Search Results (5,153)

The study focuses on the experimental investigation of the impact of using coconut oil (CO) as a phase-change material (PCM) for heat storage on the root-zone temperature within a greenhouse in Adana, Türkiye. The study examines the efficacy of PCM as latent heat-storage material and predicts root-zone temperature using three machine learning algorithms. The dataset used in the analysis consists of 2658 data at hourly resolution with six variables from February to April in 2022. A greenhouse with PCM shows a remarkable increase in both ambient (0.9–4.1 °C) and root-zone temperatures (1.1–1.6 °C) especially during the periods without sunlight compared to a conventional greenhouse. Machine learning algorithms used in this study include Multivariate Adaptive Regression Splines (MARS), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Hyperparameter tuning was performed for all three models to control model complexity, flexibility, learning rate, and regularization level, thereby preventing overfitting and underfitting. Among these algorithms, R² values for testing data listed from largest to smallest are MARS (0.95), SVR (0.96), and XGBoost (0.97), respectively. The results emphasize the potential of machine learning approaches for applying thermal energy storage systems to agricultural greenhouses. In addition, it provides insight into a net-zero energy greenhouse approach by storing heat in a bio-based PCM, alongside its implementation and operational procedures. Full article

As semiconductor technologies advance, SRAM cells deployed in space systems face heightened sensitivity to radiation-induced soft errors. In conventional 6T SRAM, when high-energy particles strike sensitive nodes, single-event upsets (SEUs) may occur, flipping stored bits. Furthermore, with aggressive scaling, charge sharing among adjacent devices can trigger single-event multi-node upsets (SEMNU). To address these reliability concerns, this study presents a radiation-hardened SRAM design, SHWA18T, tailored for space applications. The proposed architecture is evaluated against IASE16T, PRO14T, PRO16T, QCCS, SIRI, and SEA14T. Simulation analysis demonstrates that SHWA18T achieves improved performance, particularly in terms of critical charge and write capability. The design was implemented in 90 nm CMOS technology at a 1 V supply. With enhanced robustness, the cell withstands both SEUs and SEMNUs, thereby guaranteeing stable data retention in space environments. Full article

The distribution of grain particles within a silo influences heat and moisture transfer during stored grain ventilation, leading to grain quality losses. A study on porosity distribution analysis and ventilation tests was conducted in a pilot silo with a height of 3 m, a diameter of 1.5 m, and a conical dome height of 0.85 m. The E-B constitutive model was incorporated into the secondary development of FLAC^3D 5.0 to analyze the vertical pressure distribution in the grain bulk. An anisotropic porosity distribution model for the maize bulk was developed, accounting for both vertical pressure and segregation mechanisms. The differences in airflow and heat transfer during ventilation between isotropic and anisotropic porosity distributions were quantified. A nonlinear model was innovatively proposed to predict the temperature front curve (TFC) during ventilation as affected by porosity variation. The results indicate that friction between the maize kernel and the silo wall led to vertical pressure at the center of the bottom that was 10.7% higher than that near the wall. The average surface porosity of the maize bulk was 2.8% higher than at the bottom. This led to a minimum porosity of 0.409 at the center of the silo bottom, due to the combined effect of impact during the loading process and vertical pressure. The numerical simulation demonstrated excellent consistency with the experimental data. At a supply vent air velocity of 0.126 m/s, an increase in the maize bulk height from 0.725 m to 2.9 m resulted in reductions in airflow rate and average relative humidity of 20.3% and 9.67%. The airflow velocity near the wall was 13.4% higher than that in the center, leading to a faster cooling rate in the peripheral region compared to the center of the maize bulk. The airflow velocity based on the isotropic porosity model was higher at the center than that predicted by the anisotropic model, whereas the opposite trend was observed near the wall. The temperature front during ventilation based on the anisotropic porosity model exhibited a concave curve. A nonlinear model was developed to predict this temperature front, showing strong agreement with computational data. Full article

