Number Of Connections Research Articles

Decision-making techniques based on aggregation operators and similarity measures under q-rung orthopair hesitant fuzzy connection numbers and their application

The concept of q-rung orthopair hesitant fuzzy set represents an advancement and extension of hesitant fuzzy sets, encompassing both fuzzy sets and q-rung orthopair fuzzy sets. q-rung orthopair hesitant fuzzy set characterizes a set of membership and non-membership grades within the interval [0, 1], which enhances its adaptability compared to existing methods. This flexibility proves invaluable in providing more insightful data about various objects. The primary objective of this research is to introduce a decision-making technique in the context of q-RHF using the theory of set pair analysis (SPA). q-RHFS effectively handles ambiguous data by incorporating membership and non-membership grades, while the connection number (CN) based on SPA theory manages the intricacies of uncertainty and certainty structures by relying on "identity", "discrepancy" and "contrary" grades. Building on the relationship between q-RHFS and the connection number of set pair analysis, a comprehensive framework known as q-rung hesitant fuzzy connection number set (qHCNs) is developed. This model not only addresses uncertainty, but also offers valuable insights. Furthermore, this research introduces similarity measures derived from qHCN and examines their advantages through illustrative examples. Additionally, a novel approach to decision modeling utilizing these measures applied to medical diagnosis is also introduced. The application of this established model contributes to an effective approach and demonstrated its soundness and efficiency. In addition, a detailed comparative study is conducted with the existing models and advantages of proposed model. The research concludes with a summary of the authors' findings, highlighting the consistency and effectiveness of their work.

Construction of Landscape Ecological Risk Collaborative Management Network in Mountainous Cities—A Case Study of Zhangjiakou

The prevention of ecological risks is a critical determinant influencing sustainable development. Driven by rapid socio-economic development, the ecosystems of mountainous cities within agro-pastoral transition zones are increasingly vulnerable to complex disturbances, constituting a significant threat to sustainable development and human well-being. To help achieve sustainable development, it is essential to conduct research on addressing and mitigating ecological risks from the perspective of collaborative management networks in mountainous cities. Taking Zhangjiakou as the study area, this paper employed the land use transfer matrix and standard deviation ellipse methods to analyze the dynamic land use changes. Additionally, using Fragstats 4,2 to calculate the landscape indices with land use data, this paper evaluated the landscape ecological risk (LER) from 2000 to 2020. Furthermore, the social network analysis (SNA) method was utilized to explore the spatial correlation characteristics of the LER. The findings indicate that: (1) Cultivated land and grassland were the predominant land use types in Zhangjiakou. During 2000–2020, Zhangjiakou experienced significant changes in land use, dominated by the transfer among cultivated land, forestland, and grassland. It indicated that the issue of unstable ecological land use continued to exist. Affected by human activities, construction land showed a consistent upward trend, primarily concentrated in the urban built-up areas and areas along the Jing-Zhang Railway. (2) The LER of Zhangjiakou was predominantly characterized by low risk, medium risk, and high risk levels. In the transitional areas and foothills, the LER was relatively higher. During 2000–2020, Zhangjiakou showed a declining trend of LER. This suggested that the ecological protection policies in Zhangjiakou were effective, leading to an improvement in the local ecological environment. (3) The LER in Zhangjiakou demonstrated a spatial clustering pattern that exhibited an upward trend, which was supported by both spatial autocorrelation and the SNA analysis. In the LER collaborative management network, Xuanhua, Qiaodong, Qiaoxi, Wanquan and Zhangbei consistently upheld pivotal roles. Based on the number of inward and outward connections, 16 counties in Zhangjiakou were classified into four categories and three zones accompanied by corresponding recommendations. The findings of this study can serve as a valuable reference for subsequent landscape pattern optimization and ecological restoration in Zhangjiakou.

Joint spectrum and power allocation scheme based on value decomposition networks in D2D communication networks

Device-to-device (D2D) communications allow short-range communication devices to multiplex cellular-licensed spectrum to directly establish local connections for ultra-high number of terminal connections and greater system throughput. However, spectrum sharing also brings serious interference to the network. Therefore, a reliable and efficient resource allocation strategy is important to mitigate the interference and improve the system spectral efficiency. In this paper, we investigated spectrum access and power allocation in D2D communications underlay cellular networks based on deep reinforcement learning with the aim of finding a feasible resource allocation strategy to maximize data rate and system fairness. We proposed a value decomposition network-based resource allocation scheme for D2D communication networks. Our proposed scheme avoids frequent information exchanges among D2D users by centralized training, while allowing D2D users to make distributed joint resource allocation decisions. Simulation results show that the proposed scheme has stable convergence and good scalability, and can effectively improve the system capacity.

