Distance Cost Research Articles

Recovery of compaction resources in internal dump of gently inclined composite coal seam open-pit mine

In the production and construction process of a slowly inclined composite coal seam surface mine in China, the problem of coal compression by the non-working slope is common, but the research on coal recovery is usually conducted for the end slope compression problem. Due to the small economic and reasonable stripping ratio in the early stage of infrastructure construction in an open-pit mine, the buried deep coal seam has not yet been mined and is covered by internal dumping. In recent years, with the gradual increase of the economic and reasonable stripping ratio, the mining of coal seam covered by internal dumping site has become feasible. This paper puts forward the mining scheme for this problem. In this paper, a mathematical model is established, and the trench between the inner dump and the non-working side is used as the working platform for coal seam mining. The relationship between stripping height, working line length and mining width of coal seam is studied. The variation law of production stripping ratio, remining amount, repeated stripping amount and mining width is further analyzed. At the same time, the influence of production stripping cost on the economic and reasonable stripping ratio is studied, and the relationship between transportation cost and discharge distance is established, so as to indirectly determine the relationship between mining width and economic and reasonable stripping ratio. Finally, it is found that the relationship between mining net income and mining width is first proportional and then inversely proportional. Taking an open-pit mine as an example, through the quantitative analysis of economic indicators, the net income of coal mining under different mining widths is calculated. The results show that when the mining width is 271 m, the economic benefit of the inner row mining is the highest, and the net income reaches 2021.4575 million yuan. At this time, the production stripping ratio is 5.25, which is less than the economic and reasonable stripping ratio of 6.95. The practical significance of the results is to improve the overall economic benefits and recovery rate of mining enterprises, and ensure the safe, green and efficient mining of mines.

What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA.

Modelling population connectivity is central to biodiversity conservation and often relies on resistance surfaces reflecting multi-generational gene flow. ResistanceGA (RGA) is a common optimization framework for parameterizing these surfaces by maximizing the fit between genetic distances and cost distances using maximum likelihood population effect models. As the reliability of this framework has rarely been studied, we investigated the conditions maximizing its accuracy for both prediction and interpretation of landscape features' permeability. We ran demo-genetic simulations in contrasted landscapes for species with distinct dispersal capacities and specialization levels, using corresponding reference cost scenarios. We then optimized resistance surfaces from the simulated genetic distances using RGA. First, we evaluated whether RGA identified the drivers of the genetic patterns, that is, distinguished Isolation-by-Resistance (IBR) patterns from either Isolation-by-Distance or patterns unrelated to ecological distances. We then assessed RGA predictive performance using a cross-validation method, and its ability to recover the reference cost scenarios shaping genetic structure in simulations. IBR patterns were well detected and genetic distances were predicted with great accuracy. This performance depended on the strength of the genetic structuring, sampling design and landscape structure. Matching the scale of the genetic pattern by focusing on population pairs connected through gene flow and limiting overfitting through cross-validation further enhanced inference reliability. Yet, the optimized cost values often departed from the reference values, making their interpretation and extrapolation potentially dubious. While demonstrating the value of RGA for predictive modelling, we call for caution and provide additional guidance for its optimal use.

An automatic method for extracting sand dunes based on slope cost distance from digital elevation models

Sand dunes significantly impact climate, water resources, and human infrastructure, reflecting sand sources and wind conditions. However, their complex and heterogeneous terrain leads to fragmented and poorly continuous dune extraction results. Focusing on the Badain Jaran Desert, this study developed a sand dune extraction method using slope cost distance analysis with FABDEM data. First, positive and negative terrains were segmented using 30 m resolution digital elevation data. Next, slope patches were classified based on specific criteria: slope less than 1.5°, patch area greater than 0.01 km2, and patches entirely within negative terrains. The filtered patches served as source data, and slope raster data were used to calculate slope cost distance. Sand dunes and interdune areas were delineated based on a predefined slope cost threshold. Post-processing, such as smoothing and vector conversion, yielded the final extraction results. The method effectively extracted compound transverse megadunes and pyramid megadunes, addressing misclassification issues and accurately defining interdune areas. It demonstrated faster processing speeds compared to traditional methods, with high precision (89.38 % & 90.37 %), accuracy (96.5 % & 92.89 %), and recall (94.77 % & 98.63 %). The results are more complete and less fragmented, aiding in the extraction of micro-scale dune features and revealing macro-scale spatial patterns, which are beneficial for studying dune formation, development, and wind dynamics.

