Search

The purpose of this article is to evaluate the quality of research management of students in agricultural universities of Ukraine using benchmarking. The external evaluation of agricultural higher education institutions (HEIs) of Ukraine was conducted on the basis of the data of the State Scientific Institution "Institute of education content modernization" and the Ministry of Education and Science of Ukraine regarding the results of the All-Ukrainian competition of student scientific papers in the fields of knowledge and specialties in 2019/2020 and 2020/2021 academic years. The leaders according to the results of participation in the II round of the competition in the analyzed period were 9 HEIs: Mykolaiv National Agrarian University (NAU), National University of Life and Environmental Sciences of Ukraine, Kharkiv Petro Vasylenko National Technical University of Agriculture, Lviv NAU, Sumy NAU, Vinnytsia NAU, Poltava State Agrarian Academy, Kharkiv NAU named after V. V. Dokuchayev, Polissia National University. They annually had more than 10 winners of the competition of various degrees. Other agricultural HEIs of Ukraine should focus on their indicators in order to improve the quality of research management of students. The obtained results enable agricultural universities not only to determine the leaders based on the number of works awarded with I, II or III degree diplomas in the All-Ukrainian competition, but also to evaluate, compared to them, their strengths and weaknesses; to get new ideas for research management of students; to plan further scientific activities taking into account the indicators of HEIs-leaders. [ABSTRACT FROM AUTHOR]

Background: Advancements over the past decade in DNA sequencing technology and computing power have created the potential to revolutionize medicine. There has been a marked increase in genetic data available, allowing for the advancement of areas such as personalized medicine. A crucial type of data in this context is genetic variant data which is stored in variant call format (VCF) files. However, the rapid growth in genomics has presented challenges in analyzing and comparing VCF files. Results: In response to the limitations of existing tools, this paper introduces a novel web application that provides a user-friendly solution for VCF file analyses and comparisons. The software tool enables researchers and clinicians to perform high-level analysis with ease and enhances productivity. The application's interface allows users to conveniently upload, analyze, and visualize their VCF files using simple drag-and-drop and point-and-click operations. Essential visualizations such as Venn diagrams, clustergrams, and precision–recall plots are provided to users. A key feature of the application is its support for metadata-based file grouping, accomplished through flexible data matrix uploads, streamlining organization and analysis of user-defined categories. Additionally, the application facilitates standardized benchmarking of VCF files by integrating user-provided ground truth regions and variant lists. Conclusions: By providing a user-friendly interface and supporting essential visualizations, this software enhances the accessibility of VCF file analysis and assists researchers and clinicians in their scientific inquiries. [ABSTRACT FROM AUTHOR]

The Municipal Solid Waste Management (MSWM) system has been used as a unit of study to apply the Life Cycle Assessment (LCA) approach. Kanjur waste processing facility Mumbai, India was selected for technological performance assessment. The paper analyses multiple strategies for managing the MSW and the comparison of strategies is done to study impact on Global Warming Potential (GWP), Eutrophication Potential (GW) and Acidification potential (AP), which are the critical impact areas of MSW processing. The scenarios included were transportation, material recovery, direct landfill with the recovery of LFG, composting of the source-separated organic fraction with landfilling and biomining. Landfilling and the release of LFG (Landfill Gas), electricity generation from LFG, flaring of LFG were other assessed interventions. The scenarios were modelled using the CML impact characterisation method. The results highlight that sorting, biomining, composting, landfilling with energy recovery has the minimal impact on the environment compared to strategies where direct landfilling was considered. On a life cycle view, the transportation of waste to the processing site caused a significant global warming impact and is a hotspot, which can be considered for impact mitigation. Initiatives like RDF reuse, leachate recirculation, LFG recovery, biomining are other circular practices that lead to a significant reduction in GWP, EP and AP. [ABSTRACT FROM AUTHOR]

Background: Automated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available. This domain has become increasingly popular, thanks to modern machine learning algorithms. However, the automated evaluation of HG systems is still an open problem, especially on a larger scale. Results: This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems. Utilizing curated datasets, our approach tests these systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypotheses accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. Conclusions: Dyport is an open-source benchmarking framework designed for biomedical hypothesis generation systems evaluation, which takes into account knowledge dynamics, semantics and impact. All code and datasets are available at: https://github.com/IlyaTyagin/Dyport. [ABSTRACT FROM AUTHOR]

Advances in quantum computers may pose a significant threat to existing public-key encryption methods, which are crucial to the current infrastructure of cyber security. Both RSA and ECDSA, the two most widely used security algorithms today, may be (in principle) solved by the Shor algorithm in polynomial time due to its ability to efficiently solve the discrete logarithm problem, potentially making present infrastructures insecure against a quantum attack. The National Institute of Standards and Technology (NIST) reacted with the post-quantum cryptography (PQC) standardization process to develop and optimize a series of post-quantum algorithms (PQAs) based on difficult mathematical problems that are not susceptible to being solved by Shor's algorithm. Whilst high-powered computers can run these PQAs efficiently, further work is needed to investigate and benchmark the performance of these algorithms on lower-powered (constrained) devices and the ease with which they may be integrated into existing protocols such as TLS. This paper provides quantitative benchmark and handshake performance data for the most recently selected PQAs from NIST, tested on a Raspberry Pi 4 device to simulate today's IoT (Internet of Things) devices, and provides quantitative comparisons with previous benchmarking data on a range of constrained systems. CRYSTALS-Kyber and CRYSTALS-Dilithium are shown to be the most efficient PQAs in the key encapsulation and signature algorithms, respectively, with Falcon providing the optimal TLS handshake size. [ABSTRACT FROM AUTHOR]

