Directional Distance Research Articles

Efficiency analysis of geological exploration, mineral production, and land restoration in China's provinces under natural change

China is one of the world's largest producers of mineral resources and possesses abundant mineral reserves, and its mining industry thus plays a crucial role in the country's economic development. However, most studies overlook the importance of geological exploration activities preceding mineral production and the impact of land restoration on said production under external environmental factors. Therefore, this research utilizes the meta recycle undesirable dynamic three-stage directional distance function (DDF) under exogenous model to investigate geological exploration stage efficiency, mineral production stage efficiency, and land restoration stage efficiency in different regions of China. The research findings are as follows. (a) The overall efficiency in the east region surpasses that of the other regions. (b) The central region exhibits the lowest efficiency in the geological exploration stage and mineral production stage. (c) Land restoration stage efficiency in all regions needs improvement due to the impact of precipitation.

Assessing the Impact of China’s Industry on Health: A Dynamic Three-Stage Bootstrap Model with an Endogenous Fuzzy Directional Distance Function

Evaluating the industrial eco-system is essential for promoting resource efficiency, environmental development, and public health protection. However, traditional Data Envelopment Analysis (DEA) methods are prone to biases and rely on precise numerical values, which pose significant challenges when evaluating the industrial ecosystem in China, particularly given the prevalent data uncertainties and environmental complexities. Additionally, DEA models are typically static, fail to capture the long-term trends and dynamic characteristics of the industrial ecosystem. To address these issues, this study proposes a dynamic three-stage model with an endogenous fuzzy directional distance function. The model integrates the generalized smooth bootstrap method and fuzzy comparison techniques to correct errors and account for data uncertainties, improving the accuracy and scientific validity of eco-efficiency assessments. Chinese enterprises face challenges such as excessive resource consumption, environmental pollution, and health risks, necessitating a more comprehensive and flexible evaluation system to adapt to the complex and dynamic nature of the industrial ecosystem. By focusing on industrial production, environmental governance, and health threats (IPEGHT) in China, the research aims to provide a robust framework for enhancing industrial eco-efficiency in a dynamic and uncertain environment. The results show: (1) The proposed method for selecting endogenous direction vectors is applied to the industrial production stage, thereby identifying the subsequent improvement directions for industrial production in various provinces. (2) The idea of integration is adopted to introduce the generalized smooth bootstrap method and fuzzy mathematics, to conduct a precise and comprehensive evaluation of the IPEGHT industrial eco-system. (3) From 2011 to 2021, China’s IPEGHT industrial eco-system efficiency showed a step-like distribution, and attention needs to be paid to environmental governance. The proposed method’ application can facilitate the integration of DEA and fuzzy mathematics, and fosters a more sustainable industrial eco-system.

A novel texture descriptor Octagonal-based Directional Distance Local Binary Pattern (ODLBP) for image classification

A novel texture descriptor Octagonal-based Directional Distance Local Binary Pattern (ODLBP) for image classification

A comparison of the accuracy and feasibility of a low-cost mobile application versus higher-cost handheld 3D scanner for digital ear prosthetics.

Facial prosthetics are an important means to rehabilitate patients with congenital or acquired facial defects. However, with a time-consuming manual workflow and workforce shortage, access to facial prosthetics is limited in Australia and worldwide, especially for rural and remote patients. Optical 3D scanning has been increasingly integrated in digitizing data. With the development of TrueDepth® camera technology on smartphones, there is increasing availability of mobile applications which can generate 3D images to improve accessibility and reduce cost. This study compares the accuracy of mobile phone applications to high resolution 3D scanners for auricular data acquisition. We conducted a case-control study comparing the EM3D smartphone application (EM3D) with the EinScan Pro 2× Plus Shining 3D handheld scanner (EinScan) in 22 healthy participants equating to 44 ears, using CloudCompare software analysis. On average, EM3D acquired images 2.5 minutes quicker than the EinScan. The mean absolute directional distance difference was 1.10 mm, within the accepted deviation range of 2 mm. Out of the 44 ears, only 1 ear (2.27%) did not meet the accepted value of accuracy within 2 mm. The average completeness was 85% and the overall quality of images obtained from EinScanand EM3D were 53.5% and 57.7%, respectively, through observational analysis. Mobile iPhone applications such as EM3D are a viable alternative to 3D handheld scanners such as EinScan. This study demonstrates reliable results in accuracy, and improved results in time, cost and operational feasibility.