An integrated multi-criteria decision making approach for selecting the starting location of railroad projects

PurposeThe successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.Design/methodology/approachBased on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.FindingsThe study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.Originality/valueThis study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

Roughness Ecosystem-based Approach to Estimate the Exposure Area of Tsunami on a Coastal City: A Case Study in Bengkulu City, Indonesia

Bengkulu City, as the main activity center in Bengkulu Province, is experiencing significant population growth. However, geographically, the city is vulnerable to tsunami disasters. High population growth outside the line with coastal area management also increases the risk of tsunami disasters in Bengkulu City due to green land conversion into built-up land. This condition is exacerbated by coastal abrasion that threatens the coastline, brings residential settlements closer to the shoreline, and conflicts over spatial utilization of the coastal green belt. Therefore, implementing tsunami mitigation measures, mainly through coastal ecosystem-based strategies, is crucial. This study compares the spatial modeling of tsunami inundation in Bengkulu City with three roughness scenarios of coastal vegetation: coastal forest and mangrove. These scenarios include a scenario without coastal vegetation, a scenario based on existing conditions, and a scenario with optimized coastal vegetation. Spatial modeling was conducted using cost distance analysis modeling to calculate the effectiveness of coastal vegetation in reducing tsunamis. The results showed that coastal forests and mangroves in existing conditions could effectively reduce tsunamis by 3.84% compared to land cover without coastal forests and mangroves. The coastal vegetation optimization scenario has effectively reduced the exposure area by 5.00% compared to that without vegetation. The coastal vegetation optimization scenario also has an effectiveness of 1.20% compared to the existing condition. The findings underscore the critical role of coastal vegetation as a natural barrier against tsunamis and emphasize the potential advantages of optimizing land cover to improve coastal protection.

Distance: A 2024 re-examination

Distance, a concept that continues to be relevant and rather complex, is far from being obsolete despite its diminishing role in the age of virtual space. It represents spatial separation, a notion that was first quantified in Egypt some 5,000 years ago. The evolution of distance can be traced through five distinct phases: natural (absolute and relative distances), relational (time and cost distances, distance decay, friction of distance, and distanciation), technological (extensibility, accessibility, and speed), virtual (proximity, digital, cognitive, and metaphorical distances), and optional (optional and estimated distances). These historical phases have built upon each other, leading to our contemporary experience of all distance classes depending on specific separation cases. Modern humans may overcome any distance on earth, one way or another, unless prevented from doing so by political national sovereignties.

The visual landscape of rock art in Qeydu Valley in Teymare in the Central Iranian Plateau

Qeydu Valley is a part of the Teymare region, which contains one the largest clusters of rock art in the central Iranian Plateau. Analyzing about 495 panels in more than 100 sites, this paper is intended to provide an insight into the landscape of rock art in Qeydu Valley. The paper explores the intersection of rock art positioning, movement patterns, and visual landscape in the study area. Total and cumulative viewshed, cost distance, and least-cost path analysis have been used to reconstruct the visibility dimension of the rock art locations in movement across the region. The total and cumulative viewshed analysis suggest that the panels showing a hunting theme benefit from a higher degree of visibility compared to the other petroglyphs.