Existing methods for the secure transaction and circulation of remote sensing image data primarily focus on post-event investigation, lacking a reliable mechanism for secure distribution and fair trading of data. To address this issue, this study proposes a trusted transaction method that integrates a watermark based on Paillier homomorphic encryption, blockchain, and smart contract. This method leverages the homomorphic property of the Paillier cryptosystem to imperceptibly embed the ciphertext of the watermark generated from transaction information into encrypted remote sensing image data. The data buyer at the receiving end decrypts the key pair using the private key, thereby decrypting the data to obtain the watermarked plaintext. Simultaneously, transaction records are immutably stored on trusted blockchain nodes via smart contracts. Throughout the entire transaction process, data encryption/decryption and watermark embedding/extraction are symmetric. The experimental results demonstrate that the watermark can be effectively extracted after encryption, thereby supporting transaction verification and traceability. Furthermore, the three smart contracts designed in this study all exhibit strong execution performance. In particular, the smart contract employed for verification demonstrated an average execution latency of only 0.19 s per instance. Through enforcing the retrieval of parameters and storage credentials from the blockchain, the proposed method effectively constrains malicious behavior from both parties, offering a novel technical approach to facilitate consensus and mutual trust. Full article

In the era of big data and rapid information growth, investors encounter a complex financial environment characterized by extensive data, conflicting investment objectives, and markets that are unpredictable due to economic and policy fluctuations. Hence, asset selection is vital for both investors and researchers. Multi-objective optimization algorithms balance multiple objectives to find optimal solutions and are widely used in engineering, economics, etc. This paper proposes a multi-objective decomposition optimization algorithm integrated with differential grouping (DG-MOEA/D). Initially, the algorithm employs the recursive spectral clustering differential grouping (RDGSC) technique to identify dependencies among variables, grouping them to reduce interactions between the variables. It then uses MOEA/D-UTEA to optimize each group, with an external archive for storing and updating solutions. Experimental results on the DTLZ and LSMOP test functions show that the DG-MOEA/D algorithm greatly outperforms the other seven comparison algorithms. When used in real-world scenarios like stock and bond asset allocation, the algorithm continues to outperform other methods, demonstrating its significant advantages in practical applications. Full article

Progressive myopia in children is a highly prevalent condition in societies worldwide and is often treated with compounded low-dose atropine sulfate (AS) eye drops without preserving agents to avoid irritation/sensitisation. Surprisingly, there is a lack of data regarding the in-use stability of contamination-free LDPE dispenser units (CFDs) for this compounded multidose product, which causes uncertainty among prescribers and patients in Europe. Thus, our aim was to compare the effect of different dispenser types on the chemical and microbial stability of unpreserved AS eye drops (0.01% w/w). A dripping simulation was performed to obtain information on microbial stability over 4 weeks through plating and separately over 12 weeks through direct inoculation, HPLC and pH analysis. For CFDs, no contamination was found after 4, 8 or 12 weeks of use when stored at 23 or 4 °C as opposed to the control. AS content remained within 0.01 ± 0.0002% w/w after 12 weeks, with higher chemical stability at 4 °C despite decreasing pH. A stress test confirmed the validity of the CFD system. In conclusion, using CFDs and refrigerated storage was found to be safe for compounded unpreserved AS eye drops over 12 weeks of use. Full article

Ex situ plant conservation in Poland is shaped by a dual institutional framework that distinguishes between agricultural genetic resources and the protection of rare and endangered wild flora. The National Centre for Plant Genetic Resources (NCPGR) focuses on cultivated taxa and Crop Wild Relatives (CWRs), whereas the PAS Botanical Garden Seed Bank prioritizes wild species of high conservation concern, including those listed under the EU Habitats Directive (HD). This review examines the legal and institutional foundations of ex situ conservation in Poland within global and regional regimes (CBD, ITPGRFA, EU Habitats Directive) and analyzes a harmonized dataset of 1458 species. Comparative analyses show significant discrepancies in institutional holdings: CWRs are relatively well represented in the gene bank, whereas many HD species remain underrepresented, particularly those producing non-orthodox seeds that cannot be stored by conventional methods. Conversely, rare wild taxa maintained in the Seed Bank often fall outside agricultural genetic resource frameworks. The limited overlap between these collections highlights gaps in coordination, data integration, and methodological development. Strengthening institutional synergy, developing approaches for non-orthodox seeds, and enhancing international cooperation will be crucial for Poland to meet the goals of the Kunming–Montreal Global Biodiversity Framework and the Global Strategy for Plant Conservation. Full article

