Multiple Demand Research Articles

Bridging Experimental Neuroscience and Clinical Neuropsychology: Fluid Intelligence in Frontal Lobe Assessments

This viewpoint explores the gap between theoretical frameworks in experimental neuroscience and clinical neuropsychology. It highlights how John Duncan's theory of the Multiple Demand (MD) system, which links the frontal lobe to fluid intelligence (g), helps explain general performance on classical executive tests. However, it also discusses how traditional scores often fail to capture the complexity of behaviours associated with frontal lobe damage, and we suggest that developing improved scoring methods could be useful for integrating experimental and clinical neuropsychology insights.

Delivery optimization for collaborative truck–drone routing problem considering vehicle obstacle avoidance

Drone participating in logistics delivery has gained much concern due to its potential superiority of operational efficiency and service costs. How to optimize the hybrid vehicle delivery trip is still a crucial issue. Besides, complex urban environment and regulatory no-through zones bring more rigorous challenges. The aim is to develop an efficient routing solution that considers obstacles encountered by the truck and drone, ensuring the timely delivery. This paper proposes an efficient deep reinforcement learning to optimize the collaborative truck–drone routing problem (C-TDRP) under vehicle obstacle avoidance trail. Specifically, the trucks deploy a fleet of drones to serve the random distributed consumers subject to minimum delivery cost. Homogeneous drones serve multiple demand nodes per trip with limited energy carrying capacity. We design the C-TDRP formulation with studying the feasibility of obstacle avoidance routing. Owing to the computational complexity of the proposed mathematical model, an intelligent optimization algorithm based on deep reinforcement learning is developed, named pointer network (Ptr-Net), which can capture the position weights associated with network sequence automatically. Through rigorous experimental validations, the results demonstrate that the vehicle energy consumption of final trajectory reduces by more than 42%, even for large-scale delivery scenarios. Our proposed method not only enhances the optimization performance but also lays the foundation for more intelligent and adaptable logistics delivery in complex urban environments.

The language network ages well: Preserved selectivity, lateralization, and within-network functional synchronization in older brains.

Healthy aging is associated with structural and functional brain changes. However, cognitive abilities differ from one another in how they change with age: whereas executive functions, like working memory, show age-related decline, aspects of linguistic processing remain relatively preserved (Hartshorne et al., 2015). This heterogeneity of the cognitive-behavioral landscape in aging predicts differences among brain networks in whether and how they should change with age. To evaluate this prediction, we used individual-subject fMRI analyses ('precision fMRI') to examine the language-selective network (Fedorenko et al., 2024) and the Multiple Demand (MD) network, which supports executive functions (Duncan et al., 2020), in older adults (n=77) relative to young controls (n=470). In line with past claims, relative to young adults, the MD network of older adults shows weaker and less spatially extensive activations during an executive function task and reduced within-network functional synchronization. However, in stark contrast to the MD network, we find remarkable preservation of the language network in older adults. Their language network responds to language as strongly and selectively as in younger adults, and is similarly lateralized and internally synchronized. In other words, the language network of older adults looks indistinguishable from that of younger adults. Our findings align with behavioral preservation of language skills in aging and suggest that some networks remain young-like, at least on standard measures of function and connectivity.

A Bayesian learning and pricing model with multiple unknown demand parameters

A Bayesian learning and pricing model with multiple unknown demand parameters

Partially embedded metabarrier to suppress surface waves in granular mediaa).

The gravity-induced depth-dependent elastic properties of a granular half-space result in multiple dispersive surface modes and demand the consideration of material heterogeneity in metabarrier designs to suppress surface waves. Numerous locally resonant metabarrier configurations have been proposed in the literature to suppress Rayleigh surface waves in homogeneous media, with little focus on extending the designs to a heterogeneous half-space. In this work, a metabarrier comprising partially embedded rod-like resonators to suppress the fundamental dispersive surface wave modes in heterogeneous granular media known as first order PSV (PSV1; where P is the longitudinal mode and SV is the shear-vertical mode) and second order PSV (PSV2) is proposed. The unit-cell dispersion analysis, together with an extensive frequency-domain finite element analysis, reveals preferential hybridization of the PSV1 and PSV2 modes with the longitudinal and flexural resonances of the resonators, respectively. The presence of the cutoff frequency for the longitudinal-resonance hybridized mode facilitates straightforward suppression of the PSV1 mode, while PSV2 mode suppression is possible by tailoring the hybridized flexural resonance modes. These PSV1 and PSV2 bandgaps are realized experimentally in a granular testbed comprising glass beads by embedding 3D-printed resonator rods. Also explored are novel graded metabarriers capable of suppressing both PSV1 and PSV2 modes over a broad frequency range for potential applications in vibration control and seismic isolation.

Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses

Accurately predicting electricity load is crucial for maintaining the stability of modern power systems. Most studies focus on deterministic predictions, while uncertainty prediction and demand responses (DR) need further research. This paper proposes Bilinear-DRTFT, a load forecasting model that supports uncertainty and considers DR. To comprehensively evaluate model performance, this study combines five deterministic evaluation metrics and three uncertainty evaluation metrics on the test set. The research findings include:1. Incorporating DR effectively enhances the model's performance in both deterministic and uncertainty prediction; 2. Model performance continually improves with refined DR, achieving better uncertainty reduction when incorporating dynamic DR signals considering critical peak electricity prices, reaching expected performance at 80 % and 90 % confidence levels with narrow PINAW; 3. After adding DR, high-quality feature engineering provides the basis for the simplification of feedforward network; 4. Analyzing monthly uncertainty predictions reveals specific time periods on which DR policies should focus. These conclusions are validated in two experimental regions.

Optimal configuration of hydrogen storage capacity of hybrid microgrid considering peak regulation and frequency modulation requirements

This study proposes an innovative hydrogen storage capacity optimization configuration method that considers multiple demand factors, addressing the issue that traditional methods for optimizing hydrogen storage capacity in hybrid microgrids cannot simultaneously balance economic efficiency and grid connection quality. This method breaks through the traditional optimization framework and adopts a double-layer optimization model, combining the peak shaving operation cost of the hybrid microgrid with the interactive power deviation and power fluctuation penalty terms of the interconnection line during the frequency regulation stage, achieving dual optimization of economy and grid connection quality. Through precise mathematical modeling and efficient optimization algorithms, we have successfully obtained the minimum operating cost corresponding to the minimum power fluctuation, the minimum interconnection line interactive power deviation penalty term, and the optimal hydrogen storage capacity configuration scheme. Identify two typical days through cluster analysis. Option 1 (considering peak shaving, frequency regulation, and hydrogen electrolysis) has the lowest net load and a total operating cost of 3.0331 million yuan, which is better than Option 2 (3.0973 million yuan) and Option 3 (5.0398 million yuan). This plan effectively reduces wind and solar power waste, shortens the operating time of thermal power units, and demonstrates the rationality and economy of optimizing hydrogen storage capacity configuration.

Multi-demand-side resource regulation capacity assessment for distribution network safety and security

Taking into account the multiple demand differences and the existence of distribution network faults, the study conducts a multiple demand-side resource regulation capability assessment for distribution network safety and security as a way to achieve distribution network resource regulation optimization. The study considers multiple demands and adopts the correlation depth wandering algorithm to assess node faults in the distribution network, and proposes a quantitative assessment strategy for multiple demand-side resource regulation capability, and constructs an index system for assessing the resource regulation capability of the distribution network, and carries out the assessment of the resource regulation capability of the multiple demand-side resources. The results show that the regulation efficiency of wind power, thermal power, solar power and hydro power is above 20%, i.e., the combined regulation efficiency of the four energy sources is able to reach above 80%. The results show that the multi-demand-side resource regulation capability for distribution network security can be significantly improved, and the findings of the study have an important practical value for the electric power industry in terms of resource regulation and security, as well as providing a strong support for the sustainable development of the industry.

Inventory rationing, admission control, and production capacity allocation in a make‐to‐stock/make‐to‐order manufacturing system

AbstractThis paper considers a manufacturing system in which products are produced in both make‐to‐stock (MTS) and make‐to‐order (MTO) modes. Production of MTS and MTO products is done in batches, incurs a setup cost, and is non‐preemptive. The inventory of MTS products fulfills the demand of multiple classes, and each class demand can be satisfied or rejected. Customer orders for MTO production can be accepted or rejected, and their size is the same as the production batch. The primary goal of this paper is to study a policy that coordinates inventory rationing, admission control, and production capacity allocation to maximize the system's profit. We formulate the problem as a Markov decision process model and identify the structure of optimal control policies. We investigate the effect of inventory rationing on the profit by comparing its performance to that of the system with a first‐come‐ first‐serve policy to allocate inventory to multiple demand classes and study the extent to which the benefit of inventory rationing can be affected by system parameter changes. We also propose a heuristic that manages control decisions from linear threshold functions. Our test results from numerical examples show that the average percentage difference between the optimal and heuristic policies is within 1.2%.

