Field Personnel Research Articles

A Benchmarking Model for HRM Elements of Large Mining Industry: ISM-Based Approach

While human resource management (HRM) elements concerning the expansive mining industry have been acknowledged, they still need to be explored. This paper presents a novel and strategic endeavour to construct a benchmark model linking HRM elements specific to the large mining industry. Drawing from an extensive literature review, crucial HRM elements pertinent to the mineral industry are systematically identified and thoroughly examined. The study utilises the interpretive structural modelling (ISM) technique within the context of major Indian mining industries to unveil latent relationships among these HRM-related elements. Through six-phased interactions, a foundational model is established, illuminating these relationships. The findings are poised to positively impact the mining industry, contributing to enhanced system sustainability. Furthermore, this paper represents a pioneering effort as the first research to elucidate the relationship model of HRM elements within the mining industry. It encourages HRM researchers to delve further into the mining industry to address its challenges. This research serves as a foundational study and provides a platform for further investigations by HRM experts, researchers and field personnel, enabling a multifaceted exploration of HRM perspectives.

A 1+ Mechanism Model for Predicting the Mixed-Oil Concentration in Multiproduct Pipelines

Summary Petroleum products are frequently transported successively through the same multiproduct pipeline. Due to turbulent and convective diffusion mass transfer, two adjacent oils will mix with each other, forming a mixed-oil segment. Accurate and rapid prediction of mixed-oil concentration is crucial for the precise management of mixed-oil segments. Conventional 1D modeling methods exhibit shortcomings in accurately representing the asymmetric distribution characteristics of mixed-oil concentration curves, and high-dimensional models are not practically applied due to their prohibitive computational time costs. Building on the 1D model framework, this paper proposes a “1+” mechanism model by considering the convective mass transfer behavior between the turbulent core region and the laminar boundary layer, and new governing equations and corresponding numerical solution methods are also introduced. Simulation experiments affirm the ability of the new model to characterize the asymmetric distribution features of mixed-oil concentration curves, along with its high computational efficiency in engineering applications. This is demonstrated by the computational time of approximately 30 seconds for simulating a pipeline of 300 km in length (Δx = 10 m, Δt = 1 second, CPU: i5-12500H, RAM: 16 GB). When applied to pipelines in industrial scenarios, the new model is shown to accurately predict the distribution of mixed-oil concentration curves. The research findings are significantly beneficial in assisting field personnel to gain advanced insights into the mixed-oil concentration distribution at the station, enabling timely and well-informed strategies for handling mixed-oil segment, thereby enhancing the operational efficiency of multiproduct pipelines.

Research on Sintering Machine Axle Fault Detection Based on Wheel Swing Characteristics

During the sintering process in iron production, wheel swing is a sign of sintering machine trolley axle faults, which may lead to the wheel falling off and affect the production operation of the sintering machine system in serious cases. To solve this problem, this paper proposes a fault detection and localization method based on the You Only Look Once version 9 (YOLOv9) object detection algorithm and frame difference method for detecting sintering machine trolley wheel swing. The wheel images transmitted from the camera were sent to a trolley wheel and side panel number detection model that was trained on YOLOv9 for recognition. The wheel recognition boxes of the previous and subsequent frames were fused into the wheel region of interest. In the wheel region of interest, the difference operation was carried out. The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers’ average precision was 99.2% in fault localization. Utilizing the aforementioned method to construct a system for detecting wheel swing can provide technical support for fault detection of the trolley axle on the sintering machine.

An overwinter protocol for detecting wolverines and other carnivores at camera traps paired with automated scent dispensers.

Camera traps deployed with olfactory attractants are used to survey rare and elusive carnivores. Study areas with deep snowpack and rugged terrain present challenges and risks to field personnel, who traditionally must revisit camera stations regularly to refresh attractants. In such locations, alternative overwinter survey protocols that include a persistent attractant would improve both the safety and efficiency of camera-trap surveys. We present a protocol for installing camera traps and automated scent dispensers on trees at above-average maximum snow depth to eliminate the need for interim service visits and to enable standardized surveys to be conducted throughout the year. Our protocol proved to be effective at attracting and detecting numerous and repeated visits by wolverines, fishers, and other carnivores in two montane regions of the western contiguous United States. The volume, timing, and composition of liquid scent lure released by automated scent dispensers can be varied to target multiple species of interest, and the dispenser can be used insituations where bait rewards may influence the behavior of target species and/or pose human safety concerns.