The design of automotive human–machine interfaces (HMIs) for global consumers' needs to cater to a broad spectrum of drivers. This paper comprises benchmark studies and explores how users from international markets—Germany, China, and the United States—engage with the same automotive HMI. In real driving scenarios, N = 301 participants (premium vehicle owners) completed several tasks using different interaction modalities. The multi-method approach included both self-report measures to assess preference and satisfaction through well-established questionnaires and observational measures, namely experimenter ratings, to capture interaction performance. We observed a trend towards lower preference ratings in the Chinese sample. Further, interaction performance differed across the user groups, with self-reported preference not consistently aligning with observed performance. This dissociation accentuates the importance of integrating both measures in user studies. By employing benchmark data, we provide insights into varied market-based perspectives on automotive HMIs. The findings highlight the necessity for a nuanced approach to HMI design that considers diverse user preferences and interaction patterns. [ABSTRACT FROM AUTHOR]

A highly accurate iteration of the Liu & Zenner multiaxial fatigue criterion is presented and validated against the FatLim dataset. The proposed criterion modifies the mean stress parameters of the original criterion while keeping the amplitude parameters unchanged. The investigation of the best‐performing stress amplitude parameters revealed that existing multiaxial fatigue criteria that rely on dimensional weight parameters exhibit a notable prediction bias. This bias leads to inaccurate fatigue assessments. The paper sheds light on the extent of this bias and its implications for common engineering practices. By introducing the refined criterion, the study addresses these limitations and provides a more reliable framework for multiaxial fatigue analysis. The comprehensive validation against the FatLim dataset demonstrates the superiority of the proposed criterion in accurately predicting fatigue behavior under diverse loading conditions. The findings underscore the significance of considering both amplitude and mean stress parameters in multiaxial fatigue criteria, offering valuable insights for enhancing the accuracy of fatigue assessments in engineering applications. Highlights: A data‐driven validation process aids in selecting fatigue criteria for complex loading conditionsNew insights gained regarding the limitations of existing criteria under partial and overload casesNew integral fatigue criterion yields the highest result accuracy among studied criteriaThe proposed approach improves the accuracy of fatigue analysis and design of machine elements [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to critically evaluate the evolution of Assurance of Learning (AoL) in business education and identify gaps and responsibilities in higher institution landscapes moving into the future. This comes amidst increasing structural reforms, an increasingly digitalised world, accelerated by the COVID-19 pandemic and wider scrutiny of graduate competencies for job readiness. Design/methodology/approach: A systematic literature review comprising 27 journal articles published between 2018 and 2022 is the methodology used in this research. This approach is justified as it provides a recent synopsis of current trends on AoL and encompasses the changes over the course of the COVID-19-induced higher education landscapes. Findings: The systematic literature review revealed a strong flavour for AoL measurement through the students' perspectives, with little emerging from faculty insights. Only six out of the 27 articles were framed in a non-English speaking background, revealing that most studies were still concentrated in a US or English environment. Also, while papers on AoL have increased in numbers from 2018 to 2022, there remains scant literature on AoL measurements related to the COVID-19 pandemic and recent digital technologies, and how these would have changed given the rapid shift to online or hybrid environments. Originality/value: A comprehensive review of AoL literature from 2018 to 2022 is undertaken through Leximancer, which reveals conceptual and relational ties between core themes and concepts of interest. The findings inform business school leaders of the current state of AoL processes, by combining the views of students, faculty and managers. [ABSTRACT FROM AUTHOR]

This paper is a collaborative effort between academic researchers and practitioners to consider the conditions under which global benchmarking may be used as a tool for supporting urban transformation. Reflecting on WWF's One Planet City Challenge and UN-Habitat's Guiding Principles for City Climate Action Planning, the paper suggests that the practice of global benchmarking can be transformative through encouraging organisational learning and reflection, building relationships between cities and global and trans-local organisations, and governing for structurally transformative qualities. However, the practice of benchmarking is not without potential tensions: they may reify existing practices rather than reforming them, be less usable for or accessible to cities in lower income countries, and may neglect issues of climate justice, which are not easily reduced to comparative measures of success or failure. This suggests that a wholesale reliance on benchmarking as a mode of governing climate change might risk marginalising certain issues and amplifying others. We conclude by recommending improved material and technical support for urban data collection and suggest that benchmarking should be combined with a broader suite of performance indicators and reflective practices in order to support urban transformation. [ABSTRACT FROM AUTHOR]

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines. [ABSTRACT FROM AUTHOR]

Recent advancements in single-cell sequencing technologies have generated extensive omics data in various modalities and revolutionized cell research, especially in the single-cell RNA and ATAC data. The joint analysis across scRNA-seq data and scATAC-seq data has paved the way to comprehending the cellular heterogeneity and complex cellular regulatory networks. Multi-omics integration is gaining attention as an important step in joint analysis, and the number of computational tools in this field is growing rapidly. In this paper, we benchmarked 12 multi-omics integration methods on three integration tasks via qualitative visualization and quantitative metrics, considering six main aspects that matter in multi-omics data analysis. Overall, we found that different methods have their own advantages on different aspects, while some methods outperformed other methods in most aspects. We therefore provided guidelines for selecting appropriate methods for specific scenarios and tasks to help obtain meaningful insights from multi-omics data integration. [ABSTRACT FROM AUTHOR]

This paper develops a unified port performance index (PPI) considering different cargo categories and the multi-dimensional nature of port performance indicators/dimensions. This study has used the quintile method to construct PPI. Further, the PPI obtained from the quintile method is compared with weighted unified index (PPIPCA) using principal component analysis (PCA), extensively used for index development in the literature. A pilot index development is demonstrated using secondary panel data for 12 major Indian ports on five significant dimensions, namely, operations, physical infrastructure, technical infrastructure, finance, and socio-economic. Results show that the JNPT port outperforms all other ports under the container cargo category. Likewise, Kandla port in the liquid port category and Paradip port in the other (dry & break bulk) cargo category are on top. Also, qualitatively similar results and insights are obtained with the PPIPCA. Subsequently, the panel data regression and efficiency analysis are performed to demonstrate the utility of the proposed index. The results affirm that the operations, physical infrastructure, and socio-economic dimensions have a positive and significant impact on port financial performance. The present study operationalises some key unexplored port performance indicators/dimensions that can enable effective decision-making. [ABSTRACT FROM AUTHOR]

