Search Results (1,240)

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

Green hydrogen, produced via renewable-powered electrolysis, offers a promising path toward deep decarbonisation in energy systems. This study investigates the major technological, infrastructural, and economic challenges facing green hydrogen production in Jordan—a resource-constrained yet renewable-rich country. Key barriers were identified through a structured survey of 52 national stakeholders, including water scarcity, low electrolysis efficiency, limited grid compatibility, and underdeveloped transport infrastructure. Respondents emphasised that overcoming these challenges requires investment in smart grid technologies, seawater desalination, advanced electrolysers, and policy instruments such as subsidies and public–private partnerships. These findings are consistent with global assessments, which recognise similar structural and financial obstacles in scaling up green hydrogen across emerging economies. Despite the constraints, over 50% of surveyed stakeholders expressed optimism about Jordan’s potential to develop a competitive green hydrogen sector, especially for industrial and power generation uses. This paper provides empirical, context-specific insights into the conditions required to scale green hydrogen in developing economies. It proposes an integrated roadmap focusing on infrastructure modernisation, targeted financial mechanisms, and enabling policy frameworks. Full article

Against the background of intensifying human–land contradictions, evaluation of land use benefits and identification of obstacles are crucial for sustainable land management and socioeconomic development. Taking Henan Province as an example, this research employed the entropy weight method and TOPSIS model to assess the land use benefits across its cities from 2011 to 2020, a period of rapid land use transformation, analyzed their spatiotemporal evolution, and identified key obstacles via an obstacle degree model. The results showed the following. (1) The social land use benefits consistently exceeded the ecological and economic benefits, with steady improvements observed in both the individual and comprehensive benefits. Spatially, the benefits showed a “one city dominant” pattern, decreasing gradually from the central region to the south, north, east, and west, with this spatial gradient further intensifying over time. (2) Economic factors were the primary obstacles, with significantly higher obstruction degrees than social or ecological factors. The main obstacles were the general budget revenue of government finance per unit land area, domestic garbage removal volume, and total retail sales of social consumer goods per unit land area. (3) The policy implications focus on strengthening regional differentiated development by leveraging Zhengzhou’s core role to boost the land-based economic benefits, integrating social–ecological strengths with agricultural modernization, and promoting “core–periphery linkage” to narrow gaps through targeted industrial and infrastructure strategies. This study could provide region-specific insights for sustainable land management in agricultural provinces undergoing rapid urbanization. Full article

Massive MIMO has emerged as a key enabler in modern wireless communication, delivering unparalleled spectral efficiency and connectivity. Yet, as antenna arrays become larger, significant obstacles arise in handling channel state information (CSI) feedback and the computational burden. This paper proposes a groundbreaking angular-domain transmission method that transitions from the conventional time–frequency domain to the angular domain. By employing projection-based transforms, akin to the FFT-based OFDMA model that introduced frequency-domain transmission with subcarriers, this technique exploits the inherent sparsity of massive MIMO channels in the angular domain, enabling data flows to be seamlessly mapped onto physical paths or rays. The resulting sparsity reduces signaling overhead and streamlines system complexity, making massive MIMO viable for next-generation networks. Simulation and empirical studies highlight how angular-domain strategies reduce feedback requirements, support Tera-bps data rates, and facilitate scalable designs for ultra-large-scale MIMO. Full article

Blind and visually impaired (BVI) people face significant challenges in perception, navigation, and safety during travel. Existing infrastructure (e.g., blind lanes) and traditional aids (e.g., walking sticks, basic audio feedback) provide limited flexibility and interactivity for complex environments. To solve this problem, we propose a real-time travel assistance system based on deep learning. The hardware comprises an NVIDIA Jetson Nano controller, an Intel D435i depth camera for environmental sensing, and SG90 servo motors for feedback. To address embedded device computational constraints, we developed a lightweight object detection and segmentation algorithm. Key innovations include a multi-scale attention feature extraction backbone, a dual-stream fusion module incorporating the Mamba architecture, and adaptive context-aware detection/segmentation heads. This design ensures high computational efficiency and real-time performance. The system workflow is as follows: (1) the D435i captures real-time environmental data; (2) the processor analyzes this data, converting obstacle distances and path deviations into electrical signals; (3) servo motors deliver vibratory feedback for guidance and alerts. Preliminary tests confirm that the system can effectively detect obstacles and correct path deviations in real time, suggesting its potential to assist BVI users. However, as this is a work in progress, comprehensive field trials with BVI participants are required to fully validate its efficacy. Full article

