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Professional STEM societies have been identified as an important lever to address STEM diversity, equity, and inclusion. In this "Perspectives" article, we chronicle the highlights of the first Amplifying the Alliance to Catalyze Change for Equity in STEM Success (ACCESS+) convening held in September 2021. Here, we introduce the three-part ACCESS+ approach using a model that entails (i) completion of a DEI self-assessment known as the equity environmental scanning tool, (ii) guided action plan development and iteration, and (iii) sustained participation in a community of practice.

The Internet of Things (IoT) has revolutionized the way we interact with everyday objects by connecting sensors/actuators to the Internet to monitor and control various aspects of our environment. As the number of IoT devices continues to grow, efficient medium access control protocols are needed to ensure that communication takes place in a reliable and scalable manner. In this paper, we investigate the performance of the Asynchronous Pulse Code Multiple Access (APCMA) protocol in massive IoT scenarios through simulations and analysis. We show that APCMA is able to outperform CSMA/CA in terms of the number of successfully transmitted messages in scenarios with up to 30,000 transmitting sensor nodes while also being able to utilize the channel more efficiently. We also examine the sensitivity of APCMA’s performance with respect to its parameters.

The buildings sector consumes a significant amount of raw materials and energy resources, with high-energy consumption and environmental impact. To achieve net-zero emissions, it is crucial to address the substantial carbon footprint generated by buildings in operation. A promising solution lies in the development of renewable and sustainable building materials capable of efficiently storing thermal energy to regulate indoor temperature without relying on operational energy (HVAC systems). Here we report on a wood-phase change material (PCM) composite, referred to as PCM-wood, which holds potential for energy-efficient buildings. The composite shows excellent thermal regulation capability with a melting enthalpy of 113 J g−1 at 22 °C and solidification enthalpy of 114 J g−1 at 21 °C. Despite some loss of mass and in enthalpy of melting, the PCM-wood showcases stable thermal regulation performance over 50 heating/cooling cycles and the wood modification does not negatively impact the tensile strength of the wood material. Hence, the PCM-wood composite combines structural performance and efficient energy storage with the ability to passively regulate indoor temperature, buffering fluctuations of more than 6 °C. Such a passive thermal regulation strategy has the potential to significantly reduce energy consumption in the building sector and to help achieve net-zero buildings by reducing energy consumption and mitigating CO2 emissions.

We focus on finding a correlation between the asymmetries of electroencephalography (EEG) signals and subjective well-being (SWB) when changed on short time scales via environmental conditions. Most research in this field focuses on frontal alpha asymmetry. We systematically examine different sensor locations and filter the sensor data into the delta band, the theta band, the alpha band, the beta band, and the gamma band, or leave the EEG signal unfiltered. We confirm that frontal alpha asymmetry is correlated to SWB. However, asymmetries between other sensors and/or filtering the data to other bands also shows a linear correlation to SWB values. Asymmetries of anterior brain regions show statistically significant results not only in the alpha band but also in the delta band and theta band, or when the data is not filtered into a specific band. Asymmetries of posterior regions show a trend to be correlated to SWB when EEG activity is higher on the opposite hemisphere and filtered into different frequency bands. Thus, our results let us conclude that focusing just on frontal sensors and the alpha band might not reveal the whole picture of brain regions and frequency bands involved in SWB.

Subjective well-being (SWB) describes how well people experience and evaluate their current condition. Previous studies with electroencephalography (EEG) have shown that SWB can be related to frontal alpha asymmetry (FAA). While those studies only considered a single SWB score for each experimental session, our goal is to investigate such a correlation for individuals with a possibly different SWB every 60 or 30 s. Therefore, we conducted two experiments with 30 participants each. We used different temperature and humidity settings and asked the participants to periodically rate their SWB. We computed the FAA from EEG over different time intervals and associated the given SWB, leading to pairs of (FAA, SWB) values. After correcting the imbalance in the data with the Synthetic Minority Over-sampling Technique (SMOTE), we performed a linear regression and found a positive linear correlation between FAA and SWB. We also studied the best time interval sizes for determining FAA around each SWB score. We found that using an interval of 10 s before recording the SWB score yields the best results.

