Discovering Organizational Hierarchy through a Corporate Ranking Algorithm: The Enron Case

The detection of early-warning signals of performance deterioration can help technical support teams in taking swift remedial actions, thus ensuring rigor in production support operations of large scale software systems. Performance anomalies or deterioration, if left unattended, often result in system slowness and unavailability. In this paper, we presents a simple, intuitive and low-overhead technique for recognizing the early warning signs in near real time before they impact the system The technique is based on the inverse relationship which exists between throughput and average response time in a closed system. Because of this relationship, a significant increase in the average system response time causes an abrupt fall in system throughput. To identify such occurrences automatically, Individuals and Moving Range (XmR) control charts are used. We also provide a case study from a real-world production system, in which the technique has been successfully used. The use of this technique has reduced the occurrence of performance related incidents significantly in our daily operations. The technique is tool agnostic and can also be easily implemented in popular system monitoring tools by building custom extensions.

We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level.

Centrality is an important measure to identify the most important actors in a network. This paper discusses the various Centrality Measures used in Social Network Analysis. These measures are tested on complex real-world social network data sets such as Video Sharing Networks, Social Interaction Network and Co-Authorship Networks to examine their effects on them. We carry out the correlation analysis of these centralities and plot the results to recommend when to use those centrality measures. Additionally, we introduce a new centrality measure - Cohesion Centrality based on the cohesiveness of a graph, develop its sequential algorithm and further devise a parallel algorithm to implement it.

BackgroundIn collaboration with the Mobile Monitoring of Cognitive Change (M2C2; 1‐U2C‐AG060408‐01) Project, our team developed a tablet‐based electronic Stroop (eStroop) task. Initial development explored direct translation of the paper‐Stroop (pStroop) often used in clinical settings, and found that this resulted in ceiling effects. Herein we report on initial validation of the eStroop task from the iPACES v3 clinical trial.MethodInitial validation examined eStroop data (n = 59 sessions) serially collected from ten MCI and caregiver participants in the iPACES v3 clinical trial (mean age = 70.2, mean years of education = 16.9). Included in analyses were eStroop data for both traditional metrics (i.e., overall accuracy by block condition), response time, as well as typically constructed difference/ratio scores (i.e., AC ratio). After a baseline tele‐neuropsychological evaluation in which paper Stroop was administered, at predetermined study intervals (typically weekly), participants were automatically prompted to complete the eStroop on their iPACES Android tablet. In the eStroop, participants were presented with four blocks of 40 trials: (1) color words in black font, (2) congruent word and font color; (3) incongruent words with font color; (4) mixed congruent and incongruent trials.ResultThe paper Stroop AC Ratio (total seconds to complete paper A, divided by time to complete paper C), was moderately negatively correlated (r = ‐0.52, p < .001) with average response time on block three of eStroop (incongruent words with font color). The pStroop AC Ratio was also significantly negatively correlated with number of errors on block one (r = ‐0.42, p < .001), two (r = ‐0.26, p = 0.04), three (r = ‐0.39, p = 0.002), and four (r = ‐0.26, p = .049). A weak, significant positive correlation (r = 0.44, p < .001) was observed between the AC ratio from pStroop and eStroop. Very weak non‐significant correlations between paper Stroop AC Ratio and average response time on blocks one, two, and four of eStroop were observed.ConclusionThese pilot data provide preliminary evidence that the eStroop can serve as a remotely and self‐administered digital test for serial assessment of executive function and will be further evaluated in our current iPACES study.

A three-dimnensional vision system for on-line operation that aids a collision avoidance system for an industrial robot is developed. Because of the real-time requirement, the process that locates and describes the obstacles must be fast. To satisfy the safety requirement, the obstacle model should always contain the physical obstacle entirely. This condition leads to the bounding box description of the obstacle, which is simple for the computer to process. The image processing is performed by a Machine Intelligence Corporation VS-100 machine vision system. The control and object perception is performed by the developed software on a host Digital Equipment Corporation VAX 11/780 Computer. The resultant system outputs a file of the locations and bounding descriptions for each object found. When the system is properly calibrated, the bounding descriptions always completely envelop the obstacle. The response time is data-dependent. When using two cameras and processed on UNIX time sharing mode, the average response time will be less than 2 s if eight or fewer objects are present. When using all three cameras, the average response time will be less than 4 s if eight or fewer objects are present.

Rapid prototyping is an automated manufacturing process that quickly builds physical models from CAD files of three-dimensional prototypes. It dramatically speeds up design and manufacturing processes and substantially reduces the production cost. With the advanced Web technologies, rapid prototyping manufacturing systems can be further integrated and more effectively utilized. We have designed a distributed web platform where users can design prototypes, simulate manufacturing, and issue ordering requests for real manufacturing through browser interfaces. However, an improper scheduling approach may degrade system utilization and throughput significantly. To solve this problem, we propose a real-time scheduling approach to maximize system utilization and minimize the average response time with the best effort. Using this approach, new requests can be accepted dynamically and all accepted requests can be finished before deadlines. We show that the scheduling overhead can be neglected and the average response time is very close to the optimal solution. With the proposed scheduling approach, the Web rapid prototyping platform is able to fully utilize Web resources for rapid prototyping manufacturing and the quality of service is significantly improved.

