bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-UAV Cooperative Transportation Using Dynamic Control Allocation and a Reinforcement Learning Compensator
Authors: Li Shuai , Zanotto Damiano
Published: November 2021

This paper proposes a new trajectory tracking method for a 6 degree-of-freedom (DOF) cable-suspended payload controlled by a team of quadrotors. Using the modeling convention of reconfigurable cable-driven parallel robots (RCDPRs) the coupled dynamics of the payload and the quadrotors are derived. Based on this dynamic model, a new dynamic control allocation approach is introduced to optimally distribute the virtual control input (i.e., the wrench to be exerted on the payload) among the cables and generate reference positions for the quadrotors on-line, while avoiding collisions between quadrotors and accounting for cable tension constraints. Furthermore, a new reinforcement-learning (RL) compensator is proposed to reduce tracking errors caused by the constraints in the quadrotors’ thrusts. Numerical simulations are conducted to validate the proposed approach.
Design Form and Function Prediction From a Single Image
Authors: Edwards Kristen M. , Addala Vaishnavi L. , Ahmed Faez
Published: November 2021

Estimating the form and functional performance of a design in the early stages can be crucial for a designer for effective ideation Humans have an innate ability to guess the size, shape, and type of a design from a single view. The brain fills in the unknowns in a fraction of a second. However, humans may struggle with estimating the performance of designs in the early stages of the design process without making prototypes or doing back-of-the-envelope calculations. In contrast, machines need information about the full 3D model of a design to understand its structure. Machines can estimate the performance using pre-defined rules, expensive numerical simulations, or machine learning models. In this paper, we show how information about the form and functional performance of a design can be estimated from a single image using machine learning methods. Specifically, we leverage the image-to-image translation method to predict multiple projections of an image-based design. We then train deep neural network models on the predicted projections to provide estimates of design performance. We demonstrate the effectiveness of our method by predicting the aerodynamic performance from images of aircraft models. To estimate ground truth aero-dynamic performance, we run CFD simulations for 4045 3D aircraft models from the ShapeNet dataset and use their lift-to-drag ratio as the performance metric. Our results show that single images do carry information for both form and functional performance. From a single image, we are able to produce six additional images of a design in different orientations, with an average Structural Similarity Index score of 0.872. We also find image-translation methods provide a promising direction in estimating the performance of design. Using multiple images of a design (gathered through image-translation) to predict design performance yields a recall value of 47%, which is 14% higher than a base guess, and 3% higher than using a single image. Our work identifies the potential and provides a framework for using a single image to predict the form and functional performance of a design during the early-stage design process. Our code and additional information about our work are available at http://decode.mit.edu/projects/formfunction/.
Geometry Enhanced Generative Adversarial Networks for Random Heterogeneous Material Representation
Authors: Chen Hongrui , Liu Xingchen
Published: November 2021

The representation of material structure geometry is essential to the reconstruction, physical simulation, and the multiscale structure design with Random Heterogeneous Material (RHM). Traditional approaches to material structure representation often need to balance the trade-off between efficacy and accuracy. Recently, deep learning-based techniques have been adopted to reduce the computational time of RHM reconstruction. However, existing approaches generally lack guarantees over key RHM characteristics, including Minkowski functionals and correlation functions.

We propose a novel approach to geometrically enhancing the deep learning-based RHM representation by introducing Minkowski functionals, a set of topological and geometrical characteristics of material structure, into the training of conditional Generative Adversarial Networks (cGAN). This hybrid approach combines the feature learning capability of deep learning with the well-established material structure characteristics, greatly improving the accuracy of the RHM representation while maintaining its efficiency. The effectiveness of the proposed hybrid approach is validated through the reconstruction of a wide range of natural and manmade materials, including Voronoi foam structures, femur, and sandstone. Through computational experiments, we demonstrate that geometrically enhancing the training of cGAN for RHM representation not only significantly decreases the representation error in Minkowski functionals between input sample materials and reconstructed results, but also improves the performance of other material structure characteristics, such as two-point correlation functions.
A Tripolar Electromyography Device With Active Electrode-Skin Impedance Imbalance Compensation
Authors: Towse Alexander J. , Fortune Benjamin C. , Pretty Chistopher G. , Hayes Michael P.
Published: November 2021

This paper discusses the development of a tripolar EMG device featuring electrode impedance compensation circuitry. The device also includes circuitry to test the effectiveness of these features at improving EMG signal quality. Due to various factors, the electrode-skin impedance of different electrodes is typically imbalanced. This imbalance increases EMG susceptibility to electrical noise. These issues can be mitigated by applying impedance compensation. This was done for a tripolar configuration specifically to also reduce interference due to crosstalk.

The development process and design choices behind the device features are discussed, with particular focus on the impedance compensation circuit. This includes key components used, and the justification behind their selection.

