Pieces Of Information Research Articles

A Comparison of Channel Selection for the Assimilation of CrIS Radiances for NWP

Abstract With hyperspectral instruments measuring radiation emitted by Earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on the information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for the Cross-track Infrared Sounder (CrIS) was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati has 224 channels also present in our optimal selection, which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3% and 4.5% for temperature and humidity, respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various matrices, the results in terms of analysis error are similar. Significance Statement Satellites measure radiation from Earth and its atmosphere in the thermal infrared. Those radiance data contain thousands of measurements, called channels, and thus, a selection needs to be done retaining most of the information content since the large number of individual pieces of information is not usable for numerical weather prediction systems. The goal of this paper is to find an optimal selection for the instrument CrIS and to compare this selection with selections made for different numerical weather prediction systems. It was found that even though the channels selected optimally were different in terms of which channels were selected compared to other selections, the results in terms of precision of the analysis are similar and the results in terms of analysis error are similar due to the nature of hyperspectral instruments, which have multiple Jacobians overlapping.

Bayesian Focal Mechanism Estimation from P-, S-Wave Amplitudes, and Polarities for a Microearthquake Sequence in Irpinia, Italy

ABSTRACT The P-wave peak distribution in azimuth and takeoff angles, corrected for the distance through empirical attenuation laws, can reveal the radiation pattern amplitude of the source (Tarantino et al., 2019). This piece of information, jointly combined with the available polarities and S-wave peak amplitudes, can provide the full focal mechanism, constraining the solution also when only a few seismic station records are available, that is, in the case of microseismicity. We proposed a new technique, named P-, S-wave amplitude, and polarities (P-SAP), designed to compute the focal mechanism by jointly inverting the P-, S-wave amplitude ratios and P-wave polarities in a Bayesian framework and employing the octree strategy (Fang et al., 1996) to explore the space of possible solutions. The outputs are strike, dip, and rake angles of the most likely triplet (principal and auxiliary planes) with the related uncertainties, as well as other multiple solutions, if present. We tested the methodology to synthetic data, and we applied it to a microseismic sequence that occurred in Irpinia region, southern Italy. A background microseismicity occurs in a volume delimited by the faults activated during the 1980 M 6.9 Irpinia earthquake. This faults system is complex and composed of northwest–southeast-striking normal faults along the Apennines chain and an approximately east–west-oriented strike-slip fault, deep-seated in the Potenza area. A network of three-component accelerometers and velocimeters are currently deployed to monitor the area. The major topic of this work is validating the proposed P-SAP methodology for microseismicity studies. We also inferred the optimum stress tensor of the sequence, confirming that the microseismicity is controlled by the regional stress field and can reveal characteristics useful to highlight behaviors of larger-scale seismicity.

Организация информационно-аналитического обеспечения оперативно-розыскной деятельности органов внутренних дел по розыску лиц, пропавших без вести, в условиях контртеррористической операции и вооруженных конфликтов

The planning and implementation of operational search activities play a pivotal role in the search for missing persons amidst counter-terrorist operations and armed conflicts. Key to these efforts is the utilization of data that can aid in the identification or recognition of the missing individual. Such data includes physical descriptions in the form of verbal portraits or photographic impressions, basic identifying information, fingerprint cards, and handwriting samples. These pieces of information serve to individualize the sought-after person and can be instrumental in their identification. The establishment of new databases of operational search information, including databases of genomic codes of individuals, could prove to be a valuable tool in obtaining information about unidentified bodies and linking them to missing persons. However, the implementation of such databases raises a number of technical, legal, and ethical issues that must be considered. This article aims to examine the specifics of organizing information and analytical support for the operational activities of internal affairs bodies in the search for missing persons in conditions of counter-terrorist operations and armed conflicts, utilizing available resources accessible to operatives. In conclusion, the effective organization of information and analytical support is paramount in facilitating the search for missing persons within the challenging contexts of counter-terrorist operations and armed conflicts. Leveraging various data sources, including physical descriptions and emerging technologies like genomic databases, offers promising avenues for enhancing identification processes. However, the integration of such resources necessitates careful navigation of technical, legal, and ethical considerations. By addressing these complexities, internal affairs bodies can optimize their operational efforts and contribute significantly to resolving cases of missing individuals amidst complex security environments.

