Abstract
Currently, copper is approved as an active substance among plant protection products and is considered effective against more than 50 different diseases in different crops, conventional and organic. Tomato has been cultivated for centuries, but many fungal diseases still affect it, making it necessary to control them through antifungal agents, such as copper, making it the primary form of fungal control in organic farming systems (OFS). The objective of this work was to determine whether exogenous copper applications can affect AOX mechanisms and nitrogen use efficiency in tomato plant grown in OFS. For this purpose, plants were sprayed with 'Bordeaux' mixture (SP). In addition, two sets of plants were each treated with 8 mg/L copper in the root substrate (S). Subsequently, one of these groups was also sprayed with a solution of 'Bordeaux' mixture (SSP). Leaves and roots were used to determine NR, GS and GDH activities, as well as proline, H2O2 and AsA levels. The data gathered show that even small amounts of copper in the rhizosphere and copper spraying can lead to stress responses in tomato, with increases in total ascorbate of up to 70% and a decrease in GS activity down to 49%, suggesting that excess copper application could be potentially harmful in horticultural production by OFS.

Abstract
Background Breast symmetry is an essential component of breast cosmesis. The Harvard Cosmesis scale is the most widely adopted method of breast symmetry assessment. However, this scale lacks reproducibility and reliability, limiting its application in clinical practice. The VECTRA (R) XT 3D (VECTRA (R)) is a novel breast surface imaging system that, when combined with breast contour measuring software (Mirror (R)), aims to produce a more accurate and reproducible measurement of breast contour to aid operative planning in breast surgery. Objectives This study aims to compare the reliability and reproducibility of subjective (Harvard Cosmesis scale) with objective (VECTRA (R)) symmetry assessment on the same cohort of patients. Methods Patients at a tertiary institution had 2D and 3D photographs of their breasts. Seven assessors scored the 2D photographs using the Harvard Cosmesis scale. Two independent assessors used Mirror (R) software to objectively calculate breast symmetry by analysing 3D images of the breasts. Results Intra-observer agreement ranged from none to moderate (kappa - 0.005-0.7) amongst the assessors using the Harvard Cosmesis scale. Inter-observer agreement was weak (kappa 0.078-0.454) amongst Harvard scores compared to VECTRA (R) measurements. Kappa values ranged 0.537-0.674 for intra-observer agreement (p < 0.001) with Root Mean Square (RMS) scores. RMS had a moderate correlation with the Harvard Cosmesis scale (r(s) = 0.613). Furthermore, absolute volume difference between breasts had poor correlation with RMS (R-2 = 0.133). Conclusion VECTRA (R) and Mirror (R) software have potential in clinical practice as objectifying breast symmetry, but in the current form, it is not an ideal test.

Abstract
Background: During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they have been evaluated using different datasets.Purpose: This paper presents a comprehensive comparison of various COVID-19 detection methods based on self-reported information using the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), a large health surveillance platform, which was launched in partnership with Facebook.Methods: Detection methods were implemented to identify COVID-19-positive cases among UMD-CTIS participants reporting at least one symptom and a recent antigen test result (positive or negative) for six countries and two periods. Multiple detection methods were implemented for three different categories: rule-based approaches, logistic regression techniques, and tree-based machine-learning models. These methods were evaluated using different metrics including F1-score, sensitivity, specificity, and precision. An explainability analysis has also been conducted to compare methods.Results: Fifteen methods were evaluated for six countries and two periods. We identify the best method for each category: rule-based methods (F1-score: 51.48% -71.11%), logistic regression techniques (F1-score: 39.91% -71.13%), and tree-based machine learning models (F1-score: 45.07% -73.72%). According to the explainability analysis, the relevance of the reported symptoms in COVID-19 detection varies between countries and years. However, there are two variables consistently relevant across approaches: stuffy or runny nose, and aches or muscle pain.Conclusions: Regarding the categories of detection methods, evaluating detection methods using homogeneous data across countries and years provides a solid and consistent comparison. An explainability analysis of a tree-based machine-learning model can assist in identifying infected individuals specifically based on their relevant symptoms. This study is limited by the self-report nature of data, which cannot replace clinical diagnosis.

Abstract

Abstract
To ensure the safety of passengers concerning virus propagation, such as COVID-19, and keep the turnaround time at low levels, airlines should seek efficient aircraft boarding strategies in terms of both physical distancing and boarding times. This study seeks to analyze the impact of different boarding strategies in the context of the International Air Transport Association's recommendations during the pandemic to reduce interference and physical contact between passengers in airplanes. Boarding strategies such as back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal have been tested in this context. This study extends the previous literature using discrete event simulation to evaluate the impact of the occupation of the middle seat by family members only. This study also analyses the impact of having passengers carrying hand luggage and priority passengers on the performance of these strategies concerning boarding times. In general, the simulation results revealed a 15% improvement in boarding times when the reverse pyramid strategy is used compared to a random strategy, which essentially results from a reduction in the boarding interferences between passengers. The results also show that Steffen's strategy is the best performing, while the blocks strategy results in the worst performance. This study has practical implications for airline companies concerning both operation efficiency and passenger safety.

Abstract
This research focuses on developing a wearable device that aims to enhance problem-solving and communication abilities within the context of Industry 4.0. The wearable is being developed in the Continental Advanced Antenna, and it allows operators to notify material shortages on the manufacturing line and helps minimize workflow disturbance. The wearable gives a list of missing materials using context-aware computing, allowing operators to identify and prioritize the missing item quickly. We used the Quick and Dirty usability testing approach to ensure the device's usability and efficacy, allowing quick feedback and iterative modifications throughout the development process. Experienced consultants of project participated initial tests on the device and found that it has the potential to improve efficiency and communication in an industrial setting. However, further testing involving end users is necessary to optimize the device for the unique demands of the production environment. This paper offers valuable insights into the lessons learned from the project and proposes potential future research directions.