Abstract
Antarctic deserts, such as the McMurdo Dry Valleys (MDV), represent extremely cold and dry environments. Consequently, MDV are suitable for studying the environment limits on the cycling of key elements that are necessary for life, like nitrogen. The spatial distribution and biogeochemical drivers of nitrogen-cycling pathways remain elusive in the Antarctic deserts because most studies focus on specific nitrogen-cycling genes and/or organisms. In this study, we analyzed metagenome and relevant environmental data of 32 MDV soils to generate a complete picture of the nitrogen-cycling potential in MDV microbial communities and advance our knowledge of the complexity and distribution of nitrogen biogeochemistry in these harsh environments. We found evidence of nitrogen-cycling genes potentially capable of fully oxidizing and reducing molecular nitrogen, despite the inhospitable conditions of MDV. Strong positive correlations were identified between genes involved in nitrogen cycling. Clear relationships between nitrogen-cycling pathways and environmental parameters also indicate abiotic and biotic variables, like pH, water availability, and biological complexity that collectively impose limits on the distribution of nitrogen-cycling genes. Accordingly, the spatial distribution of nitrogen-cycling genes was more concentrated near the lakes and glaciers. Association rules revealed non-linear correlations between complex combinations of environmental variables and nitrogen-cycling genes. Association rules for the presence of denitrification genes presented a distinct combination of environmental variables from the remaining nitrogen-cycling genes. This study contributes to an integrative picture of the nitrogen-cycling potential in MDV.

Abstract
The use of a mobile sink in wireless sensor networks has been a game changing to enable the development of smart cities applications. The mobility feature allows more effective data gathering and energy saving in the network, since the sink can be closer to source nodes, which could activate their radios only when the sink approximates. Doing so, more efficient settings can be achieved when configuring and deploying sensor nodes for a myriad of applications. However, this mobility-centric strategy can generate applications scenarios with large delays when sensor networks are monitoring the environment, which may result in considerable data losses in critical applications. To cope with that, this paper proposes an algorithm to dynamically plan the repositioning of a single mobile sink within distributed sensing applications. The algorithm considers dependability requirements associated with network connectivity, operability, and energy consumption, implicitly minimizing the energy-hole problem and connectivity issues. Simulation results are presented to demonstrate how the can be applied to move the sink through the network meeting dependability requirements. © 2022 IEEE.

Abstract
This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

Abstract
In the published article, there was an error in Table 2 as published. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm when they should be in cm. The corrected Table 2 and its caption appear below. In the published article, there was an error in Table 3 as published. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm. The correct unit is cm. The corrected Table 3 and its caption appear below. In the published article, there was an error in Figure 3 as published. The units of the Total body center of mass sway in x-axis were shown in mm in the vertical axis of the plot. The correct unit is cm. The corrected Figure 3 and its caption appear below. In the published article, there was an error in Supplementary Table S.I. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm. The correct unit is cm. The correct material statement appears below. In the published article, there was a mistake on the computation description of one of the assessed parameters (total body center of mass). A correction has been made to “Data Processing,” Paragraph 3: “For each gait cycle, we computed the 24 spatiotemporal and kinematic gait parameters listed in Table 2 and defined in (15). The total body center of mass (TBCM) sway was computed as the standard deviation of the distance (in the x/y-axis, i.e., medial-lateral and vertical directions) of the total body center of mass (TBCM), in relation to the RGBD sensor’s coordinate system, for all gait cycle frames. For each frame, TBCM’s position is the mean position of all body segments’ CM, which was obtained according to (21).” The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated. © 2022 Vilas-Boas, Rocha, Cardoso, Fernandes, Coelho and Cunha.

Abstract
Fiber Bragg grating is widely used in optical fiber applications as a filter or a sensor due to its compact size and high sensitivity to physical conditions, such as temperature and strain. The purpose of this chapter is to describe the implementation and characterization of two tuning methods for optical fiber Bragg gratings, varying the temperature or the length of the fiber. Among the methods using mechanical deformation, compression of the fiber by bending a flexible sheet aggregated with the Bragg grating has shown very interesting tuning results, reaching 19.0 nm with minimum reflection bandwidth variation over the entire tuning range. Stretching the fiber has presented several drawbacks, including breaking of the fiber and a lower tuning range of 4.9 nm. Temperature tuning technique presents good linearity between tuning range and temperature variation but at the cost of a low tuning range (0.4 nm) and a permanent high current electrical source.

Abstract
Among the innovative services provided by smart cities initiatives, emergencies management systems have stood out as a mean to prevent the occurrence of disasters in urban areas, detecting emergencies as soon as possible and triggering response actions. For that, such systems may rely on multiple emergencies detection units spread over a city, which will be used to detect abnormal situations and report them for further processing. Although the use of multi-sensors hardware units seems to be reasonable to detect a lot of emergency-related variables such as temperature, humidity, smoke, and toxic gases, cities may have different geographical zones concerning the potential negative impacts (risk) that an emergency may have until it is properly mitigated. Therefore, such risk associated to those zones should guide the deployment of emergencies detection units, but their computation is not straightforward and it may depend on different parameters. In this context, this paper proposes a mathematical model to compute mitigation zones in any city, taking as reference the availability of response centers retrieved from open geospatial databases, notably hospitals, fire departments, and police stations. An algorithm is defined to compute a critical index to each zone, which will be exploited to indicate the proportional number of detection units that should be allocated according to the total number of available units. Initial results for the city of Porto, Portugal, are presented, which are discussed when concerning the construction of practical emergencies management systems. © 2022 IEEE.