Greater Complexity Research Articles

Molecular Insights into Chemical Reactions at Aqueous Aerosol Interfaces.

Atmospheric aerosols facilitate reactions between ambient gases and dissolved species. Here, we review our efforts to interrogate the uptake of these gases and the mechanisms of their reactions both theoretically and experimentally. We highlight the fascinating behavior of N2O5 in solutions ranging from pure water to complex mixtures, chosen because its aerosol-mediated reactions significantly impact global ozone, hydroxyl, and methane concentrations. As a hydrophobic, weakly soluble, and highly reactive species, N2O5 is a sensitive probe of the chemical and physical properties of aerosol interfaces. We employ contemporary theory to disentangle the fate of N2O5 as it approaches pure and salty water, starting with adsorption and ending with hydrolysis to HNO3, chlorination to ClNO2, or evaporation. Flow reactor and gas-liquid scattering experiments probe even greater complexity as added ions, organic molecules, and surfactants alter the interfacial composition and reaction rates. Together, we reveal a new perspective on multiphase chemistry in the atmosphere.

AI for crisis decisions

Increasingly, our cities are confronted with crises. Fuelled by climate change and a loss of biodiversity, increasing inequalities and fragmentation, challenges range from social unrest and outbursts of violence to heatwaves, torrential rainfall, or epidemics. As crises require rapid interventions that overwhelm human decision-making capacity, AI has been portrayed as a potential avenue to support or even automate decision-making. In this paper, I analyse the specific challenges of AI in urban crisis management as an example and test case for many super wicked decision problems. These super wicked problems are characterised by a coincidence of great complexity and urgency. I will argue that from this combination, specific challenges arise that are only partially covered in the current guidelines and standards around trustworthy or human-centered AI. By following a decision-centric perspective, I argue that to solve urgent crisis problems, the context, capacities, and networks need to be addressed. AI for crisis response needs to follow dedicated design principles that ensure (i) human control in complex social networks, where many humans interact with AI; (ii) principled design that considers core principles of crisis response such as solidarity and humanity; (iii) designing for the most vulnerable. As such this paper is meant to inspire researchers, AI developers and practitioners in the space of AI for (urban) crisis response – and other urgent and complex problems that urban planners are confronted with.

Resolving Multielement Semiconductor Nanocrystals at the Atomic Level: Complete Deciphering of Domains and Order in Complex CuαZnβSnγSeδ (CZTSe) Tetrapods.

Semiconductor nanocrystals (NCs) with high elemental and structural complexity can be engineered to tailor for electronic, photovoltaic, thermoelectric, and battery applications etc. However, this greater complexity causes ambiguity in the atomic structure understanding. This in turn hinders the mechanistic studies of nucleation and growth, the theoretical calculations of functional properties, and the capability to extend functional design across complementary semiconductor nanocrystals. Herein, we successfully deciphered the atomic arrangements of 4 different nanocrystal domains in CuαZnβSnγSeδ (CZTSe) nanocrystals using crucial zone axis analysis on multiple crystals in different orientations. The results show that the essence of crystallographic progression from binary to multielemental semiconductors is actually the change of theoretical periodicity. This transition is caused by decreased symmetry in the crystal instead of previously assumed crystal deformation. We further reveal that these highly complex crystalline entities have highly ordered element arrangements as opposed to the previous understanding that their elemental orderings are random.

Research on the Influence Mechanism of Moisture Content on Macroscopic Mechanical Response and Microscopic Evolution Characteristic of Limestone