ASSESSMENT OF FUMMAN AGRICULTURAL PRODUCTS INDUSTRIES’ CONTRACT FARMING SCHEME AMONG PINEAPPLE FRUIT FARMERS IN OYO STATE, NIGERIA

This study assessed FUMMAN Agricultural Products Industries Plc. contract farming scheme among pineapple fruit farmers in Oyo State, Nigeria. Data on enterprise characteristics, benefits derived, constraints encountered and field personnel characteristics were gathered from 105 pineapple fruit farmers. Respondents were sampled using a multistage sampling procedure and interview schedule were used to elicit information which were analyzed using frequencies, percentages, mean, standard deviation and Pearson Product Moment Correlation (PPMC) at α0.05. Findings showed that the majority (76.2%) of the pineapple fruit farmers cultivated less than 1 hectares farm size. They derived benefits from access to stable market (x ̅=1.94) and reduction of wastage level (x ̅=1.84), while defaulting in contractual agreements (x ̅=1.45) and delay in payment (x ̅=1.38) were major constraints encountered. Integrity (x ̅=3.29), honesty (x ̅=3.29) and trustworthiness (x ̅=3.27) were the major characteristics of field personnel identified. There were significant relationships among farmers’ benefit derived (r=0.787), constraints encountered (r=-0.193) and field personnel characteristics’ assessment. It is recommended that FUMMAN should endeavour to keep to contractual agreements while ensuring extension of the contracts through highly rated field personnel

Theoretical and practical considerations in the creation of a disaster risk reduction educational strategy at the primary education level: A case study of Mexico city

Educational settings present both opportunities and challenges for students, teachers, and decision-makers in terms of creating a disaster risk reduction culture. The selection of a framework, the creation of adequate materials, and the choice of dissemination mechanisms are as important as the contents and the targets prioritized. This study reviews the disaster risk reduction educational strategy implemented in Mexico City schools since 2019 and analyzes the outcomes by 1) surveying the principals and teachers (1,788) at 714 schools, 2) conducting interviews with high-level authorities and field personnel from several agencies involved in the strategy (12), 3) reviewing official databases and actual compliance with regulations and, 4) evaluating the memorability and efficiency of educational activities and materials. The results revealed a complex network of elements associated with the implementation of the strategy, with the most important and easiest to target being a “belief of self-efficacy when facing an emergency situation” as a factor affecting involvement, a sense of self-responsibility, risk perception, and even preference regarding the type of knowledge to deliver to schoolchildren. Regarding the activities and materials implemented, different degrees of success were observed, with the highest being those related to psychosocial support (addressing actual needs during the COVID-19 pandemic) and disaster-response plans (compliance with regulations), while the modality of interaction revealed that activities involving machine interactivity had higher efficiency rates compared with human interactivity or no interactivity. The findings extracted from this case study of Mexico City have the potential to help other cities develop better policies.

Occupancy model reveals limited detectability of lichens in a standardised large‐scale monitoring

AbstractQuestionWhat are the extent and the possible causes of imperfect detection in lichens? Because lichens are sessile and lack seasonality, they should be easier to survey than animals that can move or plants and fungi with seasonal morphology, and one could therefore expect relatively high detection probabilities.Location826 standardised sampling plots across Switzerland.MethodsUsing repeated detection/non‐detection data from a national lichen survey conducted by professional lichenologists, we estimated the mean and variation in detectability for 373 tree‐living species with a multi‐species occupancy model. We also quantified the effect of species conspicuousness, identifiability and observer experience on detection probability.ResultsThe average detection probability for a single survey was unexpectedly low with an average of 0.49 (range across species: 0.25–0.74). Conspicuous species showed higher average detectability (0.56) than inconspicuous species (0.41), and identifiability as well as previous experience with a species substantially increased the probability of a person detecting it. Accounting for experience, the mean detection probabilities of observers ranged from 0.32 to 0.69.ConclusionsOur study confirms that detection probability per survey is often far below 1 also in sessile organisms, even when a standardised survey is conducted by experts. When species are seasonal (plants, fungi, etc.), survey areas are larger, or field personnel are less experienced, as is the case for many surveys and monitoring programs, detectabilities are likely to be substantially lower. We therefore argue that imperfect detection should systematically be considered in the survey design and data analysis also for sessile organisms.