Objective: Medical Visual Question Answering (Med-VQA), a key technology that integrates computer vision and natural language processing to assist in clinical diagnosis, possesses significant potential for enhancing diagnostic efficiency and accuracy. However, its development is constrained by two major bottlenecks: weak few-shot generalization capability stemming from the scarcity of high-quality annotated data and the problem of catastrophic forgetting when continually learning new knowledge. Existing research has largely addressed these two challenges in isolation, lacking a unified framework. Methods: To bridge this gap, this paper proposes a novel Evolvable Clinical-Semantic Alignment (ECSA) framework, designed to synergistically solve these two challenges within a single architecture. ECSA is built upon powerful pre-trained vision (BiomedCLIP) and language (Flan-T5) models, with two innovative modules at its core. First, we design a Clinical-Semantic Disambiguation Module (CSDM), which employs a novel debiased hard negative mining strategy for contrastive learning. This enables the precise discrimination of “hard negatives” that are visually similar but clinically distinct, thereby significantly enhancing the model’s representation ability in few-shot and long-tail scenarios. Second, we introduce a Prompt-based Knowledge Consolidation Module (PKC), which acts as a rehearsal-free non-parametric knowledge store. It consolidates historical knowledge by dynamically accumulating and retrieving task-specific “soft prompts,” thus effectively circumventing catastrophic forgetting without relying on past data. Results: Extensive experimental results on four public benchmark datasets, VQA-RAD, SLAKE, PathVQA, and VQA-Med-2019, demonstrate ECSA’s state-of-the-art or highly competitive performance. Specifically, ECSA achieves excellent overall accuracies of 80.15% on VQA-RAD and 85.10% on SLAKE, while also showing strong generalization with 64.57% on PathVQA and 82.23% on VQA-Med-2019. More critically, in continual learning scenarios, the framework achieves a low forgetting rate of just 13.50%, showcasing its significant advantages in knowledge retention. Conclusions: These findings validate the framework’s substantial potential for building robust and evolvable clinical decision support systems. Full article

Chronic disease, for which diet is a major risk factor, remains the leading cause of death in the United States, responsible for 8 out of 10 deaths. A continually growing body of research has been looking at food environments, relating them to characteristics of residents living in those environments and impacts on health outcomes. However, most of the research has been looking at Body Mass Index or obesity as the primary health outcome of such environments. This study looks at multiple health outcomes (chronic and perceived) from the Center for Disease Control’s PLACES—Local Data for Better Health dataset for Lansing, Michigan, and assesses the corresponding food environments, specifically the prevalence of fast-food outlets and convenience stores, to assess the impacts these food environments have, either directly or indirectly, on health. We find that fast-food outlets have a direct impact on certain health outcomes, while convenience stores impact certain health outcomes indirectly through food insecurity. These findings suggest that strategically balancing such environments with healthier options in underserved areas could help improve overall health. Full article

Myoglobin (Mb), a heme-containing protein, plays crucial roles in storing and transporting oxygen in muscle cells. Various Mb structures have been extensively determined using conventional cryogenic crystallography, providing valuable information for understanding the molecular mechanisms of the protein. However, this approach has limitations attributable to cryogenic temperatures and radiation damage. Serial femtosecond crystallography (SFX) using X-ray free-electron lasers is an emerging technique that enables the determination of biologically relevant room-temperature structures without causing radiation damage. In this study, we assessed the crystallization, collection, and processing of SFX diffraction data of Mb from equine skeletal muscle. Needle- and needle cluster-shaped Mb crystals were obtained using the microbatch method. Fixed-target SFX data collection was performed at the Pohang Accelerator Laboratory X-ray Free Electron Laser, yielding 1389 indexed diffraction patterns. The phase problem was solved by molecular replacement. The preliminary Mb structure determined at 2.3-Å resolution in this study exhibited subtle structural differences in the heme environment compared with previously reported Mb structures determined by SFX. These results both confirm the feasibility of myoglobin SFX experiments and establish a foundation for future time-resolved studies aiming to visualize ligand binding and oxygen transport. Full article

