Time Of Discovery Research Articles

TONE: A CHIME/FRB Outrigger Pathfinder for Localizations of Fast Radio Bursts using Very Long Baseline Interferometry

The sensitivity and field of view of the Canadian Hydrogen Intensity Mapping Experiment (CHIME) has enabled its fast radio burst (FRB) backend to detect thousands of FRBs. However, the low angular resolution of CHIME prevents it from localizing most FRBs to their host galaxies. Very long baseline interferometry (VLBI) can readily provide the subarcsecond resolution needed to localize many FRBs to their hosts. Thus, we developed TONE: an interferometric array of eight 6-m dishes to serve as a pathfinder for the CHIME/FRB Outriggers project, which will use wide field-of-view cylinders to determine the sky positions for a large sample of FRBs, revealing their positions within their host galaxies to subarcsecond precision. In the meantime, TONE’s [Formula: see text] [Formula: see text]km baseline with CHIME proves to be an excellent testbed for the development and characterization of single-pulse VLBI techniques at the time of discovery. This work describes the TONE instrument, its sensitivity, and its astrometric precision in single-pulse VLBI. We believe that our astrometric errors are dominated by uncertainties in the clock measurements which build up between successive Crab pulsar calibrations which happen every [Formula: see text] [Formula: see text]h; the wider fields-of-view and higher sensitivity of the Outriggers will provide opportunities for higher-cadence calibration. At present, CHIME-TONE localizations of the Crab pulsar yield systematic one-dimensional localization errors of [Formula: see text] [Formula: see text]asec — considerably better than the resolution achievable with seeing limited followup observations in the optical/near-infrared from the ground.

Salutary impact of spontaneous oxidation in CH3NH3SnI3 on CZTS-based solar cell

From the time of discovery, CH3NH3SnI3 has been a promising candidate in photovoltaics due to its outstanding optoelectronic properties. However, stabilization was not easy to achieve in CH3NH3SnI3-based solar cells. Because CH3NH3SnI3 was used as an absorber, its naturally-occurring self-doping property spontaneously modified band alignment, which increased carrier recombination and decreased the efficiency of solar cell gradually. In this paper, for the first time, we have presented detailed study on use of CH3NH3SnI3 as a hole transport layer in prototype solar cell having configuration: CH3NH3SnI3/CZTS/CdS/ZnO/AZO, using SCAPS software. To understand the effect of spontaneous self-doping property of CH3NH3SnI3 on solar cell performance, the analysis of variation in solar cell performance parameters, band alignment conduction band, valance band, Fermi levels, charge density, current density, conductance, capacitance and recombination rate was performed as a function of increasing CH3NH3SnI3 carrier concentration. It was found that, when used as an hole transport layer, the inherent self-doping property of CH3NH3SnI3 became a helpful trait to increase hole extraction and spontaneously enhanced our device efficiency. Thus, the inherent self-doping property of CH3NH3SnI3 transformed from curse to boon when we leveraged CH3NH3SnI3 as an hole transport layer in our solar cell device.

First record of a hydrozoan (Cnidaria, Hydrozoa) growing on a sigalionid scaleworm (Annelida, Sigalionidae)

The scaleworm Pelogenia fimbriata (Hartman, 1939) collected from Secas Islands, Panama, during the Allan Hancock Pacific Expedition in 1916, was not only recognized as an undescribed species at the time of discovery but has also revealed a new finding almost nine decades after its description. A paratype of P. fimbriata was examined in the Natural History Museum of Los Angeles County, USA and the hydroid Bimeria vestita Wright, 1959 was found attached to an anterior scale of the worm. The interaction between B. vestita and P. fimbriata in this study represents the first record of epibiosis of a hydroid on a sigalionid scaleworm. This discovery contributes to our understanding of epibiosis in Hydrozoa and opens new avenues for further research in this area.