Returns to workplace connectivity: evidence from Brazil

ABSTRACT We empirically investigate the quantitative effect of workplace connectivity on workers’ wages. Connectivity measures a worker’s rank within the workplace’s connection hierarchy, considering the number of co-workers in the same occupation and firm (workplace) and the relative tenure of immediate co-workers. We construct a connectivity index as the ratio of a worker’s tenure-weighted connections to the total number of connections in the workplace, using a worker’s job tenure and the connections formed over time to obtain a relative measurement of their connectivity. Using a unique dataset from the Brazilian Annual Social Information Report (RAIS), we estimate the (fixed effect) wage equation, controlling for work and firm heterogeneity. Our results show positive and statistically significant returns to connectivity. The effect is particularly pronounced for workers in administrative services and industrial goods production, those at the bottom of the connectivity distribution, and those in medium-sized firms. We also find that a worker’s connectivity is more relevant for wages than their tenure.

Размежевание или сплочение? (Динамика сетевой структуры политических телеграм-каналов: моделирование и эмпирический анализ)

In this paper, the authors propose novel methodology to analyze the dynamics of large-scale online network structures caused by significant exogenous shocks (foreign policy crises and the onset of military conflicts). The article focuses on diagnosing cohesion or polarization processes. As a first step, on the basis of the existing theoretical traditions, the authors analyze mechanisms at the micro-level that may lead to consolidation or polarization. Among these, they reveal mechanisms based on changes in position and mechanisms based on changes in identity. As a second step, the authors determine the “network projection” of these mechanisms’ actions: the changes in the structures of interaction networks between individuals they lead to. At this point mathematical modeling plays a key role, allowing for specific predictions regarding network behavior, expressed in changes in observable metrics. Four network indicators are proposed: the number of communities (clusters), modularity, the number of connections, and the average degree of nodes. As a result of computational experiments, the expected dynamics of all the indicators is calculated, which is compared with the corresponding mechanisms of changes in political positions or identity. As a third step, these predictions are compared with the indicators calculated from the empirical data. For the empirical analysis the article uses messages from the Russianlanguage political and news telegram channels, corresponding in time to the start of a special military operation (February-March, 2022), and those that chronologically occur 10 months earlier or later (April, 2021, and December, 2022). The general sample of such messages contains those with hyperlinks to other channels. It is the presence of such hyperlinks is considered to be a sign of the connection and is used to build a network. For each time period, the same network parameters are measured as for the computational data. Comparison of the observed dynamics with what is predicted by the models indicates that in the case of the special military operation, one key mechanism was at work — cohesion through the expansion of in-group boundaries, based on shifting to higher-order identity categories.

A Magnesium Organic Framework Fluorescent Sensor for Selective Detection of Nitrofuran Antibiotics and Inorganic Pollutants

ABSTRACTA novel 3D magnesium‐based metal organic framework (Mg‐MOF) has been solvothermally synthesized using 5‐(pyridin‐4‐yl)isophthalic acid. The ligand has demonstrated to preserve two types of coordination modes depending on the connection mode of the pyridine group. The uncoordinated pyridine groups provide Lewis base sites (LBSs), whereas the coordinated ones promote to form a rarely reported eight‐connected tetranuclear Mg clusters. Benefiting from the multinuclearity with high number of connections, the framework keeps good performance of high thermal stability until 500°C. Furthermore, the framework exhibits potential open metal sites (OMSs) benefiting from the terminal coordinated water groups of the metal clusters. Owing to the abundant active sites, the Mg‐MOF exhibits good sensitivity to nitrofuran antibiotics and Cr2O72−, with high KSV (at 104 M−1 level) and low limit of detection (LOD) values (∼10−6 M). Density functional theory (DFT) investigation reveals that the interchange of the HOMO–LUMO energy levels of the framework and antibiotics is responsible for the sensing activity. In addition, the Mg‐MOF is greatly recyclable in five cycles while maintaining the structural rigidity and sensing activity, making it a promising luminescence probe material.

