Benchmark Analysis Research Articles

Evolution of transport, accessibility and tourism competitiveness in Extremadura (Spain): a benchmarking analysis

The efficiency of the transportation options available to tourists, in terms of convenience and time, directly impacts the competitiveness of a destination in the tourism sector. The aim of this study is to compare how the indicators of accessibility and connectivity of transportation have evolved across Spain's Autonomous Regions. To achieve this, we used indicators related to the sufficiency and connectivity of air and high-speed train services, as well as land accessibility and connectivity, as provided by Exceltur's Monitur reports. For a dynamic analysis, which is essential in assessing this type of infrastructure, we considered the values of these indicators from the first Monitur report (2010) and the most recent one (2018). Based on these values for the 17 Autonomous Regions of Spain, we identified the regions that have gained relative competitiveness and those that have experienced a relative decline. The dynamic analysis conducted in this study has allowed us to classify the regions according to the evolution of their relative competitiveness. It highlights the key areas that destination managers should focus on to design strategies that enhance their competitive position.

In Silico Benchmarking of Fatigue Life Estimation Models for Passive SMD Solder Joints Under Thermal Cycling

Related to microelectronics’ reliability, lifetime estimation methods have gained importance, especially for surface-mounted devices. The virtual testing of electronic assemblies necessitates the geometry modeling and finite element analysis of the solder joint. The effect of the simplification of the solder geometry on the predicted lifetime is an open question. Furthermore, there is still not yet straightforward guidance for the choice of the material model and fatigue lifetime model. In this study, the impact of the geometry input method, the material model and the lifetime model choice is investigated on two different surface-mounted capacitors in a simulation-based benchmark analysis under thermal cyclic loading. Four different types of solder geometry modeling approaches are compared, among which one is a physics-based approach. Ten different fatigue models founded on plastic and viscoplastic material models are benchmarked. The results show that the component standoff height and the solder volume have a positive effect on the lifetime, while the capacitor size has a slightly negative effect on the lifetime. The results also suggest that approximate geometries can be used to replace the physics-based model with a restriction for the minimum standoff height.

BO-KM: A comprehensive solver for dispersion relation of obliquely propagating waves in magnetized multi-species plasma with anisotropic drift kappa-Maxwellian distribution

The observation of superthermal plasma distributions in space reveals a multitude of distributions with high-energy tails, and the kappa-Maxwellian distribution is a type of non-Maxwellian distribution that exhibits this characteristic. However, accurately determining the multiple roots of the dispersion relation for superthermal plasma waves propagating obliquely presents a challenge. To tackle this issue, we have developed a comprehensive solver, BO-KM, utilizing an innovative numerical algorithm that eliminates the need for initial value iteration. The solver offers an efficient approach to simultaneously compute the roots of the kinetic dispersion equation for oblique propagation in magnetized plasmas. It can be applied to magnetized superthermal plasma with multi-species, characterized by anisotropic drifting kappa-Maxwellian, bi-Maxwellian distributions, or a combination of the two. The rational and J-pole Padé expansions of the dispersion relation are equivalent to solving a linear system's matrix eigenvalue problem. This study presents the numerical findings for kappa-Maxwellian plasmas, bi-Maxwellian plasmas, and their combination, demonstrating the solver's outstanding performance through benchmark analyses. Program summaryProgram Title: BO-KMCPC Library link to program files:https://doi.org/10.17632/pr9cvjrvfv.1Licensing provisions: BSD 3-clauseProgramming language: MatlabNature of problem: To efficiently solve for multiple roots of the kinetic dispersion relation in superthermal plasma distributions with high-energy tails observed in space, we have developed BO-KM, a novel and comprehensive solver that employs a unified framework for computing uprathermal (or thermal) waves and instabilities. This solver is applicable to magnetized multi-species collisionless plasmas with anisotropic drift kappa-Maxwellian, bi-Maxwellian distributions, or a combination of both. Furthermore, BO-KM incorporates a submodule dedicated to the perpendicular propagation dispersion relation of bi-Kappa plasmas, thereby significantly improving computational efficiency at high κ values.Solution method: The method converts the kinetic plasma dispersion relation based on rational expansion (for the kappa-Maxwellian model) and J-pole Padé expansion (for the bi-Maxwellian model) into an equivalent linear eigenvalue system. This transformation effectively turns the root-finding task into an eigenvalue problem, enabling the simultaneous determination of roots using standard eigenvalue libraries.Additional comments including restrictions and unusual features: Kinetic relativistic effects are not included in the present version yet.