Revisiting fiscal performance of non-special category states of India: a global Malmquist index approach

Purpose This paper aims to analyse the fiscal performance of non-special category states of India. Design/methodology/approach This study applied a global Malmquist productivity index (GMPI) to measure the state fiscal performance, using eight fiscal indicators to measure the fiscal stance of 17 non-special category states of India over the period 2000–2020. Findings The findings reveal significant inter-state variations in all the fiscal indicators over the period. Goa, Haryana, Kerala, Chhattisgarh, Odisha and Jharkhand have been the best fiscal performers in recent years. The states Chhattisgarh, Jharkhand and Odisha present an interesting case; their fiscal performance index (productivity) score increased more than 1% during the sample period as compared to other states. This improvement is primarily associated with revenue efficiency, expenditure quality and size of capital outlay. Research limitations/implications The conventional Malmquist productivity index uses a geometric mean form of two contemporaneous measures of productivity change and this index faces a potential linear programming infeasibility problem in measuring cross-period directional distance functions. Therefore, productivity growth measured using indexes should be interpreted with caution. Therefore, the results obtained based on the GMPI might be missing the sensitive check. Originality/value This study is a pioneering initiative in India, constructing a fiscal performance index to measure and rank the non-special category states of the country based on GMPI, thus addressing limitations in previous studies on index formation techniques and selecting fiscal indicators.

Productivity growth of Spanish tourist wineries using a directional distance approach

This paper analyses, in a two-step procedure, the productivity growth of 69 Spanish wineries (period 1998-2022) which offer tourism activities at their installations. To do so, we firstly calculate the total factor productivity (TFP) change using a directional distance function (DDF) and the Luenberger-Hicks-Moorsteen (LHM) TFP indicator introduced by Ang and Kerstens (2017), which allows us to correctly distinguish between technological change (TC), technical inefficiency change (TINC) and scale inefficiency change (SINC). Although a high efficiency is found in median terms, productivity decreased during the period in cumulative terms. This is explained by two contrasting forces: a positive TINC and SINC, and a negative TC. In a second step, we analyse the effect of some factors on TFP change and its components using a dynamic panel data model. Several practical implications are proposed based on these outcomes.

Fast and Nondestructive Mechanical Characterization of Thermally Sprayed and Laser Cladded Coatings: Automated Surface Acoustic Wave Spectroscopy as a New Tool for Quality Control and Research

AbstractLaser-induced surface acoustic wave spectroscopy (LISAWS) allows quick and nondestructive evaluation of the elastic properties such as the Young’s modulus of coatings, surfaces and surface-near bulk materials. Furthermore, the mechanical weakening due to cracks and pores can be evaluated, as they influence the propagation of surface waves as well. This makes the method a fast, accurate and versatile tool for surface characterization, and it is increasingly used in research and development, and quality control. The short measurement time of the LISAWS method allows the time-efficient distribution of the effective Young's modulus over the coated surface to be determined. For this purpose, an industrial LAwave measurement system was automated to allow for processing of a larger number of samples and fast mappings. The investigated coating materials were thermally sprayed Al2O3 insulation coatings and WC-reinforced 316L steel coatings on brake disks produced by laser cladding, respectively. For the Al2O3 coatings, the correlation between the Young's modulus and its areal distribution is shown for different process parameters, such as spray gun movement direction or spray distance, and compared with results from pull-off tests. For the WC/316L-coated brake disks, the distribution of the wave velocity over the coated surfaces or the two coated sides of different disks with varying coating qualities is used to assess the coating quality and homogeneity.

Path-based DEA models in multiplier form and returns-to-scale analysis

AbstractData envelopment analysis (DEA) models appear in envelopment and multiplier forms, which are in a primal-dual relationship. In this paper, we derive the general multiplier form of path-based models, encompassing radial, directional distance function, and hyperbolic distance function models as special cases. We investigate the economic interpretation of the multiplier models and uncover the link between shadow profit inefficiency and technical inefficiency provided by path-based models. Using the optimality conditions for the primal-dual pair, we precisely describe the two-way relationship between the optimal solutions of the multiplier model and the supporting hyperplanes of the technology set at the projection. This relationship serves as a mathematical justification for extending one of the early approaches to measuring returns-to-scale (RTS) onto the entire class of path-based models. Moreover, we demonstrate the eligibility of this method by revealing the fact that the set of all strongly efficient benchmarks for the assessed unit in path-based models does not need to belong to a single strongly efficient face of technology set. This finding changes the view on traditional approaches to RTS measurement that rely on supporting hyperplanes encompassing a single strongly efficient face of the technology set. In this new perspective, we propose two methods for RTS measurement. The first is based on the hyperplanes at the projection, and the second method adapts the minimum face method to be suitable for path-based models. Both methods are fully justified and brought to an algorithmic form.