Exploring the self, others, and the world: the interplay between experiences abroad in adolescence, identity processes, and ethnic prejudice

Study abroad experiences can be an important asset for individuals’ personal development and well-being. However, their impact on social identity domains and prejudice is still unexplored. The current research examined whether the ways in which youth retrospectively narrate their abroad experiences in adolescence were intertwined with their national and European identity processes and ethnic prejudice. A total of 117 Italian youth (M age = 22.71, SD = 2.50; 53.8% females) who participated in a mobility program in adolescence completed an online questionnaire and a retrospective narrative interview. Agency and self-event connections in the narratives were directly linked to identity processes in national and European domains. European identity processes were associated with different levels of affective and cognitive prejudice. Across both identity domains, the number of self-event connections in the narratives was indirectly linked to lower affective prejudice via increased in-depth exploration. These findings highlight the importance of abroad experiences for consolidating self- and other-oriented views.

Relationship between the rumen microbiome and liver transcriptome in beef cattle divergent for feed efficiency

BackgroundFeed costs account for a high proportion of the variable cost of beef production, ultimately impacting overall profitability. Thus, improving feed efficiency of beef cattle, by way of determining the underlying genomic control and selecting for feed efficient cattle provides a method through which feed input costs may be reduced whilst also contributing to the environmental sustainability of beef production. The rumen microbiome dictates the feed degradation capacity and consequent nutrient supply in ruminants, thus potentially impacted by feed efficiency phenotype. Equally, liver tissue has been shown to be responsive to feed efficiency phenotype as well as dietary intake. However, although both the rumen microbiome and liver transcriptome have been shown to be impacted by host feed efficiency phenotype, knowledge of the interaction between the rumen microbiome and other peripheral tissues within the body, including the liver is lacking. Thus, the objective of this study was to compare two contrasting breed types (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI) over contrasting dietary phases (zero-grazed grass and high-concentrate), based on gene co-expression network analysis of liver transcriptome data and microbe co-abundance network of rumen microbiome data. Traits including RFI, dry matter intake (DMI) and growth rate (ADG), as well as rumen concentrations of volatile fatty acids were also included within the network analysis.ResultsOverall, DMI had the greatest number of connections followed by RFI, with ADG displaying the fewest number of significant connections. Hepatic genes related to lipid metabolism were correlated to both RFI and DMI phenotypes, whilst genes related to immune response were correlated to DMI. Despite the known relationship between RFI and DMI, the same microbes were not directly connected to these phenotypes, the Succiniclasticum genus was however, negatively connected to both RFI and ADG. Additionally, a stepwise regression analysis revealed significant roles for both Succiniclasticum genus and Roseburia.faecis sp. in predicting RFI, DMI and ADG.ConclusionsResults from this study highlight the interactive relationships between rumen microbiome and hepatic transcriptome data of cattle divergent for RFI, whilst also increasing our understanding of the underlying biology of both DMI and ADG in beef cattle.

Abnormal Structural–Functional Coupling and MRI Alterations of Brain Network Topology in Progressive Supranuclear Palsy

BackgroundProgressive supranuclear palsy (PSP) can cause structural and functional brain reconstruction. There is a lack of knowledge about the consistency between structural–functional (S–F) connection networks in PSP, despite growing evidence of anomalies in various single brain network parameters.PurposeTo study the changes in the structural and functional networks of PSP, network's topological properties including degree, and the consistency of S–F coupling. The relationship with clinical scales was examined including the assessment of PSP severity, and so on.Study TypeRetrospective.SubjectsA total of 51 PSP patients (70.04 ± 7.46, 25 females) and 101 healthy controls (64.58 ± 8.84, 58 females).Field Strength/Sequence3‐T, resting‐state functional MRI, diffusion tensor imaging, and T1‐weighted images.AssessmentA graph‐theoretic approach was used to evaluate structural and functional network topology metrics. We used the S–F coupling changes to explore the consistency of structural and functional networks.Statistical TestsIndependent samples t tests were employed for continuous variables, χ2 tests were used for categorical variables. For network analysis, two‐sample t tests was used and implied an false discovery rate (FDR) correction. Pearson correlation analysis was used to explore the correlations. A P‐value <0.05 was considered statistically significant.ResultsPSP showed variations within and between modules. Specifically, PSP had decreased network properties changes (t = −2.0136; t = 2.5409; t = −2.5338; t = −2.4296; t = −2.5338; t = 2.8079). PSP showed a lower coupling in the thalamus and left putamen and a higher coupling in the visual, somatomotor, dorsal attention, and ventral attention network. S–F coupling was related to the number of network connections (r = 0.32, r = 0.22) and information transmission efficiency (r = 0.55, r = 0.28). S–F coupling was related to basic academic ability (r = 0.39) and disinhibition (r = 0.49).Data ConclusionPSP may show abnormal S–F coupling and intramodular and intermodular connectome in the structural and functional networks.Level of Evidence3Technical EfficacyStage 3