Testing found the tripolar electrode configuration had limited effect on crosstalk interference. Fortunately, the impedance compensation circuit could successfully correct for impedance imbalance. This led to a marked reduction in noise due to electrical interference, such as from 50Hz mains hum.
Task Space Planning With Complementarity Constraint-Based Obstacle Avoidance
Authors: Sinha Anirban , Sarker Anik , Chakraborty Nilanjan
Published: November 2021

In this paper, we present a task space-based local motion planner that incorporates collision avoidance and constraints on end-effector motion during the execution of a task. Our key technical contribution is the development of a novel kinematic state evolution model of the robot where the collision avoidance is encoded as a complementarity constraint. We show that the kinematic state evolution with collision avoidance can be represented as a Linear Complementarity Problem (LCP). Using the LCP model along with Screw Linear Interpolation (ScLERP) in SE(3), we show that it may be possible to compute a path between two given task space poses by directly moving from the start to the goal pose, even if there are potential collisions with obstacles. Scalability of the planner is demonstrated with experiments using a physical robot. We present simulation and experimental results with both collision avoidance and task constraints to show the efficacy of our approach.
Neurocognitive Effects of Incentivizing Students to Improve Performance Through Repeat Attempts in Design Settings
Authors: Shah Devanshi , Kames Elisabeth , Morkos Beshoy
Published: November 2021

The goal of the study is to examine the effectiveness of using an incentivized “test/retest” scenario to improve participants’ performance during stressful situations. The study makes use of an electroencephalography (EEG) machine to detect participants’ stress levels during a one-minute typing test. The typing test administered was a standard, “story-typing” test. A total of 23 student participants were randomly divided into two cohorts: the control cohort and the experimental cohort. Participants were asked to complete a preliminary questionnaire self-assessing their ability to handle stressful situations. Both cohorts were then asked to complete the typing test (hereafter referred to as T1) and fill out an Emotional Stress Reaction Questionnaire (ESRQ), indicating their emotions during the typing test. The participants were then asked to complete the typing test and accompanying ESRQ a second time (hereafter referred to as T2). However, prior to the second test, the participants in the experimental cohort were told that the participant that shows the most improvement in their typing speed (measured in words per minute) will receive a $100 gift card.

This stimulus is used to increase the already stressful situation for the experimental cohort and examine whether participants’ brain activity changes when the “retest” is incentivized. Each participant’s EEG data and heartrate were measured through the duration of the experiment and t-tests and regression analyses were used to determine if a statistically significant difference existed between cohorts (control vs. experimental) or within cohorts (T1 vs. T2).

The results show that there were no significant changes in brain activity, emotions, or typing performance for the control group of participants (no reward offered). However, the experimental group showed an increase in EEG sensor activity; specifically, the sensors that control vision and emotion. Interestingly, the participant’s performance was found to be correlated to their emotional responses, rather than their EEG sensor data. Additionally, the experimental groups’ positive emotions were increased for the second typing test, which is incentivized. The findings lay a foundation for design settings scenarios where preparatory practices can be incorporated.
Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors
Authors: Hafizur Rahman Molla , Xie Charles , Sha Zhenghui
Published: November 2021

Design thinking is essential to the success of a design process as it helps achieve the design goal by guiding design decision-making. Therefore, fundamentally understanding design thinking is vital for improving design methods, tools and theories. However, interpreting design thinking is challenging because it is a cognitive process that is hidden and intangible. In this paper, we represent design thinking as an intermediate layer between human designers’ thought processes and their design behaviors. To do so, this paper first identifies five design behaviors based on the current design theories. These behaviors include design action preference, one-step sequential behavior, contextual behavior, long-term sequential behavior, and reflective thinking behavior. Next, we develop computational methods to characterize each of the design behaviors. Particularly, we use design action distribution, first-order Markov chain, Doc2Vec, bi-directional LSTM autoencoder, and time gap distribution to characterize the five design behaviors. The characterization of the design behaviors through embedding techniques is essentially a latent representation of the design thinking, and we refer to it as design embeddings. After obtaining the embedding, an X-mean clustering algorithm is adopted to each of the embeddings to cluster designers. The approach is applied to data collected from a high school solar system design challenge. The clustering results show that designers follow several design patterns according to the corresponding behavior, which corroborates the effectiveness of using design embedding for design behavior clustering. The extraction of design embedding based on the proposed approach can be useful in other design research, such as inferring design decisions, predicting design performance, and identifying design actions identification.
Nonlinear Spectral Properties of Elastic Waves Propagating Along a Pantographic Metamaterial With Local Inertia Amplifiers
Authors: Settimi Valeria , Lepidi Marco , Bacigalupo Andrea
Published: November 2021

Pantographic mechanisms can be introduced in the cellular periodic microstructure of architected metamaterials to achieve functional effects of local inertia amplification. The paper presents a one-dimensional pantographic metamaterial, characterized by an inertially amplified tetra-atomic cell. An internally constrained two-degrees-of-freedom model is formulated to describe the undamped free propagation of harmonic waves in the weakly nonlinear regime. A general asymptotic approach is employed to analytically determine the linear and nonlinear dispersion properties. Analytical, although asymptotically approximate, functions are obtained for the nonlinear wavefrequencies and waveforms, which show significant nonlinear effects including softening/hardening bending of the backbone curves and synclastic/anticlastic curvatures of the invariant manifolds.
Prescribing and Self-Regulating Heated Water-Based Exercise by Rating of Perceived Exertion in Older Individuals With Hypertension
Authors: Marçal Isabela Roque , Fernandes Bianca , do Amaral Vanessa Teixeira , Pelaquim Renato Lopes , Ciolac Emmanuel Gomes
Published: November 2021

