Statistical Basis Research Articles

Log‐TF‐IDF and NETCONF‐Based Network Switch Anomaly Detection

ABSTRACTIn this study, we propose and evaluate a model that utilizes both log data and state data to detect abnormal conditions in network switches. Building upon our previous research and drawing inspiration from TF‐IDF used in natural language processing to measure word importance, we propose a statistical method, Log‐TF‐IDF, to quantify the rarity of each log pattern in the log data. Furthermore, based on this Log‐TF‐IDF, we introduce the AB Score, which quantifies how abnormal the current log pattern is. Our findings indicate that the AB Score is notably higher and more volatile in abnormal conditions. We confirm that anomaly detection is feasible through the AB Score, which has the advantage of being computationally efficient due to its statistical basis. We combined the metrics generated during the AB Score calculation with resource data collected with NETCONF and developed a machine‐learning model to detect abnormal conditions in network switches. We confirm that this model can detect abnormal conditions with an F1 score of 0.86 on our collected dataset, confirming its viability for detecting abnormal states in network equipment.

A Predictive Compact Model of Effective Travel Time Considering the Implementation of First-Mile Autonomous Mini-Buses in Smart Suburbs

An important development task for the suburbs of smart cities is the transition from rigid and economically inefficient public transport to the flexible order-based service with autonomous vehicles. The article proposes a compact model with a minimal input data set to estimate the effective daily travel time (EDTT) of an average resident of a suburban area considering the availability of the first-mile autonomous vehicles (AVs). Our example case is the Järveküla residential area beyond the Tallinn city border. In the model, the transport times of the whole day are estimated on the basis of the forenoon outbound trips. The one-dimensional distance-based spatial model with 5 residential origin zones and 6 destination districts in the city is applied. A crucial simplification is the 3-parameter sub-model of the distribution of distances on the basis of the real mobility statistics. Effective travel times, optionally completed with psycho-physiological stress factors and psychologically perceived financial costs, are calculated for all distances and transportation modes using the characteristic speeds of each mode of transport. A sub-model of switching from 5 traditional transport modes to two AV-assisted modes is defined by an aggregated AV acceptance parameter ‘a’ based on resident surveys. The main output of the model is the EDTT, dependent on the value of the parameter a. Thanks to the compact and easily adjustable set of input data, the main values of the presented model are its generalizability, predictive ability, and transferability to other similar suburban use cases.

Power analysis for personal light exposure measurements and interventions.

Light exposure regulates the human circadian system and more widely affects health, well-being, and performance. With the rise in field studies on light exposure's effects, the amount of data collected through wearable loggers and dosimeters has also grown. These data are more complex than stationary laboratory measurements. Determining sample sizes in field studies is challenging, as the literature shows a wide range of sample sizes (between 2 and 1,887 from a recent review of the field and approaching 105 participants in first studies using large-scale 'biobank' databases). Current decisions on sample size for light exposure data collection lack a specific basis rooted in power analysis. Therefore, there is a need for clear guidance on selecting sample sizes. Here, we introduce a novel procedure based on hierarchical bootstrapping for calculating statistical power and required sample size for wearable light and optical radiation logging data and derived summary metrics, taking into account the hierarchical data structure (mixed-effects model) through stepwise resampling. Alongside this method, we publish a dataset that serves as one possible basis to perform these calculations: one week of continuous data in winter and summer, respectively, for 13 early-day shift-work participants (collected in Dortmund, Germany; lat. 51.514° N, lon. 7.468° E). Applying our method on the dataset for twelve different summary metrics (luminous exposure, geometric mean, and standard deviation, timing/time above/below threshold, mean/midpoint of darkest/brightest hours, intradaily variability) with a target comparison across winter and summer, reveals required sample sizes ranging from as few as 3 to more than 50. About half of the metrics-those that focus on the bright time of day-showed sufficient power already with the smallest sample. In contrast, metrics centered around the dark time of the day and daily patterns required higher sample sizes: mean timing of light below mel EDI of 10 lux (5), intradaily variability (17), mean of darkest 5 hours (24), and mean timing of light above mel EDI of 250 lux (45). The geometric standard deviation and the midpoint of the darkest 5 hours lacked sufficient power within the tested sample size. Our novel method provides an effective technique for estimating sample size in light exposure studies. It is specific to the used light exposure or dosimetry metric and the effect size inherent in the light exposure data at the basis of the bootstrap. Notably, the method goes beyond typical implementations of bootstrapping to appropriately address the structure of the data. It can be applied to other datasets, enabling comparisons across scenarios beyond seasonal differences and activity patterns. With an ever-growing pool of data from the emerging literature, the utility of this method will increase and provide a solid statistical basis for the selection of sample sizes.

