The database search, spanning publications from 1971 to 2022, identified 155 articles matching inclusion criteria: individuals (18-65 years of age, regardless of gender) using substances, involved in the criminal justice system, and consuming licit or illicit psychoactive substances, without unrelated psychopathology, engaged in treatment programs or subject to judicial intervention. A selection of 110 articles for detailed analysis was made, consisting of 57 from Academic Search Complete, 28 from PsycINFO, 10 from Academic Search Ultimate, 7 from Sociology Source Ultimate, 4 from Business Source Complete, 2 from Criminal Justice Abstracts, and 2 from PsycARTICLES; manual searches added further records. These studies produced a selection of 23 articles, all of which effectively answered the research question, thereby forming the complete sample in this revisionary work. The findings reveal that treatment serves as an effective strategy implemented by the criminal justice system, reducing criminal relapse and/or drug use, and addressing the criminogenic consequences of imprisonment. Selleck Glecirasib Thus, interventions emphasizing treatment ought to be selected, albeit with ongoing shortcomings in evaluation, monitoring, and scientific publications on treatment efficacy for this particular group.

Induced pluripotent stem cell (iPSC) models of the human brain represent a promising avenue for advancing our knowledge of the neurotoxic effects stemming from drug use. However, the fidelity of these models in representing the actual genomic architecture, cellular functions, and drug-induced alterations is an issue that needs further clarification. This JSON schema, list[sentence], returns new sentences, each structurally distinct from the prior.
Advancing our understanding of how to shield or counteract molecular alterations connected with substance use disorders necessitates models of drug exposure.
Neural progenitor cells and neurons, a novel model generated from induced pluripotent stem cells derived from postmortem human skin fibroblasts, were directly compared to the donor's isogenic brain tissue. Employing a combination of RNA cell-type and maturity deconvolution analyses and DNA methylation epigenetic clocks calibrated on adult and fetal human tissue, we characterized the maturation of cell models ranging from stem cells to neurons. Employing this model, we sought to determine its potential in substance use disorder research by comparing gene expression signatures in morphine- and cocaine-treated neurons, respectively, to those observed in postmortem brain tissue from individuals diagnosed with Opioid Use Disorder (OUD) and Cocaine Use Disorder (CUD).
Within each human subject (N = 2, with two clones each), the frontal cortex's epigenetic age mirrors the skin fibroblasts' epigenetic age, closely approximating the donor's chronological age. Stem cell generation from fibroblast cells establishes an embryonic epigenetic clock. The subsequent cellular differentiation, from stem cells to neural progenitor cells to neurons, demonstrates progressive maturation.
RNA gene expression readouts and DNA methylation profiles are powerful biomarkers. In neurons originating from an individual who succumbed to an opioid overdose, morphine treatment prompted modifications in gene expression comparable to those previously noted in opioid use disorder.
The immediate early gene EGR1, whose expression is differentially affected by opioid use, is found in brain tissue.
Our approach involves the generation of an iPSC model from human postmortem fibroblasts. This model allows for a direct comparison with its matched isogenic brain tissue and can be utilized to simulate perturbagen exposure, analogous to that seen in opioid use disorder. Studies using postmortem brain cell models, specifically including cerebral organoids, in conjunction with this model, hold great potential for illuminating the mechanisms of drug-induced alterations in the brain.
We describe a new iPSC model, originating from human post-mortem fibroblasts, which is directly comparable to isogenic brain tissue. This model is suitable for modeling perturbagen exposures, such as those linked to opioid use disorder. Subsequent studies utilizing postmortem brain cell models, including cerebral organoids, and analogous systems, can prove instrumental in comprehending the mechanisms governing drug-induced alterations within the brain.

