Relevant Benchmark Research Articles

Benthic marine litter in the coastal zone of Bejaia (Algeria) as indicators of anthropogenic pollution

We studied the abundance of seafloor litter in the bay of Bejaia, Algeria, in order to better understand its sources and distribution and provide scientists with the technical basis for further monitoring and reduction measures. Data were collected during trawl surveys dedicated to fishing activity, between 40 and 200m. After each trawl, litter items were manually sorted and analyzed. The highest concentration of 58,998 items/ha was recorded to the east of the bay. Overall, plastic was the dominant component of the litter with 88% of the total amount, in weight. Most of the waste was of terrestrial origin, with rivers and beaches being the main sources due to population density, highly developed tourism during the summer season, and inadequate waste management. Overall, the results provide relevant benchmarks for future monitoring and are discussed in terms of poor waste management, a critical issue in the region.

Importance Sampling-Based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models

We propose an importance sampling (IS)-based transport map Hamiltonian Monte Carlo procedure for performing a Bayesian analysis in nonlinear high-dimensional hierarchical models. Using IS techniques to construct a transport map, the proposed method transforms the typically highly complex posterior distribution of a hierarchical model such that it can be easily sampled using standard Hamiltonian Monte Carlo. In contrast to standard applications of high-dimensional IS, our approach does not require IS distributions with high fidelity, which makes it computationally very cheap. Moreover, it is less prone to the notorious problem of IS that the variance of IS weights can become infinite. We illustrate our algorithm with applications to challenging dynamic state-space models, where it exhibits very high simulation efficiency compared to relevant benchmarks, even for variants of the proposed method implemented using a few dozen lines of code in the Stan statistical software. The article is accompanied by supplementary material containing further details, and the computer code is available at https://github.com/kjartako/TMHMC. These are also supplementary materials for this article are available online.

An alert-assisted inspection policy for a production process with imperfect condition signals

We study the inspection scheduling decisions for a production process that goes through a hidden defective state before its failure. The production process is equipped with a predictive model, generating alert and no-alert signals. An alert signal indicates that production process is in the defective state, while a no-alert signal indicates it is in the healthy state. The signals are imperfect, meaning that an alert signal can be generated for a healthy process and a no-alert signal can be generated for a defective process. Only a costly inspection can detect the true condition. We introduce a new inspection policy, which generalizes the age-based inspection policy that performs planned inspections at predetermined intervals, by considering that an inspection can also be triggered by a certain number of alerts from the predictive model. To optimize the proposed inspection policy, a stochastic dynamic programming model is formulated with the objective of minimizing the long-run expected cost rate. The performance improvement achieved by the optimal policy is quantified by comparing it to practically relevant benchmark policies. Numerical experiments with a set of realistic problem instances show that adding alert-triggered inspections to traditional age-based inspection scheduling brings up to 44% reduction in the expected cost rate when the predictive model is sufficiently accurate. Characterizing the performance of the optimal policy at a given level of imperfectness is especially useful in practice as it allows making an assessment on how much can be invested to justify a certain level of improvement in the predictive model.

An Empirical Assessment of Real Activities Manipulation Measures

ABSTRACTI empirically assess the extent to which real earnings management metrics capture opportunistic behavior versus firms' fundamental factors such as performance. For the traditional proxies proposed by Roychowdhury (2006), I find (1) the economic magnitude of the proxies to be high relative to two relevant benchmarks; (2) they exhibit persistence; and (3) they vary predictably with performance. These findings suggest that the traditional proxies likely not only capture opportunistic behavior, but also likely reflect fundamental factors. I also examine several adjusted proxies based on refinements proposed by subsequent studies. I find that those proposed by Vorst (2016) and those based on Kothari, Mizik, and Roychowdhury (2016) seem to be the most effective at attenuating correlation with underlying fundamentals. Additional simulation tests on bias and power reveal that, between the two adjusted proxies, those based on Kothari et al.'s (2016) are generally more preferable.JEL Classifications: M41; M1.

Quality metrics in minimally invasive gynecologic surgery.

