Determining the Identity of Corpses Using Fingerprints: Results from Practice and Analysis of Process Used in the Republic of Serbia

In today’s world, when there is a constant fight against organized crime and terrorism, when we have cases of mass accidents (plane crashes, train crashes, buses, etc.), the constant need for precise and quick identification of persons is evident in these cases. When we have situations with a large number of dead in various conditions, as well as complete or only parts of the body being on the spot, there is a need to use scientific and forensic methods in order to find out the reliable identity of these people. Furthermore, there is a need, in some cases, to identify persons who committed suicide, were killed, or died a natural death (accidental death) and who do not have documents according to which their identity can be determined. The aim of this paper will, however, be to identify a group of persons who need to be identified, known as unidentified corpses. Method. Describe and discuss the way of determining identity based on dactyloscopic data, which provides accurate and unambiguous identification, using fingerprints. Results. The identity was determined in 1271 cases of unidentified corpses by dactyloscopic comparison of fingerprints with a database containing fingerprints of about 8,000,000 indisputably identified persons. It was confirmed in 1139 cases. Conclusion. The high degree of identification in our research, as much as 89.6%, makes this method rightly represented as a standard method for confirming a person’s identity.

The “Global Change Data Base” GCDB Facilitates a Transition to Clean Energy and Sustainability

This article presents the opportunities for constructing a global data base picturing underlying trends that drive global climate change. Energy-related CO₂ emissions currently represent the key impact on climate change and thus become here the object of deep, long-term and historiographic analysis. In order to embrace all involved domains of technology, energy economy, fuel shares, economic efficacity, economic structure and population, a “Global Change Data Base” (GCDB) is suggested, based on earlier worldwide accepted data repositories. Such a GCDB works through regressions and statistical analysis of time series of data (on extensive magnitudes such as energy demand, population or Gross Domestic Product, GDP) as well as generation of derived data such as quotients of the former, yielding intensive magnitudes that describe systems and their structural properties. Moreover, the GCDB sets out to compute the first and second time derivatives of said magnitudes (and their percentual shares) which indicate new long-term developments already at very early phases. The invitation to participate in this foresight endeavour is extended to all readers. First preliminary GCDB results quantitatively portray the evolutionary structural global dynamics of economic growth, sectoral economic shifts, the shifts within energy carriers in various economic sectors, the ongoing improvements of energy intensity and energy efficiency in many economic sectors, and the structural changes within agricultural production and consumption systems.

A Lightweight Visual Navigation and Control Approach to the 2022 RoboMaster Intelligent UAV Championship

In this paper, an autonomous system is developed for drone racing. On account of their vast consumption of computing resources, the methods for visual navigation commonly employed are discarded, such as visual-inertial odometry (VIO) or simultaneous localization and mapping (SLAM). A series of navigation algorithms for autonomous drone racing, which can operate without the aid of the information on the external position, are proposed: one for lightweight gate detection, achieving gates detection with a frequency of 60 Hz; one for direct collision detection, seeking the maximum passability in-depth images. Besides, a velocity planner is adopted to generate velocity commands according to the results from visual navigation, which are enabled to perform a guidance role when the drone is approaching and passing through gates, assisting it in avoiding obstacles and searching for temporarily invisible gates. The approach proposed above has been demonstrated to successfully help our drone passing-through complex environments with a maximum speed of 2.5 m/s and ranked first at the 2022 RoboMaster Intelligent UAV Championship.

Analysis of a σ⁵⁴ Transcription Factor L420P Mutation in Context of Increased Organic Nitrogen Tolerance of Photofermentative Hydrogen Production in Cereibacter sphaeroides Strain 2.4.1 Substrain H2

Photofermentative hydrogen production with non-sulfur purple bacteria like Cereibacter sphaeroides (formerly Rhodobacter sphaeroides) is a promising and sustainable process to convert organic waste into the energy carrier hydrogen gas. However, this conversion is inhibited by elevated organic nitrogen concentrations in the substrate, which limits its applicability to nitrogen-poor organic waste. We present genomic and transcriptomic insights into a substrain of Cereibacter sphaeroides strain 2.4.1 that shows unexpected high levels of photofermentative hydrogen evolution when fed with glutamate. Genome sequencing revealed 222 single nucleotide variances (SNVs) between the reference genome of C. sphaeroides strain 2.4.1 and the analyzed substrain H2. These affect 61 protein coding genes. A leucine-proline exchange is present in the σ⁵⁴ factor (rpoN2 gene), a global hydrogen and nitrogen metabolism regulator. We propose a model how this mutation alters DNA-binding properties that explain the unexpected organic nitrogen tolerance of hydrogen production. Transcriptomic analyses under varying glutamate concentrations support this finding. Thus, we present the first thorough genomic and transcriptomic analysis of a Cereibacter strain that shows promising metabolic characteristics for biotechnological hydrogen gas production from organic waste. These results suggest a potential target for strain optimization. Possibly, our key finding can be transferred to other hydrogen producing microorganisms.

New Geographical (Im)materialities in Rural Spaces for a Renewed Countryside in the Global North. Some Key Comments in the Rural Geography Debate

From the point of view of the new (im)materialities and the relevance of vernacular house in the process of rural change and restructuring, this contribution comments some possible innovative ways of research in rural studies. The objective of the study is to bring the attention about the relevance of vernacular houses in the process of global rural change and restructuring and their particular expressions in localities and vernacular houses. The methodology in qualitatively based on auto-biographical and ethnographical research based in three houses of study in a marginal rural area of central Spain. The main conclusions suggest a process of hybridization of people and vernacular houses with two different circuits: new comers and traditional populations.

