Limited Capacity Research Articles

Презумпция необходимой зрелости сознательно-волевой сферы физического лица

The article examines the types of legal presumptions, provides an analytical review of the literature, as well as the opinions of civil scientists on the topic under study. Based on the theoretical analysis, a definition of the concept of civil capacity of an individual as a presumption of the necessary maturity of the conscious-volitional sphere of an individual has been developed. It is stated that, in the presence of legal facts indicating a negative impact on the conscious-volitional sphere of an individual, such a presumption can be refuted. It is concluded that the refutation of the presumption of the necessary maturity of the conscious-volitional sphere of an individual is the restriction of the civil capacity of an individual, and the legal facts are the statutory grounds for limiting the capacity. The given author's definition allows us to clarify the legal essence and features of the legal capacity of an individual and its limitations, which have theoretical and practical significance in legal science. Keywords: presumption, refutation of presumption, civil law, family law, objective law, legal personality, legal capacity, legal capacity, limitation of legal capacity, legal capacity, legal capacity to commit a delict, social maturity, conscious-volitional sphere, reasonableness, conscientious behavior, legal fact, individual, guardian, trustee.

Coordination of NPCs in multi-agent systems based on behavior trees

Abstract. This paper addresses the design and coordination challenges of controlling non-player characters' (NPCs) behaviors in multi-agent systems (MAS) using behavior trees (BTs), which are preferred over finite state machines (FSMs) due to their hierarchical structure and ease of maintenance. While BTs effectively resolve the question of "What to do next?" for individual NPCs, their application in MAS, particularly with the integration of a blackboard system for central control, reveals limitations in efficiency, robustness, and heuristic capacity as system complexity increases. To explore solutions to these challenges, this study analyzes various algorithms that enhance the functionality of behavior trees within MAS. The research focuses on three primary areas: the optimization of behavior trees, the development of behavior tree search algorithms, and the improvement of communication algorithms within BTs. Methods involve a comparative analysis of existing and new algorithmic approaches to identify and address inefficiencies in NPC coordination. The findings indicate that advanced behavior tree configurations, when combined with innovative search and communication strategies, significantly improve the coordination, robustness, and operational efficiency of MAS. These enhancements allow for more dynamic and responsive NPC interactions in complex gaming environments.

Maize-Straw Biochar Enhances Soil Properties and Grain Yield of Foxtail Millet in a Newly Reclaimed Land

Large-scale land reclamation has become common in northwestern China; however, low soil fertility and poor soil water-holding capacity limit agricultural production on these reclaimed lands, requiring increased fertilizer and irrigation inputs. Biochar, produced from agricultural waste, has shown potential in improving soil quality and water-holding capacity. In this two-year field study (2021 and 2022), we investigated the effects of biochar produced from maize straw on soil properties and grain yield of foxtail millet grown on newly reclaimed land. Three biochar treatments (3000, 4500, and 6000 kg ha−1) were compared to a control (CK) with no biochar application. Biochar application resulted in increased soil organic matter, total phosphorus, total nitrogen, soil enzyme activity, and soil organic acid content. It also significantly decreased soil pH and bulk density. Compared with the CK, biochar increased available nitrogen from 29.7% to 108% in 2021 and 37.0% to 88.4% in 2022. Similarly, biochar increased available phosphorus from 64.7% to 143% in 2021 and 41.9% to 96.5% in 2022. Grain yields ranged from 3092 to 4753 kg ha−1. Biochar treatments increased grain yield compared to the CK, ranging from 12.2% to 24.6% in 2021 and 27.1% to 53.7% in 2022. Correlation analysis revealed that soil pH was negatively related to soil oxalic acid content, phosphorus content, and sucrase activity. Available nitrogen and phosphorus contents were negatively related to soil bulk density and positively related to catalase activity. Soil water content was negatively correlated with soil bulk density and positively correlated with organic matter. In conclusion, biochar improved the rhizosphere soil pH and the effectiveness of soil fertility in the newly reclaimed soil, resulting in an enhanced grain yield of foxtail millet.

