12,641 research outputs found
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
Sleep abnormalities can have severe health consequences. Automated sleep
staging, i.e. labelling the sequence of sleep stages from the patient's
physiological recordings, could simplify the diagnostic process. Previous work
on automated sleep staging has achieved great results, mainly relying on the
EEG signal. However, often multiple sources of information are available beyond
EEG. This can be particularly beneficial when the EEG recordings are noisy or
even missing completely. In this paper, we propose CoRe-Sleep, a Coordinated
Representation multimodal fusion network that is particularly focused on
improving the robustness of signal analysis on imperfect data. We demonstrate
how appropriately handling multimodal information can be the key to achieving
such robustness. CoRe-Sleep tolerates noisy or missing modalities segments,
allowing training on incomplete data. Additionally, it shows state-of-the-art
performance when testing on both multimodal and unimodal data using a single
model on SHHS-1, the largest publicly available study that includes sleep stage
labels. The results indicate that training the model on multimodal data does
positively influence performance when tested on unimodal data. This work aims
at bridging the gap between automated analysis tools and their clinical
utility.Comment: 10 pages, 4 figures, 2 tables, journa
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Targeting Fusion Proteins of HIV-1 and SARS-CoV-2
Viruses are disease-causing pathogenic agents that require host cells to replicate. Fusion of host and viral membranes is critical for the lifecycle of enveloped viruses. Studying viral fusion proteins can allow us to better understand how they shape immune responses and inform the design of therapeutics such as drugs, monoclonal antibodies, and vaccines. This thesis discusses two approaches to targeting two fusion proteins: Env from HIV-1 and S from SARS-CoV-2. The first chapter of this thesis is an introduction to viruses with a specific focus on HIV-1 CD4 mimetic drugs and antibodies against SARS-CoV-2. It discusses the architecture of these viruses and fusion proteins and how small molecules, peptides, and antibodies can target these proteins successfully to treat and prevent disease. In addition, a brief overview is included of the techniques involved in structural biology and how it has informed the study of viruses. For the interested reader, chapter 2 contains a review article that serves as a more in-depth introduction for both viruses as well as how the use of structural biology has informed the study of viral surface proteins and neutralizing antibody responses to them. The subsequent chapters provide a body of work divided into two parts. The first part in chapter 3 involves a study on conformational changes induced in the HIV-1 Env protein by CD4-mimemtic drugs using single particle cryo-EM. The second part encompassing chapters 4 and 5 includes two studies on antibodies isolated from convalescent COVID-19 donors. The former involves classification of antibody responses to the SARS-CoV-2 S receptor-binding domain (RBD). The latter discusses an anti-RBD antibody class that binds to a conserved epitope on the RBD and shows cross-binding and cross-neutralization to other coronaviruses in the sarbecovirus subgenus.</p
Mapeamento do processo logístico de cargas de resinas líquidas
Com este trabalho procurou-se criar uma análise de possibilidades e necessidades
para a implementação de um processo logístico de expedição automatizado por via do
mapeamento do processo, partindo da realização de um estudo bibliográfico nas áreas de
logística, processos de negócio e desenvolvimentos tecnológicos da atualidade aplicados a
esta área de negócio que permita analisar a forma como diversos autores descrevem cada
um deles e de que forma estes podem contribuir para a eficiência dos processos e
sustentabilidade dos negócios.
A análise feita com apoio de um elemento da gestão da empresa onde se realizou este
estudo revelou-se benéfica e adequada ao método necessário para o desenvolvimento do
mesmo, levando a atingir os objetivos delineados. Na análise desenvolvida e mapeando o
atual processo de expedição de resinas líquidas da empresa EuroResinas foi possível
encontrar potenciais causas para problemas no processo atual e potenciais oportunidades de
melhoria. A partir desta análise foi possível sugerir melhorias e formas de implementar a
automatização em algumas partes do processo mapeado pela via de instalação de
equipamentos e tecnologia de última geração.
Este estudo permitiu desbravar o caminho inicial para a implementação do processo
logístico de expedição de resinas líquidas da EuroResinas de forma completamente
automatizada para que possa ser utilizado do ponto de vista de self-service por parte do
motorista do camião.
No final do estudo, o mapeamento do processo “as-is” permitiu retirar conclusões
relevantes para a implementação do processo automatização sugerindo um mapeamento de
processo “to-be” e delineando o trabalho futuro nesse sentidoThis work aimed to create an analysis of possibilities and needs for the implementation
of a logistic process of automated dispatch through process mapping, starting from the
realization of a bibliographic study in the areas of logistics, business processes and
technological developments of the current applied to this business area that allows analyzing
how several authors describe each of them and how they can contribute to the efficiency of
the processes and sustainability of the business.
The analysis made with the support of an element of the management of the company
where this study was carried out proved to be beneficial and appropriate to the method
necessary for its development, leading to achieving the objectives outlined. In the analysis
developed and mapping the current process of dispatch of liquid resins of the company
EuroResinas it was possible to find potential causes for problems in the current process and
potential opportunities for improvement. From this analysis it was possible to suggest
improvements and ways to implement automation in some parts of the process mapped
through the installation of equipment and state-of-the-art technology.
This study allowed us to explore the initial path for the implementation of EuroResinas
logistics process of liquid resins in a completely automated way so that it can be used from the
point of view of self-service by the truck driver.
At the end of the study, the mapping of the "as-is" process allowed us to draw relevant
conclusions for the implementation of the automation process suggesting a "to-be" process
mapping and to outline future work in this direction
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH
Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)
Concealed Automated Trash Bin with Shredder for Solid Wastes
oai:ojs2.journals.cspc.edu.ph:article/45The common difficulty in populated developing countries like the Philippines is inappropriate waste management practices. The improper use of waste bins and waste segregation are some of those. One of the major causes is the irresponsibility of the people. As expected, the consequences are environmental and health risks experienced by people. The countermeasure to minimize these risks is the solution proposed by people, particularly the development of an automated segregation system. The waste bins are designed to be concealed to conserve space, slow down the decomposition rate and reduce the foul odor of waste. The design is fully automated to minimize direct contact with the waste. The classifying section is capable of collecting and segregating waste using a gripper, servo motors, ultrasonic, capacitive, and photoelectric sensors. To conserve power, the segregated waste is held in a storage bin prior to shredding. Shredded waste is routed to their respective transport bins for collection after shredding.The ultrasonic sensors provide data about the capacity of the transport bins and allow the GSM module to send an SMS informing the concerned authority regarding the bins’ status. These messages facilitate easier waste collection. Two tests were conducted to determine the performance of the prototype: response-time and garbage level detection tests. The result shows that the prototype performed well and can successfully achieve the desired function. It took 23.745 and 2.711 seconds to collect and segregate the waste, respectively. Likewise, the monitoring system successfully expedited the checking of the waste bins. However, it is recommended to include quality of output and SMS delay tests. These tests can improve the overall performance of the prototype
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