Pest monitoring models play a vital role in enabling informed decisions for pest control and effective management strategies. In the context of smart farming, various approaches have been developed, surpassing traditional techniques in both efficiency and accuracy. However, the application of Few-Shot Learning (FSL) methods in this domain remains limited. In this study, we aim to bridge this gap by leveraging Transfer Learning (TL). Our findings highlight the considerable efficacy of TL techniques in this context, showcasing a significant 24% improvement in mAP performance and a 10% reduction in training time, thereby enhancing the efficiency of the model training process.
Over the years, many solutions have been suggested in order to improve object detection in maritime environments. However, none of these approaches uses flight information, such as altitude, camera angle, time of the day, and atmospheric conditions, to improve detection accuracy and network robustness, even though this information is often available and captured by the UAV. This work aims to develop a network unaffected by image-capturing conditions, such as altitude and angle. To achieve this, metadata was integrated into the neural network, and an adversarial learning training approach was employed. This was built on top of the YOLOv7, which is a state-of-the-art realtime object detector. To evaluate the effectiveness of this methodology, comprehensive experiments and analyses were conducted. Findings reveal that the improvements achieved by this approach are minimal when trying to create networks that generalize more across these specific domains. The YOLOv7 mosaic augmentation was identified as one potential responsible for this minimal impact because it also enhances the model’s ability to become invariant to these image-capturing conditions. Another potential cause is the fact that the domains considered (altitude and angle) are not orthogonal with respect to their impact on captured images. Further experiments should be conducted using datasets that offer more diverse metadata, such as adverse weather and sea conditions, which may be more representative of real maritime surveillance conditions. The source code of this work is publicly available at https://github.com/ipleiria-robotics/maritime-metadata-adaptation.
The data exchange between different sectors of society has led to the development of electronic documents supported by different reading formats, namely portable PDF format. These documents have characteristics similar to those used in programming languages, allowing the incorporation of potentially malicious code, which makes them a vector for cyberattacks. Thus, detecting anomalies in digital documents, such as PDF files, has become crucial in several domains, such as finance, digital forensic analysis and law enforcement. Currently, detection methods are mostly based on machine learning and are characterised by being complex, slow and mainly inefficient in detecting zero-day attacks. This paper aims to propose a Benford Law (BL) based model to uncover manipulated PDF documents by analysing potential anomalies in the first digit extracted from the PDF document’s characteristics. The proposed model was evaluated using the CIC Evasive PDFMAL2022 dataset, consisting of 1191 documents (278 benign and 918 malicious). To classify the PDF documents, based on BL, into malicious or benign documents, three statistical models were used in conjunction with the mean absolute deviation: the parametric Pearson and the non-parametric Spearman and Cramer-Von Mises models. The results show a maximum F1 score of \(87.63\%\) in detecting malicious documents using Pearson’s model, demonstrating the suitability and effectiveness of applying Benford’s Law in detecting anomalies in digital documents to maintain the accuracy and integrity of information and promoting trust in systems and institutions.