We aimed to analyze the usefulness of the 6–20 rating of perceived exertion (RPE) scale for prescribing and self-regulating high-intensity interval (HIIE) and moderate-intensity continuous (MICE) aerobic exercise performed in a heated swimming pool (32 °C). Fifteen older individuals (65 ± 3 years) treated for hypertension underwent a symptom-limited maximal graded exercise test to determine their heart rate at anaerobic threshold, and respiratory compensation point. On different days, participants were randomized to HIIE (walking/jogging between 11 and 17 of RPE; 25 min) and MICE (walking at 11–13 of RPE; 30 min). Heart rate during the low-intensity intervals of HIIE and MICE remained below the graded exercise test’s heart rate at anaerobic threshold (−7 ± 18 bpm/−16 ± 15 bpm) and respiratory compensation point (−18 ± 18 bpm/−30 ± 16 bpm), respectively, and maintained in the aerobic training zone during the high-intensity intervals of HIIE (+8 ± 18 bpm/−4 ± 19 bpm). The RPE scale at 15–17 is a useful tool for prescribing and self-regulating heated water-based HIIE and may have important implications for water-based exercise in older individuals with hypertension.
The Effects of Concussions on Batting Performance in Major League Baseball Players: A Critically Appraised Topic
Authors: Turbyfill Lindsey R. , Hatley Logan J. , Needle Alan R.
Published: November 2021

Clinical Scenario: The impact of concussion in baseball athletes is far reaching although typically less studied than collision sports. The neuromotor sequelae of these injuries can have lasting effects on the high level of coordination needed in baseball skills. In professional athletes, the long-term effects of concussion combined with the high demands for performance can potentially shorten athletes’ careers. Clinical Question: Do Major League Baseball players who have had a concussion compared with those with no history of concussions have decreased batting performance after they return to full participation? Summary of Key Findings: Seven articles meeting criteria for level 2 evidence were identified in this critically appraised topic. Four of 7 studies found deficits in batting performance following concussion. However, the 3 studies that did not identify differences lacked a comparative control group. Clinical Bottom Line: Evidence supports a relationship between concussion and decreased batting performance. This suggests there is a demand for screening and rehabilitation strategies aimed at improving sport-specific neuromotor and coordination skills in these individuals. Strength of Recommendation: Collectively, the body of evidence included to answer the clinical question aligns with the strength of recommendation of B.
Detecting Flavobacterial Fish Pathogens in the Environment via High-Throughput Community Analysis
Authors: Testerman Todd , Beka Lidia , McClure Emily Ann , Reichley Stephen R. , King Stacy , Welch Timothy J. , Graf Joerg
Published: November 2021

Early detection of a deadly disease outbreak in a population can be the difference between mass mortality or mitigated effects. In the present study, we evaluated and compared two molecular techniques for detecting economically impactful aquaculture pathogens.
Temporal and Gene Reassortment Analysis of Influenza C Virus Outbreaks in Hong Kong, SAR, China
Authors: Daniels Rodney S. , Galiano Monica , Ermetal Burcu , Kwong Jasmine , Lau Chi S. , Xiang Zheng , McCauley John W. , Lo Janice
Published: November 2021

Influenza C virus (ICV) infection of humans is common, with the great majority of people being infected during childhood, though reinfection can occur throughout life. While infection normally results in “cold-like” symptoms, severe disease cases have been reported in recent years.
On the Program of Russian Research in the Field of Controlled Thermonuclear Fusion and Plasma Technologies
Authors: Ilgisonis V. I. , Ilyin K. I. , Novikov S. G. , Olenin Yu. A.
Published: November 2021

The main scientific problems solved within the “Development of controlled fusion technologies and innovative plasma technologies” federal project, part of the comprehensive program “Development of equipment, technologies, and scientific research in the field of atomic energy use in the Russian Federation for the period up to 2024” are described. A brief history of development of the project is given, and the description of its main components, participants, anticipated results, and possibilities for the future is p-resented. This paper is a foreword to the materials of the journal issue entirely devoted to the Tokamak with Reactor Technologies (TRT) project, which is to be developed within the federal project.
Association of Parvovirus B19 with Rheumatic Diseases
Authors: Au Chi Kit , Lai Tin Lok , Yim Cheuk Wan
Published: November 2021

Human parvovirus B19 infections are well reported to be associated with different autoimmune disorders. They can either mimic or trigger autoimmune diseases, such as systemic lupus erythematous (SLE), rheumatoid arthritis (RA), and vasculitis. A lack of awareness about this infection can result in delays in diagnosis and poor care. In this review, the basic biology and clinical aspects of the parvovirus, human immune response, and the pathogenesis in the rheumatic diseases are discussed.
Uveitis and Rheumatological Diseases
Authors: Cheung Chloe Kwong Yee , Wong Mandy Oi Man , Chan Carmen Kar Mun , Chung Ho Yin
Published: November 2021