Cause of death statistics-how to avoid misinterpretation of mortality data

National mortality registers provide important data for monitoring population health. Analyses of cardiovascular mortality in particular-and especially mortality from coronary heart disease-are frequently the basis for conclusions about the quality of healthcare and prevention. To be able to interpret disease-specific mortality differences between countries and changes in mortality over time, it is necessary to know the basics of monocausal cause-of-death statistics and the associated limitations in comparative statements.Conclusions from disease-specific mortality data can be problematic due to the highly subjective selection of diseases that are entered on adeath certificate after apost-mortem examination. In death certification, unawareness of the different "roles" of specific diseases-known at the time of death-within the causal chain leading to death can result in incomplete death certificates as adata basis for mortality statistics. Comparisons of disease-specific mortality rates are difficult due to different proportions of non-informative-including unknown-causes of death and due to country-specific preferences for which diseases are recorded on adeath certificate-especially in the prevalence of multimorbidity. Apopulation'smorbidity is therefore only reflected to alimited extent by mortality rates. Conceptual ambiguities with regard to the concepts of lethality and mortality when describing mortality rates can also lead to erroneous conclusions.Training of medical staff on how to complete adeath certificate and the introduction of an electronic death certificate can improve the quality of mortality data. Irrespective of this, knowing potential pitfalls when analyzing mortality data will improve the quality of health reporting.

Patterns and Relationships of Pesticide Use in Agricultural Crops of Latin America: Review and Analysis of Statistical Data

The excessive use of pesticides in agriculture causes problems related to public health; biodiversity; the contamination of water bodies, soil and air; and general environmental degradation, including interactions with climate change effects. The aim of this work was to describe the patterns of pesticide use in 17 Latin American countries and their associations with the total harvested area and the harvested areas of the five main crops on the basis of statistics reported by the Food and Agriculture Organization from 1990 to 2021. Principal component analysis (PCA) revealed four different patterns among countries according to the magnitude of pesticide application: Brazil (G1) > Argentina (G2) > Colombia and Mexico (G3) > Central and South America (G4). Together, more than 1.2 million tons of active ingredients is applied annually, carrying harmful effects and risks. In the joint analysis of both datasets (applied pesticides and harvested area), different correlations were observed between the amount of pesticides applied and the harvested area; for example, in G1 and G2, positive and significant correlations were detected between the applied amounts of total pesticides, herbicides, insecticides and bactericides and the total area harvested by the main crop, but in G3, there was a negative correlation with the five main crops.

Multi-parameter coupling modeling method and hybrid mechanism of concrete-encased CFST hybrid structures

Concrete-encased concrete-filled steel tubular (CFST) hybrid structures consist of encased CFST components and reinforced concrete (RC) encasement, posing unique challenges related to their hybrid mechanisms and coupling effects. This study addresses these issues by proposing an automatic and efficient Finite Element (FE) modeling method, which accounts for multiple constitutive models of confined concrete, meticulous grid division and reasonable interaction. The FE modeling method was packaged with a user-friendly graphical interface in the software ‘Auto CECFST’, which has been shared on GitHub(https://github.com/CECFST/Auto-CECFST.git). The modeling method has been verified by test results on the strength, stiffness and deformation capacity. Utilizing the refined FE modeling method, 7776 FE models are generated based on an orthogonal combination of 7 critical parameters of concrete-encased CFST hybrid structures. To accurately define failure modes, stress ratio (φ) was proposed to achieve quantitative analysis on the failure mode, followed by a comprehensive investigation into the full-range analysis of the hybrid mechanism of two components. Moreover, further exploration focused on multi-parameter coupling effects of critical mechanism characteristics, including strength, stiffness, and deformation capacity, elucidating the hybrid mechanism and mechanical similarities between single and multi-chord structures. Based on the above analysis, the balance of strength and deformation ability could be achieved by certain parameter ranges. Finally, available strength and stiffness calculation methods are validated against experimental and FE results under three typical failure modes, revealing the advantages and limitations of calculation methods on the basis of mechanics and data statistics, which provides a basis for security structural design.

