Miracle-dz/UniFAR-FTUs huggingface dataset:
12th DIMACS Implementation Challenge: VRPTW track

The DIMACS Implementation Challenges address questions of determining realistic method performance where worst case analysis is overly pessimistic and probabilistic models are too unrealistic: experimentation can provide guides to realistic method performance where analysis fails. Experimentation also brings methodic questions closer to the original problems that motivated theoretical work. It also tests many assumptions about implementation methods and data structures. It provides an opportunity to develop and test problem instances, instance generators, and other methods of testing and comparing performance of methods. And it is a step in technology transfer by providing leading edge implementations of methods for others to adapt. The 12th Implementation Challenge is dedicated to the study of Vehicle Routing problems, bringing together research in both theory and practice. This rendition of the Challenge is part of the DIMACS Special Focus on Bridging Continuous and Discrete Optimization and will be capped by a workshop hosted by DIMACS at Rutgers University in April 6-8, 2022. This Challenge is held in honor of David S. Johnson and includes activities dedicated to him and his many contributions to the study of methods.
Changes in the Ileal, but Not Fecal, Microbiome in Response to Increased Dietary Protein Level and Enterotoxigenic <italic toggle='yes'>Escherichia coli</italic> Exposure in Pigs

The relationship between porcine gut microbiota composition and health is an important area of research, especially due to the need to find alternatives to antimicrobial use to manage disease in livestock production systems. Previous work has indicated that lower crude dietary protein levels can reduce the impacts of postweaning colibacillosis, which is a porcine diarrheal disease caused by enterotoxigenic Escherichia coli (ETEC). Here, to explore the complex interactions between the gut microbiota, protein nutrition, and ETEC exposure, the microbial compositions of both ileal digesta and feces were analyzed with or without ETEC exposure from pigs fed a lowor high-protein diet. Since ETEC colonization is mostly localized to the ileum, changes in the small intestinal microbiota were expected in response to ETEC exposure. This was supported by the study findings, which identified significant microbiota changes in ileal samples but not in fecal samples. Both increased dietary protein and ETEC exposure impacted on ileal microbiota alpha diversity (richness and diversity indices) and beta diversity (structure, stability, and relative taxon abundances) at certain sampling points, although the combination of a high-protein diet and ETEC exposure had the most profound impact on ileal microbiota composition. An understanding of how infection and nutrition lead to microbiota changes is likely to be required if dietary strategies are to be developed for the management of enteric diseases. IMPORTANCE Gut bacterial communities have been shown to play a key role in pig health and development and are strongly influenced by host diet, but studies highlighting the complex interactions between nutrition, gut infections and the microbiome tend to focus on bacterial populations in the feces and not other important gut locations. We found that alteration of dietary protein level and exposure to a pathogenic microorganism, enterotoxigenic Escherichia coli (ETEC), changed bacterial populations in the distal small intestine (i.e., the ileum). We found that the most profound changes occurred in pigs fed a high-protein diet in combination with exposure to ETEC, showing a clear interaction between dietary composition and exposure to a key pathogen. These changes were not observed in the fecal samples, revealing the importance of studying biologically pertinent sites in the gut, and so the data will help to inform the development of alternative management strategies for enteric disorders.
The role of complete blood count parameters in distinguishing complicated and uncomplicated appendicitis

Introduction Acute appendicitis (AA) is a prevalent disease that can be lifethreating if left untreated. There have been advances in imaging techniques recently, which facilitated the hand of physicians, but they are not widely available and require expertise to interpret. This makes diagnosis of appendicitis challenging for physicians working in rural regions. Complete blood count (CBC) is still the first line of test for a patient with abdominal pain in emergency departments (1). It is an easy and quick test to evaluate hematologic parameters which can be changed due to infection, hemorrhage and genetic disorders. Systemic inflammatory response is generally associated with white blood cell (WBC) count (2). However, WBC has no role in differentiating simple and complicated appendiceal disease. In addition to WBC count, there are several other markers that can be used as a sign of inflammatory conditions. For instance, neutrophil-to-lymphocyte ratio (NLR) is an indicator of subclinical inflammation (3). Also, red blood cell distribution width (RDW) has been reported to be in relation to infection (4). Furthermore, platelets play a crucial role in inflammation and their size can change as a result of inflammatory conditions. Therefore, mean platelet volume (MPV), which is a measurement of the average size of platelet in the blood, may have diagnostic potential in various diseases (5,6).
Tracing the Effect of Computerized Feedback on Learner Summaries: A Qualitative Analysis of Structural Change