Uveitis is the most common ophthalmological disorder in the field of rheumatology, accounting for a significant proportion of visual morbidity, both locally and internationally. Causative factors can be divided into infectious and noninfectious etiologies. The diagnosis of uveitis is a major challenge due to heterogeneity in presentation. The disease course may be acute monophasic, recurrent, or chronic relapsing. Complications include posterior synechiae, secondary cataract, ocular hypertension or glaucoma, macular edema, retinal vascular occlusion, epiretinal membrane, and so on, and ultimately visual loss. Antimicrobial therapy is indicated for infection, whereas noninfectious uveitis warrants a combination of steroids, immunosuppressives, and anti-inflammatory agents. With the advancement of biologics, treatment strategies in chronic, noninfectious uveitis have had multiple breakthroughs, particularly in treatment-resistant cases. This article provides a review of the diagnostic approach to uveitis based on symptomatology and ophthalmological findings, and discussion of relevant treatment modalities and strategies.
Toward Taming the Overhead Monster for Data-flow Integrity
Authors: Feng Lang , Huang Jiayi , Huang Jeff , Hu Jiang
Published: November 2021

Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks. However, its real-world application has been quite limited so far because of the prohibitive performance overhead it incurs. Moreover, the overhead is enormously difficult to overcome without substantially lowering the DFI criterion. In this work, an analysis is performed to understand the main factors contributing to the overhead. Accordingly, a hardware-assisted parallel approach is proposed to tackle the overhead challenge. Simulations on SPEC CPU 2006 benchmark show that the proposed approach can completely enforce the DFI defined in the original seminal work while reducing performance overhead by 4×, on average.
Theoretical Study on V Atom Supported on N and P-Doped Defective Graphene for Electrocatalytic Nitrogen Reduction
Authors: Song Wei , Wang Ran , Liu Xiao , Guo Yongliang , Fu Ling , He Chaozheng
Published: November 2021

Ammonia (NH3) is one of the most extensively produced chemicals worldwide, and it plays an important and indispensable role in the global economy. At present NH3 is mainly produced by the traditional Haber-Bosch process operated at high pressure and temperature, which results in massive energy consumption and carbon dioxide emissions. The electrochemical nitrogen reduction reaction (NRR) can allow the production of NH3 from nitrogen and water under ambient conditions and is regarded as a sustainable alternative to the Haber–Bosch process because of its low energy consumption and limited environmental impact. In this study, using density functional theory calculations, we designed a monovacancy defective graphene (MVG) doped with various nitrogen and phosphorus atoms and a single vanadium atom (VN1–3@MVG and VP1–3@MVG) to be used as electrocatalysts. The results revealed that N- and P-doping are beneficial for N2 adsorption and activation and can effectively reduce the energy barrier of the NRR, especially for P-doping. Among the synthesized electrocatalysts, double P-doped V@MVG demonstrated the best catalytic activity with a low free energy barrier of 0.43 eV. This paper reports the development of an efficient catalyst for electrochemical NH3 synthesis and provides valuable insights on the design of electrocatalysts with high activity and stability.
Numerical Investigation on Diffusion-Induced Cracking in Solid Electrolyte of Composite Electrode and Its Impact on the Li-Ion Conductivity
Authors: Huang Ying , Zhang Fangzhou , Huang Qiu-An , He Yaolong , Zhang Jiujun
Published: November 2021

In this paper, the cracking of the solid electrolyte (SE) and its impacts on the effective Li-ion conductivity of composite electrodes of all-solid-state lithium-ion batteries (ASSLIBs) are investigated numerically. A two-dimensional finite element (2D FEM) model was developed for composite electrodes in which active material particles (AM particles) are embedded in the solid electrolyte. The 2D FEM model can successfully calculate and simulate the diffusion-induced stress, the generation of solid electrolyte cracks (SE cracks), and the Li-ion transport. The degradation of Li-ion conductivity for cracked composite electrodes is calculated with the homogenization method. It is revealed that the diffusion-induced volume variation in AM particles can generate significant stress and thus SE cracking in composite electrodes of ASSLIBs. The calculated results suggest that swelling AM particles are more favorable than shrinking AM particles for the structural stability of composite electrodes. It is also demonstrated that the evolution of the conductivity with the propagation of SE cracking is consistent with the percolation theory. The fundamental understating of the SE cracking and its impact in this paper may benefit the design of novel ASSLIBs with more stable performance and a longer lifespan.
A Robust Electrochemical Sensor for Determination of Thiamethoxam Based on Electrochemical Reduced Graphene Oxide-Cationic Pillar[6]arene Composite Film
Authors: Wu Yanju , Li Zikang , Han Dongyang , Duan Qunpeng , Wang Fei
Published: November 2021

On the surface of a glassy carbon electrode, electrochemically reduced graphene oxide-cationic pillar[6]arene (ErGO-CP6) composite film was constructed using a pulsed potential method. UV–vis spectra, SEM, Raman spectra and electrochemical experiments were applied to characterize the composite film. It was then used as a new electrochemical sensing platform for determination of thiamethoxam. Due to the synergistic effect of ErGO and CP6, this composite film shows a higher sensitivity and better selectivity toward thiamethoxam than that of ErGO film. The linear range from 1.0 × 10−7 to 1.3 × 10−5 mol l−1 was obtained by differential pulse voltammetry. Meanwhile, the method was applied to cucumber and tomato samples in a recovery test. The recovery was between 92.0% and 98.7%, and the results are satisfactory. This study presents a promising electrochemical sensing platform for rapid and sensitive analysis of thiamethoxam.
A new anesthesia protocol enabling longitudinal lung-function measurements in neonatal rabbits by micro-CT
Authors: Ferrini Erica , Leo Ludovica , Corsi Luisa , Catozzi Chiara , Salomone Fabrizio , Ragionieri Luisa , Pennati Francesca , Stellari Franco Fabio
Published: November 2021