Context Physical exercise (PE) is an effective treatment for depression, alone or as an adjunct. Objective There is a lack of indicators regarding the frequency, intensity, duration, and type of physical exercise (PE). This study aims to synthesize and analyze the dose-effect of different PE protocols in adult subjects in the treatment of depression, based on the analysis of randomized controlled trials (RCTs). Data Sources The search was conducted using Web of Science, PubMed, and Cochrane Library electronic databases. Study Selection Studies with an exercise-based intervention published by December 31, 2021 were identified. RCTs and meta-analyses involving adults with depression were also included; 10 studies were selected, including a total of 956 subjects. Study Design Systematic review and meta-analysis. Level of Evidence Level 1. Results Effect sizes were summarized using standardized mean differences (95% confidence interval) by effected randomized models. The results reinforce that exercise appears to be beneficial in improving depression among adults aged 18 to 65 years. Interventions lasting above 150 minutes per week of moderate intensity and group interventions seem to have a more significant effect on reducing depression. Studies have revealed that aerobic exercise, compared with resistance or flexibility, has a more positive effect on depression. Conclusion PE can be a way to reduce depression and can be used as a possible adjunctive tool for pharmacological and/or alternative treatments. Considering the findings of this study, it is important that health professionals (eg, exercise physiologists, physicians, nurses, psychologists) promote the practice of PE as a complementary alternative and act early to prevent the worsening of depression. PROSPERO Registration Number CRD42020188909
The present study explores the influence of self-determined motivation and the interplay of positive and negative affect on anxiety levels among individuals engaged in gym practitioners during the second COVID-19 lockdown. A total of 196 exercisers (29.17 ± 10.77) were enrolled in the present study, of which 112 (57.1%) were women and 84 (42.9%) were men. The survey included sociodemographic data, as well as validated instruments measuring self-determined motivation, positive and negative affect, and anxiety states related to the COVID-19 pandemic. The results revealed a positive association between self-determined motivation and positive affect (β = 0.36, CI = 0.12, 0.37; p < 0.001), and a negative association between self-determined motivation and negative affect (β = -0.17, CI = -0.31, -0.01; p = 0.03). Moreover positive, and negative affect are negatively (β = -0.33, CI = -0.43, -0.24; p < 0.001) and positively (β = 0.72, CI = 0.57, 0.82; p < 0.001) associated to anxiety, respectively. Thus, this study appears to emphasize the impact of self-determined motivation on affect as a potential buffer against anxiety levels, particularly in a context where practitioners found themselves restricted in their usual gym practices.
As Europe prepares itself for a new downturn, this paper proposes to examine the determinants of hotel Revenue per Available Room (RevPAR) through literature review, and contribute to improving hotels’ performance by understanding the weight of the occupancy rate and the Average Daily Rate (ADR) on RevPAR, after the pandemic. A quantitative methodology was used, collecting data from STR Share Center and Our World in Data, such as ADR, occupancy rate, RevPAR, and COVID-19 confirmed cases. Results show the overwhelming effect of COVID-19 on hotel performance, conducing to ADR, occupancy rate, and RevPAR decline, and highlighting a co-movement of these indicators during COVID-19. After the lifting of major COVID-19 restrictions, RevPAR had a greater influence from ADR in some European countries, but the occupancy rate should not be disregarded. The findings, however, suggest the absence of the revenge travel phenomenon. The relationship between the number of COVID-19 cases and the decrease in RevPAR is not statistically significant, implying the existence of other factors that probably also had impact. The different measures adopted by governments to contain the virus, and each country's dependency on tourism, led to different impacts on hotel performance. This study helps hoteliers to know how to measure performance and the RevPAR drivers that can improve it, taking into account the situations that differ by country, as well as variables that are not controllable.
This article aims to uncover the inception, thematic structure, and forthcoming paths of excellence models (EMs) research and to shed light on the relationship between EMs and total quality management (TQM). We analysed 950 empirical articles from 1989 to 2022, published in 396 scientific journals indexed on Clarivate Analytics Web of Science Core Collection™ and Scopus, from all subject categories. We used content analysis, regression, citation, co-citation, and co-occurrence of research topics. The results unveiled the foundational clusters, current thematic streams, and forthcoming paths constituting EMs’ autonomous research structure. They also show that most researchers look to EMs as independent management instruments beyond TQM. This article helps researchers of different fields to investigate EMs efficiently and reliably since it identifies research trends and forthcoming paths, along with the most relevant authors, journals, and articles on EMs subjects. It enables managers to utilise EMs more effectively and evolve towards innovative training approaches to meet sustainable development challenges. This article is the most comprehensive bibliometric analysis and integrated systematic literature review of EMs research. It is also the first study to investigate EMs as a stand-alone research subject adding knowledge to its relationship with TQM and discovering latent and emerging research topics.