In this study, we examined how students made changes in their summaries of complex scientific texts, supported by a technology-enhanced feedback system. Referring to system-generated indices that describe student mental models of texts, we conducted qualitative reviews on selected cases in which students in a graduate-level course wrote at least two revisions of the summaries of texts. Learners used feedback to introduce more relevant concepts and build an integrated structure in a text by connecting concepts across adjacent sentences
Unified Approach for Robust Stability Analysis of Buck Converters with Discrete-Time Sliding Mode Control

This paper proposes a novel discrete-time sliding mode (DTSM) control approach to address the robust stability problem of buck converters with multiple disturbances. The contributions lie in the “unified” modelling and controller design. In modelling, all the possible model uncertainties and external disturbances are considered and further classified into two cases. It can also be extended to the situations with individual/several disturbances. While for the controller design, differing from the traditional DTSM based on the nominal model, the disturbances are directly introduced in the process, giving the robust stability condition and four new regulation subranges. It is suitable for both nominal and perturbed systems. Finally, the influences of the sampling time and disturbances on the control performance are investigated. Simulations and experiments confirm the benefits of the unified approach with greater accuracy and wider applications.
Long non ‐ coding RNA HOTAIR knockdown alleviates lipopolysaccharide ‐ induced acute respiratory distress syndrome and the associated inflammatory response by modulating the microRNA ‐ 30 a ‐ 5 p / PDE 7 A axis

Acute respiratory distress syndrome (ARDS) is a severe pulmonary disease, which can be modulated by certain long non‐coding (lnc)RNAs. The present study aimed to inves‐ tigate the regulatory mechanism of lncRNA HOTAIR in ARDS and the inflammatory response induced by lipopolysaccharide (LPS). The mRNA expression levels of HOTAIR, microRNA (miR)‐30a‐5p and PDE7A were determined using reverse transcription‐quantitative PCR, while a MTT assay was used to assess the viability of the MLE‐12 cells and ELISA was used to determine the concentration of different inflamma‐ tory factors [tumor necrosis factor (TNF)‐α, IL‐1β and IL‐6]. The interactions between miR‐30a‐5p and HOTAIR/PDE7A were predicted using TargetScan and StarBase databases and verified using a dual‐luciferase reporter assay. The protein expression levels of PDE7A were determined using western blot analysis. Mouse models of LPS‐induced ARDS were established to investigate the suppressive effect of HOTAIR knockdown on ARDS in vivo. lncRNA HOTAIR was increased in LPS‐treated MLE‐12 cells and in a ARDS mouse model. HOTAIR knockdown decreased the concentra‐ tion of TNF‐α, IL‐1β and IL‐6, and increased cell viability in vitro. miR‐30a‐5p upregulation decreased TNF‐α, IL‐1β and IL‐6 concentrations, and increased cell viability in vitro. HOTAIR targeted miR‐30a‐5p and miR‐30a‐5p targeted PDE7A. miR‐30a‐5p downregulation and PDE7A upregulation reversed the suppressive effect of HOTAIR knockdown on the concentrations of TNF‐α, IL‐1β and IL‐6, and the positive effect of HOTAIR knockdown on cell viability in vitro. HOTAIR knockdown also attenuated ARDS and the inflammatory response induced by LPS in vivo. The suppression of HOTAIR alleviated ARDS and the inflammatory response induced by LPS by modulating the miR‐30a‐5p/PDE7A axis. These results provide a potential therapeutic strategy for ARDS.
MATH-SHU 236 Data Representation and Diffusion maps