Micro-computed tomography (micro-CT) imaging is an emerging technology with many applications in small animals, for example, the study of pulmonary diseases, although clear guidelines and critical mass of evidence are still missing in the preclinical literature. The neonatal rabbit is a valuable model for studying pulmonary development. However, the longitudinal monitoring of lung function by micro-CT can be challenging. Distinctive datasets corresponding to the end-inspiration and end-expiration phases need to be generated and analyzed to derive lung-functional parameters. The quality of CT scans and the reliability of parameters obtained remain highly dependent on the anesthesia protocol used. Three different anesthetic protocols were tested. The combination of dexmedetomidine 0.25 mg/kg injected intraperitoneally followed by 1% isoflurane was found to facilitate CT imaging at 4 and 11 days after birth. Contrarily, isoflurane and ketamine-xylazine were found unsuitable and thus not investigated further. Total lung volumes significantly increased at day 11 compared with baseline in both respiratory phases, whereas lung tissue remained constant. As expected, functional residual capacity, air-to-tissue ratio, and minute ventilation were significantly increased at day 11 in each animal. Those parameters were correlated with inspiratory capacity, compliance, elastance, and resistance of both respiratory system and tissue component, as measured by flexiVent. Lung development was also evaluated by histomorphometric analyses. In conclusion, we have identified a safe and suitable anesthesia protocol for micro-CT imaging in neonatal rabbits. Moreover, the possibility to longitudinally measure lung function in the same subject dramatically reduced the intraexperimental variability.
Enabling Device‐to‐Device Transmission for NOMA‐Aided Systems
Authors: Le Anh-Tu , Nguyen Nhan Duc , Do Dinh-Thuan , Munochiveyi Munyaradzi
Published: November 2021

To utilize the close transmission, we assume that the device‐to‐device (D2D) link is activated to improve the performance of the far user. We consider two groups of users in the nonorthogonal multiple access‐ (NOMA)‐ aided wireless system. These features are necessary for massive connectivity in future wireless systems. The system performance also shows suitable performance at far distance users. To evaluate the performance in detail, we derive novel closed form expressions of outage probability. In practical situations impaired by channel uncertainty, it is necessary to evaluate the impact of channel error levels on outage probability. Our numerical results indicated that the transmit power at the base station and channel error level are the main impacts on system performance. Despite these impacts, our obtained numerical results demonstrated that the proposed scheme can still increase energy efficiency and achieve significant outage performance via many practical challenges.
Nanolayered Double Hydroxide Inhibits the Pathogenicity of Vibrio parahaemolyticus
Authors: Wang Cang , Ma Xiaoyi , Hu Jie , Tian Xiaopeng , Zhang Hao , Dong Dongxue , Fang Zaixi , Lyu Mingsheng , Wang Shujun , Lu Jing
Published: November 2021

Nanolayered double hydroxide (LDH) is a type of anion layered inorganic compound whose bacteriostatic properties have recently garnered much attention. Vibrio parahaemolyticus is a marine pathogen that can lead to aquaculture diseases and substantial economic losses. Therefore, our study assessed the mechanisms by which Mg/Al-LDH prevents V. parahaemolyticus infection. Our results demonstrated that Mg/Al-LDH not only inhibited V. parahaemolyticus growth but also biofilm formation. Moreover, coupling Mg/Al-LDH with hydrogen peroxide and UV irradiation further inhibited the growth and biofilm formation of V. parahaemolyticus. Additionally, Mg/Al-LDH was found to adversely affect DNA and the gelling ability of chitosan. Furthermore, exposing V. parahaemolyticus to Mg/Al-LDH led to a 54.73% and 4.3% inhibition in the expression of the toxic genes tlh and trh, respectively. Mg/Al-LDH also improved the symptoms of V. parahaemolyticus infection in Penaeus vannamei, making this a promising candidate to prevent pathogenic bacteria infection in aquaculture.
Analysis of Cost Overrun and Schedule Delays of Infrastructure Projects in Low Income Economies: Case Studies in Ethiopia
Authors: Melaku Belay Solomon , Tilahun Seifu , Yehualaw Mitiku , Matos Jose , Sousa Helder , Workneh Endalew Temesgen
Published: November 2021

