Index Monitoring Research Articles

A human-centered data analysis approach for long-term assessment of human health quality

Internet of Things (IoT)-based health monitoring is a key focus in Healthcare 4.0 for continuously tracking Vital signs and other health biometrics. While IoT technologies like sensors, connectivity, hardware, software, and batteries have been extensively studied, there has been a lack of emphasis on evaluating human-centered health capabilities using long-term data collected by the Internet of Medical Things (IoMT). This paper tackles the problem of human-centered Vital signs-based health long-term assessment by proposing a hybrid approach that includes model-free multi-profile monitoring, optimization models, control charts, and profile capability indices. In this new approach, a curves dissimilarity index-based profile monitoring method is employed to address the complexity and outlier elimination issues in the traditional regression model-based profile monitoring approach, and a mixed-integer linear mathematical model is developed to assign human-centered tolerance to the dissimilarity index of each Vital sign profile. Also, control charts are employed to monitor the health of individuals according to each Vital sign, and overall. In addition, the profile capability index and non-confirming probability values are interpreted as long-term health measures. The efficacy of the newly proposed method is demonstrated in a simulation study based on an individual’s six Vital signs generated data set using MATLAB, CPLEX, and MINITAB.

Developing an EEG-based model to predict awakening after cardiac arrest using partial processing with the BIS Engine.

Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. We sought to determine whether internal EEG subparameters extracted by the Bispectral Index (BIS) monitor, a device commonly used to estimate depth-of-anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest. In this retrospective cohort study, we trained a 3-layer neural network to predict recovery of consciousness to the point of command following versus not based on 48 hours of continuous EEG recordings in 315 comatose patients admitted to a single US academic medical center after cardiac arrest (Derivation cohort: N=181; Validation cohort: N=134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine (i.e., the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. Our model was trained on hourly-averaged measurements of these internal subparameters. We compared this model's performance to the modified Westhall qualitative EEG scoring framework. Maximum prognostic accuracy in the Derivation Cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, our model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristic curve: 0.86, accuracy: 0.87, sensitivity: 0.83, specificity: 0.88, positive predictive value: 0.71, negative predictive value: 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest. In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative gold-standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

Real-time monitoring of asphalt pavement structure fatigue response based on tri-axis accelerometer

ABSTRACT Fatigue damage is a notable distress of asphalt pavements. However, monitoring the development process of such distresses in real-time remained challenges for researchers. In this paper, the self-developed sensing facility has been utilized to obtain aggregate kinematic response of semi-circular specimen during the fatigue loading process in the upper-middle (H/2), lower-middle (L/2) and lower-right (L/3) positions of the specimen. Angle and acceleration variation in the X, Y, and Z directions were analysed independently at three positions. The correlation between the kinematic characteristic of the aggregate and the vertical deformation of the asphalt mixture specimen is established. The results illustrated that the asphalt mixture viscoelasticity attenuation during fatigue test caused the angle accumulation and acceleration response change of the specimen. The angle accumulation in the X-axis direction and acceleration variation in Y-axis direction of the aggregate at the H/2 position exhibited a significant three-stage change. Angle-Accumulation Rate (AAR) of change is suggested as a long-term monitoring index for the fatigue development of the asphalt mixture. The proposal of AAR index creatively combines the long-term kinematic behaviour and mechanical behaviour of asphalt mixture, making long-term and real-time monitoring of the asphalt pavement health from the perspective of kinematics become possible.

Impact of audiovisual content on Facebook and its relationship with charitable actions

The audiovisual and graphic content published on Facebook by the Segunda Oportunidad Foundation of Quito in 2023 is analyzed. Strategies are derived from the study that make gains or losses in donations visible, which contribute to the subsistence of the animals. The qualitative-quantitative research is developed from the use of the analytical-synthetic method in the description of the object of study, through the separation, decomposition and union of the object by classes, arriving at a correspondence relationship where contents are identified, metrics and interactions. Interviews, content analysis and monitoring indices are used as research techniques. The results demonstrate the importance of managing visuality and its relationship with the results obtained on social networks

Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model

As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, the proposed approach eliminates the need for explicit modeling and leverages a novel optimization algorithm for data denoising. Additionally, a new noise-resistant monitoring index is introduced to enhance monitoring reliability. The paper is structured into two main sections for validation. The first section addresses advanced data preprocessing, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the parameters of Random Singular Value Decomposition (RSVD). This step effectively minimizes noise, reduces manual intervention, and handles high-dimensional data. The second section focuses on analyzing the data characteristics using the Random Matrix Theory (RMT) and establishing novel condition monitoring indicators to achieve more reliable monitoring outcomes. The proposed methodology captures the intricate relationships among key variables within the system, providing a more robust framework for condition monitoring. Applied to a marine diesel engine lubrication system, the method demonstrates significant improvements in noise immunity and monitoring reliability. Comparative analyses of condition monitoring models before and after denoising reveal that the relative error of the proposed monitoring index under varying noise amplitudes is within 1%, substantially lower than that of other indices. Furthermore, the monitoring accuracy is improved by 4.95% when the proposed index is employed for system condition monitoring.

A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot

Automated monitoring of the rice leaf area index (LAI) using near-ground sensing platforms, such as inspection robots, is essential for modern rice precision management. These robots are equipped with various complementary sensors, where specific sensor capabilities partially overlap to provide redundancy and enhanced reliability. Thus, leveraging multi-sensor fusion technology to improve the accuracy of LAI monitoring has become a crucial research focus. This study presents a rice LAI monitoring model based on the fused data from RGB and multi-spectral cameras with an ensemble learning algorithm. The results indicate that the estimation accuracy of the rice LAI monitoring model is effectively improved by fusing the vegetation index and textures from RGB and multi-spectral sensors. The model based on the LightGBM regression algorithm has the most improvement in accuracy, with a coefficient of determination (R2) of 0.892, a root mean square error (RMSE) of 0.270, and a mean absolute error (MAE) of 0.160. Furthermore, the accuracy of LAI estimation in the jointing stage is higher than in the heading stage. At the jointing stage, both LightGBM based on optimal RGB image features and Random Forest based on fused features achieved an R2 of 0.95. This study provides a technical reference for automatically monitoring rice growth parameters in the field using inspection robots.

The water-energy nexus under ENSO variability in four Colombian hydropower basins

ABSTRACT The El Niño-Southern Oscillation (ENSO) has diverse effects on the interannual variability of river flows in Colombia. Given that 70% of the country's electricity supply is provided by hydroelectric dams located in different regions and operated by different companies linked by a nationally integrated system, the decisions taken by operators in response to ENSO can be just as varied. The present study investigates the spatial and temporal distribution of the ENSO effects in four case studies, its relation to hydropower generation and prices, and assesses the reliability of global ENSO indices for monitoring and anticipating local effects. The results show ENSO-driven variations in streamflow and precipitation in all cases, but their intensity and duration are highly dependent on location. La Niña led to positive precipitation and stream flow anomalies, while El Niño led to negative anomalies. In two dams, Hidrosogamoso and Betania, the ENSO influence is stronger, and has a higher correlation with reservoir operations with lags greater than one month. Urra has more stationary annual operations given the reduced ENSO effects in the reservoir’s basin. On the whole, ENSO indices are insufficient in describing the local variations completely. And operator’s size, market share, and installed capacity contribute to increasing flexibility in operations while facing ENSO challenges.

An IoT-enabled monitoring and control system for electrochromic smart windows

Electrochromic (EC) smart windows are highly efficient in preventing solar irradiation. Nevertheless, an insufficient automated monitoring and control system for EC windows has been observed. This study outlined the framework for designing a control system that effectively regulated the voltage polarity of an EC smart window. Integrating Internet of Things (IoT) capabilities and environmental sensors enhanced the smart window control system. This integration allowed for automated monitoring and adjustment of light intensity (below and above 30,000 lx), temperature (below and above 32 °C), and UV index (below 3 and above 7) for the smart window. The magnitude of the current supplied from the output load of the operational circuit module was directly correlated with the switching time of the electrochromic window. Moreover, voltage fluctuation was observed during the evaluation testing of the control system, which was ascribed to the reliability of the breadboard platform. This study also provided a functional design analysis for a Wi-Fi-enabled control system, presented insights into manual and automatic control modes under diverse environmental conditions, and improved user interaction through real-time data visualization using the IoT Blynk platform. Overall, this study presented a range of cost-effective approaches for managing the dynamic EC window using Wi-Fi and sensor-enabled technology.