Controlling and planning cash flows at the enterprise

In today's market economy, effective cash flow management is important for ensuring the stability andcompetitiveness of an enterprise. Financial controlling, as a key component of the overall strategic management system,plays an important role in optimizing cash flows, monitoring resource attraction and risk management. It includes thedevelopment and implementation of a cash management policy, the use of analytical tools to forecast and assess financialrisks, and the development of strategies to minimize the negative impact of various factors. Financial controlling helpscompanies to ensure effective operation in difficult conditions, such as in a state of war or unstable market conditions, byresponding quickly to changes in the external environment. The controlling system includes not only monitoring expensesand income, but also developing and implementing strategies to optimize resources and maintain the company's financialstability. Operational financial controlling, which focuses on internal data, is a key tool for responding quickly to changesin the financial condition of the company. Methodological tools such as direct-costing, break-even point, ABC analysis,functional cost analysis, SWOT analysis, benchmarking and portfolio analysis help to identify and correct deviations fromtargets, increase cost efficiency and improve the financial stability of the company. Implementation of a financialcontrolling system not only improves financial transparency but also strengthens the confidence of investors and creditors,as well as maintains the company's stable development in a competitive market. Thus, financial controlling is an integralpart of effective financial resources management, which allows companies to achieve short-term and long-term financialgoals, maintain competitiveness and ensure sustainable development in the market.

A mini-review of single-cell Hi-C embedding methods

Single-cell Hi-C (scHi-C) techniques have significantly advanced our understanding of the 3D genome organization, providing crucial insights into the spatial genome architecture within individual nuclei. Numerous computational and statistical methods have been developed to analyze scHi-C data, with embedding methods playing a key role. Embedding reduces the dimensionality of complex scHi-C contact maps, making it easier to extract biologically meaningful patterns. These methods not only enhance cell clustering based on chromatin structures but also facilitate visualization and other downstream analyses. Most scHi-C embedding methods incorporate strategies such as normalization and imputation to address the inherent sparsity of scHi-C data, thereby further improving data quality and interpretability. In this review, we systematically examine the existing methods designed for scHi-C embedding, outlining their methodologies and discussing their capabilities in handling normalization and imputation. Additionally, we present a comprehensive benchmarking analysis to compare both embedding techniques and their clustering performances. This review serves as a practical guide for researchers seeking to select suitable scHi-C embedding tools, ultimately contributing to the understanding of the 3D organization of the genome.

When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs

The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models (LLMs) such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that LLMs and transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that LLMs and transformer PLMs tend to score GPT-generated text 10-20% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that even though the LLMs and transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly (in part) due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.

Benchmarking of key performance factors in textile industry effluent treatment processes for enhanced environmental remediation

This article presents a comprehensive benchmarking analysis of merit performance factors in the Effluent Treatment Plants (ETP) of the textile industry. The study aims to identify and evaluate key factors that contribute to the efficient operation and performance of ETPs. The performance of ETP was analyzed by valuable data gained from figures of PH, Dissolved oxygen, Dissolved solids, Suspended solids, Density, COD and BOD. The technical trends showed the deviations in the working conditions of Effluent Treatment Plant by variation in temperature. This variation is achieved by varying the settling time of wastewater in the sedimentation tank during the working process. The required dosing, plant efficiency and economic factors were taken into account. The Plant efficiency was determined to be 83.5% at normal conditions of water entering at temperature of 30°C and pressure of 1 atm along with addition of coagulants and flocculants in the wastewater. While the efficiency of the ETP plant was calculated about 88% using a Compact photometer at elevated conditions of temperature such as > 45°C, while at other temperatures the efficiency decreases significantly due to several reasons. The operating time of water treatment was decreased due to the variations in temperature of wastewater while other conditions kept constant like pressure, flow rates of water and chemicals (Polyacrylamide and Polymeric Ferric Sulfate). The usage of coagulants and flocculants at optimum conditions has been discussed in this study.

Consumer Satisfaction Benchmarking Analysis Using Group Decision Support System (GDSS) PROMETHEE Methodology in a GIS Environment

In today’s competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global performance results or they are not able to differentiate consumer perceptions using location analytics. This research develops a novel framework to assist multi-branch companies in mapping the consumer satisfaction performance of their stores, expanding conventional customer relationship management to the spatial context. The framework developed proposes a decision model that combines the Group Decision Support extension of the PROMETHEE and CRITIC methods in a GIS environment to generate satisfaction performance mappings. The developed decision-making framework converts consumer responses into satisfaction performance maps, allowing the company’s stores and their competitors to be evaluated. Moreover, it provides insight into the potential opportunities and threats for each store. The performance of the proposed framework is highlighted through a case study involving a multi-branch coffeehouse company in a Greek city. Finally, a tool developed to assist the computational part of the framework is presented.