NPGPT: natural product-like compound generation with GPT-based chemical language models

Natural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been common for many years. However, the intricate structures of these compounds present challenges in terms of structure determination and synthesis, particularly compared to the efficiency of high-throughput screening of synthetic compounds. In recent years, deep learning-based methods have been applied to the generation of molecules. In this study, we trained chemical language models on a natural product dataset and generated natural product-like compounds and verified the performance of the generated compounds as a drug candidate library. The results showed that the distribution of the compounds generated was similar to that of natural products. We also evaluated the effectiveness of the generated compounds as drug candidates. Our method can be used to explore the vast chemical space and reduce the time and cost of drug discovery of natural products.

Broken Drill Bits During Surgical Procedures: A Review of 156 Patient Safety Events

Background Broken surgical drill bits pose a risk to patient safety. A routine review of event reports submitted to the Pennsylvania Patient Safety Reporting System (PA-PSRS) uncovered an increase in the number of event reports related to broken drill bits, prompting an exploration to better understand patterns of drill bit breakage in Pennsylvania. Methods We queried the PA-PSRS database to identify event reports describing a broken drill bit during surgery submitted between January 1, 2021, and December 31, 2023. Event reports were manually coded to identify the procedure, anatomic location of breakage in the patient’s body, timing of discovery, and fragment fate. Results A total of 156 relevant event reports were identified. The procedure being performed was determined in 64 of 156 event reports. Fracture repair procedures were most commonly reported to encounter a broken drill bit (27 of 64; 42.2%). We were able to determine the anatomic location in the patient’s body where the drill bit breakage occurred in 108 of 156 event reports. The most commonly reported anatomic location was the femur (27 of 108; 25.0%). We were able to determine the fate of the broken drill bit in 141 of 156 event reports, and most drill bits were retained in the patient (99 of 141; 70.2%). Conclusion This data analysis provides insight into broken drill bits in Pennsylvania facilities. Because drill bits are commonly broken pieces of equipment and the fragments from these broken pieces are frequently retained, it is important to prevent drill bit breakage to improve patient safety. Table 2 (251188) outlines prevention strategies established by equipment manufacturers and previous research. Implementing these strategies can address breakage across multiple procedures and patients. Plain Language Summary While a rare occurrence, surgical instruments may break during a procedure. In the case of drill bits, which are one of the most frequently broken surgical instruments, pieces that are left inside of a patient could cause harm, and may migrate to other parts of the body. When Patient Safety Authority researchers noted an increased number of events involving broken and retained drill bits during surgery, they analyzed data from the Pennsylvania Patient Safety Reporting System (PA-PSRS) to help decrease the risk of broken drill bits in surgery. In studying cases involving a drill bit breaking during surgery, researchers learned that most of these events occurred during fracture repair procedures and joint replacement surgery, and drill bits most frequently broke in the femur (thigh bone). In most event reports, the broken drill bit was discovered during the procedure, but in 2.6% of event reports (4 out of 156 reports) it was discovered after the operation. Removing drill bit fragments is recommended; however, surgeons may decide to leave them in place if removal risks damage to the surrounding area, and in many event reports broken drill bits were retained following the surgery with no further intervention. Hospitals and surgical facilities should look to existing literature and device manufacturers’ guidelines for prevention strategies, such as recommended surgical techniques; drill bit sterilization, reprocessing, and storage; and general safety measures, some of which are summarized in the article.

(Invited) Nanosheet Antibody Mimics for Diagnosis and Therapy of Bacterial Infections

