Comparative Benchmarking Research Articles

A chromosome-scale genome and transcriptomic analysis of the endangered tropical tree Vatica mangachapoi (Dipterocarpaceae).

Vatica mangachapoi is a tropical tree species native to Southeast Asia. It has long been valued as a timber species because the wood resists decay, but it is now considered vulnerable to extinction due to habitat loss and overexploitation. Here, we present the first chromosome-level genome assembly of V. mangachapoi that we created by combining data from PacBio long read sequencing with Hi-C proximity ligation and Illumina short-read sequencing. The assembled genome was 456.21 Mb, containing 11 chromosome and a BUSCO score of 93.4%. From the newly assembled genome, 46,811 protein-coding genes were predicted. Repetitive DNA accounted for 53% of the genome. Phylogenomic and gene family analyses showed that V. mangachapoi diverged from a common ancestor of Gossypium raimondii 70 million years ago. Transcriptome analyses found 227 genes that were differentially expressed in the leaves of plants grown in normal soil relative to plants grown in dry, coastal, sandy soil. For these genes, we identified three significantly enriched with GO terms: responses to organonitrogen compounds, chitin-triggered immunity, and wound response. This genome provides an important comparative benchmark not only for future conservation work on V. mangachapoi but also for phylogenomics work on Dipterocarpaceae.

Deep learning tools for the cancer clinic: an open-source framework with head and neck contour validation

BackgroundWith the rapid growth of deep learning research for medical applications comes the need for clinical personnel to be comfortable and familiar with these techniques. Taking a proven approach, we developed a straightforward open-source framework for producing automatic contours for head and neck planning computed tomography studies using a convolutional neural network (CNN).MethodsAnonymized studies of 229 patients treated at our clinic for head and neck cancer from 2014 to 2018 were used to train and validate the network. We trained a separate CNN iteration for each of 11 common organs at risk, and then used data from 19 patients previously set aside as test cases for evaluation. We used a commercial atlas-based automatic contouring tool as a comparative benchmark on these test cases to ensure acceptable CNN performance. For the CNN contours and the atlas-based contours, performance was measured using three quantitative metrics and physician reviews using survey and quantifiable correction time for each contour.ResultsThe CNN achieved statistically better scores than the atlas-based workflow on the quantitative metrics for 7 of the 11 organs at risk. In the physician review, the CNN contours were more likely to need minor corrections but less likely to need substantial corrections, and the cumulative correction time required was less than for the atlas-based contours for all but two test cases.ConclusionsWith this validation, we packaged the code framework and trained CNN parameters and a no-code, browser-based interface to facilitate reproducibility and expansion of the work. All scripts and files are available in a public GitHub repository and are ready for immediate use under the MIT license. Our work introduces a deep learning tool for automatic contouring that is easy for novice personnel to use.

Rapid Influence Maximization on Social Networks: The Positive Influence Dominating Set Problem

