Search Results (5,512)

The construction sector presently consumes about 40% of global energy and generates 36% of CO₂ emissions, making facade retrofits a priority for decarbonising buildings. This review clarifies how ventilated facades (VFs), wall assemblies that interpose a ventilated air cavity between outer cladding and the insulated structure, address that challenge. First, the paper categorises VFs by structural configuration, ventilation strategy and functional control into four principal families: double-skin, rainscreen, hybrid/adaptive and active–passive systems, with further extensions such as BIPV, PCM and green-wall integrations that couple energy generation or storage with envelope performance. Heat-transfer analysis shows that the cavity interrupts conductive paths, promotes buoyancy- or wind-driven convection, and curtails radiative exchange. Key design parameters, including cavity depth, vent-area ratio, airflow velocity and surface emissivity, govern this balance, while hybrid ventilation offers the most excellent peak-load mitigation with modest energy input. A synthesis of simulation and field studies indicates that properly detailed VFs reduce envelope cooling loads by 20–55% across diverse climates and cut winter heating demand by 10–20% when vents are seasonally managed or coupled with heat-recovery devices. These thermal benefits translate into steadier interior surface temperatures, lower radiant asymmetry and fewer drafts, thereby expanding the hours occupants remain within comfort bands without mechanical conditioning. Climate-responsive guidance emerges in tropical and arid regions, favouring highly ventilated, low-absorptance cladding; temperate and continental zones gain from adaptive vents, movable insulation or PCM layers; multi-skin adaptive facades promise balanced year-round savings by re-configuring in real time. Overall, the review demonstrates that VFs constitute a versatile, passive-plus platform for low-carbon buildings, simultaneously enhancing energy efficiency, durability and indoor comfort. Future advances in smart controls, bio-based materials and integrated energy-recovery systems are poised to unlock further performance gains and accelerate the sector’s transition to net-zero. Emerging multifunctional materials such as phase-change composites, nanostructured coatings, and perovskite-integrated systems also show promise in enhancing facade adaptability and energy responsiveness. Full article

Recent technological advancements have resulted in the emergence of portable electronic devices (PEDs), including mobile phones equipped with satellite communication capabilities. These devices generally emit higher power, which can potentially cause electromagnetic interference to GPS antennas. This study uses both simulation and experimental methods to evaluate the interference path loss (IPL) between PEDs located inside an A320 aircraft and an external GPS antenna. The effects of PED location, antenna polarization, and frequency bands on IPL were simulated and analyzed. Additionally, measurement experiments were conducted on an A320 aircraft, and statistical methods were used to compare the experimental data with the simulation results. Considering the front-door coupling of both spurious and intentional radiated emissions, the measured IPL is up to 15 ± 3 dB lower than the $IP{L}_{target}$. This result should be interpreted with caution. This issue offers new insights into the potential risks of electromagnetic interference in aviation environments. The findings help quantify the probability of interference with GPS antennas. Furthermore, the modeling simplification method used in this study may be applicable to the analysis of other large and complex structures. Full article

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

In Europe, corporate sustainability reporting through the double materiality assessment was formally introduced with the Corporate Sustainability Reporting Directive in response to the European Sustainability Reporting Standards. The double materiality assessment is essential not only to determine the scope of corporate sustainability reporting but also to guide companies toward an efficient allocation of resources and shape corporate sustainability strategies. However, although EFRAG represents the technical adviser of the European Commission, there are numerous “interoperable” standards related to the assessment of double materiality, including the Global Reporting Initiative (GRI), or UNI 11919-1:2023. This research intends to systematically analyze similarities and divergences between the most widespread double materiality assessment standards at the global scale, highlighting their strengths and weaknesses and trying to identify a comparable path toward the creation of a set of common guidelines. This analysis is carried out through the systematic study of seven standards and by answering nine questions ranging from generic ones, such as “what is the concept of double materiality?”, to more technical questions like “does the standard identify thresholds?”, but adding original prospects such as “does the standard refer to different types of capital?”. Findings highlight that EFRAG, UNI 11919-1:2023, and GRI represent the most complete and least-discretionary standards, but some methodological aspects need to be enhanced. In the double materiality assessment, companies must identify key stakeholders, material topics and material risks, and must develop the double materiality matrix, promoting transparent disclosure, continuous monitoring, and stakeholders’ engagement. While comparability is principally required among companies operating within the same sector and of similar size, this does not preclude the possibility of comparing firms across different sectors with respect to specific indicators, when appropriate or necessary. Full article

