Search Results (3,005)

We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. This enables the prediction of initial droplet configurations that evolve into target HR imprints after a specified spreading time. The developed neural network architecture aims at learning to tune the refinement level of its residual convolutional blocks by using function approximators that are trained to map a given film thickness to an appropriate refinement level indicator. We use multiple stacks of convolutional layers, the output of which is translated according to the refinement level indicators provided by the directly connected function approximators. Together with a non-linear activation function, the translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. We believe that this work holds value for the semiconductor manufacturing and packaging industry. Specifically, it enables desired layouts to be imprinted on a surface by squeezing strategically placed droplets with a blank surface, eliminating the need for customized templates and reducing manufacturing costs. Additionally, this approach has potential applications in data compression and encryption. Full article

Background: Anhedonia, defined as the diminished capacity to experience pleasure, represents a core negative symptom in first-episode psychosis (FEP) with profound implications for functional outcomes and long-term prognosis. Despite its clinical significance, comprehensive understanding of anhedonia prevalence, underlying mechanisms, and optimal intervention strategies in early psychosis remains limited. Objectives: To systematically examine the prevalence and characteristics of anhedonia in FEP patients, explore neurobiological mechanisms, identify clinical correlates and predictive factors, and evaluate intervention efficacy. Methods: Following PRISMA 2020 guidelines, we conducted comprehensive searches across PubMed, Embase, PsycINFO, and Web of Science databases from January 1990 to June 2025. Studies examining anhedonia and negative symptoms in FEP patients (≤24 months from onset) using validated assessment instruments were included. Quality assessment was performed using appropriate tools for study design. Results: Twenty-one studies comprising 3847 FEP patients met inclusion criteria. Anhedonia prevalence ranged from 30% at 10-year follow-up to 53% during acute phases, demonstrating persistent motivational deficits across illness trajectory. Factor analytic studies consistently supported five-factor negative symptom models with anhedonia as a discrete dimension. Neuroimaging investigations revealed consistent alterations in reward processing circuits, including ventral striatum hypofunction and altered network connectivity patterns. Social anhedonia demonstrated stronger associations with functional outcomes compared to other domains. Epigenetic mechanisms involving oxytocin receptor methylation showed gender-specific associations with anhedonia severity. Conventional antipsychotic treatments showed limited efficacy for anhedonia improvement, while targeted psychosocial interventions demonstrated preliminary promise. Conclusions: Anhedonia showed high prevalence (30–53%) across FEP populations with substantial clinical burden (13-fold increased odds vs. general population). Meta-analysis revealed large effect sizes for anhedonia severity in FEP vs. controls (d = 0.83) and strong negative correlations with functional outcomes (r =·−0.82). Neuroimaging demonstrated consistent ventral striatum dysfunction and altered network connectivity. Social anhedonia emerged as the strongest predictor of functional outcomes, with independent suicide risk associations. Conventional antipsychotics showed limited efficacy, while behavioral activation approaches demonstrated preliminary promise. These findings support anhedonia as a distinct treatment target requiring specialized assessment and intervention protocols in early psychosis care. Full article

This study aimed to assess the vulnerability of transportation networks and identify critical nodes and regions within a multimodal transportation system. While previous research has predominantly focused on centrality measures to evaluate node importance from an accessibility perspective, this study emphasizes the need to evaluate network vulnerability comprehensively in response to rapid socioeconomic changes. We propose a vulnerability function that integrates network topology and connectivity. First, we defined the vulnerability of individual nodes and regional clusters. Second, we developed a methodology to evaluate the defined vulnerabilities systematically. Finally, we applied the framework to Korea’s multimodal transportation network, conducting a case study to validate the effectiveness and applicability of the proposed function. Conclusively, this study presents a comprehensive vulnerability assessment framework for multimodal transportation networks, offering valuable insights to support robust decision making for enhancing sustainable and efficient transportation systems. Full article

Under the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiotemporal evolution of topological patterns, ecological processes, and ecosystem services (ES) in Wuhan from the “pattern–process–function” perspective. To overcome the lag in research concerning the coupling of ecological processes, functions, and spatial patterns, we explore the long-term dynamic evolution of ecosystem structure, process, and function by integrating multi-source data, including remote sensing, enabling comprehensive spatiotemporal analysis from 2000 to 2020. Addressing limitations in current EN optimization approaches, we integrate morphological spatial pattern analysis (MSPA), use circuit theory to identify EN components, and conduct spatial optimization accurately. We further assess the effectiveness of two scenario types: “pattern–function” and “pattern–process”. The results reveal a distinct “increase-then-decrease” trend in EN structural attributes: from 2000 to 2020, source areas declined from 39 (900 km²) to 37 (725 km²), while corridor numbers fluctuated before stabilizing at 89. Ecological processes and functions exhibited phased fluctuations. Among water-related indicators, water conservation (as a core function), and modified normalized difference water index (MNDWI, as a key process) predominantly drive positive correlations under the “pattern–function” and “pattern–process” scenarios, respectively. The “pattern–function” scenario strengthens core area connectivity (24% and 4% slower degradation under targeted/random attacks, respectively), enhancing resistance to general disturbances, whereas the “pattern–process” scenario increases redundancy in edge transition zones (21% slower degradation under targeted attacks), improving resilience to targeted disruptions. This complementary design results in a gradient EN structure characterized by core stability and peripheral resilience. This study pioneers an EN optimization framework that systematically integrates identification, assessment, optimization, and validation into a closed-loop workflow. Notably, it establishes a quantifiable, multi-objective decision basis for EN optimization, offering transferable guidance for green infrastructure planning and ecological restoration from a pattern–process–function perspective. Full article

