynomial in the size of the program. We show that the consistency restriction does not limit the expressiveness, and whether a DReX program is consistent can be checked efficiently. We report on a prototype implementation, and evaluate it using a representative set of text processing tasks.
Gainsharing: a few questions, and fewer answers.

Gainsharing is an important organization development intervention, yet many of the basic questions have not been answered. This paper addresses these questions by developing a conceptual model and using the model to review the case study literature on gainsharing . The plans varied in terms of structural factors, implementation factors, and situational factors, and a majority were successful. Gainsharing impact was found in five key areas: organizational effectiveness, individual quality of work life, ideas and innovation, labor-management cooperation, and pay. Relevant research is briefly reviewed and several unanswered questions are identified for future research.
A Heuristically-Aided Algorithm for Mutual Exclusion in Distributed Systems

A heuristically-aided algorithm to achieve mutual exclusion in distributed systems is presented which has better performance characteristics than previously proposed algorithms. The algorithm makes use of state information, which is defined as the set of states of mutual exclusion processes in the system. Each site maintains information about the state of other sites and uses it to deduce a subset of sites likely to have the token. Consequently, the number of messages exchanged for a critical section invocation is a random variable between 0 and n (n is the number of sites in the system). It is shown that the algorithm achieves mutual exclusion and is free from deadlock and starvation. The effects of a site crash are discussed, as are those of a communication-medium failure on the proposed algorithm. Methods of recovery from these failures are suggested. The performance of the algorithm is studied using a simulation technique (and an analytic technique for low and heavy traffics of requests for critical section execution). >
Novel micelle formulation of curcumin for enhancing antitumor activity and inhibiting colorectal cancer stem cells

Background and methods: Curcumin has extraordinary anticancer properties but has limited use due to its insolubility in water and instability, which leads to low systemic bioavailability. We have developed a novel nanoparticulate formulation of curcumin encapsulated in stearic acid-g-chitosan oligosaccharide (CSO-SA) polymeric micelles to overcome these hurdles. Results: The synthesized CSO-SA copolymer was able to self-assemble to form nanoscale micelles in aqueous medium. The mean diameter of the curcumin-loaded CSO-SA micelles was 114.7 nm and their mean surface potential was 18.5 mV. Curcumin-loaded CSO-SA micelles showed excellent internalization ability that increased curcumin accumulation in cancer cells. Curcumin-loaded CSO-SA micelles also had potent antiproliferative effects on primary colorectal cancer cells in vitro, resulting in about 6-fold greater inhibition compared with cells treated with a solution containing an equivalent concentration of free curcumin. Intravenous administration of curcumin-loaded CSO-SA micelles marginally suppressed tumor growth but did not increase cytotoxicity to mice, as confirmed by no change in body weight. Most importantly, curcumin-loaded CSO-SA micelles were effective for inhibiting subpopulations of CD44+/CD24+ cells (putative colorectal cancer stem cell markers) both in vitro and in vivo. Conclusion: The present study identifies an effective and safe means of using curcumin-loaded CSO-SA micelles for cancer therapy.
Online Social Networking Sites and Mental Health Research

Socializing and networking was transformed in the technological era by the introduction of social networking sites (SNSs). These online sites contain an abundance of information about individual preferences, interests, types, and frequency of social interactions, etc. However, scientific studies that have utilized SNS activity data to aid our understanding of mental health disorders are scarce. This is partly due to the practicalities of accessing SNS data and methodological issues of large-scale data collection, but also because the construct validity of SNS measures is unknown. By and large, the literature to date has attempted to link various SNSs measures to various mental health symptomologies, mostly collected using self-report measures rather than data generated by SNSs. Although such research has demonstrated some preliminary and putative associations between SNS activity and mental health measures, the current literature is still in its infancy and arguably lacks rigor in design, offering limited insights into its theoretical significance and plausibility. In this review, we will provide an account of the theoretical importance of using data generated from SNSs in mental health research and provide a brief overview of the literature published in this area to date.
Residual Admission Capacity in Optical Burst Switching Networks and Its Application in Configuration of Loss Guaranteed Tunnels

This paper studies how the capacity utilization can be improved by proper configuration of loss guaranteed tunnels in optical burst switching (OBS) networks. We show that the method of computing residual bandwidth in IP networks is not applicable to OBS networks due to the bufferless nature. We propose a more accurate metric, residual admission capacity (RAC), to measure the amount of link residual capacity. We apply the concept of RAC in loss budget partitioning and loss threshold selection. First, we develop an RAC based loss budget partitioning (RAC-LBP) algorithm. The RAC-LBP is efficient in capacity utilization and flexible with loss threshold settings. We then develop an RAC based loss threshold selection (RAC-LTS) algorithm. Using the loss thresholds determined by the RAC-LTS can improve the capacity utilization efficiency.
A Two-Stage Margin-Based Algorithm for Optimal Plug-in Electric Vehicles Scheduling

