Search Results (207)

Refugee camps have evolved from temporary shelters into permanent, complex urban systems, yet policy models often fail to address their long-term systemic marginality. To bridge this gap, this study conducts a comparative qualitative analysis of three protracted cases in the Middle East: Shatila (Lebanon), Yarmouk (Syria), and Jabalia (Gaza). Using a directed qualitative content analysis, the study evaluates secondary data, spatial reports, and institutional archives across five core System Pillars: Governance Configurations, Legal Regimes, Humanitarian Mediation, Spatial Violence, and Grassroots Agency. Findings show that conditions of urban exception are sustained by a self-reinforcing nexus of these processes. Furthermore, a dual-process model of refugee agency emerges, distinguishing short-term adaptive survival tactics (e.g., informal DIY utility grids) from constitutive practices of urban citizenship that reconfigure institutional aid protocols. In post-conflict contexts, physical destruction is systematically coupled with state-led property legislation to enforce permanent displacement. We conclude that protracted camps must be recognized as dynamic, co-produced urban systems rather than static zones of exception. Effectively breaking cycles of socio-spatial marginalization requires international actors to transition from short-term emergency assistance to rights-based metabolic planning, legal tenure protection, and formalized institutional co-production frameworks. Full article

The pervasive rise in digital platforms has fundamentally recalibrated the relationship between urban communities and their cultural landscapes, reconfiguring not only the sensory experience of these environments but also their interpretive frames and the everyday contestations that surround them. Using coffee spaces in Beijing as an empirical lens, this study integrates 630 validated POI locations, 287,632 online reviews from Dianping, GIS spatial analysis, LDA topic modelling, semiotic coding, and a fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) text classifier to examine the convergence between platform-mediated public perceptions and urban spatial differentiation. Our spatial interrogation identifies a statistically significant centre–periphery divide within this platform-curated corpus—a pattern that describes the geography of platform-mediated visibility rather than the total physical distribution of coffee outlets in Beijing, characterised by pronounced high-density agglomerations nesting within historic urban cores, central business districts, nascent knowledge hubs, and gentrifying lifestyle enclaves. Three landscape prototypes are derived from the data—business corridors, cultural experimental fields, and social-media spectacles. Each type exhibits discernibly different patterns of spatial density, adjacent land-use configurations, perceived experiential qualities, and underlying symbolic discourses. While positive sentiments dominate platform discourse, localised anxieties emerge regarding visual homogenization, intense queuing, premium pricing, and loss of place authenticity. The 630 venues analysed here represent a platform-visibility stratum—establishments sufficiently reviewed to feature on digital intermediaries—and the spatial patterns we map describe the geography of this visibility rather than the total physical distribution of coffee outlets. Full article

Urban landscapes are increasingly understood as dynamic systems in which natural, cultural, and socio-spatial layers continuously overlap and evolve. Within this perspective, heritage and memory are often treated as stable qualities of place, to be preserved or restored amid ongoing urban transformation. This paper challenges that static understanding by advancing memory-in-transformation, a theoretical framework positioned within the broader concept of Temporal Urbanism. Drawing on an integrative synthesis of landscape theory, critical heritage studies, memory studies, and urban theory, the paper argues that urban landscape transformation does not simply affect heritage and memory but actively produces, reconfigures, and redefines them over time. The framework rests on three interrelated dimensions: temporal layering, mediated interpretation, and dialectical production, through which transformation operates as a mediating process linking spatial change, collective memory, and the continuous formation of heritage landscapes. Illustrative cases (Superkilen, Copenhagen; the Slussen redevelopment, Stockholm) show how transformation can destabilize existing heritage while generating new heritage systems. The paper further introduces the notion of temporal justice, foregrounding whose memories are sustained or marginalized in processes of change, and outlines new directions for research and practice in heritage urbanism, land-use planning, landscape architecture, and sustainable urban development. Full article

Although peripheral ties are essential for social integration, traditional aging theories suggest that they naturally atrophy as older adults withdraw from physical social settings and prioritize intimate relationships. Digital technologies, however, offer new affordances that enable older adults to actively sustain such ties. Through six online focus groups with 32 older Chinese WeChat users (aged 55–74), this study examines how older adults manage their peripheral networks in the digital environment. Three findings emerge. First, older adults maintain a distinct typology of digital peripheral ties—Former Acquaintances, Present-day Acquaintances, and “Net Friends.” Second, they sustain these ties through strategically optimized observational engagement, tracking their networks with minimal interpersonal friction. Third, the benefits of these ties—practical information, identity maintenance, and direct emotional companionship—converge into a lightweight yet meaningful support circle that complements the strong-tie core. By uncovering these practices, this study shows that older adults are active curators of their digital social networks, providing empirical insight into the reconfiguration of late-life social convoys and design implications for age-friendly platforms. Full article

