Search Results (3,323)

Lacticaseibacillus rhamnosus (Lbs. rhamnosus) is renowned for its tolerance to gastric acid and adaptability to bile and alkaline conditions, and is crucial for intestinal health and immune regulation. In this study, integrated transcriptomic and proteomic analyses were employed to elucidate the response mechanisms of Lbs. rhamnosus under osmotic stress, induced by exposure to 0.6 M sodium lactate, which elevates environmental osmotic pressure. It was shown that 792 differentially expressed genes and 138 differentially expressed proteins were detected in Lbs. rhamnosus ATCC 53103 treated with osmotic stress. The differential regulation of these genes/proteins mainly includes the inhibition of fatty acid metabolism with membrane structural remodeling (downregulation of the acetyl coenzyme A carboxylase family and fatty acid binding protein family expression), dynamic homeostasis of amino acid metabolism (restriction of the synthesis of histidine, cysteine, leucine, etc., and enhancement of the catabolism of lysine, tryptophan, etc.), and survival-oriented reconfiguration of carbohydrate metabolism (gene expression related to the glycolytic pathway increases, while gene expression related to the pentose phosphate pathway decreases). These synergistic alterations in metabolic regulation may facilitate the adaptive response of Lbs. rhamnosus ATCC 53103 to osmotic stress. Overall, our findings deepen the current understanding of the stress response mechanisms in lactic acid bacteria and offer novel insights into the survival strategies employed by Lbs. rhamnosus ATCC 53103 under hyperosmotic conditions. Full article

This article examines the contemporary Portuguese family through the lens of changes in the transmission of family values, with a particular focus on the religious sphere. Using a quantitative methodology based on a questionnaire survey administered to a non-probabilistic convenience sample of 3634 respondents in Portugal, this study explores the transformations in family values and the role of religion. The findings show that current values are increasingly oriented toward individualism, emotionality, expressiveness, and empowerment, with religion no longer underpinning these values. The religious decline within the family sphere has paralleled the erosion of traditional, positional, and hierarchical values—even among individuals with religious beliefs in whom the sense of belonging is weakening—favoring individualistic and expressive values related to work, education, and leisure. The analysis reveals significant generational differences in the perception of family, indicating an ongoing process of social transformation that reflects broader structural changes in Portuguese society. Younger generations exhibit a stronger adherence to individualistic values and a weaker attachment to traditional hierarchical patterns. The data suggest a profound reconfiguration of the value foundations of the family, with important implications for family policies and for understanding contemporary family dynamics in the Portuguese context. Full article

Driven by the new wave of technological revolution and industrial transformation, firms are accelerating strategic change to gain new competitive advantages. Situated within a complex adaptive system, firms must adapt to highly dynamic and uncertain external environments by adjusting executive cognitive structures, reconfiguring resources and capabilities, and strengthening collaboration with industrial ecosystem elements; hence, digital strategic change is characterized by continuous evolution. Using a sample of Chinese A-share listed firms from 2015 to 2023, this study develops a “cognition–capability–strategy” pathway model grounded in upper echelons theory and dynamic capabilities theory to examine how executive cognitive styles, i.e., cognitive flexibility and cognitive complexity, drive digital strategic change via absorptive capacity and how environmental dynamism moderates these relationships. The findings show that executive cognition, as a decision node in strategic change, can dynamically adjust firms’ strategic paths by activating absorptive capacity in rapidly changing external information environments; environmental dynamism differentially affects the two cognitive styles. Heterogeneity tests further indicate that the role of executive cognition varies significantly with regional digital economy development levels, firm life cycle, and industry factor intensities. The study reveals how firms can respond to high environmental uncertainty through cognition–strategy alignment and resource capability reconfiguration in a complex adaptive system, providing theoretical references and practical insights for emerging economies to advance digital transformation and enhance competitiveness. Full article