Do institutions matter for agri-food imports and exports between China and Africa? A gravity model analysis

This paper aims to study the role of institutional quality on agri-food trade (import and export) between China and 48 African countries during the 2001-2021 period. The gravity model of trade was employed by estimating the Generalised Least Square (GLS) and the Poisson Pseudo-Maximum Likelihood (PPML) estimators, which include zero trade flows for panel data. The main results show that voice and accountability, control of corruption, and the rule of law all enhance agri-food trade flows. While political instability encourages agri-food exports to China. In addition, the economic size of China, the trade cost (distance), and the population of African countries also encourage agri-food trade between China and African economies. Moreover, access to the sea, exchange rate policy, and the economic size of African nations deplete China’s agri-food imports from Africa. We showed the mechanism through which each institutional variable affects China’s agri-food trade flows to African nations.

Sensitivity to network perturbations in the randomized shortest paths framework: theory and applications in ecological connectivity

The randomized shortest paths (RSP) framework, developed for network analysis, extends traditional proximity and distance measures between two nodes, such as shortest path distance and commute cost distance (related to resistance distance). Consequently, the RSP framework has gained popularity in studies on landscape connectivity within ecology and conservation, where the behavior of animals is neither random nor optimal. In this work, we study how local perturbations in a network affect proximity and distance measures derived from the RSP framework. For this sensitivity analysis, we develop computable expressions for derivatives with respect to weights on the edges or nodes of the network. Interestingly, the sensitivity of expected cost to edge or node features provides a new signed network centrality measure, the negative covariance between edge/node visits and path cost, that can be used for pinpointing strong and weak parts of a network. It is also shown that this quantity can be interpreted as minus the endured expected detour (in terms of cost) when constraining the walk to pass through the node or the edge. Our demonstration of this framework focuses on a migration corridor for wild reindeer (Rangifer rangifer) in Southern Norway. By examining the sensitivity of the expected cost of movement between winter and calving ranges to perturbations in local areas, we have identified priority areas crucial for the conservation of this migration corridor. This innovative approach not only holds great promise for conservation and restoration of migration corridors, but also more generally for connectivity corridors between important areas for biodiversity (e.g. protected areas) and climate adaptation. Furthermore, the derivations and computational methods introduced in this work present fundamental features of the RSP framework. These contributions are expected to be of interest to practitioners applying the framework across various disciplines, ranging from ecology, transport and communication networks to machine learning.

The bi-objective multi-depot split delivery location routing problems under uncertain conditions

This research focuses on addressing the multi-depot location-routing problem with split delivery and multiple objectives under uncertain conditions. A mathematical model is proposed to effectively tackle this problem by considering various constraints and considerations. The paper emphasises the use of split delivery as an effective approach for reducing transportation costs. Furthermore, the study explores bi-objective optimisation, specifically cost minimisation and distance balancing. To assess the robustness of the proposed model under different scenarios, sensitivity analysis tools are employed. Numerical examples are presented to demonstrate the effectiveness of the model. Bi-objective optimisation is conducted using the augmented ε-constraint method, enabling decision-makers to analyse trade-offs between cost minimisation and distance balancing objectives. The findings of the study reveal that an increase in demand leads to higher cost minimisation and lower distance balancing. Conversely, an increase in transportation costs results in higher distance balancing. The research also highlights the robustness of the model and its potential for cost reduction through split delivery compared to scenarios without uncertainty.

Factors Influencing the Uptake of Cervical Cancer Screening Services among Women Attending Gynecological OPD at KIUTH, Ishaka-Bushenyi, South Western Uganda