Background Value-based healthcare (VBHC) is a conceptual framework to improve the value of healthcare by health, care-process and economic outcomes. Benchmarking should provide useful information to identify best practices and therefore a good instrument to improve quality across healthcare organizations. This paper aims to provide a proof-of-concept of the feasibility of an international VBHC benchmarking in breast cancer, with the ultimate aim of being used to share best practices with a data-driven approach among healthcare organizations from different health systems. Methods In the VOICE community—a European healthcare centre cluster intending to address VBHC from theory to practice—information on patient-reported, clinical-related, care-process-related and economic-related outcomes were collected. Patient archetypes were identified using clustering techniques and an indicator set following a modified Delphi was defined. Benchmarking was performed using regression models controlling for patient archetypes and socio-demographic characteristics. Results Six hundred and ninety patients from six healthcare centres were included. A set of 50 health, care-process and economic indicators was distilled for benchmarking. Statistically significant differences across sites have been found in most health outcomes, half of the care-process indicators, and all economic indicators, allowing for identifying the best and worst performers. Conclusions To the best of our knowledge, this is the first international experience providing evidence to be used with VBHC benchmarking intention. Differences in indicators across healthcare centres should be used to identify best practices and improve healthcare quality following further research. Applied methods might help to move forward with VBHC benchmarking in other medical conditions. [ABSTRACT FROM AUTHOR]

A Global Navigation Satellite System (GNSS) is widely used today for both positioning and timing purposes. Many distinct receiver chips are available as Application-Specific Integrated Circuit (ASIC)s off-the-shelf, each tailored to the requirements of various applications. These chips deliver good performance and low energy consumption but offer customers little-to-no transparency about their internal features. This prevents modification, research in GNSS processing chain enhancement (e.g., application of Approximate Computing (AxC) techniques), and design space exploration to find the optimal receiver for a use case. In this paper, we review the GNSS processing chain using SyDR, our open-source GNSS Software-Defined Radio (SDR) designed for algorithm benchmarking, and highlight the limitations of a software-only environment. In return, we propose an evolution to our system, called Hard SyDR to become closer to the hardware layer and access new Key Performance Indicator (KPI)s, such as power/energy consumption and resource utilization. We use High-Level Synthesis (HLS) and the PYNQ platform to ease our development process and provide an overview of their advantages/limitations in our project. Finally, we evaluate the foreseen developments, including how this work can serve as the foundation for an exploration of AxC techniques in future low-power GNSS receivers. [ABSTRACT FROM AUTHOR]

This paper focuses on addressing the complex healthcare needs of patients struggling with discordant chronic comorbidities (DCCs). Managing these patients within the current healthcare system often proves to be a challenging process, characterized by evolving treatment needs necessitating multiple medical appointments and coordination among different clinical specialists. This makes it difficult for both patients and healthcare providers to set and prioritize medications and understand potential drug interactions. The primary motivation of this research is the need to reduce medication conflict and optimize medication regimens for individuals with DCCs. To achieve this, we allowed patients to specify their health conditions and primary and major treatment concerns, for example, costs of medication, interactions with current drugs, and weight gain. Utilizing data gathered from MTurk and Qualtrics, we gained insights into healthcare providers' strategies for making/customizing medication regimens. We constructed a dataset and subsequently deployed machine learning algorithms to predict optimal medication regimens for DCC patients with specific treatment concerns. Following the benchmarking different models, Random forest emerged as the top performer, achieving an accuracy of 0.93. This research contributes significantly to the enhancement of decision-making processes, empowers patients to take a more active role in their healthcare, and promotes more informed and productive discussions between patients and their care teams. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a benchmark method to identify best practices in higher education across the country, in relation with management practices of academic productivity gender gaps and their impact on family responsibilities. This paper uses a qualitative approach to collect relevant information from official web pages from sixteen international universities previously selected. All information related to best practices policies implemented in each university was charted and assigned to one of the following categories for further analysis: ranking, gender policies, gender gaps, academic productivity, and childcare responsibilities. Results from this study revealed that the main best practices implemented in the included universities were practices related to harassment prevention, gender equality at college admission, development of protocols, leadership training, scholarships for study and research programs, adequate infrastructure, childcare financial support, flexible work schedules, family leave policies, improvement of the work environment and the development of departments in charge of follow-up and regular assessment of the proposed strategies. [ABSTRACT FROM AUTHOR]

There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The mode of implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range of efficacy, and hardware costs are the primary distinctions between these algorithms. Selecting an appropriate algorithm is critical for users, as it influences the electrical efficiency of PV systems and lowers costs by reducing the number of solar panels required to achieve the desired output. This research is relevant since PV systems are an alternative and sustainable solution for energy production. The main aim of this paper is to review the current advances in MPPT algorithms. This paper first undertakes a systematic literature review (SLR) of various MPPT algorithms, highlighting their strengths and weaknesses; a detailed summary of the related reviews on this topic is then presented. Next, quantitative and qualitative comparisons of the most popular and efficient MPPT methods are performed. This comparison is based on simulation results to provide efficient benchmarking of MPPT algorithms. This benchmarking validates that intelligent MPPTs, such as artificial neural network (ANN), fuzzy logic control (FLC), and adaptive neuro-fuzzy inference system (ANFIS), outperform other approaches in tracking the MPPT of PV systems. Specifically, the ANN technique had the highest efficiency of 98.6%, while the ANFIS and FLC methods were close behind with efficiencies of 98.34% and 98.29%, respectively. Therefore, it is recommended that these intelligent MPPT techniques be considered for use in future photovoltaic systems to achieve optimal power output and maximize energy production. [ABSTRACT FROM AUTHOR]