Antibodies are widely employed as recognition elements in diagnosis and therapy of various diseases [1], however, they have some limitations such as poor stability, long discovery time, and high cost. Herein, we present a facile approach to create nanosheet antibody mimics with multivalent peptide recognition phases and luminescent rigid transition metal dichalcogenide (TMD) nanosheets for the selective recognition, detection, and eradication of pathogenic bacteria. Tripeptides with a nitriloacetate-Cu group were spontaneously assembled on TMD nanosheets via coordinate bonding, providing a variety of TMD-tripeptide assembly (TPA) antibody mimics [2]. TMD-TPA antibody mimics selectively recognized various pathogenic bacteria, including E. coli O157:H7, S. aureus, and S. typhimurium, with high binding affinities. The bacterial binding sites for TMD-TPA were identified by experiments and molecular dynamics simulations, revealing that the dynamic and multivalent interactions of TMD-TPA antibody mimics played an important role for their selective recognition and high affinity. The TMD-TPA antibody mimics allowed the rapid, selective, and sensitive detection of pathogenic bacteria from human serum and urine. In addition, the TMD-TPA antibody mimics selectively killed the pathogenic bacteria infecting the wounds of mice under near-infrared irradiation, leading to effective healing. This work demonstrates the potential of TMD-TPA antibody mimics as an alternative to protein antibodies for diagnosis and therapy.Reference[1] a) H. K. Binz, P. Amstutz, A. Pluckthun, Nat. Biotechnol. 2005, 23, 1257; b) M. Gebauer, A. Skerra, Curr. Opin. Chem. Biol. 2009, 13, 245[2] T. W. Kang, I. J. Hwang, S. Lee, S. J. Jeon, C. Choi, J. Han, Y. So, W. Son, H. Kim, C. S. Yang, J. H. Park, H. Lee, and J. H. Kim, Advanced Materials, 2021, 2101376

Formation of an Active Giant Nonsulfide Zinc System, Jinding, China: Relationships Among Tectonics, Climate, and Supergene Metal Remobilization

Abstract The oxidized portion of the giant Jinding Zn-Pb deposit, Yunnan, China, accounts for about 40% of the original metal resource, indicating a nonsulfide zinc component of 80 to 90 million tonnes (Mt), making it among the largest nonsulfide zinc concentrations known. The presence of the Jinding deposit within a rugged topographic upland undergoing active weathering and erosion suggests that the nonsulfide zinc supergene system was active at the time of discovery. The ore-bearing strata cropped out at ~2800 m elevation, and strong oxidation effects are present to at least 200 m below the premining surface. The exhumation of the late Oligocene Jinding primary ores and exposure to groundwater modification may have been initiated by late Miocene uplift that coincides with the onset of the Southeast Asia monsoonal climate. The abundance of calcium carbonate in the Jinding host strata and primary ore results in the preserved nonsulfide zinc ores being dominated by smithsonite crusts of a variety of compositions, along with a diverse assemblage of supergene Zn-Pb and other minerals reflecting the primary ore composition. Complex microstratigraphic banding in smithsonite at scales of ≤20 µm is revealed by luminescent microscopy, likely recording groundwater-related annual precipitation events. Jinding smithsonites from the Beichang and Jaiyashan ore zones have δ18OV-SMOW values (in which V-SMOW = Vienna-standard mean ocean water) that vary between 18.6 and 21.5‰, which are unusually low compared with most supergene smithsonites. These values are consistent with the cold conditions of the Jinding area (11°C mean annual temperature) and are compatible with local cold springs and travertines. The δ13CV-PDB values (in which V-PDB = Vienna-Pee Dee Belemnite) of the Jinding smithsonites range between –10.1 and –3.6‰ and are distinct from calcites formed by local hot springs and from low-18O smithsonites from other nonsulfide zinc deposits. The light carbon isotope composition of Jinding smithsonites suggests a major contribution of organic carbon and only minor, if any, contributions from local Triassic marine limestone and atmospheric CO2. The 13C-depleted carbon likely was derived from soils with C3 plant-dominated organic matter, from oxidation of hydrocarbons in the primary ore, or from dissolution of isotopically light sandstone cements or ore calcites. Reaction path models show that the reaction of descending, cool, oxidizing, meteorically derived groundwater with calcite, CO2, and primary sulfide minerals can account for much of the observed supergene mineral assemblage. The Jinding nonsulfide zinc mineralization reflects the interrelationships among tectonic, climatic, and groundwater processes in high elevation and/or latitude settings, notably related to cold groundwater. Although the preservation of thick nonsulfide zinc concentrations was impeded by the dynamic regional tectonic and climatic conditions that resulted in high precipitation and erosion rates, the size of the primary ore zone and focused groundwater transport resulted in the development of a giant nonsulfide zinc mineralization system.

