Search Results (90)

Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). Full article

This work explores the link between CEO turnover patterns and firms’ climate change exposure in a data set of over two thousand U.S. publicly traded firms. The findings demonstrate that CEO turnover is negatively associated with measures of climate change exposure developed with machine learning based on the frequency of discussions linked to climate change in the firms’ earnings conference calls. The results further indicate that this significant negative relationship exists in the year after the CEO’s departure from the firm, not before their departure. CEO turnover scenarios differ in their impact on a firm’s climate change exposure and sentiment. The focus of a firm’s management and financial analysts covering the firm can shift away from the issues of climate change. The negative and significant relationship with firms’ climate change exposure is observed particularly for forced CEO departures in firings or resignations, as well as for outsider CEO replacements. No significant relationship is found for CEO departures due to retirement or for cases of internal CEO succession. The results provide insights for decision makers, investors and boards of directors trying to evaluate the role of CEO turnover in climate change exposure at firms. Full article

Various platforms, such as Google Maps, provide information about the services of public transport companies worldwide. Operators publish the planned (static) timetable using the General Transit Feed Specification (GTFS) format, while the GTFS Realtime (GTFS-RT) specification provides live (dynamic) information about the services. In this paper, we present our dataset that was built by retrieving and pre-processing the data sources of the open data platform of BKK Futár, hosted by the Centre for Budapest Transport Company (BKK). The paper contains a well-detailed description of our methods for retrieving and pre-processing the data among statistical features. The dataset covers a one-year period in which the data collection mechanism used for realtime data was continuously improved from collecting only live vehicle positions to covering all the available feeds and increasing the query frequency. We merged the static data with the vehicle positions to filter them, yielding a clean set of tracked trips. As a result, more than 90% of the daily planned trips could be reconstructed from the responses. We provide an interactive web-based visualization for the analysis of the GTFS schedule’s, and the GTFS-RT Vehicle Positions feed’s, geospatial features. The dataset and also our methodology can serve as input for various research studies to investigate the common characteristics of delays and disruptions or predict real departure times based on the current vehicle positions and historical data. Full article

Urban rail transit (URT) systems frequently face operational challenges arising from temporal and spatial imbalances in passenger demand, resulting in inefficiencies in train scheduling and resource utilization. To address these issues, this study proposes a multi-objective optimization model that jointly plans short-turn and full-length train services. The objectives of the model are to minimize total passenger waiting time and train mileage while improving passenger load distribution across the rail line, subject to practical constraints such as departure frequency limitations, rolling stock availability, and coverage of short-turn services. To efficiently solve this model, an improved Pelican Optimization Algorithm (POA) is developed, incorporating techniques such as Tent chaotic mapping, nonlinear weight adjustment, Cauchy mutation, and the sparrow alert mechanism, significantly enhancing convergence accuracy and computational efficiency. A real-world case study based on Nanjing Metro Line 1 demonstrates that the proposed framework substantially reduces average passenger waiting times and overall train mileage, achieving a more balanced distribution of passenger loads. In addition, the study reveals that flexible-ratio dispatching strategies, representing theoretically optimal solutions, outperform integer-ratio dispatching schemes that reflect real-world operational constraints. This finding underscores that investigating the practical feasibility and optimization potential of flexible-ratio scheduling strategies constitutes a valuable direction for future research. The outcomes of this study provide a scalable and intelligent decision-support framework for train scheduling in URT systems, effectively contributing to the sustainable and intelligent development of rail operations. Full article

Wildfires, a natural part of the wildland life cycle, are experiencing a decades-long trend of increased frequency, duration, and magnitude, resulting in increased risk of fatalities and property damage. Fire suppression methods are adapting accordingly, including the increased use of aerial firefighting. Aerial firefighting, conducted in coordination with ground crews, provides real-time reconnaissance of a wildfire and performs strategic drops of retardant to contain and/or suppress the fire. These flight operations require airport and air traffic control infrastructure. The purpose of this report is to provide statistics on the U.S. aerial firefighting fleet, flight operations, and airport utilization and equipment in 2024. This information, which is not readily available, may be of use to airport planners, air navigation service providers, and policy makers. Thirty-four (34) Very Large/Large Air Tankers (VLAT/LATs) were under contract with the United States Forest Service (USFS) Multiple Award Task Order Contracts (MATOCs) in 2024. The aircraft, ranging in age from 27 to 57 years, performed 11,219 retardant drop and reposition flights. Flights operated on 88% of the days with an average of 35 flights per day and a maximum of 200 flights per day. The number of flights per aircraft across the fleet was not uniform (average 288 flights, max 465 flights). Consistent with firefighting practices, the flights operated under Visual Flight Rules (VFR), mostly in the afternoons, with an average retardant drop flight duration of 34 min. Two hundred and seven (207) airports supported at least one departure, with 14 airports supporting 50% of the departures. Eighty-six (86%) percent of the airports were towered and 84% had precision approach procedures. All but two military airports were public airports that are part of the National Plan for Integrated Airport System (NPIAS) and eligible for Airport Improvement Plan (AIP) funding. Runway length and weight bearing are limitations at several airports. Furthermore, operations are no longer limited to airports west of the Rockies, with increased operations in the mid-west and east coast. Full article