Bayesian methods have been widely used in the last two decades to infer statistical properties of spatially variable coefficients in partial differential equations from measurements of the solutions of these equations. Yet, in many cases the number of variables used to parameterize these coefficients is large, and oobtaining meaningful statistics of their probability distributions is difficult using simple sampling methods such as the basic Metropolis- Hastings algorithm--in particular, if the inverse problem is ill-conditioned or ill-posed. As a consequence, many advanced sampling methods have been described in the literature that converge faster than Metropolis-Hastings, for example, by exploiting hierarchies of statistical models or hierarchies of discretizations of the underlying differential equation. At the same time, it remains difficult for the reader of the literature to quantify the advantages of these algorithms because there is no commonly used benchmark. This paper presents a benchmark Bayesian inverse problem--namely, the determination of a spatially variable coefficient, discretized by 64 values, in a Poisson equation, based on point measurements of the solution--that fills the gap between widely used simple test cases (such as superpositions of Gaussians) and real applications that are difficult to replicate for developers of sampling algorithms. We provide a complete description of the test case and provide an open-source implementation that can serve as the basis for further experiments. We have also computed 2 x 1011 samples, at a cost of some 30 CPU years, of the posterior probability distribution from which we have generated detailed and accurate statistics against which other sampling algorithms can be tested. [ABSTRACT FROM AUTHOR]

The paper in the form of short communication deals with the phenomenon of social farming. This is a form of involvement of disadvantaged people in the integration process through agricultural activities. Based on the results of documentary analysis and benchmarking method, we presented experiences from the other European countries. In the Czech Republic, the concept is not widespread, not anchored in legislation and not supported by the public administration. Our proposals were aimed at using existing legislative possibilities, not at proposals that require changes to legal standards or the focus of financial support in the form of subsidies. We propose to use certain tools, namely a social business model or cooperation between a social service provider and a farmer. Furthermore, it is a suitable form of business for public beneficial entities, namely associations and especially institutes. We see suitability in the way of tax optimization. [ABSTRACT FROM AUTHOR]

The landscape of sarcoma care is on the cusp of a transformative era, spurred by the convergence of digital health and artificial intelligence (AI). This perspectives article explores the multifaceted opportunities and challenges in leveraging these technologies for value-based, precision sarcoma care. We delineate the current state-of-the-art methodologies and technologies in sarcoma care and outline their practical implications for healthcare providers, administrators, and policymakers. The article also addresses the limitations of AI and digital health platforms, emphasizing the need for high-quality data and ethical considerations. We delineate the promise held by the synergy of digital health platforms and AI algorithms in enhancing data-driven decision-making, outcome analytics, and personalized treatment planning. The concept of a sarcoma digital twin serves as an illustrative paradigm for this integration, offering a comprehensive, patient-centric view of the healthcare journey. The paper concludes with proposals for future research aimed at advancing the field, including the need for randomized controlled trials or target trial emulations and studies focusing on ethical and economic aspects. While the road to this transformative care is laden with ethical, regulatory, and practical challenges, we believe that the potential benefits far outweigh the obstacles. We conclude with a call to action for multidisciplinary collaboration and systemic adoption of these technologies, underscoring the urgency to act now for the future betterment of sarcoma care and healthcare at large. [ABSTRACT FROM AUTHOR]

Benchmarking serves as the foundation for selecting a database in any project. The available benchmarking tools evaluate system performance by subjecting it to random data and a set of arbitrary operations, without considering the specific characteristics of the application. The problem with these tools is that they reflect unrealistic benchmarks as they do not consider the nature, sequence, and type of queries the application will send to the database. In this paper, we introduced the approach of benchmarking the database based on the nature of interaction and queries between the application and database, and we built a benchmarking tool using Java to benchmark Redis and MongoDB as databases for a trip planning application with GTFS data of Budapest local transport data. Our study involved comparing the performance of both databases under ten different stress levels by simulating the number of querying clients. The results show that both database's performance is slightly decreased while increasing the number of clients (stress). However, Redis shows better performance compared to MongoDB. [ABSTRACT FROM AUTHOR]

Long short-term memory neural networks have been proposed as a means of creating accurate models from large time series data originating from various fields. These models can further be utilized for prediction, control, or anomaly-detection algorithms. However, finding the optimal hyperparameters to maximize different performance criteria remains a challenge for both novice and experienced users. Hyperparameter optimization algorithms can often be a resource-intensive and time-consuming task, particularly when the impact of the hyperparameters on the performance of the neural network is not comprehended or known. Teacher forcing denotes a procedure that involves feeding the ground truth output from the previous time-step as input to the current time-step during training, while during testing feeding back the predicted values. This paper presents a comprehensive examination of the impact of hyperparameters on long short-term neural networks, with and without teacher forcing, on prediction performance. The study includes testing long short-term memory neural networks, with two variations of teacher forcing, in two prediction modes, using two configurations (i.e., multi-input single-output and multi-input multi-output) on a well-known chemical process simulation dataset. Furthermore, this paper demonstrates the applicability of a long short-term memory neural network with a modified teacher forcing approach in a process state monitoring system. Over 100,000 experiments were conducted with varying hyperparameters and in multiple neural network operation modes, revealing the direct impact of each tested hyperparameter on the training and testing procedures. [ABSTRACT FROM AUTHOR]

With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper. [ABSTRACT FROM AUTHOR]

Benchmarking is a tool for evaluating organizational performance with a learning approach from oth)ers. The importance of benchmarking in every industry is clear for anyone. In the automotive industry, the performance of after-sales service agencies in Iran is evaluated every year by Iran Standard and Quality Inspection company. One of the ways to continuously improve in after-sales service agencies is benchmarking of successful and efficient examples in the network. In this paper, a benchmarking model is developed considering that the repair index and customer satisfaction are interdependent. To improve the accuracy and operationality of benchmarking, some constraints have been added to the model with the opinion of experts. Considering the dependent parameters, a data envelopment analysis model has been proposed and this model has been implemented to benchmark 20 after-sales service agencies of a car company. By solving the model and comparing it with the results of the original model, it was observed that the considered conditions changed the benchmarking and in)creased the accuracy. This paper discusses the concept of the impact and importance of dependent parameters in benchmarking, and with this concept, a benchmarking model for automotive after-sales service agencies is presented. [ABSTRACT FROM AUTHOR]