Dynamic Behaviors of Composite-Laminated Concentrically Stiffened Rectangular Plates by Substructure Modal Synthesis

Existing numerical analysis methods often incur high computational costs when directly solving multi-degree-of-freedom systems due to computer capacity limitations. To address this issue, this paper took the composite laminated concentrically stiffened rectangular plate as the research object. The modal synthesis method was employed to reduce the degrees of freedom of the model, enabling the solution of complex dynamic characteristics. In the research process, the domain decomposition theory was used to divide the research object into plate and rib substructures. Based on the spectral Chebyshev method, the substructure dynamic model of the composite laminated rectangular plate was constructed. Subsequently, the function expressions of the complex load excitation on each substructure were derived, and the fixed modal synthesis method was introduced to construct the spectral Chebyshev reduced model of the substructures. According to the artificial spring theory equivalent, the interface force, and the whole reduction model of the composite laminated concentrically stiffened rectangular plate were obtained by assembling the substructure models. The time–domain and frequency–domain dynamic behaviors of composite laminated concentrically stiffened rectangular plates are yielded by solving the whole reduced model. The novelty of this work lies in combining the spectral Chebyshev method with the modal synthesis method. The established reduced model no longer depended on mesh quality and required fewer matrix dimensions and computation time compared to the whole model, while still achieving sufficiently accurate results. Additionally, based on the reduced model, an effective parametric study scheme was proposed to analyze the impact of dimensions and material properties of rib substructures on the dynamic behaviors of the composite laminated concentrically stiffened rectangular plate. These findings can be used to guide the optimal design of laminated concentrically stiffened rectangular plates in engineering practice.

Engaging primary care patients at risk for suicide in mental health treatment: user insights to inform implementation strategy design

BackgroundGiven that the majority of suicide decedents visit primary care in the year preceding death, primary care has been identified as a key setting in which to engage patients at risk for suicide in mental health services. The objective of this research was to identify barriers and facilitators to engagement in mental health services among primary care patients at risk for suicide to inform the development of strategies to increase engagement.MethodsSeventy-four semi-structured qualitative interviews were conducted with primary care patients (n = 20), primary care (n = 18) and behavioral health (n = 12) clinicians, mental health intake coordinators (n = 4), and health system and clinic leaders (n = 20). Patients who had been referred for mental health services from primary care and reported an elevated score (≥ 1) on item 9 on the Patient Health Questionnaire at the time of referral were eligible to participate. Eligible clinicians and leaders were employed in a primary care or behavioral health setting in a single large health system with an integrated mental health program. Interviews typically lasted 30–60 min, were completed over video conference or phone, and were coded by members of the research team using a rapid qualitative analysis procedure.ResultsParticipants were primarily female (64.9%), white (70.3%) and non-Hispanic/Latine (91.9%). The most identified barriers to mental health care engagement were waitlists, capacity limits, insurance, patient characteristics, communication, collaboration, and/or difficulties surrounding travel. The most commonly cited facilitators of engagement included telehealth, integrated care models, reminders, case management support, psychoeducation, motivational enhancement, and scheduling flexibility. Concrete suggestions for improving engagement in mental health services included increasing communication between providers, streamlining referral and intake processes, providing reminders and follow ups, and advocacy for increased reimbursement for suicide risk assessment.ConclusionsResults underscore the myriad barriers patients at risk for suicide encounter when attempting to engage in mental health care in a primary care setting. Facilitators of engagement and suggestions for improving connections to care were also identified, which can inform the design of implementation strategies to improve engagement in mental health services among primary care patients at risk for suicide.Trial registrationClinicalTrials.gov Identifier: NCT05021224 (Registered August 19, 2021).

Optical networking that exploits massive wavelength/spectrum and spatial parallelisms

As DWDM transmission offers enhanced wavelength/spectrum parallelism, the capacity of optical networks has been substantially increased. Due to the theoretical capacity limit of C-band transmission over single-mode fibers, research into new frequency bands and parallel fibers has become very active. However, the hardware scale of current optical cross-connect nodes will explode with greater wavelength/spectrum and spatial parallelism. Three optical node/network architectures are presented in this paper that take advantage of one or both of these parallelism technologies. These architectures will provide a baseline for cost-effective and bandwidth-abundant future optical networks based on massive parallelism.

