From feces to data: A metabarcoding method for analyzing consumed and available prey in a bird‐insect food web

Diets play a key role in understanding trophic interactions. Knowing the actual structure of food webs contributes greatly to our understanding of biodiversity and ecosystem functioning. The research of prey preferences of different predators requires knowledge not only of the prey consumed, but also of what is available. In this study, we applied DNA metabarcoding to analyze the diet of 4 bird species (willow tits Poecile montanus, Siberian tits Poecile cinctus, great tits Parus major and blue tits Cyanistes caeruleus) by using the feces of nestlings. The availability of their assumed prey (Lepidoptera) was determined from feces of larvae (frass) collected from the main foraging habitat, birch (Betula spp.) canopy. We identified 53 prey species from the nestling feces, of which 11 (21%) were also detected from the frass samples (eight lepidopterans). Approximately 80% of identified prey species in the nestling feces represented lepidopterans, which is in line with the earlier studies on the parids' diet. A subsequent laboratory experiment showed a threshold for fecal sample size and the barcoding success, suggesting that the smallest frass samples do not contain enough larval DNA to be detected by high‐throughput sequencing. To summarize, we apply metabarcoding for the first time in a combined approach to identify available prey (through frass) and consumed prey (via nestling feces), expanding the scope and precision for future dietary studies on insectivorous birds.</p

Artificial Intelligence in Government Services: A Systematic Literature Review

The aim of this paper is to provide an overview on how artificial intelligence is shaping the digital era, in policy making and governmental terms. In doing so, it discloses new opportunities and discusses its implications to be considered by policy-makers. The research uses a systematic literature review, which includes more than one technique of data analysis in order to generate comprehensiveness and rich knowledge, we use: a bibliometric analysis and a content analysis. While artificial intelligence is identified as an extension of digital transformation, the results suggest the need to deepen scientific research in the fields of public administration, governmental law and business economics, areas where digital transformation still stands out from artificial intelligence. Although bringing together public and private sectors, to collaborate in the public service delivery, presents major advantages to policy makers, evidence has also shown the existence of negative effects of such collaboration.info:eu-repo/semantics/publishedVersio

Transport demand

Abstract The global community has witnessed a growing sense of urgency in addressing the pressing challenges stemming from climate change. Among industries most profoundly affected by this phenomenon are agriculture and forestry, i.e., the profound primary production sectors. Concurrently, the impact of these industries on climate change, as well as vice versa, the impact of climate change on the industries, has been a topic of considerable discussion for several years. One of the primary drivers of carbon emissions from these primary industries is energy consumption, not only during the production phase but also in the transportation of goods produced by these industries. Consequently, comprehending the scope and characteristics of transportation variables related to these products holds the potential to significantly benefit these industries by optimizing their logistics, identifying appropriate infrastructure investments, and in general allocating resources more effectively. This research applies a combination of qualitative and quantitative methodologies to enhance our understanding of the volume and nature of transportation variables, with cereals serving as the chosen product group, a case, for demonstrative purposes. Datasets and reports from institutions such as Luke, Tilastokeskus, and Eurostat have been scrutinized to accomplish the stated research objectives. The findings are as follows: The number of small agricultural enterprises and farms has been declining, but the total agricultural land has remained constant, suggesting that transportation impacts should still be anticipated. For instance, there are now fewer pick-up nodes and a higher volume of produce per production unit. Finland has been a prominent oat producer and exporter in Europe; however, Finnish farmers have been cultivating more barley than other cereal types. Consumption trends indicate an ongoing increase in oat demand, especially as arable areas shift northward due to climate change. Notably, 65% of domestic consumption is allocated to livestock feed, with 45% of this transported between farms without undergoing industrial processing. Human consumption accounts for 14% of domestic consumption. Furthermore, 17% of cereals produced are exported, significantly surpassing imports, rendering Finland a net cereal exporter rather than an importer. Over the last decade, the average journey length for trucks transporting cereals has been approximately 115 kilometers. Certain transportation indicators, such as transport volume and vehicle mileage, have exhibited substantial fluctuations, with 2019 witnessing a nearly threefold increase in transport volume and almost a twofold rise in vehicle mileage compared to the previous year. While the available dataset may not have provided a sufficiently solid foundation for in-depth analysis, preliminary estimates suggest a strong likelihood of interconnectedness between transport variables and trade, utilization, and production data. Consequently, it may be feasible to forecast transport demand based on data from preceding years, with the volume of transported cereals exhibiting a stronger correlation with data from the previous year than with data from the same year or two years prior. This finding may indicate that detailed, commodity level freight forecasts are possible, once the data is available

