Directional Distance Research Articles

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

BackgroundDiabetes 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.ObjectiveThis 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.MethodsTo 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.ResultsThe 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.

Reading and Writing Research Based on Surname Group

This study examines the influence of surname on academic performance in higher education. It includes an in-depth analysis of comprehensive book circulation data from the library of Nanjing Normal University, covering the period from 2012 to 2023. The study uses Kendall coefficients to assess the consistency in book circulation and renewal among the top 300 surnames of readers, employs multidimensional scaling analysis to identify differences or similarities between categorical variables through direct distance assessment, uses chi-square tests to measure the correlation between surnames above and below the mean line and various categorical variables, and conducts survival analysis to examine circulation activities related to the top 300 surnames. Results are interpreted in the context of female-dominated education and teacher preparation. The results reveal a consistent pattern in the 12-year borrowing and renewal habits of the top 300 surnames of readers. A hierachical mismatch between the reader, book category, and author reflects complex reading interests beyond academic authority. In the wake of the COVID-19 pandemic, readers with surnames above the average have shown less interest in using the book renewal service. The borrowing and renewal activity for the disappeared surnames changed within two years for foreign language books and six years for Chinese books.

Soft Tissue Facial Morphology in Growing Patients with Different Occlusal Classes.

The study of facial profiles in the dental field is very important for the diagnosis and the dental and orthodontic treatment plan. The aim of this study is to analyze the three-dimensional morphology of the faces of 269 growing patients with Class I and II occlusions, focusing on children aged between 6 and 9 years old. The analysis was conducted using a non-invasive computerized system, which allowed for the automatic collection of facial landmarks and the subsequent reconstruction of three-dimensional coordinates. The sample comprised 269 children within the specified age range. Each child's facial features were captured using the non-invasive computerized system, which utilized two infrared CCD cameras, real-time hardware for label recognition, and software for three-dimensional landmark reconstruction. Sixteen cutaneous facial landmarks were automatically collected for each participant. From these landmarks, 10 angular and 15 linear measurements, as well as five direct distance rates, were derived. The mean values for each age class were calculated separately for children with bilateral Angle Class I occlusion and compared with those for children with bilateral Class II occlusion. In all children, the left and right occlusal classes were measured as suggested by Katz. The analysis revealed notable differences, primarily in the three-dimensional angular measurements between children with Class I and II occlusions. Specifically, Class II children exhibited more convex faces in the sagittal plane and a less prominent lower jaw compared to Class I children. However, no significant differences were observed in linear measurements, except for the lower facial height rate, which varied inconsistently across age groups between the two occlusion types. the findings of this research highlight distinct three-dimensional facial morphological differences between children with Class I and II occlusions. While Class II children tended to have more convex facial profiles and less prominent lower jaws, linear measurements showed minimal variation between the two occlusion types. These results underscore the importance of three-dimensional analysis in understanding facial morphology in growing patients with different occlusal patterns.

The impact of big fund on the operational efficiency of semiconductor companies: Corporate social responsibility as a non-economic output

This study contributes to research on the total factor operational efficiency of the semiconductor company considering corporate social responsibility (CSR) by proposing a novel integer-valued nonparametric frontier method. This method adopts an endogenous directional distance function, which first selects the benchmark and then endogenously determines the directional vector. Avoiding the issue of non-integer slack adjustment increases the convenience of formulating incentive plans and calculating efficiency. We apply this method to the input–output data of 185 listed Chinese semiconductor companies from 2010 to 2022, considering integer-valued CSR as non-economic output. Global Malmquist productivity index analysis shows that the average total factor operational efficiency of companies that received investment from the National Integrated Circuit Industry Investment Fund (Big Fund) increased by 6.9%. Using the econometric technique, we verify that the total factor operational efficiency of semiconductor companies receiving Big Fund investment increased by 0.084 compared to those without Big Fund investment. Heterogeneity analysis showed that the effect of Big Fund was more significant in improving the total factor operational efficiency of state-owned, new, and non-integrated circuit semiconductor companies, implying the necessity of purposeful investment policies for the Big Fund.