Network analysis of Tourette syndrome and attention-deficit/hyperactivity disorder symptoms in children and adolescents

BackgroundWhile Tourette syndrome (TS) and attention-deficit/hyperactivity disorder (ADHD) often co-occur, the nature of the relationship between their symptoms is not well understood. Network analysis of psychopathology allow for detailed examinations of symptom interactions, providing an effective approach to explore the patterns of comorbidity between TS and ADHD symptoms.MethodsThis study included 3,958 participants (male/female = 3,004/954, age mean ± SD = 8.60 ± 2.25 years). We collected data on TS symptoms using the Motor Tic, Obsessions and Compulsions, Vocal Tic Evaluation Survey (MOVES), and ADHD symptoms using the Swanson, Nolan, and Pelham Rating Scale-IV (SNAP-IV). Network analysis was employed to construct a combined network of TS and ADHD symptoms at the symptom level. We utilized the expected influence (EI) and bridge EI metrics to explore the core and bridge symptoms within the network.ResultsThe network structure demonstrated a moderate number of non-zero connections between TS and ADHD symptoms, constituting 23.06% of all potential connections. Core symptoms in the comorbidity network included “Often has difficulty sustaining attention in tasks or play activities,” “Certain bad words or thoughts keep going through my mind,” and “Words come out that I can’t stop or control.” Bridging symptoms identified were “Words come out that I can’t stop or control,” “I do certain things like jumping or clapping over and over,” “I can’t control all my movements,” and “Often talks excessively.”ConclusionThe core and bridging symptoms identified in this study serve as potential therapeutic targets for the treatment of TS and ADHD comorbidity in clinical children and adolescents.

PSX-3 Transcriptional response to an alternative diet on liver, muscle, and rumen of beef cattle

Abstract Feed cost represents a major economic determinant within cattle production, amounting to an estimated 75% of the total variable costs. Consequently, comprehensive approaches such as optimizing feed utilization through alternative feed sources, alongside the selection of feed-efficient animals, are of great significance. Here, we investigate the effect of two diets, traditional corn-grain fed and alternative by-product based, on 14 phenotypes related to feed, methane emission and production efficiency and on multi-tissue transcriptomics data from liver, muscle, and rumen wall, derived from Nellore bulls (n = 52), 26 on each diet. To this end, diets were contrasted at the level of phenotype, gene expression, and gene-phenotype network connectivity. As regards the phenotypic level, at a P-value < 0.05, significant differences were found in favor of the alternative diet for average daily weight gain at finishing, dry matter intake at finishing, methane emission, carcass yield and subcutaneous fat thickness at the rib-eye muscle area. In terms of the transcriptional level of the 14,776 genes expressed across the examined tissues, we found 487, 484, and 499 genes differentially expressed due to diet in liver, muscle, and rumen, respectively (P-value < 0.01). To explore differentially connected phenotypes across both diet-based networks, we focused on the phenotypes with the largest change in average number of connections within diets and tissues, namely methane emission and carcass yield, highlighting, in particular, gene expression changes involving SREBF2, and revealing the largest differential connectivity in rumen and muscle, respectively. Similarly, from examination of differentially connected genes across diets, the top-ranked most differentially connected regulators within each tissue were MEOX1, PTTG1, and BASP1 in liver, muscle, and rumen, respectively. Changes in gene co-expression patterns suggest activation or suppression of specific biological processes and pathways in response to dietary interventions, consequently impacting the phenotype. The identification of genes that respond differently to diets and their associated phenotypic effects serves as a crucial steppingstone for further investigations, aiming to build upon our discoveries. Ultimately, such advancements hold the promise of improving animal welfare, productivity, and sustainability in livestock farming.