Motivated by applications arising on social networks, we study a generalization of the celebrated dominating set problem called the Positive Influence Dominating Set (PIDS). Given a graph G with a set V of nodes and a set E of edges, each node i in V has a weight bi, and a threshold requirement gi. We seek a minimum weight subset T of V, so that every node i not in T is adjacent to at least gi members of T. When gi is one for all nodes, we obtain the weighted dominating set problem. First, we propose a strong and compact extended formulation for the PIDS problem. We then project the extended formulation onto the space of the natural node-selection variables to obtain an equivalent formulation with an exponential number of valid inequalities. Restricting our attention to trees, we show that the extended formulation is the strongest possible formulation, and its projection (onto the space of the node variables) gives a complete description of the PIDS polytope on trees. We derive the necessary and sufficient facet-dening conditions for the valid inequalities in the projection and discuss their polynomial time separation. We embed this (exponential size) formulation in a branch-and-cut framework and conduct computational experiments using real-world graph instances, with up to approximately 2.5 million nodes and 8 million edges. On a test-bed of 100 real-world graph instances, our approach finds solutions that are on average 0.2% from optimality and solves 51 out of the 100 instances to optimality. Summary of Contribution: In influence maximization problems, a decision maker wants to target individuals strategically to cause a cascade at a minimum cost over a social network. These problems have attracted significant attention as their applications can be found in many different domains including epidemiology, healthcare, marketing, and politics. However, computationally solving large-scale influence maximization problems to near optimality remains a substantial challenge for the computing community, which thus represent significant opportunities for the development of operations-research based models, algorithms, and analysis in this interface. This paper studies the positive influence dominating set (PIDS) problem, an influence maximization problem on social networks that generalizes the celebrated dominating set problem. It focuses on developing exact methods for solving large instances to near optimality. In other words, the approach results in strong bounds, which then provide meaningful comparative benchmarks for heuristic approaches. The paper first shows that straightforward generalizations of well-known formulations for the dominating set problem do not yield strong (i.e., computationally viable) formulations for the PIDS problem. It then strengthens these formulations by proposing a compact extended formulation and derives its projection onto the space on the natural node-selection variables, resulting in two equivalent (stronger) formulations for the PIDS problem. The projected formulation on the natural node-variables contains a new class of valid inequalities that are shown to be facet-defining for the PIDS problem. These theoretical results are complemented by in-depth computational experiments using a branch-and-cut framework, on a testbed of 100 real-world graph instances, with up to approximately 2.5 million nodes and 8 million edges. They demonstrate the effectiveness of the proposed formulation in solving large scale problems finding solutions that are on average 0.2% from optimality and solving 51 of the 100 instances to optimality.

Evaluating an Aboriginal community controlled residential alcohol and other drug services: Use of benchmarking to examine within treatment changes in wellbeing.

Aboriginal Community Controlled Organisations (ACCO) have an important role in the Australian health-care sector. However, there has been a lack of research evaluating ACCOs in the treatment of alcohol and other drug (AOD) use. Using a benchmarking approach, the present study examined within treatment changes on measures of wellbeing for people attending a residential AOD ACCO. The study focused on The Glen, an AOD residential treatment service that is managed by the Ngaimpe Aboriginal Corporation (n=775). The Glen is a male-only service and provides treatment to both Indigenous and non-Indigenous men. The evaluation focused on measures of wellbeing (i.e. symptom distress and quality of life) collected at intake, 30 and 60 days during the person's stay. Comparative benchmarking was conducted with a cohort of men who were attending non-ACCO residential AOD treatment services (n=4457). The Glen participants demonstrated statistically significant improvements on measures of wellbeing. The Glen participants were more likely to complete treatment than participants attending non-ACCO services. Likewise, Indigenous people attending The Glen were more likely to complete treatment (compared to Indigenous people attending non-ACCO services). Rates of reliable and clinically significant change suggested that changes in quality of life were largely equivalent between The Glen and non-ACCO services, while participants attending The Glen tended to demonstrate larger reductions in symptom distress compared to the non-ACCO services. The study provides further support for the important role that ACCOs play in supporting Indigenous people in their recovery.

Lean adoption, implementation, and outcomes in public hospitals: benchmarking the US and Italy health systems

BackgroundDespite the growing interest in transformational performance improvement among nearly all countries, international benchmarking has rarely been used. Cross-comparative research could allow an appreciation of the extent of Lean’s use in healthcare and a better evaluation of possible cultural influences on Lean implementation. This study provides a comparative international benchmarking of Lean adoption, implementation, and outcomes of hospitals in the US and Italy.MethodsThe National Survey of Lean, developed in 2017 in the US and adapted in Italy in 2019 was used to compare the two healthcare systems along three dimensions: the maturity of adoption, the implementation approach, identifying both strategic and operational activities and tools, and the Lean performance, investigated through patients, employed, and affiliated staff, costs, and service provision areas. Descriptive statistics including T-tests were used to examine differences between the two countries on the study variables.ResultsLean has been adopted less by Italian public hospitals (36%) than US public hospitals (53%). Each country averages 4 years of experience with Lean. Italian hospitals reported being at a higher maturity stage while the US implemented a more system-wide approach, developing Lean in more operational units. The daily management system, leadership commitment, education and training indexes were higher or the same in the US while in Italy, hospitals had a higher self-reported performance index.ConclusionThis exploratory work is one of the first international benchmarking studies on Lean implementation in healthcare systems using a standardized survey with a common set of definitions and questions. The study identifies different forms of Lean implementation that can be adopted, both at strategic and operational levels, with related perceived outcomes. Despite the US public hospitals being more likely to report a higher number of units using Lean, a higher daily management system index and use of Lean tools, Italian hospitals report more achievements primarily due to Lean. Further research can build on these findings by examining the relationship between Lean adoption/implementation and independent, objective performance measures.

