Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.

This study introduces a novel Groundwater Flooding Risk Assessment (GFRA) model to evaluate risks associated with groundwater flooding (GF), a globally significant hazard often overshadowed by surface water flooding. GFRA utilizes a conditional probability function considering critical factors, including topography, ground slope, and land use-recharge to generate a risk assessment map. Additionally, the study evaluates the return period of GF events (GFRP) by fitting annual maxima of groundwater levels to probability distribution functions (PDFs). Approximately 57% of the pilot area falls within high and critical GF risk categories, encompassing residential and recreational areas. Urban sectors in the north and east, containing private buildings, public centers, and industrial structures, exhibit high risk, while developing areas and agricultural lands show low to moderate risk. This serves as an early warning for urban development policies. The Generalized Extreme Value (GEV) distribution effectively captures groundwater level fluctuations. According to the GFRP model, about 21% of the area, predominantly in the city’s northeast, has over 50% probability of GF exceedance (1 to 2-year return period). Urban outskirts show higher return values (> 10 years). The model’s predictions align with recorded flood events (90% correspondence). This approach offers valuable insights into GF threats for vulnerable locations and aids proactive planning and management to enhance urban resilience and sustainability.

This paper presents a quantitative exploration of the functionality of cost accounting systems and their determinants in social welfare organizations. We conducted a questionnaire survey of managers of social welfare organizations running special nursing homes for the elderly and conducted a cluster analysis based on the data collected. The questionnaire was created based on the scales used in previous studies, with some new scales developed. For data analysis, the statistical analysis environment R was used. The clValid package of R was used to assess the validity of the cluster analysis. Based on the results of the analysis in this paper, it is expected that social welfare organizations that pursue cost leadership strategies and have a strong public interest orientation will benefit greatly by being able to utilize a highly functional cost accounting system. Such organizations will be able to improve their business efficiency by utilizing cost information, and their social contribution activities based on the resulting resources will truly be a contribution to public welfare. The findings from this study are of practical significance because they can be used by business managers of social welfare organizations to review the functionality of their cost accounting systems. We also focus on the degree to which nonprofit organizations focus on social contribution activities (in this paper, we call this public interest orientation). The public interest orientation of an organization is thought to affect the functionality of the cost accounting system in the same way as the organization’s strategy, but there has not been enough quantitative research on this point. By focusing on the public interest orientation of social welfare organizations, this study contributes to deepening our knowledge in this area.

Illegal, unreported, and unregulated fishing (IUU fishing) crimes by rogue fisheries companies are rife in the sea waters of Riau Province. However, this issue is rarely reported by those provincial journalists in the online media where they work. In fact, in Riau, there are 163 online media companies and 600 competent journalists; 200 of them live in capture fisheries center areas. Apart from the journalist competency factor, the decision to make IUU fishing news can also be influenced by the fisheries company intervention that committed the crime. Besides, the policy role of media leaders—editors, editors-in-chief, and media owners—also determines journalists’ decisions to make those news stories. This research aims to analyze the influence of journalist competence and fishing company intervention on the decision to make IUU fishing news, as well as the role of media leader policy as mediators in these influences. This survey involved 100 competent journalists as respondents. Data collection was carried out through a questionnaire containing a number of closed statements measured on a 5-point Likert scale, which was distributed to respondents. The data were analyzed using the Structural Equation Modeling (SEM) method. The research results show that the fishing company intervention has a negative and significant influence on the decision to make IUU fishing news in Riau, while journalist competence does not. Additionally, media leader policy was found to play a significant role in mediating the influence of fisheries company intervention and journalist competence on the decision to make IUU fishing news. The leader policy could prevent journalists from making IUU fishing news if fisheries companies, who are responsible for those crimes, intervene and request it. Those actions of media leaders need to be questioned because they can hamper the media’s function as a means of disseminating information, educating the public, and implementing social control, especially those related to combating IUU fishing crimes.

Natural forests and abandoned agricultural lands are increasingly replaced by monospecific forest plantations that have poor capacity to support biodiversity and ecosystem services. Natural forests harbour plants belonging to different mycorrhiza types that differ in their microbiome and carbon and nutrient cycling properties. Here we describe the MycoPhylo field experiment that encompasses 116 woody plant species from three mycorrhiza types and 237 plots, with plant diversity and mycorrhiza type diversity ranging from one to four and one to three per plot, respectively. The MycoPhylo experiment enables us to test hypotheses about the plant species, species diversity, mycorrhiza type, and mycorrhiza type diversity effects and their phylogenetic context on soil microbial diversity and functioning and soil processes. Alongside with other experiments in the TreeDivNet consortium, MycoPhylo will contribute to our understanding of the tree diversity effects on soil biodiversity and ecosystem functioning across biomes, especially from the mycorrhiza type and phylogenetic conservatism perspectives.

This article describes a classification tool to cluster SARAL/AltiKa waveforms. The tool was made using Python scripts. Radar altimetry systems (e.g., SARAL/AltiKa) measures the distance from the satellite centre to a target surface by calculating the satellite-to-surface round-trip time of a radar pulse. An altimeter waveform represents the energy reflected by the earth’s surface to the satellite antenna with respect to time. The tool clusters the altimetric waveforms data into desired groups. For the clustering, we used evolutionary minimize indexing function (EMIF) with k-means cluster mechanism. The idea was to develop a simple interface which takes the altimetry waveforms data from a folder as inputs and provides single value (using EMIF algorithm) for each waveform. These values are further used for clustering. This is a simple light weighted tool and user can easily interact with it.