One of the goals of Agenda 2030 is to increase the share of renewable energy in the global energy mix. In this context, photovoltaic systems play a key role in the transition to clean energy. According to the International Energy Agency, in 2023, solar photovoltaic alone accounted for three-quarters of renewable capacity additions worldwide. Designing a performing photovoltaic system requires careful planning that takes into account various factors, both internal and external, in order to maximize energy production and optimize costs. In addition to the technical characteristics of the system (internal factors), the positions and the shapes of external buildings and surrounding obstacles (external factors) have a significant impact on the output of photovoltaic systems. However, given the complexity of these environmental factors, they cannot be treated accurately in manual design practice. For this reason, this paper proposes a Building Information Modeling-based workflow for the design of a photovoltaic system that can guide the professional step-by-step throughout the design process, starting from the embryonic phase to the definitive, and therefore more detailed, one. The developed methodology allows for an in-depth analysis of the shading, the photovoltaic potential of the building, the performance of the photovoltaic system, and the costs for its construction in order to evaluate the appropriateness of the investment. The main aim of the paper is to create a standardized procedure applicable on a large scale for photovoltaic integration within Building Information Modeling workflows. The methodology is tested on two case studies, characterized by different architectural features and geographical positions. Full article

Spinal cord injury (SCI) remains one of the toughest obstacles in neuroscience and regenerative medicine due to both severe functional loss and limited healing ability. This article aims to provide a key integrative, mechanism-focused review of the molecular landscape of SCI and the new disruptive therapy technologies that are now evolving in the SCI arena. Our goal is to unify a fundamental pathophysiology of neuroinflammation, ferroptosis, glial scarring, and oxidative stress with the translation of precision treatment approaches driven by artificial intelligence (AI), CRISPR-mediated gene editing, and regenerative bioengineering. Drawing upon advances in single-cell omics, systems biology, and smart biomaterials, we will discuss the potential for reprogramming the spinal cord at multiple levels, from transcriptional programming to biomechanical scaffolds, to change the course from an irreversible degeneration toward a directed regenerative pathway. We will place special emphasis on using AI to improve diagnostic/prognostic and inferred responses, gene and cell therapies enabled by genomic editing, and bioelectronics capable of rehabilitating functional connectivity. Although many of the technologies described below are still in development, they are becoming increasingly disruptive capabilities of what it may mean to recover from an SCI. Instead of prescribing a particular therapeutic fix, we provide a future-looking synthesis of interrelated biological, computational, and bioengineering approaches that conjointly chart a course toward adaptive, personalized neuroregeneration. Our intent is to inspire a paradigm shift to resolve paralysis through precision recovery and to be grounded in a spirit of humility, rigor, and an interdisciplinary approach. Full article

Qingke (Hordeum vulgare L. var. nudum Hook. f.), a staple crop in the Tibetan Plateau, suffers from severe yield reduction under continuous cropping (by 38.67%), yet the underlying mechanisms remain unclear. This study systematically investigated the effects of 23-year continuous cropping (23y-CC) on the nutrient dynamics, carbohydrate metabolism, and enzymatic activities in Qingke leaves across five developmental stages (T1: seedling; T2: tillering; T3: jointing; T4: flowering; T5: filling). Compared to the control (first-year planting), 23y-CC significantly reduced leaf nitrogen (N), phosphorus (P), and potassium (K) contents by 60.94%, 47.96%, and 60.82%, respectively, at early growth stages. Key nitrogen-metabolizing enzymes, including glutamate synthase (GOGAT), glutamine synthase (GS), and nitrate reductase (NR), exhibited reduced activities under 23y-CC, indicating impaired nitrogen assimilation. Carbohydrate profiling revealed lower starch and glucose contents but higher sucrose accumulation in later stages (T4–T5) under 23y-CC, accompanied by the dysregulation of sucrose synthase (SS) and invertase activities. These findings elucidate how continuous cropping disrupts nutrient homeostasis and carbon allocation, ultimately compromising Qingke productivity. This study provides novel insights into agronomic strategies for mitigating continuous cropping obstacles in Qingke. Full article

