Traditional Collection Methods Research Articles

Boosting efficiency of mussel spat collection for ecological sustainability: Identifying critical drivers and informing management

Abstract The long‐term sustainability of natural and bottom‐cultured mussel beds relies on the availability of spat (i.e. juvenile mussels). Traditional spat collection methods, which disrupt the donor population and its habitat, have prompted the adoption of suspended mussel spat collectors (SMCs) as an ecologically sustainable alternative. However, practical experience has demonstrated that SMC efficiency is subject to significant spatiotemporal variability, with the underlying biotic and abiotic drivers remaining unresolved. Based on 11‐year SMC practices in the Dutch Wadden Sea, we first validated, through field experiments, the inference that larval abundance and settlement rate in seawater are the primary determinants of SMC efficiency. Secondly, we screened the key factors driving variation in SMC efficiency using an integrated dataset that includes both management options (i.e. user‐defined factors, like SMC types) and environmental conditions (i.e. site‐specific factors, such as hydrodynamics). Lastly, we developed a predictive model to explore the sensitivity of SMC efficiency to these key factors. The efficiency of SMCs was not affected by larval abundance and settlement rate, but rather regulated by management options and environmental conditions, with the key factors identified as SMC type, substrate size, mean wave height and starfish abundance. Rope‐based SMCs outperform net‐based SMCs in terms of harvest efficiency. Both types of SMCs exhibit consistent sensitivity to environmental conditions, with harvest efficiency higher in areas with lower mean wave height and fewer starfish. Increasing substrate size (i.e. rope length and net area) in these areas has the potential to further improve SMC efficiency. Synthesis and applications: Our study highlights the importance of environmental conditions over life cycle‐related factors in regulating SMC efficiency. This offers optimistic prospects for investment in larger‐scale deployment of SMCs and maximizing efficiency through site suitability assessments beforehand. The predictive model we developed can provide information for this purpose. Furthermore, strategically adjusting management options would further optimize SMC efficiency, but it is necessary to balance the associated benefits and costs. Overall, our study underscores the predictability and controllability of SMC efficiency and informs management to maximize sustainable spat supply in both mussel culture and restoration.

Mineral Recource Prospecting and Evaluation of Dry Stream Sediments of Wadi Umm Gheig – Umm Naggat Area, Eastern Desert, Egypt

The area of Wadi Umm Gheig-Umm Naggat is located at the central part of the Eastern desert, Egypt, 50km south to El-Quseir city at the Red Sea coast. The area is dominated by metavolcanics, Older Granites, Dokhan Volcanics, intrusive cumulate gabbro and Younger Granites. This works focuses on heavy minerals distributed in dry stream sediments of Wadi Umm Gheig and Gabal Umm Nagat. The study has followed up the traditional methods for sample collection from the stream sediments and the plane study for investigation and prospecting, as well as evaluation of heavy mineral deposits. The studied bed rocks are represented mainly by monzogranite, alkali feldspar granite, syenogranite and alkali granite. The investigated heavy minerals dominating the stream sediments are given by magnetite, ilmenite, rutile, leucoxene, zircon, fluorite and apatite which are arranged in decreasing order as percentages of abundances. Few trace minerals are detected in some samples like thorite and monazite, and xenotime as well. The evaluation of heavy minerals concentration reveals higher concentration of magnetite, ilmenite and zircon at the western sector, while fluorite show random distributions within the stream sediments area.

IOT Garbage Monitoring System with Weight Sensing

This paper presents a novel approach towards efficient waste management utilizing Internet of Things (IoT) technology. The proposed system integrates garbage monitoring and weight sensing capabilities to create a smart waste management solution. Traditional waste collection methods are often inefficient and lack real-time monitoring, leading to overflows, environmental hazards, and unnecessary resource allocation. By employing IoT sensors embedded within garbage bins, real-time data on fill-level and weight can be collected and transmitted to a central server. AI calculations are applied to break down the information and predict future fill levels, optimizing waste collection schedules and routes. The system also includes a command-line interface available through web or mobile applications, enabling users to request timely pickups or report issues. Furthermore, the weight sensing functionality provides insights into the type and volume of waste being generated, facilitating better waste categorization and recycling initiatives. Field tests conducted in urban environments demonstrate the adequacy of the proposed system, reducing operational costs, minimizing environmental impact, and enhancing overall waste management efficiency. The coordination of IoT innovation with garbage monitoring and weight sensing represents a significant advancement towards sustainable and smarter cities