Navigating Asia's sustainable growth trajectory: a cross-regional comparative analysis.

This study examined the green total factor productivity (GTFP), technical progress change (GTC), and technical efficiency change (GEC) trajectories across 39 Asian countries from 2006 to 2021. GTFP was assessed across five Asian regions using the global Malmquist-Luenberger (GML) index with a slacks-based measure model and directional distance function. The results of the study show that West Asia, with a mean GTFP value of 1.0011, is ahead of Southeast Asia and South Asia, while Central and East Asia show room for improvement. Gini index analysis further shows that GTFP varies significantly within and outside the region, with large fluctuations especially in Southeast Asia and South Asia. GTC stagnated significantly in 2009 and 2015, indicating a bottleneck in technological progress, while GEC showed a positive upward trend, reflecting the improvement in resource efficiency. The 'decoupling' of GTC and GEC highlights the inconsistency between efficiency growth and the pace of technological progress, particularly in East and Central Asia. In 2021, Asia's average GTFP reaches 1.0048, signaling the region's progress towards sustainable productivity. This study provides important theoretical and practical implications by quantifying the regional differences in GTFP, GTC, and GEC, which provide a quantitative basis and decision-making reference for the formulation of green development policies in the Asian region.

Feasibility and preliminary evidence of the immediate effect on balance, functionality, and cognition in adults over 50 through postural sway-meter training.

An accelerometer-based device (postural sway-meter) is increasingly used for balance assessment, training, and fall prevention. However, limited information exists regarding its immediate effect on physical and cognitive performance, especially among middle-aged and older adults. This study aims to investigate the immediate effects of a balance training program using a postural sway-meter on balance, functional, and cognitive performance in adults over 50 years. This experimental study involved 72 participants aged 50 years and over, randomly assigned to either the intervention or control group. The intervention group underwent a 30-minute balance training session using the sway-meter with sound feedback (set at 75% of the stability limits at baseline). The control group participated in a 30-minute fall prevention knowledge session. The sway-meter measured balance during quiet stance and leaning. Functional performance was assessed using the multi-dimensional reach test (MDRT). Cognitive performance was evaluated through Trail Making Tests (TMT) A & B, as well as hand/foot reaction time assessments. Between-group comparisons at post-intervention showed no significant differences in balance, functional, and cognitive performance outcomes. Within-group analysis revealed a significant decrease in maximum reaching distance in the forward direction (MDRT) (p = 0.032, d = 0.31, 95% CI [-0.15, 0.78]) and the time to complete TMT-B (p = 0.036, d = 0.24, 95% CI [-0.22, 0.71]) in the intervention group. The control group showed a significant increase in COM angle sway excursion (lateral direction) (p = 0.011, d = 0.27, 95% CI [-0.19, 0.74]) and a decrease in TMT-A time (p = 0.031, d = 0.38, 95% CI [-0.09, 0.85]). Both groups significantly reduced hand reaction time (intervention: p = 0.036, d = 0.24, 95% CI [-0.22, 0.70]; control: p = 0.034, d = 0.20, 95% CI [-0.26, 0.66]) at post-intervention assessment. The findings of this study suggest that a single 30-minute balance training session using a postural sway-meter, delivered by a physiotherapist, is not only feasible and safe for community-dwelling older adults but also has the potential to significantly improve balance and cognitive outcomes. Enhancing the training program by increasing the amount of leaning and duration could further amplify these benefits, underscoring the need for a more robust training regimen.

Conical Free Disposal Hull estimators of directional distances and Luenberger productivity indices for general technologies

Conical Free Disposal Hull estimators of directional distances and Luenberger productivity indices for general technologies

China's energy efficiency improvement considering renewable energy substitution: Applying a dynamic two-stage undesirable non-radial directional distance function

As the country with the largest CO2 emissions in the world, China is developing renewable energy as the main way to address climate change. By 2021, China's wind and solar power generation accounted for 40 % and 37 % of the global total, but there is an imbalance in the development of renewable energy within China. Different from existing research on energy efficiency evaluation that rarely considers renewable energy, this study constructs a dynamic two-stage undesirable non-radial directional distance function that divides traditional energy efficiency into electricity production efficiency (EPE) by considering renewable energy substitution effects and energy utilization efficiency (EUE) by considering changes in the energy consumption structure. Different from other empirical results, the western provinces, with advantages in renewable energy development, their EPE has significantly improved, and EUE has also improved with the optimization of energy consumption structure, thus their overall energy performance surpass that of the eastern and central regions. In addition, by comparing the room for improvement of variables, we find that most provinces can improve their energy performance by expanding the scale of renewable energy generation and increasing the proportion of electricity to total energy consumption. Therefore, we propose policy recommendations to promote the sustainable development of renewable energy.

