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The purpose of this paper is to investigate the role of identification and disidentification processes of individuals who perform dirty work. Specifically, this study seeks to understand how identification creates resilience for volunteer workers to endure dirty work.

The present study examines the resilience of volunteers in dirty work roles by interviewing 37 volunteers at an animal shelter and observing volunteers for 72 hours. The transcripts and field notes were analyzed using a grounded theory analysis.

Volunteers construct multiple identifications and disidentifications as part of the resilience process to engage dirty and dangerous work. Volunteers switched between different (dis)identifications and communicatively reinforced (dis)identifications to overcome the physical and social stigma associated with their work.

The present study extends research on resilience into a new context: dirty work. The findings bring into question assumptions regarding resilience and how a disruption is defined in the resilience literature. Disruptions are communicatively constructed and future studies should continue to research alternative contexts to study resilience labor.

With the rapid advancement of lifestyle and technology, human lives are becoming increasingly threatened. Accidents, exposure to dangerous substances and animal strikes are all possible threats. Human lives are increasingly being harmed as a result of attacks by wild animals. Further investigation into the cases reported revealed that such events can be detected early on. Techniques such as machine learning and deep learning will be used to solve this challenge. The upgraded VGG-16 model with deep learning-based detection is appropriate for such real-time applications because it overcomes the low accuracy and poor real-time performance of traditional detection methods and detects medium- and long-distance objects more accurately. Many organizations use various safety and security measures, particularly CCTV/video surveillance systems, to address physical security concerns. CCTV/video monitoring systems are quite good at visually detecting a range of attacks associated with suspicious behavior on the premises and in the workplace. Many have indeed begun to use automated systems such as video analytics solutions such as motion detection, object/perimeter detection, face recognition and artificial intelligence/machine learning, among others. Anomaly identification can be performed with the data collected from the CCTV cameras. The camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. Many cases have been recorded where wild animals enter public places, causing havoc and damaging lives and property. There are many cases where people have lost their lives to wild attacks. The conventional approach of sifting through images by eye can be expensive and risky. Therefore, an automated wild animal detection system is required to avoid these circumstances.

The proposed system consists of a wild animal detection module, a classifier and an alarm module, for which video frames are fed as input and the output is prediction results. Frames extracted from videos are pre-processed and then delivered to the neural network classifier as filtered frames. The classifier module categorizes the identified animal into one of the several categories. An email or WhatsApp notice is issued to the appropriate authorities or users based on the classifier outcome.

Evaluation metrics are used to assess the quality of a statistical or machine learning model. Any system will include a review of machine learning models or algorithms. A number of evaluation measures can be performed to put a model to the test. Among them are classification accuracy, logarithmic loss, confusion matrix and other metrics. The model must be evaluated using a range of evaluation metrics. This is because a model may perform well when one measurement from one evaluation metric is used but perform poorly when another measurement from another evaluation metric is used. We must utilize evaluation metrics to guarantee that the model is running correctly and optimally.

The output of conv5 3 will be of size 7*7*512 in the ImageNet VGG-16 in Figure 4, which operates on images of size 224*224*3. Therefore, the parameters of fc6 with a flattened input size of 7*7*512 and an output size of 4,096 are 4,096, 7*7*512. With reshaped parameters of dimensions 4,096*7*7*512, the comparable convolutional layer conv6 has a 7*7 kernel size and 4,096 output channels. The parameters of fc7 with an input size of 4,096 (i.e. the output size of fc6) and an output size of 4,096 are 4,096, 4,096. The input can be thought of as a one-of-a-kind image with 4,096 input channels. With reshaped parameters of dimensions 4,096*1*1*4,096, the comparable convolutional layer conv7 has a 1*1 kernel size and 4,096 output channels. It is clear that conv6 has 4,096 filters, each with dimensions 7*7*512, and conv7 has 4,096 filters, each with dimensions 1*1*4,096. These filters are numerous, large and computationally expensive. To remedy this, the authors opt to reduce both their number and the size of each filter by subsampling parameters from the converted convolutional layers. Conv6 will use 1,024 filters, each with dimensions 3*3*512. Therefore, the parameters are subsampled from 4,096*7*7*512 to 1,024*3*3*512. Conv7 will use 1,024 filters, each with dimensions 1*1*1,024. Therefore, the parameters are subsampled from 4,096*1*1*4,096 to 1,024*1*1*1,024.