Role of integrated pulmonary index in respiratory monitoring of spontaneously breathing COVID-19 patients with moderate to severe respiratory symptoms

Role of integrated pulmonary index in respiratory monitoring of spontaneously breathing COVID-19 patients with moderate to severe respiratory symptoms

A151: The Monitoring of Health-Related Indices on Chinese Undergraduates Before, During, After COVID-19

Background/Purpose: China, as the first country who suffered the outbreak of COVID-19, reacted quickly to prevent the transmission of the epidemic by lockdown policy. Today, it has been one year since China declared the end of COVID-19. This research intended to explore the real effects of the pandemic on contemporary undergraduate physical fitness by screening and analyzing National Students Fitness Test data, comparing the data before, during, and after COVID-19. Method: Research has analyzed National Students Fitness Test data in the periods before, during, and after COVID-19 in a Chinese university, discussing the effects of COVID-19 such as the difference in body shape, respiratory system function, body strength, speed, flexibility quality, and endurance quality. In total, 14,708 college students participated, including 6,125 males and 8,583 females. In the analysis approach, using SPSS 26.0, paired-samples T test compared mean differences within the data. Students came from different provinces with sufficient cultural diversity, which attenuates risk of bias. Results: Before the outbreak, the BMI index of college students was 20.15, and it rose to 20.31 during the outbreak, as well as 20.45 at the end of the epidemic. Before the epidemic, the 50 m time was 8.48 s, which increased to 8.81 s during the epidemic and 8.77 s at the end of the epidemic. Before the outbreak, standing long jump was 182.1 cm, 178.9 cm during the outbreak, and 182.84 cm after the outbreak. The sit-and-reach was 13.26 cm before the outbreak, 15.9 cm during the outbreak, and 16.2 cm after the outbreak. The vital capacity was 3,230.87 before the outbreak, 3,201.52 during the outbreak, and 3,091.11 after the outbreak. The endurance index decreased first and then increased, and the overall difference was small compared with before the epidemic, while the strength index declined for three consecutive tests. Conclusion/Discussion: According to the comparison of the data before, during, and after the outbreak, the study found that the epidemic influenced the students' BMI to continue to rise, and their strength and vital capacity to continue to decline, which was greatly affected. However, endurance and explosive power rebounded after the pandemic.

Usability of smartphone-based RGB vegetation indices for steppe rangeland inventory and monitoring

Usability of smartphone-based RGB vegetation indices for steppe rangeland inventory and monitoring

A new diatom-based multimetric index (MMI-D) for ecological health monitoring in the Tropical Rift Valley Lake (Lake Hawassa, Ethiopia)

Multimetric assessment is one of the important tools for diagnosing, detecting and measuring impairment in ecosystem function in lentic ecosystems. It enhances detection capabilities across a broader variety of stressors and offers a more complete picture of ecological conditions than single metrics or biological indicators. In this context, a diatom-based multimetric index (MMI-D) has been developed to evaluate the ecological health of Lake Hawassa. Physicochemical and benthic diatom sampling was conducted at nine sites along the lakeshore, representing varying levels of human disturbance, from February to November in2015 and 2016. The sampling sites were classified a priori into three categories: minimally disturbed (three sites), moderately disturbed (three sites) and highly disturbed (three sites). This classification, was based on a clustering analysis using the percent disturbance score (PDS). Of the 24-diatom candidate metrics, only ten were chosen as core metrics for the development of MMI-D, based on redundancy analysis, reaction to environmental conditions, percent discriminatory efficiency (%DE) and box plots. The newly established MMI-D clearly distinguished between reference and non-reference sites, and between the lake’s three clusters. The MMI-D’s performance was validated using independent data sets from lakes Hawassa and Ziway and it demonstrated the best capability for discrimination between different disturbance levels. MMI-D 2-stage Least Squares (2SLS) regression analysis revealed an inverse but robust connection with the PDS, indicating its responsiveness to Lake Hawassa habitat quality degradation (n = 9, R 2 = 0.921, p < 0.001). The MMI-D revealed a high %DE (95.1%) and a negative but significant connection with nutrients, total suspended solids (TSS), and turbidity (R 2 > 0.6; p < 0.05). Generally, it can be concluded that this index is a powerful tool that could assist endusers by providing a practical method for measuring the ecological quality of Lake Hawassa.