Cultural heritage in insular regions faces increasing challenges due to overtourism, seasonal economies, and insufficient protection frameworks. This study investigates the adaptive reuse of Sarakina Mansion, a deteriorated 18th-century estate on the island of Zakynthos, as a model for integrating cultural heritage preservation with sustainable tourism. The research addresses the gap in localized strategies for heritage-led development in the context of islands with overtourism. Through a qualitative case study methodology—including site analysis, archival research, and stakeholder interviews—this paper explores how abandoned cultural assets can be reactivated to foster community engagement and diversify tourism models. Two distinct SWOT analyses were conducted as follows: one at the territorial level (Zakynthos Island) and another focused on the island’s cultural heritage. The findings highlight key obstacles such as environmental degradation and policy fragmentation, but they also reveal opportunities for adaptive reuse grounded in local identity and sustainable practices. The proposed reuse scenario for Sarakina promotes partial structural stabilization and community-driven cultural programming, aiming to create a hybrid open-air cultural hub. This study contributes a replicable framework for reimagining neglected heritage assets in overtourism-affected areas, aligned with the UN Sustainable Development Goals (SDGs). Full article

The present study was conducted in Dacope Upazila, a sub-district located within the Khulna District of the coastal region in Bangladesh. The research methods employed included the implementation of 350 household questionnaire surveys (HQSs), 12 focus group discussions (FGDs), and 20 key informant interviews (KIIs) to assess economic security status in disaster-vulnerable areas. The findings indicate that the economic well-being of the region is precarious due to a paucity of revenue sources and the occurrence of various calamitous events, induced risks, and vulnerabilities. To achieve long-term economic security for households, a considerable proportion of the population (approximately 22%) in the study areas is dependent on agricultural activities for their livelihoods. The study also revealed that approximately 22% of households in the study areas reported experiencing salinity intrusion. Furthermore, most of the households, around 68%, reported cyclones as their primary obstacle to building disaster-resilient communities. Consequently, the prevailing local and institutional strategies to ensure economic security were found to be inadequate and unsustainable in the study upazila. Therefore, the study resulted in the formulation of a conceptual framework intended to measure the contribution of economic security to the adaptability and sustainability of disaster-resilient communities in vulnerable coastal areas of Bangladesh. Full article

To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article

Enhancing the resilience of the oil and gas industry chain is essential for achieving sustainable energy development amid global industrial restructuring and the accelerating low-carbon transformation. This study identifies the core contradictions in the development of China’s OGI and constructs a comprehensive evaluation index system to assess the resilience of the industry from the four sustainability-aligned dimensions of resistance, recovery, innovation, and transformation. Using the entropy weight comprehensive evaluation model, obstacle degree model, and coupling coordination degree model, the resilience performance of China’s OGI chain is evaluated from 2001 to 2022. The results show a significant upward trend in overall resilience, with evident stage characteristics. Resistance remains relatively stable, recovery shows the most improvement, innovation steadily increases, and transformation accelerates after 2019, particularly in response to China’s dual carbon goals. Key barriers include limited CCUS deployment and insufficient downstream innovation capacity. The improved coupling coordination among resilience subsystems highlights enhanced systemic synergy. These findings offer valuable implications for strengthening the sustainability and security of energy supply chains under climate and geopolitical pressures. Full article