This work aimed to determine the factors influencing uptake of cervical cancer screening services among women attending gynecologic OPD at KIUTH, Ishaka-Bushenyi, South-western Uganda. A cross sectional study design and Quantitative approach was used. 100 women attending gynecologic OPD at KIUTH were selected using Kish Leslie formular. Data collected using a questionnaire was entered and analyzed using SPSS, presented in frequency, percentage and P-values tables. 41.2% were aged 25-31 years and the minority 2.0% being >45 years. 65.7% coming away from Ishaka; married 73.7% for 1-5 or 11-20 years each accounting for 32.5%. 37.4% while 30(30.3%) reached tertiary education level. 42.4% were unemployed whereas 7.1% were simply housewives. 50.0% had 2-4 children, and only 18.3% had <2 children. 86.9% were aware while 13.1% who weren’t. 56.8% got their information concerning cervical cancer and its screening from health facility compared to 3.7% from press. 34.7% had knowledge on screening recommendation among adult women who specified it being 22.1% every three years and 12.6% yearly. 88.4% specified it to be treatable if detected early with a P-value=<0.01*, 73.5% knows a local health facility offering the services; p-value=0.05, 53(62.4%) of 62.2% claim that results return time could influence the testing uptake, pvalue=0.18, also, 89.2% agreed that Recommendation/counseling affects the uptake of cervical cancer screening/testing p-Value=0.05, as well as having tested before 43.4% already tested had a p-value=0.05. Long distance to HF affects 75.9% aware of screening of 77.1% p-value=0.49, cervical test cost affect with 75.6% aware of the services p-value=0.55, and Gender of HW where 39.5% who were aware of the services out of 47.5% agreed that it affects uptake p-value=<0.001. According to the study findings, awareness/knowledge of cervical cancer screening/testing services among women attending KIUTH stands at 86.9%; whose info is mostly from health facility and Television/Radio. Early detection; availing of local health facility, short results return time, Recommendation/counselling and having tested before increases uptake whereas the long distance to the health facility, cervical test cost and longest distances reduces uptake of cervical cancer and screening services/testing. Keywords: Cervical cancer, Screening services, Healthy facility, Women, OPD.

Human-in-the-loop optimization of rocker shoes via different cost functions during walking

Personalised footwear could be used to enhance the function of the foot–ankle complex to a person’s maximum. Human-in-the-loop optimization could be used as an effective and efficient way to find a personalised optimal rocker profile (i.e., apex position and angle). The outcome of this process likely depends on the selected optimization objective and its responsiveness to the rocker parameters being tuned. This study aims to explore whether and how human-in-the-loop optimization via different cost functions (i.e., metabolic cost, collision work as measure for external mechanical work, and step distance variability as measure for gait stability) affects the optimal apex position and angle of a rocker profile differently for individuals during walking. Ten healthy individuals walked on a treadmill with experimental rocker shoes in which apex position and angle were optimized using human-in-the-loop optimization using different cost functions. We compared the obtained optimal apex parameters for the different cost functions and how these affected the selected gait related objectives. Optimal apex parameters differed substantially between participants and optimal apex positions differed between cost functions. The responsiveness to changes in apex parameters differed between cost functions. Collision work was the only cost function that resulted in a significant improvement of its performance criteria. Improvements in metabolic cost or step distance variability were not found after optimization. This study showed that cost function selection is important when human-in-the-loop optimization is used to design personalised footwear to allow conversion to an optimum that suits the individual.

Evaluation of teleneuropsychology services in veterans during COVID-19.

This study evaluated the quality of direct-to-home teleneuropsychology (teleNP) services that offered patients choice of service modality. We conducted a quality assurance project in a sample of Veterans (N = 143) referred for a variety of referral reasons from a Midwestern Veteran Affairs Hospital. Neuropsychological evaluations were conducted between February and June 2021 during the second wave of the COVID-19 pandemic. We evaluated teleNP rate of use, factors influencing acceptability and feasibility, and cost savings. Approximately 40% of our scheduled patients completed full direct-to-home teleNP, with about 80% of our sample choosing at least one telehealth encounter as part of their evaluation. Age was a significant factor in predicting full teleNP use, with older adults preferring in-person testing. Digital divide consults (i.e., loaned tablets) led to an increase in teleNP use. Those utilizing full teleNP (interview, assessment, feedback) benefited from cost and travel distance savings. Most Veterans in our Midwestern Veteran Affairs Hospital used teleNP services during the second wave of the COVID-19 pandemic, with utilization influenced by specific patient demographics (i.e., age) and access to technology. Offering Veteran choice of assessment modality (in-person or video teleNP) was associated with significant cost reduction due to mileage savings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Simulation of Flood Inundation Extent by Integration of HEC-HMS, GA-based Rating Curve and Cost Distance Analysis