CLUSTERING ANALYSIS FOR GROUPING SUB-DISTRICTS IN BOJONEGORO DISTRICT WITH THE K-MEANS METHOD WITH A VARIETY OF APPROACHES

Population data is an important piece of information that is useful for regional planning and development. Insight into the state of an area is more straightforward to observe if there are grouped sub-districts. In this case, data mining techniques can identify patterns and relationships in population data. The K-Means algorithm is a clustering technique that divides data into groups or clusters based on similar characteristics. This research aims to apply the K-Means method with various approaches to clustering sub-districts in the Bojonegoro district according to population data. The research method used is a quantitative method with an exploratory study in the application of the K-Means method with a variety of approaches, namely the use of the Kernel K-Means method by utilizing the mapping function to map data to a higher dimension before the clustering process. In addition, the Fast K-Means method is used, which reduces the model training time to improve the cluster-centered recalibration problem as the amount of data increases. The data source used in this research is secondary population data in the form of birth, death, migrant, and moving variables obtained from the Satu Data Bojonegoro website developed by the Bojonegoro Regency Government. It is found that the best K-Means approach is the Kernel K-Means method with a number of clusters of 5. The performance of the cluster method is evaluated by measuring the average distance within the cluster. The data coordinate pattern in the Kernel K-means method clustering shows a smooth initial trend when the value of the number of clusters is 5 so that the clusters formed are obtained clearly. The conclusion from this study's results is that the K-Means method's best approach in grouping sub-districts in Bojonegoro district is the Kernel K-Means approach.

SuiETAS: Developing and Testing ETAS-Based Earthquake Forecasting Models for Switzerland

ABSTRACT We present the development and testing of multiple epidemic-type aftershock sequence (ETAS)-based earthquake forecasting models for Switzerland, aiming to identify suitable candidate models for operational earthquake forecasting (OEF) at the Swiss Seismological Service. We consider seven model variants: four variants use parameters obtained through fitting the ETAS model to the Swiss earthquake catalog, and three use generic parameters that were fit to Californian seismicity or global seismicity from regions tectonically similar to Switzerland. The model variants use different pieces of information from the current state-of-the-art time-independent earthquake rate forecast underlying the Swiss seismic hazard model (SUIhaz2015), and one is calibrated on a larger local data set that includes smaller earthquakes by allowing a time-dependent estimate of the completeness magnitude. We test all variants using pseudoprospective short-term (7-day) forecasting experiments and retrospective long-term (30-year) consistency tests. Our results suggest that all ETAS-based models outperform the time-independent SUIhaz2015 forecast in the short term, but two of the model variants overestimate event numbers in the long term. ETAS parameters are found not to be universally transferrable across tectonic regimes, and region-specific calibration is found to add value over generic parameters. Finally, we rank all model variants based on six criteria, including the results of the pseudoprospective and retrospective tests, as well as other criteria such as model run time or consistency with the existing long-term model, using a multicriteria decision analysis approach. Based on this ranking, we propose the ETAS model calibrated on the Swiss catalog, and with the spatial background seismicity distribution of SUIhaz2015 as the ideal candidate for the first Swiss OEF system. All procedures and choices involved in the development and testing of the Swiss ETAS model follow recently established expert recommendations and can act as a reference in the establishment of time-variant earthquake forecasting models for other regions.

Increased left atrial pressure is associated with fibrosis, elevated mechanical load and ventricular diastolic dysfunction in patients undergoing AF catheter ablation