Management Training Program and Leadership Mental Development for Field Staff of PT Intan Pertiwi Industri

Management Training and Leadership Mental Coaching Program at PT INTIWI aims to enhance managerial skills and the mental well-being of field personnel. Against the backdrop of understanding the complexity of fieldwork and the increasingly dynamic market demands, this program is designed to cultivate outstanding field leaders. Training methods involve discipline training, case-based training, pre-tests, and post-tests. Evaluation is conducted through pre-tests and post-tests, as well as participant feedback and field performance monitoring. The program is expected to have a positive impact, such as increased productivity, job satisfaction, and a reduction in stress levels. The success of this program will create a more balanced work environment and contribute to the strategic goals achievement of PT INTIWI.

Enhancing Customer E-Loyalty and E-WOM: The Role of Electronic and Non-Electronic Service Quality and Customer Satisfaction (PLN Mobile Application)

This study examines the relationships between six constructs: e-service quality, customer e-satisfaction service quality, customer satisfaction, customer e-loyalty, and electronic word-of-mouth (EWOM). The study collected data from 452 respondents who are customers of PLN and have used the PLN Mobile application. The study found that customer e-loyalty can be directly and indirectly influenced by service and e-service quality. Both customer satisfaction and e-satisfaction can mediate the influence of service quality and e-service quality on customer e-loyalty. Therefore, PLN needs to ensure service quality (both through the application and field personnel) and customer satisfaction with the services provided to enhance customer satisfaction and foster electronic loyalty, encouraging customers to continue using the PLN Mobile application. The study also reveals that customer e-loyalty positively and significantly influences EWOM.

AI, Influencers, and Grit: How Apache’s 8-Year Quest To Build Its Own Drilling Advisor Achieved Full Adoption

When oil prices tumble, upstream research and development projects are among the first casualties. Many are put on the shelf. Few are ever taken off. Then there’s what happened at Apache Corp. More than a decade ago, the Houston-based independent oil and gas producer, which following a restructuring is now a subsidiary of APA Corp., was among a small cadre of operators working at the fore of automated drilling technology. As a first adopter, Apache had gone so far as to develop a newbuild automated rig design for its onshore shale fields in Texas. Then in mid-2014, crude prices plummeted nearly 60% over a 7-month period. The heady days of $100/bbl oil were replaced by the era of “lower for longer,” derailing Apache’s ambitious plan to build its pioneering prototype. That could have been the end of the story. But less than a year after the ambitious capital project was scrapped, something else emerged in its place. While AI-based automation was out, Apache’s drilling team was given a chance to develop the next best thing: an AI-based drilling advisory system. “We saw that there was at least a small opportunity to do something more with our rig data—that 1-Hz real-time data—by combining it and mashing it up with contextual data so that it could be something useful,” said Michael Behounek, a former director of drilling, completions, and workover performance and leader of the project for Apache where he spent the past 13 years before taking early retirement. Now a managing partner of an upstream digital consulting startup called Emerja, Behounek spoke at the recent SPE Annual Technical Conference and Exhibition while presenting SPE 215132. The paper outlines how after 8 years that small opportunity turned into a 10% year-over-year reduction in drilling costs. The system, a presumed multi-million dollar value creator, was adopted across all of Apache’s contracted rigs in 2018, playing an increasingly important role in the drilling of more than 1,700 wells across a wide spectrum of geologies. This track record spans nine onshore and offshore basins, with deployments of the system in the Permian Basin, Egypt, Canada, offshore Suriname, and the North Sea. Behounek said most of the reported cost savings stem from the models’ ability to trim rig time by predicting problems that would keep drillers from staying on bottom and turning to the right. That said, the paper, which is coauthored by Apache’s software partner Intellicess Inc., emphasizes that “the system only enables the opportunity—it is the field personnel and engineers taking the proper actions and decisions offered by the system that deliver the improvement.” Apache has shared several papers about the various components of the advisory program over the years but the most recent offers a holistic view of the strategy that led to companywide adoption. Of the dozens of takeaways it offers, some of the biggest follow.