This paper proposes an optimal charging strategy for plug-in electric vehicles (PEVs) to be used in electric distribution networks. The optimization algorithm is made up of two phases: 1) an optimal power flow calculation; and 2) a linear optimization. The former, while taking into account the power system technical constraints, sets the upper bounds to the recharge power for each vehicle and the latter defines the recharge profiles of each PEV. In order to test the effectiveness of the optimization algorithm, a case study was set up. The connection of 300 PEVs to the Conseil International des Grands Réseaux Électriques (CIGRÉ) European low voltage benchmark network has been simulated. The proposed algorithm has been compared with a nonoptimal charging strategy, which assigns a flat charging profile by dividing the energy requested by the desired recharge time. The results show that the optimization algorithm both complies with the energy requests set by the end users and with the technical operation limits of the network. This allows for PEVs to provide a basic-although of paramount importance-service to the grid: the smart charge.
How Civilizing Processes Continued: Towards an Informalization of Manners and a Third Nature Personality

Based on an analysis of manners books in four Western countries since 1890, this paper describes how ‘civilizing processes’ have continued in the nineteenth and twentieth centuries. The paper first focuses on three central functions of a ‘good society’ and its code of manners, and then describes how, in a long-term phase of formalizing manners and disciplining people, ‘dangerous’ emotions such as those related to physical (including sexual) violence came to be controlled in increasingly automatic ways. Thus, a second-nature or conscience-dominated type of personality became dominant. The twentieth century saw rising social constraints towards being unconstrained, and yet reflective, flexible, and alert. These pressures coincided with an informalization of manners and an emancipation of emotions: emotions that had been denied and repressed regained access to consciousness and wider acceptance in social codes. Yet it has been only since the ‘Expressive Revolution’ of the 1960s that standards of self-control have increasingly enabled people to admit, to themselves and others, to having ‘dangerous’ emotions without provoking shame, particularly the shame-fear of losing control and face. To the extent that it has become ‘natural’ to perceive the pulls and pushes of both ‘first nature’ and ‘second nature’, as well as the dangers and chances, short-term and long-term, of any particular situation or relation, a ‘third nature’ type of personality has been developing. Examples illustrating this trend also help us to understand it as a process of psychic integration triggered by continued social integration.
Microglial Cell Line Established from Prion Protein-Overexpressing Mice Is Susceptible to Various Murine Prion Strains

ABSTRACT Several lines of evidence suggest that microglia have important roles in the pathogenesis of prion diseases. Here, we establish a novel microglial cell line (MG20) from neonatal tga20 mice that overexpress murine prion protein. After exposure to Chandler scrapie, we observed the replication and accumulation of disease-associated forms of the prion protein in MG20 cells up to the 15th passage. Furthermore, MG20 cells were susceptible to ME7, Obihiro scrapie, and bovine spongiform encephalopathy agents. Thus, MG20 cell lines persistently infected with various murine prion strains provide a useful model for the study of the pathogenesis of prion diseases.
A high dimensional Directed information estimation using data-dependent partitioning

Directed Information (DI) is used to quantify the causal and dynamic relations between two signals. The main advantage of using DI compared to other measures of causality is that it does not assume an underlying signal model and thus can capture both linear and nonlinear interactions between signals. However, one major problem in computing the DI from data is the high computational cost and the unreliability of the probability density function (pdf) estimation methods. In this paper, we propose a high dimensional DI estimation method based on computing multi-information by an adaptive datadependent partitioning technique. The proposed estimation method does not assume any distribution for the data under consideration and requires no pdf estimation. The proposed method is applied on simulated data and is compared with other DI estimation methods to verify its effectiveness.
Work organisation constructs and ergonomic outcomes among European forest machine operators

The aims of the study were to: 1) create work organisation constructs for forest machine operators; 2) examine relationships between these work organisation constructs and different ergonomic outcomes. The study was carried out among forest machine operators in six European countries and was based on a questionnaire survey (n = 358). Exploratory factor analysis (EFA) was used in the formation of the construct measures of work organisation. The EFA resulted in a five-factor structure. The factors were named ‘job control’, ‘variation’, ‘job rotation’, ‘breaks’ and ‘rate of work’. Three multiple regression analyses were carried out separately for the dependent variables job satisfaction, musculoskeletal symptoms and headache/sleeping problems. All regression analyses resulted in significant overall models. The highest explained variance among the dependent variables was found for job satisfaction (R = 0.51, i.e. ‘large’ effect size). The unique contribution of the work organisation constructs varied in the models, but ‘job rotation’ contributed significantly in all regression models. The results may be used as a source for ergonomic improvements to work organisation. The present study was performed as part of the EU project ‘ErgoWood’, which resulted in a European handbook for mechanised forest operations (Gellerstedt et al. 2005). The results delivered part of the evidence base for a toolbox in this handbook regarding changes in work organisation to improve health and performance.
New wireless acoustic array node for localization, beamforming and source separation for bio-complexity bird data collection and study

