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This article aims to investigate the impact of (1) the establishment of a refugee settlement, (2) the energy demand of a host and refugee population, (3) the residence time of refugees and (4) interventions in the energy sector on sustainable utilization of the forest.

Refugee movements from the Democratic Republic of Congo and settlement construction in a Zambian host society provide the setting. An agent-based model is developed. It uses survey data from 277 Zambian households, geographic information system coordinates and supplementary data inputs.

The future forest stock remains up to 30 years without an influx of refugees. Refugee developments completely deplete the forest over time. The settlement construction severely impacts the forest, while refugees' energy needs seem less significant. Compared with the repatriation of refugees, permanent integration has no influential impact on forest resources. Interventions in the energy sector through alternative sources slow down deforestation. Once a camp is constructed, tree cutting by hosts causes forest covers to decline even if alternative energy is provided.

The analysis is useful for comparable host–refugee settings and United Nations High Commissioner for Refugees interventions in settlement situations. Forest and energy sector interventions should involve host and refugee stakeholders.

This article adds value through an agent-based model in the Zambian deforestation–refugee context. The study has a pilot character within the United Nation's Comprehensive Refugee Response Framework. It fills a gap in long-term assessments of refugee presence in local host communities.

This study aimed to estimate the economic benefits of PM2.5 emission abatement by Red Pine, Pinus Koraiensis and Quercus, using a metering model analyzing the amount of PM2.5 absorption in Korea.

To estimate the economic effects of PM2.5 adsorptions by trees, the frequency of hospital visits resulting from respiratory and circulatory diseases was estimated using a Probit model based on the data from National Health and Nutrition Survey.

The results show that Quercus and Pinus Koraiensis absorb and eliminate the largest amount of PM2.5. Reducing 1 ton of PM2.5 emission through the planting of trees leads to lower incidences of respiratory and circulatory diseases equivalent to the amount of 95 million won. When the trees planted are 2-year-old Red Pine, Pinus Koraiensis and Quercus, the resulting economic benefits of the PM2.5 abatement would amount to 481 million won, 173 million won and 1,027 million won, respectively. If the trees are 80 years old, the economic benefits are estimated to be 73 billion won for Red Pine, 103 billion won for Pinus Koraiensis and 38 billion won for Quercus.

One limitation of this study is that the weight of PM2.5 adsorbed by each leaf area entirely depended on the experimental results from a prior study and the values are likely to be different from those actually absorbed in natural surroundings. In addition, because of the lack of data from a domestic survey on the surface of leaf area or the reload flow rate of PM2.5, this study referred to data from foreign research. Unfortunately, this specific data may not reflect climatic and terrain characteristics specific to the target country. We used the annual wind speed to calculate the reload flow rate and elimination volume; however, the figures could be more accurate with hourly or daily climate variations. When estimating the health benefits of changes in PM2.5 emissions on respiratory and circulatory diseases, more segmented access to patients' hospital visits and hospital admissions are desirable. Finally, the study focused on the three major tree species of Korea, however, a more detailed study of PM2.5 reduction by various tree types is needed in the future.

This paper quantitatively assessed the amount of PM2.5 adsorption by each of the three tree species. Then, the economic benefits were calculated in terms of how much money would be saved on hospital visits thanks to the reduced PM2.5 levels and lower incidences of respiratory and circulatory system diseases. The net contribution of this study was to prove the trees' function of reducing PM2.5 as it relates to human health. We focused on the most common trees in Korea and compared them to provide new information on the species.

In cities with high density, heat is often trapped between buildings which increases the frequency and intensity of heat events. Researchers have focused on developing strategies to mitigate the negative impacts of heat in cities. Adopting green infrastructure and cooling pavements are some of the many ways to promote thermal comfort against heat. The purpose of this study is to improve microclimate conditions and thermal comfort levels in high-density living conditions in Seoul, South Korea.

This study compares six design alternatives of an apartment complex with different paving and planting systems. It also examines the thermal outcome of the alternatives under normal and extreme heat conditions to suggest strategies to secure acceptable thermal comfort levels for the inhabitants. Each alternative is analyzed using ENVI-met, a software program that simulates microclimate conditions and thermal comfort features based on relationships among buildings, vegetation and pavements.

The results indicate that grass paving was more effective than stone paving in lowering air temperature and improving thermal comfort at the near-surface level. Coniferous trees were found to be more effective than broadleaf trees in reducing temperature. Thermal comfort levels were most improved when coniferous trees were planted in paired settings.

Landscape elements show promise for the improvement of thermal conditions because it is much easier to redesign landscape elements, such as paving or planting, than to change fixed urban elements like buildings and roads. The results identified the potential of landscape design for improving microclimate and thermal comfort in urban residential complexes.