Evidence suggests affective responses to exercise can influence exercise adherence. However, there is a limited understanding of how and when to measure core affect in resistance training. As such, the objective of this systematic review was to analyze how the Feeling Scale and/or the Felt Arousal Scale have been used in resistance training to assess core affect. Focus was given to the contextual feasibility, timing, and frequency of assessment. A search in PubMed, SPORTDiscus, and PsycINFO databases was conducted (last search date July, 2022) with the purpose of including experimental and non-experimental studies, utilizing the Feeling Scale and/or the Felt Arousal Scale in resistance training, and focused on apparently healthy individuals of any age. Twenty-seven studies (N=718 participants) published between 2009-2022 were qualitatively analyzed. Both scales appeared to be able to detect core affect within a wide array of intensities, ages, and equipment. As for the timing and frequency of measurement, no apparent standardization was evident. The use of the Feeling Scale, the Felt Arousal Scale, or both, to measure core affect appears to be feasible in resistance training practices. However, a lack of methodological background raises concerns regarding the quality of previous studies' assessments and comparisons of results across studies.
The fourth industrial revolution brings many opportunities for the exploration of new business models, based on increasing digitalization that ultimately enables the prediction of the behavior of systems. Several challenges may be identified in the Industrial Management (IM) field. One of the most relevant is the opportunity to deal with real-time data and adapt the decision-making processes with agile approaches. IM learners will need to increase their awareness of these opportunities and challenges, both in professional training and in higher education. Thus, this study proposes a simulation system to support the learning process of opportunities and challenges to deal with big data from production systems’ sensors. The proposed simulation system implements simple dispatching rules for the jobs entering the production queue. Additionally, the system allows the creation of many coupled machines, each one associated with a one-level bill of materials, and a set of sensors delivering data to an excel file simulating a cloud. The study will show how to use the data in a learning experience for learners to understand the high amount of data delivered by sensors and the type of information and decisions it allows.
Shifting the communication process in the Occupational Safety and Health (OSH) field to digital can bring advantages to risk management. A digital communication system can help ensure that all workers find it easier to report hazards, accidents, and near misses. Additionally, it can make possible access to several prevention information. Since companies typically lack this type of solution, this work aimed to identify the requirements of an OSH communication system and propose a prototype. To this end, a company in the construction and maintenance sector was used as a case study. Information was collected through visits to a construction site, interviews with the workers and a focus group session. The results showed the company's interest in modernizing communication methods and dematerializing the accidents and near misses reporting. The system can be used to improve information management and maintenance activities. As a result, a prototype for a digital communication system was proposed, contributing to simplifying processes for all end users. However, it must be safeguarded, that the prototype describes preliminary results for the system to be developed. Further studies are ongoing to obtain the final solution.
The resources presented derive from a broader research carried out in 2021 in several Portuguese clothing retailers. This chapter examines the results of the working conditions reported by a group of managers during the pandemic context, namely what was the experience of working in a teleworking scheme during the periods of mandatory confinement and returning to work in the store with all the contingency measures required by law to prevent contamination by Covid-19. The approach took into account different data collection procedures, namely the analysis of the contingency measures adopted in 2020 and 2021, the questionnaire on working conditions, and the testimonies collected through interviews with managers on their personal experience of teleworking during this period. The results show that the majority of respondents consider the working conditions to be favourable, indicating the company’s constant concern about hazards and compliance with established safety rules and procedures, especially during the most critical period of the pandemic. The teleworking experience was more positive in the second period of mandatory confinement, due to the learning process when the pandemic broke out.
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The results of the study of eucalyptus bark fibers as a structure-forming material confirm the possibility of using such raw materials for the production of thermal insulation. With the help of an electron microscope, the microstructure of the bark and bark fibers was studied, which allows us to explain the mechanisms that ensure the thermal insulation properties of the fibers. Depending on the technological operations at the fiber preparation stage (mechanical grinding, cooking in ash solution, carbonation) and the use of a binder, the thermal conductivity coefficient of thermal insulation varies within 0,036–0,059 W/(m×°C) at a density of 80–220 kg/m3. Samples based on eucalyptus bark fibers demonstrate sufficient low sorption humidity for materials based on vegetable raw materials. At a relative humidity of 60%, sorption is 9,4–14,5%, and at a relative humidity of 97%, it reaches 21,6–38,5%. The samples also provide high resistance to the appearance of fungus on the fibers of the eucalyptus bark in a wet state, which indicates the durability of the structure-forming material during the operation of thermal insulation.