Psychiatric diagnoses frequently rely on a careful examination of the patient's manifestations and symptoms. While deep learning-based binary classification models have been developed to improve diagnoses, clinical integration has been impeded by the broad variety and heterogeneity of the disorders. An autoencoder-based normative model is proposed here.
Our autoencoder was trained on resting-state functional magnetic resonance imaging (rs-fMRI) scans from a group of healthy control participants. The model was subsequently utilized to evaluate the deviation of each patient's connectivity in schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) from the norm, focusing on the abnormal functional brain networks (FBNs). Independent component analysis and dual regression were integrated within the FSL (FMRIB Software Library) framework for rs-fMRI data processing. The correlation coefficients, calculated using Pearson's method, for the blood oxygen level-dependent (BOLD) time series of all functional brain networks (FBNs) were determined, and a subject-specific correlation matrix was created for each participant.
Neuropathological studies suggest a considerable role for basal ganglia network functional connectivity in bipolar disorder and schizophrenia; this role, however, is less clear in attention-deficit/hyperactivity disorder. Besides this, the unusual connectivity pattern between the basal ganglia network and the language network is more indicative of BD. Connectivity between the higher visual network and the right executive control network is particularly salient in schizophrenia (SCZ), while the connectivity between the anterior salience network and the precuneus networks is more relevant in attention-deficit/hyperactivity disorder (ADHD). The proposed model, as evidenced by the results, successfully identified functional connectivity patterns characteristic of various psychiatric disorders, aligning with existing literature. Selleck Glecirasib Patients in both independent SCZ groups exhibited comparable abnormal connectivity patterns, reinforcing the general applicability of the proposed normative model. Despite group-level disparities, closer analysis at the individual level revealed the fallacy of these observations, underscoring the significant heterogeneity of psychiatric disorders. Findings from this research point towards a precision-oriented medical technique, highlighting the individualized functional network changes of each patient, as potentially more advantageous than the standard group-diagnosis methodology.
We observed a pronounced role for basal ganglia network functional connectivity in the neuropathology of both bipolar disorder and schizophrenia, yet this role appears less evident in the context of attention-deficit/hyperactivity disorder. Selleck Glecirasib Moreover, the irregular connections between the basal ganglia network and language network are more indicative of BD than other neurological conditions. In SCZ, the connectivity between the higher visual network and the right executive control network stands out, while ADHD is predominantly associated with the connectivity between the anterior salience network and the precuneus networks. The literature suggests that the proposed model correctly identifies functional connectivity patterns that are unique to different psychiatric disorders. A shared pattern of abnormal connectivity emerged in the two independent schizophrenia (SCZ) patient groups, supporting the generalizability claim of the presented normative model. Even though group-level differences were detected, an investigation at the individual level failed to replicate these findings, underscoring a substantial degree of heterogeneity in psychiatric disorders. These findings highlight that a precision-based medical method, keyed to the unique functional network modifications of individual patients, might offer greater benefits than the traditional approach of grouping diagnoses.

An individual's lifetime experience of self-harm and aggression occurring concurrently is termed dual harm. The clarity of dual harm as a unique clinical entity depends on the existence of adequate evidentiary support. A systematic review investigated the presence of unique psychological correlates of dual harm, differentiating it from single instances of self-harm, aggression, or no harmful behavior. We pursued a critical analysis of the literature as a secondary undertaking.
PsycINFO, PubMed, CINAHL, and EThOS were searched on September 27, 2022, in the review, resulting in the identification of 31 eligible papers and their associated 15094 individuals. Risk of bias assessment was performed using a modified Agency for Healthcare Research and Quality tool, and a narrative synthesis was then undertaken.
Evaluations of variations in mental health, personality, and emotional factors were carried out on the distinct behavioral groups within the studies included. Our study uncovered weak evidence that dual harm is an independent psychological entity with particular psychological characteristics. Our examination, instead, points to the combined effect of psychological risk factors associated with self-harm and aggression as the source of dual harm.
Upon critical examination, the dual harm literature exhibited numerous limitations. Clinical implications and recommendations for future research endeavors are presented.
The study documented under CRD42020197323, and retrievable at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=197323, addresses a critical issue.
The study, whose identifier is CRD42020197323, and detailed at the link https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=197323, is evaluated in this report.

