Complex Actions Research Articles

Mapping food system drivers of the double burden of malnutrition using community-based system dynamics: a case study in Peru

BackgroundPeru is facing a double burden of malnutrition (DBM), characterized by the co-existence of undernutrition and overnutrition. Double-duty actions that concurrently target common drivers of undernutrition and overnutrition, while ensuring no unintended side effects, are recommended to effectively address the DBM. To understand these complex common mechanisms and design context-specific double-duty actions, there is a need for participatory systems approaches. This paper provides a case study of applying a community-based system dynamics approach to capture stakeholder perspectives of food system drivers of the DBM in two regions in Peru.MethodsWe implemented a multi-stage community-based system dynamics approach, which included processes for research capacity building for systems approaches, and the designing, piloting, and implementation of stakeholder workshops. A total of 36 stakeholders, representing diverse perspectives, participated in five group model building workshops. Stakeholder views are presented in a causal loop diagram that showcases the feedback mechanisms between key food system drivers of overweight and stunting in Peru.ResultsThe causal loop diagram highlights that prioritization of undernutrition over overnutrition in the policymaking process, due to Peru’s historically high levels of undernutrition, may undermine action against the DBM. It also describes potential mechanisms of unintended impacts of undernutrition policies on the DBM in Peru, including impacts related to within-family distribution and quality of food provided through food assistance programs, and unintended impacts due to regional dynamics.ConclusionsThis paper highlights the importance of a participatory approach to understand local needs and priorities when recommending double-duty actions in Peru and shares practical methodological guidance on applying participatory systems approaches in public health.

Synthesis and Characterization of 4-(1-(2H benzo[d][1,2,3]triazol-2-ylimino)ethyl)benzonitrile and amino acid alanine and its Metal Complexes

The biological actions of transition metal ion complexes with mixed ligands are very different. Amino acid and azomethine containing Schiff bases are very useful building blocks for ternary complexes. The interaction of two ligands 4-(1-(2H-benzo[d][1,2,3]triazol-2-ylimino)ethyl)benzonitrile as L1 and an amino acid alanine as L2, with newly synthesized cuprous chloride solution in a 1:1:1 molar ratio using standard reflux approach has been attempted to create unique metal(I) complexes. Elemental analysis, molecular weight, magnetic moment, thermal analysis and spectroscopic methods are used to characterize the produced ligand and metal(I) complexes. Bacillus subtilis, Escherichia coli and Candida albicans were used as test organisms in antibacterial tests of the produced compounds. Antibacterial and antifungal activity of the metal (I) complexes have been shown to be very high against ciprofloxacin, ketoconazole.

RHEOLOGICAL AND COLLOID-STRUCTURAL BEHAVIOUR OF HIGH-VISCOSITY CRUDE OIL AND EMULSION AFTER WAVE ACTION

One of the main problems in oil production is the formation of stable oil-water emulsions that cause corrosion of pipelines, malfunction of pumping equipment, and poisoning of catalysts at refineries. The features of the viscosity-temperature behaviour of high-viscosity resinous crude oil from the Russkoye field (Yamal-Nenets autonomous district) and its 30 wt% emulsion after exposure to electromagnetic and low-frequency acoustic fields are investigated. Electromagnetic treatment of crude oil leads to a decrease in the phase transition temperature and the yield strength coefficient. Low-frequency acoustic processing of the emulsion is accompanied by a decrease in the effective viscosity and the yield strength coefficient. After a complex wave action, the viscosity-temperature characteristics of crude oil continue to fall, and the stability of the formed emulsions decreases due to the intense coalescence of water-phase droplets. It is shown that the amount of released asphaltenes in oil-containing systems decreases after wave treatment. On the contrary, after a complex impact, their content in crude oil increases, while in the emulsion it continues to decrease. This is likely to occur due to the release of hydrocarbons occluded in the asphaltene structure into the liquid oil phase.