Today, several developing countries struggle to improve the cost and time performances of major infrastructure works due to various reasons. Cost overrun and delay are one of the major challenges being faced by the construction and infrastructure sector. Hence, the aim of this study is to explore the extent of cost overrun and schedule delays in building and road infrastructure projects across the Ethiopian construction industry. Primary data were collected through a structured questionnaire survey to evaluate the potential risks leading to those challenges. Various data analysis tools were employed, to investigate the critical causes of cost overrun and delays in infrastructure projects. The findings reveal that the minimum cost overrun for building construction projects is found to be 2%, whereas the maximum and average cost overruns are 248% and 35%, respectively. For road infrastructure projects, the minimum, maximum, and average cost overruns are found to be 1%, 61%, and 18%, respectively. Similarly, the minimum, maximum, and average delays recorded in building construction projects are 9%, 802%, and 143%, respectively, whereas, in road infrastructure projects, the minimum delay is found to be 3%, the maximum delay is 312%, and an average schedule delay of 110% is recorded. In addition, the top risk factors leading to cost overrun in infrastructure projects are inflation, inaccurate cost estimates, and variations, whereas the major risks causing schedule delays are variations, economic conditions, and escalation of material prices. Further, practical implications and key recommendations were provided to curb cost overrun and delay in infrastructure projects.
Implications of the Presence of Hyperdense Middle Cerebral Artery Sign in Determining the Subtypes of Stroke Etiology
Authors: Sangpetch Suchada , Wantaneeyawong Chayasak , Soontornpun Atiwat , Tiyapun Nantaporn , Tanprawate Surat , Thiankhaw Kitti
Published: November 2021

Background. Identifying stroke subtypes is crucial in choosing appropriate treatment, predicting outcomes, and managing recurrent stroke prevention. Objectives. To study the association of hyperdense middle cerebral artery sign (HMCAS) on noncontrast computed tomography (NCCT) brain and subtypes of stroke etiology. Methods. This is a retrospective hypothesis testing study. Patients aged 18 or over who had middle cerebral artery occlusion symptoms with HMCAS with verification on brain NCCT and received intravenous thrombolysis between January 2016 and June 2019 were enrolled. The demographic data, clinical outcomes, stroke subtypes, and characteristics of HMCAS were collected from medical records. Results. Ninety-nine out of 299 enrolled patients presented with HMCAS. The most common stroke subtype was cardioembolism (59%). Of the baseline characteristics, hypertension was more common in cases of large-artery atherosclerosis (LAA) (86.4%), and atrial fibrillation (AF) was the highest in cardioembolism (44.8%). HMCAS disappearance in cardioembolism was lowest compared to LAA and others (63% vs. 91% vs. 94.7%, respectively). The univariable analysis found that HMCAS disappearance is significantly associated with all stroke subtypes (Odds ratio, 95% confidence interval 10.58, 1.31-85.43;

P
=
0.027

for other and 5.88, 1.24-27.85;

P
=
0.026

for LAA). Multinomial logistic regression found that body weight and hypertension were associated with the LAA subtype. AF and intracranial hemorrhage (ICH) were associated with cardioembolism. Conclusion. The most likely diagnosis from the presence of HMCAS is cardioembolism, but the definite stroke etiologic subtype can not be identified. Combining the patient risk factors, including body weight, hypertension, and AF, with HMCAS and its characteristics will predict stroke subtypes more accurately.
Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction
Authors: Zhang Jun , Chen Xuedong
Published: November 2021

Although socially responsible investment (SRI) has developed into an important investment style, only a small number of studies discuss SRI portfolio construction. In view of the overwhelming breakthrough of machine learning in prediction, this paper proposes SRI portfolio construction models by combining a double-screening mechanism considering machine learning prediction and an extended global minimum variance (GMV) model (or extended maximum Sharpe ratio (MSPR) model), which are, respectively, named double-screening socially responsible investment (DSSRI) portfolio models I and II. The proposed models consist of two stages, i.e., stock screening and asset allocation. First, this paper develops a novel double-screening mechanism incorporating environmental, social, and corporate governance (ESG) and return potential criteria to ensure that high-quality stocks with good ESG performance and high-return potential are input into the optimal portfolio. Specifically, to obtain accurate stock return predictions, an extreme learning machine model optimized by the genetic algorithm is employed to predict stock prices. Next, to trade off the financial and ESG objectives of SRI investors, an extended GMV model (or extended MSPR model) considering the ESG factor is introduced to determine the capital allocation proportion of the stocks. We take the A-share market of China as the sample to verify the effectiveness of the proposed models. The empirical results demonstrate that compared with alternative models, the proposed models can yield better annualized return and ESG score performance as well as competitive Sharpe ratio performance.
Surrounding Rock Control Technology When the Longwall Face Crosses Abandoned Roadways: A Case Study
Authors: Jiang Zhenpeng , Wang Fangtian , Miao Kaijun , Cao Qinghua
Published: November 2021

When a working face is crossing the abandoned roadways, problems such as roof subsidence, rock fracture, and instability will occur, resulting in widespread roof fall and rib spalling, which seriously affect safe and efficient mining on the working face. In this paper, the no. 23 coal pillar working face of Juji coal mine is taken as the engineering background, a mechanical model of crossing the abandoned roadways is constructed aimed at the problem of the working face crossing the abandoned roadway group, the collapse of the abandoned roadway roof is analyzed, a scheme of crossing the abandoned roadways is designed, and the development law of the stress and plastic zone after the reinforcement scheme is stimulated and analyzed. The results show that when the working face advances to the abandoned roadway, key block B crosses the abandoned roadway and the solid coal to form a “cross-roadway long key block.” It is calculated that the minimum support resistance required for the abandoned roadway is 6700 kN. Based on the results of numerical comparison, it is concluded that filling wood pile when the working face passes through the roof abandoned roadway and adding anchor cables for reinforcement support when the working face crosses the coal seam abandoned roadway effectively reduce the stress concentration of surrounding rocks, decrease the development of the plastic zone, and achieve safe and efficient mining when the working face crosses the abandoned roadways.
Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis
Authors: Ezzati-Jivan Naser , Daoud Houssem , Dagenais Michel R.
Published: November 2021