We employ the Satellite-beacon Ionospheric scintillation Global Model of the upper Atmosphere (SIGMA), a three-dimensional radio wave propagation model, and data acquired from the Scintillation Auroral GPS Array (SAGA), a network of six Global Positioning System (GPS) receivers at Poker Flat, AK, to characterize them. Employing an inverse approach, the model's output is calibrated against GPS data to estimate the best-fit parameters describing the irregularities. To understand the E- and F-region irregularity characteristics during geomagnetically active times, we conduct a thorough examination of one E-region event and two F-region events, using two differing spectral models as input for the SIGMA algorithm. Our spectral analysis shows E-region irregularities to be elongated along the magnetic field lines, exhibiting a rod-like structure. F-region irregularities show a different morphology, with wing-like structures extending along and across magnetic field lines. The spectral index of E-region events demonstrated a smaller value compared to the spectral index of F-region events. Subsequently, the spectral slope on the ground becomes less steep at higher frequencies in contrast to the spectral slope observed at the irregularity height. A 3D propagation model, incorporating GPS observations and inversion, is employed to detail the unique morphological and spectral characteristics of E- and F-region irregularities in a limited set of examples presented in this study.

The global increase in vehicle numbers, coupled with problematic traffic congestion and a significant rise in road accidents, represent significant issues. Autonomous vehicles operating in platoons offer innovative solutions for the efficient management of traffic flow, particularly when dealing with congestion and thus minimizing accidents. Recently, research on platoon-based driving, also known as vehicle platooning, has seen significant expansion. Vehicle platooning, by strategically compacting vehicles, enhances road capacity and shortens travel times, all while maintaining safety. Connected and automated vehicles necessitate the effective application of cooperative adaptive cruise control (CACC) systems and platoon management systems. Using vehicle status data acquired via vehicular communications, CACC systems enable platoon vehicles to keep a safer, closer distance. This study proposes an adaptive strategy for vehicular platoon traffic flow and collision avoidance, built upon the CACC system. To manage congestion and prevent collisions in volatile traffic situations, the proposed approach focuses on the development and adaptation of platoons. Scenarios of obstruction are discovered throughout the travel process, and solutions to these problematic situations are articulated. Merge and join maneuvers are undertaken in order to maintain the platoon's even progression. Simulation results highlight a marked improvement in traffic flow, attributable to the successful implementation of platooning to alleviate congestion, thereby reducing travel time and preventing collisions.

We propose a novel framework, using EEG signals, to characterize the cognitive and affective brain processes in response to neuromarketing stimuli. The proposed classification algorithm, fundamentally based on a sparse representation scheme, is the cornerstone of our approach. Our strategy rests on the notion that EEG markers of mental or emotional states are located within a linear subspace. Subsequently, a test brain signal is demonstrably a linear combination of brain signals across all classes in the training set. Graph-based priors over the weights of linear combinations are incorporated into a sparse Bayesian framework for determining the class membership of brain signals. The classification rule is, moreover, generated by applying the residuals of a linear combination. Publicly accessible neuromarketing EEG data was used in experiments to show the effectiveness of our method. The employed dataset's two classification tasks, affective state recognition and cognitive state recognition, saw the proposed classification scheme surpass baseline and state-of-the-art methods in accuracy, achieving more than an 8% improvement.

Health monitoring smart wearable systems are highly sought after in the fields of personal wisdom medicine and telemedicine. By using these systems, the detecting, monitoring, and recording of biosignals becomes portable, long-term, and comfortable. High-performance wearable systems have been on the rise in recent years, driven by the development and optimization strategies within wearable health-monitoring systems, which prominently feature advanced materials and system integration. Despite progress, these domains still encounter hurdles, such as negotiating the balance between adaptability, elongation, sensor effectiveness, and the dependability of the systems. For this purpose, the evolutionary process must continue to support the growth of wearable health monitoring systems. This review, in this context, encapsulates key accomplishments and recent advancements in wearable health monitoring systems. In parallel, a strategy is outlined, focusing on material selection, system integration, and biosignal monitoring techniques. The next generation of wearable health monitoring devices, offering accurate, portable, continuous, and long-term tracking, will broaden the scope of disease detection and treatment options.

The intricate open-space optics technology and expensive equipment required frequently monitor fluid properties in microfluidic chips. APX2009 We are introducing dual-parameter optical sensors with fiber tips into the microfluidic chip in this research. Sensors were positioned throughout each channel of the chip to allow for the real-time determination of the concentration and temperature of the microfluidics. Regarding temperature, the sensitivity was 314 pm/°C, and glucose concentration sensitivity came to -0.678 dB/(g/L). APX2009 The microfluidic flow field displayed minimal alteration due to the presence of the hemispherical probe. The optical fiber sensor and microfluidic chip were integrated into a low-cost, high-performance technology. In light of this, we posit that the microfluidic chip, integrated with an optical sensor, has significant applications in drug discovery, pathological research, and material science exploration. Micro total analysis systems (µTAS) can greatly benefit from the application potential of integrated technology.