Hybrid Optimization With Deep Spiking Equilibrium Neural Network for Software Defined Network Based Congestion Prevention Routing

ABSTRACTIn the Internet of Things (IoT) network, within a period of time it constructs a huge network of millions and billions of things that incorporate with each other, leading to various technical and application problems. Also, on‐time delivery of packets is significant in software‐defined networks. In the current software‐defined network, the bandwidth overhead is not considered when an enormous amount of traffic enters the network; this may lead to network congestion. To overcome this issue, a novel method is proposed to detect network congestion based on hybrid optimization, namely, the Hybrid Gannet with Pelican Optimization (HGPO) algorithm. The node‐level congestions and the link‐level congestions, which means the buffer overflow and the multiple nodes trying to utilize the channel at the same time, must be controlled efficiently. Once the network congestion gets controlled, the shortest route path selection and congestion prevention are performed perfectly with the aid of the proposed Deep Spiking Equilibrium Neural Network (DSENN). A few route discovery frequency vectors, such as the interroute discovery time and route discovery time of each node, are determined to prevent congestion. Finally, it is implemented in the Python platform successfully, and the achieved throughput, delay, packet loss ratio, packet delivery ratio, end‐to‐end delay, and performance measures of the proposed method are 140 Mbit/s, 17 ms, 3.8%, 0.35 s, and 100%, respectively, which outperforms the other compared traditional algorithms.

A new methodology for evaluating the neighbor discovery time in schedule-based asynchronous duty-cycling wireless sensor networks

Duty cycling is a fundamental mechanism for battery-operated wireless networks, such as wireless sensor networks. Due to its importance, it is an integral part of several Medium Access Protocols and related wireless technologies. In Schedule-based Asynchronous Duty Cycle, nodes activate and deactivate their radio interfaces according to a pre-designed schedule of slots, which guarantees overlapping uptime between two neighbors, independent of the offset between their internal clocks, making communication between them possible. This paper presents a new methodology for evaluating the Neighbor Discovery Time (NDT) of Schedule-based Asynchronous Duty Cycle. Differently from previous methodologies, it accounts for the possibility of the slots in the schedules of the two neighbors not being perfectly border-aligned — an unrealistic assumption in practice. By means of simulation, we show that not taking this under consideration can lead to an overestimate of the NDT by a factor of 2 depending on the particular scenario, thus justifying the importance of our work.•We propose a new subslot-based methodology for computing the NDT of a wakeup schedule used for asynchronous duty cycling.•It replaces the traditional slot-based methodology, by dividing slots into subslots, allowing for the analysis of non-integer clock offsets between nodes, and further allowing mathematical models to consider the more realistic continuous-time case.•Our validation data shows that the slot-based methodology may overestimate NDT by a factor of up to 2, making the proposed subslot-based methodology much more precise.

Context-Driven Service Deployment Using Likelihood-Based Approach for Internet of Things Scenarios

In a context-aware Internet of Things (IoT) environment, the functional contexts of devices and users will change over time depending on their service consumption. Each iteration of an IoT middleware algorithm will also encounter changes occurring in the contexts due to the joining/leaving of new/old members; this is the inherent nature of ad hoc IoT scenarios. Individual users will have notable preferences in their service consumption patterns; by leveraging these patterns, the approach presented in this article focuses on how these changes impact performance due to functional-context switching over time. This is based on the idea that consumption patterns will exhibit certain time-variant correlations. The maximum likelihood estimation (MLE) is used in the proposed approach to capture the impact of these correlations and study them in depth. The results of this study reveal how the correlation probabilities and the system performance change over time; this also aids with the construction of the boundaries of certain time-variant correlations in users’ consumption patterns. In the proposed approach, the information gleaned from the MLE is used in arranging the service information within a distributed service registry based on users’ service usage preferences. Practical simulations were conducted over small (100 nodes), medium (1000 nodes), and relatively larger (10,000 nodes) networks. It was found that the approach described helps to reduce service discovery time and can improve the performance in service-oriented IoT scenarios.