Simulation of Flood Inundation Extent by Integration of HEC-HMS, GA-based Rating Curve and Cost Distance Analysis

Statistical modelling of travel mode choice of public university students in Freetown, Sierra Leone: the case of three campuses

ABSTRACT Though much research has been conducted on students’ travel behavior around the world, not much is known about university students in Sierra Leone. This study investigates the most common transport modes amongst public university students, the factors influencing their preference, and the impact of their choice on the environment. Through a Revealed Preference survey, using the traditional paper-and-pencil method combined with a smartphone-assisted interviewing technique, 632 students were sampled. Discrete travel choice models were developed and estimated, and the methodology was demonstrated for the three campuses under the University of Sierra Leone. The study found that more than one-third of students preferred the paratransit mode, ‘podapoda’, followed by taxi, two and three-wheelers. According to the mode choice model results and exploratory analysis, sociodemographics, travel time, cost, and trip distance are the main factors considered by students’ for selecting a transport mode. Students varied widely in their assessment of how their mode choice affects the environment. These findings highlight the need for a sustainable transport system for university trips. Acquiring appropriate transport modes, promoting active travel, and developing student residences with shorter commutes will ensure long-term land management. This will promote the sociocultural, economic, educational, and ecological aspects of campus growth.

Temporal and Spatial Evolution Analysis and Correlation Measurement of Urban–Rural Fringes Based on Nighttime Light Data

Rural–urban fringe areas serve as crucial transitional zones within urban structures, and their spatiotemporal evolution holds significant reference value for scientifically planning urban configurations. The existing research predominantly focuses on large cities, overlooking the spatiotemporal evolution mechanisms of small- to medium-sized cities. This study employs nighttime light data as the data source to ensure continuous and consistent data, overcoming administrative boundaries. Taking Taizhou City as a case study, a combination of the threshold method and an improved Mann–Kendall algorithm is employed to reveal the evolution process of fringe areas. And a gravity model is utilized to unearth the interaction relationships among regions. The results indicate that from 2010 to 2020, the urban area expanded from 1097 km2 to 2791 km2, with fringe areas experiencing initial contraction followed by gradual expansion. The central urban areas of Jiaojiang, Luqiao, and Huangyan gradually merged, forming a concentrated region. Linhai maintained a high level of attraction, while spatial gravity weakened in other areas. This study quantitatively analyzes the expansion trends of fringe areas in small- to medium-sized cities, elucidating the attractive effects of time–cost distance and land resources on development and providing valuable support for subsequent spatial planning and governance.

Effects of source-sink landscape proximity on the spatial–temporal water quality from the perspective of cost distance in an agricultural watershed

Spatial patterns of source-sink landscapes play a critical role in assessing surface water quality. However, this relation may be different due to various proximity of source-sink landscapes. The effect of the source-sink landscape proximity on surface water quality remains poorly understood. The impact of source-sink landscape proximity on the spatial–temporal water quality were quantified based on field monitoring data (2013–2019) in an agricultural watershed. A new approach considering the cost distance of landscape types, slope, and vegetation on the transformation of nutrients from one point to water bodies was used to construct different buffer zones which reflected very high, high, moderate, low, very low proximities, while the subwatershed represented the very low proximity. Then the effects of the source-sink landscape proximity were assessed based on sub-watershed and other four buffers. The resutls showed that the TN and NO3−-N were the main non-point source pollution indices with significant spatial variation (p < 0.05). The water quality index (WQI) was higher during the dry season than during the wet season, indicating that the sampling sites also experienced seasonal variations. The multi-scale analysis demonstrated the significance of the source-sink landscape proximity in improving the quality of surface water. A stronger correlation between the ratios of sink landscape and the main water quality parameters (the TN and NO3−-N) was observed in the low proximity zone, suggestiong that the low proximity zone with moderate non-point source pollution risk was a critical region in water quality management. The results provide important insights into understanding the effects of the source-sink landscape proximity on water quality and how scientific and effective measures can be made to improve water quality.