Borderline personality disorder (BPD) is a complex and heterogeneous condition characterized by emotional instability, impulsivity, and impaired regulation of interpersonal relationships. This narrative review integrates findings from recent neuroimaging, neurochemical, and treatment studies to identify core neurobiological mechanisms and highlight translational potential. Evidence from 112 studies published up to 2025 is synthesized, encompassing structural MRI, resting-state and task-based functional MRI, EEG, PET, and emerging machine learning applications. Consistent disruptions are observed across the prefrontal–amygdala circuitry, the default mode network (DMN), and mentalization-related regions. BPD shows a dominant and stable pattern of hyperconnectivity in the precuneus. Transdiagnostic comparisons with PTSD and cocaine use disorder (CUD) suggest partial overlap in DMN dysregulation, though BPD-specific traits emerge in network topology. Machine learning models achieve a classification accuracy of 70–88% and may support the tracking of early treatment responses. Longitudinal fMRI studies indicate that psychodynamic therapy facilitates the progressive normalization of dorsal anterior cingulate cortex (dACC) activity and reductions in alexithymia. We discuss the role of phenotypic heterogeneity (internalizing versus externalizing profiles), the potential of neuromodulation guided by biomarkers, and the need for standardized imaging protocols. Limitations include small sample sizes, a lack of effective connectivity analyses, and minimal multicenter cohort representation. Future research should focus on constructing multimodal biomarker panels that integrate functional connectivity, epigenetics, and computational phenotyping. This review supports the use of a precision psychiatry approach for BPD by aligning neuroscience with scalable clinical tools. Full article

Plant growth regulators (PGRs) enhance crop stress resistance but their roles in microbial-mediated phosphorus cycling within intercropping systems are unclear. Thus, We conducted a two-year field study using corn (Zea mays L. cv. Denghai 605) and soybean (Glycine max L. cv. Hedou 22) in fluvisols and luvisols soil according to World Reference Base for Soil Resources (WRB) standard. Under a 4-row corn and 6-row soybean strip intercropping system, three treatments were applied: a water control (CK), and two plant growth regulators—T1 (EC: ethephon [300 mg/L] + cycocel [2 g/L]) and T2 (ED: ethephon [300 mg/L] + 2-Diethyl aminoethyl hexanoate [10 mg/L]). Foliar applications were administered at the V7 stage (seventh leaf) of intercropped corn plants to assess how foliar-applied PGRs (T1/T2) modulated the soil phosphorus availability, microbial communities, and functional genes in maize intercropping systems. PGRs increased the soil organic phosphorus and available phosphorus contents, and alkaline phosphatase activity, but not total phosphorus. PGRs declined the α-diversity in fluvisols soil but increased the α-diversity in luvisols soil. The major taxa changed from Actinobacteria (CK) to Proteobacteria (T1) and Saccharibacteria (T2) in fluvisols soil, and from Actinobacteria/Gemmatimonadetes (CK) to Saccharibacteria (T1) and Acidobacteria (T2) in luvisols soil. Functional gene dynamics indicated soil-specific regulation, where fluvisols soil harbored more phoD (organic phosphorus mineralization) and relA (polyphosphate degradation) genes, whereas phnP gene dominated in luvisols soil. T1 stimulated organic phosphorus mineralization and inorganic phosphorus solubilization in fluvisols soil, upregulating regulation genes, and T2 enhanced polyphosphate synthesis and transport gene expression in luvisols soil. Proteobacteria, Nitrospirae, and Chloroflexi were positively correlated with organic phosphorus mineralization and polyphosphate cycling genes, whereas Bacteroidetes and Verrucomicrobia correlated with available potassium (AP), total phosphorus (TP), and alkaline phosphatase (ALP) activity. Thus, PGRs activated soil phosphorus by restructuring soil type-dependent microbial functional networks, connecting PGRs-induced shifts with microbial phosphorus cycling mechanisms. These findings facilitate the targeted use of PGRs to optimize microbial-driven phosphorus efficiency in strategies for sustainable phosphorus management in diverse agricultural soils. Full article