Background: The diet of young people in Spain has changed significantly, with a departure from a balanced dietary pattern and a greater intake of processed foods. Such food generates an acidic environment in the mouth, which promotes the multiplication of bacteria capable of causing inflammation and damage to the gums. Aim: This study aimed to determine the association between the frequency of consuming processed foods and periodontal disease, as well as sex differences, in an adolescent population. Methods: A study was conducted on 233 students aged 15 to examine the frequency of food consumption and its correlation with periodontal disease. Differences were determined via a Student’s t-test to compare the means. A chi-square test was used to compare categorical variables. The 95% confidence interval estimate was used in all cases (p < 0.05). Results: It was observed that girls have a higher mean number of healthy sextants than boys (3.26 $\pm$ 0.20 vs. 2.70 ± 0.21; p = 0.029). A statistically significant difference was noted between healthy and affected subjects in the frequency of consumption of packaged milkshakes (p = 0.003), industrial juices (p = 0.009), industrial pastries (p = 0.018), and fruits in syrup (p = 0.022). When segmented by sex, a statistically significant difference was noted in boys between healthy and affected subjects in the frequency of consumption of packaged milkshakes (p = 0.044), salty snacks (p = 0.032), and cold cuts (p = 0.033); in girls, the difference was detected in industrial juices (0.024). Conclusions: The results of this study suggest that adolescent boys are more affected periodontally than girls. In both sexes, the level of consumption of processed foods affects the presence of periodontal disease. Full article

Departures from historical wildfire regimes due to climate change have significant implications for the structure and composition of forests, as well as for fire management and operations in the Alberta region of Canada. This study analyzed the relationship between climate and wildfire and used a random forest algorithm to predict future wildfire frequencies in Alberta, Canada. Key factors driving wildfires were identified as vapor pressure deficit (VPD), sea surface temperature (SST), maximum temperature (Tmax), and the self-calibrated Palmer drought severity index (scPDSI). Projections indicate an increase in wildfire frequencies from 918 per year during 1970–1999 to 1151 per year during 2040–2069 under a moderate greenhouse gas (GHG) emission scenario (RCP 4.5) and to 1258 per year under a high GHG emission scenario (RCP 8.5). By 2070–2099, wildfire frequencies are projected to increase to 1199 per year under RCP 4.5 and to 1555 per year under RCP 8.5. The peak number of wildfires is expected to shift from May to July. These findings suggest that projected GHG emissions will substantially increase wildfire danger in Alberta by 2099, posing increasing challenges for fire suppression efforts. Full article

Multiplication, division, and square root operations introduce significant challenges in digital signal processing (DSP) systems, traditionally requiring multiple operations that increase execution time and hardware complexity. This study presents a novel approach that leverages binary logarithms to perform these operations using only addition, subtraction, and shifts, enabling a unified hardware implementation—a marked departure from conventional methods that handle these operations separately. The proposed design, involving logarithm and antilogarithm calculations, exhibits an algebraically symmetrical pattern that further optimizes the processing flow. Additionally, this study introduces innovative log-domain correction terms specifically designed to minimize computation errors—a critical improvement over existing methods that often struggle with precision. Compared to standard hardware implementations, the proposed design significantly reduces hardware resource utilization and power consumption while maintaining high operational frequency. Full article

Although RFID(radio frequency identification) tags do not require a direct line of sight, their operational range is often characterized as being limited. Indeed, in the case of passive RFID tags, the interrogating signal from the transmitter needs to reach the tag’s radio transponder and trigger a nearly omnidirectional scattered signal to be harvested by the receiver. This two-way (from Tx to the tag and back to Rx) channel exhibits increased attenuation not only due to the doubled distance (in case Tx and Rx are collocated) but also to the uncontrolled (i.e., unfocused) backscattering. In the work at hand, we propose a way to control the backscattered radiation and focus the produced beam towards the direction of the reader (the Tx-Rx device). Indeed, one can utilize the concept of retrodirective arrays to immediately control the direction of departure of the backscatter link, maximizing the scattered power towards the reader and thus delivering an increase in the operational range of the tag. This of course means that in this case, the tag should be equipped with a minimum of two element radiators. Thus, retrodirective RFID array tags are introduced in the current work to increase the operating range with minimal costs and levels of complexity since 90° hybrids are used to achieve proper backscattering. To evaluate the proposed passive tag array, performance aspects are addressed. Specifically, we examine the Bit Error Rate with respect to the Signal to Noise Ratio for the retrodirective tag, the one antenna, the broadside, and the spatial diversity array. The results prove that the proposed tag allows for a significant increase in the operational range. Full article

In this paper, we develop a multi-objective integrated optimization method for feeder buses of rail transit based on realistic considerations. We propose a bus stop selection method that considers the influence of shared motorcycles, which can score the importance of alternative bus stops and select those with the highest scores as objectives. The objective of the model in this paper is to minimize both the travel costs of passengers and the operating costs of the bus company. This is achieved by optimizing feeder bus routes, the frequency of departures, and interchange discounts to enhance the connectivity between feeder buses and rail transit. In addition, to ensure the feasibility of generated routes in the real road network, a genetic algorithm encoded with priority is used to solve this model. We use the Xingyao Road subway station in Kunming as an example, and the results show that the optimization method is effective. Full article