Children with intoeing gait are at increased risk of tripping and consequent injury, reduced mobility, and psychological issues. Quantification of tripping is needed outside the gait lab during daily life for improved clinical assessment and treatment evaluation and to enrich the database for artificial intelligence (AI) learning. This paper presents the development of a low-cost, wearable tripping monitor to log a child’s Tripping Hazard Events (THEs) and steps taken during two weeks of everyday activity. A combination of sensors results in a high probability of THE detection, even during rapid gait, while guarding against false positives and minimizing power and therefore monitor size. A THE is logged when the feet come closer than a predefined threshold during the intoeing foot swing phase. Foot proximity is determined by a Radio Frequency Identification (RFID) reader in “sniffer” mode on the intoeing foot and a target of passive Near-Field Communication (NFC) tags on the contralateral foot. A Force Sensitive Resistor (FSR) in the intoeing shoe sets a time window for sniffing during gait and enables step counting. Data are stored in 15 min epochs. Laboratory testing and an IRB-approved human participant study validated system performance and identified the need for improved mechanical robustness, prompting a redesign of the monitor. A custom Python (version 3.10.13)-based Graphical User Interface (GUI) lets clinicians initiate recording sessions and view time records of THEs and steps. The monitor’s flexible design supports broader applications to real-world activity detection. Full article

The retail sector is the most demand-sensitive echelon in the supply chain, where non-moving items accumulate and become dead stock. Existing inventory management studies focus on fast-moving products and income generation. This paper focuses on dead stock management and proposes a mixed strategy solution using a pure integer non-linear programming model that minimizes the dead stock management cost of a retail chain operator. The number of products and volume of product-related data in a retail chain system require big data analysis to ensure sustainable inventory practices that reduce waste generated from dead stock inventory. Through hypothetical data sets, the 3-store, 10-product run showed that discount percentage, expected sales success probability of a product in a store location, and disposition of unsold products were the main drivers of the decisions made by the model. The most significant cost contributors arising from these decisions were the unrecovered product cost (UPC), disposed product cost (PC), and salvage value from the successful sale of dead stock. Inventory managers must balance the effect on these cost components when they choose the strategies to use in managing dead stock. Full article

Background/Objectives: The increasing presence of “no added sugar” products in the Polish food market provides consumers and nutritionists with access to products with varying nutritional compositions. Comparing the nutritional and energy values of products with and without added sugar provides objective data on their composition, which is important for informed diet planning and for monitoring differences between product groups. Methods: The research material included a total of 1278 food products, including 744 labeled “without added sugar” and 534 containing added sugar, obtained from four online stores and three offline retail outlets in Poland in the second and third quarters of 2023. The product assessment was based on an analysis of the nutritional and energy value, expressed per 100 g of each product. Results: The quantitative analysis revealed that products with added sugar were characterized by a higher energy value and a statistically significantly higher content of saturated fatty acids, carbohydrates, and sugars. Conclusions: Comparison of selected product groups revealed significant differences in nutritional and energy values. Analyzing these differences provides a practical overview of product composition and can be a useful source of information for consumers and nutritionists. Full article

The Blockchain-Based Medical Insurance Transaction System (BMIT) developed in this study addresses key issues in traditional medical insurance—information silos, data tampering, and privacy breaches—through innovative blockchain architectural design and technical infrastructure reconstruction. Built on a consortium blockchain architecture with FISCO BCOS (Financial Blockchain Shenzhen Consortium Blockchain Open Source Platform) as the underlying platform, the system leverages FISCO BCOS’s distributed ledger, granular access control, and efficient consensus algorithms to enable multi-stakeholder on-chain collaboration. Four node roles and data protocols are defined: hospitals (on-chain data providers) generate 3D coordinate hashes of medical data via an algorithmically enhanced Bloom Filter for on-chain certification; patients control data access via blockchain private keys and unique parameters; insurance companies verify eligibility/claims using on-chain Bloom filters; the blockchain network stores encrypted key data (public keys, Bloom filter coordinates, and timestamps) to ensure immutability and traceability. A 3D-enhanced Bloom filter—tailored for on-chain use with user-specific hash functions and key control—stores only 3D coordinates (not raw data), cutting storage costs for 100 records to 1.27 KB and reducing the error rate to near zero ($1.77\%$ lower than traditional schemes for 10,000 entries). Three core smart contracts (identity registration, medical information certification, and automated verification) enable the automation of on-chain processes. Performance tests conducted on a 4-node consortium chain indicate a transaction throughput of 736 TPS (Transactions Per Second) and a per-operation latency of 181.7 ms, which meets the requirements of large-scale commercial applications. BMIT’s three-layer design (“underlying blockchain + enhanced Bloom filter + smart contracts”) delivers a balanced, efficient blockchain medical insurance prototype, offering a reusable technical framework for industry digital transformation. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article