Miracle-dz/UniFAR-FTUs huggingface dataset:
Why Good Project Management Is Not Enough: Liabilities of Incumbency and Newness

A common view is that good project management is necessary but not enough to ensure project success. This begs the question: Why not? This paper examines current factor-, process-, and capability-based explanations of project performance and finds gaps in their explanatory power. An alternative explanation is developed from organizational learning and capability theory that integrates drivers for project success and drivers for project failure in one model. Project performance is modeled as the contested outcome of the two opposing drivers, enabling unpredictable variations in project outcomes. A key contribution of the model is the inclusion of drivers of underperformance and failure. These comprise common learning barrier conditions that can reduce or negate the performance value of organizational capabilities applied to projects. Two types of conditions are described: liabilities of incumbency and liabilities of newness. Together, these negative drivers can offset or overwhelm the positive effects of organizational learning on capability accumulation, creating a net performance liability, leading to underperformance or failure— even in well-managed projects. The performance model is illustrated using a longitudinal case study. The model improves our understanding of project performance as well as our ability to explain industry data on project outcomes and anomalous inconsistencies in performance in successive projects. Implications of the model are discussed, including how it might be used in practice. The paper argues from an information systems project perspective, but the model also has relevance to other application domains.
The Safety of Cannabis Use in Pregnancy

Based on self-reported surveys conducted by the Substance Abuse and Mental Health Services Administration, cannabis use in pregnant females has increased over the years. Despite the increasing trend, the relationship between cannabis use and fetal outcomes is not fully understood. This review paper evaluates the literature investigating the short-term and long-term fetal outcomes resulting from cannabis use during pregnancy. Additionally, the risk of chronic marijuana use leading to cannabis hyperemesis syndrome has been highlighted in this paper using conclusions compiled from several case studies. Several studies linked delayed mental growth and reduced cognitive function with prenatal cannabis use, but the literature was limited to low-er-quality observational studies and could not establish causality. One systematic review investigated short-term outcomes of low birth rates and preterm deliveries, where marijuana use in conjunction with tobacco use was associated with more preterm deliveries. Another study found that six-year-old patients exposed to cannabis prenatally were more likely to score lower in different categories on the Stanford-Binet scale test, which measures intelligence. The exposure in the first, second, or third trimester was associated with lower composite or subcategory scores such as verbal reasoning or short-term memory. Despite these results, the studies evaluated had limita-tions. They could not establish a clear relationship between cannabis use and fetal outcomes, but the literature showed a similar pattern of health, social and economic inequities among the populations who self-reported cannabis and substance use and non-users. Current organization guidelines advise against the use of cannabis use during pregnancy due to mixed and limited literature. However, they emphasize the importance of the clinician in the public health efforts of education and resource-distribution in addressing these inequities.
IRONWAN: Increasing Reliability of Overlapping Networks in LoRaWAN

LoRaWAN deployments follow an ad hoc deployment model that has organically led to overlapping communication networks, sharing the wireless spectrum, and completely unaware of each other. LoRaWAN uses ALOHA-style communication where it is almost impossible to schedule transmission between networks belonging to different owners properly. The inability to schedule overlapping networks will cause internetwork interference, which will increase node-to-gateway message losses and gateway-to-node acknowledgment failures. This problem is likely to get worse as the number of LoRaWAN networks increases. In response to this problem, we propose IRONWAN, a wireless overlay network that shares communication resources without modifications to underlying protocols. It utilizes the broadcast nature of radio communication and enables gateway-to-gateway communication to facilitate the search for failed messages and transmit failed acknowledgments already received and cached in overlapping network’s gateways. IRONWAN uses two novel algorithms: 1) a real-time message interarrival predictor, to highlight when a server has not received an expected uplink message and 2) the interference predictor, to ensure that extra gateway-to-gateway communication does not negatively impact the communication bandwidth. We evaluate IRONWAN on a 1000-node simulator with up to ten gateways and a 10-node testbed with 2-gateways. The results show that IRONWAN can achieve up to 12% higher packet delivery ratio (PDR) and total messages received per node while increasing the minimum PDR by up to 28%. These improvements save up to 50% node’s energy. Finally, we demonstrate that IRONWAN has comparable performance to an optimal solution (wired and centralized) but with 2–32 times lower communication costs. IRONWAN also has up to 14% better PDR when compared to FLIP, a wired-distributed gateway-to-gateway protocol in certain scenarios.
Graphene Oxide-Lignin/Silk Fibroin/ZnO Nanobiocomposite: A Novel Bioactive Scaffold With Antibacterial Activity