Quality improvement and patient safety are relevant to the advancement of clinical care, particularly in the field of minimally invasive gynecologic surgery (MIGS). Although safety and feasibility of MIGS have been established, identification of quality metrics in this field is also necessary. Surgical quality improvement has focused on national overarching measures to reduce mortality, surgical site infections (SSIs), and complications. Quality improvement in minimally invasive surgery has additionally led to advancements in postoperative patient recovery and long-term outcomes. Process measures in minimally invasive surgery include use of bundles and enhanced recovery after surgery (ERAS) programs. However, procedure-specific quality metrics for MIGS outcomes are poorly defined at this time. Quality metrics in minimally invasive gynecology are well defined for structural measures and select process measures. Creation of relevant benchmarks for outcome measures in minimally invasive gynecologic surgery are needed.

Evaluating 3D Printers Using Data Envelopment Analysis

Data Envelopment Analysis (DEA) is an established powerful mathematical programming technique, which has been employed quite extensively for assessing the efficiency/performance of various physical or virtual and simple or complex production systems, as well as of consumer and industrial products and technologies. The purpose of the present study is to investigate whether DEA may be employed for evaluating the technical efficiency/performance of 3D printers, an advanced manufacturing technology of increasing importance for the manufacturing sector. For this purpose, a representative sample of 3D printers based on Fused Deposition Modeling technology is examined. The technical factors/parameters of 3D printers, which are incorporated in the DEA, are investigated and discussed in detail. DEA evaluation results compare favorably with relevant benchmarks from experts, indicating that the suggested DEA technique in conjunction with technical and expert evaluation could be employed for evaluating the performance of a highly technological system, such as the 3D printer.

The patient acceptable symptom state for the knee society score, oxford knee score and short form-36 following unicompartmental knee arthroplasty.

The patient acceptable symptom state (PASS) is a target value on a patient-reported outcome measures (PROM) scale beyond which patients deem themselves to have attained an acceptable outcome. This study aimed to define the PASS thresholds for generic and knee-specific PROMs at 2years after unicompartmental knee arthroplasty (UKA). Prospectively collected data of 955 patients who underwent UKA for medial osteoarthritis at a single institution was reviewed. Patients were assessed preoperatively and 2years postoperatively using the Knee Society Knee Score (KSKS), Function Score (KSFS), Oxford Knee Score (OKS), SF-36 Physical Component Score (PCS) and Mental Component Score (MCS). Responses to an anchor question assessing patients' overall rating of treatment results were dichotomized and used to determine if PASS was achieved. PASS thresholds for each PROM were selected based on the Youden index on a receiver operating characteristics (ROC) curve. Sensitivity analyses were performed for different subgroups (by age, gender, BMI), baseline score tertiles and an alternate definition of PASS. In total, 92.7% reported their current state as acceptable. The areas under the curve (AUC) for ROCs were 0.72-0.83, except for the SF-36 PCS (AUC 0.64), indicating good discriminative accuracy of the other PROMs. PASS thresholds were 85.5 for KSKS, 77.5 for KSFS, 41.5 for OKS, 49.9 for SF-36 PCS and 54.6 for SF-36 MCS. Sensitivity analyses revealed that the thresholds were robust. Patients who attained a PASS were at least 4-5 times more likely to be satisfied and have expectations fulfilled. PASS thresholds can be used to define treatment success in future outcome studies. At the individual level, they provide clinically relevant benchmarks for surgeons when assessing postoperative recovery. III.

The TR-BDF2 method for second order problems in structural mechanics

The application of the TR-BDF2 method to second order problems typical of structural mechanics and seismic engineering is discussed. A reformulation of this method is presented, that only requires the solution of algebraic systems of size equal to the number of displacement degrees of freedom. A linear analysis and numerical experiments on relevant benchmarks show that the TR-BDF2 method is superior in terms of accuracy and efficiency to the classical Newmark method and to its generalizations.