The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article

In light of the ongoing discussion concerning the energy transition of the heating sector, primarily focused on district heating and shaped by heating corporations towards an incremental transformation, an alternative direction for the energy transition of the heating sector towards electroheating—a breakthrough transformation—is presented in this paper, along with a justification of its rationale. Arguments “for” and “against” both transformation paths are provided. Analyses of the costs of transforming district heating systems along both trajectories are conducted. The opportunities of a breakthrough transformation are characterized. An alternative approach to the energy transformation of district heating systems will provoke resistance and opposition from representatives of institutions operating within the current model. Transforming the existing heating model without changing its structure will burden society with high transformation costs through demands for government guarantees to cover these expenses. The analysis presented in this paper shows that these costs can be significantly reduced if the approach to the generation and distribution of district heat is changed. Full article

The $N_k$-index (k-distance degree index) of a connected graph G was first introduced by Naji and Soner as a generalization of the distance degree concept, as $N_k\left(G\right)={\displaystyle \sum _{k=1}^{d\left(G\right)}}\left({\displaystyle \sum _{v\in V\left(G\right)}}{d}_k\left(v\right)\right)k$, where the distance between u and v in G is denoted by $d(u,v)$, the diameter of a graph G is denoted by $d\left(G\right)$, and the degree of a vertex v at distance k is denoted by $d_k\left(v\right)=\{u,v\in V\left(G\right)d(u,v)=k\}$. In this paper, we extend the study of the $N_k$-index of graphs. We introduced some graph transformations and their impact on the $N_k$-index of graph and proved that the star graph has the minimum, and the path graph has the maximum $N_k$-index among the set of all trees on n vertices. We also show that among all trees with fixed maximum-degree $\Delta$, the broom graph $B_{n,\Delta }$ (consisting of a star $S_{\Delta +1}$ and a pendant path of length $n-\Delta -1$ attached to any arbitrary pendant path of star) is a unique tree which maximizes the $N_k$-index. Further, we also defined and proved a graph with maximum $N_k$-index for a given number of n vertices, maximum-degree $\Delta$, and perfect matching among trees. We characterize the starlike trees which minimize the $N_k$-index and propose a unique tree which minimizes the $N_k$-index with diameter d and n vertices among trees. Full article

This study investigates the hydrogen permeability of injection-molded polyamide 6 (PA6) nanocomposites reinforced with organo-modified montmorillonite (OMMT) at varying concentrations (1, 2.5, 5, and 10 wt. %) for potential use as Type IV composite-overwrapped pressure vessel (COPV) liners. While previous work examined their mechanical properties, this study focuses on their crystallinity, morphology, and gas barrier performance. The precise inorganic content was determined using thermal gravimetry analysis (TGA), while differential scanning calorimetry (DSC), wide-angle X-ray diffraction (WAXD), and scanning electron microscopy (SEM) were used to characterize the structural and morphological changes induced by varying filler content. The results showed that generally higher OMMT concentrations promoted γ-phase formation but also led to increased agglomeration and reduced crystallinity. The PA6/OMMT-1 wt. % sample stood out with higher crystallinity, well-dispersed clay, and low hydrogen permeability. In contrast, the PA6/OMMT-2.5 and -5 wt. % samples showed increased permeability, which corresponded to WAXD and SEM evidence of agglomeration and DSC results indicating a lower degree of crystallinity. PA6/OMMT-10 wt. % showed the most-reduced hydrogen permeability compared to all other samples. This improvement, however, is attributed to a tortuous path effect created by the high filler loading rather than optimal crystallinity or dispersion. SEM images revealed significant OMMT agglomeration, and DSC analysis confirmed reduced crystallinity, indicating that despite the excellent barrier performance, the compromised microstructure may negatively impact mechanical reliability, showing PA6/OMMT-1 wt. % to be the most balanced candidate combining both mechanical integrity and hydrogen impermeability for Type IV COPV liners. Full article