Delta-V-optimal centralized guidance strategy for under-actuated N-satellite formations

This paper addresses the computation of Delta-V-optimal, safe, relative orbit reconfigurations for satellite formations in a centralized fashion. The formations under consideration comprise an uncontrolled chief spacecraft flying with an arbitrary number, N, of deputy satellites, where each deputy is equipped with a single electric thruster. Indeed, this represents a technological solution that is becoming widely employed by the producers of small-satellite platforms. While adopting a single electric thruster does reduce the required power, weight, and size of the orbit control system, it comes at the cost of rendering the satellite under-actuated. In this setting, the satellite can provide a desired thrust vector only after an attitude maneuver is carried out to redirect the thruster nozzle opposite to the desired thrust direction. In order to further extend the applicability range of such under-actuated platforms, guidance strategies are developed to support different reconfiguration scenarios for N-satellite formations. This paper starts from a classical non-convex quadratically constrained trajectory optimization formulation, which passes through multiple simplifications and approximations to arrive to two novel convex formulations, namely a second-order cone programming formulation, and a linear programming one. Out of five guidance formulations proposed in this article, the most promising three were compared through an extensive benchmark analysis that is applied to fifteen of the most widely-used solvers. This benchmark experiment provides information about the key distinctions between the different problem formulations, and under which conditions each one of them can be recommended.

Integrated metaheuristic approaches for estimation of fracture porosity derived from fullbore formation micro-imager logs: Reaping the benefits of stand-alone and ensemble machine learning models

Fracture porosity is one of the most effective parameters for reservoir productivity and recovery efficiency. This study aims to predict and improve the accuracy of fracture porosity estimation through the application of advanced machine learning (ML) algorithms. A novel approach was introduced for the first time to estimate fracture porosity by reaping the benefits of petrophysical and fullbore formation micro-imager (FMI) data based on employing various stand-alone, ensemble, optimisation and multi-variable linear regression (MVLR) algorithms. This study proposes a ground-breaking two-step committee machine (CM) model. Petrophysical data containing compressional sonic-log travel time, deep resistivity, neutron porosity and bulk density (inputs), along with FMI-derived fracture porosity values (outputs), were employed. Nine stand-alone ML algorithms, including back-propagation neural network, Takagi and Sugeno fuzzy system, adaptive neuro-fuzzy inference system, decision tree, radial basis function, extreme gradient boosting, least-squares boosting, least squares support vector regression and k-nearest neighbours, were trained for initial estimation. To improve the efficacy of stand-alone algorithms, their outputs were combined in CM structures using optimisation algorithms. This integration was applied through five optimisation algorithms, including genetic algorithm, ant colony, particle swarm, covariance matrix adaptation evolution strategy (CMA-ES) and Coyote optimisation algorithm. Considering the lowest error, the CM with CMA-ES showed superior performance. Subsequently, MVLR was employed to improve the CMs further. Employing MVLR to combine the CMs yielded a 57.85% decline in mean squared error and a 4.502% improvement in the correlation coefficient compared to the stand-alone algorithms. The results of the benchmark analysis validated the efficacy of this approach.

Text analysis and optimization strategy of an app’s privacy policy from the perspective of dual perception: taking Chinese shopping apps as an example

Purpose This paper aims to conduct an in-depth analysis of the shortcomings of apps’ privacy policies and to propose improvement and optimization strategies, which are of great significance for establishing a transparent and responsible privacy protection framework that ensures compliant collection and use of users’ information and effective protection of their privacy. Design/methodology/approach This paper obtained privacy policy texts for 100 shopping apps through Web crawlers and manual downloads. Based on the perspective of perceived usefulness, thematic analysis is conducted through the latent Dirichlet allocation topic model and comparison with existing policies. Based on the perspective of perceived ease of use, readability analysis is conducted through content analysis and formula calculation. Findings The apps privacy policies can be divided into seven themes. The authors benchmark these seven topics with the Personal Information Protection Law of the People’s Republic of China, the E-Commerce Law of the People’s Republic of China and the General Data Protection Regulation. It is found that there are omissions in the information collection and use and juvenile protection of the existing apps. Through the indicators’ readability analysis and calculation, it is found that the existing apps privacy policies have good performance in the readability indicators such as naming method, frame directory and so on. However, text personalization and text readability need to be improved and optimized. Originality/value At the theoretical level, this paper constructs a model from the dual perception perspectives of perceived usefulness and perceived ease of use and analyses the apps’ privacy policy texts at a fine-grained level. At the practical level, based on large-scale apps’ privacy policy text data, this paper conducts multi-dimensional research from theme analysis, authoritative law benchmarking analysis, content analysis and text readability calculation and analysis. At the same time, this paper identifies the current problems of apps’ privacy policies and puts forward countermeasure suggestions for their content improvement and optimization.