Many algorithms that were developed for solving three-dimensional bin packing problems use generic data for either experiments or evaluation. However, none of these datasets became accepted for benchmarking 3D bin packing algorithms throughout the community. To close this gap, this paper presents the benchmarking dataset for robotic bin packing problems (BED-BPP), which is based on realistic data. We show the variety of the dataset by elaborating an n-gram analysis. Besides, we propose an evaluation function, which contains a stability check that uses rigid body simulation. We demonstrated the application of our dataset on four different approaches, which we integrated in our software environment. [ABSTRACT FROM AUTHOR]

Over an 18-month period four New Zealand educational institutions--a university, a private tertiary enterprise, a wānanga, and an institute of technology/polytechnic--have engaged in a process of change influenced by technology. Their e-learning capability was benchmarked using the E-Learning Maturity Model, and this information was used to stimulate change activities. The resulting case studies, discussed in this paper, illustrate the issues that face tertiary organisations and leaders engaging with the opportunities and challenges of e-learning. Five factors have been identified as significant influences on the ability of organisations to change in response to technology in the current political and educational landscape of the New Zealand tertiary sector. [ABSTRACT FROM AUTHOR]

Remanufacturing has been widely adopted in the industrial sector due to carbon emission constraints and economic benefits. This paper discusses a closed-loop supply chain composed of an original equipment manufacturer (OEM), an authorized remanufacturer (AR), that is licensed by the OEM to carry out remanufacturing activities in the presence of strategic consumers under carbon cap-and-trade regulations. We establish a Stackelberg game model to identify the optimal manufacturing/remanufacturing decisions made by chain members, and compare the impacts of two different carbon allowance allocation rules on the optimal production decisions and profits, and on the environment. The results showed that optimal decisions in a closed-loop supply chain are affected by the carbon price, carbon allowance allocation, and consumer preferences for remanufactured products. In addition, for high-emission enterprises, the grandfathering rule performs better than the benchmarking rule, yielding higher profits and less environmental impact. The government should take into account the actual economic and production technological developments, implement the benchmarking rule for low-emission enterprises, and apply the grandfathering rule to high-emission enterprises. [ABSTRACT FROM AUTHOR]

Benchmarking is a tool for evaluating organizational performance with a learning approach from others. The importance of benchmarking in every industry is clear for anyone. In the automotive industry, the performance of after-sales service agencies in Iran is evaluated every year by Iran Standard and Quality Inspection company. One of the ways to continuously improve in after-sales service agencies is benchmarking of successful and efficient examples in the network. In this paper, a benchmarking model is developed considering that the repair index and customer satisfaction are interdependent. To improve the accuracy and operationality of benchmarking, some constraints have been added to the model with the opinion of experts. Considering the dependent parameters, a data envelopment analysis model has been proposed and this model has been implemented to benchmark 20 after-sales service agencies of a car company. By solving the model and comparing it with the results of the original model, it was observed that the considered conditions changed the benchmarking and increased the accuracy. This paper discusses the concept of the impact and importance of dependent parameters in benchmarking, and with this concept, a benchmarking model for automotive after-sales service agencies is presented. [ABSTRACT FROM AUTHOR]

The Not Structured Query Language (NoSQL) databases have become more relevant to applications developers as the need for scalable and flexible data storage for online applications has increased. Each NoSQL database system provides features that fit particular types of applications. Thus, the developer must carefully select according to the application's needs. Redis is a key-value NoSQL database that provides fast data access. On the other hand, the Apache HBase database is a column-oriented database that offers scalability and fast data access, is a promising alternative to Redis in some types of applications. In this research paper, the goal is to use the Yahoo Cloud Serving Benchmark (YCSB) to compare the performance of two databases (Redis and HBase). The YCSB platform has been developed to determine the throughput of both databases against different workloads. This paper evaluates these NoSQL databases with six workloads and varying threads. [ABSTRACT FROM AUTHOR]

Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly exploring random tree, are used heavily in autonomous robots. With the recent advances in machine learning, development of learning-based path planning algorithms has been experiencing a rapid growth. A unified path planning interface that facilitates the development and benchmarking of existing and new algorithms is needed. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learning-based path planning algorithms in 2D and 3D grid world environments. Many existing path planning algorithms are supported, e.g. A*, Dijkstra, waypoint planning networks, value iteration networks, and gated path planning networks; integrating new algorithms is easy and clearly specified. The benchmarking ability of PathBench is explored in this paper by comparing algorithms across five different hardware systems and three different map types, including built-in PathBench maps, video game maps, and maps from real world databases. Metrics, such as path length, success rate, and computational time, were used to evaluate algorithms. Algorithmic analysis was also performed on a real-world robot to demonstrate PathBench's support for Robot Operating System. PathBench is open source1. [ABSTRACT FROM AUTHOR]

The 2019 European Open Data Directive identifies geospatial data as data that could have a major impact on human activities (high-value data, HVD) and advocates its provision as open data (OD), i.e., without barriers to access and re-use. Although Croatia has implemented OD policies to support the provision of open data, many geospatial data are still not available, or if available, their level of openness ranks Croatia lower than Slovenia and Serbia on some ranking lists. Benchmarking tools have proven to be a powerful tool in identifying barriers in OD. This paper, therefore, benchmarks the level of openness and provision of geospatial HVD in Croatia, Slovenia and Serbia, using the extended and modified Global Open Data Index methodology (GODI Plus). It is expected that this will provide an answer to the status of OD policies and government engagement in OD in Croatia and identify good OD practices among the three countries analyzed. Furthermore, the results will be a baseline benchmark for future HVD analyses. The results reveal low data openness for Croatia and Serbia, high data openness for Slovenia, and a low level of government engagement in all three proposed countries. [ABSTRACT FROM AUTHOR]