Proximity-sensors on GPS collars reveal fine-scale predator-prey behavior during a predation event: A case study from Scandinavia.

Although the advent of high-resolution GPS tracking technology has helped increase our understanding of individual and multispecies behavior in wildlife systems, detecting and recording direct interactions between free-ranging animals remains difficult. In 2023, we deployed GPS collars equipped with proximity sensors (GPS proximity collars) on brown bears (Ursus arctos) and moose (Alces alces) as part of a multispecies interaction study in central Sweden. On 6 June, 2023, a collar on an adult female moose and a collar on an adult male bear triggered each other's UHF signal and started collecting fine-scale GPS positioning data. The moose collar collected positions every 2 min for 89 min, and the bear collar collected positions every 1 min for 41 min. On 8 June, field personnel visited the site and found a female neonate moose carcass with clear indications of bear bite marks on the head and neck. During the predation event, the bear remained at the carcass while the moose moved back and forth, moving toward the carcass site about five times. The moose was observed via drone with two calves on 24 May and with only one remaining calf on 9 June. This case study describes, to the best of our knowledge, the first instance of a predation event between two free ranging, wild species recorded by GPS proximity collars. Both collars successfully triggered and switched to finer-scaled GPS fix rates when the individuals were in close proximity, producing detailed movement data for both predator and prey during and after a predation event. We suggest that, combined with standard field methodology, GPS proximity collars placed on free-ranging animals offer the ability for researchers to observe direct interactions between multiple individuals and species in the wild without the need for direct visual observation.

Applying the socioecological framework as a methodological approach to combat chronic diseases challenges: a case-in-point illustration using a narrative review of factors for tuberculosis medication non-adherence in India

The “slow-moving disaster” of chronic diseases has wide-ranging social implications. While prevention of chronic diseases has shifted from individual behavioral interventions to broad socio-ecological interventions, an organizing framework for complex chronic disease situations could prove to be valuable. In this article, we proposed the socio-ecological framework as one solution to identify gaps at multiple levels and guide further research, intervention efforts, and policy direction for individual chronic diseases. We illustrate this through a case-in-point example in the form of a narrative review of factors of nonadherence to therapy for tuberculosis in India. A literature search was conducted within PubMed, Scopus, Cinahl, Medline, and the Penn State Harrell Library Resources to identify peer-reviewed articles emanating from India describing factors associated with nonadherence to tuberculosis medications. The factors for non-adherence to tuberculosis medication were reported and described as individual (age, gender, religion, socioeconomic status, etc.), interpersonal (patients, family, & friends, stigma & discrimination, patient-provider relationship, etc.), organizational, community and policy levels of the socioecological model. Furthermore, gaps in the literature pertaining to these factors of nonadherence were also identified and categorized at each level of the socioecological model. The socio-ecological framework has the potential for broader and more comprehensive application to other chronic diseases and the problems associated. Integrating and conceptualizing these contributors organizes the complexities around chronic diseases that may, in turn, inform various policymakers, researchers, program implementation experts, and field personnel in formulating the desired strategy to combat the challenges.

Modeling the soil-machine response of secondary tillage: A deep learning approach

