Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging science and investigative practice The application of social network analysis in criminal investigation

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Explorer Novel Aspects in Pattern Formation Arise from Coupling Turing Reaction-Diffusion and Chemotaxis

Recent experimental studies on primary hair follicle formation and feather bud morphogenesis indicate a coupling between Turing-type diffusion driven instability and chemotactic patterning. Inspired by these ﬁndings we develop and analyse a mathematical model that couples chemotaxis to a reaction–diffusion system exhibiting diffusion–driven (Turing) instability. While both systems, reaction–diffusion systems and chemotaxis, can independently generate spatial patterns, we were interested in how the coupling impacts the stability of the system, parameter region for patterning, pattern geometry, as well as the dynamics of pattern formation. We conduct a classical linear stability analysis for different model structures, and conﬁrm our results by numerical analysis of the system. Our results show that the coupling generally increases the robustness of the patterning process by enlarging the pattern region in the parameter space. Concerning time scale and pattern regularity, we ﬁnd that an increase in the chemosensitivity can speed up the patterning process for parameters inside and outside of the Turing space, but generally reduces spatial regularity of the pattern. Interestingly, our analysis indicates that pattern formation can also occur when neither the Turing nor the chemotaxis system can independently generate pattern. On the other hand, for some parameter settings, the coupling of the two processes can extinguish the pattern formation, rather than reinforce it. These theoretical ﬁndings can be used to corroborate the biological ﬁndings on morphogenesis and guide future
Compact EEG Features for Person Identification

In the present work, we consider the opportunity for person identification from multichannel EEG signals. Specifically, we propose a method for designing compressed characteristic features representing the aggregated EEG activity for a selected period of time. The features are obtained as the averaged values of the relations of activity between specific EEG electrodes, using different sizes of the aggregation window. Twelve sets of these features, designed through the proposed method, were evaluated in a common experimental protocol based on the well-known DEAP database to assess the practical applicability when combined with typical classifiers. The experimental results validate the practical significance of the proposed method for developing intelligent HMIs.
Empowering Firefighting Robots with loT for Proactive Area Surveillance and Fire Management

The goal of this article is to describe the development of a robot capable of assisting in surveillance across various sectors, especially in fire detection and isolation, using Internet of Things (loT) technologies. The robot incorporates multiple sensors, including temperature, humidity, and motion sensors. It is equipped with a camera and a microphone to capture and transmit data instantly to a central control system. The system then processes the data to identify any anomalies or potential security hazards and alerts the relevant authorities. The primary benefit of this initiative is its ability to provide continuous monitoring in areas that are challenging for human surveillance, such as construction sites, warehouses, or expansive outdoor locations. The robot can operate autonomously, navigate obstacles, and collect data from different points, making it an effective and reliable solution for security purposes. Additionally, it can be utilized to control small fires using a water pump.
A Performance Study: Convolutional Deep Belief Networks and Convolutional Neural Networks for Audio Classification

This paper presents an innovative approach for supervised audio classification using Convolutional Deep Belief Networks (CDBN). Using advanced Digital Signal Processing techniques, the method efficiently extracts salient features from audio signals, enabling the training of a robust Convolutional Deep Belief Network (CDBN). The performance of the proposed method is then evaluated and compared with that of Convolutional Neural Network(CNNs)-based audio classifiers trained on the same labeled data. The paper rigorously evaluates the pro-posed method against annotated data and different MFCC tuning parameters, showing its performance on par with advanced CNN-based audio classifiers. A remarkable accuracy of 94.05 % was achieved for the DBN model compared to 94.05% by the CNN model. The effect of changing various MFCC parameters on the model performance is also shown. Additionally, the paper analyzes the pros and cons of Deep Belief Networks for audio classification, offering valuable insights for future research in this field.
Results of stress analysis inferred from fault slip data along the Sudetic Marginal Fault (NE part of Bohemian Massif)

The Sudetic Marginal Fault (SMF) separates the Fore-Sudetic block on the NE and the Sudetic block on the SW of Bohemian Massif. The fault slip data along the SMF yielded two diff erent subhorizontal orientations of the maximum stress axes. The NE–SW oriented compression was computed in the Fore-Sudetic block, while the NNW–SSE compression is recorded in the Sudetic block. The faults are predominantly oriented in the NE–SW as well as in NW–SE direction. The dextral and sinistral sense of movement was depicted. The stress fi elds have operated most probably since Miocene to present day. The SMF most probably represents a barrier between the two diff erent tectonic stress regimes.
Digital Cognitive Assessment Tests for Older Adults: Systematic Literature Review