STUDY OF LUBRICANT ADDITIVES FOR EXPANSION PIPE EXPANSION IN MONODIAMETER WELLS

There is a need to improve well design and completion in order to reduce material intensity while maintaining sufficient casing cross-sections. One of the possible solutions for this is the construction of mono-diameter wells using expandable casing technology. The technology of drilling wells with the same fixed bore diameter from the wellhead to the productive formation could solve many problems and limitations in the construction of wells, opening up opportunities for the development of large oil and gas reserves that are currently classified as difficult to recover or inaccessible. The technology of drilling monodiameter wells also reduces drilling volumes, reduces the adverse impact on the environment and, ultimately, leads to minimization of construction costs.For purposeful choice of flushing fluid components, especially reagents of complex action and lubricating additives necessary for drilling of mono-diameter wells, their preliminary laboratory tests on experimental installations providing physical similarity of interaction processes (wear) of friction pairs in relation to drilling technology are required. The task of this article is to study lubricating additives for reaming of reaming pipe in mono-diameter wells for subsequent selection of the most optimal of them. The experiments were carried out on the wellknown universal machine II 5018, designed for testing friction and wear of metals and alloys, hard engineering plastics and composites. The loading system and the II 5018 work unit were upgraded to simulate the processes being studied. During the experiments, the friction coefficient and wear rate of the sample were measured. To carry out the research, various lubricating and cooling media used in drilling technology were selected.

Clinical, Metabolic and Neurohumoral Effects of L-arginine in Patients with Coronary Heart Disease in Combination with Arterial Hypertension

The objective: to analyze the impact of complex therapy using L-arginine, ubiquinone (coenzyme Q10), L-carnitine, lycopene, plant extracts and folic acid on the functional state of the endothelium, tolerance to physical activity, indicators of oxidative stress, lipid metabolism and clinical status patients with stable angina pectoris of the III functional class (FC) in comorbidity with arterial hypertension (AH) of the II grade, stage 2. Materials and methods. 100 patients with stable angina pectoris III FC and AH II grade, stage 2 aged 55–68 years participated in the study. The patients were divided into two groups: 50 patients of the 1st group received standard therapy – RAAS blocker, amlodipine, bisoprolol, acetylsalicylic acid and atorvastatin; 50 patients of the 2nd group in addition to standard therapy received a multi-component product of complex action, which includes L-arginine, ubiquinone (coenzyme Q10), Lcarnitine, lycopene, a number of plant extracts and folic acid, in a dose of 1 sachet per day. At the beginning and at the end of the study endothelial functions were assessed, daily blood pressure (BP) was monitored, physical exercise testing was performed, the weekly number of angina attacks and nitroglycerin tablet taking were recorded, oxidative status parameters were studied, quality of life was assessed, and the results were statistically processed. Results. The use of complex action preparation, which includes L-arginine, ubiquinone (coenzyme Q10), L-carnitine, lycopene, plant extracts and folic acid, as adjuvant therapy demonstrated a decrease in the level of indicators of lipid peroxidation (LPO) and an increase levels of antioxidant protection parameters. In the patients of the 2nd group a statistically significant positive dynamics of the blood pressure level was found: the median of daily systolic blood pressure (SBP) decreased to 131.78 mm (p<0.01). A significant decrease in median daytime and nighttime SBP and diastolic blood pressure (DBP) was also determined (p<0.01). The use of a multicomponent preparation of complex action led to a significant decreased of total cholesterol (CHL) by 14.5%, TG by 10.37% and low-density lipoprotein cholesterol by 12.5%. There was also an increased level of high-density lipoprotein cholesterol by 6.48% (р<0.05 for all indicators). The patients of the 2nd group demonstrated a significant increase in tolerance to physical excesses: an increase in the double product by 21.4% and the total amount of the done work by 78.4% compared to the initial values (р<0.05 for both indicators). After 2 months of treatment the difference between the groups in terms of the decrease in the weekly number of angina attacks was statistically significant (р<0.05). This positive trend was accompanied by a decrease in the median number of nitroglycerin tablets taken per week in the 2nd group from 5.4 to 3.0 (р<0.05). In the patients of the 2nd group after 2 months of observation a statistically significant improvement in quality of life indicators according to the “Seattle Angina Questionnaire” was determined (р<0.05). Conclusions. Although all study participants received identical standard antihypertensive and lipid-lowering therapy, adjuvant use of a multicomponent complex of L-arginine, ubiquinone (coenzyme Q10), L-carnitine, lycopene, plant extracts, and folic acid for 2 months demonstrated a reduction in the impact of oxidative stress with parallel restoration of antioxidant mechanisms, improvement in vascular reactivity in patients and daily control of blood pressure, contributed to the correction of the lipid profile with increased tolerance to physical exercise and, as a result of the above-mentioned effects, had a significant positive impact on the quality of life of patients.