Recent advances of sweat sampling, sensing, energy-harvesting and data-display toward flexible sweat electronics

Sweat contains diverse types of biomarkers that can mirror an individual’s health condition. The forefront research of sweat monitoring primarily focuses on sensing basic parameters, i.e., sweat rate and single electrolyte imbalances in controlled laboratory settings. However, recent works show the potential of sweat for the rich biomarkers in aspects of comprehensive health status display, timely safety alarming, and energy harvesting. The advances in wearable flexible electronics enable continuous, real-time, noninvasive detection of multiple sweat components, providing molecular-level insights into human physiology and psychology information; additionally, the efficient sweat extraction technologies of flexible electronics promote its application in energy harvesting, contributing to advancing a flexible sweat platform. This review comprehensively explores flexible sweat-based electronics, encompassing four key aspects: sweat sampling methods, sweat-based sensors, sweat-based energy harvesters, and sweat data display methods. Firstly, the traditional sweat-based platform is discussed in sweat sampling, sensing, and data analysis. Then, the development of wearable sweat sampling methods is discussed with a comparison of the traditional sweat collection methods. After that, the recent advances in sweat-based biosensors for monitoring diverse sweat analytes, such as the perspiration volume, glucose, lactate, and uric acid levels, are summarized. Subsequently, this review also highlights the recent progress and potential value of sweat-based energy harvesters in sweat-activated batteries and bio-fuel cells. Furthermore, multiple data display methods are proposed to achieve accurate feedback on health status, such as colorimetric techniques, light-emitting diodes, actuators, etc. Finally, this review concludes the main current challenges faced in practical applications of sweat-based bioelectronic systems and proposes a vision for the future evolution of this promising field.

The Integrated Online Application for Blood Bank and Donor Management System

Blood shortages pose a significant global health challenge, hindering medical interventions and causing preventable deaths. Traditional blood collection methods struggle to keep pace with demand, highlighting the need for a centralized and digital solution. We propose a online Blood Bank and Donor Management System (BBDMS) – a mobile application utilizing HTML, CSS, JavaScript, PHP, and SQL – to streamline blood donation and transfusion processes.

How to detect occluded crosswalks in overview images? Comparing three methods in a heavily occluded area

Crosswalk presence data is crucial for pedestrian safety and urban planning. However, obtaining such data at a large scale is often challenging due to the high cost associated with traditional collection methods. While automated methods based on computer vision have been explored to detect crosswalks from aerial images, a major obstacle to their application is the handling of candidate crosswalks occluded by objects or shadows in the aerial imagery. To address this challenge, this study explores different deep learning-based solutions, including the aerial-view and street-view methods, which are commonly used, and a combination of the two − dual-perspective method. Deep learning models based on Convolutional Neural Networks with VGG16 architecture were trained using 16,815 images to automatically detect crosswalks from both aerial and street view images. To compare the performance of these methods in handling occlusions, 1,378 images from a heavily occluded area were processed separately by the three methods. The results showed that the aerial-view method suffered the most when dealing with images from a heavily occluded area, resulting in the lowest accuracy, precision, recall, and F1 score among the three methods. On the other hand, the street-view method outperformed the aerial-view method significantly. The dual-perspective method demonstrated the highest accuracy and precision values, indicating its superiority in accurately predicting the location of a crosswalk. However, the street-view method exhibited the highest recall value, highlighting its superior ability to recover an occluded crosswalk among all methods.

Harnessing customized AI to create voice of customer via GPT3.5

The integration of customer feedback is universally acknowledged as crucial in the product development process. Yet, traditional feedback collection methods employed by companies, such as interviews and surveys, have remained mainly unchanged and come with limitations. Interviews often fail to accurately capture customers' needs due to communication barriers, while surveys prompt only incremental changes instead of inspiring innovation. This challenge is compounded in the service industry, where feedback is intangible and more difficult to quantify. Text analysis presents a promising solution to delve into customer preferences more deeply, providing insights that can guide the development of new products and services. Our research advances the use of generative AI, specifically the GPT engine, beyond its conventional role as a chatbot. We innovate by adapting it to extract actionable insights from customer-service interactions, offering real-time, valuable data for decision-making and representing a significant leap forward in Voice of the Customer (VoC) analysis.