Quantized Dirac cones generated by one-dimensional multi-connected optical waveguide networks

This paper introduces one-dimensional (1D) limited periodic multi-connected vacuum optical waveguide networks (LPMVOWN), a fascinating class of photonic bandgap structures designed to produce Dirac cones. Unlike previously explored materials such as graphene, metamaterials, photonic crystals, and phononic crystals, optical waveguide networks produce a sequence of Dirac cones while also exhibiting quantum properties in the angular frequencies corresponding to these cone points. We used the topological translational periodicity inherent in optical waveguide networks to establish analytical relationships governing the angular frequency, slope, and number of Dirac cones generated by the system. These relationships are used to elucidate how parameters like the waveguide length ratio of the units, the number of waveguide connections, and the waveguide material influence these properties. This analysis improves our understanding of linear, degenerate, and concatenated energy band structures, as well as their potential applications. Furthermore, it offers valuable insights for enhancing the control of electromagnetic wave propagation and provides experimentalists with greater convenience and flexibility in their research endeavours.

Algorithmic Decomposition of Railway Objects for Distributed Interlocking System

Railway interlocking systems can be implemented as distributed systems, where each part of a station is handled by a separate logical unit. The logical units of such systems form a network and communicate by interchanging messages. Such distributed architectures are well known in large industrial control systems. There are several design practices and also algorithmic task partitioning methods that are applicable in distributed control systems. Some of such methods can also be adapted in designing of railway interlocking systems as well. In the case of such systems, the communication time between components must be kept low. Namely each separate controller in a given route must be able to exchange their internal state within a limited time in order to permit the train movement authorization. This limitation could cause high traffic load, if every logical unit would be interconnected with each other. Therefore, the main goal of the minimization is to reduce the number of connections between logical units. This can achieved by distributing and assigning the topological railway objects to certain logical units.

Spatial resource arrangement influences both network structures and activity of fungal mycelia: A form of pattern recognition?

The present study investigated the behavior and wood decay ability of mycelial network of Phanerochaete velutina, a cord-forming fungus, on multiple wood blocks. We placed well-colonized wood blocks in two spatial arrangements (Circle and Cross) on a soil plate and compared the development of the mycelial network and wood decay over 116 days. In the Circle arrangement, the degree of connection (number of connected cords) of the blocks ranged from 0 to 8, with no significant differences observed across positions. However, in the Cross arrangement, the outer blocks exhibited a greater degree of connection than the inner blocks. The mass loss of the wood block was positively associated with the degree of connection and was significantly smaller in the Cross than in the Circle arrangement. These findings suggest that fungal mycelium can “recognize” the difference in the spatial arrangement of wood blocks as part of their wood decay activity.

Cryptocurrencies under climate shocks: a dynamic network analysis of extreme risk spillovers

Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.

Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption

A technique is presented that reduces the required memory of neural networks through improving weight storage. In contrast to traditional methods, which have an exponential memory overhead with the increase in network size, the proposed method stores only the number of connections between neurons. The proposed method is evaluated on feedforward networks and demonstrates memory saving capabilities of up to almost 80% while also being more efficient, especially with larger architectures.

Exploring the Evolution of Research Connectivity and Funding in Global Neurosurgical Publications

There are critical disparities in the neurosurgical care provided around the globe due to challenges in resource allocation, training, and infrastructure. Global neurosurgical collaborations have replaced classical mission trips to address these disparities. However, the development of these collaborations and the impact of research funding on their growth has not yet been systematically studied. In this article, we use a graph theoretical approach to investigate trends in funding and co-authorship between and among authors from high-income countries (HICs) and authors from low- and middle-income countries (LMICs). A bibliometric search of the global neurosurgical literature returned 307 articles between 1985 and 2020. A connectivity analysis was conducted to compute the number of co-authorships between HIC-HIC, LMIC-HIC, and LMIC-LMIC authors. The number of connections, summarized as either a global sum of connections or an average number of connections per manuscript, were analyzed in the context of time and funding through parametric statistical tests. An exponential increase in co-authorship collaboration was observed over time, especially after 2015. Notably, LMIC-LMIC collaborations appear to be rising at over twice the rate of other collaboration types. The presence of funding, in general, was associated with increased co-authorship of manuscripts by LMIC and HIC authors together (p = 0.033). A significant majority of the funding associated with LMIC-HIC co-authorships was supplied through charitable organizations and government grants (p = 0.034, p = 0.009, respectively). Most LMIC-LMIC co-authorships had no funding. This work shows significant and rapid growth in international neurosurgical partnerships, especially in HIC-LMIC and LMIC-LMIC collaborations. Also, a significant positive relationship exists between research funding and LMIC-HIC co-authorship trends. This work encourages us as a community to continue to expand our translational collaborations with LMIC neurosurgeons and establish funding mechanisms independent of HIC authors.