In Search of Medea’s Atypical Motherhood in Euripides’ Medea: Cultural and Historical Perspective

This research aims to highlight the common traits of atypical motherhood portrayed in Euripides’ tragic play, “Medea” focusing on the character of Medea. The study explores the contrast between revenge and motherhood, showcasing how Medea challenges the traditional maternal roles which often emphasize nurturing and protective qualities. Medea challenges these norms and Conventional expectations through her complex and controversial actions by navigating the intricate interplay between love, revenge, and societal norms. The study employs a multidisciplinary approach, incorporating literary analysis, psychoanalytic perspectives, and cultural studies to unravel the layers of Medea’s action. Through a detailed analysis of Medea’s character, the paper examines subtle representations of motherhood, revealing the tensions between maternal instinct and the pursuit of individual agency. Additionally, the research investigates how cultural and historical context of ancient Greece influenced the construction of maternal roles in the play. By delving into the character of Medea, this study contributes to a deeper understanding of the complex dynamics surrounding motherhood within the context of classical Greek literature. Furthermore, it invites reflection on the enduring relevance of these themes in contemporary discussions on gender, power, and societal expectations. Through this exploration, the paper aims to contribute to a nuanced understanding of maternal roles in classical literature and stimulate discourse on the portrayal of atypical motherhood in ancient narratives.

Stable training via elastic adaptive deep reinforcement learning for autonomous navigation of intelligent vehicles

The uncertain stability of deep reinforcement learning training on complex tasks impedes its development and deployment, especially in intelligent vehicles, such as intelligent surface vessels and self-driving cars. Complex and varied environmental states puzzle training of decision-making networks. Here we propose an elastic adaptive deep reinforcement learning algorithm to address these challenges and achieve autonomous navigation in intelligent vehicles. Our method trains the decision-making network over the function and optimization learning stages, in which the state space and action space of autonomous navigation tasks are pruned by choosing classic states and actions to reduce data similarity, facilitating more stable training. We introduce a task-adaptive observed behaviour classification technique in the function learning stage to divide state and action spaces into subspaces and identify classic states and actions. In which the classic states and actions are accumulated as the training dataset that enhances its training efficiency. In the subsequent optimization learning stage, the decision-making network is refined through meticulous exploration and accumulation of datasets. The proposed elastic adaptive deep reinforcement learning enables the decision-making network to effectively learn from complex state and action spaces, leading to more efficient training compared to traditional deep reinforcement learning approaches. Simulation results demonstrate the remarkable effectiveness of our method in training decision-making networks for intelligent vehicles. The findings validate that our method provides reliable and efficient training for decision-making networks in intelligent vehicles. Moreover, our method exhibits stability in training other tasks characterized by continuous state and action spaces.

Improving Female Basketball Shooting Accuracy Using Corrective and Stability Exercises

Developing effective training methods to enhance shooting accuracy in women’s basketball provides key insights into precision fundamentals, thus elevating the gameplay quality and competitiveness. This study evaluated the effectiveness of targeted corrective and stability exercises in enhancing shooting accuracy among female basketball players. The quasi-experimental study involved 70 female students, from a public middle school in Villanueva North District, Misamis Oriental, Philippines. They were divided into two groups: Corrective Exercises and Stability Exercises groups. The participants’ pretest and posttest scores were analyzed using descriptive statistics and T-tests. The results indicated that before the intervention, participants in both groups demonstrated poor shooting accuracy with slight improvement following the intervention. The comparable levels of improvement between the two groups suggest that each type of exercise is equally effective in improving basketball shooting accuracy. Results suggest that an exercise program grounded in the Principle of Motor Learning, which breaks down complex actions for more effective practice, can enhance basketball shooting accuracy despite physical asymmetries and balance issues. The study recommends that educators and coaches implement structured physical activities to boost students’ shooting accuracy. It also urges future research to explore comprehensive training programs and demographics of shooting accuracy using corrective and stability exercises.

Gold(I) N‐heterocyclic carbene (NHC) complexes containing 6‐mercaptopurine derivatives and their in vitro anticancer and anti‐inflammatory effects