Student Feedback Mining System Using Sentiment Analysis

In education, the importance of gathering and understanding student feedback cannot be over stated. Traditional methods of gathering feedback through surveys and assessments often suffer from limitations such as low response rates and subjectivity. This paper proposes a new approach to improve the feedback collection process by implementing a Student Feedback Mining System using sentiment analysis. The goal of this system is to automatically analyze and extract sentiment from student feedback and provide educators and institutions with valuable insights into students' overall satisfaction and feelings toward various aspects of their academic experience. The system uses natural language processing (NLP) techniques to process unstructured text data, identify sentiments, and categorize feedback into positive, negative, or neutral feelings. Collecting student feedback through various channels such as online surveys, discussion forums and other feedback mechanisms. Cleaning and preprocessing the collected text data to remove noise, irrelevant information and standardize the format for analysis. Using state of the art NLP algorithms and sentiment analysis techniques to determine the emotional tone of feedback. This includes classifying sentiment as positive, negative, or neutral. Grouping feedback based on specific criteria such as course content, teaching methods, facilities and overall learning experience to provide a comprehensive understanding of the various aspects affecting student satisfaction. Presenting analyzed data through intuitive visualizations and reports, making it easy for educators and administrators to interpret and respond to feedback. Creating feedback that enables continuous improvement by providing actionable insights for educators to improve their teaching methods, curriculum and overall learning environment. The proposed system aims to overcome the limitations of traditional feedback collection methods by automating the analysis process, providing real-time insights and supporting a data-driven approach to improving education. Using sentiment analysis, educators and institutions can proactively solve problems, improve the quality of education, and create a more positive and engaging learning environment.

ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ УПРАВЛІНСЬКИХ РІШЕНЬ З ВИКОРИСТАННЯМ ШТУЧНОГО ІНТЕЛЕКТУ

In today's conditions of rapid development of information technologies and constant growth of the volume of data, information support for management decisions becomes an important factor in the company's success. Information security is the tools and technologies that ensure the collection, processing, analysis and transmission of information to support effective management. The article provides a retrospective review and assessment of the current state of information support for management decisions, which emphasizes the role of information systems and technologies in data collection, storage, and processing. It has been established that traditional methods of collecting and processing information are becoming less and less effective in conditions of constant growth of its volume, which requires the use of the latest technologies, such as artificial intelligence. Artificial intelligence opens up new opportunities for improving the efficiency of management processes, thanks to its ability to analyze a large volume of data, identify hidden patterns and form optimal solutions based on the results obtained. The article examines the theoretical and practical aspects of using artificial intelligence in the information provision of management decisions at the enterprise. An analysis of the possibilities of artificial intelligence in the context of company management was carried out. The focus is on various methods and algorithms for improving decision-making processes, including machine learning, neural networks, and expert systems. As a rule, first of all, companies solve the issue of automating routine management tasks, increasing the accuracy of forecasting and reducing risks when making strategic decisions. When using artificial intelligence, it is important to ensure compliance with the limitations and potential risks caused by the use of confidential data, the possible subjectivity of analytical data, the bias of algorithms, the existence of cyber threats, etc. Under all conditions, control and responsibility for the implementation of artificial intelligence technologies in management practice is borne by a specific responsible person.

Composição florística e similaridade de uma área de conservação do semiárido paraibano, Nordeste brasileiro