The uniaxial compression experiments and acoustic emission (AE) monitoring are conducted to investigate the macroscopic mechanical behavior and microscopic fracture characteristics of limestone samples with varying moisture contents. The findings revealed that as the moisture content increases from 0 to 6.6%, there is a decrease in peak stress and an increase in peak strain. A clear trend towards greater complexity in fracture characteristics is observed with increasing moisture content. In addition, AE activities demonstrate a heightened frequency, accompanied by an elevation in the corresponding multifractal parameter Δα as the moisture content rises. These variations are attributed to the increase in moisture content, which promotes the proliferation of small-scale microcracks and inhibits their evolution into large-scale microcracks. Consequently, the damage and failure process of the limestone samples transitions from being predominantly controlled by a few large-scale microcracks to being collectively influenced by a multitude of small-scale microcracks as the moisture content increases. In conjunction with the Criterion of Microcrack Density, the correctness of the analysis above is substantiated through mathematical derivation. Further, a quantitative model that links the microcrack system to moisture content is established based on the multifractal parameter Δα. Following this, a characterization model that depicts the macroscopic mechanical properties of limestone affected by moisture content is developed. This model effectively encapsulates the quantitative relationship between moisture content and the macroscopic characteristics of limestone and is validated through fitting experimental data. This research contributes to understanding the macroscopic mechanical response and microscopic fracture characteristics of limestone samples with different moisture contents, providing valuable insights and guidance for ensuring safety during engineering construction processes.

Diversity, Origin and Evolution of the ESCRT Systems.

All eukaryotic cells possess complex intracellular membrane organization. ESCRT (Endosomal Sorting Complexes Required for Transport) plays a central role in membrane remodeling which is essential for cellular functionality in eukaryotes. Recently, it has been shown that Asgard archaea, the archaeal phylum that includes the closest known relatives of eukaryotes, encode homologs of many components of the ESCRT systems. We employed protein sequence and structure comparisons to reconstruct the evolution of ESCRT systems in archaea and identified several previously unknown homologs of ESCRT subunits, some of which can be predicted to participate in cell division. The results of this reconstruction indicate that the Last Archaeal Common ancestor already encoded a complex ESCRT system that was involved in protein sorting. In Asgard archaea, ESCRT systems evolved towards greater complexity, and in particular, the connection between ESCRT and the ubiquitin system that was previously considered a eukaryotic signature was established.

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data

BackgroundThe accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program.ResultsOur results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction.ConclusionsThe dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

Model and algorithm analysis of MPC control in aerospace engineering

With the continuous development of time and technology, the control system has thus evolved in the direction of great complexity. In the field of path control, once control algorithms cannot absolutely meet the requirements of all people, so this paper introduces an alternative algorithm-MPC. This article focuses on the development of MPC from when it is invented and its application in aerospace today with specified examples, and then lists and analyses the mainstream MPC algorithms, namely LTI, LTV, LPV, PWA and nonlinear systems. Article below also illustrates the derivation of sample of non-parametric model-based algorithms (Dynamic matrix control) and parametric model-based algorithms (Generalized Predictive Control) and the elaboration of mainstream systems and their applications in the form of an overview. Eventually gives a conclusion to the current situation of MPC and relationships among various algorithms. Note that this article is mainly aimed at scholars who are new to MPC algorithms.

Techno-Economic Optimization of Radiator Configurations in Power Transformer Cooling

In this research, a numerical approach is created to assess the effective parameters of power transformer thermal management and, as a result, improve their cooling systems. This study analyzes the radiator’s thermal performance across several arrangements and optimizes the dimensions and configurations for varied cooling loads from a techno-economic perspective. The optimization criteria were the radiator’s height (L), fin spacing (D), and number of fins (N). Due to the great complexity of the generated models, the coupled thermo-hydraulic numerical simulations were carried out on a computer cluster. An in-house radiator test facility was constructed for the experiments in order to verify the numerical model. The simulation findings accord well with the empirically obtained values. A total of 76 radiator sets were investigated. Following that, the generated findings were used to perform an optimization analysis. Finally, the response surface method was used to establish an ideal radiator layout for the specified cooling capacity at the lowest possible cost. These findings reveal that the best cooling performance is obtained when the spacing between the fins is 50 mm. Cooling capacity per unit cost rises as radiator size decreases. The cost factor and geometric details were shown to have strong connections.

A New Perspective for Computational Social Systems: Fuzzy Modeling and Reasoning for Social Computing in CPSS

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as “social sensor” and “social IoT”, these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber–physical system (CPS)-oriented information computing sublimate to the cyber–physical–social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber–physical–social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss the challenges for future research directions.

