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This paper inspects in detail the seasonality (deterministic) in container freight rates, and compares seasonality patterns in different freight rate indices. A deterministic seasonality unit root test is performed to achieve set objectives. This study concludes that all the indices (tested in this paper) exhibit significant deterministic seasonality. For January and August, there is no seasonal effect observed in all five series. At the same time, all the indices except Exports from Europe Rate Index (EEI) exhibit significant seasonal patterns in February, September, and December. All five indices exhibit significant seasonality during May, and the coefficient sign shows a drop in the freight rates. During March, October, and November; it is observed that only EEI exhibit significant seasonal patterns. The results could be beneficial for carriers and agents who are involved in the containerised freight transport business. Also, shippers could get a clear idea about the freight rates' nature across various trade routes.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

In view of the consensus that climate change is happening, scientists have documented several findings about Uganda’s recent climate, as well as its variability and change. The purpose of this study is to review what has been documented, thus it gives an overview of what is known and seeks to explain the implications of a changing climate, hence what ought to be known to create a climate resilient environment.

Terms such as “climate”, “climate change” and “climate variability” were identified in recent peer-reviewed published literature to find recent climate-related literature on Uganda. Findings from independent researchers and consultants are incorporated. Data obtained from rainfall and temperature observations and from COSMO-CLM Regional Climate Model-Coordinated Regional Climate Downscaling Experiment (CCLM CORDEX) data, European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) data and Global Precipitation Climatology Centre (GPCC) have been used to generate spatial maps, seasonal outputs and projections using GrADS 2.02 and Geographic Information System (GIS) software for visualization.

The climate of Uganda is tropical in nature and influenced by the Inter-Tropical Convergence Zone (ITCZ), varied relief, geo-location and inland lakes, among other factors. The impacts of severe weather and climate trends and variability have been documented substantially in the past 20-30 years. Most studies indicated a rainfall decline. Daily maximum and minimum temperatures are on the rise, while projections indicate a decrease in rainfall and increase in temperature both in the near and far future. The implication of these changes on society and the economy are discussed herein. Cost of inaction is expected to become huge, given factors like, the growing rate of the population and the slow expanding economy experienced in Uganda. Varied forms of adaptation to the impacts of climate change are being implemented, especially in the agricultural sector and at house hold level, though not systematically.

This review of scientific research findings aims to create a better understanding of the recent climate change and variability in Uganda and provides a baseline of summarized information for use in future research and actions.

This study aims to develop an econometric analysis of how modern agriculture can be a fundamental instrument for reducing the levels of multidimensional poverty in Uganda. It demonstrates the importance of agriculture in reducing inequalities amongst the poor while focusing on the relationship between increasing productions from modern agricultural practices and the poverty level across the country.

The study explores Box–Jenkins approach to cereal production data with the use of econometric analysis as the main tool to determine the implications of modern agricultural practices in Uganda. Most poor people around the world are in marginalized rural environments, and agriculture provides for their livelihoods. This makes agricultural development crucial for reducing multidimensional poverty on a large scale and needs development within agriculture to be enhanced. Education, health and standard of living are the three dimensions considered from the weighted indicators, amounting to 30%, to be categorized poor in the three dimensions.

Modernization of agriculture is an ultimate solution to multidimensional poverty reduction in Uganda through employment generation and the effects of food prices. Shreds of evidence support the theories that agricultural incomes together with the actual wages increase with a general rise in the rural non-agricultural economy. Results depict a close correlation between national income and GDP per capita which is a very significant indication that more application of agricultural technology would lead to a sub sequential improvement of livelihoods engaged in agricultural practices.

Agriculture remains a vital sector that employs a greater portion of the population in Uganda’s economy. Major roles have been played by the sector in the economy including employment opportunities, rural household incomes, food supplies and a reduction in poverty from a multidimensional front. Exploring the behavior of poverty level using modern agriculture as an indicator and its relationship with the poverty level arising from improved agricultural practices could provide a meaningful display of variation in poverty across the regions at the country level.

The purpose of this study is to evaluate the vulnerability of the important rangeland shrub, Atriplex leucoclada (Boiss) to both climate change and livestock grazing, within the Syrian rangelands as a representative landscape type of West Asia.

Ecologically based quantitative niche models were developed for both shrub species using maximum entropy and 13 spatially explicit GIS-based layers to predict current and future species distribution scenarios. Climatic variables varied over time in line with the predictions created from the HADCM3 global circulation model.

Results indicate that with grazing and climate change, the distribution of A. leucoclada will be reduced by 54 per cent in 2050, with the mean annual and minimum temperatures of the coldest month having the highest contribution in the model (28.7 and 21.2 per cent, respectively). The contribution of the grazing pressure, expressed by the overgrazing index, was estimated at 8.2 per cent.

These results suggest that the interaction of climate and increased grazing has the potential to favor the establishment of unpalatable species, while reducing the distribution of preferred plant species on western Asia rangelands.