The role of digital transformation in ensuring the level of technological development of industries and enterprises

In the context of the development of the data economy, the implementation of the policy of import substitution, and the increase in technological sovereignty, the study of the impact of digital transformation on increasing the level of technological development of industries and enterprises is of particular importance and relevance. The purpose of the study is to systematize, based on statistical dynamic data, the directions of digital transformation to ensure the technological development of industry. The scientific novelty of the study lies in the correlation of trends in digital transformation and technological development of the industrial sector. Description, comparative analysis, trend graphs, regression analysis, generalization and systematization of data were used as research methods. Based on the results of the study, the following conclusions were obtained: indicators of innovation activity and the effectiveness of research activities remain relatively low, while the trend of reduction in the number of researchers continues; the development of the data economy has contributed to increased investment in the development of information and communication technologies, but on average less than 2% of the population is employed in the field of information technology; the cost share of purchased domestic software is steadily increasing in government agencies while decreasing in commercial organizations, which negatively affects the implementation of information security policies and import substitution of technologies in the field of data management; Organizations have seen an increase in the use of integrated management information systems, mainly based on technologies such as ERP–systems, CRM–systems and supply chain management systems. The analysis of trends in the influence of digital transformation in ensuring the level of technological development presented in the article can be used as the initial information, statistical and analytical basis for developing programs to stimulate technological renewal of industrial sectors and enterprises.

Prospects for the economy and foreign trade of Ethiopia: fore cast estimates based on mathematical modeling

The article presents a forecast assessment of the development of the national economy and foreign trade of Ethiopia until 2030, calculated by the authors on the basis of a mathematical and systematic analysis of macroeconomic processes and methods of modeling these processes usingthe Box-Jenkins model (ARIMA). Estimation method: the exact maximum likelihood method. The topic seems relevant due to Ethiopia's accession to the BRICS alliance from 2024. Therefore, it is important to predict possible trends in Ethiopia's economic and foreign trade development, their significance for the country itself, as well as their impact on the economic dynamics of other BRICS countries and the association as a whole. The study is updated taking into account the continued increase in global volatility and the emergence of new threats, including in Africa. The subject is trends in the economic development of Ethiopia as a member of the BRICS. The purpose of the article is to develop a scientifically based forecast of the development of the economy and foreign trade of Ethiopia in the medium term. The article identifies potential threats to the development of the Ethiopian economy. The statistical basis of the study was mainly data from the United Nations Conference on Trade and Development (UNCTAD). The results of the study can be used to understand the trends of socio-economic and foreign trade development of the country, adjust this development, as well as expand and enrich the theoretical concepts of the world economy. The research and author's calculations are based solely on the current circumstances and current realities, without taking into account any fundamental changes in the politics and economy of the country under study. The author's calculations and conclusions can be useful to economists, entrepreneurs, and researchers as a basis for their further practical and scientific activities.

EsmamDS: A more diverse exceptional survival model mining approach

In this work we present an Ant Colony Optimization heuristic to find subgroups with exceptional behavior in time-to-event data. The area of time-to-event or survival data analysis has its basis in statistics, where the main goal is to predict if and when an event will happen. In other words, the main goal in survival analysis has long been to build global models able to predict the time for the occurrence of an event. Nevertheless, very often predictive models are used to compare stratified data in order to evaluate whether a variable is associated or not with the outcome. For instance, patients might be stratified according to a treatment variable (placebo or not) to compare models (survival curves) and decide on the effectiveness of the treatment. Although this is an effective approach if the variable of interest is already known, it does not provide an alternative for the cases where specialists do not know how to stratify the data, that is, if they do not know which variable could be related to the outcome. Our approach targets exactly this. Our method seeks combinations of variables that are associated, i.e. describe, subgroups of individuals with unexpected or exceptional survival curves. In this sense, we complement the literature with a descriptive approach that is able to find and characterize those groups for specialists. Our method is based on the framework of exceptional model mining. It improves on a preliminary version presented in a conference. The main enhancement was to redesign our heuristic to retrieve interesting and diverse subgroups while minimizing three aspects of redundancy: coverage; description; and model. Our second extension regards how the quality function is applied. We now allow users to control whether the quality measure compares subgroups against the population, or against individuals that do not satisfy the descriptive rule. Third, we conduct further experiments to compare the performance of our approach to state of the art algorithms with real world benchmark data sets. Finally, we also present a case study showing a possible application of our method in the bioinformatics/health domain.