As cities assume growing responsibility for climate mitigation, an important question is whether urban carbon markets can serve as effective instruments of decarbonisation. While emissions trading research has focused primarily on emissions outcomes, less attention has been paid to the institutional mechanisms underlying policy operation. This article addresses this gap through a qualitative case study of the Tokyo Cap-and-Trade System (TCTS), launched in 2010 as the world’s first mandatory city-level emissions trading scheme. Despite its official designation, the TCTS largely follows the logic of a baseline-and-credit system rather than a conventional allowance-based cap-and-trade programme. Drawing on government data, policy documents, academic studies, and interviews, the article examines how emissions trading was adapted to the metropolitan scale through policy mobility, institutional translation, and functional state rescaling. It argues that the TCTS combines carbon trading with bureaucratic oversight, monitoring and verification, technical guidance, and energy-efficiency regulation within an administratively embedded governance framework. Although substantial emissions reductions were recorded among regulated facilities, persistent surplus credits and limited trading reveal the limits of market mechanisms alone. The study shows how globally mobile climate policies are reconfigured through local institutions and metropolitan governance capacities, suggesting that successful urban decarbonisation depends as much on administrative capacity, institutional learning, and sustained regulatory engagement as on carbon pricing. Full article

Against the backdrop of accelerating globalization and the reconfiguration of ecological security patterns, border regions—serving as critical carriers of national spatial governance—exhibit pronounced spatial heterogeneity and stage-dependent characteristics in land use evolution that warrant systematic investigation. This study focuses on global border zones to reveal the trends and evolutionary pathways of land use change, thereby providing a scientific basis for border spatial governance. Using the period of 1992–2020, four phases of global land cover data from the European Space Agency (ESA) were employed. Buffer zones of 50 km, 100 km, and 150 km were established along global national boundaries, and the study period was divided into three decadal stages. Border types were classified into terrestrial borders, coastal borders, and island borders. An integrated indicator system was constructed, including the Single Land Use Dynamic Degree (SLUDD), Integrated Land Use Dynamic Degree (ILUDD), and Weighted Composite Transition Intensity. A multi-scale nested analytical framework coupled with transition matrices was applied to assess land use evolution across global, continental, and bilateral national scales. The results indicate the following: (1) Distinct evolutionary pathways exist among border types. Terrestrial borders exhibit a combination of development and ecological restoration, with staged peaks in urban expansion; coastal borders are dominated by a unidirectional conversion from cropland to built-up land; and island borders demonstrate concurrent agricultural decline, ecological recovery, and built-up expansion. (2) From 1992 to 2020, global border land use underwent a transition from high-intensity transformations to low-intensity steady adjustments. Major changes were centered on the expansion of production space, outward diffusion of built-up areas, and vegetation restructuring. High-intensity changes were concentrated near boundary zones and attenuated significantly with increasing buffer distance. (3) At the continental scale, stable hierarchical patterns emerge in terms of change intensity, spatial gradients, and transition structures. Borders in Eurasia and North America show higher activity, Africa exhibits widespread diffusion, and South America and Oceania are characterized by forest boundary reshaping and vegetation adjustments, respectively. (4) Typical border regions can be categorized into three evolutionary types: forest ecological frontiers, agricultural expansion zones, and land–sea composite corridors. Regional trajectories vary significantly, with Southeast Asia transitioning rapidly toward stabilization, West Africa retaining periodic high-intensity disturbances, and South America remaining in a low-intensity adjustment state. This study clarifies the evolutionary patterns and boundary effect ranges of global border land use, providing theoretical support and empirical references for resource allocation and ecological regulation in border regions. It also lays the groundwork for refining border typologies, quantifying driving mechanisms, and developing differentiated cross-border governance strategies. Full article