Background The global health pandemic has affected the increasing older adult population, especially those with mental illnesses. It is necessary to prevent cases of cognitive impairment in adults early on, and this requires the support of information and communication technologies for evaluating and training cognitive functions. This can be achieved through computer applications designed for cognitive assessment. Objective In this review, we aimed to assess the state of the art of the current platforms and digital test applications for cognitive evaluation, with a focus on older adults. Methods A systematic literature search was conducted on 3 databases (Web of Science, PubMed, and Scopus) to retrieve recent articles on the applications of digital tests for cognitive assessment and analyze them based on the methodology used. Four research questions were considered. Through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, following the application of inclusion and exclusion criteria, a total of 20 articles were finally reviewed. Results Some gaps and trends were identified regarding the types of digital applications and technologies used, the evaluated effects on cognitive domains, and the psychometric parameters and personal characteristics considered for validation. Conclusions Computerized tests (similar to paper-and-pencil tests) and test batteries (on computers, tablets, or web platforms) were the predominant types of assessments. Initial studies with simulators, virtual environments, and daily-life activity games were also conducted. Diverse validation methods and psychometric properties were observed; however, there was a lack of evaluations that involved specific populations with diverse education levels, cultures, and degrees of technology acceptance. In addition, these evaluations should consider emotional and usability aspects.
The frailty phenotype and its association with all-cause mortality in community-living Norwegian women and men aged 70 years and older: The Tromsø Study 2001-2016.

Aim: There is a lack of studies on frailty prevalence and the association between frailty and mortality in a Norwegian general population. Findings regarding sex differences in the association between frailty and mortality have been inconsistent. The aim of this study was to investigate the association between the frailty phenotype and all-cause mortality in men and women in a Norwegian cohort study. Methods: We followed 712 participants (52% women) aged 70 years and older participating in the population-based Tromsø 5 Study in 2001-02 for all-cause mortality up to 2016. The frailty status at baseline was defined by a modified version of Fried’s frailty criteria. Cox regression models were used to analyze the association between frailty and mortality with adjustment for age, sex, disability, comorbidity, smoking status and years of education. Results: In total, 3.8% (n=27) of participants were frail (women: 4.4%, men: 3.2%) and 38.1% (n=271) were pre-frail (women: 45.8%, men: 29.9%). During follow-up (mean 10.1 years), 501 (70%) participants died. We found an increased risk of mortality for frail elderly (multivariate-adjusted HR 4.16 (95% CI 2.40, 7.22)) compared to non-frail elderly. In sex-stratified analysis the adjusted HR was 7.09 (95% CI 3.03, 16.58) for frail men and 2.93 (95% CI 1.38, 6.22) for frail women. Results for pre-frailty showed an overall weaker association with mortality. Conclusions: While frailty was more prevalent in women than in men, the findings suggest that the association between frailty and mortality is stronger in men than in women.
Seismic evaluation of the Flemish Cap and Goban Spur pre-rift relationship prior to the opening of the North Atlantic, with support from plate reconstruction

Recent geophysical investigations of the NE Newfoundland-Irish Atlantic rifted margin pair (North Atlantic) have called into question the previously assumed conjugate relationship and rift-perpendicular extension between the Flemish Cap and Goban Spur. In this study, we present multichannel seismic reflection profiles across the margins of the Flemish Cap, Porcupine Bank, and Goban Spur, and define their structural domains (proximal, 10 necking, hyperextended, and/or exhumed mantle domains), which display varying degrees of asymmetry along strike. Observed intra-crustal reflectivity patterns are interpreted to be related to Paleozoic orogeny and/or orogenic collapse prior to the major Mesozoic rifting, consistent with this rift system having been strongly influenced by inherited Appalachian-Caledonian and Variscan structures. Reflective features within each structural domain are strikingly different across both margins, however similarities are most pronounced for the necking domains of the 15 Porcupine Bank and Flemish Cap, which is consistent with a connection between the Porcupine Bank and Flemish Cap during Early Jurassic rifting. This inference is compatible with recently published deformable plate reconstruction models, which are used herein to relocate and reconstruct the representative seismic sections back through time to the onset of rifting. The changes of paleo-positions and geometric shapes of the seismic sections in deformable plate reconstruction models over time further show complicated 3D plate kinematics for the NE 20 Newfoundland-Porcupine Atlantic margins, highlighting interplay of inherited structures, oblique extension, and poly-phase margin evolution between the Porcupine Bank, Goban Spur, and Flemish Cap and their intervening rift basins during the southern North Atlantic opening.
Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Multi-cost Job Routing and Scheduling in Grid Networks