The extensive use of Deep Neural Networks (DNNs) encourages people to design domain-specific hardware called Artificial Intelligence (AI) processors. The novel hardware makes optimizations challenging without a proper performance model that reveals working details and performance implications. This paper presents a performance model, Verrocchio, for Huawei DaVinci AI Core, which predicts the execution time of real-world DaVinci kernels. We propose specially-crafted micro-benchmarks to identify contention source, runtime behaviors, and bandwidth sharing, which significantly determine performance. Since DaVinci Core adopts a binary semaphore mechanism for synchronization, Verrocchio views each instruction as a discrete event and manages its execution time based on the programming logic. For evaluation, Verrocchio achieves average error rates of 2.62% and 2.30% in sample kernels for single-core and double-core execution. We demonstrate an optimizing process of matrix multiplications with Verrocchio, achieving speedups of 1.70× for operators and 1.53× for applications and error rates of 5.06% and 5.25%. • Detailed dissections of Huawei DaVinci AI Core, a novel AI processor. • Benchmarking the DaVinci Core bandwidth contention, the key performance factor. • Performance model for accurate execution time prediction of kernel program. • Demonstration of DaVinci kernel optimization and prediction accuracy evaluation. [ABSTRACT FROM AUTHOR]

A collection of twelve papers published in Energies, in a Special Issue on "Building Energy Audits-Diagnosis and Retrofitting", was bound together and published in 2021, focusing on the built environment. The aim was to systematically collect and analyze relevant data for obtaining adequate knowledge on the energy use profile of buildings, and was extended for the sustainability assessment of the built environment. To date, all papers have been very well received, attaining a total of 97 citations and over 15,300 views. The papers addressed historic and various building types, baselines for non-residential buildings from energy performance audits and from in-situ measurements, monitoring and data analysis, assessment of indoor environmental quality, model calibration and verification of energy savings, along with an urban audit and rating method for assessing the sustainability of the built environment. Following on from the success of this Special Issue, the decision was made to reopen and extend it to include papers related to decarbonization and sustainability, at building, city, region, and national scales. This Editorial reviews the performance of the first Special Issue and outlines the second volume on Building Energy Audits-Diagnosis and Retrofitting Towards Decarbonization and Sustainable Cities, as a Special Issue in Energies. [ABSTRACT FROM AUTHOR]

This paper presents a systematic mapping literature review about the mobile robotics competitions that took place over the last few decades in order to obtain an overview of the main objectives, target public, challenges, technologies used and final application area to show how these competitions have been contributing to education. In the review we found 673 papers from 5 different databases and at the end of the process, 75 papers were classified to extract all the relevant information using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. More than 50 mobile robotics competitions were found and it was possible to analyze most of the competitions in detail in order to answer the research questions, finding the main goals, target public, challenges, technologies and application area, mainly in education. [ABSTRACT FROM AUTHOR]

Benchmarking has been adopted by private organisations in search of commercial efficiency gains and public sector organisations in terms of efficiency, effectiveness and accountability. One such sector that has considerable potential to benefit from benchmarking is the transport sector. Focusing on the pavement management of road networks, this paper reviews a number of popular benchmarking techniques cited in the literature and undertakes a comparative analysis of their suitability with respect to a number of selection criteria. The audience for the paper is researchers and practitioners in pavement management and, consequently, the benchmarking techniques presented range from simple ratios or regression analyses to more complex frontier-based methods incorporating optimisation of weightings. Two of these frontier-based methods, Data Envelopment Analysis, a non-parametric data-orientated approach, and Stochastic Frontier Analysis, a parametric stochastic approach, are shown to be the most suitable for use in the pavement management sector. As the true level of efficiency is unknown in benchmarking, it is not possible to determine which technique is the best. Both have their advantages and disadvantages, however, we have stated a preference for Data Envelopment Analysis recognising that its non-parametric form offers a possible advantage in pavement management given the complexity and number of influencing variables. [ABSTRACT FROM AUTHOR]

Expression analysis of small noncoding RNA (sRNA), including microRNA, piwi-interacting RNA, small rRNA-derived RNA, and tRNA-derived small RNA, is a novel and quickly developing field. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length − 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (4) analyzing differential expression with DESeq2 with adjusted p-value < 0.05 or limma with p-value < 0.05 if there is very little signal and few transcripts. [ABSTRACT FROM AUTHOR]

This paper identifies the key planning stages of a post-occupancy evaluation (POE) in Higher Education Institutes (HEIs), utilising prominent guidance documentation and then develops a delineated data flow diagram representing the decision pathways (as a process map) practitioners are required to follow. Process mapping conducted also identifies inconsistencies in POE planning and implementation in practice, and develops new theory regarding the barriers to systematic and comparable POE implementation. An interpretivist and inductive research approach is utilised to develop new insights and theories on POE implementation using secondary data contained in published case study reports. The research delineates prominent POE guidance documentation using data flow diagrams to enable a cross comparative analysis to be made between case studies. A comparison of the delineated publicly available POE reports illustrate a distinct lack of consistency in the implementation of POE directly impeding the development of benchmarks and subsequent iterative improvement of future building design. Furthermore, the observed low levels of compliance (with data collection recommended in industrial guidance documentation) shows that the full utility and widely espoused sustainability benefits of POE are not being exploited in practice. Findings presented emphasise the requirement for a standardised approach to implementing POE in practice vis-à-vis simplifying the diverse array of strategies and approaches stipulated in prevailing guidance documentation. Such work is the first to adopt a process mapping approach to POEs conducted in practice. [ABSTRACT FROM AUTHOR]

Summary: Despite assurances of government action, the burden of non‐communicable diseases (NCDs) and overweight and obesity is continuing to grow at an alarming rate both globally and in Europe. The NOURISHING and MOVING policy frameworks outline a comprehensive set of policy actions across 6 domains and 16 policy areas in which national governments should take action to promote healthy diets and physical activity. Monitoring and benchmarking these policies is important for assessing progress on obesity and NCD prevention. This paper describes the participatory process for developing benchmarking tools structured around the policy areas of the NOURISHING and MOVING policy frameworks. They consist of a set of indicators and policy attributes that assess government support in promoting healthy nutrition and physical activity. They are adolescent relevant as they capture policy actions that target or impact adolescents. The benchmarking tools are designed to monitor progress on national government action on nutrition and physical activity based on aspirational standards. They will be applied in 27 European countries initially and are aimed at policymakers, researchers, and civil society, to track progress, develop the research infrastructure on effectiveness of NCD prevention policies at population level, and support advocacy efforts. [ABSTRACT FROM AUTHOR]

