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Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effectiveand burdensome. This paper pres-ents a novel Kerberos-based framework for automated, disruption-tolerant key management for control system environments. Experimental tests and their results are presented to quantify the expected performance overhead of this approach. Additionally, Zeek sensor analytics are presented to aid in monitoring the health and security of the key management framework operation.

IntroductionNumerous studies have highlighted cognitive benefits in lifelong bilinguals during aging, manifesting as superior performance on cognitive tasks compared to monolingual counterparts. Yet, the cognitive impacts of acquiring a new language in older adulthood remain unexplored. In this study, we assessed both behavioral and fMRI responses during a Stroop task in older adults, pre- and post language-learning intervention.MethodsA group of 41 participants (age:60–80) from a predominantly monolingual environment underwent a four-month online language course, selecting a new language of their preference. This intervention mandated engagement for 90 minutes a day, five days a week. Daily tracking was employed to monitor progress and retention. All participants completed a color-word Stroop task inside the scanner before and after the language instruction period.ResultsWe found that performance on the Stroop task, as evidenced by accuracy and reaction time, improved following the language learning intervention. With the neuroimaging data, we observed significant differences in activity between congruent and incongruent trials in key regions in the prefrontal and parietal cortex. These results are consistent with previous reports using the Stroop paradigm. We also found that the amount of time participants spent with the language learning program was related to differential activity in these brain areas. Specifically, we found that people who spent more time with the language learning program showed a greater increase in differential activity between congruent and incongruent trials after the intervention relative to before.DiscussionFuture research is needed to determine the optimal parameters for language learning as an effective cognitive intervention for aging populations. We propose that with sufficient engagement, language learning can enhance specific domains of cognition such as the executive functions. These results extend the understanding of cognitive reserve and its augmentation through targeted interventions, setting a foundation for future investigations.

The last deglaciation is the most recent relatively well-documented period of pronounced and fast climate warming, and, as such, it holds important information for our understanding of the climate system. Notably, while research into terrestrial organic carbon reservoirs has been instrumental in exploring the possible sources of atmospheric carbon dioxide during periods of rapid change, the underlying mechanisms are not fully understood. Here we investigate the mobilization of organic matter to the Bay of Biscay, located in the north-eastern Atlantic Ocean off the coasts of France and Spain. Specifically, we focus on the area that was the mouth of the Channel River during the last deglaciation, where an enhanced terrigenous input has been reported for the last glacial–interglacial transition. We conducted a comprehensive suite of biomarker analyses (e.g. n-alkanes, hopanes and n-alkanoic acids) and isotopic investigations (radiocarbon dating and δ13C measurements) on a high-resolution sedimentary archive. The present study provides the first direct evidence for the fluvial supply of immature and ancient terrestrial organic matter to the core location. Moreover, our results reveal the possibility of permafrost carbon export to the ocean, driven by processes such as deglacial warming and glacial erosion. These findings are consistent with observations from other regions characterized by present or past permafrost conditions on land, which have shown that permafrost thaw and glacial erosion can lead to carbon remobilization, potentially influencing atmospheric carbon dioxide levels.

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effectiveand burdensome. This paper pres-ents a novel Kerberos-based framework for automated, disruption-tolerant key management for control system environments. Experimental tests and their results are presented to quantify the expected performance overhead of this approach. Additionally, Zeek sensor analytics are presented to aid in monitoring the health and security of the key management framework operation.

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

In this paper, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations.

Conventional cyber-security intrusion detection systems monitor network traffic for malicious activity and indications that an adversary has gained access to the system. The approach discussed here expands the idea of a traditional intrusion detection system within electrical power systems, specifically power distribution networks, by monitoring the physical behavior of the grid. This is achieved through the use of high-rate distribution Phasor Measurement Units (PMUs), alongside SCADA packets analysis, for the purpose of monitoring the behavior of discrete control devices. In this work we present a set of algorithms for passively learning the control logic of voltage regulators and switched capacitor banks. Upon detection of an abnormal operation, the operator is alerted and further action can be taken. The proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site.

Data error, modification, and loss can occur in scientific computing due to numerous potential causes, including those natural, accidental, or malicious in nature. Such errors and modifications can silently impact scientific computing results if not detected and corrected or compensated for through other means. The goal of this document is to describe the ways in which integrity faults due to bit flips occur, their potential odds of occurring, and potential mitigation mechanisms in high-level, practical terms, and is broken into individual computer components and environments describing each of those elements. We conclude the report by summarizing key issues and several best practices for mitigation of the effects of bit flip-induced errors.