Abstract Background Increased left atrial filling pressures might be a hallmark feature of atrial cardiomyopathy which has been associated with an increased incidence of atrial fibrillation (AF) recurrence even after successful pulmonary vein isolation (PVI). The interplay between left atrial pressure, measured during the ablation procedure, and other parameters characterising atrial and ventricular remodelling in patients with AF remains elusive. Methods and Results Between 2018 and 2022, 182 patients undergoing PVI for paroxysmal or persistent AF were included in the study. Mean left atrial pressure (mLAP) was measured invasively during the ablation procedure after transseptal puncture. A mLAP higher than 15 mmHg was considered the cut-off value for LA hypertension. Bipolar high-density 3D electro-anatomical maps (Carto) obtained during the procedure were used to manually quantify low-voltage (<0.5 mV) and normal voltage areas of the LA surface and a global low-voltage/normal voltage ratio index was derived. Contemporary transthoracic echocardiography data were included to evaluate LV systolic and diastolic function and blood samples were obtained post-procedurally from all patients. In bivariate linear regression analysis, mLAP reached only modest correlation with E/e’ as an imaging marker of diastolic dysfunction (R=0.31, p=0.005) (Figure 1A), with particular differences in mLAP values between patients with marked diastolic impairment (E/e’ > 15) vs. subgroups with mild dysfunction ( 8 < E/e’ ≤ 15) or normal diastolic function (E/e’ ≤ 8 ) (Figure 1B), log NT proBNP (R=0.34, p<0.001) (Figure 1C) and low-voltage/normal voltage ratio index (R=0.21, p=0.014) (Figure 1D). For a total of 158 patients, data about 1 year recurrence of AF were available, of these 22 (13.9%) had a recurrence after two procedures using cryo-energy for the index PVI and radiofrequency ablation for the re-PVI (second procedure). A Kaplan-Meier analysis and log-rank test showed a trend but no statistical significance regarding an increased AF recurrence rate between patients with and without LA hypertension (χ2 = 2.63, p= 0.09) (Figure 2). Conclusion Direct intra-procedural invasive measurement of LA pressures can offer an important and convenient novel piece of information for patients undergoing catheter ablation procedures for AF, with potential implication for post-procedural recurrence risk stratification. Atrial hypertension is only partially related to LV diastolic impairment, atrial fibrosis and myocardial mechanical load burden.

Exploring undiscovered public knowledge in neuroscience.

In this essay, I argue that the combination of research synthesis and philosophical methods can fill an important methodological gap in neuroscience. While experimental research and formal modelling have seen their methods progressively increase in rigour and sophistication over the years, the task of analysing and synthesizing the vast literature reporting new results and models has lagged behind. The problem is aggravated because neuroscience has grown and expanded into a vast mosaic of related but partially independent subfields, each with their own literatures. This fragmentation not only makes it difficult to see the full picture emerging from neuroscience research but also limits progress in individual subfields. The current neuroscience literature has the perfect conditions to create what the information scientist Don Swanson called "undiscovered public knowledge"-knowledge that exists in the mutual implications of different published pieces of information but that is nonetheless undiscovered because those pieces have not been explicitly connected. Current methods for rigorous research synthesis, such as systematic reviews and meta-analyses, mostly focus on combining similar studies and are not suited for exploring undiscovered public knowledge. To that aim, they need to be adapted and supplemented. I argue that successful exploration of the hidden implications in the neuroscience literature will require the combination of these adapted research synthesis methods with philosophical methods for rigorous (and creative) analysis and synthesis.

An Analysis of Aesthetic Education and Personality Cultivation among College Students in China Based on Social Cognitive Theory

The present study aims to explore the awesome association between aesthetic education and character improvement, expressly focusing on students in China. Aesthetic education, defined as the joining of innovative and imaginative experiences into ordinary enlightening settings, has been logically seen for its conceivable impact on empowering complete new development, including cognitive, emotional, and social aspects. Character improvement, of course, consolidates the excessively long instances of contemplations, opinions, and approaches to acting that shape a solitary's outstanding characteristics and joint efforts with the world. To explore this relationship, a mixed-methods approach was taken, uniting inferential estimations, effective examination, and a pilot study. The use of inferential estimations considers the quantitative assessment of data, giving encounters into the connection between receptiveness to elegant preparation and changes in character ascribed among students. Thematic analysis, an emotional strategy, supplements these revelations by perceiving rehashing subjects and models in students' observations and experiences associated with classy learning. The pilot study was filled in as an essential assessment, refining research ways of thinking and ensuring the authenticity and faithful nature of data combination instruments. Through Surveys, interviews, and observational techniques, various components influencing character characteristics, similar to creativity, significant explanation, conclusive thinking, and social correspondence, were investigated inside the setting of trendy tutoring programs. As a rule, this study adds to the ongoing literature by offering precise pieces of information on the impact of elegant preparation on character improvement among students. The revelations have ideas for educators, policymakers, and instructive program makers, focusing on the meaning of integrating creative and imaginative experiences into enlightening frameworks to propel comprehensive development and update students' overall thriving.

