Search Results (10)

The Tibetan Plateau’s lakes, serving as critical water towers for over two billion people, exhibit divergent responses to climate change that remain poorly quantified. This study develops a deep learning framework integrating Synthetic Aperture Radar (SAR) altimetry from Sentinel-3A with bias-corrected CMIP6 (Coupled Model Intercomparison Project Phase 6) climate projections under Shared Socioeconomic Pathways (SSP) scenarios (SSP2-4.5 and SSP5-8.5, adjusted via quantile mapping) to predict lake-level changes across eight Tibetan Plateau (TP) lakes. Using a Feed-Forward Neural Network (FFNN) optimized via Bayesian optimization using the Optuna framework, we achieve robust water level projections (mean validation R² = 0.861) and attribute drivers through Shapley Additive exPlanations (SHAP) analysis. Results reveal a stark north–south divergence: glacier-fed northern lakes like Migriggyangzham will rise by 13.18 ± 0.56 m under SSP5-8.5 due to meltwater inputs (temperature SHAP value = 0.41), consistent with the early (melt-dominated) phase of the IPCC’s ‘peak water’ framework. In comparison, evaporation-dominated southern lakes such as Langacuo face irreversible desiccation (−4.96 ± 0.68 m by 2100) as evaporative demand surpasses precipitation gains. Transitional western lakes exhibit “peak water” inflection points (e.g., Lumajang Dong’s 2060 maximum) signaling cryospheric buffer loss. These projections, validated through rigorous quantile mapping and rolling-window cross-validation, provide the first process-aware assessment of TP Lake vulnerabilities, informing adaptation strategies under the Sustainable Development Goals (SDGs) for water security (SDG 6) and climate action (SDG 13). The methodological framework establishes a transferable paradigm for monitoring high-altitude freshwater systems globally. Full article

The paper intends to contribute to readers’ comprehension of the Greek telecommunications market, focusing on the strategic decisions associated with network tower-sharing analysis in Greece. The Greek telecommunications industry is described for the first time following the Structure–Conduct–Performance (SCP) paradigm of Industrial Organisation (IO), as a methodological tool of analysis. In that respect, an SCP model in its extended form is constructed, aiming to examine how structure, conduct, and performance interrelate to each other. More precisely, the SCP model explains how strategic decisions regarding tower infrastructure sharing between 2013–2022 were developed, as a result of a series of interactions and feedback effects, amongst market structure, operators’ conducts, and performances, resulting in strengthening competition and reshaping market structure with the entrance of a new player in the Greek mobile market, an independent TowerCo (Athens, Greece) in Greece. International tendencies and competition issues influencing domestic growth potentialities and alternative operators’ concentration will be addressed, too. The paper concludes with presenting a basically qualitative, explanatory interpretive analysis of the perspectives of network tower-sharing analysis in the Greek telecommunication industry, including policy recommendations for the near future and thoughts on future research, as well. Full article

This article proposes a method for ontology construction in the field of ancient architecture digitization with the aim of addressing the lack of formalization, sharing, and reusable unified description mechanisms currently observed in the field of ancient architecture digitization. This method defines the related concepts, attributes, and relationships between concepts in the digitization of ancient architecture. It employs the network ontology language OWL to model the ontology in the digitization domain of ancient architecture and realizes the visualization of the ontology in the digitization domain of ancient architecture, thereby providing effective support for the sharing and reuse of digitization knowledge of ancient architecture. Finally, an example of a wooden tower is taken to verify the effectiveness and reliability of the proposed method. Full article

With the sustainable development of intelligent fisheries, accurate underwater fish segmentation is a key step toward intelligently obtaining fish morphology data. However, the blurred, distorted and low-contrast features of fish images in underwater scenes affect the improvement in fish segmentation accuracy. To solve these problems, this paper proposes a method of underwater fish segmentation based on an improved PSPNet network (IST-PSPNet). First, in the feature extraction stage, to fully perceive features and context information of different scales, we propose an iterative attention feature fusion mechanism, which realizes the depth mining of fish features of different scales and the full perception of context information. Then, a SoftPool pooling method based on fast index weighted activation is used to reduce the numbers of parameters and computations while retaining more feature information, which improves segmentation accuracy and efficiency. Finally, a triad attention mechanism module, triplet attention (TA), is added to the different scale features in the golden tower pool module so that the space attention can focus more on the specific position of the fish body features in the channel through cross-dimensional interaction to suppress the fuzzy distortion caused by background interference in underwater scenes. Additionally, the parameter-sharing strategy is used in this process to make different scale features share the same learning weight parameters and further reduce the numbers of parameters and calculations. The experimental results show that the method presented in this paper yielded better results for the DeepFish underwater fish image dataset than other methods, with 91.56% for the Miou, 46.68 M for Params and 40.27 G for GFLOPS. In the underwater fish segmentation task, the method improved the segmentation accuracy of fish with similar colors and water quality backgrounds, improved fuzziness and small size and made the edge location of fish clearer. Full article

Recent times have seen a significant rise in interest from mobile operators, vendors, and research projects toward achieving more energy-efficient and sustainable networks. Not surprisingly, it comes at a time when higher traffic demand and more stringent and diverse network requirements result in diminishing benefits for operators using complex AI-driven network optimization solutions. In this paper, we propose the idea of tower companies that facilitate radio access network (RAN) infrastructure sharing between operators and evaluate the additional energy savings obtained in this process. In particular, we focus on the RAN-as-a-Service (RANaaS) implementation, wherein each operator leases and controls an independent logical RAN instance running on the shared infrastructure. We show how an AI system can assist operators in optimizing their share of resources under multiple constraints. This paper aims to provide a vision, a quantitative and qualitative analysis of the RANaaS paradigm, and its benefits in terms of energy efficiency. Through simulations, we show the possibility to achieve up to 75 percent energy savings per operator over 24 h compared to the scenario where none of the energy-saving features are activated. This is an additional 55 percent energy savings from sharing the RAN infrastructure compared to the baseline scenario where the operators use independent hardware. Full article