Research on the Relationship Between Financial Flexibility and Capital Structure Taking Chinese Listed Companies as Example

The acceleration of global economic integration has led to an increasingly uncertain external environment for Chinese enterprises, which are therefore required to master financial flexibility and capital structure. These are two of the basic elements in the corporate financial decision-making field, and their mastery will enable enterprises to survive and develop in this environment. The objective of this paper is to examine the relationship between financial flexibility and capital structure and to explore the potential applications of this relationship in the context of Chinese listed companies. A sample of Chinese listed companies across various industries, spanning the period from 2011 to 2021, has been obtained from the CSMAR database and used to conduct a series of statistical analysis. The software Stata has been employed to perform benchmark regression analysis, heterogeneity testing, and robustness testing on endogenous variables associated with capital structure and financial flexibility in order to gain insight into their respective influences. The results of the charts demonstrate a significant impact of financial flexibility on capital structure. In particular, there is a significant positive impact on debt structure, while growth opportunities have a negative impact. This paper indicates that firms should utilise their debt structures in a rational and strategic manner, and adjust their growth opportunities to enhance their competitive edge.

Monotonic and cyclic lateral loading of piles in low- to medium-density chalk

Offshore and other structures often rely on driven piles to carry lateral loads. However, there is currently no established design method to cover lateral loading at chalk sites, which are widespread across Northwest Europe. This paper reports monotonic and cyclic lateral load tests on highly instrumented 508 mm and 1220 mm diameter. open steel piles driven at a well-characterised chalk test site in Kent, UK, for a recent joint industry project that developed new benchmark datasets and analyses, supported by high-quality testing. The ultimate lateral pressures mobilised in the chalk are shown to be relatively low compared to its uniaxial compressive strengths (UCS) due to pile-driving damage, natural fracturing, local yielding and brittleness. Significant gaps opened between the piles and chalk during loading, that led to a substantially softer response on unloading and subsequent reloading, as well as marked axial capacity losses. Reaction curves extracted from the field measurements and applied in a one-dimensional numerical model perform well in reproducing the monotonic lateral tests. As with piles driven in other materials, one-way cyclic lateral loading led to permanent displacement accumulation and stiffness changes that were linked to the cyclic loading parameters. Both effects were more marked under biaxial cyclic lateral loading.

Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and Benchmarks

Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and Benchmarks

S1919 Evolving Burden of Liver Cancer Attributable to High BMI in the United States and Its Trend from 1990-2021: A Benchmarking Analysis

S1919 Evolving Burden of Liver Cancer Attributable to High BMI in the United States and Its Trend from 1990-2021: A Benchmarking Analysis

From Hindsight to Foresight: Understanding and Overcoming Bias in Financial Forecasts

Abstract This study conducts a comprehensive benchmarking analysis on analyst forecasts of revenue and EBITDA for exchange-traded public companies in the U.S. spanning the period from 2009 to 2022. Our findings reveal a consistent and statistically significant upward bias present in both revenue and EBITDA forecasts, on average, as evidenced by various measures of forecast error. This bias is explored at both market sector and market-wide levels of aggregation to check for macro-consistency. Recognizing the significance of these biases, we propose bias-adjustment frameworks aimed at enhancing the accuracy of future analyst forecasts pertaining to revenue and EBITDA. The implications of our study underscore the importance of mitigating such biases for improved forecasting precision in financial markets.

How to measure and manage country reputation

Countries benefit from a good reputation in terms of gaining a higher foreign direct investment inflow, a growing export volume, becoming more attractive as tourist destinations and, last but not least, by improving their populations’ well-being. Consequently, many countries endeavor to manage their public perception actively. Since communication influences perception, advertising plays a particularly important role in the latter. However, countries need to fathom which opinions they need to encourage in order to ensure others’ optimal perceptions of them. A prerequisite for tackling this challenge is a model that measures and explains country reputation. This study develops such a model. Our model conceptualizes reputation as a two-dimensional attitudinal construct, thereby avoiding one-dimensional approaches’ shortcomings. It refers to the cognition-related dimension as competence, whereas the affect-related dimension is termed likability. It measures reputation, as perceived by stakeholders, by means of six reflective indicators. Furthermore, the model employs a catalogue of 30 formative indicators — structured by means of five key constructs — to identify the drivers of country reputation. By cultivating impactful drivers, countries are able to apply targeted measures to alter their reputations’ ratings on the two dimensions (i.e. competence and likability). We employed data from Germany, the United States, and the United Kingdom to develop our model. Relevant criteria highlight the model’s reliability and validity. A benchmark analysis of the proposed model compared to that of existing approaches illustrates its superiority in terms of its convergent and criterion validity.

