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This study proposes a new demand-side management (DSM) technique, which is characterised by low computational requirements. The proposed technique relies on developing an operational matrix by the device local controller based on the device characteristics and the customer preferences. This matrix is sent to the energy management system (EMS) without the need to send any further information about the device or the customer preferences; then, the EMS chooses the optimal schedule for the device. To demonstrate the effectiveness of the proposed DSM technique, it is incorporated in an EMS that consists of three units controlled by a centralised microgrid controller (MGC). The three units managed by the MGC are the data collection and storage engine, the forecasting engine, and the optimisation engine. The EMS utilises the rolling horizon concept to manage real-time information and to provide the plug-and-play option for all controllable devices. Simulation results on a typical microgrid system show that the proposed DSM technique outperforms conventional DSM approaches in terms of the computational time.

In an attempt to capture the variety of symptoms that emerge following traumatic stress, the revision of posttraumatic stress disorder (PTSD) criteria in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has expanded to include additional symptom presentations. One consequence of this expansion is that it increases the amorphous nature of the classification. Using a binomial equation to elucidate possible symptom combinations, we demonstrate that the DSM-IV criteria listed for PTSD have a high level of symptom profile heterogeneity (79,794 combinations); the changes result in an eightfold expansion in the DSM-5, to 636,120 combinations. In this article, we use the example of PTSD to discuss the limitations of DSM-based diagnostic entities for classification in research by elucidating inherent flaws that are either specific artifacts from the history of the DSM or intrinsic to the underlying logic of the DSM's method of classification. We discuss new directions in research that can provide better information regarding both clinical and nonclinical behavioral heterogeneity in response to potentially traumatic and common stressful life events. These empirical alternatives to an a priori classification system hold promise for answering questions about why diversity occurs in response to Stressors.

The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.

Despite extensive study of the neurobiological correlates of post-traumatic stress disorder (PTSD), little is known about its molecular determinants. Here, differential gene expression and network analyses of four prefrontal cortex subregions from postmortem tissue of people with PTSD demonstrate extensive remodeling of the transcriptomic landscape. A highly connected downregulated set of interneuron transcripts is present in the most significant gene network associated with PTSD. Integration of this dataset with genotype data from the largest PTSD genome-wide association study identified the interneuron synaptic gene ELFN1 as conferring significant genetic liability for PTSD. We also identified marked transcriptomic sexual dimorphism that could contribute to higher rates of PTSD in women. Comparison with a matched major depressive disorder cohort revealed significant divergence between the molecular profiles of individuals with PTSD and major depressive disorder despite their high comorbidity. Our analysis provides convergent systems-level evidence of genomic networks within the prefrontal cortex that contribute to the pathophysiology of PTSD in humans. A transcriptome-wide characterization of the molecular pathology of post-traumatic stress disorder (PTSD) postmortem brains provides a comprehensive resource for mechanistic insight and therapeutic development.

Post-traumatic stress disorder (PTSD) is a major problem among military veterans and civilians alike, yet its pathophysiology remains poorly understood. We performed a genome-wide association study and bioinformatic analyses, which included 146,660 European Americans and 19,983 African Americans in the US Million Veteran Program, to identify genetic risk factors relevant to intrusive reexperiencing of trauma, which is the most characteristic symptom cluster of PTSD. In European Americans, eight distinct significant regions were identified. Three regions had values of P < 5 × 10−10: CAMKV; chromosome 17 closest to KANSL1, but within a large high linkage disequilibrium region that also includes CRHR1; and TCF4. Associations were enriched with respect to the transcriptomic profiles of striatal medium spiny neurons. No significant associations were observed in the African American cohort of the sample. Results in European Americans were replicated in the UK Biobank data. These results provide new insights into the biology of PTSD in a well-powered genome-wide association study.

Post-traumatic stress disorder (PTSD) can develop following severe trauma, but the extent to which genetic and environmental risk factors contribute to individual clinical outcomes is unknown. Here, we compared transcriptional responses to hydrocortisone exposure in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons and peripheral blood mononuclear cells (PBMCs) from combat veterans with PTSD ( n  = 19 hiPSC and n  = 20 PBMC donors) and controls ( n  = 20 hiPSC and n  = 20 PBMC donors). In neurons only, we observed diagnosis-specific glucocorticoid-induced changes in gene expression corresponding with PTSD-specific transcriptomic patterns found in human postmortem brains. We observed glucocorticoid hypersensitivity in PTSD neurons, and identified genes that contribute to this PTSD-dependent glucocorticoid response. We find evidence of a coregulated network of transcription factors that mediates glucocorticoid hyper-responsivity in PTSD. These findings suggest that induced neurons represent a platform for examining the molecular mechanisms underlying PTSD, identifying biomarkers of stress response, and conducting drug screening to identify new therapeutics. The authors generated stem cell-derived neurons from combat veterans with and without PTSD and found PTSD-dependent gene expression changes in response to glucocorticoids. This highlights how stress response may be altered in individuals with PTSD.