Prior to the Bovine Spongiform Encephalopathy (BSE) crisis, detailed information on beef products seemed no real necessity. However, following the outbreak of BSE, the Government felt obliged to protect consumer interest with legislation. Obligatory product information became required for beef traceability. This paper describes how the emergence of beef product information became relevant against the background of BSE. The paper describes the beef sector over time, through two case studies. From both studies, the emergence of product information exchange can be clearly noted. The first study describes the type of information exchanged, before the BSE outbreak. The second study describes the type of information exchanged after the BSE outbreak, prior to compulsory labelling.

Grounded on the technology-organization-environment (TOE) framework, the purpose of this paper is to develop a two-stage model of radio frequency identification (RFID) adoption in livestock businesses. RFID adoption is divided into two stages, acceptance and extension. It is argued that RFID adoption in livestock businesses is influenced by technological (interoperability, technology readiness), organizational (readiness, market scope), and environmental (competitive market pressure, data inconsistency) factors.

From a qualitative field study, along with the support of existing literature, the authors developed a research model, which was then validated with survey data of 318 livestock businesses in Australia. Data analysis used partial least squares structural equation modeling.

Empirical results showed that interoperability, organizational readiness, and competitive market pressure, and data inconsistency significantly influence acceptance of RFID technology in livestock businesses. In addition, the extended use of RFID is determined mainly by interoperability, technology readiness, organizational market scope, and data inconsistency. The results suggested differential effect of data inconsistency– it had a negative influence on RFID acceptance but a positive impact on the extent of its use.

This is one of the first studies to examine RFID adoption as a two-stage process. The theoretical basis was based on TOE framework and the factors were developed from a field study. The results of this study will provide insights for different livestock industry including technologists, farm managers, and market players.

‐‐ The purpose of this paper is to provide a brief introduction to software applications that make nature field guides accessible on computers, including desktop and mobile devices such as smartphones and tablets, and to describe some specific products that are available.Design/methodology/approach ‐‐ This article discusses the development and proliferation of software applications or “apps” for nature field guides. Common features of such apps and some specific examples are presented.Findings ‐‐ Numerous field guide apps are available for identifying animals, plants, and other natural phenomena using mobile computing devices or smartphones. Apps for mobile devices retain the traditional quality of portability for field guides, and most of these apps offer high quality photographs along with descriptive text. Some products include audio files for identifying bird songs or frog calls. Features for users include the ability to maintain life lists and to search guide contents using graphic icons, animal or plant markings, or colors.Originality/value ‐‐ Reviews of nature field guides have generally focused on print sources or electronic book editions of print titles. While several newspapers, magazines, and websites have published reviews of mobile field guide apps, an introduction and reviews have not yet been published in a source like Reference Reviews.

This paper argues that this animal surveillance has the potential for considerable function creep going far outside the scheme's original objectives and acts as a conduit for more problematic surveillance of humans. This results in social sorting of people with subsequent unforeseen consequences leading to discrimination and curtailment of freedoms for both animals and their owners. Ultimately this opens people up to further intrusive targeting by commercial interests and, more alarmingly, scrutiny from law enforcement agencies.

An empirical study examining an initiative involving the collection of canine DNA sources data from publicly available Cabinet, Select Committee and Scrutiny Committee records from the London Borough of Barking and Dagenham (LBBD). It also draws on news media sources, publicity material from the company running the scheme and from this and other local authorities. Methods include analysis of documents, semiotic and discourse analysis.

This paper highlights the importance of animals to surveillance studies and examines the extent to which animals are a part of the surveillant assemblage in their own right. It also demonstrates how nonhuman animals extend the reach of the surveillant assemblage.

The scheme was called a badge of considerate dog ownership, yet it is one that can be franchised to tie up with diverse income streams being described as advantageous in the age of austerity. In 2017, it was reported that this scheme was to be rolled out in other areas and was moving from being voluntary to being mandatory with the enforcement of Public Space Protection Orders (PSPOs). These have been described as “geographically defined ASBOS” that have come into force under the Anti-social Behaviour and Policing Act (2014); they often work to criminalise activities that were not previously considered illegal.

In the theorising of surveillance, animals have been largely overlooked. Epidemiological studies proliferate, yet the role of animals in many aspects of everyday surveillance has been neglected.