A phase transition coordinated monoclinic Fe-based Prussian blue for high-performance sodium-ion battery cathode

Prussian blue analogs (PBAs) are ideal cathode materials for sodium-ion batteries (SIBs) owing to their cost-effectiveness, good low-temperature performance, and facile synthesis process. Nevertheless, their actual capacity and cycling stability limit practical application. Herein, a new chelating agent, sodium tartrate, is proposed to synthesize high-quality Fe-based Prussian blue (HQ-NFF) with enhanced Na content and a stable structure through chelation effect-assisted co-precipitation. The crystal structure of HQ-NFF undergoes a phase transition to a Na-rich monoclinic phase after full discharge, leading to improved capacity and cycling stability. The HQ-NFF cathode exhibits an ultra-high capacity of 160.8 mAh g−1 and a capacity of 146.1 mAh g−1 at 2 C, with a capacity retention of 75.3 % after 150 cycles. Moreover, a 3D-printed HQ-NFF cathode with an areal loading of 7.88 mg cm−2, delivers a high areal capacity of 0.969 mAh cm−2 and good rate capability.

In situ thermal-assisted photocatalytic decarboxylation of high-concentration biomass-derived fatty acids to alkanes

Heating is a straightforward method to increase reaction rates to meet industrial production, yet photocatalysis seldom incorporates it because their intrinsic driving force depends on the separation of photogenerated carriers, which is rarely affected by temperature. Here, we report a temperature-dependent photocatalytic decarboxylation strategy for producing long-chain n-alkanes from biomass-derived fatty acids using only the photothermal effect of the SnS catalyst. Under concentrated light irradiation without any external heating, and by employing high boiling-point solvents to reach optimal reaction temperatures (∼254 °C), Cn-1 n-alkane allows a very high concentration output (∼0.5 M for stearic acid to n-heptadecane) in a single operation, which is far beyond the capacity limit of conventional photocatalysis, typically no more than the mM levels. Mechanistic studies suggest that the in-situ heat brings the standing C-chain at low temperature down onto the SnS surface, increasing strain on the C-COO- bonds and facilitating reaction with photo-induced hole-electron pairs. We demonstrate that the majority of the incident light energy, when converted to heat, accelerates the photocatalytic decarboxylation.

UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.

Decision trees for determining the fate of laboratory animals.

Facilities involved in laboratory animal research often face ethical challenges such as: what should I do with the animals that are no longer suitable for experimental purposes? One of the common answers to this question is to kill them. And while numerous scientifically justifiable reasons exist for killing laboratory animals, we must not overlook our ethical responsibility towards these sentient beings. Animal facility managers and scientists frequently find themselves in a moral dilemma, torn between furthering their research and addressing the well-being of experimental animals required for their studies. We elaborated a concept consisting of six decision trees and recommendations for making informed decisions about the need to kill laboratory animals in research facilities, considering legal and ethical considerations. The concept is based on the German regulatory perspective. However, the measures and decisions for animal welfare can be implemented in all laboratory animal facilities. These recommendations suggest several courses of action, including implementing consistent breeding plans, exploring alternative uses, reassigning surplus animals and their organs, and establishing appropriate housing capacity limits that ensure species-appropriate care. We encourage scientists and animal facility managers to develop and implement decision-making frameworks and procedures tailored to their specific facilities, in the hope that this work will promote a thoughtful and responsible approach to the complex challenges associated with the killing of laboratory animals, advancing scientific progress and the humane treatment of these animals.