The evolution of 3GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5GC). Results show that backhaul and midhaul relays maintain delays of 50–60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services. Full article

The increasing digitalization of power systems has revolutionized the functionality and efficiency of electrical protection relays. These digital relays enhance fault detection, monitoring, and response mechanisms, ensuring the reliability and stability of power networks. However, their connectivity and reliance on communication protocols introduce significant cybersecurity risks, making them potential targets for malicious attacks. Cyber threats against digital protection relays can lead to severe consequences, including cascading failures, equipment damage, and compromised grid security. This paper presents a comprehensive review of cybersecurity challenges in digital electrical protection relays, focusing on four key areas: (1) a taxonomy of cyber attack models targeting protection relays, (2) the associated risks and their potential impact on power systems, (3) existing mitigation strategies to enhance relay security, and (4) future research directions to strengthen resilience against cyber threats. Full article

The advancement of Internet of Things (IoT) technology has enabled battery-powered devices to be deployed across a wide range of applications; however, it also introduces challenges such as high energy consumption and security vulnerabilities. To address these issues, adiabatic logic circuits offer a promising solution for achieving energy efficiency and enhancing the security of IoT devices. Adiabatic logic circuits are well suited for energy harvesting systems, especially in applications such as sensor nodes, RFID tags, and other IoT implementations. In these systems, the harvested bipolar sinusoidal RF power is directly used as the power supply for the adiabatic logic circuit. However, adiabatic circuits require a peak detector to provide bulk biasing for pMOS transistors. To meet this requirement, a diode-connected MOS transistor-based voltage doubler circuit is used to convert the sinusoidal input into a usable DC signal. In this paper, we propose a novel adiabatic logic design that maintains low power consumption while optimizing energy and current fluctuations across various input transitions. By ensuring uniform and complementary current flow in each transition within the logic circuit’s functional blocks, the design reduces energy variation and enhances resistance against power analysis attacks. Evaluation under different clock frequencies and load capacitances demonstrates that the proposed adiabatic logic circuit exhibits lower fluctuation and improved security, particularly at load capacitances of 50 fF and 100 fF. The results show that the proposed circuit achieves lower power dissipation compared to conventional designs. As an application example, we implemented an ultrasonic transmitter circuit within a LoRaWAN network at the end-node sensor level, which serves as both a communication protocol and system architecture for long-range communication systems. Full article

The substantia nigra (SN) has historically been regarded as a pivotal element of the brain’s motor circuits, notably within the context of the nigrostriatal pathway and Parkinson’s disease. However, recent advancements in neuroimaging techniques, particularly tractography, have facilitated the delineation of its anatomical projections. These techniques have revealed the involvement of the SN in a more extensive array of functional networks encompassing cognitive, emotional, and motivational domains. This paper reviews the current knowledge on the structural connectivity of the SN in humans based on diffusion tensor imaging and tractography. It summarizes the main projection pathways, including classical and newly described connections, such as the direct SN pars compacta connections to the thalamus, cortico–neural inputs, and connections to limbic regions and the hippocampus. Furthermore, the text delves into the distinctions between the SN pars compacta and SN pars reticulata subregions, exploring their parcellation based on connectivity. The paper demonstrates that the SN is a functionally diversified nucleus, the implications of which are significant for the understanding of both motor and neuropsychiatric disorders. The present study addresses the paucity of comprehensive treatment in this area and provides a framework for further research on dopaminergic circuits. Full article

Background: Foster care alumni face increased health challenges across the domains of mental and physical health, yet there is a paucity of research examining the associations between care experiences, health, and sleep quality in alumni aged 30 and above. Objectives: Our exploratory mixed-method study examined the sleep quality of North American group care leavers aged 30+ to understand whether sleep quality in adulthood is associated with earlier child welfare system experiences during childhood and adolescence. Secondly, we examined the association between sleep quality and overall concurrent health. Methods: Using a convenience sample of 41 alumni of care aged 30–85 and 16 qualitative interviews, we explored the intricate connections between group care leavers’ developmental trauma, sleep quality, and health. Linear regression and qualitative content analysis were utilized to understand how sleep was related to well-being in aging care alumni. Results: Adult sleep was significantly associated with the perceived quality of their youth out-of-home placement experiences (β = 0.421, p < 0.01), controlling for friendship support networks and demographic variables. Adult sleep quality was a significant predictor of overall health (β = −0.328, p < 0.05). Qualitative interviews elucidated insights into the importance and linkages of child welfare system experiences, adult sleep, and well-being. Conclusions: Our research highlights the enduring association between child welfare placement experiences, and sleep functioning well into adulthood, even when accounting for contemporaneous social support and other demographic indicators. Practitioners should be inquiring directly about sleep, and future longitudinal research should delve deeper into the nature of sleep difficulties and their association with health and well-being. Full article