In radio monitoring, specific emitter identification (SEI) and automatic modulation classification (AMC) are typically handled independently. APX2009 In terms of their application contexts, signal models, feature extractions, and classifier constructions, the two tasks display corresponding similarities. Integrating these two tasks presents a feasible and promising opportunity to reduce overall computational complexity and improve the classification accuracy for each task. In this paper, we detail a dual-task neural network, AMSCN, capable of simultaneously determining the modulation type and transmitter origin of a received signal. The AMSCN process commences with a DenseNet and Transformer integration as the foundation for extracting noteworthy characteristics. A subsequent step implements a mask-based dual-head classifier (MDHC) to reinforce joint learning on both tasks. A multitask cross-entropy loss, comprised of the cross-entropy loss for the AMC and the cross-entropy loss for the SEI, is proposed for training the AMSCN. Our method, evidenced by experimental results, achieves performance gains for the SEI task through the incorporation of supplementary information from the AMC task. Our findings regarding AMC classification accuracy, when evaluated against prevailing single-task models, align with the current leading performance metrics. The SEI classification accuracy, however, shows a significant improvement, rising from 522% to 547%, providing strong evidence for the AMSCN's effectiveness.

Various methods for evaluating energy expenditure exist, each possessing advantages and disadvantages that should be carefully weighed when selecting the approach for particular settings and demographics. Accurate and dependable measurement of oxygen consumption (VO2) and carbon dioxide production (VCO2) is essential across all methods. A comparative study of the mobile CO2/O2 Breath and Respiration Analyzer (COBRA) was conducted against the Parvomedics TrueOne 2400 (PARVO) as a reference standard. Further measurements were used to compare the COBRA to the Vyaire Medical, Oxycon Mobile (OXY) portable instrument. Four repeated trials of progressive exercises were conducted on 14 volunteers, each averaging 24 years of age, 76 kilograms in weight, and exhibiting a VO2 peak of 38 liters per minute. Resting and walking (23-36% VO2peak), jogging (49-67% VO2peak), and running (60-76% VO2peak) activities all had VO2, VCO2, and minute ventilation (VE) continuously measured in a steady state by the COBRA/PARVO and OXY systems. To standardize work intensity (rest to run) progression across the two-day study (two trials per day), the order of system testing (COBRA/PARVO and OXY) was randomized, thereby ensuring consistent data collection. Analyzing systematic bias was integral to assessing the accuracy of the COBRA to PARVO and OXY to PARVO ratios under diverse work intensity conditions. The degree of variability within and between units was determined by interclass correlation coefficients (ICC) and 95% agreement limits. The COBRA and PARVO methods produced similar results for VO2, VCO2, and VE across a range of work intensities. For VO2, the bias standard deviation was 0.001 0.013 L/min⁻¹, with a 95% confidence interval of (-0.024, 0.027) L/min⁻¹, and R² = 0.982. Similarly, VCO2 measurements yielded a bias standard deviation of 0.006 0.013 L/min⁻¹, a 95% confidence interval of (-0.019, 0.031) L/min⁻¹, and R² = 0.982. Finally, VE measurements exhibited a bias standard deviation of 2.07 2.76 L/min⁻¹, a 95% confidence interval of (-3.35, 7.49) L/min⁻¹, and R² = 0.991.