SynBot is an open-source image analysis software for automated quantification of synapses

The formation of precise numbers of neuronal connections, known as synapses, is crucial for brain function. Therefore, synaptogenesis mechanisms have been one of the main focuses of neuroscience. Immunohistochemistry is a common tool for visualizing synapses. Thus, quantifying the numbers of synapses from light microscopy images enables screening the impacts of experimental manipulations on synapse development. Despite its utility, this approach is paired with low-throughput analysis methods that are challenging to learn, and the results are variable between experimenters, especially when analyzing noisy images of brain tissue. We developed an open-source ImageJ-based software, SynBot, to address these technical bottlenecks by automating the analysis. SynBot incorporates the advanced algorithms ilastik and SynQuant for accurate thresholding for synaptic puncta identification, and the code can easily be modified by users. The use of this software will allow for rapid and reproducible screening of synaptic phenotypes in healthy and diseased nervous systems.

Game theory elucidates how competitive dynamics mediate animal social networks

BackgroundWhile most game theoretical models assume that individuals randomly interact with all other group members, strong evidence indicates that individuals tend to preferentially interact with some of them. The position of an individual in a network affects, among other factors related to survival, its predation risk and competitive success. Here I then modified the Hawk-Dove game to explore the effect of social network structure on competitive strategy of individuals that differ in their fighting ability and may adjust their use of the Hawk, Dove and Assessor tactics to maximize their foraging success when they meet opponents they are connected with.ResultsFrom randomly generated networks, I demonstrate that phenotypic assortment by fighting ability reduces individuals’ aggressiveness and, as such, favours cooperative interactions. Furthermore, the success of individuals with the weakest fighting ability is usually highest within networks where they most frequently meet opponents with the same fighting ability as their own, suggesting they might benefit from breaking connections with strong contestants. This might be the case when strong contestants systematically rely on the aggressive Hawk tactic or the risk of being predated is low and independent of the number of neighbours. Thus, I extended the model and built a dynamic model to allow individuals not only to adjust their behaviour to local conditions but also to modify the structure of the social network. The number of connections and degree of phenotypic assortment are then affected by ecological factors (e.g. resources value and predation risk), but above all by whether individuals can reliably assess the competitive ability of their opponents and adjust their behaviour accordingly.ConclusionsThese findings provide strong evidence that behaviour can play a key role in shaping network structure and highlight the importance of considering the coevolution of network and behaviour to apprehend its consequences on population dynamics.

Quantum battery supercharging via counter-diabatic dynamics

Abstract We introduce a counter-diabatic approach for deriving Hamiltonians modeling superchargable quantum batteries (QBs). A necessary requirement for the supercharging process is the existence of multipartite interactions among the cells of the battery. Remarkably, this condition may be insufficient no matter the number of multipartite terms in the Hamiltonian. We analytically illustrate this kind of insufficiency through a model of QB based on the adiabatic version for the Grover search problem. On the other hand, we provide QB supercharging with just a mild number of global connections in the system. To this aim, we consider a spin-$1/2$ chain with $n$ sites in the presence of Ising multipartite interactions. We then show that, by considering the validity of the adiabatic approximation and by adding $n$ terms of $(n-1)$-site interactions, we can achieve a Hamiltonian exhibiting maximum QB power, with respect to a normalized evolution time, growing quadratically with $n$. Therefore, supercharging can be achieved by $O(n)$ terms of multipartite connections. The time constraint required by the adiabatic approximation can be surpassed by considering a counter-diabatic expansion in terms of the gauge potential for the original Hamiltonian, with a limited $O(n)$ many-body interaction terms assured via a Floquet approach for the counter-diabatic implementation.