Based on the system architecture and software structure of GMLC (Gateway Mobile Location Center) in 3G (third generation), a new dynamic load-balancing algorithm is proposed. It bases on dynamic feedback and imports the increment for admitting new request into the load forecast. It dynamically adjusts the dispatching probability according to the remainder process capability of each node. Experiments on the performance of algorithm have been carried out in GMLC and the algorithm is compared with Pick-KX algorithm and DFB (Dynamic FeedBack) algorithm in average throughput and average response time. Experiments results show that the average throughput of the proposed algorithm is about five percents higher than that of the other two algorithms and the average response time is four percents higher under high system loading condition.

The wireless fluid level indicator comprises a sensing and transmitting circuit for detecting and transmitting the levels of fluids from a container to the receiving circuit which receives and displays the amount of the measured volume. This has been achieved using an ultrasonic sensor (HC-SR04), a firmware (PIC16F648A/PIC16F876A microcontroller programmed in C/assembly languages), an encoder (HT12E) and a radio frequency (RF) transmitter transmitting at a bandwidth of 433MHz to an RF receiver interfaced with a decoder (HT12D), liquid crystal display (LCD) and a Buzzer. The device when tested measured water levels and transmitted same over a distance of up to 98.5 meters in open areas and up to 50 meters in highly obstructed areas. The average response time was also estimated to be 0.1 second. As a contact-less depth detection system, the device finds useful applications in measurement of fluid levels in homes and industries where concentrates, table water, transmission oils are produced, without affecting the chemical or physical properties of such substances-an advantage over the contact methods used in fluid level detection.
 Aims: To design and implement a wireless microcontroller based fluid level display system using ultrasonic sensing method.
 Study Design: By encoding the Transmitter module to sense and detect the amount of water level, while the decoder module decodes the received data and display the result.
 Place and Duration of Study: Department of Physics, University of Agriculture Makurdi, Benue State, Nigeria, between November 2018 and April 2019.
 Methodology: An RF transceiver, encoder/decoder (HT12E/HT12D), a 2x16 Liquid display module, 4MHz crystal oscillator, a buzzer and other peripherals were interfaced to two 8-bit controllers; PIC16F648A and PIC16F876A. The controllers were programmed in C/Assembly languages to enable effective communication of the system. The system was then calibrated to read and transmit water level values from a 30-centimeter deep tank over a distance of 1.0 - 98.5 meters.
 Results: The system was tested to have maximum sensitivity over a distance of 98.5 meters in non-obstructed areas and 50 meters in obstructed areas. The average response time was also estimated to be 0.5 seconds.
 Conclusion: The design and implementation of a wireless fluid level display system using ultrasonic sensing technique has been successfully done. The system workability is suitable for deployment and usage in domestic and industrial purposes.

In this letter, we analytically evaluate the average query response time of the Two-level Hierarchical Clustering based Hybrid-routing Protocol (THCHP) proposed recently for wireless sensor networks (WSNs). The accuracy of the average query response time analysis is verified via numerical simulations. Additionally, average query response time comparisons, between THCHP and a previously proposed Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network (APTEEN) protocol, show that THCHP is better suited than APTEEN for delay sensitive WSN applications.

Despite the extensive use of real-world data for retrospective, observational clinical research, our understanding of how real-world data might increase the efficiency of data collection in patient-level randomized clinical trials is largely unknown. The structure of real-world data is inherently heterogeneous, with each source electronic health record and claims database different from the next. Their fitness-for-use as data sources for multisite trials in the United States has not been established. For a subset of participants in the HARMONY Outcomes Trial, we obtained electronic health record data from recruiting sites or Medicare claims data from the Centers for Medicare & Medicaid Services. For baseline characteristics and follow-up events, we assessed the level of agreement between these real-world data and data documented in the trial database. Real-world data-derived demographic information tended to agree with trial-reported demographic information, although real-world data were less accurate in identifying medical history. The ability of real-world data to identify baseline medication usage differed by real-world data source, with claims data demonstrating substantially better performance than electronic health record data. The limited number of lab results in the collected electronic health record data matched closely with values in the trial database. There were not enough follow-up events in the ancillary study population to draw meaningful conclusions about the performance of real-world data for identification of events. Based on the conduct of this ancillary study, the challenges and opportunities of using real-world data within clinical trials are discussed. Based on a subset of participants from the HARMONY Outcomes Trial, our results suggest that electronic health record or claims data, as currently available, are unlikely to be a complete substitute for trial data collection of medical history or baseline lab results, but that Medicare claims were able to identify most medications. The limited size of the study population prevents us from drawing strong conclusions based on these results, and other studies are clearly needed to confirm or refute these findings.