In this study, a novel nanobiocomposite was synthesized using graphene oxide, lignin, silk fibroin and ZnO and used in biological fields. To synthesize this structure, after preparing graphene oxide by the Hummer method, lignin, silk fibroin, and ZnO nanoparticles (NPs) were added to it, respectively. Also, ZnO NPs with a particle size of about 18 nm to 33 nm was synthesized via Camellia sinensis extract by green methodology. The synthesized structure was examined as anti-biofilm agent and it was observed that the Graphene oxide-lignin/silk fibroin/ZnO nanobiocomposite has a significant ability to prevent the formation of P. aeruginosa biofilm. In addition, due to the importance of the possibility of using this structure in biological environments, its toxicity and blood compatibility were also evaluated. According to the obtained results from MTT assay, the viability percentages of Hu02 cells treated with Graphene oxide-lignin/silk fibroin/ZnO nanobiocomposite after 24, 48, and 72 h of incubation were 89.96%, 89.32%, and 91.28%. On the other hand, the hemolysis percentage of the synthesized structure after 24 h and 72 h of extraction was 9.5% and 11.76% respectively. As a result, the synthesized structure is hemocompatible and had no toxic effects on Hu02 cells.
The Fault Diagnosis Method of Diesel Based on Wavelet Pocket and BP Neural Network

Because of the well time-frequency spectrum disposal capability of wavelet packet, the wavelet packet algorithm is used to analyze the time - frequency characteristics of diesel vibration signals. The signal energy distributing characteristics based on wavelet packet transform. are extracted and taken as diagnostic characteristic vector, then improved BP neural network algorithm that connects additional momentum with self-adaptive learning rate was used to classify and recognize faults of diesel valves. The experimental results show the fault diagnosis method of diesel based on wavelet pocket and BP neural network is effective and feasible.
Fragment-Based Virtual Screening to Discover Potential Plasmodium PI4KIIIβ Ligands

Plasmodium species that cause malaria, a disease responsible for about half a million deaths per annum despite concerted efforts to combat it. The causative agent depends on type III beta phosphatidylinositol 4-kinase (PPI4K) during the development of merozoite. PPI4K is the only clinically validated Plasmodium kinase so far and its inhibitors are effective both in vitro and in vivo. In this work, a small library of ~22 000 fragments was virtually screened using PPI4K homology model to discover potential ligands of the enzyme. 16 virtual hits were selected based on ≤ -9.0 kcal/mol binding energy cut off and were subjected to similarity and substructure searching after they had passed PAINS screening. The derivatives obtained showed improved binding energies, which ranged from -10.00 to -13.80 kcal/mol. Moreover, the topmost ranking compound 31, with interesting drug-like quality was stable within the protein’s binding cavity during the 10 ns molecular dynamics simulation period. In addition, analysis of its binding pose revealed some unique binding interactions with PPI4K active site residues as the basis for the observed improved binding affinity. Overall, compound 31 appears to be a viable starting point for the development of PPI4K inhibitors with antimalarial activity.
Mapping the Changing Structure of Conflict Networks in North and West Africa

ABSTRACT Contemporary conflict in North and West Africa is characterized by a high degree of social and political complexity. Hundreds of rebel groups and extremist organizations are involved in a shifting series of alliances and rivalries with regional governments and with each other. These changing relationships can be represented as a social network that provides both opportunities and constraints to violent organizations. To better address this complexity, this article models the temporal evolution of both opposition and cooperation networks using detailed information on nearly 40,000 events in North and West Africa from 1997–2020. Using a relational approach called Dynamic Social Network Analysis (DSNA), the article suggests that the increasing number of belligerents, increasing density of conflictual relationships, and polarization on powerful organizations capable of conducting extensive military operations make a peaceful resolution of the North and West African conflicts more elusive than ever.
AMIDARS Flight Performance

CAI, a Division of RECON/OPTICAL, INC. has developed an Airborne Minefield Detection and Reconnaissance System (AMIDARS)* which provides passive day/night reconnaissance of minefields, armored/ground vehicles, troop concentrations, bridges/railways and buildings from unmanned reconnaissance vehicles. This paper discusses the results of the integration flight tests. Selected imagery is presented and evaluated to assess the system's performance.
Short-Term Prediction on the Time Series of PCP Speed Based on Elman Neural Network