A key problem in Grid networks is how to efficiently manage the available infrastructure, in order to satisfy user requirements and maximize resource utilization. This is in large part influenced by the algorithms responsible for the routing of data and the scheduling of tasks. In this paper, we present several multi-cost algorithms for the joint scheduling of the communication and computation resources that will be used by a Grid task. We propose a multi-cost scheme of polynomial complexity that performs immediate reservations and selects the computation resource to execute the task and determines the path to route the input data. Furthermore, we introduce multi-cost algorithms that perform advance reservations and thus also find the starting times for the data transmission and the task execution. We initially present an optimal scheme of non-polynomial complexity and by appropriately pruning the set of candidate paths we also give a heuristic algorithm of polynomial complexity. Our performance results indicate that in a Grid network in which tasks are either CPU-or data-intensive (or both), it is beneficial for the scheduling algorithm to jointly consider the computational and communication problems. A comparison between immediate and advance reservation schemes shows the trade-offs with respect to task blocking probability, end-to-end delay and the complexity of the algorithms.
Morphophylogenetic evidence reveals four new fungal species within Tetraplosphaeriaceae (Pleosporales, Ascomycota) from tropical and subtropical forest in China

﻿Abstract Tetraplosphaeriaceae (Pleosporales, Ascomycota) is a family with many saprobes recorded from various hosts, especially bamboo and grasses. During a taxonomic investigation of microfungi in tropical and subtropical forest regions of Guizhou, Hainan and Yunnan provinces, China, several plant samples were collected and examined for fungi. Four newly discovered species are described based on morphology and evolutionary relationships with their allies inferred from phylogenetic analyses derived from a combined dataset of LSU, ITS, SSU, and tub2 DNA sequence data. Detailed illustrations, descriptions and taxonomic notes are provided for each species. The four new species of Tetraplosphaeriaceae reported herein are Polyplosphaeriaguizhouensis, Polyplosphaeriahainanensis, Pseudotetraploayunnanensis, and Tetraploahainanensis. A checklist of Tetraplosphaeriaceae species with available details on their ecology is also provided.
Explorer Effect of genotyping strategies on the sustained benefit of single-step genomic BLUP over multiple generations

Background: Single‑step genomic best linear unbiased prediction (ssGBLUP) allows the inclusion of information from genotyped and ungenotyped individuals in a single analysis. This avoids the need to genotype all candidates with the potential benefit of reducing overall costs. The aim of this study was to assess the effect of genotyping strategies, the proportion of genotyped candidates and the genotyping criterion to rank candidates to be genotyped, when using ssGBLUP evaluation. A simulation study was carried out assuming selection over several discrete genera‑ tions where a proportion of the candidates were genotyped and evaluation was done using ssGBLUP. The scenarios compared were: (i) three genotyping strategies defined by their protocol for choosing candidates to be genotyped (RANDOM: candidates were chosen at random; TOP: candidates with the best genotyping criterion were genotyped; and EXTREME: candidates with the best and worse criterion were genotyped); (ii) eight proportions of genotyped candidates ( p ); and (iii) two genotyping criteria to rank candidates to be genotyped (candidates’ own phenotype or estimated breeding values). The criteria of the comparison were the cumulated gain and reliability of the genomic estimated breeding values (GEBV). Results: The genotyping strategy with the greatest cumulated gain was TOP followed by RANDOM, with EXTREME behaving as RANDOM at low p and as TOP with high p . However, the reliability of GEBV was higher with RANDOM than with TOP. This disparity between the trend of the gain and the reliability is due to the TOP scheme genotyping the candidates with the greater chances of being
Learning to merge search results for efficient Distributed Information Retrieval

Merging search results from different servers is a major problem in Distributed Information Retrieval. We used Regression-SVM and Ranking-SVM which would learn a function that merges results based on information that is readily available: i.e. the ranks, titles, summaries and URLs contained in the results pages. By not downloading additional information, such as the full document, we decrease bandwidth usage. CORI and Round Robin merging were used as our baselines; surprisingly, our results show that the SVM-methods do not improve over those baselines.
A Role of Triangular Fuzzy Neutrosophic Numbers in Solving Neutrosophic Transportation Problem

—The transportation problem (TP) is a prominent kind of linear programming problem (LPP) in which it is necessary to transfer goods from several sources to several destinations while minimizing the overall cost of transportation is its objective. Because of its many practical uses, it’s a popular tool in operation research. The utilization of neutro-sophic sets (NS) to analyze and resolve diverse decision-making challenges has quickly gained popularity. As a consequence, the neutrosophic theory is progressively being the subject of several current research investigations. In this study, we assess the TP in a neutrosophic setting, where the neutrosophic transportation problem (NTP) is expressed as a table called the neutrosophic transportation table, with triangular fuzzy neutrosophic numbers as its core elements. We make use of a score function to convert the triangular fuzzy neutrosophic values to their equivalent crisp numbers, followed by a stepwise methodology of the proposed approach to obtain the optimal solution. The outcomes are then compared with the previously acquired solutions to demonstrate the method’s effectiveness.
Environments