An Optimal Energy Absorption and Utilization Design for Deformable/Reconfigurable Modular Solar-Powered UAVs in Near Space

Maximizing the absorption and utilization of solar energy is a bottleneck issue and hot research concern for high-altitude long-endurance (HALE) solar-powered UAVs (SP-UAVs). This paper investigates the overall design and task optimization for a deformable/reconfigurable SP-UAV platform that utilizes its modular characteristics to optimize the overall solar absorption and structural weight while maximizing its mission utility. First, we establish the energy absorption/consumption model, the aerodynamic model, and the subsystem mass prediction model. Based on these models, the overall parameter design and optimization scheme of a modular SP-UAV is proposed by using heuristic intelligence algorithms. The results show that the loading ratio of the deformable modular SP-UAV increases from 8.2% to 9.8%. Then, the flight processes in both the deformed and the reconfigured cases are verified under task-oriented scenarios. Simulation results indicate that compared with comparable benchmarks with conventional layouts, deformable SP-UAVs are capable of increasing the feasible zone from 72 to 142 days. Additionally, the separation and recombination points are optimized and determined at specific latitudes and dates. A performance analysis shows that separation in the daytime can multiply, thereby increasing the flight range, although the feasible zone shrinks with increasing UAV unit number.

OMNI-DRL: Learning to Fly in Forests with Omnidirectional Images

Perception is crucial for drone obstacle avoidance in complex, static, and unstructured outdoor environments. However, most navigation solutions based on Deep Reinforcement Learning (DRL) use limited Field-Of-View (FOV) images as input. In this paper, we demonstrate that omnidirectional images improve these methods. Thus, we provide a comparative benchmark of several visual modalities for navigation: ground truth depth, ground truth semantic segmentation, and RGB images. These exhaustive comparisons reveal that it is superior to use an omnidirectional camera to navigate with classical DRL methods. Finally, we show in two different virtual forest environments that adapting the convolution to take into account spherical distortions improves the results even more.

Crisis Management Practices in Tourism SMEs During COVID-19

This study examines the influence of tourism micro, small, and medium-sized enterprises’ (SMEs) business characteristics and liquidity and their managers’ demographic characteristics and expectations for assistance regarding crisis management practices (CMP) during the COVID-19 pandemic. The survey was conducted in the Republic of Slovenia, where the majority of businesses are classified as SMEs. This study focuses on tourist agencies, lodging, and food and beverage facilities. Overall, 572 valid online questionnaires have been obtained from SME managers. The research model included five research constructs: CMPs, SMEs business characteristics, liquidity, managers’ demographic characteristics, and their expectations for assistance. Results show that managers primarily rely on labour CMPs; liquidity, assistance from stakeholders, and governmental aid in dealing with the crisis. Results also indicate that SMEs’ business characteristics, managers’ demographic characteristics, and liquidity do not influence CMPs. The amount of provided governmental aid positively influences managers’ expectations for assistance from other stakeholders of the business ecosystem, while the amount of SMEs’ liquid assets negatively influences managers’ expectations for governmental aid. The use of selected indicators enables an internationally comparable benchmarking process and facilitates the improvement of crisis management in tourism SMEs. The conclusion provides recommendations and useful information for researchers, policymakers, and managers.

On the use of neural networks for energy reconstruction in high-granularity calorimeters

We contrasted the performance of deep neural networks — Convolutional Neural Network (CNN) and Graph Neural Network (GNN) — to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.