Symposium Introduction: Recent Progress and Current Challenges in Wine Analytical Sciences.

Oenology, behind the more visible social and convivial aspects associated with the consumption of wine and spirits, has great complexity due to the coexistence of countless different styles of artisanal products, linked to the diversity of places grapes are grown and wine is produced, and its global distribution. Premium wine is not a commodity. To scientifically support such complexity requires an extremely rigorous and diversified scientific expertise, capable of supporting the continuous improvement of processes and products that are developed, evaluated, and offered on international markets. Rigorous chemical and sensory analyses are essential to this continuous process of improvement, and for several decades now have regularly brought together a large community of researchers from multidisciplinary backgrounds to share, discuss, and harmonize their knowledge and experience of wines. The 12th edition of In Vino Analytica Scientia 2022 was held in Neustadt, Germany, in July 2022. The conference was attended by over 240 delegates, from 23 nationalities, who shared different aspects of wine and spirits research, with 9 key note lectures; 37 full talks; 36 short communications, and over 200 posters. This special issue is a collection of full papers from a selection of contributed oral presentations and posters presented at the conference.

Neural complexity is increased after low doses of LSD, but not moderate to high doses of oral THC or methamphetamine.

Neural complexity correlates with one's level of consciousness. During coma, anesthesia, and sleep, complexity is reduced. During altered states, including after lysergic acid diethylamide (LSD), complexity is increased. In the present analysis, we examined whether low doses of LSD (13 and 26 µg) were sufficient to increase neural complexity in the absence of altered states of consciousness. In addition, neural complexity was assessed after doses of two other drugs that significantly altered consciousness and mood: delta-9-tetrahydrocannabinol (THC; 7.5 and 15 mg) and methamphetamine (MA; 10 and 20 mg). In three separate studies (N = 73; 21, LSD; 23, THC; 29, MA), healthy volunteers received placebo or drug in a within-subjects design over three laboratory visits. During anticipated peak drug effects, resting state electroencephalography (EEG) recorded Limpel-Ziv complexity and spectral power. LSD, but not THC or MA, dose-dependently increased neural complexity. LSD also reduced delta and theta power. THC reduced, and MA increased, alpha power, primarily in frontal regions. Neural complexity was not associated with any subjective drug effect; however, LSD-induced reductions in delta and theta were associated with elation, and THC-induced reductions in alpha were associated with altered states. These data inform relationships between neural complexity, spectral power, and subjective states, demonstrating that increased neural complexity is not necessary or sufficient for altered states of consciousness. Future studies should address whether greater complexity after low doses of LSD is related to cognitive, behavioral, or therapeutic outcomes, and further examine the role of alpha desynchronization in mediating altered states of consciousness.

FEZF2 and AIRE1: An Evolutionary Trade-off in the Elimination of Auto-reactive T Cells in the Thymus.

Autoimmune Regulator 1 (AIRE1) and Forebrain Embryonic Zinc Finger-Like Protein 2 (FEZF2) play pivotal roles in orchestrating the expression of tissue-restricted antigens (TRA) to facilitate the elimination of autoreactive T cells. AIRE1's presence in the gonads of various vertebrates has raised questions about its potential involvement in gene expression control for germline cell selection. Nevertheless, the evolutionary history of these genes has remained enigmatic, as has the rationale behind their apparent redundancy in vertebrates. Furthermore, the origin of the elimination process itself has remained elusive. To shed light on these mysteries, we conducted a comprehensive evolutionary analysis employing a range of tools, including multiple sequence alignment, phylogenetic tree construction, ancestral sequence reconstruction, and positive selection assessment. Our investigations revealed intriguing insights. AIRE1 homologs emerged during the divergence of T cells in higher vertebrates, signifying its role in this context. Conversely, FEZF2 exhibited multiple homologs spanning invertebrates, lampreys, and higher vertebrates. Ancestral sequence reconstruction demonstrated distinct origins for AIRE1 and FEZF2, underscoring that their roles in regulating TRA have evolved through disparate pathways. Furthermore, it became evident that both FEZF2 and AIRE1 govern a diverse repertoire of genes, encompassing ancient and more recently diverged targets. Notably, FEZF2 demonstrates expression in both vertebrate and invertebrate embryos and germlines, accentuating its widespread role. Intriguingly, FEZF2 harbors motifs associated with autophagy, such as DKFPHP, SYSELWKSSL, and SYSEL, a process integral to cell selection in invertebrates. Our findings suggest that FEZF2 initially emerged to regulate self-elimination in the gonads of invertebrates. As organisms evolved toward greater complexity, AIRE1 likely emerged to complement FEZF2's role, participating in the regulation of cell selection for elimination in both gonads and the thymus. This dynamic interplay between AIRE1 and FEZF2 underscores their multifaceted contributions to TRA expression regulation across diverse evolutionary contexts.