The scientific breakthrough in understanding the role of sports volunteers, as well as the importance of motivation and expectations management in decision-making, has boosted several studies, helping to reinforce the conceptual idea of a sports legacy, especially in relation to the participation and involvement of Olympic volunteers. In this sense, and reinforcing this idea, this study aimed to analyse and measure the perception of sports volunteers regarding the motivations and expectations arising from their participation in the Rio 2016 Olympic Games. The study followed a quantitative-descriptive and inferential methodology, with a validated sample of 828 responses collected through a semi-structured questionnaire adapted from the original VMS-ISE motivation scale. The results point to a reinforcement of the perceived importance and value associated with participation in the Olympic Games that translate into high levels of motivation and satisfaction by most of the volunteers involved. The most valued factors were the feeling of pleasure promoted by the experience, the possibility of personal and professional development, the passion for the Olympic Games, and the passion for sport. The majority of the candidates validated the post-event expectations as having exceeded their initial ones, and 92.0% of the volunteers would participate in a similar event again. The study allows the reinforcement of sports volunteers intentions and expectations, while validating and confirming previous studies on the importance of motivation and personal development in a successful recruitment strategy that fosters regular and consolidated participation of previous volunteers.
Background : While Enterobacteriaceae bacteria are commonly found in the healthy human gut, their colonization of other body parts can potentially evolve into serious infections and health threats. We investigate a graph-based machine learning model to predict risks of inpatient colonization by multidrug-resistant (MDR) Enterobacteriaceae. Methods: Colonization prediction was defined as a binary task, where the goal is to predict whether a patient is colonized by MDR Enterobacteriaceae in an undesirable body part during their hospital stay. To capture topological features, interactions among patients and healthcare workers were modeled using a graph structure, where patients are described by nodes and their interactions are described by edges. Then, a graph neural network (GNN) model was trained to learn colonization patterns from the patient network enriched with clinical and spatiotemporal features. Results: The GNN model achieves performance between 0.91 and 0.96 area under the receiver operating characteristic curve (AUROC) when trained in inductive and transductive settings, respectively, up to 8% above a logistic regression baseline (0.88). Comparing network topologies, the configuration considering ward-related edges (0.91 inductive, 0.96 transductive) outperforms the configurations considering caregiver-related edges (0.88, 0.89) and both types of edges (0.90, 0.94). For the top 3 most prevalent MDR Enterobacteriaceae, the AUROC varies from 0.94 for Citrobacter freundii up to 0.98 for Enterobacter cloacae using the best-performing GNN model. Conclusion: Topological features via graph modeling improve the performance of machine learning models for Enterobacteriaceae colonization prediction. GNNs could be used to support infection prevention and control programs to detect patients at risk of colonization by MDR Enterobacteriaceae and other bacteria families.