Sustainability of smart rural mobility and tourism:a key performance indicators-based approach

Abstract The concept of smart mobility and tourism has evolved from a technology-driven approach to one that focuses on sustainable solutions to address economic, social, and environmental issues. The United Nations (UN) sustainable development goals (SDGs) provide a framework for measuring and tracking progress toward sustainability goals. Key performance indicators (KPIs) are a useful tool for measuring and tracking progress towards these goals, allowing for continuous monitoring and evaluation of progress, identification of areas for improvement, and directing targeted interventions. This research aims to develop an indicators-based framework to evaluate the sustainability of smart and sustainable mobility and tourism in rural areas. Rural areas have often been neglected, or at least less prioritized, in the sustainability development of the mobility sector. The study also seeks to identify the overlap of KPIs between rural tourism and mobility, and how improved green mobility services can enhance sustainable rural tourism. Smart mobility and tourism indicators have a strong mutual relationship in rural communities, driving economic development, improving the quality of life for residents and visitors, and creating more sustainable and livable communities. Smart mobility and tourism indicators also play a crucial role in supporting the UN SDGs by providing data and insights that can inform policy and decision-making. The results of this research conclude how the target and performance setting of projects on sustainable mobility and tourism in rural communities support each other, and how they support achieving SDGs

Challenges for first-mile logistics in primary production

Abstract Primary production, particularly in the agriculture industry, has been and will be affected by climate change. Extreme weather conditions and their effects on the agriculture supply chain and agricultural field soil are highly evident and attract considerable media attention. Although the agriculture supply chain is scrutinized carefully, there is not enough focus on first-mile logistics. The first mile also affects the downstream stages of the supply chain. These project deliverables aim to enhance the understanding of the challenges of first-mile logistics in poor-bearing soils and vulnerable environments. It seeks to identify and elaborate on different challenges and proposes how agricultural machine and equipment providers might be able to provide solutions. This research applies both qualitative and quantitative methods. The scientific and gray literature and other sources are scanned to compile the challenges. The fault-tree type of analysis is carried out to categorize and structure the challenges. Next, the information value of the identified challenges is assessed by an evidence-scoring and ranking system. Industrial partners and other field experts are interviewed to ensure the validity of the findings. The quality function deployment (QFD) framework is proposed to link the identified challenges with industrial partners’ product features. Lastly, some arguments are provided in order to define the prerequisites of simulation and testing of soil–machine interaction on sensitive soils. Based on the interviews with company representatives, the report also re-examines the first-mile concept, redefines the sequential supply chain, and proposes a hybrid supply chain topology with circular transportation (between pre-harvest and post-harvest phases). Altogether, 93 challenges are identified and categorized into six different clusters, with subgroups. Through additional expert interviews, it is also found that the majority of the identified challenges are globally acknowledged, except soil compaction. Out of the 93 challenges, merely 24 are acknowledged as important by the interviewed industrial partners, which indicates different priorities of agri-machine business actors. Based on the QFD analysis, it is suggested that subsequent work packages of the LEVITOI project should enhance laboratory simulation and field-testing efforts to enable making products that cause as little soil compaction as possible in varying weather conditions

