The rapid pace of urbanization and the growing demand for urban development have precipitated financial crises in cities worldwide, particularly in funding essential projects. Traditional revenue sources are insufficient to meet the substantial costs of urban initiatives, resulting in a significant financial gap between escalating expenditures and stagnant income streams. The Land Value Capture (LVC) mechanism, a collection of innovative financial tools, has gained global recognition as an effective strategy to alleviate the financial pressures of urban development. This study introduces the LVC mechanism to the Persian academic urban planning community for the first time, systematically categorizing its implementation tools using the meta-synthesis method. Data collection involved document analysis and a comprehensive literature review, adhering to the inclusion and exclusion criteria of the PRISMA systematic review method. The data was analyzed through open and axial coding, followed by categorization. The proposed research model classifies LVC tools into four categories: tax and fee mechanisms, regulatory adjustments, public-private partnerships, and public asset and property management. It further identifies the instruments within each category and examines their application across various countries.

The emergence of big data is a radical shift in the business context, leading to a change in value creation and capture. This phenomenon is a newborn concept in the business and management literature confirmed by the growing number of publications over recent years. This paper presents an updating comprehensive bibliometric analysis to describe and assess the scientific landscape of value creation and capture based on leveraging big data in the literature. Bibliometrix and VOSviewer were selected as software tools for descriptive and network bibliometric analysis based on the Web of Science Core Collection database from 2011 until 2020. By implementing bibliometric analysis such as analysis of citations and co-occurrence of keywords, we have recognized the most prominent and influential authors, papers, journals, countries, and four potential clusters of current trends in studies. These four trends of value creation and capture from big data studies are: 1) strengthening the basic knowledge of value creation in the big data era, 2) data-driven business model and value capturing, 3) dynamic capabilities and centrality of knowledge, and 4) digital transformation of the service industry. Finally, by identifying the existing research gaps, future research directions in each cluster are demonstrated.

The Trans-Sumatra Toll Road (TSTR) located in Indonesia operation which is under the management of State-Owned Enterprises (SOE) faces several challenges and requires alternative sources of financing and income, one of which is Land Value Capture (LVC)-based area development. This research aims to identify and analyze the Critical Success Factors needed to implement land value capture in the TSTR project. The results of this research obtained five success factors with the highest ranking and a model of the relationship between variables in implementing land value capture in SOE assigned toll road operations based on the consensus of the expert. Through literature studies, 40 success factors were grouped into five categories and 14 criteria to successfully implement land value capture on toll road-based infrastructure that experts validated. The validated success factors were processed through a series of expert assessments using the Delphi-Method questionnaire, resulting in five success factors with the highest ranking in each category. The relationships between variables were further obtained from PLS-SEM modeling and were analyzed. The analysis of the relationship model produced relationships between variables including Government Policy, Toll Road Developer Business Model, Asset/Property Management, Investment Supporting Environment, LVC Planning and Specific Project Conditions. The present study result may determine factors that can ensure the successful completion of toll road projects with SOE assignment scheme.

Background and Objectives: A single reactive IgG anti-Dengue virus ELISA test in the absence of IgM antibodies or NS1antigen may denote current infection or past exposure to the virus. To determine whether IgG index value can be used toidentify true current dengue infection we conducted a prospective observational study.Materials and Methods: Suspected dengue patients (n=1745) were tested in their first specimen by MAC-ELISA, GAC-ELISA and NS1 antigen ELISA. Patients with MAC-ELISA and NS1Antigen non-reactive but GAC-ELISA reactiveresults (n=57) in their first test were followed up and repeated sampling was asked for IgG index values were calculatedaccording to the manufacturer’s instruction and classified as: low (2.2-2.5), medium (2.5-4.0) and high (>4.0).Results: 16 out of 57 patients (28.1%) had low IgG Index value whereas 26 cases (45.6%) were categorized as medium and15 (26.3%) were classified as patients with high IgG index. Nine patients with paired reactive serology or antigen positivestatus were categorised as serologically confirmed dengue fever, 11 patients as not dengue with categorical evidence of otherinfections while the rest 37 casas with clinical, radiological and laboratory parameters suggestive of dengue but no serologicalconfirmation as possible dengue. Among confirmed, possible and non-Dengue cases, 33.3, 32.4 and 0.0% had high Indexvalue in comparison with 22.2, 29.7 and 27.3% showing low Index values, respectively.Conclusion: Our results suggested a high IgG response in favour of true dengue infection than past exposure while no conclusionsshould drawn from a low or medium reactive GAC-ELISA results in the absence of IgM antibodies and NS1 Ag.

The most preferred method for the detection of foot-and-mouth disease (FMD) viral antigen and identification of viral serotype is the ELISA. Diagnostic tests with high sensitivity is necessary both to distinguish infected vaccinated animals and disease control programs for identifying the carrier animals. To detection of FMD virus, the current strategies are mainly based on capture antibody (sandwich) ELISA test. Applying laying pullets as animal bioreactor for production specific egg yolk antibody (IgY) has increased in recent years due to its high yield, affinity, low price, and quick production turnover. In the present study, we aimed to produce a concentrated and purifies IgY polyclonal antibody to design a capture antibody ELISA kit against the FMD virus (FMDV) serotype A. At first, laying hens were immunized by inactivated FMDV serotype virus and then, on days 14, 21, and 28 following vaccination, the eggs and sera were collected and then, the IgY polyclonal antibodies were extracted and purified from the chicken egg yolk using polyethylene glycol 6000–ethanol precipitation procedure. Extracts were filtered, purified by ion exchange chromatography and dialyzed. The purified IgY concentration estimated by Bradford assay, confirmed it presents by SDS-PAGE and Western blot and also its specific immune reaction by Ouchterlony double immunodiffusion and Dot blot tests. Moreover, for achieving optimum concentration of antigen/antibody (sera) in sandwich ELISA, checkerboard titration test was setup base on indirect ELISA results. Eventually, 119 previously confirmed samples (including 80 positive and 39 negative) by both Real time PCR (quantitative PCR, qPCR) and with a commercial ELISA kit, used for evaluation of sensitivity and accuracy of our developed Capture antibody ELISA kit. In this manner, the sensitivity and specificity of our designed kit was 100% and 98%, respectively. According to this, the present developed capture ELISA kit based on IgY has high sensitivity and specificity for FMD virus detection and it could be used in future as both commercial detecting and serotyping applications.