The Path to Urban Sustainability: Urban Intelligent Transformation and Green Development—Evidence from 286 Cities in China

Urban intelligence is reshaping urban innovation patterns, accelerating urban transformation, and significantly influencing green and sustainable development. By applying the non-radial directional distance function and an improved entropy method, this study measures the green development efficiency and levels across 286 Chinese cities from 2006 to 2020. The objectives of this study are twofold: first, to examine the impact of urban intelligence transformation on green development, and second, to investigate how urban intelligence influences common prosperity. The analysis employs a double/debiased machine learning model, with the “Smart City Pilot” policy as the focal point. The findings indicate that (1) urban intelligence transformation enhances both the level and efficiency of green development in Chinese cities; (2) this transformation fosters green development by driving urban innovation, upgrading industrial structures, and promoting green finance; and (3) the impact of urban intelligence varies across cities with different sizes, resource endowments, and marketization levels. Furthermore, the study constructs a common prosperity index to assess how urban intelligence contributes to residents’ well-being and social equity. The results suggest that urban intelligence transformation not only advances green development but also contributes to improving residents’ quality of life, thereby promoting a more equitable and prosperous society. These insights offer crucial policy guidance for China and other countries facing environmental and economic challenges in the digital age.

Evaluating air quality efficiency in the major Indian cities using a directional distance function approach

Background: The ongoing advancements in modern society have negatively impacted air quality, and India is one of the worst affected countries. This study aimed to evaluate the efficiency of maintaining air quality in 10 major Indian cities. Methods: The present study employed a directional distance function (DDF) within the framework of data envelopment analysis (DEA) to evaluate the efficiency of 10 major cities including Chennai, Delhi, Bengaluru, Ahmedabad, Hyderabad, Jaipur, Lucknow, Patna, Gurugram, and Thiruvananthapuram from January 01, 2018 to December 31, 2019. Results: The results indicate that air pollution is a significant issue in most cities in India. Thiruvananthapuram, Bengaluru, and Chennai were identified as the most efficient cities in terms of air quality for both 2018 and 2019 whereas Ahmedabad was noted as a purely inefficient city during the same period. Moreover, it was revealed that cities in the northern (Delhi, Lucknow, Patna), western (Ahmedabad), and northwestern (Jaipur, Gurugram) parts of India had higher levels of air pollution compared to the southern (Chennai, Bengaluru, Hyderabad, Thiruvananthapuram) part of India. Conclusion: There are significant disparities in air quality efficiency among the cities, revealing that southern cities perform better than their northern, western, and northwestern counterparts. It emphasizes the need for targeted interventions to improve air quality, particularly in cities like Delhi, Ahmedabad, and Jaipur.

Chirality sorting with 3D-arbitrarily-oriented circularly polarized ultra-long optical needle field

We propose an all-optical sorting method for chiral nanoparticles by use of a 3D-arbitrarily-oriented circularly polarized ultra-long optical needle field, which is constructed through reversing the radiation patterns from an array of spin dipole located in the focal volume of a 4Pi microscopy. It is demonstrated that particles with different chirality can be trapped at different positions along the optical needle by the chiral gradient force, and the direction and distance of sorting are determined by the orientation and length of the circularly polarized optical needle respectively, which can be easily controlled by modifying the quantity and orientation of the equivalent spin dipoles. This scheme combines the advantages of controllable sorting direction and long sorting distance, providing a feasible route toward all-optical enantiopure chemical syntheses and enantiomer separations in pharmaceuticals.

Benchmarking the medication efficiency and technological progress of diabetes drugs.