Root cause identification of performance degradation within distributed systems is often a difficult and time‐consuming task, yet it is crucial for maintaining high performance. In this paper, we present an execution trace‐driven solution that reduces the efforts required to investigate, debug, and solve performance problems found in multinode distributed systems. The proposed approach employs a unified analysis method to represent trace data collected from the user‐space level to the hardware level of involved nodes, allowing for efficient and effective root cause analysis. This solution works by extracting performance metrics and state information from trace data collected at user‐space, kernel, and network levels. The multisource trace data is then synchronized and structured in a multidimensional data store, which is designed specifically for this kind of data. A posteriori analysis using a top‐down approach is then used to investigate performance problems and detect their root causes. In this paper, we apply this generic framework to analyze trace data collected from the execution of the web server, database server, and application servers in a distributed LAMP (Linux, Apache, MySQL, and PHP) Stack. Using industrial level use cases, we show that the proposed approach is capable of investigating the root cause of performance issues, addressing unusual latency, and improving base latency by 70%. This is achieved with minimal tracing overhead that does not significantly impact performance, as well as O(log n) query response times for efficient analysis.
Modeling Research and Test Verification of the Seismic Response of a Multistage Series Liquid Tank
Authors: Yao Xiaoguang , Meng Lijun , Chu Peng , Yao Liang
Published: November 2021

Liquid storage tanks are lifeline structures and strategically very important. Heavy damages or even collapse of these facilities subjected to strong earthquakes may cause disastrous consequences. In this paper, the seismic response of a multistage series liquid storage tank was simulated by a finite element method and verified by a scaled‐down experiment. The structural flexibility of the tank and the liquid‐structure coupling characteristics between the liquid and tank wall were considered in the research. A multimass‐block and spring model was employed to be equivalent to the longitudinal vibration of the liquid in the storage tank. The relationships between the connection springs and the elements of the stiffness matrix were explicitly deduced. The seismic response analysis of a four‐stage series liquid tank was carried out, and the acceleration response, the stress response of the tank, and the vertical vibration of the liquid were obtained. The experimental results are in good agreement with the simulation results, which verifies the effectiveness of the modeling method in this paper.
The Improving Risk Informed HPV Screening (IRIS) Study: Design and Baseline Characteristics
Authors: Gage Julia C. , Raine-Bennett Tina , Schiffman Mark , Clarke Megan A. , Cheung Li C. , Poitras Nancy E. , Varnado Nicole E. , Katki Hormuzd A. , Castle Philip E. , Befano Brian , Chandra Malini , Rydzak Greg , Lorey Thomas , Wentzensen Nicolas
Published: November 2021

Background:

Cervical cancer screening with high-risk human papillomavirus (HrHPV) testing is being introduced. Most HrHPV infections are transient, requiring triage tests to identify individuals at highest risk for progression to cervical cancer. Head-to-head comparisons of available strategies for screening and triage are needed. Endometrial and ovarian cancers could be amenable to similar testing.

Methods:

Between 2016 and 2020, discarded cervical cancer screening specimens from women ages 25 to 65 undergoing screening at Kaiser Permanente Northern California were collected. Specimens were aliquoted, stabilized, and stored frozen. Human papillomavirus (HPV), cytology, and histopathology results as well as demographic and cofactor information were obtained from electronic medical records (EMR). Follow-up collection of specimens was conducted for 2 years, and EMR-based data collection was planned for 5 years.

Results:

Collection of enrollment and follow-up specimens is complete, and EMR-based follow-up data collection is ongoing. At baseline, specimens were collected from 54,957 HPV-positive, 10,215 HPV-negative/Pap-positive, and 12,748 HPV-negative/Pap-negative women. Clinical history prior to baseline was available for 72.6% of individuals, of which 53.9% were undergoing routine screening, 8.6% recently had an abnormal screen, 30.3% had previous colposcopy, and 7.2% had previous treatment. As of February 2021, 55.7% had one or more colposcopies, yielding 5,563 cervical intraepithelial neoplasia grade 2 (CIN2), 2,756 cervical intraepithelial neoplasia grade 3 (CIN3), and 146 cancer histopathology diagnoses.

Conclusions:

This robust population-based cohort study represents all stages of cervical cancer screening, management, and posttreatment follow-up.