Practical Application of the COuGH RefluX Score for Patients with Laryngopharyngeal Symptoms: Real-World Assessment in Predicting GERD and PPI Response.

The COuGH RefluX score has been validated to predict likelihood of gastroesophageal reflux disease (GERD) in patients with laryngopharyngeal symptoms (LPS) using parameters of Cough, Overweight, Globus, Hiatal Hernia, Regurgitation, and male seX. This study aimed to assess the real-world value of the COuGH RefluX score in predicting proton pump inhibitor (PPI) response in LPS patients. Patients with LPS for >3 months were prospectively enrolled, and assessed using the Reflux Symptom Index (RSI) and 24-hour impedance-pH monitoring. Based on COuGH RefluX scores, patients were designated unlikely GERD (≤2.5), inconclusive GERD (3.0-4.5), and likely GERD (≥5.0). PPI response was defined as a 50% reduction in RSI. Among 196 participants (mean age 47.8), 121 were unlikely, 60 were inconclusive, and 15 were likely to have GERD. Patients unlikely to have GERD had higher MNBI, lower hiatal hernia prevalence, and fewer confirmed GERD cases compared to inconclusive and likely GERD groups (P <0.05). Symptom severity and AET were similar across groups (P >0.05). PPI response rates were 12.4%, 45.0%, and 73.3% across the groups (P <0.001). Multivariate logistic regression showed COuGH RefluX scores and lower MNBI as independent predictors of PPI responsiveness (scores 3.0-4.5, OR =4.190, P =0.001; scores ≥5.0, OR =15.772, P <0.001; MNBI, OR =0.915, P =0.001). The COuGH RefluX score is effective in predicting GERD and guiding PPI use in LPS patients without esophagitis. The score can ensure appropriate PPI use and targeted testing during initial LPS patient encounters.

Acceptability of bispectral Index monitoring in end-of-life care for dementia.

Assessing consciousness might benefit the care for people with Alzheimer's disease and other types of dementia at the end of life by indicating distressing symptoms and moments of awareness. This could guide symptom control and increase interaction with the person with dementia. This study aims to investigate the acceptability of a biosignal measurement of consciousness, the Bispectral Index monitoring (BIS), for persons with dementia at the end of life. Qualitative study using focus groups and interviews with demonstrations of BIS monitoring, with 17 individuals with dementia and 24 family caregivers. Qualitative content analysis was based on the theoretical framework of acceptability. People with dementia did not find BIS monitoring bothersome, yet acceptability varied across participants and situations. Family caregivers considered BIS acceptable for medical situations such as palliative sedation and pain assessment. Perceived effectiveness, ethicality, and usefulness considerations underpinned reasons and concerns around acceptability. Potential end-users expressed diverse attitudes towards BIS monitoring in dementia end-of-life care. If BIS or similar technologies are to be implemented in the future, care must be taken to ensure that the device has sufficient added value, that the person with dementia and family are well informed, and that the technology does not replace human care. Future research should investigate its efficacy and feasibility in the situations that were deemed acceptable.