The results contribute to the literature by examining the effect of tree species and layout on thermal comfort levels, which has been rarely investigated in previous studies.

The accurate valuation of second-hand vessels has become a prominent subject of interest among investors, necessitating regular impairment tests. Previous literature has predominantly concentrated on inferring a vessel's price through parameter estimation but has overlooked the prediction accuracy. With the increasing adoption of machine learning for pricing physical assets, this paper aims to quantify potential factors in a non-parametric manner. Furthermore, it seeks to evaluate whether the devised method can serve as an efficient means of valuation.

This paper proposes a stacking ensemble approach with add-on feedforward neural networks, taking four tree-driven models as base learners. The proposed method is applied to a training dataset collected from public sources. Then, the performance is assessed on the test dataset and compared with a benchmark model, commonly used in previous studies.

The results on the test dataset indicate that the designed method not only outperforms base learners under statistical metrics but also surpasses the benchmark GAM in terms of accuracy. Notably, 73% of the testing points fall within the less-than-10% error range. The designed method can leverage the predictive power of base learners by incrementally adding a small amount of target value through residuals and harnessing feature engineering capability from neural networks.

This paper marks the pioneering use of the stacking ensemble in vessel pricing within the literature. The impressive performance positions it as an efficient desktop valuation tool for market users.

This study evaluates “potentials for using tourism in promoting indigenous resources for community development at Musina Municipality, Limpopo Province, South Africa.”

The study used a questionnaire survey, focus group discussions, and field observations to gather data. Microsoft Excel, Spreadsheet, cross-tabulation analysis, and manual sorting contributed to quantitative and qualitative data analyses.

The study uncovered vast significant indigenous species, resources, and tourism potentials with low impacts of indigenous species and resource benefits to the local communities. The details pointing to the actual and potential indigenous resources situations around tourism activities in Musina municipality emerged prominently. Thus, the study concluded such significant indigenous species, resources, and better tourism potentials need a well-combined strategy to channel the benefits to the local community's livelihoods.

The issue of indigenous resources, forests, trees, and tourism concerning rural community livelihoods has become of curiosity in the past few years. Nonetheless, few such studies have investigated the synergies between tourism and significant indigenous species and resources to improve their livelihoods.

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

This study aims to assess the biodiversity of the study area and estimate the carbon stock of two dry deciduous forest ranges of Banka Forest Division, Bihar, India.

The phytosociological analysis was performed and C stock estimation based on volume determination through nondestructive methods was done.

Phytosociological analysis found total 18,888 [14,893 < 10 cm (diameter at breast height) dbh] and 2,855 (1,783 < 10 cm dbh) individuals at Banka and Bounsi range with basal area of 181,035.00 cm² and 32,743.76 cm², respectively. Importance value index was highest for Shorea robusta in both the ranges. Species diversity index and dominance index, 1.89 and 1.017 at Banka and 1.99 and 5.600 at Bounsi indicated the prevalence of biotic pressure. Decreased dbh and tree height resulted in a lowered growing stock volume as 59,140.40 cm³ ha⁻¹ (Banka) and 71,306.37 cm³ ha⁻¹ (Bounsi). Total C stock at Banka and Bounsi range was 51.8 t ha^-1 and 12.56 t ha⁻¹, respectively where the highest C stock is recorded for Shorea robusta in both the ranges (9.8 t ha⁻¹ and 2.54 t ha^-1, respectively). A positive correlation between volume, total biomass and basal area of tree species with C stock was observed. R² value for Banka range was 0.9269 (volume-C stock), 1 (total biomass-C stock) and 0.647 (basal area-C stock). Strong positive correlation was also established at Bounsi range with R² value of 1. Considering the total forest area enumerated, C sequestration potential was about 194.25 t CO₂ (Banka) and 45.9 t CO₂ (Bounsi). The valuation of C stock was therefore US$2,525.25 (Banka) and US$596.70 (Bounsi).

The research found the potentiality of the study area to sequester carbon. However, for future, the degraded areas would require intervention of management strategies for restoration of degraded lands and protection of planted trees to increase the carbon sequestration potential of the area.

Present study is the first attempt to assess the phytosociology and estimate the regulatory services of forest with respect to biomass and carbon stock estimation for the Banka forest division of Bihar.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The purpose of the study is to analyse the occurrence and distribution of different tree species in Gilgit-Baltistan, Pakistan, as a baseline for further inventories, and estimate the biomass per species and plot. Furthermore, it aims to measure forest biodiversity using established formulae for tree species diversity index, richness, evenness and accumulative curve.

Field data were collected, including stratification of forest sample plots. Statistical analysis of the data was carried out, and locally appropriate allometric equations were applied for biomass estimation.