In this paper, we present simulation and experimental studies of localization and source separation for a bio-complexity bird study based on a new wireless acoustic array node named Voxnet. The Approximate Maximum Likelihood (AML) method is used to estimate blindly the direction-of-arrivals (DOAs) of sources which can be used to both locate the sources using a DOA-based localization method and generate the steering vectors in order to separate the sources via beamforming. Simulation and measured data confirmed the proper operations of the AML-based 2-D/3-D localization and 2-D source separation, as well as the Voxnet hardware nodes.
Deformable image alignment as a source of stereo correspondences on portraits

Mesh-based deformable image alignment (MDIA) is an algorithm that warps a template image onto a target by deforming a 2D control mesh in the image plane, using an image-based nonlinear optimization strategy. MDIA has been successfully applied to various nonrigid registration problems, deformable surface tracking and stabilization of scene-to-camera motion in video. In this paper we investigate the use of image-based MDIA for computing dense correspondences for 3D reconstruction of human heads from high resolution portrait images. Human heads are topolog-ically simple in 3D while providing textures which are challenging to match, such as hair and skin. We find that even with a simple piecewise affine deformation model MDIA delivers excellent correspondence results. We propose a robust, piecewise optimization scheme to compute MDIA on very high resolution images. We address issues of regular-ization and luminance correction and discuss the role of epipolar constraints. The correspondences retrieved with our approach facilitate the estimation of camera extrinsics and yield highly detailed meshes of the head.
Delay-sensitive application support in 802.11 based multi-hop network

During the recent years, research efforts have been made to study how to deploy the WLAN in a multi-hop manner. One of the many challenges to deploy a multi-hop network is how to provide delay-sensitive application support. Research have been done to study how to use DCF mode in a multi-hop network to provide delay-sensitive application support. However, few research have been made to study how to use PCF mode in a multi-hop network. In this paper, we first propose a CFP leasing scheme based on the PCF mode of 802.11 and a conservative scheduling scheme to enable voice service in a multi-hop network. Then we theoretically and numerically evaluate the performance of proposed scheme. Results show that with the proposed scheme, we can provide real-time application support for a small scale multi-hop network.
SOCIAL: A Self-Organized Entropy-Based Algorithm for Identifying Communities in Networks

The identification of communities in complex networks is important to many fields including medicine, social science, national security, and marketing. A community structure facilitates the identification of hidden relations in networks that go beyond simple topological features. Current detection algorithms are centralized and scale very poorly with the number of nodes and edges present in the network. The use of these algorithms is prohibitive when applied to large-scale networks. In this paper, we propose a Self-Organized Community Identification Algorithm (SOCIAL) based on local calculations of node entropy that enables individual nodes to independently decide the community they belong to. In our context, node entropy is defined as the individual node's satisfaction with its current community. As nodes become more "satisfied'' (entropy decreases) the community structure of a network emerges. Our algorithm offers several advantages over existing approaches including near-linear performance, identification of community overlaps, and localized management of dynamic changes in the network.
Stability Indicating LC-Method for Estimation of Paracetamol and Lornoxicam in Combined Dosage Form

A simple, specific and stability indicating reversed phase high performance liquid chromatographic method was developed for the simultaneous determination of paracetamol and lornoxicam in tablet dosage form. A Brownlee C-18, 5 μm column having 250×4.6 mm i.d. in isocratic mode, with mobile phase containing 0.05 M potassium dihydrogen phosphate:methanol (40:60, v/v) was used. The flow rate was 1.0 ml/min and effluents were monitored at 266 nm. The retention times of paracetamol and lornoxicam were 2.7 min and 5.1 min, respectively. The linearity for paracetamol and lornoxicam were in the range of 5–200 μg/ml and 0.08–20 μg/ml, respectively. Paracetamol and lornoxicam stock solutions were subjected to acid and alkali hydrolysis, chemical oxidation and dry heat degradation. The proposed method was validated and successfully applied to the estimation of paracetamol and lornoxicam in combined tablet dosage form.
The honeycomb model of _{}(ℂ) tensor products II: Puzzles determine facets of the Littlewood-Richardson cone

The set of possible spectra (\lambda,\mu,\nu) of zero-sum triples of Hermitian matrices forms a polyhedral cone. We give a complete determination of its facets, finishing a long story with recent highlights by [Helmke-Rosenthal, Klyachko, Belkale]. We introduce_puzzles_, which are new combinatorial gadgets to compute Grassmannian Schubert calculus, and will probably be the main point of interest for many readers. As the proofs indicate, the Hermitian sum problem is very naturally studied using puzzles directly, and their connection to Schubert calculus is quite incidental to our approach. In particular, we get new, puzzle-theoretic, proofs of the results in [H,Kly,HR,Be]. Along the way we give a characterization of ``rigid'' puzzles, which we use to prove a conjecture of W. Fulton: ``if for a triple of dominant weights \lambda,\mu,\nu of GL(n,C) the irreducible representation V_\nu appears exactly once in V_\lambda tensor V_\mu, then for all N\in \naturals, V_{N\lambda} appears exactly once in V_{N\lambda} tensor V_{N\mu}. ''