Bioactive glasses are known for their surface reactivity and ability to bond with bone tissue through the formation of hydroxyapatite. This study investigates the effects of substituting ultrapure water with natural geothermal waters from the Azores in the sol–gel synthesis of 45S5 and MgO-modified bioglasses. Using high-resolution X-ray photoelectron spectroscopy (XPS), we examined how the mineral composition of the waters influenced the chemical environment and network connectivity of the glass surface. The presence of trace ions, such as Mg²⁺, Sr²⁺, Zn²⁺, and B³⁺, altered the silicate structure, as evidenced by binding energy shifts and peak deconvolution in O 1s, Si 2p, P 2p, Ca 2p, and Na 1s spectra. Thermal treatment further promoted polymerization and reduced hydroxylation. Our findings suggest that mineral-rich waters act as functional agents, modulating the reactivity and structure of bioactive glass surfaces in eco-sustainable synthesis routes. Full article

Anion exchange membranes (AEMs) are vital for electrochemical energy devices such as alkaline fuel cells and water electrolyzers, enabling the use of non-precious metal catalysts despite challenges from alkaline degradation. Hyperbranched polymers (hbPs) with their globular structure, high functional group density, and simple synthesis, offer a promising platform for enhancing transport and stability. In this study, multifunctional hbPs were synthesized from 4-vinylbenzyl chloride (VBC) and 2-hydroxyethyl methacrylate (HEMA) via atom transfer radical self-condensing vinyl polymerization (ATR-SCVP) and crosslinked into polyurethane-based AEMs. Characterization confirmed successful copolymerization and crosslinking, with excellent alkaline stability. Membranes crosslinked with higher molecular weight (MW) and VBC-richer hbPs (e.g., OH-hbP1-PU) exhibited high water uptake (75%) but low ion-exchange capacity (1.54 mmol/g) and conductivity (186 µS/cm), attributed to steric hindrance and insufficient ionic network connectivity. In contrast, OH-hbP2-PU exhibited optimal properties, with the highest OH⁻ conductivity (338 µS/cm) and IEC (2.64 mmol/g), highlighting a balanced structure for efficient ion transport. This work offers a tunable strategy for high-performance AEM development through tailored hbP architecture. Full article

Fluid dispersion directly influences the transport, mixing, and efficiency of hydrogen storage in depleted gas reservoirs. Pore structure parameters, such as pore size, throat geometry, and connectivity, influence the complexity of flow pathways and the interplay between advective and diffusive transport mechanisms. Hence, these factors are critical for predicting and controlling flow behavior in the reservoirs. Despite its importance, the relationship between pore structure and dispersion remains poorly quantified, particularly under elevated flow conditions. To address this gap, this study employs pore network modeling (PNM) to investigate the influence of sandstone and carbonate structures on fluid flow properties at the micro-scale. Eleven rock samples, comprising seven sandstone and four carbonate, were analyzed. Pore network extraction from CT images was used to obtain detailed pore structure parameters and their statistical measures. Pore-scale simulations were conducted across 60 scenarios with varying average interstitial velocities and water as the injected fluid. Effluent hydrogen concentrations were measured to generate elution curves as a function of injected pore volumes (PV). This approach enables the assessment of the relationship between the dispersion coefficient and pore structure parameters across all rock samples at consistent average interstitial velocities. Additionally, dispersivity and n-exponent values were calculated and correlated with pore structure parameters. Full article

Mutualistic interactions are crucial to the structure and functioning of ecological communities, playing a vital role in maintaining biodiversity amidst environmental perturbations. In studies of meta-networks, which are groups of local networks connected by dispersal, most research has focused on the effect of dispersal on interaction networks of competition and predation, without much attention given to mutualistic interactions. Consequently, the role of different dispersal rates (between local networks and across species) in stability and network structures is not well understood. We present a competition–mutualism model for meta-networks where mutualistic interactions follow a type II functional response, to investigate stability and species abundance dynamics under varying dispersal scenarios. We specifically assess the impact of mutualism and dispersal heterogeneity, both between local networks and across species, on the structure and stability of meta-networks. We find that mutualistic meta-networks exhibit greater stability, higher total abundance, lower species unevenness, and greater nestedness compared to meta-networks with only competition interactions. Although dispersal heterogeneity across species exerts some influence, dispersal heterogeneity between local networks mainly drives the patterns observed: it reduces total abundance, increases unevenness, and diminishes compositional similarity across the meta-network. These results highlight the pivotal role of both mutualism and spatial dispersal structure in shaping ecological networks. Our work advances understanding of how mutualistic interactions and dispersal dynamics interact to influence biodiversity and stability in complex ecosystems. Full article