Changes in vegetation ecosystem carbon sinks and their response to drought in the karst concentration distribution area of Asia

Changes in net ecosystem productivity (NEP) in karst areas can have a significant impact on terrestrial ecosystem carbon cycling, yet quantifying changes in vegetation NEP and its response to factors such as drought and hydroclimate remains a difficult challenge because of its special climatic and hydrological conditions. We used remote sensing data to estimate vegetation NEP in the Asian karst concentrated distribution area (AKC), analyzed its spatial and temporal variations annually (2000−2020) and during rainy season (May–November), established the drought fluorescence monitoring index (DFMI), and used a ridge regression model to explore the response mechanism of vegetation NEP to dry and wet conditions response mechanism. The results showed the following: (1) Compared with the annual changes, the vegetation NEP changes in the rainy season differed significantly on the karst geographic divisions, in which there was a significant increasing trend in Southwest China (SC) and its karst areas, while a significant decreasing trend in the Indochina Peninsula (IP) and its karst areas. (2) DFMI was the main driver of vegetation NEP change, of which the contributions were 38.05 % and 32.82 % at the annual scale and in the rainy season, respectively, which drove the increase in SC vegetation NEP, and the decrease in IP; note that the increase in vapor pressure deficit (VPD) was the key factor causing the decrease in NEP in the IP karst area during the rainy season. (3) In the lagged effect of drought on vegetation NEP, the time scale of the lag was found to be 1 month. The study revealed differences in the changes in the vegetation carbon sinks in different karst geographic divisions. We obtained a new finding: a significant trend of decreasing vegetation NEP in the IP and its karst area was influenced by the long-term effects of changes in DFMI and VPD. Therefore, the variability of different karst areas, as well as changes in drought and water resources, should be considered in carbon-cycle regulation and vegetation restoration efforts in karst areas.

Research on Interaction Effect Between Rural Roads and Peasants' Income Based on Granger Causality Test

This study aims to address the lack of methods for adjusting the scale of online ride hailing capacity. Taking G city as an example, a dynamic monitoring index system for the scale of online ride hailing capacity is constructed by comprehensively considering factors such as order volume, driver labor hours and income levels, and the operation status of the taxi market. Pearson correlation analysis and stepwise linear regression methods are applied to screen the indicators, and key indicators and threshold standards for the dynamic adjustment mechanism of online ride hailing capacity are proposed, providing theoretical basis and decision support for the adjustment of urban online ride hailing capacity scale

Research on the Dynamic Adjustment Mechanism of Online Ride Hailing Capacity Scale

This study aims to address the lack of methods for adjusting the scale of online ride hailing capacity. Taking G city as an example, a dynamic monitoring index system for the scale of online ride hailing capacity is constructed by comprehensively considering factors such as order volume, driver labor hours and income levels, and the operation status of the taxi market. Pearson correlation analysis and stepwise linear regression methods are applied to screen the indicators, and key indicators and threshold standards for the dynamic adjustment mechanism of online ride hailing capacity are proposed, providing theoretical basis and decision support for the adjustment of urban online ride hailing capacity scale

Wavelet transform-based mode decomposition for EEG signals under general anesthesia.

Mode decomposition methods are used to extract the characteristic intrinsic mode function (IMF) from various multidimensional time series signals. We analyzed an electroencephalogram (EEG) dataset for sevoflurane anesthesia using two wavelet transform-based mode decomposition methods, comprising the empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) methods, and compared the results with those from the previously reported variational mode decomposition (VMD) method. To acquire the EEG data, we used the software application EEG Analyzer, which enabled the recording of raw EEG signals via the serial interface of a bispectral index (BIS) monitor. We also created EEG mode decomposition software to perform empirical mode decomposition (EMD), VMD, EWT, and WMD operations. When decomposed into six IMFs, the EWT enables narrow band separation of the low-frequency bands IMF-1 to IMF-3, in which all central frequencies are less than 10 Hz. However, in the upper IMF of the high-frequency band, which has a center frequency of ≥ 10 Hz, the dispersion within the frequency band covered was widespread among the individual patients. In WMD, a narrow band of clinical interest is specified using a bandpass filter in a Meyer wavelet filter bank within a specific mode-decomposition discipline. When compared with the VMD and EWT methods, the IMF that was decomposed via WMD was accommodated in a narrow band with only a small variance for each patient. Multiple linear regression analyses demonstrated that the frequency characteristics of the IMFs obtained from WMD best tracked the changes in the BIS upon emergence from general anesthesia. The WMD can be used to extract subtle frequency characteristics of EEGs that have been affected by general anesthesia, thus potentially providing better parameters for use in assessing the depth of general anesthesia.