For people with post-traumatic stress disorder (PTSD), recall of traumatic memories often displays as intrusions that differ profoundly from processing of ‘regular’ negative memories. These mnemonic features fueled theories speculating a unique cognitive state linked with traumatic memories. Yet, to date, little empirical evidence supports this view. Here we examined neural activity of patients with PTSD who were listening to narratives depicting their own memories. An intersubject representational similarity analysis of cross-subject semantic content and neural patterns revealed a differentiation in hippocampal representation by narrative type: semantically similar, sad autobiographical memories elicited similar neural representations across participants. By contrast, within the same individuals, semantically similar trauma memories were not represented similarly. Furthermore, we were able to decode memory type from hippocampal multivoxel patterns. Finally, individual symptom severity modulated semantic representation of the traumatic narratives in the posterior cingulate cortex. Taken together, these findings suggest that traumatic memories are an alternative cognitive entity that deviates from memory per se. Perl et al. show that in PTSD, hippocampal representations of autobiographical memories are similar across people with similar semantic content only for sad but not traumatic memories, pointing to altered brain state during traumatic memory recall.

The exponential deployment of electric vehicles (EVs) in the residential sectors in recent years allows better energy utilization in the decentralized and centralized levels of distribution systems due to their bidirectional operation and energy storage capabilities. However, to execute these, it is necessary to adopt residential demand side management (RDSM) to schedule energy utilization effectively to fetch economical and efficient energy consumption and grid stability and reliability, particularly during peak load conditions. The paper aims to formulate a robust and efficient RDSM technique to provide an energy utilization scheduling considering various influential factors and critical roles of EVs in RDSM. A Binary Whale Optimization Algorithm (BWOA) approach is proposed as an efficient algorithm for EV’s impact on the RDSM for better energy scheduling. A single-objective formulation is presented with detailed modelling considering economic energy utilization as the primary objective with all possible equality and inequality system operational constraints. Secondly, the impact of EVs on the RDSM is studied from various perspectives in result analysis, considering EVs as load, storage devices, and different bidirectional modes of operation with other vehicles, residential components, and grids. In addition, the EVs role and the mutual influence with the integration of renewable energy sources (RES) and energy storage devices (ESDs) are extensively analyzed to provide better residential energy management (REM) in terms of economic, environmental, robust, and reliable points of view. The load priority based on consumer choice is also incorporated in the formulation. Extensive simulation is done for the proposed approach to show the effect of EVs on REM, and the results are impressive to show the EV’s role as a load, as a storage device, and as a mutually supportive device to RES, ESD, and grid.

By combining computational, morphological, and functional analyses, this study relates latent markers of associative threat learning to overt post-traumatic stress disorder (PTSD) symptoms in combat veterans. Using reversal learning, we found that symptomatic veterans showed greater physiological adjustment to cues that did not predict what they had expected, indicating greater sensitivity to prediction errors for negative outcomes. This exaggerated weighting of prediction errors shapes the dynamic learning rate (associability) and value of threat predictive cues. The degree to which the striatum tracked the associability partially mediated the positive correlation between prediction-error weights and PTSD symptoms, suggesting that both increased prediction-error weights and decreased striatal tracking of associability independently contribute to PTSD symptoms. Furthermore, decreased neural tracking of value in the amygdala, in addition to smaller amygdala volume, independently corresponded to higher PTSD symptom severity. These results provide evidence for distinct neurocomputational contributions to PTSD symptoms.PTSD symptom severity in combat veterans was associated with enhanced sensitivity to prediction errors and lower neural tracking of value and learning rate, providing evidence for neurocomputational contributions to trauma-related psychopathology.

Neuromodulation trials for the treatment of posttraumatic stress disorder (PTSD) have yielded mixed results, and the optimal neuroanatomical target remains unclear. Here we analyzed three datasets to study brain circuitry causally linked to PTSD in military veterans. In veterans with penetrating traumatic brain injury, lesion locations that reduced probability of PTSD were preferentially connected to a circuit including the medial prefrontal cortex, amygdala and anterolateral temporal lobe. In veterans without lesions, PTSD was specifically associated with increased connectivity within this circuit. Reduced functional connectivity within this circuit after transcranial magnetic stimulation correlated with symptom reduction, even though the circuit was not directly targeted. This lesion-based ‘PTSD circuit’ may serve as a target for clinical trials of neuromodulation in veterans with PTSD. Siddiqi et al. identified lesion locations that reduced probability of PTSD. These were connected to a brain circuit in which increased connectivity was associated with PTSD, thus revealing a PTSD target circuit for therapeutic brain stimulation.