Chemical modifications are the standard for small interfering RNAs (siRNAs) in therapeutic applications, but predicting their off-target effects remains a significant challenge. Current approaches often rely on sequence-based encodings, which fail to fully capture the structural and protein–RNA interaction details critical for off-target prediction. In this study, we developed a framework to generate reproducible structure-based chemical features, incorporating both molecular fingerprints and computationally derived siRNA–hAgo2 complex structures. Using an RNA-Seq off-target study, we generated over 30,000 siRNA–gene data points and systematically compared nine distinct types of feature representation strategies. Among the datasets, the highest predictive performance was achieved by Dataset 3, which used extended connectivity fingerprints (ECFPs) to encode siRNA and mRNA features. An energy-minimized dataset (7R), representing siRNA–hAgo2 structural alignments, was the second-best performer, underscoring the value of incorporating reproducible structural information into feature engineering. Our findings demonstrate that combining detailed structural representations with sequence-based features enables the generation of robust, reproducible chemical features for machine learning models, offering a promising path forward for off-target prediction and siRNA therapeutic design that can be seamlessly extended to include any modification, such as clinically relevant 2′-F or 2′-OMe. Full article

Technology-based strategies for the prevention and management of suicidal behavior are widely referenced for identifying vulnerable groups and for supporting clinical reasoning, decision-making, and appropriate referrals. In this study, we estimated the interface and content validity evidence of an interactive mobile application developed for managing suicidal behavior. This methodological study employed psychometric parameters to evaluate the content and interface of the mobile application, following five action phases: analysis, design, development, implementation, and evaluation. A total of 27 healthcare professionals participated, selected by convenience sampling, all working within the Psychosocial Care Network across different regions of Brazil. Data were collected using an electronic form, the Delphi technique for evaluation rounds, and a Likert scale to achieve consensus. The validity analysis was based on a Content Validity Index (CVI) equal to or greater than 0.80. The results showed that GerenciaVida, a technology developed for healthcare workers—regardless of their level of care or professional category—can be used to screen for suicide risk in the general population and indicate preventive alternatives. The app demonstrated satisfactory indicators of content validity (0.974) and interface validity (0.963), reflecting clarity (0.925), objectivity (1.00), adequacy (0.925), coherence (0.962), accuracy (0.962), and clinical relevance (1.00). The development path of this mobile application provided scientific, technological, and operational support, establishing it as an innovative care tool. It consolidates valid evidence that supports the identification, risk classification, and prevention of suicidal behavior in various healthcare contexts. Full article

Urban vegetation shows significant spatial differences due to the combined effects of natural and human factors, yet fine-scale evolutionary patterns and their cross-scale feedback mechanisms remain limited. This study focuses on the Yangtze River Delta (YRD), the top economic area in China. By integrating data from multiple Landsat sensors, we built a high—resolution framework to track vegetation dynamics from 1990 to 2020. It generates annual 30-m Enhanced Vegetation Index (EVI) data and uses a new Vegetation Green—Brown Balance Index (VBI) to measure changes between greening and browning. We combined Mann-Kendall trend analysis with machine—learning based attribution analysis to look into vegetation changes across different city types and urban—rural gradients. Over 30 years, the YRD’s annual EVI increased by 0.015/10 a, with greening areas 3.07 times larger than browning. Spatially, urban centers show strong greening, while peri—urban areas experience remarkable browning. Vegetation changes showed a city-size effect: larger cities had higher browning proportions but stronger urban cores’ greening trends. Cluster analysis finds four main evolution types, showing imbalances in grey—green infrastructure allocation. Vegetation baseline in 1990 is the main factor driving the long-term trend of vegetation greenness, while socioeconomic and climate drivers have different impacts depending on city size and position on the urban—rural continuum. In areas with low urbanization levels, climate factors matter more than human factors. These multi-scale patterns challenge traditional urban greening ideas, highlighting the need for vegetation governance that adapts to specific spatial conditions and city—unique evolution paths. Full article