Background: The escalating global salinization poses a significant threat to agricultural productivity, necessitating a thorough understanding of plant responses to high salinity. Pea sprouts (Pisum sativum), a nutrient-rich crop increasingly cultivated in salinized regions, serve as an ideal model for such investigations due to their rapid growth cycle and documented sensitivity to ionic stress. Methods: In order to understand the response of pea sprouts in physiological regulation, redox-metabolic adjustments, and transcriptome reprogramming under salt stress, we investigated the effects of high salt concentrations on the ascorbic acid–glutathione cycle, endogenous hormone levels, metabolite profiles, and gene expression patterns in it. Results: Our findings reveal early-phase antioxidant/hormonal adjustments, mid-phase metabolic shifts, and late-phase transcriptomic reprogramming of pea sprouts under salt conditions. In addition, a biphasic response in the ascorbic acid cycle was found, with initial increases in enzyme activities followed by a decline, suggesting a temporary enhancement of antioxidant defenses. Hormonal profiling indicated a significant increase in abscisic acid (ABA) and jasmonic acid (JA), paralleled by a decrease in indole acetic acid (IAA) and dihydrozeatin (DZ), underscoring the role of hormonal regulation in stress adaptation. Metabolomic analysis uncovered salt-induced perturbations in sugars, amino acids, and organic acids, reflecting the metabolic reconfiguration necessary for osmotic adjustment and energy reallocation. Transcriptomic analysis identified 6219 differentially expressed genes (DEGs), with a focus on photosynthesis, hormone signaling, and stress-responsive pathways, providing insights into the molecular underpinnings of salt tolerance. Conclusions: This comprehensive study offers novel insights into the complex mechanisms employed by pea sprouts to combat salinity stress, contributing to the understanding of plant salt tolerance and potentially guiding the development of salt-resistant crop varieties. Full article

A reconfigurable microwave absorber based on double-layer metasurface is proposed for wide microwave band applications spanning 3 to 14 GHz. The absorber consists of two layers with two-dimensional array of four-semi-circular and square-ring metasurface patches loaded impedance devices, two spacers composed of honeycomb materials, and a bottom copper substrate. In order to break through the limitation of single-layer absorbers at finite resonant frequencies, a special double-layered metasurface structure is adopted. The layer I of metasurface is designed with two resonant peaks near the X band and transmission performance in the C band. Simultaneously, the layer II of metasurface is designed with a resonant peak near the C band and reflection performance in the X band. To achieve a reconfigurable effect, impedance adjustable device, such as PIN diodes, are connected between patterned metasurface cells of layer I. The simulation results revealed that the double-layer metasurface absorber can not only achieve broadband absorption effect, with the reflection value below −10 dB from 3.1 to 14.2 GHz, but also adjust the electromagnetic absorption rate, with the reflection value below −20 dB covers a bandwidth of 6.6–9 GHz. The good agreement between simulation and measurement validates the proposed absorber. Full article

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

Wind turbine blades feature complex geometries and operate under harsh conditions, including high curvature gradients, nonlinear deformations, elevated humidity, and particulate contamination. This study presents the design and kinematic analysis of a novel climbing robot based on a 10R folding metamorphic mechanism. The robot employs a hybrid wheel-leg drive and adaptively reconfigures between rectangular and hexagonal topologies to ensure precise adhesion and efficient locomotion along blade leading edges and windward surfaces. A high-order kinematic model, derived from a modified Grubler–Kutzbach criterion augmented by rotor theory, captures the mechanism’s intricate motion characteristics. We analyze the degrees of freedom (DOF) and motion branch transitions for three representative singular configurations, elucidating their evolution and constraint conditions. A scaled-down prototype, integrating servo actuators, vacuum adhesion, and multi-modal sensing on an MDOF control platform, was fabricated and tested. Experimental results demonstrate a configuration switching time of 6.3 s, a single joint response time of 0.4 s, and a maximum crawling speed of 125 mm/s, thereby validating stable adhesion and surface tracking performance. This work provides both theoretical insights and practical validation for the intelligent maintenance of wind turbine blades. Full article

In the era of digital business model innovation, multinational corporations face a dual challenge: leveraging digital technologies to overcome institutional barriers while reconfiguring value creation in cross-border operations. Grounded in institutional theory and the digital transformation literature, this study investigates how knowledge distance and political distance shape subsidiary exits through a U-shaped relationship, and how digital transformation breadth and depth differentially reconfigure these effects. We conduct empirical research on 1203 Chinese multinational enterprises from 2015 to 2019. The results indicate that both knowledge distance and political distance exhibit a U-shaped relationship with the subsidiary exit. The breadth of digital transformation strengthens the U-shaped relationship between knowledge distance and subsidiary exit but weakens the relationship between political distance and subsidiary exit. The depth of digital transformation mitigates the effects of both knowledge distance and political distance on subsidiary exit. These findings provide a novel explanatory perspective on the ‘Distance Paradox’ in internationalization theory, address a critical gap in the multinational enterprise (MNE) exit literature, and propose a modular governance blueprint for MNEs. Full article

