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The purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps.

A fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion.

To verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm.

This algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.

Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

This theoretical study aims to clarify the (a priori) ambiguous effect of homeownership on unemployment. In general, in fact, homeownership discourages job mobility, but homeowners are less likely to be unemployed than tenants, since homeownership would seem to be positively related to human capital.

This study develops a modified version of the benchmark theoretic model of the labour market – the well-known “equilibrium unemployment theory” – where homeownership affects both the “Beveridge Curve” (BC, by means of job immobility) and the “Job Creation Condition” (JCC, by means of human capital).

The general result is that an increase in homeownership increases unemployment. Therefore, policymakers could encourage job mobility, before facilitating homeownership. This policy implication, however, may not apply in the case of high inflation and/or low nominal interest rate, and when the job destruction rate depends on the homeownership rate.

The model studies the steady-state equilibrium of the labour market, so the policy implications only relate to the long-run. The model, therefore, does not consider the short-run effects of homeownership on unemployment (which may differ from the long-term results).

The model suggests a public policy characterised by large investment in rail lines and subsidised commuter fares. By promoting a more efficient allocation of workers across regions (and, thus, job mobility), indeed, this policy can be a good way to increase employment, without harming homeownership.

The practical implication of this model is also a social implication, since it relates to homeownership and housing tenure.

To the best of author’s knowledge, this is the first model that introduces the key role of homeownership in the so-called “Equilibrium unemployment theory”. Precisely, the model uses a modified version of both the BC (which includes the negative effect of homeownership on the overall job search intensity of unemployed workers) and the JCC (which includes the positive effect of homeownership on both the business start-up and the human capital of workers). By comparing these two opposite effects, this theoretical work makes clearer the net effect of homeownership on unemployment.

While the effect of the minimum wage (MW) on employment has been widely studied, less is known about its impact on hirings and separations. Whereas the adverse effects of MW on hiring are quite familiar, results of studies indicating reductions in separations are less expected. This study aims to bridge the gap between theory and practice by performing a meta-analysis, which allows for understanding the real effect of MW on employment's two components: hirings and separations.

Since mixed results cloud understanding of the issue, a meta-regression analysis was conducted. This technique permits understanding the effect of MW on labor market transitions and offers additional explanations for controversial results.

Despite the evidence that MW increasing the turnover and reducing permanence could negatively affect employment, findings from meta-regression analysis pointed out that increases in MW reduce hirings but also separations offsetting the negative effect on employment. Overall, the results imply that the standard finding that MW changes have little or no impact on employment rates reflects offsetting reductions in hiring and separations. Evidence of negative publication bias is also found.

The results emphasize the importance of looking beyond employment rates to understand the impacts of MW. Overall, the evidence implies that the standard finding that MW changes have little or no impact on employment rates reflects offsetting reductions in hiring and layoffs. In addition, the results suggest that MW tends to have a much larger impact on employment flows than on employment levels. This finding has to be considered by policymakers when they make decisions about increasing the MW. These analyses assist in clarifying debates about the effects of MW on the labor market in the changing economic environment and conduct a labor policy in the right direction.

The meta-regression analysis (MRA) conducted in this study emphasizes the importance of looking beyond employment rates to understand the impacts of MW (Brochu and Green, 2013). Overall, the evidence implies that the standard finding that MW changes have little or no impact on employment rates reflects offsetting reductions in hiring and layoffs. Therefore, the evidence from the performed MRA is consistent with those previous meta-analysis studies that found little or no evidence about MW adversely affecting employment and, at the same time, provide additional explanation for these findings. In addition, the results suggest that MW tends to have a much larger impact on employment flows than on employment levels (Dube et al., 2016).

Even though hirings are reduced due to MW, this evidence could not necessarily imply a negative effect of MW on the labor market since job searching or matching is improved. Additionally, the increases in MW could improve the quality of the job and the job attachment, which are consistent with a recruitment-retention model (Dube et al., 2007). The evidence from this MRA, which is consistent with little or no impact of MW on employment, also could imply that although the MW is set relatively high to balance the supply and demand of labor, their level is close to optimal. Setting the right level is also associated with compliance with MW. This issue deserves attention since any adverse employment effects of MW could be strengthened by incomplete coverage. The effectiveness of the entire process of developing, putting into practice and enforcing MW rules hinges on compliance.

As prior meta-regression analysis did not have the same objective, the results of this article move current research forward. Based on the analysis, future research lines are delineated, and some public policy implications are assessed.

This study deals with the main issues concerning the interplay between homeownership and labour market outcomes, namely (1) the relation between homeownership and labour market outcomes, at both the individual level and the aggregate level, and (2) the relation between homeownership and human capital.

This paper is both theoretical and empirical. A search and matching model of the labour market is developed to explain the strong relation between mortgage markets and wages. A regional panel analysis in Italy is used to verify the interplay between homeownership and wages.

Homeownership is not, by itself, a condition for receiving higher wages, but rather higher wages increase the probability to become a homeowner, since they positively affect the probability of acquiring a mortgage from the bank. Eventually, wages cause homeownership, but the reverse may not be true.

The paper focuses on the labour market, while the housing market model is restricted to the mortgage market.

The positive effect of homeownership on wages is hard to theoretically formalise and is not empirically proven. Before investigating a (potential) bidirectional relationship between homeownership and labour market outcomes, therefore, the related literature should assume a new theoretical link between homeowners and wages.