Predicting attentional allocation in real-world environments: The need to investigate crossmodal semantic guidance.

Real-world environments are multisensory, meaningful, and highly complex. To parse these environments in a highly efficient manner, a subset of this information must be selected both within and across modalities. However, the bulk of attention research has been conducted within sensory modalities, with a particular focus on vision. Visual attention research has made great strides, with over a century of research methodically identifying the underlying mechanisms that allow us to select critical visual information. Spatial attention, attention to features, and object-based attention have all been studied extensively. More recently, research has established semantics (meaning) as a key component to allocating attention in real-world scenes, with the meaning of an item or environment affecting visual attentional selection. However, a full understanding of how semantic information modulates real-world attention requires studying more than vision in isolation. The world provides semantic information across all senses, but with this extra information comes greater complexity. Here, we summarize visual attention (including semantic-based visual attention), crossmodal attention, and argue for the importance of studying crossmodal semantic guidance of attention. This article is categorized under: Psychology > Attention Psychology > Perception and Psychophysics.

Opportunities and Difficulties in the Repurposing of HDAC Inhibitors as Antiparasitic Agents

Ongoing therapy for human parasite infections has a few known drugs but with serious side effects and the problem of drug resistance, impelling us to discover novel drug candidates with newer mechanisms of action. Universally, this has boosted the research in the design and development of novel medicinal agents as antiparasitic drugs with a novel mode of action. Histone deacetylase inhibitors (HDACis) are used in a vast variety of diseases due to their anti-inflammatory properties. Drug repurposing strategies have already approved HDACis as cancer therapeutics and are now under investigation for many parasitic infections. Along with the expression of the gene, histone deacetylase (HDAC) enzymes also act as a slice of great multi-subunit complexes, targeting many non-histones, changing systemic and cellular levels signaling, and producing different cell-based specified effects. Zinc (Zn2+)- and nicotinamide adenine dinucleotide (NAD+)-dependent HDACs of parasites play pivotal roles in the alteration of gene expression of parasites. Some of them are already known to be responsible for the survival of several parasites under odd circumstances; thus, targeting them for therapeutic interventions will be novel for potential antiparasitic targets. This point of view outlines the knowledge of both class-I and class-II HDACis and sirtuin inhibitors that emerged to be the key players in the treatment of human parasitic disorders like Leishmaniasis, Schistosomiasis, Malaria, Trypanosomiasis, and Toxoplasmosis. This review also focuses on repurposing opportunities and challenges in HDAC inhibitors that are preceded by their clinical development as potent new antiparasitic drugs.