PalaCell2D: A framework for detailed tissue morphogenesis

In silico, cell based approaches for modeling biological morphogenesis are used to test and validate our understanding of the biological and mechanical processes that are at work during the growth and the organization of multi-cell tissues. As compared to in vivo experiments, computer based frameworks dedicated to tissue modeling allow us to easily test different hypotheses, and to quantify the impact of various biophysically relevant parameters. Here, we propose a formalism based on a detailed, yet simple, description of cells that accounts for intra-, inter- and extra-cellular mechanisms. More precisely, cell growth and division are described through the space and time evolution of the membrane vertices. These vertices follow a Newtonian dynamics, meaning that their evolution is controlled by different types of forces: a membrane force (spring and bending), an adherence force (inter-cellular spring), external and internal pressure forces. Different evolution laws can be applied to the internal pressure, depending on the intra-cellular mechanism of interest. In addition to the cells dynamics, our formalism further relies on a lattice Boltzmann method, using the Palabos library, to simulate the diffusion of chemical signals. The latter aims at driving the growth and migration of a tissue by simply changing the state of the cells. All of this leads to an accurate description of the growth and division of cells, with realistic cell shapes and where membranes can have different properties. While this work is mainly of methodological nature, we also propose to validate our framework through simple, yet biologically relevant benchmark tests at both single-cell and full tissue scales. This includes free and chemically controlled cell tissue growth in an unbounded domain. The ability of our framework to simulate cell migration, cell compression and morphogenesis under external constraints is also investigated in a qualitative manner.

576 Striving for Excellence: Beacon Application Submission and Achievement

Abstract Introduction The American Association of Critical-Care Nurses (AACN) Beacon Award (BA) recognizes individual nursing units with Gold, Silver, or Bronze level for excellence. The BA signifies an effective and systematic approach to policies, procedures and processes that include engagement of staff and key stakeholders; fact-based evaluation strategies for continuous process improvement; and performance measures that meet or exceed relevant benchmarks. Units designated with BA show an overall higher morale and lower turnover rate. Being a verified burn unit and Magnet designated facility, the unit practice council (upc) began the application process for Beacon. Methods The application process started in 2017 through a core group of UPC members. Different sections of the application were written through the guidance of the team leader and with support of the unit management. Data was gathered from various hospital departments and dashboards which included: leadership structures and systems, appropriate staffing and staff engagement, effective communication, knowledge management and learning development, evidence-based practice and processes, and outcome measurements. After data collection/synthesis, small group meetings were conducted to complete the application. The application was reviewed and edited by the unit management prior to submission. Highlights included: low infection rates in the unit exceeding national benchmarks, peer supporter events for burn survivors, team bonding, community outreach, multiple abstract submission/presentations from nursing at local/national conferences, high patient satisfaction scores, implementing new burn care practices based on evidence, and interdisciplinary rounds. Results Burn unit was designated as a Beacon Gold Award Recipient in 2019. This designation lasts for three years. Our burn unit became one of seven units in the nation with BA and is the second one to achieve Gold designation. During and after this process, there was an increase in staff engagement, teamwork, and exceptional quality metrics. Conclusions Persistent effort combined with teamwork and leadership can help a unit achieve Gold BA designation. This award requires ongoing work to refine processes and promote evidence-based practices.

Passive beamforming design for intelligent reflecting surface assisted MIMO systems

Intelligent reflecting surfaces (IRSs) constitute passive devices, which are capable of adjusting the phase shifts of their reflected signals, and hence they are suitable for passive beamforming. In this paper, we conceive their design with the active beamforming action of multiple-input multiple- output (MIMO) systems used at the access points (APs) for improving the beamforming gain, where both the APs and users are equipped with multiple antennas. Firstly, we decouple the optimization problem and design the active beamforming for a given IRS configuration. Then we transform the optimization problem of the IRS-based passive beamforming design into a tractable non-convex quadratically constrained quadratic program (QCQP). For solving the transformed problem, we give an approximate solution based on the technique of widely used semidefinite relaxation (SDR). We also propose a low-complexity iterative solution. We further prove that it can converge to a locally optimal value. Finally, considering the practical scenario of discrete phase shifts at the IRS, we give the quantization design for IRS elements on basis of the two solutions. Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks.

Impactul investiţiilor străine asupra dezvoltărilor durabile

There have always been concerns for the study of foreign direct investment (FDI), however the most significant contributions and theories have been developed with the expansion of multinational companies, due to the need to explain investment behaviour in order to perform forecasts at national, regional and global levels. Recent studies show that the investment decision starts from the market size, which is the most relevant criterion for investors as the market will absorb the products and services they will produce. Companies are less interested in other issues, often referring only to the GNI indicator (Gross National Income), regardless of its expression as it is considered a relevant benchmark for making direct investments, without addressing the principles of sustainable development. That said, recent theories suggest that the investment decision should also take into account social and environmental aspects, given the legal obligation of companies to declare non-financial data regarding environmental issues, i.e., the impact of business activities on the environment, safety and health, the use of renewable and non-renewable energy, greenhouse gas emissions, water use and air pollution. In such a context, the study relies on the assumption that the investment decision is mainly grounded on the GNI criteria and, based on panel data, it was shown that this was one of the most decisive factors until the onset of the COVID-19 pandemic, which is reflected by the migration of capital according to the economic results obtained. Taking into account the shock generated by the recent pandemic, the study will be prolonged to investigate the extent to which this state of affairs will be maintained in the post-COVID-19 stage.

Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations

Successful deep learning critically depends on the representation of the learned objects. Recent state-of-the-art pharmaceutical deep learning models successfully exploit graph-based de novo learning of molecular representations. Nonetheless, the combined potential of human expert knowledge of molecular representations and convolution neural networks has not been adequately explored for enhanced learning of pharmaceutical properties. Here we show that broader exploration of human-knowledge-based molecular representations enables more enhanced deep learning of pharmaceutical properties. By broad learning of 1,456 molecular descriptors and 16,204 fingerprint features of 8,506,205 molecules, a new feature-generation method MolMap was developed for mapping these molecular descriptors and fingerprint features into robust two-dimensional feature maps. Convolution-neural-network-based MolMapNet models were constructed for out-of-the-box deep learning of pharmaceutical properties, which outperformed the graph-based and other established models on most of the 26 pharmaceutically relevant benchmark datasets and a novel dataset. The MolMapNet learned important features that are consistent with the literature-reported molecular features. While deep learning models have allowed the extraction of fingerprints from the structural description of molecules, they can miss information that is present in the molecular descriptors that chemists use. Shen and colleagues present a method to combine both sources of information into two-dimensional fingerprint maps, which can be used in a wide variety of pharmaceutical tasks to predict the properties of drugs.

Occurrence and timely management of problems requiring prompt intervention among Indigenous compared with non-Indigenous Australian palliative care patients: a multijurisdictional cohort study

ObjectivesAnticipation and prompt relief of symptoms among patients with a life-limiting illness is a core element of palliative care. Indigenous Australians commonly encounter cultural barriers in healthcare that may impair...

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) bring unprecedented performance requirements for automotive systems. Graphic Processing Unit (GPU) based platforms have been deployed with the aim of meeting these requirements, being NVIDIA Jetson TX2 and its high-performance successor, NVIDIA AGX Xavier, relevant representatives. However, to what extent high-performance GPU configurations are appropriate for ADAS and AD workloads remains as an open question.This paper analyzes this concern and provides valuable insights on this question by modeling two recent automotive NVIDIA GPU-based platforms, namely TX2 and AGX Xavier. In particular, our work assesses their microarchitectural parameters against relevant benchmarks, identifying GPU setups delivering increased performance within a similar cost envelope, or decreasing hardware costs while preserving original performance levels. Overall, our analysis identifies opportunities for the optimization of automotive GPUs to further increase system efficiency.

Reconciling X-Ray and λ21 cm H I Absorption Gas Column Densities toward Obscured AGN

Abstract Hydrogen column densities inferred from X-ray absorption are typically 5–30 times larger than the neutral atomic hydrogen column densities derived from λ21 cm H I absorption toward radio-loud active galactic nuclei (AGN). Some part of the difference is ascribed to uncertainty in the spin temperature T sp = 100 K that is often used to convert λ21 cm H I absorption to N(H I). Here we propose another way to infer the gas column from H I absorption. In our Galaxy there is a nearly linear correlation between the inteferometrically measured integrated λ21 cm absorption ϒH I and reddening, ϒH I ∝ E(B–V)1.10 for ϒH I ≳ 0.7 km s−1 or E(B–V) ≳ 0.04 mag. Scaling E(B–V) then provides the total gas column density N(H) from the same dust column that is responsible for optical obscuration and X-ray absorption, without calculating N(H I). Values of N(H) so derived typically exceed N(H I) by a factor 4 because the ubiquitous Galactic λ21 cm H I absorption samples only a portion of the interstellar gas. If the well-studied case of Hydra-A is a guide, even very large disparities in X-ray and λ21 cm gas column densities can be explained by resolving the core radio continuum and inferring N(H) from λ21 cm absorption. Milky Way conditions are often invoked in discussion of obscured AGN, so the empirical relationship seen in the Milky Way should be a relevant benchmark.

Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing.

Fog computing is an emerging technology. It has the potential of enabling various wireless networks to offer computational services based on certain requirements given by the user. Typically, the users give their computing tasks to the network manager that has the responsibility of allocating needed fog nodes optimally for conducting the computation effectively. The optimal allocation of nodes with respect to various metrics is essential for fast execution and stable, energy-efficient, balanced, and cost-effective allocation. This article aims to optimize multiple objectives using fog computing by developing multi-objective optimization with high exploitive searching. The developed algorithm is an evolutionary genetic type designated as Hyper Angle Exploitative Searching (HAES). It uses hyper angle along with crowding distance for prioritizing solutions within the same rank and selecting the highest priority solutions. The approach was evaluated on multi-objective mathematical problems and its superiority was revealed by comparing its performance with benchmark approaches. A framework of multi-criteria optimization for fog computing was proposed, the Fog Computing Closed Loop Model (FCCL). Results have shown that HAES outperforms other relevant benchmarks in terms of non-domination and optimality metrics with over 70% confidence of the t-test for rejecting the null-hypothesis of non-superiority in terms of the domination metric set coverage.

ANTICORRUPTION EFFICACY IN PUBLIC PROCUREMENT

According to the results of studies of scientists researches publications over the past 30 years and empirical studies on the public procurement organization, it is distributed on electronic platforms. Attention is focused on the dynamic aspects of the of an such organization development. A review of studies on aspects of the effects of electronic public procurement on their impact on country budget expenditures is made.The interdisciplinary nature of publications on public procurement studied in the field of technology, management and social interaction is highlighted. Although electronic public procurement should be fairly widespread in the world, a form such as electronic auctions is not often used.The experience of Ukraine shows that electronic auctions have the potential to improve public procurement. It focuses on the development of partially automatic applications of electronic auctions, which can reduce the administrative costs of buyers and sellers, as well as the transaction costs of budgets in general. Reducing such costs is a significant addition to the savings in lowering prices during the electronic auction and is a significant factor in the effectiveness of modern electronic public procurement.The author's systematization of the components of the effectiveness of the electronic public procurement system is proposed, despite the fact that its calculation should be systematically automated. An algorithm is proposed for stimulating a responsible executor — budgetary institutions — as a result of effective budget procurement.The grounds have been formed for identifying elements of further modernization of public procurement using electronic auctions on an electronic platform as an effective tool in the field of preventing money laundering, proposals for a national financial audit of the relevant public procurement benchmarks on an electronic platform.

Multi-model evaluation of phenology prediction for wheat in Australia

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

MONEY LAUNDERING: MACROECONOMIC ASSESSMENT METHODS AND CURRENT TREND IN UKRAINE

The paper examines the scientists’ approaches to the evaluation of money laundering. Attention is drawn to the macroeconomic results of such an analysis. The research on the scope of money laundering and its impact on the country’s economy is reviewed. Relevant literature and selected techniques have been highlighted and are widely available in the world. The focus is on discussing the macroeconomic implications of money laundering and on finding methods for evaluating the various scales and conclusions reached by various scholars on the scope and flows of money laundering.
 An authors’ approach to the analysis of the phenomenon of money laundering is proposed based on the study of the links between the level of money laundering with the level of vulnerability of economic sectors and the spread of other negative socio-economic phenomena. Using unique empirical data obtained as a result of an expert survey in the field of money laundering, conducted a statistical analysis and test of the statistical hypothesis regarding the presence of interconnections and to determine the essential dependence of money laundering level on the selected indicators’ subgroups of macroeconomic nature for analysis based on Pearson and Kendall’s Taw correlation coefficients.
 The conclusions on the correlation between the level of money laundering in Ukraine and macroeconomic processes in Ukraine are substantiated: shadowing of economic relations, optimization of tax burden at the initiative of business entities and «prosperity» of money laundering due to strong corruption ties in this sphere.
 The grounds for determining the priorities of further state policy in the sphere of anti-money laundering have been formed, for the national financial monitoring of relevant benchmarks of increased risk of financial transactions in specific sectors of the economy and possible further methodological use of sectoral analysis (with wider coverage of the national economic sectors) money laundering risks.