A Low-Cost Monitoring Platform and Visual Interface to Analyse Thermal Comfort in Smart Building Applications Using a Citizen–Scientist Strategy

Smart building issues are critical for current energy and comfort managing aspects in built environments. Nevertheless, the diffusion of smart monitoring solutions via user-friendly graphical interfaces is still an ongoing issue subject to the need to diffuse a smart building culture and a low-cost series of solutions. This paper proposes a new low-cost IoT sensor network, exploiting Raspberry Pi and Arduino platforms, for collecting real-time data and evaluating specific thermal comfort indicators (PMV and PPD). The overall architecture was accordingly designed, including the hardware setup, the back-end and the Android user interface. Eventually, three distinct prototyping platforms were deployed for initial testing of the general system, and we analysed the obtained results for different building typologies and seasonal periods, based on collected data and users’ preferences. This work is part of a large educational and citizen science activity.

A Lightweight Keypoint Matching Framework for Morphometric Landmark Detection

Geometric morphometrics has become an important approach in insect morphology studies because it capitalizes on advanced quantitative methods to analyze shape. Shape could be digitized as a set of landmarks from specimen images. However, the existing tools mostly require manual landmark digitization, and previous works on automatic landmark detection methods do not focus on implementation for end-users. Motivated by that, we propose a novel approach for automatic landmark detection, based on visual features of landmarks and keypoint matching techniques. While still archiving comparable accuracy to that of the state-of-the-art method, our framework requires less initial annotated data to build prediction model and runs faster. It is lightweight also in terms of implementation, in which a four-step workflow is provided with user-friendly graphical interfaces to produce correct landmark coordinates both by model prediction and manual correction. The utility iMorph is freely available at https://github.com/ha-usth/WingLanmarkPredictor, currently supporting Windows, MacOS, and Linux.

Crowdsourced Evaluation of Robot Programming Environments: Methodology and Application

Industrial robot programming tools increasingly rely on graphical interfaces, which aim at rendering the programming task more accessible to a wide variety of users. The usability of such tools is currently being evaluated in controlled environments, such as laboratories or companies, in which a group of participants is asked to carry out several tasks using the tool and then fill out a standardized questionnaire. In this context, this paper proposes and evaluates an alternative evaluation methodology, which leverages online crowdsourcing platforms to produce the same results as face-to-face evaluations. We applied the proposed framework in the evaluation of a web-based industrial robot programming tool called Assembly. Our results suggest that crowdsourcing facilitates a cost-effective, result-oriented, and reusable methodology for performing user studies anonymously and online.

Multivariate Curve Resolution Alternating Least Squares applied to Chromatographic Data: From the Basics to the Recent Advances

This revision presents applications of multivariate curve resolution alternating least squares (MCR-ALS) applied to chromatographic data. Initially, the fundamentals and recent advances of the MCR-ALS method will be presented. Several critical issues such as data organization, advantages of the modelling, constraints, evaluation of ambiguity and the use for mathematical separation is discussed. An extensive revision of the papers on MCR-ALS applied to chromatographic data reported up to 2020 is presented. A practical example of an innovative application of cholesterol lowering drugs using supercritical fluid chromatography (SFC) is described highlighting important aspects of the method. At the end, a list of links to MCR-ALS algorithms and graphical interfaces developed in Matlab, R and Python 3 is provided.

A simulation and analytic hierarchy process based decision support system for air cargo warehouse capacity design

Warehouse design is critical in the air cargo industry, where service standards are high and competition is fierce. The designers of air cargo warehouses consider various criteria like costs, system failure risks, customer perception, and marketing power to evaluate alternative designs. The issue of designing an air cargo warehouse is generally considered by design and consulting companies, which have broad experience in operational design are lacking in suggesting robust alternatives that build upon on a well-structured theoretical framework. The alternative warehouse designs are generated by considering different combinations of resource capacities such as the number of gates, workstations and storage areas. The evaluation of these alternative designs requires the use of analytical methodologies for multi-criteria decision-making. At this point, two methodologies from literature may be helpful: (i) simulation, which is a popular tool to evaluate operational performance and (ii) the analytical hierarchy process (AHP), which provides the ability to evaluate qualitative as well as quantitative criteria. In this study, a flexible and user-friendly decision support system (DSS) is developed based on combining simulation and AHP. The DSS is used to generate design alternatives, evaluate their quantitative performance via simulation, and subsequently rank the best scoring ones via AHP according to a set of quantitative as well as qualitative decision criteria. The graphical interfaces are designed with a keen eye on man-machine interaction to increase their functionality. The environment is tested and validated with real-time data in one of Europe’s largest air cargo carriers.

The contribution of Geomatics to increase safety and security in ports

In this paper, the advantages achievable from the use of two prototype systems that are being developed to increase safety and security in ports are shown. Both systems start by monitoring environmental parameters in harbors, and then process data acquired. The first system has been conceived to be helpful to port communities (port authorities, pilots) to optimize harbor waterside management (ship’s navigation and cargo, dock performances, boat moorings, refloating of stranded ships, water quality control). By monitoring and processing sea level and atmospheric pressure in port areas, it can help port communities, e.g., to choose the best time when a ship with a certain draft can enter or leave a harbor, or to plan the best route inside the basin for that vessel (port safety). The second system, instead, has been designed for port protection purposes: by monitoring and processing the Earth’s magnetic field below the sea surface in harbors (where the natural field is disturbed by a high artificial component), it is able to detect the possible presence of intruders (e.g., divers) swimming underwater in prohibited areas (port security). Here, the results of monitoring and processing activities of the two systems performed in Livorno and La Spezia harbors are shown (Italy). The processing procedures and the graphical interfaces of the systems are based on applications under development by the research team the author belongs to, by using C# and C++ languages; Matlab environment has been employed for simulations.

Analysis of modern human-computer interfaces

This article describes two research methods that are currently used in the study of graphical interfaces. The examined aspect is human-computer interaction (HCI), which is carried out by means of manipulators, which are input devices, and by means of which the tester performs the tasks set in the research scenario, which are presented using a graphical interface (GUI). The analysis covers the path the cursor follows, its speed and time. The path that the cursor takes is also drawn, and it is divided into stages because there are intermediate elements between the start and end elements. Due to the fact that it is impossible to describe numerically the feelings of the examined person, and these feelings are important for the study, the so-called usability tests, in which, among others, the ergonomics of controllers and the graphic interface itself was examined.

Reactor Design Pattern

In object-oriented (OO) applications, objects collaborate through message passing, which results in these objects being coupled and mutually dependent. These dependencies can be reactive, i.e. such that, for example, the state change of one object, requires automatic reaction in all dependent objects. Examples of such reactive dependencies can be found in various software systems, including rich graphical interfaces, spreadsheet systems, animation, robotics, etc. Unlike the reactive paradigm which natively provides abstractions and mechanisms for the management of reactive dependencies, the OO paradigm lacks proper support. Object-oriented applications developers often resort to the use of ad-hoc solutions or design patterns such as the well-known Observer pattern, which are not suitable for managing more complex scenarios. In this paper we offer a novel design pattern (REACTOR), which utilizes a graph data structure to improve the management of reactive dependencies in OO applications.

Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage

Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 executable files, sample data sets, and user manual are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_Multi-Q2.html.