Seedbed preparation constitutes a highly important step in the crop establishment process since it directly influences both quality and yield expectation in crop fields. The quality of seedbed preparation is usually assessed by the operator of the tractor or a supervising farmer and thus dependent on qualified field personnel which are scarce today. Subsequent to the visual assessment of the current seedbed structure, a qualified operator can manually adapt the secondary tillage machine in order to optimize resource efficiency (e.g., fuel consumption) while maintaining optimal seedbed preparation according to local soil conditions. In this work, we propose to use an automated approach for real-time in-situ measurement of seedbed quality. Stereo cameras mounted in the front and the back of the tractor provide images and depth-information. Additionally, telemetry data such as working speed, power take-off (PTO) speed, fuel consumption and engine torque utilization of the tractor are logged. Based on this data, we develop a Deep-Learning-based model to predict seedbed quality (measured in form of the Roughness Coefficient), engine torque utilization and engine fuel rate. Thus, we model the interaction of the soil and the machine (soil-machine response) when preparing the seedbed with different machine configurations and environment states. For training and evaluating our model, we collected data from 16 field runs with varying soil types ranging from loamy sand to silty clay in Baden-Württemberg, Germany. Based on this data, we demonstrate that our novel model is able to predict the soil-machine response on previously unseen fields with high accuracy – in our setting with a relative root mean squared error (rRMSE) between 12.2 and 14.4 %. Our proposed model has two main advantages: (1) it allows farmers to plan the optimal machine configuration in advance by simulating different machine configurations and thus reduce the complexity for the operator and (2) it can serve as a basis for the training of intelligent control agents.

The Pattern of Blood Pressure Distribution and Analysis of Influencing Factors of Pre (Hypertension) Among Young Adults: A Cross-Sectional Survey in Western India

Background: Raised blood pressure is one of the significant preventable causes of cardiovascular diseases. The Global Burden of Diseases report 2019 has identified high systolic blood pressure as the leading risk factor for attributable deaths accounting for about 1.4 million deaths in India. The study aimed to determine the prevalence of prehypertension and hypertension, along with their associated risk factors, among young adults. Methods: A cross-sectional study was conducted in urban science colleges in Gujarat. A multicomponent questionnaire comprised of demographic, dietary and lifestyle characteristics was administered to the participants (18-23 years). Data on weight, height and blood pressure were collected by trained field personnel. The categorical variables were tested by a chi-square test, while the t-test was used to compare quantitative variables. Results: Overall, 415 participants (65% female) were included in the study, with a mean age of 19.5 years. The prevalence of prehypertension and hypertension was 23.4% and 4.8%, respectively. The multivariate analysis showed that the male gender, not being underweight, and overweight was found to be associated with elevated blood pressure. Conclusions: There is a pressing need to identify cardiovascular risk factors at younger ages through regular screening, with more focus should be put on the adaptation of healthy lifestyles.

Tata Kelola Rekam Medis Berbasis Elektronik dalam Kasus Pelaporan Pasien COVID-19 dengan Metode Rapid Application Development di RSUD AL IHSAN

Information on hospitalized patients with COVID-19-19 is available through the Corona Virus Disease 2019 Reporting Information System. As with the problem in every hospital, the officer in charge of collecting COVID-19 patient data is still not effective, such as recording data and lab reports of suspected COVID-19 cases before entering them into the information system using COVID-19 recording forms and Microsoft Excel. This of course results in late and inaccurate data delivery, resulting in difficulties in monitoring the spread of COVID-19 and COVID-19 mitigation programs in hospitals. In this investigation, the researchers used Rapid Application Development (RAD), a software development technique that prioritizes rapid iterations of the development cycle. Development teams can use the RAD methodology to concentrate on prototyping work and then quickly iterate depending on input from users or related parties. With this method, we have more leeway to adapt to future COVID-19-19 reporting requirements and rules. The creation of an information system that allows medical personnel or field personnel to relay case data directly from the scene is an example of the implementation of RAD in COVID-19-19 reporting This allows for proper data collection. The results obtained from the application of methods with problems that occur in COVID-19 reporting cases The process of collecting and reporting COVID-19 data becomes more effective, this system can also help officers in monitoring COVID-19 cases so that they can provide the necessary information to the COVID-19 response team.