Diabetes poses a serious global challenge, given its increasing prevalence, detrimental effects on public health, and substantial economic burden. Since 1950s, tens of drugs have been approved by the United States (US) Food and Drug Administration (FDA). In the past decade, the medical community and regulatory agencies have moved away from the glucose-centric paradigm and increasingly call for a holistic approach to assess different treatments' benefits and harms. This study aimed to assess the medication efficiency and technological progress of Type 2 Diabetes (T2D) drugs, by considering their physiological outcomes, including both benefits (i.e., glucose lowering and weight loss) and adverse effects (mortality), relative to dosing frequency. To derive medication efficiency, this study utilized data from the US FDA and prominent meta-analyses. Given that both the benefits and adverse effects of medications are multidimensional, this study employed a nonparametric frontier method, the data envelopment analysis (DEA) model, to integrate these factors into a measure of medication efficiency. Physiological outcomes could assume both positive and negative values. Adverse effects were regarded undesirable outputs. The DEA model was built under the framework of directional distance function and was able to handle negative and undesirable values which naturally arose in the case of T2D medications. The paper presented a ranking of 20 T2D drugs in terms of medication efficiency. Three of them were able to attain the highest medication efficiency, all of which were in the GLP-1 class, including oral Semaglutide, subcutaneous Semaglutide and Dulaglutide. However, the other two GLP-1 drugs, Lixisenatide and Liraglutide, were less efficient. The average medication efficiency of drugs approved post-2010 was significantly higher than pre-2010 drugs. High dose frequency, low HbA1c reduction and insignificant weight loss were the main driving factors behind inefficiencies. Overall, medication efficiency provided an alternative perspective on treatment effectiveness other than conventional measures such as cost-effectiveness.

A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER.

Measuring carbon emission performance in China's energy market: Evidence from improved non-radial directional distance function data envelopment analysis

The most complex challenge facing the energy market is identifying effective solutions to reduce CO2 emissions (CEs) and enhance environmental performance (EP). Coal production within the power sector is the primary source of these emissions. In this study, we developed a novel linear programming model that accounts for undesirable outputs to assess the EP of 15 power enterprises in eastern China from 2016 to 2020. In addition, we employed a global non-radial Malmquist-Luenberger productivity index (GNML) to analyse the mechanisms influencing changes in efficiency among these enterprises. Our findings indicate that, while the EP of the power industry in eastern China improved, it remains at a relatively low level and exhibits instability. Moreover, technological efficiency (TE) and scale efficiency (SE) play a significant role in determining production efficiency within the sector. Therefore, it is essential for industry managers to implement standardized production management regulations, enhance technological development and scale investments, and strengthen control over unintended emissions that could facilitate energy transition.

Optimizing Supply Chain Efficiency Using Cross-Efficiency Analysis and Inverse DEA Models

Our study employs cross-efficiency analysis (CEA) and machine learning techniques to optimize supply chain performance. By integrating inverse DEA models with directional distance functions, we measure operational efficiency across various decision-making units (DMUs), accounting for undesirable outputs such as excess costs and emissions. Our results indicate a 20% improvement in market recognition efficiency and a 15% increase in earnings persistence efficiency after model application. Additionally, machine learning classifiers, including Random Forest and Support Vector Machines, further enhanced predictive accuracy, with Random Forest achieving the lowest mean absolute error of 0.07. These findings underscore the effectiveness of advanced analytical models in improving supply chain resilience and decision-making accuracy, contributing to sustainable operational performance.

Simulated GP clinic closure: effects on patient access in the Irish Mid-West.

Rural communities can experience more barriers to accessing health care than their urban counterparts, largely due to fewer healthcare staff and services, and geographical isolation. The purpose of this study is to examine the availability of GP practices in rural communities across the Mid-West of Ireland and the potential impact of practice closure on patient access. GP clinic locations were identified in Ireland's Mid-West, specifically counties Limerick and Clare. Administrative subdivisions of both counties, Small Areas (SAs), were identified and their XY geographic centre coordinates recorded. SAs were indexed into six levels of rurality according to Irish Central Statistics Office urban/rural classifications (1, cities; 2, satellite urban towns; 3, independent urban towns; 4, rural areas with high urban influence; 5, rural areas with moderate urban influence; 6, highly rural/remote areas). The direct linear distance from the centre of each SA to its respective closest GP clinic was calculated. Simulated closure of each GP clinic was assessed programmatically by removing practices from the overall dataset and calculating the new direct linear distance from each SA to the next closest GP clinic. The majority of the SAs in County Clare (63%) and County Limerick (66%) are classified as rural (rurality index ≥4), with the exception of Limerick City, where all SAs were defined as urban. Rural SAs have longer travel distances to GP clinics than their urban counterparts, and these distances are greater with increasing rurality of a population. Simulated closure of GP clinics revealed increasing travel distances to the next closest clinic with increasing level of rurality in a stepwise fashion (r2=0.31). Rural community dwellers across the Mid-West of Ireland face longer travel distances to GP clinics than their urban counterparts. Thus rural communities will be, on average, more adversely affected should their local GP clinic close. While these findings are unsurprising, our methodology calculates a discrete number that can be used to rank vulnerability of local communities. Rural areas are particularly vulnerable to GP clinic closure, and maintaining a solid foundation of primary care in these areas will require careful service and workforce planning.