Matching Technique Research Articles

The impact of access to agricultural advisory services on input use and farm performance: Evidence from Senegal

AbstractPerformant agricultural advisory services (AAS) in Africa are crucial for improving the agricultural productivity and food security of the farm households. Yet, evidence‐based study on the impacts of AAS on farm performance are scarce, especially in West African countries. This study evaluates the impact of access to AAS on input use and farm performance in Senegal, employing the instrumental variable and control function approaches accounting for selection bias. The approaches are also employed to several sub‐samples to see if the impacts are heterogeneous across crop types and fertilizer use, and matching techniques are applied to test the robustness of the results. Results show that the highest positive impact of AAS is on yield, while the impacts on gross margin and improved seeds uptake are very modest. Access to AAS has no effect on fertilizer uptake. It also finds that AAS impacts positively cereal production and farmers using no fertilizer but has no effect on the legume and horticultural crop production and on farmers using fertilizer. Given the Senegalese government and donors have put a lot of effort into developing an efficient AAS system, a consideration of the identified weaknesses is required to improve its efficiency. [EconLit Citations: Q12, Q16]

Migraine Association with Alzheimer's Disease Risk: Evidence from the UK Biobank Cohort Study and Mendelian Randomization.

Epidemiological studies on the association between migraine and Alzheimer's disease (AD) risk have yielded inconsistent conclusions. We aimed to characterize the phenotypic and genetic relationships between migraine and AD. To investigate the association between migraine and the risk of AD by analyzing data from a large sample of 404,318 individuals who were initially free from all-cause dementia or cognitive impairment, utilizing the UK Biobank dataset. We employed Cox regression modeling and propensity score matching techniques to examine the relationship between migraine and subsequent occurrences of AD. Additionally, the study utilized Mendelian randomization (MR) analysis to identify the genetic relationship between migraine and the risk of AD. Migraine patients had a significantly increased risk of developing AD, compared to non-migraine patients (adjusted hazard ratio (HR) = 2.34, 95% confidence interval (CI) = 2.01-0.74, P < 0.001). Moreover, the propensity scores matching analyses found that migraine patients had a significantly higher risk of developing AD compared to non-migraine patients (HR = 1.85, 95%CI = 1,68-2.05, P < 0.001). Additionally, the MR suggested that significant causal effects of migraine on AD risks were observed [odds ratio (OR) = 2.315; 95% confidence interval (CI) = 1.029-5.234; P = 0.002]. Moreover, no evidence supported the causal effects of AD on migraine (OR = 1.000; 95%CI = 0.999-1.006; P = 0.971). The present study concludes that migraine patients, compared to a matched control group, exhibit an increased risk of developing AD. Moreover, migraine patients exhibit an increased predisposition of genetic susceptibility to AD. These findings hold significant clinical value for early intervention and treatment of migraines to reduce the risk of AD.

Association of scrub typhus with the risk of venous thromboembolism and long-term mortality: a population-based cohort study.

The existing literature lacks studies examining the epidemiological link between scrub typhus and deep vein thrombosis (DVT) or pulmonary embolism (PE), and the long-term outcomes. The objective of this study is to explore the potential association between scrub typhus and the subsequent risk of venous thromboembolism, and long-term mortality. This nationwide cohort study identified 10,121 patients who were newly diagnosed with scrub typhus. Patients with a prior DVT or PE diagnosis before the scrub typhus infection were excluded. A comparison cohort of 101,210 patients was established from the general population using a propensity score matching technique. The cumulative survival HRs for the two cohorts were calculated by the Cox proportional hazards model. After adjusting for sex, age, and comorbidities, the scrub typhus group had an adjusted HR (95% CI) of 1.02 (0.80-1.30) for DVT, 1.11 (0.63-1.93) for PE, and 1.16 (1.08-1.25) for mortality compared to the control group. The post hoc subgroup analysis revealed that individuals younger than 55 years with a prior scrub typhus infection had a significantly higher risk of DVT (HR: 1.59; 95% CI: 1.12-2.25) and long-term mortality (HR: 1.75; 95% CI, 1.54-1.99). The scrub typhus patients showed a 16% higher risk of long-term mortality. For those in scrub typhus cohort below 55 years of age, the risk of developing DVT was 1.59 times higher, and the risk of mortality was 1.75 times higher. Age acted as an effect modifier influencing the relationship between scrub typhus and risk of new-onset DVT and death.