Elman neural network is a classical kind of recurrent neural network. It is well suitable to predict complicated nonlinear dynamics system like progressing cavity pump (PCP) speed due to its greater properties of calculation and adaptation to time-varying with the comparison of BP neural network. This paper provides one method to create, predict, and decide the model of PCP speed based on Elman neural network. At the same time, short-term prediction is made on time series of PCP speed using this model. The results of the experiment show that the model owns higher precision, steadier forecasting effect and more rapid convergence velocity, displaying that this kind of model based on Elman neural network is feasible and efficient to predict short-term PCP speed.
Integrating Spatial Data Mining Technique to Identify Potential Areas for Forest Production Using GIS and Remote Sensing

The purpose of the present study was to find potential areas for forest production using Spatial Data Mining (SDM) technique, GIS, and remote sensing. Artificial Neural Network (ANN) was the data mining method used. Current land use, average annual rainfall, soil erosion, slope, and fire hazards were selected as the criteria for the forest production. For this study, an ASTER-VNIR (Level 1B) satellite image acquired on 11 July 2006 was used. The image was classified into eight land use classes. Digital Elevation Model (DEM) was used to derive the slope map (20 m). The normalized Difference Vegetation Index (NDVI) was applied to detect areas of vegetation cover. A model was developed to evaluate areas vulnerable to soil erosion. Thematic paper maps (rainfall and soil) of the study area were screen digitized and converted to the raster format. The Alyuda NeuroIntelligence 2.2 application was used to implement the standard Back-Propagation (BP) algorithm for ANN modeling. Training stage of ANN helped to identify the optimal neural network structure. The final result was saved in spreadsheet format and converted to the GIS format for the output process, which was evaluated to produce the final map. This map was used to separate the suitable areas for the forest production into three suitability classes. The results showed that 19.65%, 28.43%, and 51.93% land areas were most suitable, suitable and less suitable, respectively, for the forest production. The SDM is more powerful than conventional cartography modeling technique for land suitability analysis.
A UHPLC-Mass Spectrometry View of Human Melanocytic Cells Uncovers Potential Lipid Biomarkers of Melanoma

Melanoma is the deadliest form of skin cancer due to its ability to colonize distant sites and initiate metastasis. Although these processes largely depend on the lipid-based cell membrane scaffold, our understanding of the melanoma lipid phenotype lags behind most other aspects of this tumor cell. Here, we examined a panel of normal human epidermal and nevus melanocytes and primary and metastatic melanoma cell lines to determine whether distinctive cell-intrinsic lipidomes can discern non-neoplastic from neoplastic melanocytes and define their metastatic potential. Lipidome profiles were obtained by UHPLC-ESI mass-spectrometry, and differences in the signatures were analyzed by multivariate statistical analyses. Significant and highly specific changes in more than 30 lipid species were annotated in the initiation of melanoma, whereas less numerous changes were associated with melanoma progression and the non-malignant transformation of nevus melanocytes. Notably, the “malignancy lipid signature” features marked drops in pivotal membrane lipids, like sphingomyelins, and aberrant elevation of ether-type lipids and phosphatidylglycerol and phosphatidylinositol variants, suggesting a previously undefined remodeling of sphingolipid and glycerophospholipid metabolism. Besides broadening the molecular definition of this neoplasm, the different lipid profiles identified may help improve the clinical diagnosis/prognosis and facilitate therapeutic interventions for cutaneous melanoma.
An approach for optimizing pavement design–redesign parameters in PPP projects

Modeling the elasticity modulus of unbound granular pavement materials has attracted significant research interest because of its importance in pavement design particularly in PPP/BOT projects. These efforts have been hampered by three factors: (i) inability to capture the correlations between the asphalt and granular layers and the subgrade, (ii) inadequate modeling of the effects of external factors on the elasticity modulus of unbound materials, and (iii) widespread use of linear statistical relationships to model a complex and non-linear phenomenon. In this paper genetically optimized neural networks and falling weight deflectometer (FWD) back-analysis results from a newly constructed BOT project in Athens, Greece, are employed in order to evaluate pavement section design parameters. It is shown that parameter values adopted during design do not co-inside with those observed from the back-analysis studies. Further, the results indicate that the relative estimation error for the modulus of elasticity of the unbound material does not exceed 25%, while the correlation between actual and predicted values is 86%, both suggesting that the proposed approach models the physical phenomenon adequately, a finding with important practical implications particularly in PPP projects.
Analysis of traffic crash data in Kentucky (2009-2013).