Many information systems involve data about people. In order reliably to associate data with particular individuals, it is necessary that an effective and efficient identification scheme be established and maintained. There is remarkably little in the information technology literature concerning human identification. Seeks to overcome that deficiency by undertaking a survey of human identity and human identification. Discusses techniques including names, codes, knowledge‐based and token‐based identification, and biometrics. Identifies the key challenge to management as being to devise a scheme which is practicable and economic, and of sufficiently high integrity to address the risks the organization confronts in its dealings with people. Proposes that much greater use be made of schemes which are designed to afford people anonymity, or which enable them to use multiple identities or pseudonyms, while at the same time protecting the organization′s own interest. Describes multi‐purpose and inhabitant registration schemes, and notes the recurrence of proposals to implement and extend them. Identifies public policy issues. Of especial concern is the threat to personal privacy that the general‐purpose use of an inhabitant registrant scheme represents. Speculates that, where such schemes are pursued energetically, the reaction may be strong enough to threaten the social fabric.

The purpose of this paper is to provide a framework for radio frequency identification (RFID) technology adoption considering company size and five dimensions of analysis: RFID applications, expected benefits business drivers or motivations barriers and inhibitors, and organizational factors.

A framework for RFID adoption derived from literature and the practical experience on the subject is developed. This framework provides a conceptual basis for analyzing a survey conducted with 114 companies in Brazil.

Many companies have been developing RFID initiatives in order to identify potential applications and map benefits associated with their implementation. The survey highlights the importance business drivers in the RFID implementation stage, and that companies implement RFID focusing on a few specific applications. However, there is a weak association between expected benefits and business challenges with the current level of RFID technology adoption in Brazil.

The paper is not exhaustive, since RFID adoption in Brazil is at early stages during the survey timeline.

The main contribution of the paper is that it yields a framework for analyzing RFID technology adoption. The authors use this framework to analyze RFID adoption in Brazil, which proved to be a useful one for identifying key issues for technology adoption. The paper is useful to any researchers or practitioners who are focused on technology adoption, in particular, RFID technology.

We propose cloud IoT based LMS (Livestock Management System) with three features. i) Animal healthcare monitoring and recording using IoT sensors via wearable collar, ii) Animal livestock identification using UID for animals (smart tag) and owners (smart card), iii) QR code reading, processing and display of the details in mobile via wireless technologies.

The developed animal monitoring device is used to detect animal physiological parameters such as body temperature; physical gestures like sitting, standing, eating and heartbeat, environmental parameters such as air temperature and relative humidity. Also, e-animal husbandry information network management system is the comprehensive web-based animal husbandry software designed for better interaction between veterinary hospital, veterinary doctor, owner, farmer and animal husbandry management.

Animal monitoring device mounted on the neck sense the values and predict the health status of the animal by using cloud IoT analytics platform. The accuracy of the system is 90 per cent and it can be well placed in the livestock environment.

This research is carried out in livestock cows located in Tirunelveli district. The practical difficulty was in placing sensors on the animal. The digital feed from the farmers and the veterinary hospital is input in the animal husbandry management software.

The developed system can be implemented for monitoring the health status of the animal from anywhere using mobile applications. Also, the digitized animal information helps the government to take the right decisions on policies and fund allocation.

The implemented system can be easily scaled up to large environments by using wireless communication and animal husbandry data will be available immediately. UID scheme for animals can uniquely identify the animal and its details.

The proposed work implements novel livestock monitoring and analytics system along with Aadhar (Unique ID) for animal. The proposed UID scheme is innovative and unique.

Inclusion of animals in emergency contingency planning is not yet common practice in many countries. The purpose of this paper is to assess the need for and viability of such inclusion in Latin America.

The study surveyed 1,882 pet owners in urban areas in Colombia, Costa Rica and Mexico to evaluate perceptions of pet owners with regard to their animals in emergencies.

Overall 74.5 percent (confidence interval 72.5‐76.5) said they would take their animals if evacuated. Only 16 percent (14.5‐17.8) would leave their animals behind. Regular vaccination was carried out by 70.5 percent of owners (68.0‐72.5): from 63.6 percent (60.8‐66.2 percent) in Mexico to 87.5 percent (84.3‐90.0) in Colombia. People in lower socio‐economic levels were less likely to take animals to the vet, or to vaccinate or identify them, and more likely to leave their animal behind during evacuation.

Results indicate both the need and the likely success of an animal contingency planning process in urban areas of Latin America.

This is the first survey of its kind concerning disaster preparedness for pets in developing countries, and counters the common perception that in such countries owners are less attached to their pets and that inclusion of animals in emergency contingency planning is not important.