A series of eight N‐heterocyclic carbenes (NHC) gold(I) complexes, involving 1,3‐bis(2,6‐diisopropylphenyl)imidazol‐2‐ylidene (iPr) ligand in combination with 6‐mercaptopurine derivatives (HL1–8), has been prepared and thoroughly characterized, including elemental analysis, mass spectrometry, infrared and multinuclear NMR spectroscopy, and single crystal X‐ray analysis. The complexes, showing general composition of [Au (iPr)(Ln)] 1–8, were evaluated for their in vitro cytotoxicity against four human cancer cell lines including A2780 (ovarian) and A2780R (ovarian Cisplatin resistant), PC3 (prostate) and MCF‐7 (breast), and normal human MRC‐5 cells (lung fibroblasts). The complexes revealed significant cytotoxicity, with the best IC50 values ≈ 3.4–6.4 μM against A2780 and reasonable selectivity. Cellular effects of the selected complexes on the A2780 cells were evaluated using various flow cytometry assays. Complexes 1, 3, and 4 showed a strong pro‐apoptotic effect and a strong effect on the loss of mitochondrial membrane potential. These findings indicate that their major mechanism of action is based on the collapse of the mitochondrial metabolism and activation of the intrinsic signaling pathway of apoptosis, consequently resulting in cell death. The complexes 1–8 revealed only negligible effect on the production of inflammatory‐related cytokine (TNF‐α), as well as the activation of nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB) or peroxisome proliferator‐activated receptor gamma (PPARγ). Moreover, the shotgun proteomic analysis was performed, and the obtained results suggest that the mechanism of action of complexes 1, 3, and 4 differs somewhat from that of Auranofin.

Targeting nuclear receptor corepressors for reversible male contraception

Despite numerous female contraceptive options, nearly half of all pregnancies are unintended. Family planning choices for men are currently limited to unreliable condoms and invasive vasectomies with questionable reversibility. Here, we report the development of an oral contraceptive approach based on transcriptional disruption of cyclical gene expression patterns during spermatogenesis. Spermatogenesis involves a continuous series of self-renewal and differentiation programs of spermatogonial stem cells (SSCs) that is regulated by retinoic acid (RA)-dependent activation of receptors (RARs), which control target gene expression through association with corepressor proteins. We have found that the interaction between RAR and the corepressor silencing mediator of retinoid and thyroid hormone receptors (SMRT) is essential for spermatogenesis. In a genetically engineered mouse model that negates SMRT-RAR binding (SMRTmRID mice), the synchronized, cyclic expression of RAR-dependent genes along the seminiferous tubules is disrupted. Notably, the presence of an RA-resistant SSC population that survives RAR de-repression suggests that the infertility attributed to the loss of SMRT-mediated repression is reversible. Supporting this notion, we show that inhibiting the action of the SMRT complex with chronic, low-dose oral administration of a histone deacetylase inhibitor reversibly blocks spermatogenesis and fertility without affecting libido. This demonstration validates pharmacologic targeting of the SMRT repressor complex for non-hormonal male contraception.

Intracellular ion accumulation in the genesis of complex action potential dynamics under cardiac diseases.

Intracellular ions, including sodium (Na^{+}), calcium (Ca^{2+}), and potassium (K^{+}), etc., accumulate slowly after a change of the state of the heart, such as a change of the heart rate. The goal of this study is to understand the roles of slow ion accumulation in the genesis of cardiac memory and complex action-potential duration (APD) dynamics that can lead to lethal cardiac arrhythmias. We carry out numerical simulations of a detailed action potential model of ventricular myocytes under normal and diseased conditions, which exhibit memory effects and complex APD dynamics. We develop a low-dimensional iterated map (IM) model to describe the dynamics of Na^{+}, Ca^{2+}, and APD and use it to uncover the underlying dynamical mechanisms. The development of the IM model is informed by simulation results under the normal condition. We then use the IM model to perform linear stability analyses and computer simulations to investigate the bifurcations and complex APD dynamics, which depend on the feedback loops between APD and intracellular Ca^{2+} and Na^{+} concentrations and the steepness of the APD response to the ion concentrations. When the feedback between APD and Ca^{2+} concentration is positive, a Hopf bifurcation leading to periodic oscillatory behavior occurs as the steepness of the APD response to the ion concentrations increases. The negative feedback loop between APD and Na^{+} concentration is required for the Hopf bifurcation. When the feedback between APD and Ca^{2+} concentration is negative, period-doubling bifurcations leading to high periodicity and chaos occurs. In this case, Na^{+} accumulation plays little role in the dynamics. Finally, we carry out simulations of the detailed action potential model under two diseased conditions, which exhibit steep APD responses to ion concentrations. Under both conditions, Hopf bifurcations leading to slow oscillations or period-doubling bifurcations leading to high periodicity and chaotic APD dynamics occur, depending on the strength of the ion pump-Na^{+}-Ca^{2+} exchanger. Using functions reconstructed from the simulation data, the IM model accurately captures the bifurcations and dynamics under the two diseased conditions. In conclusion, besides using computer simulations of a detailed high-dimensional action-potential model to investigate the effects of slow ion accumulation and short-term memory on bifurcations and genesis of complex APD dynamics in cardiac myocytes under diseased conditions, this study also provides a low-dimensional mathematical tool, i.e., the IM model, to allow stability analyses for uncovering the underlying mechanisms.