Abstract We present here a floristic survey of the angiosperms found in the Reserva Ecológica Olho d’Água das Onças (REODO) in the semiarid region of Paraíba State, Brazil, to provide a basis for proactive conservation projects there. Fifteen excursions were carried out between August/2022 and April/2024 employing traditional methods of collecting and herborization of botanical material. The collections obtained were incorporated into the Manuel de Arruda Câmara Herbarium (HACAM) at the State University of Paraíba (UEPB), Campus I. We examined the floristic relationships between the study area and 15 other floristic surveys throughout the semiarid region of Paraíba State using a similarity analysis based on the Jaccard index. Two hundred and fifty species distributed among 168 genera and 50 families of angiosperms were identified with the following growth forms: herbaceous (74 spp.), subshrub (60 spp.), shrub (48 spp.), climber (45 spp.), arboreal (17 spp.), epiphytic (3 spp.), and parasitic (3 spp.). The most representative families were Fabaceae (38 spp.), Euphorbiaceae (24 spp.), Convolvulaceae (19 spp.), Malvaceae (18 spp.), Asteraceae (11 spp.), and Poaceae (11 spp.). The most representative genera were Ipomoea L. (8 spp.), Croton L. (7 spp.), Sida L. (7 spp.), Chamaecrista (L.) Moench (6 spp.), and Mimosa L. (5 spp.). Heteropterys arcuata C. Pessoa & Amorim (Malpighiaceae) and Solanum fernandesii V.S. Samp. & R. Moura (Solanaceae) were recorded for the first time in Paraíba State; Urvillea stipitata Radlk (Sapindaceae) represents a new record for the Caatinga. Similarity analysis revealed that the flora of REODO is more closely related to that of protected areas throughout the semiarid region of Paraíba State, underscoring its conservation priority. The study area was therefore found to contain significant floristic richness, especially within the context of the Caatinga, with 30 species endemics to this domain.

Mosquitoes, Lymphatic Filariasis, and Public Health: A Systematic Review of Anopheles and Aedes Surveillance Strategies.

Lymphatic Filariasis (LF) affects over 120 million people in 72 countries, with sub-periodic filariasis common in the Pacific. Wuchereria bancrofti has three physiological races, each with a unique microfilarial periodicity, and each race is isolated to a specific geographical region. Sub-periodic W. bancrofti is transmitted by various Aedes mosquito species, with Aedes polynesiensis and Aedes samoanus being the primary vectors in Samoa. The Aedes scutellaris and Aedes kochi groups are also important vectors in the South Pacific Islands. Anopheles species are important vectors of filariasis in rural areas of Asia and Africa. The Anopheles gambiae complex, Anopheles funestus, and the Anopheles punctulatus group are the most important vectors of W. bancrofti. These vectors exhibit indoor nocturnal biting behaviour and breed in a variety of habitats, including freshwater, saltwater, and temporary water bodies. Effective vector surveillance is central to LF control and elimination programs. However, the traditional Human Landing Collection (HLC) method, while valuable, poses ethical concerns and risks to collectors. Therefore, this review critically analyses alternative trapping tools for Aedes and Anopheles vectors in LF-endemic regions. We looked at 14 research publications that discussed W. bancrofti vector trapping methods. Pyrethrum Spray Catches (PSC), one of the seven traps studied for Anopheles LF vectors, was revealed to be the second most effective strategy after HLC, successfully catching Anopheles vectors in Nigeria, Ghana, Togo, and Burkina Faso. The PSC method has several drawbacks, such as the likelihood of overlooking exophilic mosquitoes or underestimating Anopheles populations. However, exit traps offered hope for capturing exophilic mosquitoes. Anopheles populations could also be sampled using the Anopheles Gravid Trap (AGT). In contrast, the effectiveness of the Double Net Traps (DNT) and the CDC Light Trap (CDC LT) varied. Gravid mosquito traps like the OviArt Gravid Trap (AGT) were shown to be useful tools for identifying endophilic and exophilic vectors during the exploration of novel collection techniques. The Stealth trap (ST) was suggested for sampling Anopheles mosquitoes, although specimen damage may make it difficult to identify the species. Although it needs more confirmation, the Ifakara Tent Trap C design (ITT-C) showed potential for outdoor mosquito sampling in Tanzania. Furvela tent traps successfully captured a variety of Anopheles species and are appropriate for use in a variety of eco-epidemiological settings. By contrast, for Aedes LF vectors, no specific sampling tool was identified for Aedes niveus, necessitating further research and development. However, traps like the Duplex cone trap, Resting Bucket Trap (RB), and Sticky Resting Bucket trap (SRB) proved effective for sampling Aedes albopictus, offering potential alternatives to HLC. This review emphasises the value of looking into alternative trapping methods for Aedes and Anopheles vectors in the LF-endemic region. Further research is required to determine the efficacy of novel collection techniques in various contexts, even if PSC and AGT show promise for sampling Anopheles vectors. The identified traps, along with ongoing research, provide valuable contributions to vector surveillance efforts in LF-endemic regions, enabling LF control and elimination strategies to advance.