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

The Innovation Behaviour of Turkish Manufacturing Firms: An Analysis of Additionality

Innovation and R&amp;D activities have significant effects on economic development and firm success. Innovation is a key factor in economic development through productivity gains. However, firms do not perform the socially optimal level of innovation due to market failures. Therefore, innovation activity is largely supported by governments for both developed and developing countries with the aim of creating additionality. While additionality effects from government supports are widely discussed for developed countries, there is scarce evidence for developing countries. The aim of this article is to analyse innovation behaviour of Turkish firms based on firm characteristics. Further, the behavioural additionality of government support is also analysed in order to provide a full picture. For this aim, the innovation structure is analysed using a multinomial logit model and the additionality effects are analysed using the propensity score matching (PSM) technique. Results indicate that firms that are profit-oriented, produce for the internal market and have an internal R&amp;D unit, are more likely to implement organization and/or marketing innovation in comparison with product innovation. In addition, the same pattern is observed in companies that received government support. The results additionality indicate positive effects on behavioural additionality; however, this effect is evaluated to be limited.

Varus/valgus stability in imageless robotic-assisted total knee arthroplasty applying three-dimensional assessment of varus/valgus stress X-rays.

The postoperative varus/valgus stability assessment in stress X-rays has been established as an evaluation index. However, it is performed by the two-dimensional (2D) method rather than the three-dimensional (3D) method. This study aimed to identify the precision and reproducibility of measuring varus/valgus stress X-rays three-dimensionally and to examine varus/valgus stability under anesthesia in imageless robotic assisted total knee arthroplasty (rTKA). This prospective study analyzed 52 consecutive rTKAs (five males, 67 ± 5.3 years; 47 females, 74 ± 5.9 years). Postoperative varus/valgus stress X-rays in knee extension under anesthesia at manual maximum stress were three-dimensionally assessed by 2D-3D image matching technique using the 3D bone and component models. Varus/valgus angle between components (VV angle) in no stress, valgus stress, varus stress, medial joint opening (MJO), and lateral joint opening (LJO) were evaluated, clarifying this method's precision and reproducibility and valgus/varus stability. All parameters' precision and reproducibility had <1° mean differences and high intra- and inter-class correlation coefficients. Bland-Altman plots showed no fixed and proportional bias. Non-stress VV angle, valgus VV angle, varus VV angle, MJO, and LJO were 3.6 ± 1.2°, 1.0 ± 1.4°, 7.1 ± 1.9°, 1.5 ± 1.0mm, and 2.8 ± 2.7mm, respectively. This prospective study demonstrated that (1) the three-dimensional measurement method provided sufficient precision and reproducibility, and (2) the rTKAs could achieve good postoperative varus/valgus stability with a small standard deviation.

Re-considering Re-partnering: New Insights about Gender and Sexuality in the Study of Second Union Formation

Abstract Past studies have established the existence of a persistent gender gap in re-partnering, wherein women are less likely to re-partner than men in the general population. Existing theories and explanations focus on women’s and men’s socio-demographic characteristics as mechanisms determining their opportunities, needs, and attractiveness in the re-partnering process. However, this work assumes that people are heterosexual and overlooks sexual minorities despite growing scholarly interest in union formation and dissolution among Lesbian, Gay, and Bisexual (LGB) people. We investigate whether and how the gender gap in re-partnering intersects with sexual identity to highlight the role of gender relations as a social force that shapes union formation outcomes among both the heterosexual and LGB population. We use retrospective data on cohabitation and marriage histories from the British Understanding Society survey (UKHLS) to estimate event history models. We confirm the existence of a gender gap favoring men among heterosexuals but find that lesbian women are more likely to re-partner than gay men. We do not observe a gender gap among bisexuals. Results are robust to accounting for compositional differences between groups using exact matching techniques. These findings suggest that the persistent gender gap found in past studies is not as universal as previously presumed and that sexual identity plays a vital role in re-partnering outcomes. Therefore, sociologists should explicitly incorporate the gender relational context into models and theories that explain gender differences in union formation outcomes.