Built cultural heritage in East Africa is often interpreted and taught through monument-based, stylistic, or externally derived frameworks that insufficiently account for the region’s overlapping environmental, cultural, historical, and socio-spatial processes. Responding to this theoretical and pedagogical gap, this article develops a regionally grounded theoretical model of East African built heritage as a layered system, with particular attention to Kenya, Tanzania, and Uganda. Methodologically, the study adopts a qualitative, interpretive approach, combining a critical literature review, analysis of selected built heritage layers, and expert perspectives from East African and European/international respondents. The findings show that East African built heritage is shaped by five interrelated layers: environmental foundations, indigenous and vernacular systems, Swahili/coastal Indian Ocean urbanism, colonial reconfiguration, and postcolonial/contemporary transformation. These layers do not form a linear historical sequence; rather, they overlap, interact, are disrupted, and are reinterpreted through material practices, spatial transformation, memory, environmental knowledge, and everyday use. The article argues that East African built heritage is defined by a layered system that is hybrid, fragile, unevenly recognized, partly intangible, and negotiated. It concludes that architectural education must move beyond object-based conservation knowledge transfer toward competency building in historical interpretation, environmental understanding, community engagement, adaptive reuse, and critical design intervention. Full article

Advanced Metering Infrastructure (AMI) systems require bidirectional wireless communication for remote meter reading, disconnection, and demand–response management across large numbers of smart meters. LoRaWAN Class-C is a strong candidate for large-scale AMI due to its long range, low infrastructure cost, and native downlink support, but gateway placement must simultaneously satisfy uplink Pure-ALOHA capacity, downlink duty-cycle limits, and geographic coverage constraints in non-uniform device distributions. This paper proposes a greedy capacity-coverage gateway placement algorithm for a LoRaWAN AMI deployment serving 89,350 smart meters in Lam Luk Ka district, Pathum Thani Province, Thailand, based on real GIS building-footprint data. The algorithm seeds each gateway at the densest unserved 300 m grid cell, assigns devices within the planning radius R up to the tighter of the uplink Pure-ALOHA capacity and the downlink duty-cycle capacity, both computed for a 98% packet delivery target, and continues placing gateways until 98% of devices are geographically covered. For the 15 min reporting interval, the algorithm yields K* = 137 gateways, only 7% above the arithmetic lower bound of 128, and requires 2.9× fewer gateways than iterative K-means under the same placement constraints. An interval-based scenario analysis across five reporting periods of 5, 10, 15, 30, and 60 min reveals that 15 min is the crossover design point where longer intervals are limited by the downlink duty-cycle and shorter intervals are limited by uplink Pure-ALOHA collision, making 15 min the point at which both constraints are simultaneously near-binding. RX2 reconfiguration from SF10 to SF7 is shown to be essential: the default configuration yields only 121 devices/gateway at the 15 min rate, requiring 739 gateways, while SF7 reconfiguration raises capacity 5.8x to 702 devices/gateway. A Monte Carlo simulation with Urban Okumura–Hata path loss and log-normal shadowing validates the placement, achieving a mean uplink PDR of 98.0% and downlink PDR of 99.9%, both meeting the 98% design target. Class-C energy consumption is 112,787 mJ per 900 s reporting cycle, 434x that of Class-A (260 mJ), confirming that Class-C bidirectional AMI is feasible only for mains-powered smart meters. Full article

This article analyzes the territorial reconfiguration of Rondônia at the beginning of the 21st century, emphasizing the concept of “Matogrossization” as a framework for understanding the profound socio-spatial transformations resulting from the expansion of globalized agribusiness, particularly the associated monocultures of soy and corn. This concept refers to the diffusion of productive practices and territorial logics characteristic of Mato Grosso, applied to Rondônia, which has led to a significant restructuring of the regional agrarian space. Methodologically, the research comprised three distinct stages: bibliographic and documentary research, cartographic mapping, and fieldwork in both Rondônia and Mato Grosso. The growth of commodity production has driven territorial appropriation aimed at export markets, resulting in a “rush” for new lands in Rondônia. This process has intensified the development of peripheral agricultural regions and caused drastic changes in the Amazonian landscape, including accelerated deforestation. It has also deepened rural conflicts over land and territory, highlighting the resistance of peasant communities and Amazonian peoples. Demographically, there is a decline in the rural population and an increase in urban growth, driven by migration from the countryside to the cities, evidencing socio-economic transformations and spatial reorganization. The concept of “matogrossization” explains this new territorial logic, which reconfigures land use to connect Rondônia to global agribusiness. Full article