Heterogeneous strategy and performance decomposition of regional industries in China: From the perspective of resource misallocation

AbstractThe rapid expansion of China's industrial sector has been accompanied by energy consumption and environmental pollution. Accurately identifying the sources of inefficiency of industrial sector is crucial for achieving sustainable development. To address this issue, this study develops a novel decomposition approach that combines the concepts of natural and managerial disposability with an aggregate directional distance function approach to measure the performance of regional industries in China from 2006 to 2019. In the proposed approach, the overall inefficiency is decomposed into technical inefficiency and structural inefficiency. The latter component, which captures inefficiency associated with resource misallocation within a country, is further divided into mix and scale elements. The results show that there remains much room for potential improvement in both the operational and environmental performance of China's industrial system, particularly in the central area. Furthermore, structural inefficiency is the primary contributor to the overall inefficiency of Chinese industry under natural disposability, whereas technical inefficiency appears as the main source of the inefficiency under managerial disposability. The results also reveal great disparities in terms of the overall inefficiency and the sources of inefficiency of Chinese industrial sector among regions between different mitigation strategies. Given the substantial structural effect on the performance of Chinese industries across regions, it is necessary to accelerate the establishment of market mechanisms to promote efficient allocation of resources.

Lessons from the Pilot Project of Korean ETS on the Local Landscape of Economy

For a sustainable landscape of local economies, many researchers have emphasized the importance of field-oriented differentiation in government policies. In particular, the Paris Agreement, based on the bottom-up approach, aims to maximize the participation of all economic agents, in contrast to the top-down approach of the Tokyo Protocol. In response to these global paradigm shifts in the local landscape, local governments in Korea have made significant efforts to adapt to sustainable development during the pilot phase of emission trading scheme (ETS), during the period from 2015 to 2020. This study evaluates the performance of these local government policies in the transition to a carbon-zero economy. Using the general non-radial directional distance function (GNDDF), we found that Gyeongsang Province demonstrated enhanced environmental total factor productivity (TFP) during the pilot project, whereas the Seoul metropolitan area lagged behind due to a lack of governance. As the economic center of Korea, Seoul showed poor environmental performance because of the arbitrary elimination of green belt areas and unchecked land development, resulting in environmental degradation, a trend common in many developing countries facing climate adaptation challenges. To address these urbanization issues, this study concludes that a balanced approach combining stricter regulations with market-oriented promotional incentives is essential for optimizing the transition of local economies to a sustainable landscape.

Decreased diaphragm moving distance measured by ultrasound speckle tracking reflects poor prognosis in amyotrophic lateral sclerosis

ObjectiveDecreased cephalocaudal diaphragm movement may indicate respiratory dysfunction in amyotrophic lateral sclerosis (ALS). We aimed to evaluate diaphragm function in ALS using ultrasound speckle tracking, an image-analysis technology that follows similar pixel patterns. MethodsWe developed an offline application that tracks pixel patterns of recorded ultrasound video images using speckle-tracking methods. Ultrasonography of the diaphragm movement during spontaneous quiet respiration was performed on 19 ALS patients and 21 controls to measure the diaphragm moving distance (DMD) in the cephalocaudal direction during a single respiration. We compared respiratory function measures and analyzed the relationship between the clinical profiles and DMD. ResultsDMD was significantly lower in ALS patients than in the control group (0.6 ± 1.4 mm vs 2.2 ± 2.2 mm, p < 0.01) and positively correlated with phrenic nerve compound motor action potential amplitude (R = 0.63, p = 0.01). DMD was negatively correlated with the change in the ALS Functional Rating Scale-Revised scores per month after the exam (R = −0.61, p = 0.02), and those with a larger rate of decline had a significantly lower DMD (p = 0.03). ConclusionsDiaphragm ultrasound speckle tracking enabled the detection of diaphragm dysfunction in ALS. SignificanceDiaphragm ultrasound speckle tracking may be useful for predicting prognosis.