A critical appraisal of traditional methods of construction progress monitoring

PurposeThe purpose of this study is to investigate the current construction progress monitoring (CPM) process in relation to the contractual obligations, how project management teams carry out this activity in the field and why teams continue to adopt the current method. The study aims to provide a comprehensive understanding of the current monitoring process and its effectiveness, identify any shortcomings and propose recommendations for improvements that can lead to better project outcomes.Design/methodology/approachThe study conducted semi-structured interviews with 28 construction management practitioners to explore their views on contractual requirements, traditional progress monitoring practices and advanced monitoring methods. Thematic analysis was used to identify existing processes, practices and incentives for advanced monitoring.FindingsStandard construction contracts mandate current progress monitoring practices, which often rely on manual, document-centric and labor-intensive methods, leading to slow and erroneous progress reporting and project delays. Key barriers to adopting advanced tools include rigid contractual clauses, lack of incentives and the absence of reliable automated tools. A holistic automated approach that covers the entire CPM process, from planning to claim management, is needed as a viable alternative to traditional practices.Research limitations/implicationsThe study's findings can inform researchers, stakeholders and decision-makers about the existing monitoring practices and contribute to enhancing project management practices.Originality/valueThe study identified contractually mandated progress monitoring processes, traditional methods of collecting, transferring, analyzing and dispensing progress-related information and potential incentives and points of departure towards technologically advanced methods.

Assessment of a 60-Biomarker Health Surveillance Panel (HSP) on Whole Blood from Remote Sampling Devices by Targeted LC/MRM-MS and Discovery DIA-MS Analysis.

Telehealth, accessing healthcare and wellness remotely, should be a cost-effective and efficient way for individuals to receive care. The convenience of having a reliable remote collection device for blood tests will facilitate access to precision medicine and healthcare. Herein, we tested a 60-biomarker health surveillance panel (HSP), containing 35 FDA/LDT assays and covering at least 14 pathological states, on 8 healthy individuals' ability to collect their own capillary blood from a lancet finger prick and directly compared it to the traditional phlebotomist venous blood and plasma collection methods. All samples were spiked with 114 stable-isotope-labeled (SIL) HSP peptides and quantitatively analyzed by liquid chromatography-multiple reaction monitoring-mass spectrometry (LC/MRM-MS) scheduled method targeting 466 transitions from 114 HSP peptides and by a discovery data-independent acquisition mass spectrometry (DIA-MS) method. The average peak area ratio (PAR) of the HSP quantifier peptide transitions from all 8 volunteers' capillary blood (n = 48), venous blood (n = 48), and matched plasma (n = 24) was <20% coefficients of variation (CV). Heat map analysis of all 8 volunteers demonstrated that each individual had a unique biosignature. Biological replicates from capillary blood and venous blood clustered within each volunteer in k-means clustering analysis. Pearson statistical analysis of the three biofluids indicated that there was >90% similarity. Discovery DIA-MS analysis of the same samples using a plasma spectral library and a pan-human spectral library identified 1121 and 4661 total proteins, respectively. In addition, at least 122 FDA-approved biomarkers were identified. DIA-MS analysis reproducibly quantitated (<30% CV) ∼600-700 proteins in capillary blood, ∼800 proteins in venous blood, and ∼300-400 proteins in plasma, demonstrating that an expansive biomarker panel is possible with current mass spectrometry technology. Both targeted LC/MRM-MS and discovery DIA-MS analysis of whole blood collected on remote sampling devices are viable options for personal proteome biosignature stratification in precision medicine and precision health.