Ego- and Geo-Centered References: A Functional Neuroimagery Study

Introduction: The integration of vestibular, visual, and somatosensory cues allows the perception of space through the orientation of our body and surrounding objects with respect to gravity. The main goal of this study was to identify the cortical networks recruited during the representation of body midline and the representation of verticality. Methods: Thirty right-handed healthy participants were evaluated using fMRI. Brain networks activated during a subjective straight-ahead (SSA) task were compared to those recruited during a subjective vertical (SV) task. Results: Different patterns of cortical activation were observed, with differential increases in the angular gyrus and left cerebellum posterior lobe during the SSA task, in right rolandic operculum and cerebellum anterior lobe during the SV task. Discussion: The activation of these areas involved in visuo-spatial functions suggests that bodily processes of great complexity are engaged in body representation and vertical perception. Interestingly, the common brain networks involved in SSA and SV tasks were comprised of areas of vestibular projection that receive multisensory information (parieto-occipital areas) and the cerebellum, and reveal a predominance of the right cerebral and cerebellar hemispheres. The outcomes of this first fMRI study designed to unmask common and specific neural mechanisms at work in gravity- or body-referenced tasks pave a new way for the exploration of spatial cognitive impairment in patients with vestibular or cortical disorders.

Long-Term Patterns in Forest Soil CO2 Flux in a Pacific Northwest Temperate Rainforest

Soil CO2 efflux (Fs) plays an important role in forest carbon cycling yet estimates of Fs can remain unconstrained in many systems due to the difficulty in measuring Fs over long time scales in natural systems. It is important to quantify seasonal patterns in Fs through long-term datasets because individual years may show patterns that are not reflective of long-term averages. Additionally, determining predictability of net patterns in soil carbon flux based on environmental factors, such as moisture and temperature, is critical for appropriately modeling forest carbon flux. Ecosystems in moderate climates may have strong CO2 efflux even during winter, and so continuous quantification of annual variability is especially important. Here, we used a 2008–2023 dataset in a lowland temperate forest ecosystem to address two main questions: (1) What are the seasonal patterns in Fs in a highly productive temperate rainforest? (2) How is average Fs across our study area predicted by average coincident temperature, soil moisture and precipitation totals? Data showed clear seasonality where Fs values are higher in summer. We also find Fs across our measurement network was predicted by variation in abiotic factors, but the interaction between precipitation/moisture and temperature resulted in greater complexity. Specifically, in spring a relatively strong relationship between air temperature and Fs was present, while in summer the relationship between temperature and Fs was flat. Winter and autumn seasons showed weak positive relationships. Meanwhile, a negative relationship between precipitation and Fs was present in only some seasons because most precipitation falls outside the normal growing season in our study system. Our data help constrain estimates of soil CO2 fluxes in a temperate rainforest ecosystem at ~14–20 kg C ha−1 day−1 in summer and autumn, and 6.5–10.5 kg C ha−1 day−1 in winter and spring seasons. Together, estimates suggest this highly productive temperate rainforest has annual soil-to-atmosphere fluxes of CO2 that amount to greater than 4.5 Mg C ha−1 year−1. Sensitivity of such fluxes to regional climate change will depend on the balance of Fs determined by autotrophic phenological responses versus heterotrophic temperature and moisture sensitivity. Relatively strong seasonal variation coupled with comparatively weak responses to abiotic variables suggest Fs may be driven largely by seasonal trends in autotrophic respiration. Accordingly, plant and tree responses to climate may have a stronger effect on Fs in the context of climate change than temperature or moisture changes alone.

Landscape and domains of possible future threats from a societal point of view

AbstractWe are currently amid multiple interconnected and complex crises within the different dimensions of our society—a polycrisis, and the future promises even greater complexity in terms of threats. However, the existing crisis typologies and crisis identification often have an introspective organisational focus and tend to concentrate on a limited set of historically known threats. This study takes an exploratory, futures studies‐based approach with a broader societal perspective. Through environmental scanning using 1714 literature sources and consultations with 54 experts, we have identified 153 potential future crises spanning various societal domains. We utilize the PESTLE framework to outline the possible emergence of these crises. Our findings reveal a considerably larger number of potential crisis phenomena than previously proposed, aiming for objective sensemaking of the complex crisis landscape. The results also demonstrate crises vary in their temporal development, albeit evaluating this is challenging. This study challenges the prevalent myopic and subjective prioritisation‐based crisis identification and provides a comprehensive yet comprehensible list of possible future crises. In the current context of siloed decision‐making structures, both public and private organisations can use this list to enhance their future‐oriented preparedness and assess their responsibilities in managing these crises, as well as identifying necessary stakeholders.