Investigation on possibility of mangrove regeneration: a case study from Indian Sundarbans.

Mangroves serve as biotic fences of estuarine belts consisting of salt-tolerant plants that grow in intertidal zones and can be regenerated naturally as well as artificially. Mangrove regeneration refers to the process of restoring or rebuilding mangrove ecosystems that have been already degraded, damaged, or lost from their native place through the monitoring of geomorphological, taxonomical, pedological, and ecological aspects. The primary objective of this study was based on the remote sensing application through several important health monitoring indices over the minute part of Indian Sundarbans to prove the possibility of mangrove regeneration. Change detection analysis in the indices proves that there was possible growth and development in mangrove colonization over the same. The secondary objective is to comprehend the association of field-related study with the remotely sensed data on species diversity assessment over the same where the native species are Avicennia marina (Peyara Bain), Avicennia officinalis (Jat Bain), Avicennia alba (Kalo Bain), Aegialitis rotundifolia (Tora), Acanthus ilicifolius (Horgoj Kanta), Bruguiera gymnorrhiza (Kankra), Ceriops tagal (Goran), and Aegiceras corniculatum (Khalisi). The latter objective is associated with the tertiary one that unveils the correlation between several physico-chemical properties of soil samples taken from the field with the species accommodation.Textural classes are grouped between clay and silty clay that helps to get the foundation of hard wood-basedspecies (Avicennia type) specifically with the salinity ranges between 200 ppt (Site 2) and 633 ppt (Site 1)approximately which presents that the zone is highly saline zone as specifically Avicennia species can sustain highsalinity. Importance Value Index (IVI) and species diversity indices have been run to support whether the zone is diversified or not.Equilibrium between deforestation and regeneration through mangrove plantation has to be attained in the nearfuture as deforestation for the livelihood purposes exists, so this newly born island locally named Dorabagdamangrove patch, upper part (Gopalganj) of Kaikhali village, would be the best experimental study site on mangroveregeneration.

The on-Site Monitoring and Specimen-Making of Ectoparasites on Rodents and Other Small Mammals.

The ectoparasites of rodents and other small mammals usually involve five categories of arthropods-fleas, sucking lice, gamasid mites, chigger mites, and occasionally, ticks. These ectoparasites are medically important, serving as vectors for diseases such as plague, murine typhus, scrub typhus, forest encephalitis, Lyme disease, and other zoonoses. Field surveys, collection, and specimen preparation of ectoparasites are crucial for studying taxonomy, faunistics, ecology, and epidemiology. They are also essential for vector surveillance. The present protocol summarizes the on-site monitoring and specimen-making of ectoparasites of rodents and other sympatric small mammals. Besides the collection and specimen preparation of small mammal hosts, the protocol describes in detail the collection, fixation, specimen-making, and taxonomic identification of ectoparasites and provides some monitoring indices. The on-site monitoring indices include the host density index and the infestation indices of ectoparasites (prevalence, mean abundance, mean intensity). The methodologies outlined in this protocol provide technical guidance and references for vector monitoring (surveillance) and control. Key features • Collection and specimen preparation of small mammal hosts, including rodents (rats, mice, and voles) and other sympatric small mammals (shrews, tree shrews, and pikas). • Collection, fixation, specimen-making, and taxonomic identification of ectoparasites, including fleas, sucking lice, gamasid mites, chigger mites, and ticks. • On-site monitoring indices-host density index and infestation indices of ectoparasites: prevalence, mean abundance, and mean intensity. Graphical overview Flowchart for collection and specimen-making of ectoparasites and their hosts. A. Process for the collection and specimen preparation of small mammal hosts. B. Collection and fixation of ectoparasites. C. Process for specimen-making for different categories of ectoparasites.