Webshell attacks are becoming more and more prevalent every year. Webshells are malicious scripts injected into web servers in the aim to confiscate persistent and remote access through simple HTTP requests on web browsers. Through webshells, attackers can remotely access confidential data and execute system commands. Actually, threat actors use webshells as an initial foothold to compromise the network infrastructure and cause dramatic damages. The impacts of webshell attacks are enormous, ranging from basic malicious actions, such as exposing sensitive data and upload more dangerous malware, to cause denial of services and compromise external networks and hence put the whole infrastructure at risk. Webshell attacks are hazardous since they can persist for a long time without being noticed by inexperienced administrators and ordinary malware scanners. In the literature, several machine learning-based models were proposed for the detection of PHP webshells. In this paper, we propose and experiment the ability of a simple deep learner model for the detection of multi-language webshells. The aim is to highlight existing challenges on the detection of webshell attacks and outline the way forward. Through analyzing source file scripts, the proposed model is designed to be able to distinguish webshells from benign files. Due to the absence of benchmark datasets for webshell detection, we collected a reasonable in size dataset for the validation process. We compared the performance of the proposed model with recent state-of-the-art systems. We also experimented source-code and opcode-based PHP detection models and the impact of presence of near-duplicates in datasets. Experimental results showed that: (1) the proposed deep learner outperforms all the experimented systems for four tested languages: PHP, JSP, ASP and ASPX with more than 98.27% of accuracy, (2) source-code based detection models are more effective than opcode-based detection models for PHP webshells, (3) the presence of near-duplicates causes higher but biased performance of webshell detection models and (4) more attention should be paid for the detection of webshells with advanced coding tricks such as letter slicing and code splitting. [ABSTRACT FROM AUTHOR]

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas. As of December 2022, the GitHub repository1 has reached 2,000 stars and 380 forks, which demonstrates the utility of the proposed open-source framework through the wide usage by the GNN community. In this paper, we present an updated version of our benchmark with a concise presentation of the aforementioned framework characteristics, an additional medium-sized molecular dataset AQSOL, similar to the popular ZINC, but with a real-world measured chemical target, and discuss how this framework can be leveraged to explore new GNN designs and insights. As a proof of value of our benchmark, we study the case of graph positional encoding (PE) in GNNs, which was introduced with this benchmark and has since spurred interest of exploring more powerful PE for Transformers and GNNs in a robust experimental setting. [ABSTRACT FROM AUTHOR]

While function modeling has been around in engineering design research since the 1960s, there have been no systematic, comparative studies devoted to assessing the adequacy of function modeling frameworks in light of engineering design objectives. This systematic assessment and comparison – called benchmarking – is now recognized as a central research issue in current function modeling research, but insight into how this benchmarking can be done is at present limited. In this paper, we attempt to improve our insight into how benchmarking can be done for a specific but important engineering context: function optimization of reverse-engineered systems. We argue that the capacity to produce technical advantage predictions, viz. predictions concerning the improved functional performance of a redesigned technical system, is an important benchmark criterion in this context. We subsequently illustrate the utility of the criterion by assessing two prominent function modeling frameworks in terms of it. Throughout the paper, we use a case study of the design of an electric wok to clarify and illustrate our ideas. [ABSTRACT FROM AUTHOR]

Over the past decade or so and through the onset of the COVID-19 pandemic, a number of airlines have risen to global prominence in Asia (especially China), the Middle East, Latin America, Africa, and elsewhere. As each develops, many industry observers are questioning the extent to which these airlines will rival current industry heavyweights around the world. This paper aims to provide a basis for assessing what constitutes a major global carrier in the airline industry, including the likelihood of being so in the foreseeable future. Three key metrics are employed to gauge the global reach of an airline and its corresponding major global carrier status and rank: wide-bodied fleet size, the number of international destinations flown to, and continents served (squared for adequate amplification). Data are sourced from the Centre for Aviation (CAPA) airline database. The paper concludes that major global carrier status for an airline is best viewed in relation to key competitors including the ranking location of those directly above and below any given airline. This ranking approach is therefore about insights generated and is not premised on precise locations on a leader board. [ABSTRACT FROM AUTHOR]

In the context of overcoming the COVID-19 pandemic consequences, the concept of total quality management should serve as the basis for rethinking the role of customer service for travel services, in accordance with the requirements of the international service quality standards. The paper aimed to approach the theoretical, methodological and practical problems of assessing the quality of the travel services of regional enterprises that organize rural tourism tours in Ukraine. The main attention was paid to the analysis of the quality of components of standard travel services on the directions of rural tourism routes having hidden features. To assess the services quality, in accordance with their latent features in the field of rural tourism, the authors used the benchmarking methodology, which combines the number of statistical comparison methods based on multidimensional objects. The article offers an algorithm for the benchmarking of the quality of services provided by rural tourism tour operators. As part of the benchmarking tools for assessing the quality of service provided by travel business entities, the authors used the taxonomic analysis method. The approaches proposed by the authors to assessing the quality of travel services allow rural tourism business entities to form the measures to restore the sale and rendering of travel services in the context of overcoming the COVID-19 pandemic consequences. [ABSTRACT FROM AUTHOR]

Background: Social support is a key dimension of personal recovery for patients with severe mental disorders. Although clinicians and health professionals should monitor the social support resources of patients in order to provide effective treatment, no specific tool or intervention exist for that purpose. Egonet is a computer-assisted intervention for health and social services that enables the mapping, assessment and fostering of patients' social support networks. Aims: This paper describes the intervention and evaluates the appropriateness and acceptability of its implementation in a clinical setting. Method: We described the computer-assisted intervention. We used a questionnaire to assess how appropriate and acceptable clinicians found a preliminary version to be. We also collected patients' level of satisfaction with their social support networks before and after the intervention and carried out qualitative interviews. Results: Egonet is made up of four components: (1) a computer-assisted interview for mapping social networks, (2) a remote data server, (3) a data-mining module and (4) a customisable reporting tool. Egonet was implemented for 232 patients by 91 clinicians from five services. The intervention was perceived by clinicians as appropriate and useful for clinical practice, although it was noted that considerable effort was required to obtain its benefits. Patients were slightly more satisfied with their social support after the intervention than before, and expressed the opinion that more attention should be paid to the social support network. Conclusions: EGONET is appropriate and acceptable for use in work related to the social support networks of patients with psychiatric disorders. [ABSTRACT FROM AUTHOR]