The result that “homeownership is not, by itself, a condition for receiving higher wages” has positive implications for human and social development. If homeownership could lead to better labour market outcomes, indeed, socio-economic inequalities would increase in the society, because homeownership would be the starting point of a “lucky” circle that increases the well-being of people who are already wealthy.

First, this study clearly explains why the microeconomic result that homeowners are more likely to be employed than tenants is consistent – at the aggregate level – with a negative relation between homeownership and better labour market outcomes. Second, the related literature has largely ignored the social implications of the topic. A potential bidirectional relation between homeownership and (better) labour market outcomes, indeed, could imply an increase in the well-being of people who are already wealthy.

This article aims to help ensure high-quality subject access to Swedish lesbian, gay, bisexual, transgender, queer and intersexual (LGBTQI) fiction, and aims to identify challenges that librarians consider important to address, on behalf of themselves and end users.

A web-based questionnaire comprising 35 closed and open questions, 22 of which were required, was sent via online channels in January 2022. By the survey closing date, 20 March 2022, 82 responses had been received. The study was intended to complement an earlier study targeting end users.

Both this study of librarians and the previous study of end users have painted a dismal image of online search services when it comes to searching for LGBTQI fiction. The need to consult different channels (e.g. social media, library catalogues and friends), the inability to search more specifically than for the broad LGBTQI category and suboptimal search interfaces were among the commonly reported issues. The results of these studies are used to inform the development of a dedicated Swedish LGBTQI fiction database with an online search interface.

The subject searching of fiction via online services is usually limited to genre with facets for time and place, while users are often seeking characteristics such as pacing, characterization, storyline, frame/setting, tone and language/style. LGBTQI fiction is even more challenging to search because indexing practices are not really being standardized or disseminated worldwide. This study helps address this important gap, in both research and practical applications.

The paper employs a unique online user-generated housing search dataset and introduces a novel measure for housing demand, namely “contacts per listing” as explained by hedonic, geographic and socioeconomic variables.

The authors explore housing demand by employing an extensive Internet search dataset from a German housing market platform. The authors apply state-of-the-art artificial intelligence, the eXtreme Gradient Boosting, to quantify factors that lead an apartment to be in demand.

The authors compare the results to alternative parametric models and find evidence of the superiority of the nonparametric model. The authors use eXplainable artificial intelligence (XAI) techniques to show economic meanings and inferences of the results. The results suggest that hedonic, socioeconomic and spatial aspects influence search intensity. The authors further find differences in temporal dynamics and geographical variations.

To the best of the authors’ knowledge, it is the first study of its kind. The statistical model of housing search draws on insights from decision theory, AI and qualitative studies on housing search. The econometric approach employed is new as it considers standard regression models and an eXtreme Gradient Boosting (XGB or XGBoost) approach followed by a model-agnostic interpretation of the underlying effects.

Houses are durable, so an imbalance between demand and supply occurs after time has passed since initial construction. The purpose of this study is to quantify the extent of this imbalance for existing houses, focusing on the heterogeneity across property segments.

This study uses a unique data set on the “inquiry volume” that each property received from an online real estate portal to measure the volume of demand in relation to supply. Simple regressions are conducted in the resale condominium market across the Tokyo metropolitan area.

The inquiry volume successfully tracked a recent expected trend in which demand relative to supply is stronger for condominiums in reasonably priced areas, condominiums in convenient, accessible locations, condominiums built within the last 20 years and compact and spacious units. This study also confirms that these trends cannot be captured through heterogeneity in price levels, which has been widely used in previous studies on measuring housing preferences.

As an indicator of conditions in the housing market, the property-level inquiry volume has strong potential to provide useful information for supply strategies and for the sustainable use of existing housing stocks.

The originality of this paper is the use of information on the buyer side, which is typically unobservable.

Many studies have shown that the intensity and the number of hurricanes are likely to increase. This paper aims to look at the immediate effects of hurricanes on the time on the market, share of houses sold and percentage of houses with price cuts in the housing market using the metropolitan statistical area-level data in Florida.

Using a difference-in-difference method, the authors estimate the impact that a hurricane has on the housing markets.

The authors find that a hurricane has a positive and significant effect on the time on the market. A hurricane leads to a delay of the sale of a typical house in Florida by five days. The authors test for within-year seasonality and show that these effects change with seasonality of the housing market. Markets with seasonal housing prices tend to be affected more by hurricanes than those where housing prices are not seasonal. The authors also show that effects of a hurricane are transient and fade away in a few months. The results remain significant as the hurricane intensity changes.

This is the first study to look at the short-term effects of the hurricanes and how their effects vary based on seasonality of the markets.

This study aims to discover how domain familiarity and language affect the cognitive load and the strategies applied for the evaluation of search engine results pages (SERP).

This study used an experimental research design. The pattern of the experiment was based upon repeated measures design. Each student was given four SERPs varying in two dimensions: language and content. The criteria of students to decide on the three best links within the SERP, the reasoning behind their selection, and their perceived cognitive load of the given task were the repeated measures collected from each participant.

The evaluation criteria changed according to the language and task type. The cognitive load was reported higher when the content was presented in English or when the content was academic. Regarding the search strategies, a majority of students trusted familiar sources or relied on keywords they found in the short description of the links. A qualitative analysis showed that students can be grouped into different types according to the reasons they stated for their choices. Source seeker, keyword seeker and specific information seeker were the most common types observed.

This study has an international scope with regard to data collection. Moreover, the tasks and findings contribute to the literature on information literacy.