The generation of fine coal particles during mining and processing presents significant environmental and logistical challenges, particularly in coal-dependent, developing countries like South Africa (SA). This review critically evaluates the technical viability of fine coal briquetting as a sustainable waste-to-energy solution within a SA context, while drawing from global best practices and comparative benchmarks. It examines abundant feedstocks that can be used for valorization strategies, including fine coal and agricultural biomass residues. Furthermore, binder types, manufacturing parameters, and quality optimization strategies that influence briquette performance are assessed. The co-densification of fine coal with biomass offers a means to enhance combustion efficiency, reduce dust emissions, and convert low-value waste into a high-calorific, manageable fuel. Attention is also given to briquette testing standards (i.e., South African Bureau of Standards, ASTM International, and International Organization of Standardization) and end-use applications across domestic, industrial, and off-grid settings. Moreover, the review explores socio-economic implications, including rural job creation, energy poverty alleviation, and the potential role of briquetting in SA’s ‘Just Energy Transition’ (JET). This paper uniquely integrates technical analysis with policy relevance, rural energy needs, and practical challenges specific to South Africa, while offering a structured framework for bio-coal briquetting adoption in developing countries. While technical and economic barriers remain, such as binder costs and feedstock variability, the integration of briquetting into circular economy frameworks represents a promising path toward cleaner, decentralized energy and coal waste valorization. Full article

Low Earth orbit (LEO) satellite networks deliver superior real-time performance and responsiveness compared to conventional satellite networks, despite technical and economic challenges such as high deployment costs and operational complexity. Nevertheless, rapid topology changes and severe energy constraints of LEO satellites make real-time routing a persistent challenge. In this paper, we employ field-programmable gate arrays (FPGAs) to overcome the resource limitations of on-board computers (OBCs) and to manage energy consumption effectively using the Eclipse-Aware Routing (EAR) algorithm, and we implement the K-Shortest Paths (KSP) algorithm directly on the FPGA. Our method first generates multiple routes from the source to the destination using KSP, then selects the optimal path based on energy consumption rate, eclipse duration, and estimated transmission load as evaluated by EAR. In large-scale LEO networks, the computational burden of KSP grows substantially as connectivity data become more voluminous and complex. To enhance performance, we accelerate complex computations in the programmable logic (PL) via pipelining and design a collaborative architecture between the processing system (PS) and PL, achieving approximately a 3.83× speedup compared to a PS-only implementation. We validate the feasibility of the proposed approach by successfully performing remote routing-table updates on the SpaceWire-based SpaceWire Brick MK4 network system. Full article

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications. Full article

This study investigates the relationship between sovereign credit rating transitions and domestic equity market performance, focusing on Greece from 2004 to 2024. Although credit ratings are central to sovereign risk assessment, their immediate influence on financial markets remains contested. This research adopts a multi-method analytical framework combining algebraic combinatorics and time-series econometrics. The methodology incorporates the construction of a directed credit rating transition graph, the partially ordered set representation of rating hierarchies, rolling-window correlation analysis, Granger causality testing, event study evaluation, and the formulation of a reward matrix with optimal rating path optimization. Empirical results indicate that credit rating announcements in Greece exert only modest short-term effects on the Athens Stock Exchange General Index, implying that markets often anticipate these changes. In contrast, sequential downgrade trajectories elicit more pronounced and persistent market responses. The reward matrix and path optimization approach reveal structured investor behavior that is sensitive to the cumulative pattern of rating changes. These findings offer a more nuanced interpretation of how sovereign credit risk is processed and priced in transparent and fiscally disciplined environments. By bridging network-based algebraic structures and economic data science, the study contributes a novel methodology for understanding systemic financial signals within sovereign credit systems. Full article