Flexible encoding of multiple task dimensions in human cerebral cortex

IntroductionCognitive models have proposed that behavioral tasks can be categorized along at least three dimensions: the sensory-motor modality of the information, its representational format (e.g., location vs. identity), and the cognitive processes that transform it (e.g., response selection). Moreover, we can quickly and flexibly encode, represent, or manipulate information along any of these dimensions. How is this flexibility in encoding such information implemented in the cerebral cortex?MethodsTo address this question, we devised a series of functional magnetic resonance imaging (fMRI) experiments in each of which participants performed two distinct tasks that differed along one of the three dimensions.ResultsUsing multivariate pattern analysis of the fMRI data, we were able to decode between tasks along at least one task dimension within each of the cortical regions activated by these tasks. Moreover, the multiple demand network, a system of brain regions previously associated with flexible task encoding, was largely composed of closely juxtaposed sets of voxels that were specialized along each of the three tested task dimensions.DiscussionThese results suggest that flexible task encoding is primarily achieved by the juxtaposition of specialized representations processing each task dimension in the multiple demand network.

Optimization configuration strategy for regional energy systems based on multiple uncertainties and demand response

With the opening of the power market and the development of the energy Internet, the optimal allocation of regional energy systems has become the key to achieving energy efficiency and economic balance. The article studies how to achieve this balance by optimizing the allocation of regional energy systems under the influence of price fluctuations and load demand uncertainty in the electricity market. This study introduces a real-time electricity price adjustment mechanism to stimulate user participation in energy adjustments and improve energy utilization efficiency. By constructing an optimization model based on multiple uncertainties and comprehensive demand response, uncertainty factors such as energy market price fluctuations and climate change were considered, and user demand response was integrated. The research results indicate that electricity price fluctuations have a significant impact on system operation, while CSP power plant thermal storage fluctuations have a relatively small impact. After the introduction of demand response, the electricity load can be reduced to zero during specific periods, and the adjustment of electricity prices stimulates user participation and improves the consumption rate of renewable energy. The total revenue of the system increased by 54.147 million yuan, demonstrating the potential of optimized configuration in reducing costs and improving efficiency. This study provides important references for building more efficient and sustainable energy systems.

Creative connections: the neural correlates of semantic relatedness are associated with creativity

The associative theory of creativity proposes that creative ideas result from connecting remotely related concepts in memory. Previous research found that higher creative individuals exhibit a more flexible organization of semantic memory, generate more uncommon word associations, and judge remote concepts as more related. In this study (N = 93), we used fMRI to investigate brain regions involved in judging the relatedness of concepts that vary in their semantic distance, and how such neural involvement relates to individual differences in creativity. Brain regions where activity increased with semantic relatedness mainly overlapped with default, control, salience, semantic control, and multiple demand networks. The default and semantic control networks exhibited increased involvement when evaluating more remote associations. Finally, higher creative people, who provided higher relatedness judgements on average, exhibited lower activity in those regions, possibly reflecting higher neural efficiency. We discuss these findings in the context of the neurocognitive processing underlying creativity. Overall, our findings indicate that judging remote concepts as related reflects a cognitive mechanism underlying creativity and shed light on the neural correlates of this mechanism.

Task and stimulus coding in the multiple-demand network.