Dream sharing is a universal practice, and various incentives have been identified, including emotional processing, emotional relief, and demands for containment. Shared dreams can contribute to an individual’s understanding of social reality during traumatic and stressful events. The present study examined dreams shared on social network sites (SNS) during the first COVID-19 lockdown, applying a group-analytic approach. A qualitative dream content analysis conducted by a group of researchers analyzed 30 dreams shared on SNS, focusing on their contents, dominant emotions, and unique group processes. The dream content analysis yielded three meaningful and coherent themes: (1) dominant threats: enemy, danger, and COVID-19; (2) emotional fusion: confusion and despair alongside recovery and hope; and (3) group processes characterized by movement between being alone and being together. The results deepen our understanding of both unique social and psychological group processes and of people’s main experiences and key psychological coping mechanisms in times of collective trauma and natural disasters. They also demonstrate the transformative potential of dreamtelling for individuals’ coping experiences and building hope through the creative social relationships formed within SNS groups. Full article

In recent years, the development of wireless technologies has led to fast growth in mobile networks, especially with the rise of 5G New Radio (5G NR). A huge number of base stations (BSs) are mandatory to serve the growth of mobile services, which has led to concerns about the increase in electromagnetic field (EMF) radiation exposure levels. To control the overall power emitted by EMF transmitters, international bodies have set maximum exposure limits. This paper investigates the compliance distances (CDs) of shared sites by a group of Mobile Network Operators (MNO) as multi-operators operating with multi-technology and sharing the same tower. The study investigated the CDs of the most two commonly used types of sharing sites, macro and indoor-Based solution sites (IBS). In addition, the study analyzed the power densities and total exposure ratios for the general public and occupational workers in each sharing scenario. The results showed that, compared with a single MNO, the CD increased by 41% in the case of two MNOs, 73% for three MNOs, and 100% for four MNOs. The EMF site sharing scale-up formula was used to estimate the increase in CDs for N number of MNOs assuming that all MNOs use the same site configuration. In addition, the results showed that 5G has the highest contribution to the total exposure ratio (TER) at the CD in the main direction of the antennae. Full article

In the global trucking industry, vertical collaboration between shippers and carriers is attained by intermediaries, called brokers. Brokers organize carriers for a shipper in accordance with its quality and price requirements, and support carriers to collaborate horizontally by sharing a large distribution order from a shipper. Brokers also act as trustees, preventing the passing of private information of any party to the others. Despite these benefits, intermediaries in the trucking industry are involved in several sustainability problems, including high costs, high levels of carbon emissions, high percentages of empty miles, low-capacity utilizations, and driver shortages. Several studies have acknowledged the importance of improving collaboration to address these problems. Obviously, the major concern of brokers is not collaboration, but rather to optimize their own gains. This paper investigates the potential of blockchain technology to improve collaboration in the trucking industry, by eliminating brokers while preserving their responsibilities as organizers and trustees. This paper extends the transportation control tower concept from the logistics literature, and presents a system architecture for its implementation through smart contracts on a blockchain network. In the proposed system, the scalability and privacy of trucking operations are ensured through integration with privacy-preserving off-chain computation and storage solutions (running outside of the blockchain). The potential of this design artifact for fostering collaboration in the trucking industry was evaluated by both blockchain technology experts and trucking industry professionals. Full article

The so-called Internet of Things (IoT) has attracted increasing attention in the field of computer and information science. In this paper, a specific application of IoT, named Safety Management System for Tower Crane Groups (SMS-TC), is proposed for use in the construction industry field. The operating status of each tower crane was detected by a set of customized sensors, including horizontal and vertical position sensors for the trolley, angle sensors for the jib and load, tilt and wind speed sensors for the tower body. The sensor data is collected and processed by the Tower Crane Safety Terminal Equipment (TC-STE) installed in the driver’s operating room. Wireless communication between each TC-STE and the Local Monitoring Terminal (LMT) at the ground worksite were fulfilled through a Zigbee wireless network. LMT can share the status information of the whole group with each TC-STE, while the LMT records the real-time data and reports it to the Remote Supervision Platform (RSP) through General Packet Radio Service (GPRS). Based on the global status data of the whole group, an anti-collision algorithm was executed in each TC-STE to ensure the safety of each tower crane during construction. Remote supervision can be fulfilled using our client software installed on a personal computer (PC) or smartphone. SMS-TC could be considered as a promising practical application that combines a Wireless Sensor Network with the Internet of Things. Full article

Developments in the wind power industry have enabled a new generation of wind turbines with longer blades, taller towers, higher efficiency, and lower maintenance costs due to the maturity of related technologies. Nevertheless, wind turbines are still blind machines because the control center is responsible for managing and controlling individual wind turbines that are turned on or off according to demand for electricity. In this paper, we propose a communication network architecture for smart-wind power farms (Smart-WPFs). The proposed architecture is designed for wind turbines to communicate directly and share sensing data in order to maximize power generation, WPF availability, and turbine efficiency. We also designed a sensor data frame structure to carry sensing data from different wind turbine parts such as the rotor, transformer, nacelle, etc. The data frame includes a logical node ID (LNID), sensor node ID (SNID), sensor type (ST), and sensor data based on the International Electrotechnical Commission (IEC) 61400-25 standard. We present an analytical model that describes upstream traffic between the wind turbines and the control center. Using a queueing theory approach, the upstream traffic is evaluated in view of bandwidth utilization and average queuing delay. The performance of the proposed network architectures are evaluated by using analytical and simulation models. Full article