An efficient and robust gradient reinforcement learning: Deep comparative policy

Recently, actor-critic architectures such as deep deterministic policy gradient (DDPG) are able to understand higher-level concepts for searching rich reward, and generate complex actions in continuous action space, and widely used in practical applications. However, when action space is limited and has dynamic hard margins, training DDPG can be problematic and inefficiency. Since real-world actuators always have margins and interferences, after initialization, the actor network is likely to be stuck at a local optimal point on action space margin: actor gradient orients to the outside of action space but actuators stop at the margin. If the hard margins are complex, dynamic and unknown to the DDPG agent, it is unable to use penalty functions to recover from local optimum. If we enlarge the random process for local exploration, the training could be in potential risk of failure. Therefore, simply relying on gradient of critic network to train the actor network is not a robust method in real environment. To solve this problem, in this paper we modify DDPG to deep comparative policy (DCP). Rather than leveraging critic-to-actor gradient, the core training process of DCP is regulated by a T-fold compare among random proposed adjacent actions. The performance of DDPG, DCP and related algorithms are tested and compared in two experiments. Our results show that, DCP is effective, efficient and qualified to perform all tasks that DDPG can perform. More importantly, DCP is less likely to be influenced by the action space margins, DCP can provide more safety in avoiding training failure and local optimum, and gain more robustness in applications with dynamic hard margins in the action space. Another advantage is that, complex penalty for margin touching detection is not required, the reward function can always be brief and short.

QCD phase diagram and equation of state in background electric fields

The phase diagram and the equation of state of quantum chromodynamics (QCD) is investigated in the presence of weak background electric fields by means of continuum extrapolated lattice simulations. The complex action problem at nonzero electric field is circumvented by a novel Taylor expansion, enabling the determination of the linear response of the thermal QCD medium to constant electric fields—in contrast to simulations at imaginary electric fields, which, as we demonstrate, involve an infrared singularity. Besides the electric susceptibility of QCD matter, we determine the dependence of the Polyakov loop on the field strength to leading order. Our results indicate a plasma-type behavior with a negative susceptibility at all temperatures, as well as an increase in the transition temperature as the electric field grows. Published by the American Physical Society 2024

Efficacy and Mechanism of the Action of Zinc Complex Nitrilo-tris-Methylenephosphonic Acid As an Inhibitor of Corrosion in Low-Carbon Steel in Neutral Bromide- and Iodide-Containing Aqueous Media

Efficacy and Mechanism of the Action of Zinc Complex Nitrilo-tris-Methylenephosphonic Acid As an Inhibitor of Corrosion in Low-Carbon Steel in Neutral Bromide- and Iodide-Containing Aqueous Media

Path planning for robots in preform weaving based on learning from demonstration

AbstractA collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.

Adaptive hybrid reasoning for agent-based digital twins of distributed multi-robot systems

The digital twin (DT) mainly acts as a virtual exemplification of a real-world entity, system, or process via multiphysical and logical models, allowing the capture and synchronization of its functions and attributes. The bridge between the actual system and the digital realm can be utilized to optimize the system’s performance, and forecast and predict its behavior. Incorporating intelligent and adaptive reasoning mechanisms into DTs is crucial to enable them to reason, adapt, and take efficacious actions in complex and dynamic environments. To this end, we introduce an approach for deploying agent-based DTs for cyber-physical systems. The foundation pillars of this approach are (1) integrating the concepts, entities, and relations of Zeigler’s modeling and simulation framework from the perspective of agent-based DTs; (2) utilizing an expandable and scalable architecture for designing and materializing these twins, which handily enables extending and scaling physical and digital assets of the system; and finally (3) a two-tier reasoning strategy; reactive and rational models are conceptually redefined in the context of the modeling and simulation framework of agent-based DTs and technically deployed to boost the adaptive reasoning and decision-making function of DTs. As a result, an implemented simulation and control platform for a multi-robot system demonstrates the approach’s applicability and feasibility, manifesting its usability and efficiency. The platform represents physical entities as autonomous agents, creates their DTs, and assigns adequate reasoning capability to promote adaptive planning, autonomous resource management, and flexible logical decision-making to handle different situations and scenarios.