Control and Modelling of Laser Shock Peening without Coating (LSPwC) Texture of AISI 9310 Steel.

LSPwC is an important development of Laser shock peening (LSP) technology, and surface texturing is an effective method to improve tribological properties. The combination of these is expected to innovate a new surface texturing technology with a strengthing effect, but no one has attempted it. In this paper, a new LSPTwC technology combining them is innovatively proposed and validated on AISI 9310 steel, which is commonly used in helicopter transmission components for surface texturing. The LSPTwC surface was studied using an optical microscope, electron microscope, energy dispersive spectrometer, and so on. The results proved that LSPTwC is an effective texturing method of AISI 9310 steel, which modulates the texture and improves the properties, such as the microhardness increased by more than 10%. A model for calculating the texture and process parameters is also constructed on a statistical basis, and a modeling method for textured surfaces is proposed. It is verified that the calculation results and the constructed model are highly consistent with the test, with a diameter deviation <3% and depth deviation <4%. The above results can provide the experimental basis, process design method, and calculation model for single-point LSPwC texturing of AISI 9310 steel parts for helicopters, which have application value.

A study on impact of tinnitus masking on psychophysical and tinnitus tuning curve in participants with hearing impairment suffering from severe tinnitus

Background: Tinnitus can be treated by using different pedagogies of management approaches in which tinnitus masking has been acclaimed most evidence-based approach. Psychophysical tuning curve and tinnitus tuning curve explored as least interested procedures in tinnitus research. This noteworthy but nonspecific psychophysical domain may throw a light on need of the present research. Aim of the study was to find out the effect of tinnitus masking on Psychophysical tuning curve and tinnitus tuning curve. Method: A total of 30 native Bengali speaking participants were included in this study, having mean age of 45.9 years and SD was 6.46. All the participants had unilateral subjective tinnitus with Mild to Moderately severe sensorineural hearing loss. Quasi-experimental research design was used. Dual channel audiometer (MAICO MA 53) with supra-aural TDH 39 headphone was used for psychoacoustic measurements of tinnitus (including pitch matching, loudness matching, minimum masking level etc.) and to provide tinnitus masking therapy. MATLAB version 9.5 was used to develop noise to assess PTC and TTC using Sennheiser HD 201 headphone routed through MAICO MA 53 audiometer via stereo jack and cable. Tinnitus masking therapy was carried out for 30 sessions by using regimen of MML +20 dB of masking level for 30 minutes duration for each session with matched pitch. PTC and TTC were measured before and 30 sessions-after tinnitus masking therapy. PTC was measured using 1000 Hz pure tone at 10 dBSLl as reference followed by four narrow band noise with centre frequency of 500 Hz, 1000 Hz, 2000 Hz, 4000 Hz pertaining to 312 Hz band width as developed by MATLAB software. TTC was measured using the same noise with reference to matched tinnitus pitch. All the above-mentioned conditions forced choice procedure was carried out in dBHL frame work. Results: Wilcoxon sign rank test was used at 95% confidence level to check the influence of tinnitus masking therapy on PTC and TTC measures. Significant changes were portrayed at 1000 Hz and 4000 Hz respectively in PTC measure and at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz respectively in TTC measures. Conclusion: This study preliminarily provides information regarding influence on tinnitus pitch domains which has been psychoacoustically systematically measured as PTC and TTC. These two psychoacoustic signatures may be treated as prognostic biomarker of tinnitus management in future using replicable research using different methodology or using statistical basis of data mining. At that point conclusive remarks may not throw a clear indication based on the results found in this study.

Stochastic Extensions of the Elo Rating System

This work studies how the Elo rating system can be applied to score-based sports, where it is gaining popularity, and in particular for predicting the result at any point of a game, extending its statistical basis to stochastic processes. We derive some new theoretical results for this model and use them to implement Elo ratings for basketball and soccer leagues, where the assumptions of our model are tested and found to be mostly accurate. We showcase several metrics for comparing the performance of different rating systems and determine whether adding a feature has a statistically significant impact. Finally, we propose an Elo model based on a discrete process for the score that allows us to obtain draw probabilities for soccer matches and has a performance competitive with alternatives like SPI ratings.