The expansion of Internet-of-Things (IoT) deployments in 5G New Radio (NR) networks has made periodic measurement reporting a growing burden for low-mobility devices, which benefit little from frequent updates yet must report as often as highly mobile ones. At present, User Equipment (UE) transmits MeasurementReport messages at a fixed ReportInterval—typically 240 ms—regardless of mobility. This continuous transmission needlessly depletes UE battery energy and consumes critical uplink signaling capacity. This paper proposes a five-level adaptive ReportInterval selection scheme driven by the statistical properties of Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR). A low-mobility criterion combines four statistical conditions—the variance and gradient of both RSRP and SINR—through a logical AND, while a two-stage hysteresis mechanism (a 3 dB margin and a 2 s holding timer) suppresses unnecessary level transitions. The scheme is slice-agnostic: By relying on observed signal statistics rather than network-slice labels, it serves low-mobility mMTC and stationary eMBB devices while leaving URLLC and high-mobility UEs at their standard configuration. In Monte Carlo simulations over the 3GPP TR 38.901 Urban Micro (UMi) channel model (200 UEs, 300 s, 100 iterations), the algorithm attains a classification accuracy of 91.32% and a sensitivity of 98.77%. Based on the DRX energy model, it yields an estimated 10.87% reduction in average UE power (from 28.15 to 25.09 mW) together with a 51.09% reduction in the network-wide MeasurementReport count. The hysteresis mechanism cuts level transitions by a factor of 31.33 (from 6852.7 to 218.7 per iteration), substantially lowering RRC reconfiguration signaling. Operating at O(n) complexity on the gNodeB and using only conventional MeasConfig signaling, the scheme requires no protocol additions or UE-side modifications, making it directly deployable on existing 3GPP Release 17 infrastructure as a gNB-side software update. Full article

Millimeter-wave vehicular communication links are prone to blockage and suffer from severe path loss, and high mobility leads to rapidly time-varying channels. In addition, large-scale reconfigurable intelligent surface (RIS) arrays impose substantial channel-estimation overhead and phase-optimization complexity. To address these issues, a group-based and position-aided phase-optimization method is proposed for RIS-assisted millimeter-wave vehicular communications. First, an RIS-assisted uplink system is modeled with a multi-antenna base station (BS), an RIS configured as a uniform planar array (UPA) and a single-antenna vehicular terminal. Channel expressions are formulated for the direct vehicle–BS link, the vehicle–RIS link and the RIS–BS link. Rician fading, line-of-sight (LoS)-dominated millimeter-wave propagation, mobility-induced Doppler shifts and a standardized path-loss model for urban microcell street-canyon scenarios are incorporated to characterize the RIS-assisted vehicular cascaded channel. Based on this model, an optimization problem for the RIS phase-shift matrix is formulated under discrete phase-shift constraints to maximize the achievable rate per unit bandwidth. To avoid the exponential increase in complexity caused by conventional exhaustive search as the number of RIS reflecting elements increases, a successive refinement algorithm is introduced to derive an equivalent channel-gain expression. The original phase-optimization problem is then transformed into an element-wise iterative update process, thereby reducing the computational complexity of large-scale RIS phase configuration. To further reduce the reliance on full channel state information (CSI), two low-overhead phase-optimization schemes are designed. In the group-based scheme, the RIS reflecting elements are partitioned into several subgroups, with all elements in each subgroup constrained to share the same phase shift. This design reduces both the channel-estimation dimensionality and the number of optimization variables. In the position-aided scheme, the spatial coordinates of the BS, RIS and vehicle are used to derive the link distances and the associated angles of arrival and departure. Based on these geometric parameters, the vehicle–RIS–BS cascaded channel is reconstructed and a corresponding phase-alignment strategy is designed. The simulation results demonstrate that both proposed schemes achieve rates of approximately 6.5 bits ${\mathrm{s}}^{-1}{\mathrm{H}\mathrm{z}}^{-1}$ at a transmit power of 30 dBm and outperform existing phase-optimization techniques. When the successive refinement algorithm is applied, the computation time required for phase optimization with a 256-element RIS remains below 0.01 s. Under high-mobility conditions, both proposed schemes approach the performance upper bound achieved with perfect CSI, demonstrating strong robustness to channel variations. Full article

The transition to Beyond fifth generation of wireless networks (B5G) and sixth generation of wireless networks (6G) exposes the severe interference and coverage limitations of conventional cell-centric architectures. To overcome these bottlenecks, this paper presents a scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs). The proposed framework integrates a digital twin (DT) loop within an Open-RAN (O-RAN) architecture, employing multi-agent deep reinforcement learning (MADRL) and fractional programming (FP) for real-time joint active and passive beamforming optimization. Extensive Monte Carlo simulations in a dense urban environment demonstrate a 45% increase in spectral efficiency, a 30% reduction in uplink interference, and an 84% reduction in coverage holes compared to legacy 5G networks. Ultimately, these results provide network operators with a cost-effective, standards-compliant blueprint to extend non-line-of-sight (NLOS) coverage by 40% without incurring the prohibitive capital expenditure (CAPEX) of dense active hardware deployments. Furthermore, the proposed architecture demonstrates a competitive 10–15% margin of improvement in spectral efficiency over recent state-of-the-art DRL-based RIS frameworks. Full article