Apparent prevalence and selected risk factors of methicillin-resistant Staphylococcus aureus and non-aureus staphylococci and mammaliicocci in bulk tank milk of dairy herds in Indiana, Ohio, and Michigan

The purpose of this study was to determine the apparent prevalence and risk factors of methicillin-resistant Staphylococcus aureus and non-aureus staphylococci and mammaliicocci (NASM) in bulk tank milk (BTM) obtained from 300 dairy farms that belong to a cooperative collecting milk from Indiana, Michigan, and Ohio. Dairy field personnel recorded information about selected farm level risk factors and collected and froze BTM samples (n = 300) that were sent to Michigan State University researchers. Milk samples were thawed at room temperature and pre-enriched by adding 1 to 4 mL of Mueller-Hinton broth supplemented with 6.5% NaCl and incubated at 37°C for 24 h. Subsequently, 10 µL was plated on mannitol salt agar and Mueller-Hinton agar supplemented with 2.5% NaCl containing 2 mg/L oxacillin and 20 mg/L aztreonam. Colonies that grew on the selective media were subcultured on blood agar and identified using MALDI-TOF mass spectrometry. Phenotypic methicillin resistance was tested using cefoxitin disk diffusion. Conventional PCR was used to detect mecA and mecC in phenotypically resistant isolates. Of 550 isolates that were obtained from mannitol salt agar plates and 10 isolates from Mueller-Hinton agar plates, 16 species of NASM accounted for 84% of staphylococci, while S. aureus accounted for the remaining 16%. Among S. aureus, 4 isolates from 4 farms (1.3%) demonstrated phenotypic resistance to methicillin resistance but none carried mecA or mecC genes. Among NASM, 45 isolates from 40 farms (13.3%) demonstrated phenotypic resistance to methicillin. However, only 13 NASM isolates (7 Mammaliicoccus sciuri, 2 Staphylococcus haemolyticus, 1 Mammaliicoccus fleuretti, 1 Staphylococcus epidermidis, 1 Staphylococcus saprophyticus, and 1 Staphylococcus hyicus) from 13 farms were positive for mecA, whereas all were negative for mecC. Thus, the prevalence of mecA-positive NASM in BTM was 4.3%. Based on molecular results, this study demonstrated a low prevalence of methicillin resistance NASM from BTM samples collected from farms in the Upper Midwest. Dairy farms that contained ≤200 lactating cows and had swine located on the farm had a higher prevalence of methicillin-resistant NASM than smaller farms that did not contain swine.

RTO 사고 예방을 위한 메타버스 기반 안전 서비스 플랫폼

This paper implements a Metaverse-based safe operation service platform for safe maintenance and operation of RTO. A Regenerative Thermal Oxidation (RTO) is a device that prevents volatile organic compounds (VOCs) gas generated from chemical plants, paint production and coating manufacturing, and metal surface treatment from being discharged into the atmosphere. If the plant is operated 24 hours a day, the RTO also continues to operate. In order to operate safely and preserve accident prediction, guidance and safety education on equipment are necessary.
 Most of the 24-hour RTOs are long-distance, so if the equipment fails, there is a limit to immediate response, and an inefficient maintenance situation occurs. In particular, long-distance monitoring and safety training for operation are needed due to the immature handover caused by the aging trend of field personnel, complex equipment use, and personnel turnover.
 A virtual space is constructed based on a metaverse, and an assembly manual for equipment structure, circuit diagrams, component characteristics, and detailed composition is shaped in 3D using digital twin technology. To this end, 3D modeling technology generates a mesh based on the design data of the RTO equipment and generates a texture of the equipment based on the actual picture. Through the reconstructed 3D virtual space in the field for 3D virtual space configuration, the user's parts are tracked by delivering immersive low latency and high-quality data to remote XR devices, PCs, and smartphones based on multi-user. At this time, AR glass (smartphone) is used to share the location of the remote user with the site as location data.
 Most studies on the safe operation of RTO devices have focused on hardware safety through field monitoring and data acquisition for pressure, temperature, and VOCs emissions through sensor IoT. Most of the plants are located at a long distance, and the time to stop the plant for repair and repair work is limited. Accordingly, monitoring, which was previously operated as a 2D screen, is combined with 3D space to provide an intuitive and visible environment and efficiency.
 By predicting the failure state of the facility through metaverse-based monitoring, the efficiency of maintenance can be improved, and productivity and market competitiveness can be secured by minimizing downtime. In addition, education and training in virtual space reduces the risk of failure and maintains a high level of facility maintenance to establish a safe working environment. Therefore, the system's overall safety platform can cope with serious disasters and minimize environmental impacts such as gas leaks as well as equipment failures.