The heterogeneity in recently published knowledge graph embedding models’ implementations, training, and evaluation has made fair and thorough comparisons difficult. To assess the reproducibility of previously published results, we re-implemented and evaluated 21 models in the PyKEEN software package. In this paper, we outline which results could be reproduced with their reported hyper-parameters, which could only be reproduced with alternate hyper-parameters, and which could not be reproduced at all, as well as provide insight as to why this might be the case. We then performed a large-scale benchmarking on four datasets with several thousands of experiments and 24,804 GPU hours of computation time. We present insights gained as to best practices, best configurations for each model, and where improvements could be made over previously published best configurations. Our results highlight that the combination of model architecture, training approach, loss function, and the explicit modeling of inverse relations is crucial for a model’s performance and is not only determined by its architecture. We provide evidence that several architectures can obtain results competitive to the state of the art when configured carefully. We have made all code, experimental configurations, results, and analyses available at https://github.com/pykeen/pykeen and https://github.com/pykeen/benchmarking. [ABSTRACT FROM AUTHOR]

In this paper, a novel artificial intelligence technique for the estimation of near-optimal resource management is proposed. The model utilizes a two-stage data envelopment analysis to find the best-practice frontier of the decision-making units. By employing this data, a supervised multi-layer Artificial Neural Network is exercised. This network is capable of predicting the frontier for the near future by receiving input and mediator variables. In the next step, a genetic algorithm is formed to find an optimal input value for the artificial neural network, such that the overall performance of decision-making units in the near future is maximized. The proposed algorithm allows the managers to set some restrictions on the whole system, including the minimum efficiency and the maximum change on resources. The performance of the presented technique is reviewed on 31 branches of an insurance company, during the years 2015 to 2018. The results show that the developed algorithm can efficiently maximize the overall performance of decision-making units. [ABSTRACT FROM AUTHOR]

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations. • ASI-based GHI nowcasts outperform persistence model under cloudy conditions. • Under clear skies, three ASIs reveal more accurate nowcasts than persistence. • ASIs forecasts are more accurate from persistence at increasing forecast horizons. [ABSTRACT FROM AUTHOR]

Topology optimization has developed tremendously and new approaches, algorithms and applications are appearing on a daily basis. However, how to fairly evaluate and compare new concepts and ideas to existing ones is an open question due to the broadness of modelling approaches, geometry parameterizations and physical applications. Ideally, the community should define common benchmark examples but again, how to define benchmarks that are generally applicable? In the lack or impracticality of common benchmarks, the responsibility of fair evaluation of contributions is up to authors but the literature shows multiple examples where this seems to be challenging. This note lists a number of recommendations, tools and concepts that papers in the field of topology optimization (and possibly other fields) should consider to represent good scientific practise. [ABSTRACT FROM AUTHOR]

The minimum distance models have undoubtedly represented a significant advance for the establishment of targets in Data Envelopment Analysis (DEA). These models may help in defining improvement plans that require the least overall effort from the inefficient Decision Making Units (DMUs). Despite the advantages that come with Closest Targets, in some cases unsatisfactory results may be given, since improvement plans, even in that context, differ considerably from the actual performances. This generally occurs because all the effort employed to reach the efficient DEA frontier is channeled into just a few variables. In certain contexts these exorbitant efforts in some inputs/outputs become unapproachable. In fact, proposals for sequential improvement plans can be found in the literature. It could happen that the sequential improvement plans continue to be so demanding in some variable that it would be difficult to achieve such targets. We propose an alternative approach where the improvement plans require similar efforts in the different variables that participate in the analysis. In the absence of information about the limitations of improvement in the different inputs/outputs, we consider that a plausible and conservative solution would be the one where an equitable redistribution of efforts would be possible. In this paper, we propose different approaches with the aim of reaching an impartial distribution of efforts to achieve optimal operating levels without neglecting the overall effort required. Therefore, we offer different alternatives for planning improvements directed towards DEA efficient targets, where the decision-maker can choose the one that best suits their circumstances. Moreover, and as something new in the benchmarking DEA context, we will study which properties satisfy the targets generated by the different models proposed. Finally, an empirical example used in the literature serves to illustrate the methodology proposed. [ABSTRACT FROM AUTHOR]

Drawing on the critical discourse analysis of journals and working papers from 2011-2020 referring to the at-risk of poverty or social exclusion composite indicator (AROPE), we shed light on how benchmarks technicize academic discourse, particularly in its part contributed by economists. First developed to measure progress towards the poverty target set in the EU's Europe 2020 strategy, AROPE has easily permeated academic debate. Its anchoring in statistical procedures and expertise has allowed it to function in this debate as a neutral and purely technical measurement tool, obscuring the interests and normative choices underlying its design and implementation. As a result, the discursive practices associated with the benchmark have led to the reproduction and legitimization of the anti-poverty policy objectives of the Europe 2020 strategy. Simultaneously, AROPE has provided a 'cognitive infrastructure' that enabled an economic view of the world geared towards raising competitiveness. It has made it possible to assess which Member State is doing well and which is doing poorly, and making recommendations on how the laggards should improve. Our analysis shows that benchmarks such as AROPE support the process of shaping Europe as a supranational entity, creating a picture of common European problems with uniform definitions, on the basis of which it is possible to divide Member States into better and worse performers and even promote common solutions through good practices. We conclude by highlighting that the academic discourse around AROPE, generated mainly by economists, reflects the growing interdependence of the academic and political spheres and the pressure for research to have social and political impact. [ABSTRACT FROM AUTHOR]