Optimizing and investigating the charging time of phase change materials in a compact-latent heat storage using pareto front analysis, artificial neural networks, and numerical simulations

There's a growing emphasis on adopting eco-friendly energy sources to mitigate greenhouse gas emissions and foster sustainability. While renewable energy sources like solar power offer numerous benefits, they have some limitations. Additionally, storing electricity generated from solar panels can be costly and challenging due to limitations in battery technology and storage capacity. Therefore, phase change material-based thermal energy storage systems offer a promising solution to these challenges. These systems also play a crucial role in enhancing buildings' energy efficiency and sustainability. The dimensionality of these systems affects their performance. Traditional systems often rely on fixed dimensions, leading to non-uniform thermal conditions within the storage medium. To address these challenges, adopting a compact-latent heat storage (C-LHS) mechanism was recommended herein. Besides, some fins were inserted into the C-LHS system to alter the heat transfer dynamics within the device. Three of the critical parameters of the fins were changed to examine their impact on the charging time. Artificial neural networks and genetic algorithms were employed to determine the optimal positioning of fins to minimize the duration of material to get both part (melt fraction of 0.8) and full (melt fraction of 1) melting capacities. Here, the primary aim was to present a predictive model capable of accurately forecasting the charging time of the material. In all the presented finned samples, the entire PCM melted in less than 15,110 s (4 h and 12 min) and was able to fully utilize the energy absorption capacity in latent form, whereas in the non-finned system, only 68.6 % of the material managed to melt within the entire duration of the investigation (5 h). After analyzing the data, two optimal configurations of OS1 and OS2 with the minimum time for melt fractions of 0.8 and 1 were introduced. In the OS2 configuration, it took 16,200 s (4 h and 30 min) to absorb 4929 kJ of energy. The non-finned sample absorbed only 3250 kJ of energy during the same period. In General, OS2 achieved total energy absorption 51.6 % faster than the non-finned sample. Therefore, the introduced system has the potential to increase absorbed energy in the rest of the daylight hours to further amounts by increasing the number of units and employing a larger volume of material.

Energy storage configuration method for distribution networks based on moment difference analysis

The integration of a high proportion of distributed PV into distribution networks can cause power backfeeding and voltage limit violations. This paper introduces moment difference analysis theory for distribution networks, reframing PV consumption as the balancing moment difference equations. When maximum node voltage approaches the upper limit, the moment difference between PV and load stabilizes to an approximately constant, termed the standard moment difference. The standard moment difference represents the limit of the network's capacity to consume distributed PV. Essentially, the PV moment is the target for integration, while the load moment serves as the resource to consume the PV moment. Based on this theory, a method for energy storage configuration is proposed. Simplifying a complex multi-branch distribution network into single-branch lines and solving linear equations determines the optimal storage configuration. This method was tested on the Jibei Power Grid and the IEEE 33 and 69 bus systems, increasing computational efficiency by 1611.47–4973.82 times compared to the PSO algorithm, while maintaining an error margin of less than 2.6 %. Furthermore, the discharging strategy reduces the ESS capacity to between 12.39 % and 31.69 % of the original estimate, thereby enhancing both the economic and practical appeal of the approach.

An exact branch-and-price-and-cut algorithm for a practical and large-scale dial-a-ride problem

Vehicle routing problems are encountered in various practical transportation scenarios. One such problem is the dial-a-ride problem (DARP), which involves transporting efficiently passengers from their origin to their destination and whose objective is to design vehicle routes minimizing total costs and accommodating all pickup-and-delivery requests. The solution must also adhere to capacity limitations, time windows, and a maximum ride time for each passenger. This paper proposes an exact branch-price-and-cut algorithm to solve a practical variant of the DARP, where both the passengers and vehicle fleet are heterogeneous, and other practical constraints such as break requirements and maximum route duration are imposed. In this algorithm, column generation which alternates between solving a master problem and subproblems, is used to compute lower bounds in the search tree. We develop a labeling algorithm to efficiently handle these subproblems. The effectiveness of the algorithm is evaluated on a real-world case study involving 849 heterogeneous passengers and more than 70 available vehicles, as well as on 10 smaller instances (with 300 to 820 trips) extracted from another very large real-world dataset. To the best of our knowledge, this 849-trip instance is the largest DARP instance reported to be solved to optimality in the literature.

Electrophysiological Correlates of Visual Memory Search.