Generalising deep-learning models to perform well on unseen data domains with minimal retraining remains a significant challenge in computer vision. Even when the target task—such as quantifying the number of elements in an image—stays the same, data quality, shape, or form variations can deviate from the training conditions, often necessitating manual intervention. As a real-world industry problem, we aim to automate stock level estimation in retail cabinets. As technology advances, new cabinet models with varying shapes emerge alongside new camera types. This evolving scenario poses a substantial obstacle to deploying long-term, scalable solutions. To surmount the challenge of generalising to new cabinet models and cameras with minimal amounts of sample images, this research introduces a new solution. This paper proposes a novel ensemble model that combines DenseNet-201 and Vision Transformer (ViT-B/8) architectures to achieve generalisation in stock-level classification. The novelty aspect of our solution comes from the fact that we combine a transformer with a DenseNet model in order to capture both the local, hierarchical details and the long-range dependencies within the images, improving generalisation accuracy with less data. Key contributions include (i) a novel DenseNet-201 + ViT-B/8 feature-level fusion, (ii) an adaptation workflow that needs only two images per class, (iii) a balanced layer-unfreezing schedule, (iv) a publicly described domain-shift benchmark, and (v) a 47 pp accuracy gain over four standard few-shot baselines. Our approach leverages fine-tuning techniques to adapt two pre-trained models to the new retail cabinets (i.e., standing or horizontal) and camera types using only two images per class. Experimental results demonstrate that our method achieves high accuracy rates of 91% on new cabinets with the same camera and 89% on new cabinets with different cameras, significantly outperforming standard few-shot learning methods. Full article

The long-term monocropping of red kidney beans in agricultural fields can lead to the occurrence of soil-borne diseases. Alterations in the composition of the soil microbial community are a primary cause of soil-borne diseases and a key factor in continuous cropping obstacles. Research exploring how different cultivation modes can modify the diversity and composition of the rhizosphere microbial community in red kidney beans, and thus mitigate the effects of continuous cropping obstacles, is ongoing. This study employed three cultivation modes: the continuous monocropping of red kidney beans, continuous monocropping of soybeans, and red kidney bean–soybean intercropping. To elucidate the composition and diversity of rhizosphere microbial communities, we conducted amplicon sequencing targeting the V3-V4 hypervariable regions of the bacterial 16S rRNA gene and the ITS1 region of fungal ribosomal DNA across distinct growth stages. The obtained sequencing data provide a robust basis for estimating soil microbial diversity. We observed that, under the intercropping mode, the composition of both bacteria and fungi more closely resembled that of soybean monocropping. The monocropping of red kidney beans increased the richness of rhizosphere bacteria and fungi and promoted the accumulation of pathogenic microorganisms. In contrast, intercropping cultivation and soybean monocropping favored the accumulation of beneficial bacteria such as Bacillus and Streptomyce, reduced pathogenic fungi including Alternaria and Mortierell, and exhibited less microbial variation across different growth stages. Compared to the monocropping of red kidney beans, these systems demonstrated more stable microbial structure and composition. The findings of this study will inform sustainable agricultural practices and soil management strategies. Full article

Nanoradiopharmaceuticals integrate nanotechnology with nuclear medicine to enhance the precision and effectiveness of radiopharmaceuticals used in diagnostic imaging and targeted therapies. Nanomaterials offer improved targeting capabilities and greater stability, helping to overcome several limitations. This review presents a comprehensive overview of the fundamental design principles, radiolabeling techniques, and biomedical applications of nanoradiopharmaceuticals, with a particular focus on their expanding role in precision oncology. It explores key areas, including single- and multi-modal imaging modalities (SPECT, PET), radionuclide therapies involving beta, alpha, and Auger emitters, and integrated theranostic systems. A diverse array of nanocarriers is examined, including liposomes, micelles, albumin nanoparticles, PLGA, dendrimers, and gold, iron oxide, and silica-based platforms, with an assessment of both preclinical and clinical research outcomes. Theranostic nanoplatforms, which integrate diagnostic and therapeutic functions within a single system, enable real-time monitoring and personalized dose optimization. Although some of these systems have progressed to clinical trials, several obstacles remain, including formulation stability, scalable manufacturing, regulatory compliance, and long-term safety considerations. In summary, nanoradiopharmaceuticals represent a promising frontier in personalized medicine, particularly in oncology. By combining diagnostic and therapeutic capabilities within a single nanosystem, they facilitate more individualized and adaptive treatment approaches. Continued innovation in formulation, radiochemistry, and regulatory harmonization will be crucial to their successful routine clinical use. Full article