Our investigation of the relationship between coffee and subclinical inflammation involved the use of linear regression models to explore associations with biomarkers such as C-reactive protein (CRP), interleukin-13 (IL-13), and adipokines including adiponectin and leptin. A formal causal mediation analysis was undertaken to understand the part played by coffee-related biomarkers in the observed association between coffee consumption and type 2 diabetes. In the final analysis, we explored the effect modification of coffee type and smoking status. Sociodemographic, lifestyle, and health-related factors were incorporated into the corrective procedures applied to all models.
During a median observation period of 139 years in the RS cohort and 74 years in the UKB cohort, 843 and 2290 cases of incident T2D were documented, respectively. Drinking one more cup of coffee each day was associated with a 4% lower probability of type 2 diabetes (RS, hazard ratio 0.96 [95% CI 0.92-0.99], p=0.0045; UKB, hazard ratio 0.96 [0.94-0.98], p<0.0001), a lower HOMA-IR score (RS, log-transformed -0.0017 [-0.0024 to -0.0010], p<0.0001), and a decrease in CRP (RS, log-transformed -0.0014 [-0.0022 to -0.0005], p=0.0002; UKB, log-transformed -0.0011 [-0.0012 to -0.0009], p<0.0001). Our study also showed an association between high coffee consumption and high serum adiponectin and IL-13 levels, alongside low serum leptin levels. Coffee consumption's impact on CRP levels partially explained the inverse relationship between coffee intake and type 2 diabetes occurrence. (Average mediation effect RS =0.105 (0.014; 0.240), p=0.0016; UKB =6484 (4265; 9339), p<0.0001). The proportion of this effect attributed to CRP varied from 37% [-0.0012%; 244%] (RS) to 98% [57%; 258%] (UKB). The other biomarkers displayed no mediating influence. Consumers who never smoked, former smokers, and those who regularly consumed ground (filtered or espresso) coffee generally exhibited a more pronounced association between coffee consumption and T2D and CRP.
A potential mechanism underlying the beneficial association between coffee consumption and reduced type 2 diabetes risk involves the partial modulation of subclinical inflammation. Ground coffee consumption combined with a non-smoking lifestyle may yield the largest rewards. Inflammation, adipokines, and biomarkers as potential mediators of the relationship between coffee consumption and type 2 diabetes mellitus, analyzed through follow-up studies and mediation analysis.
The potential benefit of coffee consumption in lowering type 2 diabetes risk may be partially explained by its influence on subclinical inflammation. Ground coffee consumers and non-smokers stand to gain the most from these options. Inflammation, adipokines, and type 2 diabetes mellitus are examined in relation to coffee consumption through mediation analysis and follow-up studies, highlighting biomarkers.

Genome annotation of Streptomyces fradiae, coupled with sequence alignment against a local protein library, led to the identification of a novel epoxide hydrolase (EH), SfEH1, for the purpose of extracting microbial EHs with specific catalytic properties. Employing Escherichia coli BL21(DE3), the sfeh1 gene, which codes for SfEH1, was cloned and overexpressed in a soluble state. selleckchem The temperature and pH conditions that are optimal for the production of recombinant SfEH1 (reSfEH1) and reSfEH1-expressing E. coli (E. coli) are paramount. Activity levels of E. coli/sfeh1 (30) and reSfEH1 (70) underscore the more pronounced impact of temperature and pH on the activity of reSfEH1 compared to that of intact E. coli/sfeh1 cells. E. coli/sfeh1's catalytic efficiency was tested on thirteen common mono-substituted epoxides; a subsequent evaluation revealed the highest activity (285 U/g dry cells) for rac-12-epoxyoctane (rac-6a), and (R)-12-pentanediol ((R)-3b) (or (R)-12-hexanediol ((R)-4b)), corresponding to an enantiomeric excess (eep) of up to 925% (or 941%), approaching a 100% conversion ratio. The process of enantioconvergent hydrolysis of rac-3a (or rac-4a) exhibited regioselectivity coefficients (S and R) quantifiable at 987% and 938% (or 952% and 989%), as determined through calculation. By employing both kinetic parameter analysis and molecular docking simulations, the high and complementary regioselectivity was unequivocally established.