This report documents an analysis of traffic crash data in Kentucky for the years of 2009 through 2013. A primary objective of this study was to determine average crash statistics for Kentucky highways. Rates were calculated for various types of highways and for counties and cities. Difference criteria were used for exposure. Average and critical numbers and rates of crashes were calculated for various types of highways in rural and urban areas. These rates used crashes identified on highways where traffic volumes were available. Improved methods of identifying crash locations have resulted in higher rates for the last couple of years. The crash rate data can be used in Kentucky’s procedure to identify locations that have abnormal rates or numbers of crashes. The other primary objective of this study was to provide data that can be used in the preparation of the problem identification portion of Kentucky’s Annual Highway Safety Plan. County and city crash statistics were analyzed. A summary of results and recommendations in several problem identification areas is presented. These general areas include; alcohol involvement, occupant protection, speed, teenage drivers, pedestrians, bicycles, motorcycles, trucks, and vehicle defects. Other areas included in the analysis for which specific recommendations were not made include, school bus crashes and train crashes. The crash data are contained in the Collision Report Analysis for Safer Highways (CRASH) data base. This data base is updated daily so the number of crashes in a given calendar year will continue to change for a substantial time after the end of that year.
110 W low cost, efficient PV-grid interface for rural electrification

PV-utility interface systems for remote rural South Africa are investigated: contemporary topologies are assessed and a unique concept formulated. Based on this, a low power 110 W prototype unit was developed using a highly integrated, low-cost microcontroller. The PV-panel attaches (via a DC-bus capacitor filter) to an H-Bridge. The H-bridge pulse width modulates the DC bus to create a high quality low voltage sine-wave (after an LC filter). The sine-wave is then stepped up to the utility voltage by a standard 50 Hz mains transformer. Preliminary experimental results are included.
Review of guided-wave structural health monitoring

In this paper we present the state of the art in the field of guided-wave structural health monitoring (SHM). We begin with an overview of damage prognosis, and a description of the basic methodology of guided-wave SHM. We then review developments from the open literature in various aspects of this truly multidisciplinary field. First, we discuss different transducer technologies, including both piezoelectric and non-conventional popular and non-conventional piezoelectric transducers. Next, we examine guided-wave theory, tracing its early history down to modern developments. Following this, we detail the efforts into models for guided-wave excitation by SHM transducers. Then, we review several signal processing related works. The next topic in Section 6 is guided-wave SHM system development, and we explore various packaging ideas, integrated solutions and efforts to examine robustness to different service conditions. We also highlight the broad spectrum of applications in which this technology has been tested. We then present some investigations that have attempted to combine guided-wave approaches with other complementary SHM technologies for better system performance. Finally, we propose desirable developments for further advancement of this field.
Design and Realization of Submarine Voyage Training Simulation System

The traditional submarine voyage training has problems of high cost, long period and high risk. In this paper, a submarine voyage training simulation system was proposed based on virtual reality and submarine control theory. At first, the construction of the system and the functions of modules were described. The coach control system based on digital chart was realized, which can achieve functions of training environment and parameters setting. The model library of virtual ocean scene was built and then the scene was rended by Vega Prime. In order to remedy the deficiency of the network, a DR algorithm was explored, which can satisfy the realtime requirements of scene simulation and achieved the frame alignment between channels and coherence of observers.
Design of a Ka‐Band Microstrip Antenna Array with Sharped‐Beam Pattern and High Gain

A Ka‐band microstrip array antenna is presented in this article. It operates at 35.1 GHz and can realize sharped‐beam pattern and high gain. The antenna array consisting of 14 ×8 elements is fabricated on Rogers RT5880 substrate with 0.254‐mm thickness. It is center‐fed by a coax probe, and the symmetrical feed network is two‐stage cascaded with unequal amplitudes and the same phase. The total size of the array is 110 ×67 mm2. The simulated and test results show that the antenna array has a gain of 25 dB, the 3 dB beamwidths in azimuth, and elevation plane are less than 6° and 10° respectively, the sub lobe suppression is −18 dB. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2517–2520, 2013
Learning During Design for Six Sigma Projects—A Preliminary Investigation in Behavioral Healthcare

Abstract As individual members of an organization engage in activities, such as continuous quality improvement efforts, they learn. However, it is not yet well known what impact this type of learning has on individuals/teams within organizations. To examine this issue, this research used an action research, multiple case study approach to examine the use of structured, continuous quality improvement methods in behavioral/mental healthcare organizations. It explored how the use of Design for Six Sigma generated individual learning among team members as they worked to improve their organization by designing/redesigning their operational processes. Findings suggest that learning occurs through hands-on practice and guidance/support from others. In addition, communication, collaboration, and teamwork helped to overcome some of the challenges faced and facilitate learning.