Fecal matters: implementing classical Coleoptera species lists with metabarcoding data from passerine bird feces

Diversity inventories are critical to creating accurate species range maps and estimating population sizes, which in turn lead to better informed landscape and wildlife management decisions. Metabarcoding has facilitated large-scale environmental diversity surveys. However, the use of a metabarcoding approach with bird feces to survey arthropod diversity is still relatively undeveloped. The aim of this study was to see if and how a metabarcoding approach with bird feces could contribute to a saproxylic Coleoptera survey of traditional insect traps. We compared two methods of surveying saproxylic Coleoptera diversity (metabarcoding birds feces and deploying traditional traps) over two elevations in a mountain system. The two methods caught different species and different levels of functional guild richness. The metabarcoding method successfully recorded both distinct and overlapping portions of diversity from traditional collections, and the approach was also effective in signaling the presence of both rare species and nine country records. Our results show that metabarcoding Passerine bird feces can be successful when used alongside traditional collection methods to capture a broad diversity of saproxylic Coleoptera. This method, however, has quantitative and qualitative limitations, including the inability to produce species abundance data as well as the generation of false positives and negatives due to biases within the metabarcoding pipeline.Implications for insect conservationAs many terrestrial ecosystems lose insect diversity, insect diversity surveys are essential to understand the scope of the loss. Despite metabarcoding approach shortcomings, the declining costs and shorter survey and processing time required for this approach compared to traditional survey methods indicate that it can be a valuable addition to the toolkit for saproxylic Coleoptera diversity surveys.

The SisaMob Information System: Implementation of Digital Data Collection as a Tool for Surveillance and Vector Control in the State of São Paulo

Information systems are essential instruments in managing resources, in the evaluation of the epidemiological situation, and for decision-making at all hierarchical levels. Technological advances have allowed the development of systems that meet these premises. Therefore, it is recommended to consider the optimization of data entry and its immediate georeferencing in order to obtain information in real time. To meet this objective, we describe the application introduction process for the implementation of the digital collection of primary data and its integration with the database through synchronization with the SisaWeb platform (Information System for surveillance and control of Aedes aegypti), developed to meet the needs of the Arbovirus Surveillance and Control Program in the state of São Paulo, Brazil. For this purpose, the application-SisaMob-was conceived in the Android Studio development environment, Google®, following the same guidelines as the traditional collection method. Tablets equipped with the Android® operating system were used. To evaluate the implementation of the application, a semi-structured test was applied. The results highlighted that 774.9% (27) of the interviewees evaluated its use positively and, replacing the standard bulletin, 61.1% (22) of the users considered it regular to excellent. The automatic collection of geographic coordinates represented the greatest innovation in the use of the portable device, with reductions in errors and in the time taken to complete the report in the field. The integration to SisaWeb allowed obtaining information in real-time, being easily presented in tabular and graphic modes and spatially arranged through maps, making it possible to monitor the work at a distance, and allowing preliminary analyses during the data collection process. For the future, we must improve the mechanisms for assessing the effectiveness of information, increase the potential of the tool to produce more accurate analyses, which can direct actions more efficiently.

Reducing chronic disease may just be a walk in the park

Wearable technology allows the collection of real-world granular data at scales that would be impossible using traditional collection methods. Master etal. demonstrate the power of this technology to estimate the risk of disease based on daily step counts.1.

Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making

Zooplankton are fundamental to aquatic ecosystem services such as carbon and nutrient cycling. Therefore, a robust evidence base of how zooplankton respond to changes in anthropogenic pressures, such as climate change and nutrient loading, is key to implementing effective policy-making and management measures. Currently, the data on which to base this evidence, such as long time-series and large-scale datasets of zooplankton distribution and community composition, are too sparse owing to practical limitations in traditional collection and analysis methods. The advance of in situ imaging technologies that can be deployed at large scales on autonomous platforms, coupled with artificial intelligence and machine learning (AI/ML) for image analysis, promises a solution. However, whether imaging could reasonably replace physical samples, and whether AI/ML can achieve a taxonomic resolution that scientists trust, is currently unclear. We here develop a roadmap for imaging and AI/ML for future zooplankton monitoring and research based on community consensus. To do so, we determined current perceptions of the zooplankton community with a focus on their experience and trust in the new technologies. Our survey revealed a clear consensus that traditional net sampling and taxonomy must be retained, yet imaging will play an important part in the future of zooplankton monitoring and research. A period of overlapping use of imaging and physical sampling systems is needed before imaging can reasonably replace physical sampling for widespread time-series zooplankton monitoring. In addition, comprehensive improvements in AI/ML and close collaboration between zooplankton researchers and AI developers are needed for AI-based taxonomy to be trusted and fully adopted. Encouragingly, the adoption of cutting-edge technologies for zooplankton research may provide a solution to maintaining the critical taxonomic and ecological knowledge needed for future zooplankton monitoring and robust evidence-based policy decision-making.