Representative circular 556 forest sample plots of 1,000 m² contained 13,135 trees belonging to nine tree species with a total aboveground biomass of 12,887 tonnes. Sixty-eight per cent of the trees were found between 2,600 and 3,400 masl; approximately 63 per cent had a diameter at breast height equal to 30 cm, and 45 per cent were less than 12 m in height. The Shannon diversity index was 1.82, and Simpson’s index of diversity was 0.813.

Rough terrain, long distances, harsh weather conditions and location of forest in steep narrow valleys presented challenges for the field crews, and meant that fieldwork took longer than planned.

Estimating biomass in Gilgit-Baltistan’s forests using locally developed allometric equations will provide transparency in estimates of forest reference levels, National Forest Monitoring System in Pakistan and devising Reducing Emissions from Deforestation and Forest Degradation national strategies and for effective implementation.

This paper presents the first detailed forest inventory carried out for the dry temperate and semi-arid cold region of Gilgit-Baltistan, Pakistan.

The purpose of this study is to fill the gap and apply a fault tree analysis (FTA) in detention lists of Black Sea Region published port state reports from 2005 to 2016. The study analyzes valid records of 2,653 detained ships with 6,374 deficiencies based on a strategic management approach. This paper sets up FTA technique to assess the detention probability of a random ship which calls the Black Sea Region with the help of detention lists published within subject years.

This paper is not published elsewhere, and it is based on an original work, which figures out detention probability of a regular ship at Black Sea Region port state control from published lists of Black Sea Memorandum of Understanding (MoU). By utilizing these detention lists, a generic fault tree diagram is drawn. Those probabilities could be used strategically with the most seen deficiencies in the region which all could guide the users, rule makers and the controllers of the maritime system.

FTA has conducted based on the data which was collected from website of BS MoU detention lists that published from 2005 to 2016. Those lists have been published on monthly basis from 2011 to 2016 and on quarterly basis from 2005 to 2010. Proper detention records have been included into the research, whereas some missing records were excluded. Subject lists have been harmonized and rearranged according to Black Sea MoU Detention Codes which was published on October 2017 at Black Sea MoU’s website. According to BS MoU Annual Reports, 58,620 ships were inspected from 2005 to 2016 as seen in Table 1. Those ships were inspected by each member country’s PSOs in the light and guidance of predefined selection criteria of the region. Detention frequency of inspected ships detected as 0.103116 which explains any ship that called any port in the Black Sea Region could be 10% detained after inspected by PSO. Also, each intermediate event-calculated frequency enlightens the probabilities of nonconformities of ships. Although those deficiencies show structural safety and security nonconformities, those probabilities also prove us that management side of the ships are not enough to manage and apply a safety culture. By the light of that, ship owners/managers could see the general nonconformities according to regional records and could manage their fleet and each ship as per those necessities.

In the light of the above analysis, the future research on this subject could be studied on other regions which might enable a benchmark opportunity to users. Also, insurance underwriters have their own reports and publications that could clarify different points of view for merchant mariners and regulators. In this research, FTA is used as a main method to figure out the root causes of the detentions. For future researches, different qualitative and quantitative methods could be used under the direction of subjects.

Detention frequency of inspected ships detected as 0.103116 which explains any ship that called any port in the Black Sea Region could be 10% detained after inspected by PSO. Also, each intermediate event-calculated frequency enlightens the probabilities of nonconformities of ships. Although those deficiencies show structural safety and security nonconformities, those probabilities also prove us that management side of the ships are not enough to manage and apply safety culture. By the light of that, ship owners/managers could see the general nonconformities according to regional records and could manage their fleet and each ship as per those necessities.

With the nature of carriage, shipping business carry out its essential economic attendance in world trade system via inclusion in national and international transportation. As a catalyst in international trade, shipping itself enables time, place and economic benefits to users (Bosneagu, Coca and Sorescu, 2015). Social and institutional pressures generate shipping industry as one of the most regulated global industries which creates high complexity. Industry evolved to multi-directional structure ranges from international conventions (IMO and ILO) to “supra-national interferences” (EU directives), to regional guidance (MoUs) to national laws (flag states). Ship operators endeavor to adopt/fit its industry environment where rules are obvious. With adaptation of industrial environment, ship operators are able to create an important core competency.

This study enlightens the most recorded deficiencies and analyzed them with the help of fault three method. These calculated frequencies/probabilities show the most seen nonconformities and the root causes of detentions in the Black Sea Region in which those results will be benefited strategically that enables a holistic point of view that guide the owners/managers, charterers/sellers/shippers, classification societies, marine insurance underwriters, ship investors, third parties, rule makers and the controllers of the system to apply safety culture.