Timely ambulance allocation is essential for Emergency Medical Services (EMS) to deliver life-saving care effectively. Conventional methods often struggle to adapt to the unpredictable nature and locations of emergencies. Within EMS, efficient resource management is crucial for ensuring rapid and effective responses. While much emphasis has been placed on optimizing the deployment of ambulances from fixed stations, managing specialized critical care response units-known as Charlie vehicles in Qatar EMS-presents a distinct challenge. These rapid response cars are vital for providing advanced care in challenging situations, and their dynamic deployment requires a more flexible management strategy. Effectively relocating Charlie vehicles to areas with high anticipated demand after they have responded to an emergency introduces unique challenges that differ from traditional ambulance redeployment approaches. This paper proposes a novel dynamic redeployment system specifically for optimizing the allocation of critical care response vehicles, including those involved in patient transfers. Utilizing a Deep Reinforcement Learning (DRL) framework, we create a deep scoring network that prioritizes and navigates various dynamic factors at each station. Experiments using real-world data from Qatar EMS demonstrate that our system significantly outperforms existing methods. For instance, our approach achieves faster average response times and improved critical response rates compared to the leading baseline method. Notably, we observe a substantial 21.55% reduction in average response time (AveRT) and an 18.34% increase in relative response time (RelaRT) in comparison to actual operational metrics. Our approach effectively shortens the time needed to reach patients, thereby increasing the likelihood of timely treatment and improving overall patient care outcomes.

We introduce a refined way to diffusely explore complex networks with stochastic resetting where the resetting site is derived from node centrality measures. This approach differs from previous ones, since it not only allows the random walker with a certain probability to jump from the current node to a deliberately chosen resetting node, rather it enables the walker to jump to the node that can reach all other nodes faster. Following this strategy, we consider the resetting site to be the geometric center, the node that minimizes the average travel time to all the other nodes. Using the established Markov chain theory, we calculate the Global Mean First Passage Time (GMFPT) to determine the search performance of the random walk with resetting for different resetting node candidates individually. Furthermore, we compare which nodes are better resetting node sites by comparing the GMFPT for each node. We study this approach for different topologies of generic and real-life networks. We show that, for directed networks extracted for real-life relationships, this centrality focused resetting can improve the search to a greater extent than for the generated undirected networks. This resetting to the center advocated here can minimize the average travel time to all other nodes in real networks as well. We also present a relationship between the longest shortest path (the diameter), the average node degree and the GMFPT when the starting node is the center. We show that, for undirected scale-free networks, stochastic resetting is effective only for networks that are extremely sparse with tree-like structures as they have larger diameters and smaller average node degrees. For directed networks, the resetting is beneficial even for networks that have loops. The numerical results are confirmed by analytic solutions. Our study demonstrates that the proposed random walk approach with resetting based on centrality measures reduces the memoryless search time for targets in the examined network topologies.

With the worldwide digitalisation of medical records, electronic health records (EHRs) have become an increasingly important source of real-world data (RWD). RWD can complement traditional study designs because it captures...

Background: Misoprostol is an analogue of prostaglandin (PGE1) and the first registered PGN used for the treatment of peptic ulcer disease. Misoprostol should not be used in pregnant women but it can be used in termination of intrauterine fetal death (IUFD). It has been used for the induction of abortion in the first and second trimester of pregnancy. Misoprostol has already been proved to be effective and efficient for second trimester since 1987. There is insufficient information available from clinical trials to support their use to induce labor in third trimester of pregnancy. Aim of the study: The aim of this study was to describe the effect of misoprostol in third trimester of pregnancy. Methods: This retrospective study was carried out in two large hospitals in Dhaka city, which is the capital of a South Asian country, Bangladesh. The record of the studied subject was taken from the Department of Obstetrics and Gynecology of Alpha Hospital and the maternity ward of Beta Hospital from June 2018 to August 2019. The actual names of the two hospitals are pseudonym for privacy. A detailed history of each patient was taken and physical and obstetric examination were done. In total 52 patients were investigated and data were analyzed by SPSS version 23.0. Results: In this study, the primi gravida response time was found as the highest. On an average 20.34 hours was needed for the primi gravida category. In single, 2 and multi-parity cases the average pain response times were found as 16.3125, 10.3375 and 14 hours respectively. In Primi, 2nd, 3rd and 5th gravida cases the average delivery times were found 13.0714, 12.5938, 9.0769 and 3 hours respectively. Against single, two and three doses of misoprostol, the average pain response times were found 7.6875, 11.5375 and 19.25 hours respectively. Conclusion: In this study, it had been found that, most of the patient of IUFD was ‘primigravid’ (30.8%) of total respondents having complication with medical and endocrinal disease. Pre- eclampsia is more common in prim gravid than multi. There is history of thyroid dysfunction. In some cases, now a day, GDM is the cause behind IUFD.

We report an exploratory study that investigated the interaction of trait and task in determining duration judgment. High and low absorption subjects (determined by median split along the Absorption Scale) viewed a series of paired slides, and were required to relate to each pair in one of two tasks: A metaphor-production task, and a story-production one. These tasks were carried out for an objective interval of fifteen minutes, following which the subject was required to verbally estimate this duration, retrospectively. In addition, from the individual protocols we measured the average time till response and the average time of response. A significant interaction between absorption and task was obtained for the latter two variables. In addition, a main effect for task was found for the duration estimation. These and other results are assessed in terms of both a cognitive-timer model for time estimation and a contextualistic approach to temporal processing.