In the human brain, a multiple-demand (MD) network plays a key role in cognitive control, with core components in lateral frontal, dorsomedial frontal and lateral parietal cortex, and multivariate activity patterns that discriminate the contents of many cognitive activities. In prefrontal cortex of the behaving monkey, different cognitive operations are associated with very different patterns of neural activity, while details of a particular stimulus are encoded as small variations on these basic patterns (Sigala etal, 2008). Here, using the advanced fMRI methods of the Human Connectome Project and their 360-region cortical parcellation, we searched for a similar result in MD activation patterns. In each parcel, we compared multivertex patterns for every combination of three tasks (working memory, task-switching, and stop-signal) and two stimulus classes (faces and buildings). Though both task and stimulus category were discriminated in every cortical parcel, the strength of discrimination varied strongly across parcels. The different cognitive operations of the three tasks were strongly discriminated in MD regions. Stimulus categories, in contrast, were most strongly discriminated in a large region of primary and higher visual cortex, and intriguingly, in both parietal and frontal lobe regions adjacent to core MD regions. In the monkey, frontal neurons show a strong pattern of nonlinear mixed selectivity, with activity reflecting specific conjunctions of task events. In our data, however, there was limited evidence for mixed selectivity; throughout the brain, discriminations of task and stimulus combined largely linearly, with a small nonlinear component. In MD regions, human fMRI data recapitulate some but not all aspects of electrophysiological data from nonhuman primates.

Obtaining Conservative Estimates of Integrated Profitability for a Single-Period Product in an Own-Branding-and-Manufacturing Enterprise with Multiple Owned Channels

The achievable capacity index (ACI) is a simple and efficient approach for estimating the profitability of newsboy-type products, wherein profitability is defined as the probability of achieving the target profit by optimizing the order quantity. At present, the ACI is applicable to single retail stores (i.e., single demand) but not to multiple sales channels (i.e., multiple demand). This paper presents an integrated achievable capacity index (IACI) by which to measure the aggregate profitability of multiple mutually independent channels under normally distributed demand. An unbiased IACI estimator is also developed, to which is applied the Taylor expansion to approximate its sampling distribution, wherein the sizes, means, and variances of demand differ in each channel. Furthermore, overestimates due to sampling error are avoided by deriving the lower confidence bound for the IACI. This paper also provides generic tables to aid managers seeking conservative estimates of profitability. The applicability of the proposed scheme is demonstrated numerically using a real-world example involving an own-branding-and-manufacturing (OBM) enterprise with multiple owned channels.

Functional dissociation of the language network and other cognition in early childhood.

Is language distinct from other cognition during development? Does neural machinery for language emerge from general-purpose neural mechanisms, becoming tuned for language after years of experience and maturation? Answering these questions will shed light on the origins of domain-specificity in the brain. We address these questions using precision fMRI, scanning young children (35 months to 9 years of age) on an auditory language localizer, spatial working memory localizer (engaging the domain-general multiple demand [MD] network), and a resting-state scan. We create subject-specific functional regions of interest for each network and examine their selectivity, specificity, and functional connectivity. We find young children show domain-specific, left-lateralized language activation, and that the language network is not responsive to domain-general cognitive load. Additionally, the cortically adjacent MD network is selective to cognitive load, but not to language. These networks show higher within versus between-network functional connectivity. This connectivity is stable across ages (examined cross-sectionally and longitudinally), whereas language responses increase with age and across time within subject, reflecting a domain-specific developmental change. Overall, we provide evidence for a double dissociation of the language and MD network throughout development, in both their function and connectivity. These findings suggest that domain-specificity, even for uniquely human cognition like language, develops early and distinctly from mechanisms that presumably support other human cognition.

Tracking Components of Bilingual Language Control in Speech Production: An fMRI Study Using Functional Localizers.

When bilingual speakers switch back to speaking in their native language (L1) after having used their second language (L2), they often experience difficulty in retrieving words in their L1. This phenomenon is referred to as the L2 after-effect. We used the L2 after-effect as a lens to explore the neural bases of bilingual language control mechanisms. Our goal was twofold: first, to explore whether bilingual language control draws on domain-general or language-specific mechanisms; second, to investigate the precise mechanism(s) that drive the L2 after-effect. We used a precision fMRI approach based on functional localizers to measure the extent to which the brain activity that reflects the L2 after-effect overlaps with the language network (Fedorenko et al., 2010) and the domain-general multiple demand network (Duncan, 2010), as well as three task-specific networks that tap into interference resolution, lexical retrieval, and articulation. Forty-two Polish-English bilinguals participated in the study. Our results show that the L2 after-effect reflects increased engagement of domain-general but not language-specific resources. Furthermore, contrary to previously proposed interpretations, we did not find evidence that the effect reflects increased difficulty related to lexical access, articulation, and the resolution of lexical interference. We propose that difficulty of speech production in the picture naming paradigm-manifested as the L2 after-effect-reflects interference at a nonlinguistic level of task schemas or a general increase of cognitive control engagement during speech production in L1 after L2.

Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension.

Current large language models (LLMs) rely on word prediction as their backbone pretraining task. Although word prediction is an important mechanism underlying language processing, human language comprehension occurs at multiple levels, involving the integration of words and sentences to achieve a full understanding of discourse. This study models language comprehension by using the next sentence prediction (NSP) task to investigate mechanisms of discourse-level comprehension. We show that NSP pretraining enhanced a model's alignment with brain data especially in the right hemisphere and in the multiple demand network, highlighting the contributions of nonclassical language regions to high-level language understanding. Our results also suggest that NSP can enable the model to better capture human comprehension performance and to better encode contextual information. Our study demonstrates that the inclusion of diverse learning objectives in a model leads to more human-like representations, and investigating the neurocognitive plausibility of pretraining tasks in LLMs can shed light on outstanding questions in language neuroscience.

Sustainable multi-biofuel production with stochastic lead time and optimum energy utilization under flexible manufacturing

Many nations obtain their energy from fossil fuels. The benefit of renewable energy is that it decreases the dependence on conventional fuel, and minimizes carbon emissions. However, the commercial feasibility of multi-biofuel production poses a major challenge. A resilient and cost-efficient biofuel supply chain network is essential for commercialization. Furthermore, disruption risks arise from operational downtime, labor strikes, natural disasters, and uncertainty embedded in the data compromise the effectiveness of tactical and strategic level supply chain planning. In line with the aforementioned requirements, a multi-biofuel supply chain network model that reduces the total system cost and accounts for both disruption and operational risks is proposed. The proposed model determines the optimal production–distribution quantities and supports facility location and capacity decisions against multiple supply and demand interruption scenarios. The lead time crashing cost is utilized here to reduce the lead time, which increases because of transportation disruptions. To decrease energy expenses and carbon emissions, a variable production rate is applied for creating an economic way to advance the manufacturing industry. The model aims to show bioenergy’s effect on making a sustainable multi-biofuel supply chain network. Two numerical examples are used to illustrate the effectiveness of the proposed model. It is seen that by utilizing the presented method, the production cost of biofuels is significantly reduced through minimized energy consumption and carbon emissions. Moreover, the impact of critical parameters on the total system cost is explained through sensitivity analysis and graphical representations. The effort provides a systematic guideline for designing various environmental, economic, and social aspects of a sustainable supply chain network for bioenergy under minimized consumption. Multi-biofuel producers, distributors, investors, and the regulators surely be benefitted from the proposed model.

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

The spontaneous activities of the multiple demand network are related to individual differences in indirect replies comprehension

This study aims to investigate the brain networks engaged in the comprehension of indirect language, as well as the individual difference in this capacity. Specially, we aim to determine whether the difference is solely influenced by the difference in individuals’ default network (DN)/language network or whether it also relies on the networks associated with processing of complex cognitive tasks, particularly the multiple demand network (MDN). Conversational indirectness scale (CIS) scores in the interpretation dimension were used as a behavioral indicator of the indirect comprehension tendency. Reading time difference between indirect replies and direct replies collected through a self-paced reading experiment was deemed as a behavioral indicator of comprehension speed of indirect replies comprehension. The two behavioral indicators were combined with resting-state functional magnetic resonance imaging (rs-fMRI). The behaviour-rfMRI analysis showed that ALFF value of right SPL and the functional connectivity (FC) between the right SPL and right IPL/SMA/ITG/Precuneus/bilateral IFG were positively correlated with the interpretation dimension of CIS scores. In addition, the ALFF value of right fusiform gyrus, the FC between the right fusiform gyrus and right precuneus, and the FCs between right SPL and right IPL/Precuneus/IFG were negatively correlated with indirect replies comprehension speed. Overlapping of these regions with large-scale brain network revealed that the right SPL was mainly located in the MDN, and the right fusiform gyrus was mainly located in the language network. Additionally, the areas showing functional connectivity with these regions were primarily located in the MDN, with a smaller subset located in the DN. Our findings suggest that the ability of individuals to actively and rapidly acquire indirect meaning relies not only on the support of the DN and the language network, but also requires collective support from the MDN.