In the context of industrial applications, ensuring medium access control is a fundamental challenge. Industrial IoT devices are resource-constrained and must guarantee reliable communication while reducing energy consumption. The IEEE 802.15.4e standard proposed time-slotted channel hopping (TSCH) to meet the requirements of the industrial Internet of Things. TSCH relies on time synchronization and channel hopping to improve performance and reduce energy consumption. Despite these characteristics, configuring an efficient schedule under varying traffic conditions and interference scenarios remains a challenging problem. The exploitation of reinforcement learning (RL) techniques offers a promising approach to address this challenge. AI enables TSCH to dynamically adapt its scheduling based on real-time network conditions, making decisions that optimize key performance criteria such as energy efficiency, reliability, and latency. By learning from the environment, reinforcement learning can reconfigure schedules to mitigate interference scenarios and meet traffic demands. In this work, we compare various reinforcement learning (RL) algorithms in the context of the TSCH environment. In particular, we evaluate the deep Q-network (DQN), double deep Q-network (DDQN), and prioritized DQN (PER-DQN). We focus on the convergence speed of these algorithms and their capacity to adapt the schedule. Our results show that the PER-DQN algorithm improves the packet delivery ratio and achieves faster convergence compared to DQN and DDQN, demonstrating its effectiveness for dynamic TSCH scheduling in Industrial IoT environments. These quantifiable improvements highlight the potential of prioritized experience replay to enhance reliability and efficiency under varying network conditions. Full article

The rapid expansion of broadband wireless communication systems, including 5G, satellite networks, and next-generation IoT platforms, has created a strong demand for antenna architectures capable of real-time beam control, compact integration, and broad frequency coverage. Traditional reflectarrays, while effective for narrowband applications, often face inherent limitations such as fixed beam direction, high insertion loss, and complex phase-shifting networks, making them less viable for modern adaptive and reconfigurable systems. Addressing these challenges, this work presents a novel wideband planar metasurface that operates as a polarization rotation reflective metasurface (PRRM), combining 90° polarization conversion with 1-bit reconfigurable phase modulation. The metasurface employs a mirror-symmetric unit cell structure, incorporating a cross-shaped patch with fan-shaped stub loading and integrated PIN diodes, connected through vertical interconnect accesses (VIAs). This design enables stable binary phase control with minimal loss across a significantly wide frequency range. Full-wave electromagnetic simulations confirm that the proposed unit cell maintains consistent cross-polarized reflection performance and phase switching from 3.83 GHz to 15.06 GHz, achieving a remarkable fractional bandwidth of 118.89%. To verify its applicability, the full-wave simulation analysis of a 16 × 16 array was conducted, demonstrating dynamic two-dimensional beam steering up to ±60° and maintaining a 3 dB gain bandwidth of 55.3%. These results establish the metasurface’s suitability for advanced beamforming, making it a strong candidate for compact, electronically reconfigurable antennas in high-speed wireless communication, radar imaging, and sensing systems. Full article

As Chinese cities move toward stock-based development, the redevelopment of inefficient industrial land has become essential for urban spatial restructuring and sustainable transformation. Building on Lefebvre’s triadic theory of spatial production, this study establishes a comprehensive analytical framework consisting of spatial practice, representations of space, and representational spaces, aiming to elucidate the mechanisms underlying spatial reconfiguration. Through a multi-case inductive approach, twelve representative cases from Zhejiang Province are systematically analyzed to reveal the fundamental logic driving spatial reconstruction within the context of inefficient land redevelopment. The results reveal the following: (1) In the process of inefficient land redevelopment, spatial practice involves land reuse and functional integration, representations of space reflect institutional planning, and representational spaces shape meaning through cultural identity and user experience. These dimensions interact dynamically to drive the transformation of both the form and meaning of inefficient land. (2) The redevelopment of inefficient land in Zhejiang can be classified into two primary models: increment-driven and qualitative transformation, which are further divided into seven subtypes. The increment-driven model includes enterprise-initiated renewal, integrated upgrading, platform empowerment, and comprehensive remediation; the qualitative transformation model comprises mine remediation, cultural empowerment, and use conversion. (3) Significant differences exist between these models: the increment-driven model emphasizes land expansion and floor area ratio improvement, while the qualitative transformation model enhances land value through mine restoration, cultural embedding, and functional transformation. This study extends the application of spatial production theory within the Chinese context and offers theoretical support and policy insights for the planning and governance of inefficient industrial land redevelopment. Full article