Polycotyly, an interesting characteristic of seed-bearing dicotyledonous plants with more than two cotyledons, represents one of the least explored plant characters for utilization, even though cotyledon number was used to classify flowering plants in 1682. Gymnosperm and angiosperm species are generally known to have one or two cotyledons, but scattered reports exist on irregular cotyledon numbers in many plant species, and little is known about the extent of polycotyly in plant taxa. Here, we attempt to update the documentation of reports on polycotyly in plant species and highlight some lines of research for a better understanding of polycotyly. This effort revealed 342 angiosperm species of 237 genera in 80 (out of 416) families and 160 gymnosperm species of 26 genera in 6 (out of 12) families with reported or cited polycotyly. The most advanced research included the molecular-based inference of the phylogeny of flowering plants, showing a significant departure from the cotyledon-based classification of angiosperm plants, and the application of genetic cotyledon mutants as tools to clone and characterize the genes regulating cotyledon development. However, there were no reports on breeding lines with a 100% frequency of polycotyly. Research is needed to discover plant species with polycotyly and to explore the nature, development, genetics, evolution, and potential use of polycotyly. Full article

Freight transportation, dominated by trucks, is an integral part of trade and production in the USA. Given the prevalence of large truck crashes, a comprehensive investigation is imperative to ascertain the underlying causes. This study analyzed 2017–2021 Texas crash data to identify factors impacting large truck crash rates and injury severity and to locate high-risk zones for severe incidents. Logistic regression models and bivariate analysis were utilized to assess the impacts of various crash-related variables individually and collectively. Heat maps and hotspot analysis were employed to pinpoint areas with a high frequency of both minor and severe large truck crashes. The findings of the investigation highlighted night-time no-passing zones and marked lanes as primary road traffic control, highway or FM roads, a higher posted road speed limit, dark lighting conditions, male and older drivers, and curved road alignment as prominent contributing factors to large truck crashes. Furthermore, in cases where the large truck driver was determined not to be at fault, the likelihood of severe collisions significantly increased. The study’s findings urge policymakers to prioritize infrastructure improvements like dual left-turn lanes and extended exit ramps while advocating for wider adoption of safety technologies like lane departure warnings and autonomous emergency braking. Additionally, public awareness campaigns aimed at reducing distracted driving and drunk driving, particularly among truck drivers, could significantly reduce crashes. By implementing these targeted solutions, we can create safer roads for everyone in Texas. Full article

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies. Full article

The ascendency of the craft beer movement within the brewing industry may be attributed to its commitment to unique flavours and innovative styles. Mixed-culture fermentation, celebrated for its novel organoleptic profiles, presents a modelling challenge due to its complex microbial dynamics. This study addresses the inherent complexity of modelling mixed-culture beer fermentation while acknowledging the condition monitoring limitations of craft breweries, namely sporadic offline sampling rates and limited available measurement parameters. A data-driven solution is proposed, utilising an Autoregressive Recurrent Neural Network (AR-RNN) to facilitate the production of novel, replicable, mixed-culture fermented beers. This research identifies time from pitch, specific gravity, pH, and fluid temperature as pivotal model parameters that are cost-effective for craft breweries to monitor offline. Notably, the autoregressive RNN fermentation model is generated using high-frequency multivariate data, a departure from intermittent offline measurements. Employing the trained autoregressive RNN framework, we demonstrate its robust forecasting prowess using limited offline input data, emphasising its ability to capture intricate fermentation dynamics. This data-driven approach offers significant advantages, showcasing the model’s accuracy across various fermentation configurations. Moreover, tailoring the design to the craft beer market’s unique demands significantly enhances the model’s practicable predictive capabilities. It empowers nuanced decision-making in real-world mixed-culture beer production. Furthermore, this model lays the groundwork for future studies, highlighting transformative possibilities for cost-effective model-based control systems in the craft beer sector. Full article

We aimed to capture the fluctuations in the dynamics of body positions and find the characteristics of them in patients with idiopathic normal pressure hydrocephalus (iNPH) and Parkinson’s disease (PD). With the motion-capture application (TDPT-GT) generating 30 Hz coordinates at 27 points on the body, walking in a circle 1 m in diameter was recorded for 23 of iNPH, 23 of PD, and 92 controls. For 128 frames of calculated distances from the navel to the other points, after the Fourier transforms, the slopes (the representatives of fractality) were obtained from the graph plotting the power spectral density against the frequency in log–log coordinates. Differences in the average slopes were tested by one-way ANOVA and multiple comparisons between every two groups. A decrease in the absolute slope value indicates a departure from the 1/f noise characteristic observed in healthy variations. Significant differences in the patient groups and controls were found in all body positions, where patients always showed smaller absolute values. Our system could measure the whole body’s movement and temporal variations during walking. The impaired fluctuations of body movement in the upper and lower body may contribute to gait and balance disorders in patients. Full article