Neural perspectives on morality due to beguiling mechanisms.

We consider deception an example of behavior that challenges traditional explanations of moral behavior. Beguiling mechanisms, by which we mean deceptiveness with charming seduction for diversion, subtly influence moral sensitivity and judgment in moral dilemma situations. The duality of beguiling mechanisms is important to grasp, including how they relate to the ambiguity of situations. Further, we view moral behavior as quasi-adaptive, affectively based, and reliant on the processes of social cognition, arising out of a set of domain-general primitive predispositions that aggregate to produce moral "mindsets" and increasingly complex moral actions. Building on recent theoretical developments, contend that morality involves a complex heterarchical-hierarchical neurological architecture, where activity is dynamically and contextually dependent, as well as dependent on evolved brain structures and early life year socialization. We contribute to conceptualizing moral behavior from an integrated modern neural perspective. This provides a balance between moral decisions as situational, emotional, and genetically completed non-conscious processes, and the more traditional view of conscious reasoning. Beguiling mechanisms illustrate an integrative model of morality, consistent with emerging insights from affective and cognitive neuroscience.

Learning interactions with socio-technical systems : Ageragogical perspective on the importance of interactions and relationships in learning processes with older people

The approach of socio-technical systems (STS) for asuccessful life in old age focuses on the combination of technical and sociocultural factors in one. Technological systems have long since ceased to be limited to simple functions; instead, they enable customizable applications, interactive use combined with complex communication and action strands. The associated interactions with the technical system suggest aperspective on interactions and relationships. Research results show that individualizable functions of STSs are rated positively in terms of acceptance and use by older people. From ageragogical perspective, questions arise regarding the appropriation of older people and the design of learning processes (socio-technical learning arrangements). Arelational view of learning and educational processes relating to the appropriation and acquisition of competences for designing and using STS in real-world situations is developed based on aformative qualitative research design of the "RUBYDemenz" project. In particular, data/results from interviews with voluntary learning facilitators, professional practice facilitators, focus groups and research workshops are utilized. Empirical findings can be used to develop starting points for educational and learning processes to expand competences in the context of technology use. Approaches to education, training and further training follow on from this as does the question of whether the research/development process itself must be designed as alearning process in which all participants are involved as learners, with the aim of achieving the best possible fit between the technical solutions and the intended areas of application in real-world situations.

DVF:Multi-agent Q-learning with difference value factorization

In cooperative multi-agent games, agents are required to learn effective cooperative behaviors within complex action spaces. An effective approach is to utilize the Individual-Global-Max (IGM) principle to decompose the global reward signal into individual contributions from each agent. However, existing methods confront two significant challenges: ensuring the accuracy of the value function decomposition process and guaranteeing monotonic improvement in joint policies. These challenges become exacerbated in scenarios involving non-monotonic matrices. To address these challenges, we introduce a novel and flexible value factorization method called Difference Value Factorization (DVF). The key idea of our method is to transform the IGM principle into a new form DVF-IGM, which addresses non-monotonic constraints by ensuring the consistency of the IGM process between the joint difference value and the complex non-linear sum of independent difference values. A centralized evaluator is employed to estimate global Q-values, which not only enhances expressiveness but also constructs difference values for updating individual value functions. We demonstrate that DVF-IGM is an equivalent transformation of IGM and that DVF has the monotonic improvement property. Empirically, our method has been shown to maintain and recover the optimal policy in non-monotonic matrix games and achieve state-of-the-art performance in cooperative tasks within the StarCraft Multi-Agent Challenge (SMAC).

Attributing digital covert action: the curious case of WikiSaudiLeaks

ABSTRACT How can digital covert action be attributed? This paper revisits one of the most complex, most significant, and most mysterious digital covert actions of our time: a 2015 hack-and-leak case known among investigators as ‘WikiSaudiLeaks’ that so far has evaded attribution. We argue that WikiSaudiLeaks was not a stand-alone event, but a puzzle piece in a larger covert action campaign that involved advanced computer network exploitation, computer network attack, persistent deception, and a creative influence and disinformation effort. By disintegrating the larger event into its components, limited attribution becomes possible. We present the most detailed and comprehensive investigation of this case to date, attribute at least one component of the larger event to Iranian intelligence, and draw conceptional conclusions.