Individuals who use cannabis regularly encounter negative health outcomes, yet they are hesitant to seek treatment. selleckchem To lessen cannabis use and heighten functionality in those concurrently burdened by insomnia, the challenge of insomnia deserves specific attention. The preliminary efficacy of a tailored telemedicine-delivered CBT for insomnia in individuals with regular cannabis use for sleep (CBTi-CB-TM) was meticulously examined and refined through an intervention development study.
This study, a single-blind, randomized trial, investigated the efficacy of two approaches for chronic insomnia in 57 adults, 43 of whom were women (average age 37.61 years). One group (n=30) underwent Cognitive Behavioral Therapy for Insomnia combined with Cannabis Use Management (CBTi-CB-TM), while the other group (n=27) received sleep hygiene education (SHE-TM). Insomnia (Insomnia Severity Index [ISI]) and cannabis use (Timeline Followback [TLFB] and daily diary) self-reported assessments were conducted with participants at pre-treatment, post-treatment, and 8-week follow-up time points.
The CBTi-CB-TM intervention produced a considerably more positive impact on ISI scores than the SHE-TM condition, signified by a difference of -283, a standard error of 084, a statistically significant p-value (P=0004), and a large effect size (d=081). By the 8-week follow-up, an impressive 18 out of 30 (600%) participants in the CBTi-CB-TM group, were in remission from insomnia, a rate far surpassing that of the SHE-TM group where only 4 out of 27 (148%) experienced remission.
A probability of 00003 (P=00003) corresponds to the outcome 128. The TLFB demonstrated a slight decrease in cannabis use over the past 30 days for both conditions (=-0.10, SE=0.05, P=0.0026); the CBTi-CB-TM group experienced a significant decrease in the percentage of days cannabis was used within two hours of bedtime after treatment, demonstrating a decrease of 29.179% in the usage compared to a 26.80% increase in the control group (P=0.0008).
Non-treatment-seeking individuals with regular cannabis use for sleep can benefit from CBTi-CB-TM's demonstrably feasible, acceptable, and preliminary effective strategies for sleep and cannabis-related improvements. Given the sample's inherent limitations in terms of generalizability, these findings advocate for the implementation of adequately powered randomized controlled trials extending the duration of follow-up.
For non-treatment-seeking cannabis users relying on cannabis for sleep, CBTi-CB-TM emerged as a feasible, acceptable, and demonstrably preliminary effective approach to enhancing both sleep and cannabis-related outcomes. The sample's characteristics may limit the generality of these findings, but they strengthen the case for randomized controlled trials of ample power, incorporating longer follow-up durations.

Facial reconstruction, commonly referred to as facial approximation, is a widely accepted alternative technique in forensic anthropological and archaeological settings. Employing this technique, the generation of a virtual facial representation from a person's skull remains proves valuable. Three-dimensional (3-D) traditional facial reconstruction, a process sometimes called manual or sculptural reconstruction, has been established for over a century. However, its subjective character and need for anthropological training have been long acknowledged. In the past, the progression of computational technologies facilitated numerous attempts at designing a more suitable approach to 3-D computerized facial reconstruction. This approach to the method depended upon the anatomical connection between the face and skull, and subdivided into computational strategies for semi- and automated use. Multiple representations of faces can be generated with greater speed, flexibility, and realism through the use of 3-D computerized facial reconstruction. Moreover, cutting-edge tools and technologies consistently produce stimulating and credible research, and likewise support collaborative projects spanning multiple disciplines. The utilization of artificial intelligence has initiated a groundbreaking transformation in the field of 3-D computerized facial reconstruction, introducing novel discoveries and procedures within the academic community. This paper, drawing upon the last 10 years of scientific publications, provides an overview of 3-D computerized facial reconstruction, its development trajectory, and potential future challenges in achieving further improvements.

The surface free energy (SFE) of nanoparticles (NPs) profoundly influences the interfacial interactions that occur within colloidal suspensions. Because of the diverse physical and chemical properties of the NP surface, determining SFE is not a simple task. While effective for determining surface free energy (SFE) on smooth surfaces, direct force measurement methods, such as colloidal probe atomic force microscopy (CP-AFM), encounter limitations in providing dependable measurements on surfaces roughened by nanoparticles (NPs). In order to establish the SFE of NPs, we developed a trustworthy method, utilizing Persson's contact theory to incorporate the impact of surface roughness within CP-AFM experiments. Across a collection of materials, exhibiting variations in surface roughness and chemical make-up, we established the SFE. The proposed method's reliability is evidenced by the polystyrene SFE determination process. Following this procedure, the supercritical fluid extraction (SFE) values for bare and functionalized silica, graphene oxide, and reduced graphene oxide were obtained and their accuracy was demonstrated. selleckchem The innovative method empowers CP-AFM to accurately and dependably ascertain the size distribution of nanoparticles possessing a variegated surface structure, a determination typically unattainable via conventional experimentation for surface-modified nanoparticles.

The bimetallic spinel transition metal oxide anode, ZnMn2O4, has attracted significant attention because of the promising bimetallic interaction and high theoretical storage capacity.