Abstract
The present paper aims to identify the main trends and gaps in the digitalization of manufacturing SMEs. The most significant literature on this emergent theme was gathered through a criterion search, resulting in discovering 4060 related documents. To narrow this considerable number of documents, a bibliometric analysis was performed. A database was exported from Clarivates' Web of Science for clusters analysis in VOSviewer's software. Afterward, it was possible to identify the top authors and documents. Four trends were identified: one Asian, headed by China; another Anglo-Saxon, led by the USA; and two European trends, run by Italy and Germany. Furthermore, were identified two research gaps: (1) The development of pro-environmental technology and (2) digital readiness models for manufacturing SMEs.

Abstract
This paper deals with multi-objective location-routing problems involving distribution centres and a set of customers. It proposes a new two-stage solution method that comprehends the concept of sectorization. Distribution centres are opened, and the corresponding opening cost is calculated. A subset of customers is assigned to each of them and, in this way, sectors are formed. The objective functions in assigning customers to distribution centres are the total deviation in demands of sectors and the total deviation in total distance of customers from centroid of sectors, which must be minimized Afterward, a route is determined for each sector to meet the demands of customers. At this stage, the objective function is the total distance on the routes in the sectors, that must be minimized Benchmarks are defined for the problem and the results acquired with the two-stage method are compared to those obtained with NSGA-II. It is observed that NSGA-II can achieve many non-dominated solutions.

Abstract
In this paper, we report on new findings about the results of an empirical study which aims to investigate how the COVID-19 pandemic has been shaping nomadic work practices and also challenging the lifestyles of digital nomads (DN). To do this, we collected textual data from posts in a Reddit community. We argue that, in order to understand how to design technical solutions for the so-called 'new normal' working conditions, one way to approach this is to understand how digital nomads are being impacted in their work practices and routines, and also how they are seeing the future of their technology-mediated work-life space. Finally, we show how evidence collected from DNs about their experiences and difficulties perceived during the pandemic period can inform CSCW researchers worldwide about future design-oriented strands.

Abstract
Echocardiography-based cardiac boundary tracking provides valuable information about the heart condition for interventional procedures and intensive care applications. Nevertheless, echocardiographic images come with several issues, making it a challenging task to develop a tracking and segmentation algorithm that is robust to shadows, occlusions, and heart rate changes. We propose an autonomous tracking method to improve the robustness and efficiency of echocardiographic tracking. A method denoted by hybrid Condensation and adaptive Kalman filter (HCAKF) is proposed to overcome tracking challenges of echocardiograms, such as variable heart rate and sensitivity to the initialization stage. The tracking process is initiated by utilizing active shape model, which provides the tracking methods with a number of tracking features. The procedure tracks the endocardium borders, and it is able to adapt to changes in the cardiac boundaries velocity and visibility. HCAKF enables one to use a much smaller number of samples that is used in Condensation without sacrificing tracking accuracy. Furthermore, despite combining the two methods, our complexity analysis shows that HCAKF can produce results in real-time. The obtained results demonstrate the robustness of the proposed method to the changes in the heart rate, yielding an Hausdorff distance of 1.032 +/- 0.375 while providing adequate efficiency for real-time operations.

Abstract
[No abstract available]

Abstract
The consumption of antibiotics, such as sulfonamides, by humans and animals has increased in recent decades, and with it their presence in aquatic environments. This contribute to the increasing of bacterial resistant genes, making the treatment of infectious diseases more difficult. These antibiotics are usually detected by taking a water sample to a laboratory and quantifying it using expensive methods. Recently, digital colorimetry, has emerged as a new method for detecting sulfonamides in water. When a reagent comes into contact water sample containing sulfonamides, a color is produced from which we can infer the concentration of sulfonamides. To ensure that the color is not affected by the illumination when taking a photograph, a color reference target is positioned next to the sample to correct the colors. This method has already been implemented in smartphones to provide a faster and more practical tool that can be used immediately when collecting water samples. Despite this improvement, the algorithms used can still be outperformed by the use of machine learning. In this work, we presented a machine learning approach and a mobile app to solve the problem of sulfonamides quantification. The machine learning approach was designed to run locally in the mobile device, while the mobile application is transversal to Android and iOS operation systems.