Observation: “∗” is closer to “ ” than to “◦” and “ ” in Euclidean distance. Clusters: each circle forms one cluster. What wrong with it? Use diffusion-based distance and take advantage of the local information: (a) If placing a heat source at “ ”, the heat is likely to diffuse faster to “ ” than to “ ∗ ” and “ ◦ ”. (b) Why? The resistance between “ ” and “ ◦ ” could be higher than that between “ ” and “ ”. Question: Can we derive a distance based on the heat diffusion process on graph?
Atrial Fibrillatory Rate Characterization Extracted from Implanted Cardiac Monitor Data

The aim of this study is to characterize atrial fibrillatory rate (AFR) extracted from a cohort of continuously monitored atrial fibrillation (AF) patients as function of episode duration and onset time. The f-wave signal used to compute the AFR was extracted from a single lead ECG strip of the AF episodes stored in an Implantable Cardiac Monitor (ICM) in a cohort of 99 patients. The f-wave signals were obtained from 1400 AF episodes using a spatiotemporal QRST cancellation process and the AFR was estimated as the fundamental frequency of a model fitted to the extracted f-waves. We studied the relationship between AFR and episode duration and episode onset time, respectively. AFR (median (interquartile range)) was significantly lower (p-value<0.05) in short episodes (<20 min) (5.15 (0.66) Hz) than in longer episodes (5.30 (0.74) Hz). AFR was significantly higher for episodes with onset time at night (00-06) (5.34 (0.82) Hz) than for episodes with onset during the day (10-20) (5.21 (0.70) Hz). Significant differences were also found between the relative AFR (ratio between the AFR and the average AFR of the patient) and episode duration (Short: 99.2 (9.3) %; Long: 100.0 (8.9) %). Data extracted from ICMs shows that that nighttime AF onset and longer duration AF episodes are more common in patients with higher AFR.
Serum chromium, glycosylated hemoglobin level and body mass index among control and uncontrol sudanese patients with type II diabetes mellitus: Correlation study

Background: Chromium is an essential micronutrient which is required for the normal functioning of insulin and regulation of blood sugar levels. It acts as a vital antioxidant for maintaining insulin homeostasis. In diabetes mellitus, the free radical production is increased and levels of antioxidants like chromium, vanadium, selenium and manganese are reduced. Aim: To study the level of serum chromium level in patients with type 2 diabetes mellitus and its association with glycemic control. Materials and Methods: One hundred and twenty individuals were enrolled in this study, classified into 60 type II diabetes mellitus (type II DM) patients and 60 apparently health as control group. Serum chromium and Glycosylated Hemoglobin (HbA1c) level were measured using atomic absorption spectrometry and Ichroma. Results: The results showed significant decrease in serum chromium level in type II DM patients (0.0151±0.005) when compared with healthy group (0.122±0.691) with p-value (0.002). In addition to that there was significant decrease in mean concentration of serum chromium level in controlled diabetic patients (0.0206± 0.003) when compared with uncontrolled diabetic patients (0.0120±0.002) with P-value (0.04). Also there was significant positive correlation between chromium level and Body Mass Index (BMI) (R-value 0.450, P-value 0.014), and significant negative correlation between chromium level and age (R-value- 0.660, P-value 0.011) , a significant strong negative correlation between chromium level and HbA1c (R-value -0.843, P-value 0.0260). Conclusion: The study concluded that, serum chromium level is significantly decrease in type II DM.
Improved Discrimination Between Healthy and Hypertensive Individuals Combining Photoplethysmography and Electrocardiography