Biodiversity indices and Random Forests reveal the potential for striped skunk (Mephitis mephitis) fecal microbial communities to function as a biomarker for oral rabies vaccination.

Wildlife disease surveillance and monitoring poses unique challenges when assessing rates of population vaccination, immunity, or infection prevalence. Non-invasively detected biomarkers can help reduce risk to both animal and field personnel during wildlife disease management activities. In this study, we investigated the utility of fecal microbiome data collected from captive striped skunks (Mephitis mephitis) in predicting rabies virus vaccination and infection status. We sequenced the hypervariable region 4 (V4) of the bacterial 16S gene and estimated alpha and beta diversity across timepoints in three groups of skunks: vaccination then rabies virus infection, sham vaccination then rabies virus infection, and rabies virus infected without vaccination. Alpha diversity did not differ among treatment groups but beta diversity between treatments was statistically significant. The phyla Firmicutes and Proteobacteria were dominant among all samples. Using Random Forests, we identified operational taxonomic units (OTUs) that greatly influenced classification of fecal samples into treatment groups. Each of these OTUs was correlated with fecal volatile organic compounds detected from the samples for companion treatment groups in another study. This research is the first to highlight striped skunk microbiome biodiversity as a vaccination biomarker which pushes the frontier on alternative methods for surveillance and monitoring of vaccination and disease in wildlife populations.

An approach to the background, methods and challenges of research in disasters

Disasters are phenomena of global distribution and increasing impact, with a social and interdisciplinary nature and that often require the use of quantitative and qualitative methods to be investigated. The purpose of this paper is to explore the historical background of disaster research, the current use of epidemiological methods in disasters, as well as the difficulties and future challenges of research in this field. This study considers bibliographic elements and specific non-bibliographic events that have determined the historical evolution of disaster research, as well as its conceptual framework, particularly in relation to the approaches from which the research has been carried out and the range of methods epidemiological used. This is not a standard review. Current disaster research is developed for a wide variety of purposes, including the identification and characterization of populations exposed or at risk of disaster; the study and establishment of the exposure model for each specific type of disaster; the estimation of the incidence and prevalence of the different types of disaster and the quantification of the impacts in the short, medium and long term. Research is also carried out in the field of disasters to assess needs, plan relevant and efficient types of aid; model and predict the occurrence and to design adequate and efficient prevention, preparedness, mitigation and rehabilitation strategies. Even so, the predominant research on disasters is still very much focused on certain partial aspects of the phenomenon such as the response to the disaster and directed towards specific types of disasters, especially those of rapid onset. There is still little multidisciplinary and collaborative research among researchers from the different areas of interest that constitute the disaster phenomenon. Important challenges persist in disaster research, including the design of a common conceptual framework that allows multidisciplinary research in disasters, the development of specific methods to investigate complex emergencies (complex humanitarian emergencies) or disasters related to climate change; the development of a specific approach to investigate slow-onset disasters and their social, political and cultural factors, as well as research to develop feasible and cost-efficient prevention and response strategies. Solving these challenges will depend to a large extent to which the investigative capacity of managers and heads of organizations and field personnel working in disasters is improved.

The efficacy of performing field surveys during the dry and rainy seasons to estimate species richness and composition of local bat assemblages

AbstractHere we assessed the efficacy of performing field surveys during both dry and rainy seasons to estimate species richness and composition of phyllostomid bats in 12 sites of the Brazilian Atlantic Forest. Our goal was to elucidate two opposite scenarios. If bat species compositions differ between the dry and rainy seasons, then field surveys in both seasons will be considered complementary for estimating local bat assemblages. In contrast, if bat species compositions in the dry season are a nested subset of the species present in the rainy season, then field surveys in both seasons will be considered redundant. Our results showed that field surveys performed only during the rainy season exhibited the same efficacy in recording bat species as field surveys considering both dry and rainy seasons combined. Consequently, the rare species sampled in the rainy season contributed to the uniqueness of communities in terms of species composition. Considering that time, funding, and the number of field personnel available for work are important factors when determining the sampling effort in highly biodiverse regions like the tropics, our results can be useful in establishing protocols for research projects with limited resources aiming to create a comprehensive list of species.