749. GENDER DIFFERENCE IN ESOPHAGEAL SQUAMOUS CELL CARCINOMA

Abstract In Japan, esophageal cancer is often more common in men, with a lower incidence in women than in men, accounting for 12% of the total number of cancers. In Japan and the West, SCC is involved in alcohol and tobacco. These habits are characteristically more common among men. Women are more likely to be diagnosed at an earlier stage than men. Therefore, we examined the prognosis, stage, and pathology of 145 men and 30 women who underwent radical surgery for esophageal cancer over the past 5 years. (Result) p stage is male: stage 0:7(5%), stage 1:35(25%), stage II: 58(40%), stage III:35(25%), stage IV:10(7%) female: Stage 0: 2 (7%), Stage 1: 12 (40%), Stage II: 6 (20%), Stage III: 9 (30%), Stage IV: 1 (3%). Significantly more men had advanced cancer. No difference was observed regarding Ly and V. Although there were no differences in location and histology, depth of invasion and lympho node metastasis were significantly enhanced in males. (table 1) Kaplanmier survival rate after 5 years was significantly better for women than for men. (Fig 1).(Conclusion) Compared to men, women had less advanced cancer at the time of discovery and a better 5-year survival rate.

An asset management modelling framework for wind turbine blades considering monitoring system reliability

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades.

An Adaptive 3D Neighbor Discovery and Tracking Algorithm in Battlefield Flying Ad Hoc Networks with Directional Antennas.

Neighbor discovery and tracking with directional antennas in flying ad hoc networks (FANETs) is a challenging issue because of dispersed node distribution and irregular maneuvers in three-dimensional (3D) space. In this paper, we propose an adaptive 3D neighbor discovery and tracking algorithm in battlefield FANETs with directional antennas. With time synchronization, a flying node transmits/receives the neighbor discovery packets sequentially in each beam around it to execute a two-way handshake for neighbor discovery. The transmitting or receiving status of each discovery slot depends on the binary code corresponding to the identification of the node. Discovered neighbor nodes exchange their 3D positions in tracking slots periodically for node tracking, and the maximum tracking period is determined by node velocity, beamwidth, and the minimum distance between nodes. By configuring the relevant parameters, the proposed algorithm can also apply to two-dimensional planar ad hoc networks. The simulation results suggest that the proposed algorithm can achieve shorter neighbor discovery time and longer link survival time in comparison with the random scanning algorithm in scenarios with narrow beamwidth and wide moving area. When the frame length increases, the protocol overhead decreases but the average neighbor discovery time increases. The suitable frame length should be determined based on the network range, node count, beamwidth, and node mobility characteristics.

Surface modification of graphene and fullerene with Sulfur (S), Selenium (Se), and Oxygen (O): DFT Simulation for enhanced zidovudine delivery in HIV treatment

HIV is one of the most threatening health conditions with a highly increasing rate, affecting millions of people globally, and from its time of discovery until now, its potential cure cannot be explicitly defined. This challenge of having no/low effective drugs for the subjected virus has called for serious attention in the scientific world of virus disease therapeutics. Most of these drugs yields low effectiveness due to poor delivery; hence, there is a need for novel engineering methods for efficient delivery. In this study, two nanomaterilas (graphene; GP, and fullerene; C60) were modelled and investigated with sulfur (S), selenium (Se), and oxygen (O) atoms, to facilitate the delivery of zidovudine (ZVD). This investigation was computationally investigated using the density functional theory (DFT), calculated at B3LYP functional and Gd3bj/Def2svp level of theory. Results from the frontier molecular orbital (FMO), revealed that the GP/C60_S_ZVD complex calculated the least energy gap of 0.668 eV, thus suggesting a favourable interactions. The study of adsorption energy revealed chemisorption among all the interacting complexes wherein GP/C60_S_ZVD complex (-1.59949 eV) was highlighted as the most interacting system, thereby proving its potential for the delivery of ZVD. The outcome of this research urges that a combination of GP and C60 modified with chalcogen particularly, O, S, and Se can aid in facilitating the delivery of zidovudine.