Low-altitude urban environments pose significant challenges to multi-UAV trajectory planning because of dense buildings, constrained airspace, dynamic obstacles, inter-UAV conflicts, and terminal-area congestion. This study proposes a hierarchical three-dimensional cooperative trajectory-planning framework integrating dynamic-risk-aware Ant Colony Optimization (ACO) with cooperative Model Predictive Control–Gray Wolf Optimizer (MPC-GWO). Environmental costs and predicted dynamic-obstacle risks are incorporated into the ACO global search to generate risk-aware reference trajectories, while a sliding-window GWO improves trajectory smoothness and execution feasibility. During online execution, cooperative MPC-GWO combines dynamic-obstacle prediction, inter-UAV separation constraints, reconfigurable formation switching, and goal-neighborhood safety control to achieve adaptive obstacle avoidance, cooperative replanning, and orderly terminal arrival. Thirty-run Monte Carlo simulations show that the proposed method achieves a success rate of 93.3% ± 25.4% and the highest composite score of 96.20 ± 5.30, with zero dynamic-obstacle and inter-UAV collisions. Ablation experiments verify the effectiveness of the dynamic prediction, formation reconfiguration, and terminal safety-control mechanisms. The average online replanning time remains below 0.5 s, demonstrating satisfactory safety, coordination, adaptability, and real-time performance in small- to medium-scale simulated urban scenarios. Full article

Vehicle-to-Everything (V2X) communication is an essential building block of intelligent transportation systems, supporting high-data-rate vehicle-to-infrastructure (V2I) services, and ultra-reliable low-latency vehicle-to-vehicle (V2V) communication. However, in dense urban environments, V2X services can be significantly degraded by the presence of severe blockage, fast channel variations, and high levels of interference. Intelligent Reflecting Surfaces (IRSs) can be employed to reconfigure wireless propagation environments to improve V2X communication. However, the joint optimization of transmit power, spectrum reuse, and IRS reflection coefficients is a mixed-integer non-linear problem, which is further complicated by the fast vehicular mobility and time-varying interference in V2X networks. To tackle this challenging problem, this work proposes a scalable and deployable decentralized hierarchical multi-agent deep reinforcement learning (DH-MDRL) framework. The key design principle is the separation of control timescales, whereby each V2V link functions as an autonomous agent that responds to local observations at a fast timescale and determines its transmit power and spectrum reuse decisions, while the IRS controller at the base station (BS), using global network observations, updates the IRS reflection coefficients at a slower timescale. This hierarchical architecture reduces coordination signaling associated with centralized resource allocation while enabling distributed resource allocation. The IRS-assisted V2X network is modeled as a Markov decision process, where the reward design is tailored to optimize the V2I sum data rate while guaranteeing the latency and reliability constraints associated with safety-critical V2V communication. Simulation results show that the proposed DH-MDRL framework outperforms conventional schemes without IRSs and achieves an excellent trade-off between V2V link constraints’ satisfaction probability and V2I link sum data rates compared to centralized resource allocation approaches. Full article

Amidst booming digital economy and tightening climate governance, enhancing green total-factor energy efficiency has become pivotal for socioeconomic transformation. Whether digital economy drives urban green energy transition remains unresolved, particularly regarding factor mobility mechanisms and spatial spillovers. Using panel data of 281 Chinese cities from 2011 to 2022, this study applies Double Machine Learning and Spatial Durbin Models to examine digital economy’s impact on urban green energy efficiency. Findings indicate that (i) digital economy significantly enhances local green energy efficiency through green technological innovation, green finance development, and industrial upgrading; (ii) it facilitates talent and capital agglomeration toward digitally advanced regions, with innovation factor mobility serving as a crucial mediator; and (iii) significant spatial positive correlation exists, where digital economy generates pronounced spillovers to neighboring cities that exceed direct effects, fostering regional synergies. This research overcomes conventional methodological limitations and pioneers integrating innovation factor mobility into digital economy-green transition analysis, revealing factor reconfiguration as the core mechanism. Findings provide policy implications for cross-regional digital-energy coordination, factor marketization reforms, and differentiated green strategies. Full article