Local feature matching using deep learning: A survey

Local feature matching enjoys wide-ranging applications in the realm of computer vision, encompassing domains such as image retrieval, 3D reconstruction, and object recognition. However, challenges persist in improving the accuracy and robustness of matching due to factors like viewpoint and lighting variations. In recent years, the introduction of deep learning models has sparked widespread exploration into local feature matching techniques. The objective of this endeavor is to furnish a comprehensive overview of local feature matching methods. These methods are categorized into two key segments based on the presence of detectors. The Detector-based category encompasses models inclusive of Detect-then-Describe, Joint Detection and Description, Describe-then-Detect, as well as Graph Based techniques. In contrast, the Detector-free category comprises CNN Based, Transformer Based, and Patch Based methods. Our study extends beyond methodological analysis, incorporating evaluations of prevalent datasets and metrics to facilitate a quantitative comparison of state-of-the-art techniques. The paper also explores the practical application of local feature matching in diverse domains such as Structure from Motion, Remote Sensing Image Registration, and Medical Image Registration, underscoring its versatility and significance across various fields. Ultimately, we endeavor to outline the current challenges faced in this domain and furnish future research directions, thereby serving as a reference for researchers involved in local feature matching and its interconnected domains. A comprehensive list of studies in this survey is available at https://github.com/vignywang/Awesome-Local-Feature-Matching.

The impact of the COVID pandemic on health, healthcare utilization, and healthcare spending

This study investigates the impact of the COVID pandemic on healthcare utilization, spending, and health measures among the U.S. population during the first year of the pandemic. We utilize data from the Medical Expenditure Panel Survey and employ propensity score matching techniques to analyze the variation in healthcare outcomes due to the pandemic.Our findings indicate that the pandemic significantly reduced mental health status, the intensity of office, outpatient, and emergency room visits, and a corresponding decline in healthcare spending. On the other hand, we find improvement in health-related quality of life for most individuals, except for blacks, individuals with a high school degree or less, the uninsured, and the low-income population. The findings highlight disparities during the pandemic and the need for increased efforts to promote health equity.

Edge-Computing Oriented Real-Time Missing Track Components Detection

Track integrity is critical for railroad safety. Traditional track inspections are either labor-intensive or require centralized data processing, making them susceptible to human error and lapses between data collection and situation awareness. The advent of deep learning and computer vision provides promising potential for automated track inspections. However, few existing systems are edge-computing oriented or provide inspection results in real time. In this study, a novel ultra-portable system for real-time detection of track components, such as spikes, bolts, and clips, is developed by integrating the cutting-edge YOLOv8 object detection model with a tailored template matching algorithm. In this system, YOLOv8 serves to recognize track components, while the template matching algorithm discerns missing components based on predefined patterns. Field blind testing results verified the exceptional performance of the model in detecting track components and a remarkable speed of 98.12 frames per second. Leveraging these detection results, the proposed template matching technique displayed an impressive recall rate of 90% and an accuracy rate of 90.77% in identifying missing components. The proposed system provides an affordable and versatile solution for track inspection, aiming to improve railway safety.

The Prognostic Value of Serum Sialic Acid in Patients with Nasopharyngeal Carcinoma: A Propensity Score Matching Study.