Congenital heart disease: Epidemiological, genetic and evolutive profil.

Congenital heart disease is a heterogeneous group of malformations and one of the most common causes of mortality in children. The aim of this study was to investigate the clinical, genetic and evolutive characteristics of congenital heart disease. A retrospective, descriptive study was carried out between 2020 and 2023 at the pediatrics and neonatology department of Mongi Slim university hospital of Tunis. All children with confirmed congenital heart disease were included. Forty-five patients were included, representing 5.7‰ of all admissions. The sex ratio was 1.4. A prenatal diagnosis of congenital heart disease was established in 9% of cases. The median age at the time of discovery was 18 days. The initial symptomatology was respiratory distress in 64% of cases. The main reasons for performing a cardiac ultrasound were heart murmur in 38% followed by polymalformative assessment in 27% of cases. Most of the cardiopathies were atrial septal defects (42%) and ventricular septal defects (40%). Cyanotic heart diseases represented 29% of cases, conotruncal ones 13% and ductodependent ones 16%. Congenital heart disease was associated with a genetic anomaly in 53% of patients, including 15 cases of trisomy 21 and four Di-George syndromes. The treatment was mainly medical (38%), associated with surgery in 5 cases. Death occurred in nine patients, representing a mortality rate of 20%. Efforts still need to be made to improve pre- and post-natal diagnosis and ensure rapid treatment in order to reduce morbidity and mortality in our country.

Ant-observer: A new approach for automatic acquisition and autonomous analyses of individual species abundance and interactions

Traditional methods, such as direct observations or manually photographing bait stations, are limited in both data collection efficiency and accuracy. Consequently, they fail to capture the dynamic changes and complex relationships between ant species. We developed a novel system as a potential solution that combines processed consumer-grade camera equipment and software that acquires data needed for in situ experiments. This software features a user-friendly graphics user interface (GUI) for data collection and post-hoc analysis from videos captured by the camera equipment. Researchers can use this system to (1) count species abundance per frame; (2) calculate species speed; (3) detect ant interactions; and (4) count the abundance of different subcastes within species on baits. To demonstrate the complete workflow, we applied this system on ant videos collected in Hong Kong. Practical guides (supplement materials) detailing suggestions for camera equipment and software operations in both field and laboratory studies are provided. We present the results of ten in situ experiments of ants (each lasting 1–1.5 h) conducted in Hong Kong though processed consumer-grade camera equipment, with videos analyzed using the newly developed software. The analysis captured a range of variables related to ant foraging behavior, including resource discovery time, nestmate recruitment rate, and interspecific interactions. Our results confirm that the developed software provides an integrated and efficient approach for data collection, extraction, and analysis in in situ experiments, enhancing the capabilities of insect ecology research.