NAVIGATION IN E-GOVERNMENT: THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE FORMATION OF THE LEGAL FRAMEWORK FOR THE PROTECTION OF INTELLECTUAL PROPERTY RIGHTS

The integration of artificial intelligence (AI) into electronic government (e-government) systems is revolutionizing public administration by enhancing efficiency and improving service delivery. However, the adoption of AI technologies in this context also raises complex legal challenges, particularly concerning intellectual property (IP) rights. Traditional IP laws, which were developed with human authorship in mind, struggle to accommodate the unique characteristics of AI-generated content. This article examines how AI is reshaping the legal framework for IP protection within e-government systems, highlighting the implications and challenges that arise from this technological shift. The primary aim of this study is to explore the role of AI in the formation of IP law frameworks within e-government, focusing on how current laws address—or fail to address—the challenges of AI-generated content. The methodology includes a comprehensive literature review, analysis of legislative documents, case studies, and a benchmarking analysis to compare approaches across jurisdictions. Additionally, expert interviews provide insights into practical considerations and emerging trends in the field. The results indicate that while some jurisdictions, such as the European Union, are actively adapting their IP laws to address AI's impact, most existing frameworks remain inadequate for protecting AI-generated works. Divergent approaches across countries reveal a lack of international harmonization, which complicates cross-border collaboration and legal enforcement. The analysis also highlights the importance of public-private partnerships and sector-specific IP protections, which can address the unique needs of different e-government applications. From a forward-looking perspective, the study underscores the need for flexible, AI-specific IP protections that promote innovation while safeguarding IP rights. International cooperation will be essential for establishing consistent standards, facilitating global e-government initiatives, and supporting the responsible use of AI in public services. By fostering a balanced and adaptive IP framework, policymakers and stakeholders can help build a resilient digital ecosystem that accommodates future advancements in AI technology.

Benchmark Analysis of a Prismatic Type-high Temperature Gas-cooled Reactor with the ENDF/B-VII.0, ENDF/B-VII.1 and ENDF/B-VIII.0 Nuclear Data Libraries

In this work we performed a benchmark analysis of the High Temperature Engineering Test Reactor (HTTR) fully-loaded start-up critical core with a 30-bundle loading configuration using the Monte Carlo code Serpent 2 with the recently released nuclear data libraries ENDF/B-VII.0, ENDF/B-VII.1 and ENDF/B-VIII.0. The purpose of the work is to reveal the impacts of using different ENDF nuclear data libraries on neutronics calculations of a prismatic high temperature gas-cooled reactor (HTGR). The benchmark results obtained with Serpent 2 were compared against the available experimental values and those attained by different computer codes (MCNP5 and SCALE) using the nuclear data library ENDF/B-VII.0. The comparative results showed good agreement between Serpent 2, the experimental values, MCNP5 and SCALE. The results also exhibited a notable difference between using ENDF/B-VII.0, ENDF/B-VII.1 and ENDF/B-VIII.0 in predicting the neutronics parameters of the HTTR that suggests further investigation in future work.

A benchmarking analysis of sunlight and daylight availability in a highly dense urban area in southern China

The availability of daylight and sunlight in dense cities has increasingly received attentions. This article presented a benchmarking analysis of daylight & sunlight availability in six urban residential layouts at Guangzhou in southern China. As representatives of old or new towns, these layouts were achieved using local regulations. The benchmarking system was built based on an urban building under various levels of obstruction (horizontally infinite length). Daysim (Radiance) and T-sun were applied to calculate daylight illuminance and sunlight hours at building facades, respectively. Some findings were achieved as follows: 1) For sunlight availability, significant effects of urban layout and orientation can be found in both towns. Most layouts achieved similar sunlight hours as an urban building with high and heavy obstructions, while several southeast-oriented and southwest-oriented layouts had the values like a building with the medium obstruction. No clear differences of sunlight hours can be found between southeast and southwest orientations. 2) For daylight availability, most buildings in both towns had similar frequencies of proper vertical illuminance as an urban building with the medium obstruction. However, the frequencies of excessive vertical illuminance significantly varied in layout, façade position, and orientation and corresponded with the values of an urban building with different obstructions. Effects of layout were found to be clear at low and middle façade positions, while significant effects of orientation were only found at higher façade positions. 3) Generally, a daylighting analysis using the dynamic climate-based model could achieve more practical results than the approach of sunlight hours.