Elevated serum sialic acid (SA) is one of the indicators of poor prognosis in various malignant tumors. This study intends to determine the relationship between serum SA levels and survival prognosis in nasopharyngeal carcinoma (NPC). From 2014 to 2016, NPC patients with no distance metastasis undergoing intensity-modulated radiotherapy (IMRT) were retrospectively analyzed. The serum SA levels before initial treatment were measured, and an optimal cut-off level was determined by X-tile software. A propensity score matching (PSM) technique was applied to reduce intergroup differences between the low serum SA level group and the high serum SA level group. Chi-square tests were utilized for comparing intergroup differences, Kaplan-Meier approach was utilized for plotting survival curves, and univariate and multivariate Cox proportional hazards regression models were employed for analyzing prognostic factors. Overall, 293 NPC patients with no distance metastasis were included. The optimal cut-off level of serum SA was 65.10 mg/dl. The baseline levels after PSM were more balanced compared to those before PSM. Survival analysis showed that the locoregional relapse-free survival (LRRFS, p=0.010), distant metastasis-free survival (DMFS, p=0.014), progression-free survival (PFS, p=0.009), and overall survival (OS, p=0.015) survival curves of the low serum SA level group and high serum SA level group were statistically significant differences. Univariate analysis showed that American Joint Committee on Cancer (AJCC) stage, T stage, N stage, neoadjuvant chemotherapy (NC), and serum SA expression level were factors influencing the prognosis of NPC patients. Multivariate analysis showed that high serum SA expression level was related to worse PFS and OS in NPC patients with no distance metastasis. High serum SA level (SA > 65.10 mg/dl) before treatment is associated to poor survival outcomes in NPC and is an independent adverse prognostic factor in NPC patients with no distance metastasis.

Adaptive unscented Kalman filter methods for identifying time‐variant parameters via state covariance re‐updating

AbstractThe conventional parameter identification process generally assumes that parameters remain constant. However, under extreme loading conditions, structures may exhibit nonlinear behavior, and parameters could demonstrate time‐variant characteristics. The unscented Kalman filter (UKF), as an efficient online recursive estimator, is widely used for identifying parameters of nonlinear systems. Nevertheless, it exhibits limitations when attempting to identify time‐variant parameters. To address this issue, this paper proposes a covariance matching technique that produces an array of adaptive UKF algorithms. Firstly, the sensitivity parameter η is defined to identify the instant when the parameter change occurs, and its threshold is calculated based on the sensitivity parameter time history curve. Secondly, an adaptive forgetting factor is introduced to simultaneously update the innovation, cross, and state covariance matrices when the kth‐step sensitive parameter surpasses the threshold. Finally, a secondary correction forgetting factor (SCFF) is employed to further re‐update the state covariance values at the identified damage locations. This creative step enhances the adaptive capability and optimizes the identification accuracy of the proposed algorithms. Both the numerical simulations and shaking table test demonstrate that the proposed adaptive algorithms can efficiently identify the time‐variant stiffness‐type parameters, and accurately capture their time‐variant characteristics.

Motion-guided and occlusion-aware multi-object tracking with hierarchical matching

In the field of multi-target tracking, the widely embraced tracking-by-detection paradigm has rapidly progressed with the refinement of detectors and matching techniques. However, the paradigm of joint detection and tracking is relatively limited, and it is difficult to model complex scenes, such as the complexities introduced by camera motion and occlusion. In this work, a hierarchical joint detection and tracking framework is proposed, namely MSPNet. From a temporal concern, a motion-guided feature aggregation module is proposed to address the complexities of multi-frame variations. From a spatial concern, an occlusion-aware head and hierarchical spatial association are proposed to handle the challenges of occlusion. Extensive experiments on MOT challenging benchmarks demonstrate that the MSPNet can effectively reduce false negatives and improve the accuracy of tracking while outperforming a wide range of existing methods.