Filth Fly Parasitoid (Hymenoptera: Pteromalidae) Monitoring Techniques and Species Composition in Poultry Layer Facilities.

Muscid flies, especially house flies (Musca domestica L.) (Diptera: Muscidae), are a major pest of poultry layer facilities. Augmentative biological control of muscid flies with pteromalid wasps has gained increased attention in recent years. Knowing which pteromalid species are present in a specific area could produce more effective filth fly control. The purpose of this project was to survey parasitoid populations in poultry layer facilities in central and southeastern Pennsylvania from June through September. Two genera of parasitoids, Spalangia and Trichomalopsis, were collected over the course of the survey. Overall, out of 3,724 parasitized pupae the species collected in order of most to least common were Spalangia cameroni Perkins, Spalangia nigroaenea Curtis, Trichomalopsis spp., and Spalangia endius Walker. House fly parasitism overall and by each parasitoid species varied by location and over the four study months. A second objective was to evaluate a new parasitoid trap for surveying parasitoid wasp populations. This device uses a combination of house fly third instars and development media. This was compared to a more traditional method, the sentinel bag, which uses only fly pupae. A higher proportion of Spalangia spp. emerged from the new trap design and more Trichomalopsis spp. emerged from the sentinel bag. This suggests that using this new device alongside the traditional collection method may result in more accurate sampling of pteromalid populations.

Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives

The question regarding the actual usage of built environments is of immense importance in behavioral research. Yet traditional methods of collecting and analyzing data on movements and activities often lack needed accuracy and granularity. Thus, this article reviewed and summarized the applicability of emergent GPS trajectory mining approaches in the field of architecture from geographic, semantic, and quantitative perspectives, respectively. Accordingly, three experiments based on a case study using real GPS trajectory data from visitors to the Palace Museum in China were conducted to examine the usefulness and weakness of the aforementioned approaches. The findings revealed that although all three dimensions of the trajectory mining approaches had the potential to provide useful information for architectural and urban design, the higher the dimensionality in utilizing the data, the more effective the approach was in discovering generalizable knowledge of human behavioral pattern. Furthermore, the results suggested that to gain insights into the typological characteristics of human behaviors related to the built environments, the contribution of trajectory data alone was limited, hence, conventional field surveys and questionnaires which contain information on individual characteristics and spatial features should be used in conjunction. Future research and practical implications were outlined.

An assessment of indigenous knowledge of apitherapy in the Oromia region, Ethiopia

Apitherapy is the use of bee products such as honey, pollen, propolis, royal jelly, bee venom, and wax is used to prevent or treat illness and promote healing. The objectives of this study are to assess and document indigenous knowledge on the medicinal values of bee products in Ethiopia and generate baseline information on indigenous practices of the medicinal value of beehive products for further study. This study was conducted in beekeeping potential areas of Oromia Regional state by selecting seven zones and from each zone two districts and a total of 14 districts (Woreda) based on their potential for beekeeping, the existence of long-aged bee-keepers and accessibility. using a purposive sampling method and questionnaires were distributed to the elders and well-known traditional healers of the study areas. In answering the questionnaires 22-100 ages dwellers or respondents participated. The survey was conducted in 7 zones namely Jimma, Ilu ababora, West wollega, East wollega, East Shoa, West shoa, and Buno Bedele. Simple descriptive statistics were used to analyze the data obtained by collected questionnaires. The result of the study showed that 49.3% of Apis mellifera honey and 31.9% of stingless bee honey, majorly used bee products for apitherapy, and 70.3 % of the bee products used using traditional collection method. According to this study’s results, the traditional drug was prepared from honey in liquid form which comprises the biggest portion mark of 63.4% of respondents. The majority of the respondents strongly agree with bee products as a treatment for different diseases such as asthma, cough, common cold, TB, and wounds. A major challenge faced during using of bee products as medicine is the absence of awareness of apitherapy as it was reported by 28.8%. of the respondents. Thus, it can be concluded that bee products are well-known apitherapy in the Oromia region and it was recommended that awareness creation of bee products as apitherapy and further research on the medicinal value of beehive products for confirmation as medicine for different diseases since it may be a solution for various diseases resisting modern drugs.