Cardiovascular disease is one of the leading causes of death, with hypertension (HT) being its main risk factor. Its complications can be avoided with early treatment, but since these patients do not present any symptoms, HT is often detected at very advanced stages. This work presents a model for estimating blood pressure (BP) from electrocardiographic (ECG) and photoplethysmographic (PPG) signals, which can be easily obtained by means of wearable continuous monitoring devices. ECG, PPG and BP recordings from 86 patients were analyzed.A total of 34 standard and new features based on previous works were defined, such as pulse arrival times (PAT), and morphological characteristics of PPG signal. 37 classification models, ranging from Logistic Regression, Support Vector Machines (SVM), Nearest Neighbors, Naive Bayes or Coarse Trees were trained to compare discrimination results. The classifier that provided the highest performance when comparing normotensive patients with prehyperten-sive and hypertensive patients were Coarse Tree, providing an F1 score of 85.44% (Se of 86.27% and Sp of 77.14%). The use of PPG and ECG features has successfully discriminated between healthy and hypertensive individuals and, thus, could be used to detect HT by embedding these techniques in wearable devices.
Developing Collective Knowledge in an Electronic Business Space

Virtual teams have been increasingly cited as an efficient and flexible novel form of organisational arrangements affected by the emergence of the electronic business space. The purpose of forming such teams is a new (cid:145)knowledge creation(cid:146). The focus of this paper, therefore, is the process of collective (cid:145)knowing(cid:146) resulting in novel combinations of products and services. The paper presents the preliminary results of a qualitative research study on seven virtual partnerships and proposes an initial conceptual model of the knowledge creative processes taking place in virtual business relationships.
Pre-sowing treatments for enhancing germination of Guibortia coleosperma and Amblygonocarpus andongensis seeds from Kazuma Forest Reserve, Botswana

Department of Range and Forest Resources, Faculty of Natural Resources Botswana University of Agriculture and Natural Resources, Private Bag 0027, Gaborone, Botswana. Department of Wildlife and Aquatic Resources, Faculty of Natural Resources Botswana University of Agriculture and Natural Resources, Private Bag 0027, Gaborone, Botswana. Department of Forestry and Range Resources, P. O. Box 140, Kasane, Botswana
Utility Pricing in the Prosumer Era: An Analysis of Residential Electricity Pricing in California (Job Market Paper Draft)

Advanced metering infrastructure (AMI) as well as home energy management systems (HEMS) are enabling residential consumers, or ”prosumers”, to actively interact with electricity systems. In regulated retail sectors, residential rate structures greatly influence this interaction. Despite its important role, no consensus exists with respect to the ideal design of rates. Among the many factors that keep the debate open, the limited empirical literature turns the focus of the discussion around theoretical prescriptions. This study contributes with an applied analysis of residential rate structures, in the context of California’s electricity sector. We focus on a scenario in which AMI and HEMS are widely adopted. To measure welfare changes under different pricing regimes, we embed a detailed model of household behavior into a framework that combines elements from peak-load pricing and capacity expansion. This approach allows us to capture a wide variety of temporal and spatial demand substitution patterns without the need of estimating a large number of parameters. We calibrate the model using data of appliance ownership, census household counts, weather patterns, and a model of California’s electricity network. Our analysis compares four tariffs, including real-time pricing and three variants of a time-of-use program, and uses a flat rate structure as a reference case. We find that the increase in consumer surplus for the average household is not greater than 2 dollars per month. However, depending on the appliance stock, weather patterns and the specific rate structure some households may benefit up to ten times more than others. Overall our results suggests that targeting different rates to household with different characteristics is a superior strategy than defaulting all customers into any variant of a time-of-program. JEL Classification: C44, C61, D41, D61, L51, L94
Reinforcement Learning and Monte-Carlo Methods