Publication performance of university orthopedic trauma surgery in Germany

HintergrundZu den Leistungskriterien einer Universitätsklinik gehören ihre Publikationsaktivitäten. Ziel der vorliegenden bibliometrischen Untersuchung war es, die Publikationsaktivitäten deutscher unfallchirurgischer Universitätskliniken in einem Benchmarking vergleichend darzustellen.Material und MethodikDie Publikationsleistung der Führungsmannschaften, bestehend aus Chef- und Oberärzten, Sektions- und Bereichsleitern von 39 deutschen unfallchirurgischen Universitätskliniken wurde über 10 Jahre (01.01.2010 bis 31.12.2019) erfasst. Berücksichtigt wurden alle Publikationen, die in PubMed gelistet waren und bei denen die entsprechenden Personen Erst- oder Letztautor waren. Zusätzlich wurde der Impact-Faktor (IF) bestimmt.ErgebnisseInsgesamt wurden 4438 Veröffentlichungen erfasst, publiziert von 381 Chirurgen. Der Anteil der publizierenden Mitarbeiter betrug 72,8 %. Publiziert wurde in 545 Journalen. Der durchschnittliche IF aller Publikationen war 1,81. Die Publikationsaktivitäten der Kliniken zeigten eine hohe Streubreite, dies galt sowohl für die Publikationsanzahl als auch für die generierten IF des einzelnen Mitarbeiters. Die Publikationsaktivität reichte von durchschnittlich 16,4 Publikationen pro Mitarbeiter in der bestplatzierten Klinik bis 1,5 Publikationen bei der letztplatzierten. Gleiches ergab die Summe der IF. In der nach diesem Maßstab bestplatzierten Klinik erzielte der einzelne Mitarbeiter durchschnittlich kumuliert 42,1 IF verglichen mit 1,7 IF bei der letztplatzierten.SchlussfolgerungDie Publikationsleistung deutscher unfallchirurgischer Universitätskliniken zeigt eine hohe Varianz, wie dies auch bei anderen Disziplinen gefunden wurde. Die Ursachen müssen offen bleiben, eine unterschiedliche Forschungsmotivation ist aber nicht auszuschließen.

A comparative benchmarking study of multi-storey residential buildings in two periods of energy saving efforts: the 1980s and the 2010s

In 1990, Technological Institute (TI) in Denmark made a benchmarking study of 92 typical multi-storey buildings covering 23 000 dwellings. The study included measurement data from the 1970s and the years after the energy crises. This study showed that over a period of less than 20 years a significant reduction in energy consumption took place. In a new similar study, TI and Aalborg University have analysed 62 buildings covering 18 000 dwellings including measurement data from the last 20 years. This time, the data covers a period with an increasing focus on the carbon-emission impacts of energy consumption. As opposed to the first benchmarking study, the new 20-years study shows that the heat consumption has been almost constant over the last 20 years. This paper presents a comparative study of the two sets of measurements and evaluates energy saving efforts and individual building energy performance. Furthermore, the paper compares two different ways of deriving benchmarks from the data and demonstrates how utilizing change-point models/energy signature as opposed to the more traditional mean annual values per heated area, significantly increases the usability.