Truck-Drone Pickup and Delivery Problem with Drone Weight-Related Cost

Truck-drone delivery is widely used in logistics distribution for achieving sustainable development, in which drone weight greatly affects transportation cost. Thus, we consider a new combined truck-drone pickup and delivery problem with drone weight-related cost in the context of last-mile logistics. A system of integer programming is formulated with the objective of minimizing the total cost of the drone weight-related cost, fixed vehicle cost and travel distance cost. An improved adaptive large neighborhood search algorithm (IALNS) is designed based on the characteristics of the problem, several effective destroy and repair operators are designed to explore the solution space, and a simulated annealing strategy is introduced to avoid falling into the local optimal solution. To evaluate the performance of the IALNS algorithm, 72 instances are randomly generated and tested. The computational results on small instances show that the proposed IALNS algorithm performs better than CPLEX both in efficiency and effectiveness. When comparing the truck-drone pickup and delivery problem with drone weight-related cost to the problem without drone weight-related cost, it is found that ignoring the drone weight constraints leads to an underestimate of the total travel cost by 12.61% based on the test of large instances.

Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China

To alleviate travel congestion at peak periods or on congested routes, some measures for urban rail transit (URT) systems including fare incentive schemes and subsidy policies have been widely adopted. Existing measures can alleviate demand during peak periods or on congested routes to a certain extent, but they do not consider the passengers’ satisfaction with their travel experience. Consequently, this study aims to investigate passengers’ acceptance of a fare incentive policy offering a discounted fare during off-peak periods or on uncongested routes before it is implemented in Nanjing, China. To understand passengers’ acceptance of the policy, this study explores the effects of the policy on passengers’ route choices. Furthermore, the differences among passengers with respect to different travel purposes and travel times in route choices have been analyzed. A revealed preference (RP) survey and a stated preference (SP) survey consisting of 463 samples from URT passengers in Nanjing, China, were conducted and analyzed using a random-parameter multinomial logit (RPMNL) model. Results show that socio-economic variables, travel characteristics, travel cost, departure time, and travel distance significantly affect passengers’ route choices. Furthermore, the route choices of passengers with different travelling times and purposes vary. Commuters are most sensitive to travel cost during off-peak periods, followed by non-commuters who travel during peak periods. Departure time and travel distance most significantly affect the route choices of non-commuters during peak periods and off-peak periods, respectively. The findings of this study could assist transit agencies in designing attractive fare incentive policies for public transit passengers and offer valuable insights into effective demand management strategies. Moreover, the lessons derived from this study may guide the implementation of fare incentive policies in Nanjing, China, and elsewhere.

Adaptive semi-paired query hashing for multi-modal retrieval

Multi-modal hashing has attracted enormous attention in large-scale multimedia retrieval, owing to its advantages of low storage cost and fast Hamming distance computation. Existing multi-modal hashing methods assume that all multi-modal data are well paired and then encode the paired multiple modalities into joint binary codes. However, it is not ensured that all data are fully paired in practical applications. In this paper, we present an adaptive semi-paired query hashing method, which facilitates learning the hash codes for semi-paired query samples. The proposed method performs projection learning and cross-modal reconstruction learning to maintain the semantic consistency between multi-modal data. Meanwhile, the semantic similarity structure and the complementary multi-modal information are preserved by hash codes to obtain a discriminative hash function. In the encoding stage, the missing modality features are completed via the learned cross-modal reconstruction matrices. In addition, the multimodal fusion weights are fine-tuned adaptively for the new query data to capture the modality difference. The extensive experiment results on three benchmark datasets show that our proposed algorithm outperforms state-of-the-art multi-modal hashing methods.