This paper focusses on the linguistic construal of scientific knowledge in teaching-learning contexts. It aims to map the organisational structure of classificatory texts included in science textbooks and to identify the lexical resources associated with the verbalisation of classification systems. The study draws upon systemic functional linguistics (Halliday, 2014Halliday, M. A. K. (2014). Halliday’s introduction to functional grammar (4th ed., Revised by Christian M. I. M. Matthiessen). Routledge. https://doi.org/10.4324/9780203431269 https://doi.org/10.4324/9780203431269... ) and the Sydney School’s genre studies (Martin & Rose, 2008Martin, J. R., & Rose, D. (2008). Genre relations: Mapping culture. Equinox. ). The main methodological steps comprised the creation of a corpus of 100 classificatory texts, extracted from natural sciences textbooks (2nd and 3rd cycles of elementary education) used in Portugal, and the identification of their stages, based on the schematic structure of the taxonomic report genre. The results show a significant lack of obligatory stages, as well as the extended use of an optional stage: Orientation. The classification system stage was further analysed, focusing on its semantic and lexicogrammatical patterns. An irregular presence of different semantic elements was identified, as well as the use of lexicogrammatical resources that do not construe classification knowledge per se. It is argued that instructing students on the classificatory genre’s contextual and textual properties is essential to enhance their ability to comprehend and produce texts. Keywords: genre; textbooks; natural sciences; scientific classification; taxonomic report
Background: Custom-made alloplastic temporomandibular joint replacement (ATMJR) is not validated in irradiated patients. However, in specific situations, after previous reconstructive surgical failures, the authors hypothesized the role of a customized ATMJR after radiotherapy. Methods: A 65-year-old male patient was referred to Instituto Português da Face-Lisbon, Portugal-after failed attempts of mandibular reconstruction secondary to oral carcinoma resection and partial hemi-mandibulectomy plus radiotherapy of 60 total Grays. Primary reconstruction was performed with fibula free flap. Due to failure, secondary reconstructions were performed with osteosynthesis plate without success. The patient was unable to have adequate mastication and deglutition due to a severe crossbite. The authors treated the patient with an extended customized alloplastic temporomandibular joint replacement (F0M2). Results: With 3 years of follow-up, the patient showed an improvement in masticatory function, mandibular motion, pain levels, and overall quality of life. No complications were observed related to ATMJR. Conclusions: The presented case described how ATMJR, although not a validated option after radiotherapy, can be considered to restore functionality in complex cases with bone and soft tissues problems.
This study aims to assess the influence of root canal preparation, irrigation needle design and its placement depth in the irrigation flow of confluent canals during syringe irrigation. A mandibular molar presenting two confluent canals in its mesial root was sequentially prepared and scanned by micro-computed tomography after mechanical preparation up to ProTaper Next system sizes X2 (25/.06v), X3 (30/.07v) and X4 (40/.06v). In each of the root canal preparation models, a side-vented and an open-ended needle at 5, 3 and 2 mm from the working length were included, and irrigation flow was assessed by a validated computational fluid dynamics model. The results revealed that the irrigant flowed out of the confluent canals mainly through the canal that did not have the needle. Apical penetration and renewal of the irrigant were most efficiently achieved with the use of a 30G open-ended needle and a 30/.07v preparation.
The formulation of magnetic ionic liquids (MILs) or organic salts based on lanthanides as anions has been explored. In this work, a set of choline-family-based salts, and two other, different cation families, were combined with Gadolinium(III) and Terbium(III) anions. Synthetic methodologies were previously optimized, and all organic salts were obtained as solids with melting temperatures higher than 100 °C. The magnetic moments obtained for the Gd(III) salts were, as expected, smaller than those obtained for the Tb(III)-based compounds. The values for Gd(III) and Tb(III) magnetic salts are in the range of 6.55–7.30 MB and 8.22–9.34 MB, respectively. It is important to note a correlation between the magnetic moments obtained for lanthanides, and the structural features of the cation. The cytotoxicity of lanthanide-based salts was also evaluated using 3T3, 293T, Caco2, and HepG2 cells, and it was revealed that most of the prepared compounds are not toxic.
A tissue or organ may fail due to trauma, tumor, congenital disease, or other pathology. When that occurs, the standard clinical procedure includes transferring healthy tissue to the damaged site in the same patient or transplanting a functional organ from a donor. Nevertheless, it is clear to see that there are great inconveniences: the shortage of donors, rejection risk, and the diseases broadcast. It has become essential to develop new medical therapies in this area, and tissue engineering (TE) evolved to be a solution. While there are numerous approaches in the TE domain, in this chapter, we will only focus on using additive manufacturing (AM) technologies to build 3D constructs aiming to mimic the native human tissues. Difficulties and constraints are presented, followed by the available methods to manufacture. Some examples are provided for hard and soft tissue, exploring the future expectations on the field.
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