A Possible Reconstruction of Hominini Phylogeny

The Interplay between Experimental Data and Uncertainty Analysis in Quantifying CO₂ Trapping during Geological Carbon Storage

Numerical simulation is a widely used tool for studying CO₂ storage in porous media. It enables the representation of trapping mechanisms and CO₂ retention capacity. The complexity of the involved physicochemical phenomena necessitates multiphase flow, accurate fluid and rock property representation, and their interactions. These include CO₂ solubility, diffusion, relative permeabilities, capillary pressure hysteresis, and mineralization, all crucial in CO₂ trapping during carbon storage simulations. Experimental data is essential to ensure accurate quantification. However, due to the extensive data required, modeling under uncertainty is often needed to assess parameter impacts on CO₂ trapping and its interaction with geological properties like porosity and permeability. This work proposes a framework combining laboratory data and stochastic parameter distribution to map uncertainty in CO₂ retention over time. Published data representing solubility, residual trapping, and mineral trapping are used to calibrate prediction models. Geological property variations, like porosity and permeability, are coupled to quantify uncertainty. Results from a saline sandstone aquifer model demonstrate significant variation in CO₂ trapping, ranging from 17% (P10 estimate) to 56% (P90), emphasizing the importance of considering uncertainty in CO₂ storage projects. Quadratic response surfaces and Monte Carlo simulations accurately capture this uncertainty, resulting in calibrated models with an R-squared coefficient above 80%. In summary, this work provides a practical and comprehensive framework for studying CO₂ retention in porous media, addressing uncertainty through stochastic parameter distributions, and highlighting its importance in CO₂ storage projects. 

A Position-based Hybrid Routing Protocol for Clustered Flying Ad Hoc Networks

Unmanned aerial vehicles (UAVs) have been used to establish flying ad hoc networks (FANETs) to support wireless communication in various scenarios, from disaster situations to wireless coverage extensions. However, the operation of FANETs faces mobility, wireless network variations and topology challenges. Conventional mobile ad hoc network and vehicular ad hoc network routing concepts have rarely been applied to FANETs, and even then they have produced unsatisfactory performance due to additional challenges not found in such networks. For instance, position-based routing protocols have been applied in FANET, but have failed to achieve adequate performance in large networks. Clustering solutions have also been used in large networks, but with a significant overhead in keeping track of the complete topology. Hence, to solve this problem, we propose a hybrid position-based segment-by-segment routing mechanism for clustered FANETs. This approach facilitates traffic engineering across multiple wireless clusters by combining position-based inter-cluster routing with a rank-based intra-cluster routing approach capable of balancing traffic loads between alternative cluster heads. Simulation results show that our solution achieves, on average, a lower power consumption of 72.5 J, a higher throughput of 275 Mbps and a much lower routing overhead of 17.5% when compared to other state-of-the-art end-to-end routing approaches.

Plant Proteins Availability in Europe and Asia: A Causality Analysis of Climate, Demographics, and Economic Factors

The article examines the availability of plant-based proteins in Europe and Asia, considering the challenges posed by climate, demographics, and economics. The availability of these proteins is crucial given the growing impact of climate, economic, and social variables. Indeed, these factors play a decisive role in the production and accessibility of plant-based proteins across countries. The study employed a causality analysis method using regression models to determine the relative impact of these factors on protein availability. Two indicators were prioritized: total national production and the daily accessible quantity per person. This approach made it possible to construct hypothetical trajectories, showcasing the interrelations between the different variables. The results show that the availability of plant-based proteins varies across regions. Factors such as rising temperatures, increasing pollutants, and rising prices of plant proteins are particularly concerning. In this context, legumes appear as a promising alternative. They offer resilience against climatic variations while being an excellent protein source. The findings also encourage rethinking our consumption. Meat, with its significant ecological footprint, should see its consumption decrease in favor of plant-based proteins, ensuring a more sustainable diet. To facilitate this transition, the importance of appropriate public policies and incentives for producing and consuming plant proteins is emphasized.

The Future of Artificial Intelligence Will Be “Next to Normal”—A Perspective on Future Directions and the Psychology of AI Safety Concerns

This paper introduces the AI “next to normal”-thesis, suggesting that as Artificial Intelligence becomes more ingrained in our daily lives, it will transition from a sensationalized entity to a regular tool. However, this normalization has psychosocial implications, particularly when it comes to AI safety concerns. The “next to normal”-thesis proposes that AI will soon be perceived as a standard component of our technological interactions, with its sensationalized nature diminishing over time. As AI’s integration becomes more seamless, many users may not even recognize their interactions with AI systems. The paper delves into the psychology of AI safety concerns, discussing the “Mere Exposure Effect” and the “Black Box Effect”. While the former suggests that increased exposure to AI leads to a more positive perception, the latter highlights the unease stemming from not fully understanding its capabilities. These effects can be seen as two opposing forces shaping the public’s perception of the technology. The central claim of the thesis is that as AI progresses to become normal, human psychology will evolve alongside with it and safety concerns will diminish, which may have practical consequences. The paper concludes by discussing the implications of the “next to normal”-thesis and offers recommendations for the industry and policymakers, emphasizing the need for increased transparency, continuous education, robust regulation, and empirical research. The future of AI is envisioned as one that is seamlessly integrated into society, yet it is imperative to address the associated safety concerns proactively and not take the normalization effects take ahold of it.