Strange a construction: The “A egy N” in Hungarian

AbstractThe paper gives a detailed description of the “A egy N” construction in Hungarian based on a thorough investigation of carefully collected corpus data. Utterances containing this construction express a speaker-related (mostly derogatory, but sometimes appreciative) value judgement. The morphological, syntactic, and pragmatic characteristics of the construction are presented. Furthermore, some formally and pragmatically similar constructions are also discussed and some misleading pieces of information in the earlier literature are debunked.

Enhancing Leadership Management Through Fusion of Employee Information: A Unified Framework

In the dynamic environments of today’s organizations, efficient leadership management becomes an essential requirement for driving organizational achievement and developing a culture of excellence. The core of this endeavor lies in the ability to use improved data analytics to facilitate interpreting and optimizing employee performance. In line with this, this research presents a fusion framework that integrates multiple pieces of information and extracts useful insights about employee performance which can leader’s decision-making process. In particular, we integrate hypothesis testing for handling outliers and anomalies in fused information, then we introduce Random Forest (RF) to perform forecasting and analysis of the fused information about employee performance through examining the complicated interactions between employee-related features such as work-life balance, job happiness, and education level. Using a case study of IBM employees, the proposed fusion approach explores multifaceted features persuading employee abrasion and workforce dynamics. Extensive experimentations in the real world demonstrate the effectiveness of the proposed fusion framework is at supporting evidence-based tactics for organizational performance and staff retention as well as refining leadership decision-making.

A SURVEY ON PREDICTION OF DIABETIC LEVELS USING MACHINE LEARNING ALGORITHAM

Diabetes is a serious, long-lasting illness that makes blood sugar levels high. It can lead to many health problems. By 2040, it's expected that 1 in 10 adults worldwide will have diabetes, reaching a staggering 642 million people. This is a big concern and needs attention. Scientists are using advanced technology like machine learning to predict diabetes. They studied data from hospital check-ups in Luzhou, China, which had 14 pieces of information about each person. They used two different methods like decision trees, random forests to make predictions. They tested these methods to see how well they worked, using a technique called five-fold cross-validation. To make sure their findings were reliable; they also did separate tests with a large group of healthy people and those with diabetes. Because the data wasn't evenly balanced between healthy and diabetic people, they adjusted it to make it fair. They found that random forests were the best at predicting diabetes accurately, with about 81% accuracy when using all the information available. They also used techniques like principal component analysis and minimum redundancy maximum relevance to make the data easier to work with. KEYWORDS : Diabetes, blood sugar, predictions, hospital, China, methods, tests, data, balance, forests, accuracy, analysis.

Intestinal Helminth Infections among Primary School Aged Pupils in Awka North Local Government Area, Anambra State, Nigeria

This study was conducted to determine the prevalence of helminthiasis in primary school children in Awka North Local Government Area of Anambra State, Nigeria. A total of 172 faecal samples were collected and analyzed using Kato-Katz technique. Other pieces of information concerning demography were obtained through structured questionnaires. Data was analyzed using chi-square test and P˂0.05 were considered significant. Overall, 45 (26.16 %) of the samples were infected while 127 (73.84 %) were negative. Among the schools sampled, Community Primary School, Amowere recorded the highest infection prevalence of 58.18% while no infection was recorded in Umuoji Primary School, Ebenebe (0.00 %). In relation to class, pupils in class four were the most infected 12 (31.58 %) while class one children had the least prevalence 5 (12.19 %). Gender-wise, males recorded the highest prevalence of 28.43 % with female children having a lower prevalence of 22.86 %. In terms of age, children between the age bracket of 7–9 years were most infected 31 (30.09 %) with the least prevalence occurring in the 4–6 years age group. In total, four helminth parasites were found in the stool samples of the children, A. lumbricoides 35 (20.59 %), Hookworm 7 (4.16 %), and T. trichiura 5 (2.19 %). Among the predisposing factors studied, school and type of parasite were found to be statistically significant (P˂0.05). Mass deworming of primary school children should be undertaken regularly by health officers as well as enlightenment programs of pupils on proper hygiene practices and sanitation.

A Roman Fleet Soldier’s Gravestone from Scarbantia (TRHR 38)

In his paper the author deals with a funerary verse epitaph from Scarbantia (Sopron) published by him earlier (TRHR 38). Based on his new restoration, the gravestone (stela) was erected to a veteran by his wife who served earlier as fleet soldier. He was most probably called Britto. Several vulgar Latin phenomena can be observed in the inscription that was erected in the second half of the third century or at the very beginning of fourth c. The type of the metrical formula resiste viator et lege can be found in several Pannonian verse inscriptions. Based on the pieces of information given in the problematical parts of lines 2 and 3 that concern the wife, at least one line is missing at the beginning of the text and she erected the grave monument and the funerary epitaph to herself as well.