Historic science, technology, engineering and mathematics (STEM) disciplinary cultures were founded in a system that was predominately male, white, heterosexual, and able-bodied (i.e., “majority”). Some societal norms have changed, and so has demand for inclusive STEM engagement. However, legacy mental models, or deeply held beliefs and assumptions, linger and are embedded in the STEM system and disciplinary cultures. STEM reform is needed to maximize talent and create inclusive professions, but cannot be achieved without recognizing and addressing norms and practices that disproportionately serve majority vs. minoritized groups. As leading voices in disciplinary work and application, disciplinary and professional societies (Societies) are instrumental in shaping and sustaining STEM norms. We, leaders of the Amplifying the Alliance to Catalyze Change for Equity in STEM Success (ACCESS+) project, recognize the need to provide Society diversity, equity, and inclusion (DEI) change leaders with tools necessary to foster systemic change. In this Perspectives article, we present the Equity Environmental Scanning Tool (EEST) as an aid to help Society DEI change leaders elucidate legacy mental models, discern areas of strength, identify foci for advancement, and benchmark organizational change efforts. We share our rationale and work done to identify, and, ultimately, adapt a Society DEI self-assessment tool from the United Kingdom. We share background information on the UK tool, content and structural changes made to create the EEST, and an overview of the resulting EEST. Ultimately, we seek to increase awareness of a Society-specific DEI self-assessment tool designed to help Society DEI change leaders advance inclusive reform.

ABSTRACT Professional STEM societies have been identified as an important lever to address STEM diversity, equity, and inclusion. In this Perspectives article, we chronicle the highlights of the first Amplifying the Alliance to Catalyze Change for Equity in STEM Success (ACCESS+) convening held in September 2021. Here, we introduce the three-part ACCESS+ approach using a model that entails (i) completion of a DEI self-assessment known as the equity environmental scanning tool, (ii) guided action plan development and iteration, and (iii) sustained participation in a community of practice.

Science, technology, engineering, and mathematics (STEM) professional societies (ProSs) are uniquely positioned to foster national-level diversity, equity, and inclusion (DEI) reform. ProSs serve broad memberships, define disciplinary norms and culture, and inform accrediting bodies and thus provide critical levers for systems change. STEM ProSs could be instrumental in achieving the DEI system reform necessary to optimize engagement of all STEM talent, leveraging disciplinary excellence resulting from diverse teams. Inclusive STEM system reform requires that underlying “mental models” be examined. The Inclusive Professional Framework for Societies (IPF: Societies) is an interrelated set of strategies that can help ProSs change leaders (i.e., “boundary spanners”) and organizations identify and address mental models hindering DEI reform. The IPF: Societies uses four “I's”—Identity awareness and Intercultural mindfulness (i.e., equity mindset) upon which inclusive relationships and Influential DEI actions are scaffolded. We discuss how the IPF: Societies complements existing DEI tools (e.g., Women in Engineering ProActive Network's Framework for Promoting Gender Equity within Organization; Amplifying the Alliance to Catalyze Change for Equity in STEM Success' Equity Environmental Scan Tool). We explain how the IPF: Societies can be applied to existing ProS policy and practice associated with common ProS functions (e.g., leadership, membership, conferences, awards, and professional development). The next steps are to pilot the IPF: Societies with a cohort of STEM ProSs. Ultimately, the IPF: Societies has potential to promote more efficient, effective, and lasting DEI organizational transformation and contribute to inclusive STEM disciplinary excellence.

In this narrative review, we focus on the role of quantitative EEG technology in the diagnosis and prognosis of patients with unresponsive wakefulness syndrome and minimally conscious state. This paper is divided into two main parts, i.e., diagnosis and prognosis, each consisting of three subsections, namely, (i) resting-state EEG, including spectral power, functional connectivity, dynamic functional connectivity, graph theory, microstates and nonlinear measurements, (ii) sleep patterns, including rapid eye movement (REM) sleep, slow-wave sleep and sleep spindles and (iii) evoked potentials, including the P300, mismatch negativity, the N100, the N400 late positive component and others. Finally, we summarize our findings and conclude that QEEG is a useful tool when it comes to defining the diagnosis and prognosis of DOC patients.