Wastewater Nitrate Reduction to Ammonia By a Co-Centered Molecular Catalyst

The U.S. spends $2.2 billion annually on remediation of harmful algal blooms (eutrophication), primarily caused by anthropogenic nitrogen pollution in wastewater discharge.1 Eutrophication perturbs coastal ecosystems by disrupting food chains, decreasing biodiversity, and causing nutrient deficiencies.2 Selective nitrate reduction (NO3RR) of wastewater is an attractive remediation strategy that prevents eutrophication and recovers a value-added commodity chemical in the form of ammonia. Recovering ammonia from nitrate in fertilizer runoff and municipal wastewater could offset up to 13% of ammonia produced worldwide through the Haber-Bosch process.3–6 With renewable-sourced energy, this strategy would also offset 320,000 metric tons of CO2-eq in greenhouse gas emissions.7 In this study, we present a Co-centered macrocycle, called Co-DIM, as a homogeneous molecular catalyst for the selective reduction of nitrate to ammonia. The advantages of Co-DIM include high water solubility, tunable product selectivity by ligand modification, and a wide range of pH operability.8 Despite comprehensive studies of promising molecular catalysts performed in the CO2 reduction (CO2RR) literature,9 NO3RR has not received a similar level of attention, and comparative frameworks do not yet exist to critically evaluate catalyst activity, selectivity, and stability. In this study we combine electroanalytical techniques in model systems to bench-scale, membrane-separated electrolysis in real wastewater. By doing so, we quantify the performance of a model NO3RR electrocatalyst, and demonstrate a generalizable approach for comparative benchmarking against heterogeneous and homogeneous reduction catalysts for other reactions (e.g., CO2RR).Determining the reaction mechanism of homogeneous molecular electrocatalysis requires careful understanding of the reaction environment. Using electroanalysis techniques, such as cyclic voltammetry (CV), and electrode surface identity modification (through atomic layer deposition and self-assembled monolayers), we were able to probe the electron transfer kinetics of Co-DIM activation and NO3RR catalysis. We found that Co-DIM is activated by an outer-sphere electron transfer mechanism, thereby deemphasizing electrode material identity for catalysis. We also leveraged key features of the obtained sigmoidal CVs to build the first ever TOF-eta relationship for a NO3RR molecular catalyst. Finally, we demonstrated greater than 50% removal of nitrate using Co-DIM in electrolysis of municipal wastewater. With this experiment, we present the novelty of using rational cell architecture and observable electrocatalysis in real wastewater for substantial nitrate removal and ammonia recovery. This work aims to encourage a framework for NO3RR evaluation to foster next-generation catalyst design and device implementation for large-scale nitrate removal from wastewater.(1) Dodds, W. K.; Bouska, W. W.; Eitzmann, J. L.; Pilger, T. J.; Pitts, K. L.; Riley, A. J.; Schloesser, J. T.; Thornbrugh, D. J. Eutrophication of U.S. Freshwaters: Analysis of Potential Economic Damages. Environ. Sci. Technol. 2009, 43 (1), 12–19. https://doi.org/10.1021/es801217q.(2) Kemp, W. M.; Boynton, W. R.; Adolf, J. E.; Boesch, D. F.; Boicourt, W. C.; Brush, G.; Cornwell, J. C.; Fisher, T. R.; Glibert, P. M.; Hagy, J. D.; Harding, L. W.; Houde, E. D.; Kimmel, D. G.; Miller, W. D.; Newell, R. I. E.; Roman, M. R.; Smith, E. M.; Stevenson, J. C. Eutrophication of Chesapeake Bay: Historical Trends and Ecological Interactions. Mar. Ecol. Prog. Ser. 2005, 303, 1–29. https://doi.org/10.3354/meps303001.(3) Wastewater: The Untapped Resource; Unesco, Ed.; The United Nations world water development report; UNESCO: Paris, 2017.(4) Food and Agriculture Organization of the United Nations. World Fertilizer Trends and Outlook to 2020. 2020, 38.(5) Kato, T.; Kuroda, H.; Nakasone, H. Runoff Characteristics of Nutrients from an Agricultural Watershed with Intensive Livestock Production. J. Hydrol. 2009, 368 (1), 79–87. https://doi.org/10.1016/j.jhydrol.2009.01.028.(6) Lang, M.; Li, P.; Yan, X. Runoff Concentration and Load of Nitrogen and Phosphorus from a Residential Area in an Intensive Agricultural Watershed. Sci. Total Environ. 2013, 458–460, 238–245. https://doi.org/10.1016/j.scitotenv.2013.04.044.(7) Smith, C.; Hill, A. K.; Torrente-Murciano, L. Current and Future Role of Haber–Bosch Ammonia in a Carbon-Free Energy Landscape. Energy Environ. Sci. 2020, 13 (2), 331–344. https://doi.org/10.1039/C9EE02873K.(8) Xiang, Y.; Zhou, D.-L.; Rusling, J. F. Electrochemical Conversion of Nitrate to Ammonia in Water Using Cobalt-DIM as Catalyst. J. Electroanal. Chem. 1997, 424, 1–3.(9) Costentin, C.; Robert, M.; Savéant, J.-M. Catalysis of the Electrochemical Reduction of Carbon Dioxide. Chem Soc Rev 2013, 42 (6), 2423–2436. https://doi.org/10.1039/C2CS35360A.