Mathematical model for predicting the yield of apple trees on rootstock 62-396

Traditionally, forecasting apple tree yields was carried out on the basis of statistics of already obtained long-term yield dynamics or based on generative formations formed on the tree. The purpose of the work is to identify the most significant soil parameters for the formation of the apple tree varieties yield on the dwarf rootstock 62-396 and to build a mathematical model for predicting fruit yield in the conditions of the Central Chernozem region of the Russian Federation. Field research was carried out in 2004-2022 in industrial apple tree plantations at the age of full fruiting in the Lipetsk (two farms) and Tambov (eight farms) regions. Seven horticultural soil types have been studied. Based on statistical analysis, a mathematical model of apple tree productivity was obtained in the form of an analytical dependence on three factors. A geometric interpretation of the regression model is given using the Mathcad symbolic mathematics system in the form of response surfaces and the corresponding level lines. Practical recommendations are given for choosing rational parameters within selected intervals of factor variation.

GGR Handbook of Rock and Mineral Analysis Chapter 1 (Part 1) Geoanalytical Metrology

This chapter (Geoanalytical Metrology) is a contribution to the Geostandards and Geoanalytical Research Handbook of Rock and Mineral Analysis – an online textbook that is a fully revised and updated edition of Handbook of Silicate Rock Analysis, which was written by Philip J. Potts and published in 1987 by Blackie and Son (Glasgow). This second edition comprises chapters, written by prominent research scientists, designed to provide comprehensive overviews of the relevant techniques for the elemental characterisation of rocks and minerals. Chapters are designed to allow new practitioners to the field (including research students) to attain a comprehensive understanding of the theory, practice and capabilities of each technique, as well as being of benefit to established research geoanalysts. In addition to the content, chapter titles have been revised where appropriate to reflect progress in this field.Chapter 1, Part 1 (from Section 1 of the handbook dealing with fundamentals of measurement and instrument design) first sets out the overarching conventions that operate in analytical chemistry, including a description of the international organisations and systems that regulate the standards governing the discipline. This is followed by coverage of the statistical basis on which geoanalytical data sets are treated, analysed and interpreted, which summarises most of the relevant tests and terminology employed in this field. The methods by which the calibration of measured signals from instrumental techniques is tackled, followed by method validation, which covers aspects including measurement uncertainty, bias, precision and trueness. Sections detailing metrological traceability and quality management conclude this chapter.

‘No status – no census!’ The causes and consequences of the 1971 and 1981 Northern Ireland census boycotts

ABSTRACT Despite Northern Ireland having been established on the basis of statistics from the 1911 census and the centrality of religious demography to many accounts of its conflict, little has been written about the political history of census-taking there. This paper contributes to addressing this oversight by exploring two related controversies in that history: the 1971 and 1981 census boycotts. Through use of archive materials, it demonstrates how concerns about the privacy of personal information collected by the census against the backdrop of the Troubles, were mixed with broader motivations whereby the census was seen as a symbolic target for protest and boycott in the context of discrimination against Catholics and the republican movement’s campaign for political status for its prisoners, with the 1981 boycott taking place during the hunger strike of that year. Despite a perhaps surprisingly low rate of overall non-response to the census given calls for it to be boycotted, the boycotts did have a significant impact and a legacy that lasted into the 1990s—by which time republicans’ attitude towards the census had changed for the positive.

Identification of Superior Forage Pearl Millet (Pennisetum glaucum (L.) R.Br.) Inbred Lines

The experiment was conducted during kharif, (July–November, 2022) at ICAR–Indian Institute of Millet Research (IIMR), Rajendranagar, Hyderabad, India. In this experiment, 81 forage pearl millet (Pennisetum glaucum (L.) R.Br.) lines was evaluated for its genetic diversity and variability. For all variables examined, an analysis of variance revealed a considerable variation between lines. Leaf-to-stem ratio, first-cut and second-cut of green fodder yield, as well as dry fodder yield, all had high PCV and GCV values. For Plant height, Leaf length, Number of leaves tiller-1, Number of tillers plant-1, Stem thickness, and Regeneration ability however, moderate PCV and GCV values have been identified. Plant height, Number of leaves tiller-1, Leaf-to-stem ratio, Green fodder yield in first cut, the Green fodder in second cut, and the yield of dry fodder all showed high heritability and high genetic advance. On the basis of Mahalanobis D2 statistics for clustering, genetic diversity was examined. The Tocher method was used to grouping of genotype lines into 11 clusters. The most lines were found in cluster I (52), followed by cluster VI with 14 lines, cluster III with 6 lines, and cluster IX with 2 lines. Cluster VI has the greatest intra-cluster distance (86.59), followed by clusters I (54.11), III (52.99), and IX (52.30). Between cluster III and IX had observed greater inter-cluster distance of 594.54 followed by cluster III and X (471.67), cluster IX and XI (458.76) and cluster I and IX (410.82). Intercrossing of these clusters lines might be useful to develop better forage pearl millet hybrids.