Clean4TSDB: A Data Cleaning Tool for Time Series Databases

Billions of data points are generated by devices equipped with thousands of sensors, leading to significant data quality issues in time series data. These errors not only complicate time series data management but also compromise the accuracy and reliability of analysis based on such data. Given the noteworthy characteristics of time series data, existing cleaning methods struggle to provide adequate repairs, and tools supporting expressive constraints for time series remain scarce. To address this, we develop Clean4TSDB, a specialized data cleaning system for time series databases. This system integrates three key modules: expressive data quality constraint discovery, violation detection, and multivariate time series repairing, forming a comprehensive "profiling-detection-repair" workflow. Technically, we introduce TSDD, a data quality constraint that effectively captures contextual relationships within multivariate time series, and implement an efficient algorithm for its automated mining. Leveraging both row- and column-based constraints, we propose an effective time series cleaning algorithm. From a system standpoint, Clean4TSDB is pre-configured for seamless integration with time series databases like Apache IoTDB. Using user-provided and algorithmically-mined constraints, it effectively identifies various error patterns and offers reliable cleaning solutions. Furthermore, we establish a comprehensive library of state-of-the-art time series repair algorithms to meet the diverse needs of different management scenarios.

Impact of Active learning model and prior knowledge on discovery time of elusive relevant papers: a simulation study

Software that employs screening prioritization through active learning (AL) has accelerated the screening process significantly by ranking an unordered set of records by their predicted relevance. However, failing to find a relevant paper might alter the findings of a systematic review, highlighting the importance of identifying elusive papers. The time to discovery (TD) measures how many records are needed to be screened to find a relevant paper, making it a helpful tool for detecting such papers. The main aim of this project was to investigate how the choice of the model and prior knowledge influence the TD values of the hard-to-find relevant papers and their rank orders. A simulation study was conducted, mimicking the screening process on a dataset containing titles, abstracts, and labels used for an already published systematic review. The results demonstrated that AL model choice, and mostly the choice of the feature extractor but not the choice of prior knowledge, significantly influenced the TD values and the rank order of the elusive relevant papers. Future research should examine the characteristics of elusive relevant papers to discover why they might take a long time to be found.

A SURVEY OF VERTEBRATES’ ROADKILL WITHIN A 10 KM RADIUS FROM THE CENTRE OF KUALA PILAH TOWN, NEGERI SEMBILAN

Assessing animal roadkill provides valuable insights into the diversity and abundance of different species within a particular area. This study has the potential to uncover factors influencing the occurrence of animal roadkill. However, there is a scarcity of studies focusing on animal roadkill in small, secluded areas such as the Kuala Pilah district, Negeri Sembilan. Thus, this study evaluates the composition and distribution of vertebrate roadkill within a 10 km radius from the centre of Kuala Pilah Town, with a focus on temporal (time of discovery), spatial (types of roads and types of surrounding areas), and environmental factors (the presence of streetlights). The road segments located within a 10 km radius from Kuala Pilah Town have been examined in ten weeks and the found carcasses were identified by their taxonomic groups and photographed. Further, their discovery location has been observed and marked with coordinates. A chi square test was then performed to evaluate the occurrence of animal roadkill with all recorded data. This study revealed 48 animal carcasses, with a taxonomic composition of 27.08% birds (13 individuals), 50% mammals (24 individuals), 18.75% reptiles (9 individuals), and 4.17% amphibians (2 individuals). The roadkill patterns showed that squirrels and cats as the most prevalent species found, with higher carcass rates found in plantation/forest areas and on state roads. Roadkill occurrences increased between 7 a.m. and 1 p.m., while streetlights had no noticeable impact on the numbers. Overall, mammals turned out to be the most frequent vertebrate class involved in roadkill with time being the only significant factor influencing the numbers. With a better understanding of the factors influencing vertebrate roadkill, mitigation strategies such as fencing, roadside vegetation clearance, and vehicle speed restrictions might be devised to minimize the adverse impacts of road infrastructure on local animal populations.

Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset

Abstract. Wildfires are increasingly impacting social and environmental systems in the United States (US). The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis Fire-Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the US. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3×106 wildfires in the US between 1992 and 2020 . For each wildfire, we added physical (e.g., weather, climate, topography, and infrastructure), biological (e.g., land cover and normalized difference vegetation index), social (e.g., population density and social vulnerability index), and administrative (e.g., national and regional preparedness level and jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models. The FPA FOD-Attributes dataset is available at https://doi.org/10.5281/zenodo.8381129 (Pourmohamad et al., 2023).