Integration of computer networks and artificial neural networks for an AI-based network operator

This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outline the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.

Scoring Multi-hop Question Decomposition Using Masked Language Models

Question answering (QA) is a sub-field of Natural Language Processing (NLP) that focuses on developing systems capable of answering natural language queries. Within this domain, multi-hop question answering represents an advanced QA task that requires gathering and reasoning over multiple pieces of information from diverse sources or passages. To handle the complexity of multi-hop questions, question decomposition has been proven to be a valuable approach. This technique involves breaking down complex questions into simpler sub-questions, reducing the complexity of the problem. However, it’s worth noting that existing question decomposition methods often rely on training data, which may not always be readily available for low-resource languages or specialized domains. To address this issue, we propose a novel approach that utilizes pre-trained masked language models to score decomposition candidates in a zero-shot manner. The method involves generating decomposition candidates, scoring them using a pseudo-log likelihood estimation, and ranking them based on their scores. To evaluate the efficacy of the decomposition process, we conducted experiments on two datasets annotated on decomposition in two different languages, Arabic and English. Subsequently, we integrated our approach into a complete QA system and conducted a reading comprehension performance evaluation on the HotpotQA dataset. The obtained results emphasize that while the system exhibited a small drop in performance, it still maintained a significant advance compared to the baseline model. The proposed approach highlights the efficiency of the language model scoring technique in complex reasoning tasks such as multi-hop question decomposition.

Adoption of identity theft protection services in social media: A PMT investigation

In the last few years, social media has become a part of our lives. It is not a website anymore. It offers us blogging, watching videos, and connecting with more people. In our social media profiles, we create our virtual identity. We share every piece of information connected to our lives. Social media has become a very important part of our lives. Social media changes itself tremendously; it also makes it possible for criminals to take their work to another level. Many crimes happen on social media. One of them is identity theft. As we create our own identities on social media. There is a high risk of identity theft. For this reason, many identity theft protection services have been created. With identity theft protection, we are able to protect our identities on social media. Those identity theft production services are my best product because they help us identify identity threats and get rid of them. They give us protection from identity theft in exchange for some monthly fees. But those services are not common in our nation. We don't know if those services will work in our country or not. With this thesis, we will be able to find out the adoption intention of social media identity theft protection services in our country through PMT investigations.

Navigating Global Talent: An Overview of Information Systems in International Human Resource Management (IHRM)

All through the long haul, Overall Human Resource The chiefs (IHRM) has gone through a huge turn of events. At previously confined to administrative tasks, it has now emerged as a fundamental accessory essential to various leveled accomplishment. Data innovation progressions, which have adjusted the IHRM scene, have sped up this change. The way HR processes are carried out within organizations has been reclassified as a result of the inevitable reception of data frameworks. From the combination and ability to the utilization and sharing of data, every part of IHRM has been reshaped. As well as making conventional HR works more powerful, the advanced upheaval plays raised HR's part in forming business systems. Associations' HR rehearsals have changed in perspective as a result of using data frameworks. HR experts currently have additional opportunity to focus on essential drives because of routine undertakings being mechanized, bringing about more prominent arrangement with the general objectives of the association. This strategic repositioning of HR within the framework of the business necessitated a reevaluation of personnel functions and expertise. This article examines the extensive impact that information systems have on IHRM and draws on existing literature and empirical evidence for its analysis. The numerous benefits and disadvantages of implementing information systems in IHRM are explained. In addition, it provides a comprehensive analysis of the shifting duties of HR professionals in the digital age. The expanded productivity that data frameworks accommodate HR processes is one of the most outstanding benefits of IHRM. Tasks that required a lot of time and resources in the past can now be simplified and completed with unprecedented speed and precision. This efficiency benefit engages HR divisions to apportion more resources towards key drives, appropriately overhauling their obligation to legitimate accomplishment. In addition, information-driven direction facilitates data framework integration into IHRM. By handling the power of examination, HR specialists can assemble significant pieces of information into workforce examples and execution estimations. HR procedures that are customized to meet the changing requirements of the labor force and the business can be created utilizing this significant knowledge. However, despite the numerous advantages, there are a few drawbacks to incorporating data frameworks into IHRM. The boss among these is the risk of becoming overly dependent on innovation, which may make the board's human resources component questionable. Likewise, worries about information protection and security are huge, especially in this period of rigid administrative prerequisites. Overall, the integration of data frameworks into IHRM has opened up a new era of opportunity and change. While it has unquestionably improved HR practices' proficiency and viability, it also necessitates a reevaluation of HR tasks and responsibilities. By discovering some sort of amicability between mechanical turn of events and human-driven approaches, affiliations can utilize the most extreme limit of information systems to drive key HR drives and achieve efficient advantage in the overall business community. KEY WORDS International Human Resource Management, Information Technology, Strategic Partnership, Digital Transformation, HR Processes, Organizational Strategy, Data-driven Decision-making, Personnel Functions, Technology Applications, Global Talent Management.