The purpose of this research is to identify the current reverse logistics practices adopted by soft drink companies and the prominent factors which can decide the efficiency and effectiveness of the entire process of the reverse logistics channel. The paper employs Pareto analysis and the Analytical Hierarchy Process (AHP) method on data collected from logistics professionals involved in the software industry in Sri Lanka using two questionnaires. As the prominent factors, transportation, accidents, packaging, a method of storage, the cleaning process and sorting process was identified and the first four prominent factors have a higher influence on both measures of efficiency and effectiveness. They can also identify the external factors which can emerge inefficiencies due to outsourced dealers. Lack of previous literature on the subject matter and the difficulty to access the filed data were the main limitations of this study. The identified factors will help to identify the correct root causes for the inefficiencies of the current reverse logistics practices and concentrating on these factors will give an opportunity for the soft drink industry players to successfully implement a sustainable green supply chain which reduces waste at each stage of its forwards and reverse logistics process. Transportation, Accidents, Packaging, and Storage have been previously identified as considerations in reverse logistics processes and the current study showed that they have a higher impact on both efficiency and effectiveness on reverse logistics and these factors should be given specific consideration while in the operations.

This paper brings focus and attention to the aspect of time within health information behaviour. The purpose of this paper is to critically assess and present strengths and weaknesses of utilising the infodemiology approach and metrics as a novel way to examine temporal variations and patterns of online health information behaviour. The approach is shortly exemplified by presenting empirical evidence for temporal patterns of health information behaviour on different time-scales.

A short review of online health information behaviour is presented and methodological barriers to studying the temporal nature of this behaviour are emphasised. To exemplify how the infodemiology approach and metrics can be utilised to examine temporal patterns, and to test the hypothesis of existing rhythmicity of health information behaviour, a brief analysis of longitudinal data from a large discussion forum is analysed.

Clear evidence of robust temporal patterns and variations of online health information behaviour are shown. The paper highlights that focussing on time and the question of when people engage in health information behaviour can have significant consequences.

Studying temporal patterns and trends for health information behaviour can help in creating optimal interventions and health promotion campaigns at optimal times. This can be highly beneficial for positive health outcomes.

A new methodological approach to study online health information behaviour from a temporal perspective, a phenomenon that has previously been neglected, is presented. Providing evidence for rhythmicity can complement existing epidemiological data for a more holistic picture of health and diseases, and their behavioural aspects.

The farmers used several information sources to gather information about the climatic variability and modern agricultural practices to cope with climate change. The choice of adaptation strategies and the successful implication of adaptation strategies depend on accurate, timely information on the climate variability and precise technical details of adaptation strategies. By keeping the importance of climate information and agricultural extension information in the center, this study aims to conduct a micro-level evaluation of farmers’ choice of climate information, agriculture extension services and agricultural credit sources. This study’s primary objective is to understand how the different sources of climate information and agricultural extension influence farm household adaptation decisions.

This study has been conducted in three subs agro-climatic zone of the Middle Gangetic Plain region, which falls in India’s Bihar state. This paper has randomly selected seven districts from these three subs agro-climatic zone to collect the data. The analysis of this study is based on survey data collected from 700 farm households. This study has used descriptive statistics and a logistic regression model to assess the sources of climate information, agricultural extension and credit sources and how these sources influence farm households’ adaptation decisions.

The result of this study shows farmers are using different traditional (sharing experience, newspaper and radio), information and communication technology (mobile and TV) and institutional arrangements (agricultural officer and meteorological department) in the study area. The study’s finding identifies different farm households’ different sources and how these options farming farmers’ adaptation decisions. The study further revealed that institutional factors such as extension services and access to information on climate change increase the probability of adopting knowledge-intensive adaptation strategies such as soil conservation, water conservation, crop insurance and planting horticulture and vegetables.

The study has conducted a micro-level assessment of adaptation behavior at the local level to understand the factor influencing the adaptation decision. This study’s finding is useful in designing the appropriate policy framework for the farm household’s capacity building to enhance their technical skills and awareness toward the institutional arrangements.

This paper’s finding pointed out institutional arrangements’ requirement to improve adaptive capacity to make long-term strategic decisions to cope with climate change.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Between 2006 and 2011, Nicaragua shipped an average of US$9.4 million per year of smallholder-produced fresh taro (Colocasia esculenta) to the USA; however, by 2016, the US market for Nicaraguan taro had effectively collapsed. The purpose of this paper is to analyze the short-lived taro boom from the perspective of complex adaptive systems, showing how shocks, interactions between value chain actors, and lack of adaptive capacity among chain actors together contributed to the collapse of the chain.

Primary data were collected from businesses and smallholders in 2010 and 2016 to understand the actors involved, their business relations, and the benefits and setbacks they experienced along the way.

The results show the capacity of better-off smallholders to engage in a demanding market, but also the struggles faced by more vulnerable smallholders to build new production systems and respond to internal and external shocks. Local businesses were generally unprepared for the uncertainties inherent in fresh horticultural trade or for engagement with distant buyers.

Existing guides and tools for designing value chain interventions will benefit from greater attention to the circumstances of local actors and the challenges of building productive inter-business relations under higher levels of risk and uncertainty.

This case serves as a wake-up call for practitioners, donors, researchers, and the private sector on how to identify market opportunities and the design of more robust strategies to respond to them.