IntroductionDivorce is a contributor to family instability within sub-Saharan Africa, and specifically within Namibia, an increasing number of children are exposed to its impact. However, not all children react uniformly to the impact of parental divorce, and many children may be resilient. Understanding what promotes resilience in children post-divorce in African contexts is vital, given the unique socio-cultural context. Therefore, this study aimed to understand how some children are capable of resilience despite exposure to parental divorce in Namibia.MethodsA multiple case study design was employed to assess the lived experiences of children aged 9–12 post-parental divorce in Windhoek. Using the Child and Youth Resilience Measurement (CYRM-12) scale, 24 children exposed to parental divorce were screened for resiliency. The Q-Methodology, with visual material, was utilized with a sub-sample of 12 children who scored high on the CYRM (50% girls, mean age = 11) to eliminate some of the challenges associated with gathering qualitative data from younger children. The PQ Method 2.35 software program was used for data analysis.ResultsBy-person factor analysis identified four statistically significant profiles. A third (33%) of participants loaded on a factor emphasizing “quality parent-child relationships” and a further 33% emphasizing “effective parent conflict resolution.” The final two factors emphasized “healthy school attachment” (17%) and “strong community attachment” (17%). All children emphasized a stable, loving familial environment, and frequent visitation with the non-custodial father.DiscussionOur findings suggest that multiple social ecologies nurture resilience in children exposed to parental divorce in Namibia. Support should be extended beyond the perimeters of the nuclear family, and relationships with extended family members, peer groups, school, and the wider community can play an important role in children's adjustment. The study highlights the importance of contextually grounded resilience as some factors that are emphasized for children from more Western communities do not reflect as strongly in the results of this study. Other factors, including a stronger reliance on community and factors such as the school, peers, and extended family members, may play a bigger role in child resilience post-divorce in Namibia.

In recent years, attacks such as the Stuxnet malware have demonstrated that cyberattacks against control systems cause extensive damage. These attacks can result in physical damage to the networked systems under their control. In this paper, we discuss our approach for detecting such attacks by distinguishing between programs running on a programmable logic controller (PLC) without having to monitor communications. Using power signatures generated by an attached, high-frequency power measurement device, we can identify what a PLC is doing and when an attack may have altered what the PLC should be doing. To accomplish this, we generated labeled data for testing our methods and applied feature engineering techniques and machine learning models. The results demonstrate that Random Forests and Convolutional Neural Networks classify programs with up to 98% accuracy for major program differences and 84% accuracy for minor differences. Our results can be used for both online and offline applications.

In this paper, we showcase a complete, end-To-end, fault tolerant, bandwidth and latency optimized architecture for real time utilization of data from multiple sources that allows the collection, transport, storage, processing, and display of both raw data and analytics. This architecture can be applied for a wide variety of applications ranging from automation/control to monitoring and security. We propose a practical, hierarchical design that allows easy addition and reconfiguration of software and hardware components, while utilizing local processing of data at sensor or field site ('fog computing') level to reduce latency and upstream bandwidth requirements. The system supports multiple fail-safe mechanisms to guarantee the delivery of sensor data. We describe the application of this architecture to cyber-physical security (CPS) by supporting security monitoring of an electric distribution grid, through the collection and analysis of distribution-grid level phasor measurement unit (PMU) data, as well as Supervisory Control And Data Acquisition (SCADA) communication in the control area network.

We describe the fully automated workflow path developed for the ingest and analysis of liquid chromatography mass spectrometry (LCMS) data. With the help of this computational workflow, we were able to replace two human work days to analyze data with two hours of unsupervised computation time. In addition, this tool also can compute confidence intervals for all its results, based on the noise level present in the data. We leverage only open source tools and libraries in this workflow.

In recent years, attacks such as the Stuxnet malware have demonstrated that cyberattacks against control systems cause extensive damage. These attacks can result in physical damage to the networked systems under their control. In this paper, we discuss our approach for detecting such attacks by distinguishing between programs running on a programmable logic controller (PLC) without having to monitor communications. Using power signatures generated by an attached, high-frequency power measurement device, we can identify what a PLC is doing and when an attack may have altered what the PLC should be doing. To accomplish this, we generated labeled data for testing our methods and applied feature engineering techniques and machine learning models. The results demonstrate that Random Forests and Convolutional Neural Networks classify programs with up to 98% accuracy for major program differences and 84% accuracy for minor differences. Our results can be used for both online and offline applications.

In this paper, we showcase a complete, end-To-end, fault tolerant, bandwidth and latency optimized architecture for real time utilization of data from multiple sources that allows the collection, transport, storage, processing, and display of both raw data and analytics. This architecture can be applied for a wide variety of applications ranging from automation/control to monitoring and security. We propose a practical, hierarchical design that allows easy addition and reconfiguration of software and hardware components, while utilizing local processing of data at sensor or field site ('fog computing') level to reduce latency and upstream bandwidth requirements. The system supports multiple fail-safe mechanisms to guarantee the delivery of sensor data. We describe the application of this architecture to cyber-physical security (CPS) by supporting security monitoring of an electric distribution grid, through the collection and analysis of distribution-grid level phasor measurement unit (PMU) data, as well as Supervisory Control And Data Acquisition (SCADA) communication in the control area network.

We describe the fully automated workflow path developed for the ingest and analysis of liquid chromatography mass spectrometry (LCMS) data. With the help of this computational workflow, we were able to replace two human work days to analyze data with two hours of unsupervised computation time. In addition, this tool also can compute confidence intervals for all its results, based on the noise level present in the data. We leverage only open source tools and libraries in this workflow.