Spatial health and life sciences business ecosystem:a case study of San Diego

Abstract Purpose: Increasing competition in global markets requires many countries to seek new growth sectors. In addition, the nature of competition is changing. This paper applies the business ecosystem concept and studies San Diego as a spatial health and life sciences ecosystem. The purpose of this paper is to identify issues that should be considered in design of innovation policies and regional industry development. Design/methodology/approach: The research approach is built on a literature review of business ecosystems and spatial innovation. The empirical study is based on semi-structured interviews, observations, and information gathering and verification during field research. Findings: The results include a description of the ecosystem structure and dynamics. This paper demonstrates the bottom-up nature of San Diego’s health and life sciences ecosystem without a dominant lead actor, and presents prerequisites for fostering spatial ecosystems. Research limitations/implications: A single case may not be able to offer a generalized picture of this topic. However, the study raises several considerations for researchers and decision-makers involved in innovation policy design. Future work should extend the study and involve other spatial and substance contexts to compare findings and to pursue a more generic picture of innovation ecosystems and networks. Originality/value: This paper demonstrates that applying the concept of business ecosystems to the spatial context provides new insights in terms of dynamic mechanisms and factors contributing to economic growth in a particular location. Understanding how to facilitate the creation of successful spatial ecosystems is in the focal point of innovation policies

Building Value in ITS Services by Analysing Information Service Supply Chains and Value Attributes

ITS services need to be developed and there is potential for new business opportunities and investors, but the value of information must be assessed properly. This paper presents a framework of attributes that contribute to information value; the list is derived from the literature on information value and valuation. The attributes are discussed from the viewpoint of stakeholders in the service supply chain. A value asymmetry proposition is formulated and strategies to mitigate the asymmetry problem are discussed, introducing three basic strategies to tackle the problem

Deployment and analysis of Cooperative Intelligent Transport System pilot service alerts in real environment

Abstract The industry is providing vehicles with advanced features and technologies that allows vehicles to connect and communicate with their nearby environment. These technologies’ umbrella term, Cooperative Intelligent Transportation Systems (C-ITS) are aimed to enhance road traffic efficiency, safety and assist the drivers in multiple ways. The C-ITS communication system should be able to offer the functional benefits to different sets of use-cases, each having a particular sub-set of requirements. In this paper some of the relevant use-case scenarios utilizing road weather and traffic information are studied in terms of communication technology. The key requirements for C-ITS use-case scenarios are analyzed and an evaluation of the ITS-G5 protocol stack is performed. The performance of ITS-G5 in use-case scenarios is considered testing messages (alerts) on road weather and traffic information in realistic environments. The results indicate that the performance of ITS-G5 in tested use-case scenarios offers 90–98% success rate in the delivery of safety messages at a transmission frequency of 10Hz. Also, ITS-G5 delivers safety alerts with a minimum delay so that it satisfies the C-ITS use-case requirements in real environments. The C-ITS pilot platform also performs efficiently in terms of transmitting packets from a safe distance with minimum network latency and packet loss between vehicles and infrastructure. C-ITS pilot use-cases were tested on the platform developed and tailored by the Finnish Meteorological Institute (FMI)

Non-visual sensing of metallic pavement markers from a moving vehicle

Abstract Snow and ice covering paved surfaces cause problems for roads and other vehicles operating in cold regions. There are notable risks for road accidents and reduced performance of vehicle movements, e.g., in industrial facilities such as ports, when road markings and marked pathways are not visually observable. In this study, metallic paved surface markers identified by metal sensors are tested. The sound signals captured by a metal detector attached to the vehicle are used to detect the markings. Results indicate that the tested method for reading markings through snow is effective at lower speeds. The main advantages of the tested technology are effectiveness in snowy and icy surface conditions, low upfront cost detection technology in some environments, robust system without moving parts, high sampling rate, and low operating costs. The procedure is not restricted to specific environmental conditions, such as snow and ice; it also has the potential to detect markings through layers of mud, tree leaves or sand. The detectors have application prospects in intelligent transportation system applications at airports, logistics terminals and industrial facilities