A hybrid methodology for distribution level photovoltaic power production forecasting verified at the distribution system of Cyprus

Accurate photovoltaic (PV) power forecasting is an essential tool that enables the efficient integration of solar electricity and energy trading. This work proposes a novel methodology for the implementation of a forecasting tool that provides aggregated PV production day-ahead forecasts for the PV systems installed at a distribution level. Specifically, the tool is a cloud-based platform, comprising of a data quality block, a weather forecasting model, a machine learning power prediction step and an up-scaling aggregation stage. In this context and in the absence of a fully observable distribution system, the aggregated PV generation was estimated using a clustering approach and up-scaling the measured generation of reference systems to the aggregated installed capacity. The results demonstrated that the implemented tool exhibited high forecasting accuracies, lower than 10% given by the mean absolute percentage error when applied to the distribution system of Cyprus. Furthermore, the comparative benchmarking of the up-scaling techniques against the estimated aggregated PV generation demonstrated that the best-performing approach was the hybrid model, which provided a normalised root mean square error of 10.29% and mean absolute percentage error of 9.11%. Finally, useful information is provided for establishing a robust day-ahead forecasting methodology that is based on an optimal supervised learning and hybrid up-scaling approach.

STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.

Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.

Multi-objective optimization for community detection in multilayer networks

Community detection in multilayer networks plays a key role in revealing the multiple aspects of information spreading and in comprehending the relationships and interactions within and between each layer. However, most existing algorithms are prone to local optimality, and they are also difficult to extend to high-dimensional networks. To address these challenges, we propose here a multi-objective algorithm for community detection that is based on the genetic algorithm. In particular, the modularity is introduced to optimize each network layer iteratively, and the local search is combined with genetic operations to overcome local optimality. Comparative benchmarks with other algorithms on artificial and real-world networks show that the proposed algorithm performs better, especially on high-dimensional networks.

Risk and protective factors for the development of gambling-related harms and problems among Australian sexual minority men

BackgroundSexual minority men (SMM) often experience stressful social environments dominated by stigma and discrimination. SMM are typically more likely than heterosexual men to engage in certain risky behaviours such as problem gambling. This study aimed to compare gambling behaviour among SMM and examine potential risk factors (erroneous gambling cognitions, gambling outcome expectancies, hazardous alcohol use, impulsivity, and psychological distress; as well as perceived stigma and discrimination for the SMM participants) and potential protective factors (resilience, social support, and community connectedness) for problem gambling severity and gambling-related harms among SMM living in Australia.MethodsAn online survey, with an over-representation of SMM participants and problem, moderate-risk, and low-risk gamblers, was completed by 101 SMM (mean age = 28.5) and 207 heterosexual men (mean age = 26.4).ResultsSMM were found to have significantly lower levels of problem gambling severity compared with heterosexual men, and report significantly lower gambling participation, frequencies and expenditure on any gambling activity, casino table games, horse racing/greyhound betting, sports betting, and keno. However, in the SMM group, 38.3% were classified in the problem gambling category of the Problem Gambling Severity Index and 27.6% were classified in the moderate-risk gambling category. There were no significant differences between groups in gambling-related harms. Multiple regression analyses revealed that problem gambling severity and related harms were independently predicted by higher levels of impulsivity and erroneous gambling cognitions for both groups.ConclusionsLower frequency of gambling behaviours among SMM and similar risk factors predicting problem gambling severity/harms for both groups suggests that problem gambling is not pronounced among SMM. This study adds new evidence to the gambling literature which can be used as comparative benchmarks for future research.

Model-based adjustment for conditional benchmarking

AbstractQuantitative benchmarking is widely used in the industry to compare relative performance across a sample of organizations. A key analytical challenge lies in obtaining accurate measures of intrinsic organizational performance net of contextual or exogenous influences. In this paper, we propose a model-based adjustment approach for comparative benchmarking that allows the analyst to recover targeted metrics for specific aspects of innate performance. We outline the statistical theory underpinning our method, provide simulations to demonstrate its properties and describe practical examples for computation. The managerial relevance of the method is demonstrated via two real-world transport industry applications: adjusting for economies of scale and density in benchmarking average costs of urban metros and for service characteristics in benchmarking metro journey times.