God is not back: the long-term effects of Soviet secularism

ABSTRACT How effective was Soviet secularization and what is its long-term legacy? This paper investigates the lasting effects of Khrushchev’s Virgin Lands Campaign (1950s) on the current level of Orthodox religiosity in eastern Russia using an original district (raion)-level dataset that combines historical and contemporary evidence. By employing various matching techniques and an instrumental variable approach, I identify significant effects of the Virgin Lands cultivation on diminishing the role of religion even 60 years later. Moreover, I demonstrate that the resettlement of Communist Youth League (Komsomol) members and the destruction of traditional social relations may have worked as key mechanisms that contributed to the legacy effects. This study offers one of the first micro-level empirical investigations into the impact of Communism-driven secularization, and contributes to the emerging inquiry into the historical legacy of communism.

Velocity estimation of thunderstorm movement and dealiasing of single Doppler radar during convective events

The use of meteorological radars in monitoring current weather conditions is crucial regarding the observation of the evolution and dissipation of thunderstorms. Thus, Doppler velocities being measured in each radar scan and velocity vectors derived from Numerical Weather Prediction (NWP) models—that are usually not as highly resolving as radar scans—are combined, as a monitoring utility during the evolution of a convective weather event. The objective of this study is to develop a new method that allows the implementation of a thunderstorm movement velocity estimation technique combining block matching and optical flow techniques. This new method constitutes a nowcasting (NWC) application that enables the use of a single Doppler radar without the need of using NWPs. The method relies on the estimation of the thunderstorm movement vector velocities (Doppler velocity) for each constant altitude plan position indicator (CAPPI) and through correction for aliasing errors to obtain 3D vector velocity fields for convective systems. The performance of the method is evaluated for selected case studies of convective thunderstorms under different synoptic scale conditions over Greece, a geographical area with challenges in forecasting due to its sharp relief and the need for optimization of the use of radar products.

Research on feature extraction method for different levels of cracks and pitting in spur gear based on dynamic characteristic templates

Health monitoring and fault diagnosis play a pivotal role in ensuring the safety of helicopter flights. Dynamic analysis has proven to be effective in accurately capturing the characteristics of crack and pitting signals. However, its application in fault diagnosis has limitations. This study introduces a feature extraction method based on the dynamic characteristic templates (DCTs) for different levels of cracks and pitting in spur gears. This method serves as a bridge between dynamic analysis and fault diagnosis, facilitating a comprehensive assessment of gear health. The integration of improved sparrow search algorithm (ISSA) and variational mode decomposition matching (VMDM) techniques enhances the search and matching capabilities of target signal based on DCTs. Based on above research, this study reveals distinctive morphological characteristics in the signals associated with crack and pitting under different levels and these characteristics serve as valuable index for fault diagnosis.

A survey of feature matching methods

AbstractFeature matching plays a crucial role in computer vision, with applications in visual localization, simultaneous localization and mapping (SLAM), image stitching, and more. It establishes correspondences between sets of feature points from multiple images, enabling various tasks. Over the years, feature matching has witnessed significant development, with an increasing number of methods being applied. However, different methods exhibit different degrees of applicability in different scenarios and requirements due to their different rationales. To cope with these issues, a comprehensive analysis and comparison of matching methods are essential. Existing reviews often lack coverage of deep learning models and focus more on feature detection and description, neglecting the matching process. This survey investigates feature detection, description, and matching techniques within the feature‐based image‐matching pipeline. Representative methods, their mechanisms, and application scenarios are also briefly introduced. In addition, comprehensive evaluations of classical and state‐of‐the‐art methods are conducted through extensive experiments on representative datasets. Particularly, matching‐based applications are compared to fully demonstrate the advantages of the methods. Lastly, this survey highlights current problems and development directions in matching methods, serving as a reference for researchers in the field.