In the previous note we considered policy evaluation and optimization when the full specification of the MDP is given, and the major challenge is computational. In the learning setting, the MDP specification, especially the transition function P and sometimes the reward functionR, is not known to the agent. Instead, the agent can take actions in the environment and observe the state transitions, and may find approximate solutions to policy evaluation or optimization after accumulating some amounts of interaction experience, or data. While computation remains an important aspect in the learning setting, this course will largely focus on sample efficiency, that is, the problem of achieving a learning goal using as little data as possible. There are 3 major challenges in reinforcement learning, which many discussions in this course will center around: – Temporal credit assignment In RL, the value obtained by the agent is the result of decisions made over multiple steps, and a fundamental question is how we should credit the outcome to each of the preceding decisions that lead to it. The challenge is addressed by applying principles of dynamic programming. It is often mentioned to contrast other machine learning paradigms, such as supervised learning, where the agent directly observes supervisory signals and the problem lacks a long-term nature. – Generalization In many challenging RL problems, the state (and action) space is large and the amount of data available is relatively limited. An agent will not succeed in its learning objective unless it generalizes what is learned about one state to other states. This challenge is encountered in other machine learning paradigms as well, and is often addressed by function approximation techniques [1]. – Exploration (and exploitation) In RL, the characteristics of the data are largely determined by how the agent chooses actions during data collection. Taking actions to collect a dataset that provides a comprehensive description of the environment, i.e., performing good exploration, is a highly nontrivial problem. Sometimes when assessed by some online measures (more on this in Section 2), the agent needs to balance between pursuing the value obtainable with current knowledge (exploitation) and sacrificing performance temporarily to learn more (exploration). In either case, the challenge is often addressed by the principle of optimism in face of uncertainty [2].
MONITORING OF ORGANIC VOLATILE COMPOUNDS IN CRAFT BEERS DURING FERMENTATIVE PROCESS

The present study aimed to evaluate the formation of volatile organic compounds (VOCs) during the fermentation process of three different craft beers, using gas chromatography with headspace sampling. The production of VOCs, which are largely responsible for the flavors of the drink, makes up unique organoleptic characteristics for each beer. In this work, it was possible to observe the different profiles for the Pilsen, Witbier and IPA style beers, when quantifying the analytes methanol, ethanol, n-propanol, iso-butanol, 3-methyl-1-butanol (iso-amyl alcohol), acetaldehyde, ethyl acetate and iso-amyl acetate. Pilsen beer, with low fermentation, presented the ethanol content in the same concentration range as Witbier beer, as expected, but with higher alcohols and lower esters than the other two. Witbier and IPA beers, both of high fermentation, presented isoamyl alcohol concentration above expectations, while IPA beer, present higher concentrations of n-propanol, ethyl acetate and isoamyl acetate in relation to Pilsen and Witibier, which is related to the complexity of its recipe.
Principal Kernel Analysis: A Tractable Methodology to Simulate Scaled GPU Workloads

Simulating all threads in a scaled GPU workload results in prohibitive simulation cost. Cycle-level simulation is orders of magnitude slower than native silicon, the only solution is to reduce the amount of work simulated while accurately representing the program. Existing solutions to simulate GPU programs either scale the input size, simulate the first several billion instructions, or simulate a portion of both the GPU and the workload. These solutions lack validation against scaled systems, produce unrealistic contention conditions and frequently miss critical code sections. Existing CPU sampling mechanisms, like SimPoint, reduce per-thread workload, and are ill-suited to GPU programs where reducing the number of threads is critical. Sampling solutions on GPUs space lack silicon validation, require per-workload parameter tuning, and do not scale. A tractable solution, validated on contemporary scaled workloads, is needed to provide credible simulation results. By studying scaled workloads with centuries-long simulation times, we uncover practical and algorithmic limitations of existing solutions and propose Principal Kernel Analysis: a hierarchical program sampling methodology that concisely represents GPU programs by selecting representative kernel portions using a scalable profiling methodology, tractable clustering algorithm and detection of intra-kernel IPC stability. We validate Principal Kernel Analysis across 147 workloads and three GPU generations using the Accel-Sim simulator, demonstrating a better performance/error tradeoff than prior work and that century-long MLPerf simulations are reduced to hours with an average cycle error of 27% versus silicon.