Malpractice Cases in Breast Surgery: An Assessment of Litigation Involving Surgeons.

Improving patient safety and quality are priorities in health care. The study of malpractice cases provides an opportunity to identify areas for quality improvement. While the issues surrounding malpractice cases in breast cancer are often multifactorial, there are few studies providing insight into malpractice cases specifically related to common breast cancer surgical procedures. We sought to characterize the factors in liability cases involving breast cancer surgery. Closed cases from 2008 to 2019 involving a breast cancer diagnosis, a primary responsible service of general surgery, surgical oncology, or plastic surgery, and a breast cancer procedure were reviewed using data from the Controlled Risk Insurance Company (CRICO) Strategies Comparative Benchmarking System database, a national repository of professional liability data. A total of 174 malpractice cases were reviewed, of which 41 cases were closed with payment. Plastic surgeons were most commonly named (64%, 111/174), followed by general surgeons (30%, 53/174), and surgical oncologists (6%, 10/174). The most common allegation was error in surgical treatment (87%, 152/174), and infection, cosmetic injury, emotional trauma, foreign body, and nosocomial infections represented the top five injury descriptions. On average, indemnity payments were larger for high clinical severity cases. Technical skills, followed by clinical judgment, were the most commonly named contributing factors. The average payment per case was $130,422. Malpractice cases predominantly involve technical complications related to plastic surgery procedures. Better understanding of the malpractice environment involving surgical procedures performed for breast cancer may provide practical insight to guide initiatives aimed at improving patient outcomes.

HFBB model test for tall buildings: A comparative benchmark with a full-aeroelastic model

A novel approach to full-aeroelastic model design is presented, considering continuous structures and coupled modes. Its validity is proven by building a full-aeroelastic CAARC standard tall building model featuring 12-degrees-of-freedom at 1:360 scale. A complete description of its design, manufacturing, tuning, and dynamic identification is reported. The model is tested in a novel wind-tunnel experimental campaign performed at the CRIACIV facility (Prato, Italy). A comparative study of aerodynamic and full-aeroelastic response in terms of base moment and accelerations shows that the aerodynamic model tends to overpredict the response along the broader side of the model. The contribution of second modes is found to be relevant for induced acceleration at the rooftop.

Overcoming uncollapsed haplotypes in long-read assemblies of non-model organisms

BackgroundLong-read sequencing is revolutionizing genome assembly: as PacBio and Nanopore technologies become more accessible in technicity and in cost, long-read assemblers flourish and are starting to deliver chromosome-level assemblies. However, these long reads are usually error-prone, making the generation of a haploid reference out of a diploid genome a difficult enterprise. Failure to properly collapse haplotypes results in fragmented and structurally incorrect assemblies and wreaks havoc on orthology inference pipelines, yet this serious issue is rarely acknowledged and dealt with in genomic projects, and an independent, comparative benchmark of the capacity of assemblers and post-processing tools to properly collapse or purge haplotypes is still lacking.ResultsWe tested different assembly strategies on the genome of the rotifer Adineta vaga, a non-model organism for which high coverages of both PacBio and Nanopore reads were available. The assemblers we tested (Canu, Flye, NextDenovo, Ra, Raven, Shasta and wtdbg2) exhibited strikingly different behaviors when dealing with highly heterozygous regions, resulting in variable amounts of uncollapsed haplotypes. Filtering reads generally improved haploid assemblies, and we also benchmarked three post-processing tools aimed at detecting and purging uncollapsed haplotypes in long-read assemblies: HaploMerger2, purge_haplotigs and purge_dups.ConclusionsWe provide a thorough evaluation of popular assemblers on a non-model eukaryote genome with variable levels of heterozygosity. Our study highlights several strategies using pre and post-processing approaches to generate haploid assemblies with high continuity and completeness. This benchmark will help users to improve haploid assemblies of non-model organisms, and evaluate the quality of their own assemblies.