This study investigates how the integration of artificial intelligence (AI) transforms job practices within a leading European infrastructure company. Grounded in the Feeling Economy framework, the research explores the shift in task composition following AI implementation, focusing on the emergence of new roles, required competencies, and the ongoing reconfiguration of work. Using a qualitative, single-case study methodology, data were collected through semi-structured interviews with ten employees and company documentation. Thematic analysis revealed five key dimensions: the reconfiguration of job tasks, the improvement of efficiency and quality, psychological and adaptation challenges, the need for AI-related competencies, and concerns about dehumanisation. Findings show that AI systems increasingly assume repetitive and analytical tasks, enabling workers to focus on strategic, empathetic, and creative responsibilities. However, psychological resistance, fears of job displacement, and a perceived erosion of human interaction present implementation barriers. The study provides theoretical contributions by empirically extending the Feeling Economy and task modularisation frameworks. It also offers managerial insights into workforce adaptation, training needs, and the importance of ethical and emotionally intelligent AI integration. Additionally, this study highlights that the Feeling Economy must address AI’s epistemic risks, emphasising fairness, transparency, and participatory governance as essential for trustworthy, emotionally intelligent, and sustainable AI systems. Full article

This study presents a reconfigurable planar photonic device capable of dynamically switching between optical filter and absorber functionalities by exploiting the phase transition properties of GeTe, a chalcogenide phase-change material. The device adopts a Metal–Dielectric–PCM architecture composed of silver (Ag), silicon dioxide (SiO₂), and GeTe layers, each playing a distinct role: the silver layer governs the transmission and absorption efficiency, the SiO₂ layer controls the resonance conditions, and the GeTe layer determines the device’s scattering behavior via its tunable optical losses. Numerical simulations revealed that the structure enables high RGB transmission in the amorphous state and broadband absorption in the crystalline state. By adjusting geometric parameters—especially the metallic thickness—the device exhibits finely tunable spectral responses under varying polarizations and incidence angles. These findings highlight the synergistic interplay between material functionality and layer configuration, positioning this platform as a compact and energy-efficient solution for applications in tunable photonics, optical sensing, and programmable metasurfaces. Full article

Bias Temperature Instability (BTI)-induced aging of transistors is a serious concern in modern electronic circuits, yet its effects on the operation of mixed-signal circuits have not been extensively studied. In this work, initially we analyze how BTI-induced aging degradation influences the analog front end of Flash analog-to-digital converters (ADCs). BTI-induced aging leads to substantial increments in the offset voltage of the ADC comparators, which in turn affect their trip point voltage, leading to the alteration of the ADC’s performance characteristics, such as gain, full-scale error and integral nonlinearity. Thus, erroneous responses are generated. Next, we propose a low-cost BTI-induced aging mitigation technique based on a circuit reconfiguration method which periodically alters the average voltage stress on the ADC comparators’ transistors. The proposed method limits the comparators’ offset voltage development, restricting the shift in their trip point voltage. Consequently, the impact of aging on the performance characteristics of the ADC is drastically reduced, and its reliability is improved. According to our simulations, after two years of operation, the gain error is reduced by 95.43%, the full-scale error is reduced by 63.31% and the integral nonlinearity is reduced by 63.00%, with respect to operation without applying the proposed aging mitigation technique. Full article

Over Over the past years, the field of Machine Learning (ML) and Deep Learning (DL) has seen strong developments both in terms of software and hardware, with the increase of specialized devices. One of the biggest challenges in this field is the inference phase, where the trained model makes predictions of unseen data. Although computationally powerful, traditional computing architectures face limitations in efficiently managing requests, especially from an energy point of view. For this reason, the need arose to find alternative hardware solutions, and among these, there are Field Programmable Gate Arrays (FPGAs): their key feature of being reconfigurable, combined with parallel processing capability, low latency and low power consumption, makes those devices uniquely suited to accelerating inference tasks. In this paper, we present a novel approach to accelerate the inference phase of a multi-layer perceptron (MLP) using BondMachine framework, an OpenSource framework for the design of hardware accelerators for FPGAs. Analysis of the latency, energy consumption, and resource usage, as well as comparisons with respect to standard architectures and other FPGA approaches, is presented, highlighting the strengths and critical points of the proposed solution. The present work represents an exploratory study to validate the proposed methodology on MLP architectures, establishing a crucial foundation for future work on scalability and the acceleration of more complex neural network models. Full article