Relationship between Pro-Inflammatory Cytokines and Gut Microbiome in Chronic Coronary Syndrome Patients Undergoing Coronary Angiography: A Cross-Sectional Study

Background: Chronic coronary syndrome (CCS) patients have a high mortality rate globally. Atherosclerosis, a cause of CCS, is influenced by inflammation. Pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-1 (IL-1), and interleukin-6 (IL-6) have a key role in the process of atherosclerosis. Moreover, gut microbiota dysbiosis can lead to leaky gut syndrome, subsequently triggering abnormal immune responses and contributing to diseases, including atherosclerosis and coronary artery disease (CAD). Objective: To study the relationship between pro-inflammatory cytokines and gut microbiome in CCS patients undergoing coronary angiography. Material and Methods: Participants were divided into two groups by using statistical matching techniques with age and gender, as CCS patients and healthy participants. Each patient’s blood was collected on the day of the appointment. All patients’ feces were collected one day before an appointment. The present research was a cross-sectional study. Results: Fifty-three patients, including 28 CCS patients and 25 healthy participants were enrolled. CCS patients had a higher level of TNF-α compared to healthy participants with statistical significance at 79.31 pg/mL. Phascolarctobacterium, Sutterella, and Prevotella could distinguish CCS patients from healthy participants based on receiver operating characteristic (ROC) analysis. Proteus and Phascolarctobacterium were positively correlated with TNF-α. Conclusion: There is a potential relationship between gut microbiome composition and inflammatory biomarkers in CCS patients. Pro-inflammatory cytokines and specific bacterial genera may be related to indicate significant CAD in CCS patients undergoing coronary angiography. Keywords: Cardiovascular disease; Interleukin-1; Interleukin-6; Tumor necrosis factor-alpha; Pro-inflammatory cytokines; Gut microbiome; Chronic coronary syndrome

Design and simulation of three-junction all perovskite tandem solar cells: A path to enhanced photovoltaic performance

Despite numerous publications on two-junction tandems using SCAPS-1D simulation, there is a gap in research regarding three-junction tandems. This paper explores the advantages of three junction all-perovskite tandem solar cells (3J-APTSC) where we introduce a new methodology utilizing two-step filtered spectrum and current matching techniques in SCAPS-1D to design realistic tandem solar cells. Crucial steps involve determining filtered spectrum and current matching points among sub-cells for tandem optimization. Our designed three-terminal tandem solar cell exhibits promising photovoltaic parameters, including VOC: 3.76 V, JSC: 10.70 mA/cm2, FF: 69.00 %, and PCE: 26.24 %, achieved through optimized active layer thicknesses. This thorough examination of 3J-APTSC offers insights that could drive the future development of highly efficient tandem solar cells.

Seismicity under a Dormant Volcano: Unveiling Active Crustal Faulting beneath Piton des Neiges, La Réunion

ABSTRACT Volcanic environments are commonly associated with seismic activity. The two prominent shield volcanoes of La Réunion island—Piton des Neiges and Piton de la Fournaise, exhibit sustained seismic activity. Whereas the seismicity at Piton de la Fournaise is tied to its volcanic activity, the seismic activity beneath Piton des Neiges, which has been dormant for 27,000 yr, remains poorly understood. The occurrence of earthquakes under the north flank of Piton des Neiges, often felt by the population, led to the deployment of several seismic stations in the area since 2012. In this study, we employ template matching and double-difference relocation techniques to construct a high-resolution catalog of the region. Our results reveal that the seismicity observed under Piton des Neiges is primarily concentrated on a northeast-dipping fault located in the oceanic crust beneath the volcanic edifice. Analysis of focal mechanisms indicates that this structure operates as a reverse fault. In the vicinity of this primary fault, we also identify secondary seismicity clusters with similar orientation and focal mechanisms. The region has experienced continuous seismicity since 1999, with occasional periods of increased swarmlike activity in 2011 and 2018. These fluctuations in seismicity rate do not correlate with markers of deep magma transfers often observed before eruptions at the Piton de la Fournaise volcano. Beyond the crustal faults highlighted in this study, it is noteworthy that the majority of earthquakes in northern La Réunion island exhibit consistent reverse focal mechanisms. These observations suggest that seismic events may not be driven by deep magmatic activity but, rather, result from regional tectonic stress and edifice loading on pre-existing faults.