The diagnosis and prognosis of patients with severe chronic disorders of consciousness are still challenging issues and a high rate of misdiagnosis is evident. Hence, new tools are needed for an accurate diagnosis, which will also have an impact on the prognosis. In recent years, functional Magnetic Resonance Imaging (fMRI) has been gaining more and more importance when diagnosing this patient group. Especially resting state scans, i.e., an examination when the patient does not perform any task in particular, seems to be promising for these patient groups. After preprocessing the resting state fMRI data with a standard pipeline, we extracted the correlation matrices of 132 regions of interest. The aim was to find the regions of interest which contributed most to the distinction between the different patient groups and healthy controls. We performed feature selection using a genetic algorithm and a support vector machine. Moreover, we show by using only those regions of interest for classification that are most often selected by our algorithm, we get a much better performance of the classifier.

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

In this paper, we investigate our mobility-assisted and adaptive broadcast routing mechanism, called Mobility Tolerant Firework Routing (MTFR), which utilizes the concept of potentials for routing and improves node reachability, especially in situations with high mobility, by including a broadcast mechanism. We perform detailed evaluations by simulations in a mobile environment and demonstrate the advantages of MTFR over conventional potential-based routing. In particular, we show that MTFR produces better reachability in many aspects at the expense of a small additional transmission delay and intermediate traffic overhead, making MTFR a promising routing protocol and feasible for future mobile Internet infrastructures.

In realizing the network environment assumed by the Internet-of-Things, network slicing has drawn considerable attention as a way to enhance the utilization of physical networks (PNs). Meanwhile, slicing has been shown to cause interdependence among sliced virtual networks (VNs) by propagating traffic fluctuations from one network to others. However, for interconnected networks with mutual dependencies, known as a network of networks (NoN), finding a reliable design method that can cope with environmental changes is an important issue that is yet to be addressed. Some NoN models exist that describe the behavior of interdependent networks in complex systems, and previous studies have shown that an NoN model based on the functional networks of the brain can achieve high robustness, but its application to dynamic and practical systems is yet to be considered. Consequently, this paper proposes the Physical−Virtual NoN (PV-NoN) model assuming a network-slicing environment. This model defines an NoN availability state to deal with traffic fluctuations and interdependence among a PN and VNs. Further, we assume three basic types of interdependence among VNs for this model. Simulation experiments confirm that the one applying complementary interdependence inspired by brain functional networks achieves high availability and communication performance while preventing interference among the VNs. Also investigated is a method for designing a reliable network structure for the PV-NoN model. To this end, the deployment of network influencers (i.e., the most influential elements over the entire network) is configured from the perspective of intra/internetwork assortativity. Simulation experiments confirm that availability or communication performance is improved when each VN is formed assortatively or disassortatively, respectively. Regarding internetwork assortativity, both the availability and communication performance are improved when the influencers are deployed disassortatively among the VNs.

An essential application of wireless sensor networks is to successfully respond to user queries. Query packet losses occur in the query dissemination due to wireless communication problems such as interference, multipath fading, packet collisions, etc. The losses of query messages at sensor nodes result in the failure of sensor nodes reporting the requested data. Hence, the reliable and successful dissemination of query messages to sensor nodes is a non-trivial problem. The target of this paper is to enable highly successful query delivery to sensor nodes by localized and energy-efficient discovery, and recovery of query losses. We adopt local and collective cooperation among sensor nodes to increase the success rate of distributed discoveries and recoveries. To enable the scalability in the operations of discoveries and recoveries, we employ a distributed name resolution mechanism at each sensor node to allow sensor nodes to self-detect the correlated queries and query losses, and then efficiently locally respond to the query losses. We prove that the collective discovery of query losses has a high impact on the success of query dissemination and reveal that scalability can be achieved by using the proposed approach. We further study the novel features of the cooperation and competition in the collective recovery at PHY and MAC layers, and show that the appropriate number of detectors can achieve optimal successful recovery rate. We evaluate the proposed approach with both mathematical analyses and computer simulations. The proposed approach enables a high rate of successful delivery of query messages and it results in short route lengths to recover from query losses. The proposed approach is scalable and operates in a fully distributed manner.