In everyday life, we frequently engage in 'hybrid' visual and memory search, where we look for multiple items stored in memory (e.g., a mental shopping list) in our visual environment. Across three experiments, we used event-related potentials to better understand the contributions of visual working memory (VWM) and long-term memory (LTM) during the memory search component of hybrid search. Experiments 1 and 2 demonstrated that the FN400 - an index of LTM recognition - and the CDA -an index of VWM load - increased with memory set size (target load), suggesting that both VWM and LTM are involved in memory search, even when target load exceeds capacity limitations of VWM. In Experiment 3, we used these electrophysiological indices to test how categorical similarity of targets and distractors affects memory search. The CDA and FN400 were modulated by memory set size only if items resembled targets. This suggests that dissimilar distractor items can be rejected before eliciting a memory search. Together, our findings demonstrate the interplay of VWM and LTM processes during memory search for multiple targets.

Multifunctional 1–octadecanol/expanded graphite/Fe3O4 composite phase change materials with effective solar–to–thermal and magnetic–to–thermal conversion and storage

Composite phase change materials (CPCMs) with multifunctional thermal conversion and storage abilities have demonstrated exceptional properties for diverse energy storage applications recently; however, these composite materials often encounter limitations of low heat storage capacity and high cost. In this study, novel 1–octadecanol (OD)/expanded graphite (EG)/Fe3O4 CPCMs were facilely prepared and characterized for multifunctional thermal conversion and storage properties. The composites showed excellent thermal energy storage capacities, reaching 185.5 J/g at 80 % OD accompanied by high crystallization fractions of above 95 %. In addition, they exhibited great thermal conductivities, excellent leakage resistance, thermal stability, and reliability. Under simulated solar irradiation, the prepared OD/EG/Fe3O4 CPCMs exhibited a rapid temperature increase from 34 to 70 °C, achieving solar–to–thermal conversion efficiency of 80.2–90.5 %. This behavior can be attributed to its ability to capture and convert photons into thermal energy, facilitated by the synergistic effects of the conjugated double–bond system of EG and localized surface plasmon resonance of Fe3O4 nanoparticles. Furthermore, the prepared OD/EG/Fe3O4 CPCMs exhibited superparamagnetic properties, accelerating their temperatures from 32 °C to 88 °C within 11–23 s when subjected to an alternating magnetic field. This demonstrates magnetic–to–thermal conversion and storage ability. These findings highlight the potential of OD/EG/Fe3O4 CPCMs as promising and cost–effective materials for various practical thermal storage applications.

Advancements and mechanisms of stem cell-based therapies for spinal cord injury in animals.

Spinal cord injury (SCI) is a neurodegenerative disorder of the central nervous system (CNS) that can lead to permanent loss of sensation and voluntary movement beyond the affected area. Extensive preclinical and clinical trials have been conducted to evaluate the safety and effectiveness of stem cells for the treatment of various CNS diseases or disorders, including SCI. However, several challenges hinder nerve cell regeneration in the injured spinal cord, such as extensive cell loss, limited neural cell regeneration capacity, axonal disruption, and the presence of growth-inhibiting molecules, particularly astroglial scarring or glial scars at the injury site in chronic cases. These obstacles pose significant challenges for physicians in restoring normal motor and sensory nerve function in both humans and animals following SCI. This review focuses on SCI pathogenesis, the mechanisms underlying the therapeutic potential of Mesenchymal Stem Cells (MSCs) in SCI, and the potential of stem cell-based therapies as promising avenues for treatment. This review article also included relevant preclinical and clinical data from animal studies.