Variability and Diversity Studies Using Morpho-physiological Traits in Wheat (Triticum aestivum L.) under Irrigated and Rainfed Conditions

A study was conducted at Rice and Wheat Research Centre, Malan and Department of Genetics and Plant Breeding, CSK HPKV, Palampur, Himachal Pradesh, India during October, 2013 to May, 2014 to identify traits for developing reliable selection criteria and conduct diversity analysis. In the present investigation, fifty-six diverse wheat genotypes were evaluated in an alpha-design using various yield attributes and drought indices under irrigated and moisture-stress environments over three environments in two locations. Results from the study indicated significant differences for almost all the traits under all three environments and pooled environments. Variability studies showed high phenotypic and genotypic coefficient of variability with high heritability coupled with high genetic advance for seedling vigour under irrigated conditions as well as under moisture-stress conditions along with membrane stability index, dry matter accumulation and grain yield. On the basis of low DSI and high DRI values and relative ranking of morpho-physiological traits, seven genotypes HPW 368, NI 5439, CM 33, CM 19, HPW 236, CM 11 and HPW 349 have been found to be moisture-stress tolerant. Association studies showed that grains spike-1, harvest index, thousand grain weight, excised leaf water loss and days to 75% maturity are most important traits for selection of tolerant genotypes. On the basis of D2 statistics, 56 genotypes were grouped into twelve main clusters and genotypes HPW 277, HPW 297 and NIAW 1415 were found superior as well as diverse for most of the traits. Hence, these genotypes can be used in breeding programmes aimed at enhancing drought tolerance.

Machine Learning and Artificial Intelligence for a Sustainable Tourism: A Case Study on Saudi Arabia

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond.

Mobility as a Service (MaaS) for university communities: Modeling preferences for integrated public transport bundles

In order to investigate the role that Mobility as a Service (MaaS) could play in university communities to reduce car dependency and moderate car-oriented travel behavior, this paper examines individuals’ stated interest in adopting MaaS bundles in academic environments, where its potential is still largely underexplored. The study involves a large-scale survey campaign carried out within a university community in Milan (Italy), comprising 1873 answers from faculty members, technical-administrative staff, and students. The paper discusses the factors affecting behavioral intentions towards a potential MaaS adoption on the basis of aggregate statistics and discrete choice models estimates. This research highlights that there is a real opportunity to market MaaS in university communities, but an accurate user-centered design of the MaaS solutions is needed, based on individuals’ preferences and actual mobility needs. Results suggest that MaaS has a broader potential user base among individuals under 35 years old and Public Transport subscribers, and that MaaS bundles involving shared mobility services are attractive by residents in the city center, while reserved parking at interchange facilities is more attractive to commuters coming from suburban areas.

Uncovering Universal Characteristics of Homing Paths using Foraging Robots

Homing–the incredible ability of animals to navigate back to their homes from unfamiliar places is surprisingly widespread and crucial for their survival. However, the physical understanding of this phenomenon is not yet developed. Here we use a light-controlled robot mimicking foraging and homing behavior to investigate this phenomenon. The robot as a forager is a self-propelled active particle programed to undergo an in-plane active Brownian (AB) motion whose velocity vector v undergoes rotational diffusion of magnitude Dr. During the homing phase, the robot undergoes guided motion toward a positive light gradient aided by repeated reorientations, directing it back to its home. Our key finding is an interesting optimal behavior where the mean homing time becomes independent of Dr beyond a critical value posited as a signature of enhanced efficiency. We develop a first-passage-based theoretical model of homing motion, which elucidates this finding as well as accurately captures quantitative features of the homing trajectories in the form of temporal autocorrelation function of the robot's orientation. Inspired by the paradigm of stochastic resetting processes, we also perform an alternative homing motion in a computer, which integrates an AB motion with course correction resets, corroborating our experimental findings. Finally, we test our model on the publicly available data on homing pigeons and capture similar key characteristics of the homing trajectories. Together, these results offer valuable insights into the physics of homing dynamics, providing a statistical basis for its robustness across the animal kingdom. Published by the American Physical Society 2024