SNX32 is a host restriction factor that degrades African swine fever virus CP204L via the RAB1B-dependent autophagy pathway.

African swine fever virus (ASFV) causes a highly contagious and deadly disease in domestic pigs and European wild boars, posing a severe threat to the global pig industry. ASFV CP204L, a highly immunogenic protein, is produced during the early stages of ASFV infection. However, the impact of CP204L protein-interacting partners on the outcome of ASFV infection is poorly understood. To accomplish this, coimmunoprecipitation and mass spectrometry analysis were conducted in ASFV-infected porcine alveolar macrophages (PAMs). We have demonstrated that sorting nexin 32 (SNX32) is a CP204L-binding protein and that CP204L interacted and colocalized with SNX32 in ASFV-infected PAMs. ASFV growth and replication were promoted by silencing SNX32 and suppressed by overexpressing SNX32. SNX32 degraded CP204L by recruiting the autophagy-related protein Ras-related protein Rab-1b (RAB1B). RAB1B overexpression inhibited ASFV replication, while knockdown of RAB1B had the opposite effect. Additionally, RAB1B, SNX32, and CP204L formed a complex upon ASFV infection. Taken together, this study demonstrates that SNX32 antagonizes ASFV growth and replication by recruiting the autophagy-related protein RAB1B. This finding extends our understanding of the interaction between ASFV CP204L and its host and provides new insights into exploring the relationship between ASFV infection and autophagy.IMPORTANCEAfrican swine fever (ASF) is a highly contagious and acute hemorrhagic viral disease with a high mortality near 100% in domestic pigs. ASF virus (ASFV), which is the only member of the family Asfarviridae, is a dsDNA virus of great complexity and size, encoding more than 150 proteins. Currently, there are no available vaccines against ASFV. ASFV CP204L represents the most abundantly expressed viral protein early in infection and plays an important role in regulating ASFV replication. However, the mechanism by which the interaction between ASFV CP204L and host proteins affects ASFV replication remains unclear. In this study, we demonstrated that the cellular protein SNX32 interacted with CP204L and degraded CP204L by upregulating the autophagy-related protein RAB1B. In summary, this study will help us understand the interaction mechanism between CP204L and its host upon infection and provide new insights for the development of vaccines and antiviral drugs.

Study Some Methods To Measure The Reliability System Neutrosophically

Industry has developed greatly nowadays, and it has been accompanied by great complexity in machines and devices. Researches that seeks to obtain high efficiencies for these machines have emerged , such as reliability theory. Due to the verity and complexity of the machines, we resort to using the neutrosophic reliability that includes cases excluded from classical reliability. The aim of this paper is to define the neutrosophic parallel system and study neutrosophic methods of calculating the neutrosophic reliability, where the basic concept neutrosophic adjacency matrix of system are present by defined neutrosophic adjacency matrix of neutrosophic graph. Two methods for calculate the neutrosophic reliability are defined conformity to neutrosophic logic which are neutrosophic minimal path method and neutrosophic tracing method. some applications have been introduced.

Alcohol and the dopamine system.

The mesolimbic dopamine pathway plays a major role in drug reinforcement and is likely involved also in the development of drug addiction. Ethanol, like most addictive drugs, acutely activates the mesolimbic dopamine system and releases dopamine, and ethanol-associated stimuli also appear to trigger dopamine release. In addition, chronic exposure to ethanol reduces the baseline function of the mesolimbic dopamine system. The molecular mechanisms underlying ethanol´s interaction with this system remain, however, to be unveiled. Here research on the actions of ethanol in the mesolimbic dopamine system, focusing on the involvement of cystein-loop ligand-gated ion channels, opiate receptors, gastric peptides and acetaldehyde is briefly reviewed. In summary, a great complexity as regards ethanol´s mechanism(s) of action along the mesolimbic dopamine system has been revealed. Consequently, several new targets and possibilities for pharmacotherapies for alcohol use disorder have emerged.