Investigating the Future of Freight Transport Low Carbon Technologies Market Acceptance across Different Regions

Fighting climate change has become a major task worldwide. One of the key energy sectors to emit greenhouse gases is transportation. Therefore, long term strategies all over the world have been set up to reduce on-road combustion emissions. In this context, the road freight sector faces significant challenges in decarbonization, driven by its limited availability of low-emission fuels and commercialized zero-emission vehicles compared with its high energy demand. In this work, we develop the Mobility and Energy Transportation Analysis (META) Model, a python-based optimization model to quantify the impact of transportation projected policies on freight transport by projecting conventional and alternative fuel technologies market acceptance as well as greenhouse gas (GHG) emissions. Along with introducing e-fuels as an alternative refueling option for conventional vehicles, META investigates the market opportunities of Mobile Carbon Capture (MCC) until 2050. To accurately assess this technology, a techno-economic analysis is essential to compare MCC abatement cost to alternative decarbonization technologies such as electric trucks. The novelty of this work comes from the detailed cost categories taken into consideration in the analysis, including intangible costs associated with heavy-duty technologies, such as recharging/refueling time, cargo capacity limitations, and consumer acceptance towards emerging technologies across different regions. Based on the study results, the competitive total cost of ownership (TCO) and marginal abatement cost (MAC) values of MCC make it an economically promising alternative option to decarbonize the freight transport sector. Both in the KSA and EU, MCC options could reach greater than 50% market shares of all ICE vehicle sales, equivalent to a combined 35% of all new sales shares by 2035.

A Simulation-Based Optimization Evaluation of Operating Room in Healthcare under Limitation Capacity: A Multi-objective Approaches

A Simulation-Based Optimization Evaluation of Operating Room in Healthcare under Limitation Capacity: A Multi-objective Approaches

Assessment of electrochemical removal mechanisms of diverse transition metal dichalcogenides for caesium ion removal in wastewater treatment

Abstract Effective treatment of radioactive wastewater is crucial for broader nuclear energy adoption, with caesium radionuclides (most exist in the form of caesium chloride) presenting challenges due to their long half-life and biological hazards. Conventional adsorbents like zeolites and carbon-based materials, including graphene, face limitations in adsorption capacity due to the formation of electric double layers (EDL). This has led to the investigation of alternatives such as transition metal dichalcogenides (TMDs) e.g., MoS2, MoSe2, and WSe2, which offer promising galleries for caesium ion removal. Aside from extensively studied MoS2, there is limited research on the adsorption mechanisms and capacities of other TMDs like MoSe2 and WSe2. Here, we conduct a comparative study examining the removal mechanisms and capacities of exfoliated MoS2, MoSe2, and WSe2 nanosheets, alongside an evaluation of these properties in relation to graphene. Our investigation reveals distinct removal mechanisms and capacities among these three materials for capturing caesium ions in a variety of mechanisms. MoS2 nanosheets primarily utilise a pseudocapacitive charge storage mechanism via intercalation, as evidenced by a total charge storage of 0.78 C g1, with only 2.6% stored via EDL formation. In contrast, MoSe2 predominantly relies on EDL formation, with almost 60% of the total 0.54 C g1 charge storage attributed to this mechanism. Lastly, WSe2 exhibits a combination of both charge storage behaviours, with a total charge storage of 0.77 C g1, of which 14% is due to EDL formation. This research highlights the potential efficacy of TMDs as viable materials for caesium removal, offering an appealing alternative to conventional adsorbents and likely fostering advancements in water treatment technologies.

Joint Power Allocation and Hybrid Beamforming for Cell-Free mmWave Multiple-Input Multiple-Output with Statistical Channel State Information.

Cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) can effectively overcome the shadow fading effect and provide macro gain to boost the throughput of communication networks. Nevertheless, the majority of the existing studies have overlooked the user-centric characteristics and practical fronthaul capacity limitations. To solve these practical problems, we introduce a resource allocation scheme using statistical channel state information (CSI) for uplink user-centric cell-free mmWave MIMO system. The hybrid beamforming (HBF) architecture is deployed at each access point (AP), while the central processing unit (CPU) only combines the received signals by the large-scale fading decoding (LSFD) method. We further frame the issue of maximizing sum-rate subject to the fronthaul capacity constraint and minimum rate constraint. Based on the alternating optimization (AO) and fractional programming method, we present an algorithm aimed at optimizing the users' transmit power for the power allocation (PA) subproblem. Then, an algorithm relying on the majorization-minimization (MM) method is given for the HBF subproblem, which jointly optimizes the HBF and the LSFD coefficients.