New method for assigning cardinal weights in multi-criteria decision-making: the constant weight ratio method

A new method is proposed to convert ordinal ranking of a number of criteria and an additional piece of information into numerical weights. A literature review of methods for assigning cardinal weights based on ordinal ranking is performed, as well as an analysis of their behaviour. The new method, called ‘constant weight ratio’ (CWR), enables better adjustment to the decision-maker’s preferences than purely ordinal ranking methods. It also solves the problem of the excessive decrease in the weight of the most important criterion (or criteria) when the total number of criteria is large and the weight of the most important criterion (or criteria) must be high. It is achieved via three simple steps and flexible input data. The additional piece of information may be: (i) the relative importance of the criteria, i.e., the weight ratio, (ii) the total weight of the most important set of criteria, or (iii) the weight of the most important criterion. The proposed method is applied to two case studies in the cultural sector to illustrate that the resulting weights are equivalent to other methods requiring more input data from the decision maker.

Unveiling realistic mobility patterns with home–origin–destination data aggregation

The availability of increasingly abundant mobility data in recent years has opened up new avenues for researchers to unravel human mobility patterns. Data aggregation methods have been introduced to gain a quantitative understanding of collective individual movements using these data. Nevertheless, the widely adopted origin–destination (OD) aggregation method for human mobility data lacks an essential piece of information: home location, which plays a vital role in characterizing individual movement patterns. In this study, we propose a novel data aggregation approach called home–origin–destination (HOD) with the aim of improving the accuracy of human mobility estimation. We compare the performance of various data aggregation methods for estimating population distribution. Our experimental results reveal more realistic mobility patterns when incorporating estimated home information, where individuals move out in the morning and return home before midnight. To further evaluate the effectiveness of the HOD approach, we conduct an entropy analysis to measure the unpredictability of human mobility. The HOD results exhibit lower entropy values than those in the other two cases, OD and home–destination (HD). These findings underscore the importance of incorporating home information in understanding and modeling human mobility. By leveraging the HOD data aggregation method, we can achieve more accurate population distribution estimates and capture the inherent dynamics of human movement

Nativeness is a binary concept —Invasiveness and its management are not

In interdisciplinary fields such as biodiversity conservation or invasion science —where multiple perspectives from diverse disciplines often need to converge for effective environmental management, it is crucial to minimise terminological confusion in order to understand and transmit concepts accurately. The diversity of perspectives can exert a substantial influence on defining key terms in those interdisciplinary fields, potentially resulting in confusion. A lively topic within invasion science concerns the definitions of nativeness, non-nativeness, and invasiveness. While some academics dismiss the nativeness concept because it cannot be objectively defined, others advocate for its categorization, and a third perspective posits it as a binary term. Here we argue the inherent binary nature of nativeness, even when our capacity to observe is challenging. Nativeness (and consequently, non-nativeness) is an intrinsic and binary property of a species (i.e. the set of populations of a species) in a place, which should remain a central piece of information in biodiversity conservation and ecosystem restoration. In contrast, invasiveness, which relies on quantitative metrics (including abundance, spread, or impacts), should not be defined on binary terms. This underscores the importance of offering diverse, context-specific management strategies to deal with it. We illustrate the consistency of nativeness' binary nature and the need to rely on diverse management options to address different invasion scenarios with the example of the freshwater crayfish in the Iberian Peninsula.