Location-based mobile services have been in use, and studied, for a long time. With the proliferation of wireless networking technologies, users are mostly interested in advanced services that render the surrounding environment (i.e., the building) highly intelligent and significantly facilitate their activities. In this paper our focus is on indoor navigation, one of the most important location services. Existing approaches for indoor navigation are driven by geometric information and neglect important aspects, such as the semantics of space and user capabilities and context. The derived applications are not intelligent enough to catalytically contribute to the pervasive computing vision. In this paper, a novel navigation mechanism is introduced. Such navigation scheme is enriched with user profiles and the adoption of an ontological framework. These enhancements introduce a series of technical challenges that are extensively discussed throughout the paper.
Motion planning for humanoid robots in environments modeled by vision

The context of this work is vision-based motion planning for humanoid robots in an unknown environment. We present an efficient combination of on-line 3D environment modeling and motion planning methods for humanoid robots (e.g whole body motion and walking ) in non-static environment. To construct the model of the environment, we rely on 3D occupancy grid that is updated incrementally by stereo vision. As the dimension of configuration space is high for humanoid robots, a roadmap based method is used for motion planning. However, as the environment is not static, it is necessary to update the roadmap after receiving new visual information. In other words, the nodes and edges which are in collision, based on the new update of the environment, must be erased. Moreover, preliminary steps are necessary for considering the environment as a non-static model. As we construct the model incrementally by vision, several thousands points would appear in each update of the model. Therefore, updating the roadmap raises algorithmic complexity issues. Our approach is an extension of a recent idea to cope with the problem. After presenting the approach, we implement our method by planning a collision-free motion in a 3D occupancy grid model generated by HRP2 based on stereo vision. As the robot navigates in the environment, it receives updated information through its on-board cameras and refreshes the 3D model of the environment incrementally. Conventionally, the 3D model can be composed of up to millions of voxel. If the statuses of some voxels change, our method uses these changes to update the last roadmap locally. This updated roadmap is then reused for further motion planning. We evaluate our algorithm by measuring processing time and memory usage in each step and compare them with its descendant.
Tacit Knowledge Loss in South African State-Owned Companies

: This study is an interdisciplinary research that sought to explore knowledge loss risks interdependently in relation to the knowledge management (KM) and human resource management (HRM) disciplines in South African state-owned companies (SOCs). This study highlighted the significant role of knowledge-based HRM systems, which will naturally enhance the management capacity of tacit knowledge to mitigate risks and assist SOCs to deliver on a developmental mandate, as well as ensure their survival and the economic growth of developing nations. The paper aims to explore the connection between KM and HRM practices for the effective management of enterprise tacit knowledge loss risks using the SOCs of South Africa as a case. This study is a mixed methods research project, using the exploratory sequential design as a research strategy. Data were collected qualitatively through interviews with 20 purposively nominated human resource managers, and survey data was collected randomly from 25% (145 of the 585 responses) of the workers employed in SOCs. Qualitative data were analysed thematically using Atlas.ti and Statistical data were analysed using SAS and exploratory factor analysis. The relationship between HRM and KM practices in lessening the risks inherent in tacit knowledge loss, mainly from voluntary turnover, is underdeveloped in many South African SOCs. The study established that the role of HRM in supporting KM is limited and underdeveloped in many SOCs, largely because they are lagging in key structures and knowledge-centric cultures, behaviours and HRM practices to drive the effective management of company knowledge. This study assists HRM executives and KM practitioners to develop knowledge-driven HRM practices in order to mitigate risks from the loss of tacit knowledge, whilst ensuring sustained performance in public enterprises. The study and proposed strategies seek to influence the discourse, policies, practices and theories on KM and HRM praxis in companies to mitigate the risks associated with the loss of tacit knowledge.
Marine Mammals in a Changing World

Planet Earth is the most unusual one among the known planets since 71% of its surface is covered by oceans full of life. Life originated and diversifi ed in the oceans along millions of years. At present, the oceans are largely responsible for sustaining planet life and in regulating the climate (IUCN 2009, Hoegh-Guldberg and Bruno 2010). Climate change is thoroughly accepted as a global concern (IPCC 2007). Rising atmospheric gas concentrations have increased global average temperatures by approximately 0.2°C per decade over the past 30 years
Priority threat management for imperilled species of the Queensland Brigalow Belt

In this report, we present a costed and prioritised set of feasible threat management strategies for protecting 179 of the most threatened native plant and animal species of the Brigalow Belt bioregion, a highly modified biodiversity hotspot covering 20% of Queensland, Australia. The 12 strategies outlined here were designed through a consultation process with 40 experts and stakeholders in biodiversity and land management of the region, using the best available scientific data and expert knowledge.