Layout Optimization of Wave Energy Park Based on Multi-Objective Optimization Algorithm

Abstract In this paper, both semi-analytical method and numerical simulation is applied to investigate the hydrodynamic behavior of large arrays of point-absorber wave energy converters (WECs). To analyze wave interactions among multiple WECs within an array, a semi-analytical model is developed based on the potential flow theory and the matched eigen-function expansions method. The fluid domain is divided into two kinds of regions: interior regions underneath the cylinders and an exterior region surrounding all the cylinders. The matched eigenfunction expansions method is employed to solve the radiation potential problem in each domain, and the hydrodynamic coefficients and motion response of the cylinders in the array are evaluated. To validate the accuracy of the semi-analytical method, WAMIT is adopted to simulate the wave energy park numerically and compared with the results by the semi-analytical model. The hydrodynamic characteristics and power absorption performance of the WECs within the wave energy park are analyzed. The power performance of a wave energy park is studied as functions of layout geometry, incident wave direction and separation distance between WECs respectively. Finally, MOPSO based on a surrogate model is used to optimize the layout of wave energy array.

Assessing performance of water supply operation of water companies in China: A union dynamic multi-activity network data envelopment analysis

Urban growth and the increasing urban water demand highlight the importance of performance analysis in the water supply industry. Both water supply companies (WSCs) and water supply and sewage companies (WSSCs) produce and deliver potable water for city dwellers. Water authorities use benchmarks to monitor the two types of companies’ operations. The internal operation process and heterogeneities of observations were not considered for relative performance evaluation by conventional DEA models. To overcome these limitations, this study proposes a union dynamic multi-activity network data envelopment analysis (DMANDEA) model to extend static network DEA to a more general case for dynamic efficiency assessment of the water supply operations of water companies. For the first time, the model allows for the decomposition of the water supply operation into water production (WP) and water consumption (WC) substages and performance comparisons for water companies by considering their heterogeneity simultaneously. Specifically, by using a non-radial directional distance function (DDF), the inefficiencies for substages and the entire system can be both assessed dynamically. This model is applied to a sample of 246 water companies in China in 2016–2018. The overall, period, and substages’ group efficiencies, meta efficiency, and technology gap ratio (TGR) for 179 WSCs and 67 WSSCs are all provided. The empirical results reveal that: (1) the average group efficiencies of WSCs and WSSCs are 0.4567 and 0.6611, respectively. The meta efficiencies of WSCs and WSSCs are 0.4523 and 0.4703, respectively. The TGR of WSCs (0.994) is greater than that of WSSCs (0.7067). (2) WSSCs perform better than WSCs do, which suggests that economies of scope do exist in WSSCs. However, WSCs possess the best technology for water supply operations because of their high TGR. (3) The average meta efficiency for all the observed water companies (0.4572) is not optimistic, which correlates more with inefficiency in the WP stage. The causes stem from the natural monopoly, lack of competition, and relatively lower water tariffs. The Chinese water supply industry requires diversification of ownership and operation modes, and gradual increases in water tariffs. (4) Water companies in the eastern region outperform those in other regions. Regional cooperation, such as technology sharing, the transfer of managerial experience, and information exchange, needs to be emphasized.

The performance of alternative GPS tracking devices: a case report on wild boars (Sus scrofa)

BackgroundTelemetry studies on terrestrial mammals have been carried out almost exclusively using neck collar-mounted devices. However, collars are not suitable for all species and all age classes and may therefore compromise animal welfare and data quality requirements. Here, we evaluate the use of non-collar tracking devices on wild boars (Sus scrofa), a species for which collars may be problematic due to neck shape and seasonal body mass variation. We tested pelt-glued tags and three types of GPS ear tags. In addition, animals were marked with plain cattle ear tags.ResultsThe tested GPS tags exhibited various technical challenges related mostly to satellite coverage of the study area and tag loss. However, GPS devices enabled moderate movement monitoring of weeks to months, whereas plain cattle ear tags offered information on displacement over longer time intervals. The longest direct dispersal distance (163 km) by a wild boar sounder was discovered by cattle tags.ConclusionsData volume and quality obtained from small tags are inferior to data provided by GPS collars, but low-fix rate tags may enable monitoring of individuals with reduced invasiveness. Moreover, these tags enable tracking in cases where the collar is not an option. The low price and small size of the devices together with technical developments may offer cost-effective tools for future studies on dispersal and survival.