Impact:

The IRIS study is a unique and highly relevant resource allowing for natural history studies and rigorous evaluation of candidate HrHPV screening and triage markers, while permitting studies of biomarkers associated with other gynecologic cancers.
High Prediagnosis Inflammation-Related Risk Score Associated with Decreased Ovarian Cancer Survival
Authors: Brieger Katharine K. , Phung Minh Tung , Mukherjee Bhramar , Bakulski Kelly M. , Anton-Culver Hoda , Bandera Elisa V. , Bowtell David D.L. , Cramer Daniel W. , DeFazio Anna , Doherty Jennifer A. , Fereday Sian , Fortner Renée Turzanski , Gentry-Maharaj Aleksandra , Goode Ellen L. , Goodman Marc T. , Harris Holly R. , Matsuo Keitaro , Menon Usha , Modugno Francesmary , Moysich Kirsten B. , Qin Bo , Ramus Susan J. , Risch Harvey A. , Rossing Mary Anne , Schildkraut Joellen M. , Trabert Britton , Vierkant Robert A. , Winham Stacey J. , Wentzensen Nicolas , Wu Anna H. , Ziogas Argyrios , Khoja Lilah , Cho Kathleen R. , McLean Karen , Richardson Jean , Grout Bronwyn , Chase Anne , Deurloo Cindy McKinnon , Odunsi Kunle , Nelson Brad H. , Brenton James D. , Terry Kathryn L. , Pharoah Paul D.P. , Berchuck Andrew , Hanley Gillian E. , Webb Penelope M. , Pike Malcolm C. , Pearce Celeste Leigh
Published: November 2021

Background:

There is suggestive evidence that inflammation is related to ovarian cancer survival. However, more research is needed to identify inflammation-related factors that are associated with ovarian cancer survival and to determine their combined effects.

Methods:

This analysis used pooled data on 8,147 women with invasive epithelial ovarian cancer from the Ovarian Cancer Association Consortium. The prediagnosis inflammation-related exposures of interest included alcohol use; aspirin use; other nonsteroidal anti-inflammatory drug use; body mass index; environmental tobacco smoke exposure; history of pelvic inflammatory disease, polycystic ovarian syndrome, and endometriosis; menopausal hormone therapy use; physical inactivity; smoking status; and talc use. Using Cox proportional hazards models, the relationship between each exposure and survival was assessed in 50% of the data. A weighted inflammation-related risk score (IRRS) was developed, and its association with survival was assessed using Cox proportional hazards models in the remaining 50% of the data.

Results:

There was a statistically significant trend of increasing risk of death per quartile of the IRRS [HR = 1.09; 95% confidence interval (CI), 1.03–1.14]. Women in the upper quartile of the IRRS had a 31% higher death rate compared with the lowest quartile (95% CI, 1.11–1.54).

Conclusions:

A higher prediagnosis IRRS was associated with an increased mortality risk after an ovarian cancer diagnosis. Further investigation is warranted to evaluate whether postdiagnosis exposures are also associated with survival.

Impact:

Given that pre- and postdiagnosis exposures are often correlated and many are modifiable, our study results can ultimately motivate the development of behavioral recommendations to enhance survival among patients with ovarian cancer.
Synchronous Development of Acute Megakaryoblastic Leukaemia and Disseminated Melanoma following Treatment of a Germ Cell Tumour: A Case Report
Authors: Gullapalli Veena , Hsu Hannah , Bhargava Vanita , Presgrave Peter
Published: November 2021

Somatic malignant transformation of germ cell tumours is a well-described but poorly understood phenomenon. It is characterized by differentiation of pluripotent teratoma cells into somatic tumour cells. Following malignant transformation, the most common histologies are sarcomas and primitive neuroectodermal tumours; however, other subtypes have been recognized including melanoma, leukaemia, and renal cell carcinoma. We report a case of a 38-year-old male who had recently completed treatment for a mediastinal germ cell tumour with teratomatous components. He presented several months after completion of chemotherapy with metastatic lesions in his spine and liver accompanied with severe pancytopenia. He was subsequently diagnosed with acute megakaryoblastic leukaemia (AMKL), and a biopsy of a liver lesion was consistent with metastatic melanoma. This case illustrates the simultaneous development of 2 rare malignant entities: mediastinal germ cell tumour-associated AMKL and somatic malignant transformation to melanoma. It also highlights the importance of close surveillance to detect these metastatic sequelae and the emerging role of tumour sequencing to establish targetable pathways.
Bilateral Orbitofrontal Repetitive Transcranial Magnetic Stimulation in Frontal Lobe Epilepsy: A Case Report
Authors: Mikellides Georgios , Michael Panayiota , Gregoriou Angelos , Schuhmann Teresa , Sack Alexander T.
Published: November 2021

Epilepsy is a common and severe neurological disorder affecting millions of people worldwide. Nowadays, antiseizure medications (ASMs) are the main treatment for most epilepsy patients, although many of them do not respond to ASMs and suffer from drug-resistant epilepsy (DRE). Alternative and novel treatment methods have been offered nowadays, showing promising results for the treatment of DRE. Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive method that has become increasingly popular in the last decades. This article reports a patient with frontal lobe epilepsy. We aimed to investigate whether bilateral orbitofrontal (OFC) low-frequency rTMS (LF-rTMS) is feasible and tolerable, safe, and potentially clinically effective in treating epileptic seizures. The patient’s satisfaction with rTMS therapy was self-reported to be high, as rTMS helped in reducing the frequency of the focal attacks and completely abolished the preceding feeling of fear and panic. Therefore, bilateral OFC rTMS treatment can be well tolerated in patients with frontal epilepsy although the findings of the present case report with regard to clinical efficacy warrant further investigation.