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

O rio Puraquequara, e seu afluente direito, o igarapé Água Branca, localizam-se fora da zona urbana de Manaus e estão com a bacia ainda protegida por floresta primária. Têm características geológicas, pedológicas e climatológicas de igarapés naturais de terra-firme da Amazônia Central. Foram coletadas amostras de água nos meses de novembro de 1998 (período seco) e abril de 1999 (período chuvoso) e determinados os parâmetros temperatura, pH, turbidez, condutividade, alcalinidade, dureza, DQO, Ca2+, Mg2+, Na+, K+, SiO2, NO3-, NO2-, PO4(3-), SO4(2-), Cl-, NH4+ e Fe total. As águas apresentaram pH entre 3,8 e 4,1 passando a menos ácidas na estiagem. A alcalinidade e a turbidez são, em geral, mais elevadas na estiagem, enquanto a condutividade, dureza e DQO são maiores no período mais chuvoso. SiO2 e o Cl- foram os ânions mais abundantes, com contribuição maior do primeiro no Puraquequara e menor no Água Branca, enquanto o Cl- tem comportamento oposto. Na+, Fe total e NH4+ são os cátions mais abundantes e predominam, no geral, no período mais seco. O NH4+ é o único cátion que tende a aumentar sua contribuição para jusante do igarapé no período úmido. Os teores de K+ são mais elevados do que os de Mg2+ e este que os de Ca2+, e são todos superados pelo Na+. Os teores da NH4+ e NO3-, acima de 0,2 mg/L e 0,5 mg/L, respectivamente, são indícios de contaminação. Essas características definem as águas da bacia como muito diluídas, com predominância dos ânions sobre os cátions e correlacionáveis as águas de cor preta.The Puraquequara River and its right-bank's tributary, the Água Branca stream, are located in the outskirts of the Manaus urbanized area and their basin is still harbored by the primary rain forest cover. They exhibit 'terra firme' geologic, pedologic and climatological characteristics pertaining to the Amazonian region. Samples for this research were collected in November 1998 (dry season) and April 1999 (rainy season). Water temperature, pH, turbidity, conductivity, alkalinity, hardness, COD, Ca2+, Mg2+, Na+ e K+, SiO2, NO3-, NO2-, PO4(3-), SO4(2-), Cl-, NH4+ and Fe were analysed. The water is acid (3.8 to 4.1) turning into less acid in the dry season. The alkalinity and turbidity are higher in the dry season whereas conductivity, hardness and COD are higher in the rainy season. SiO2 and Cl- are the main anionic elements where the former's contribution is higher in the Puraquequara and lower in the Água Branca while the latter shows inverse contribution. Na+, Fe, and NH4+ are the main cations with their higher contribution in the dry season. NH4+ is the only cation that tends to increase its contribution downstream in the wet season. K+ contents is higher than that of Mg+ and Ca2+ yet Na+ is the highest. The higher than 0.2 mg/L and 0.5 mg/L amounts of NH4+ and NO3- respectively, are traces of water contamination. These characteristics define these waters as very diluted with anions predominating over cations and correlated to the black water of the region.

The concept of biologically inspired networking has been introduced to tackle unpredictable and unstable situations in computer networks, especially in wireless ad hoc networks where network conditions are continuously changing, resulting in the need of robustness and adaptability of control methods. Unfortunately, existing methods often rely heavily on the detailed knowledge of each network component and the preconfigured, that is, fine-tuned, parameters. In this paper, we utilize a new concept, called attractor perturbation (AP), which enables controlling the network performance using only end-to-end information. Based on AP, we propose a concurrent multipath traffic distribution method, which aims at lowering the average end-to-end delay by only adjusting the transmission rate on each path. We demonstrate through simulations that, by utilizing the attractor perturbation relationship, the proposed method achieves a lower average end-to-end delay compared to other methods which do not take fluctuations into account.