Seeking sustainable efficient global agricultural production with nexus approach

This study aims to estimate the shadow prices of water and carbon dioxide (CO2) in global agriculture under three scenarios: pursuing economic efficiency, sustainable development goals (SDGs), and environmental protection. It assesses the social cost of environmental impacts compared to actual prices. This study also evaluates substitutability among water, labor, and capital in the three scenarios for developing relevant agricultural policies depending on distinct agricultural conditions. Social costs tend to be underestimated because they are not directly observed. Our society has faced an overproduction of agriculture in terms of CO2 emissions and the exploitation of water. This study employs a method of a directional technology distance function for global agricultural production based on Food and Agriculture Organization data on agricultural inputs and outputs. The following three critical findings were obtained: First, the average shadow price of water under the economic scenario was close to the current actual water prices, and water prices would increase if agricultural production accomplished the three SDGs (Goal 2, 6, and 13). Second, labor is substituted for water under the environmental scenario because water prices increase rapidly. In contrast, water is complementary to labor under the SDGs scenario, implying that labor becomes more important for increasing food production. Third, the current CO2 prices are lower than those in the economic scenario. These values are significantly lower than those under the SDGs and environmental scenarios. Based on these results, we conclude that agricultural products are often undervalued because the appropriate value of water is overlooked in real markets. The contribution to reducing CO2 in agriculture is insufficient, owing to current low carbon taxes.

Analysing cost-effectiveness in dynamic network DEA: a directional distance function approach

Analysing cost-effectiveness in dynamic network DEA: a directional distance function approach

Three-dimensional moisture transport fabric for enhanced moisture management in protective clothing

Effective moisture management within the microenvironment of protective clothing is crucial for maintaining garment performance and wearer comfort. The inherent properties of fabrics and the structural of protective garments often hinder the discharge of sweat to the external environment. Here, we present a novel 3D moisture transport fabric, engineered with gradient wetting properties along both the thickness and length dimensions. This fabric can transport moisture from the inner to the outer surface within approximately 5 s, achieving a maximum accumulative one-way transport capability of 1189.1 in the thickness direction. Additionally, by further directing moisture along the constructed wetting gradient on the fabric's outer surface, the moisture diffusion distance in the gradient direction was up to 2.6 times greater than in other directions. This approach will enhance moisture transport and management within protective clothing and can be further extended to the development of materials for oil-water separation, wound dressings, flexible microfluidics, and fuel cell membranes.

Effect of DG penetration on hybrid relay coordination using user-defined dual-setting directional overcurrent relays and distance relays

ABSTRACT The efficient coordination of the distance and directional overcurrent relays (DOCRs) is jeopardized by the massive deployment of renewable distributed generation (DG) into electrical power networks. The two key aspects influencing the relay coordination are the bidirectional flow of current and increased short-circuit currents due to DG integration. This study investigates the consequences of variable DG penetration on the distance and dual-setting DOCRs (DS-DOCRs) coordination. As a result, common optimal relay settings have been obtained that can be utilized for variable DG penetration levels in modified IEEE-14 bus test system. For the distance relays (DRs), a fixed zone 1 and variable zone 2 operation have been considered. The proposed combined protection scheme is tested on a modified IEEE-14 bus test system using DS-DOCRs and DRs. The relay coordination problem is formulated as a constrained non-linear optimization problem and solved by genetic algorithm (GA) and gray wolf optimization (GWO). In this paper, DS-DOCRs with user-defined relay characteristics have demonstrated superior performance in line protection compared to conventional DOCRs with normal inverse (NI) characteristics. In addition, a comprehensive comparative analysis of optimization algorithms has been performed between GA and GWO algorithms.

Economic sanctions and energy efficiency: Evidence from Iranian industrial sub-sectors

Improving energy efficiency is vital for reducing energy consumption and can have substantial impacts on reducing carbon emissions. This study investigates the impact of sanctions on Iran's energy efficiency across different industrial sub-sectors from 2015 to 2019. We compute a sanction intensity index for each industrial sub-sector using principal component analysis. This index measures how much each sub-sector has been affected by sanctions. Additionally, energy efficiency is measured using the directional distance function method, considering the emission impacts as undesirable outputs. We use fixed effects regression analysis, adjusting for various manifestations of cross-sectional (spatial) and temporal dependence and controlling for other sectoral drivers of energy efficiency. We find a significant negative effect of sanction intensity on energy efficiency at the industrial sub-sector level in Iran. The negative effect of sanctions on energy efficiency is significantly larger for industrial units with higher import dependency.