Search Results (815)

Although the bell pepper (Capsicum annuum L.) is sensitive to water stress, little information is available on proper irrigation management methods for bell pepper cultivation in the northern South Korean climate. We compared the effects of different irrigation methods on crop growth and water productivity in two bell pepper varieties (Maldonado and Nagano) at different irrigation durations (ending 3 h before sunset and ending 4 h before sunset) and irrigation quantities (placing two, three, and four drippers capable of irrigating at 2 L·h⁻¹) over approximately 280 days by performing in-depth analysis of various growth indicators. The plant height of Maldonado increased as the irrigation amount increased in all irrigation treatments of T1 and T2. In Nagano, there was no significant difference in plant height between D3 and D4. Overall, the irrigation treatments produced a higher difference in yield in Maldonado plants than in Nagano plants. WP tended to increase inversely to reduction in irrigation quantity in Groups 1 (May–June) and 4 (November–December) of both varieties, and response to irrigation stop time varied among the varieties. We inferred that the optimal irrigation method for bell pepper cultivation in northern South Korea is to supply irrigation at the D3 level and adjust the irrigation end time according to the variety and crop strength. Full article

This article demonstrates that the application of the variation method to purely information-theoretic models can lead to the discovery of fundamental equations in physics, such as Schrödinger’s equation. Our solution, expressed in terms of information parameters rather than physical quantities, suggests a profound implication—Schrödinger’s equation can be viewed as a unique physical expression of a more profound informational formalism, inspiring new avenues of research. Full article

Whale blow, the vapor exhaled during respiration of cetaceans, provides valuable genetic information to monitor health status and population dynamics. However, obtaining samples of sufficient quality and quantity remains a challenge, particularly for small odontocetes. Here, we developed both field and laboratory protocols optimized for the genetic analysis of blow samples of short-finned pilot whales (Globicephala macrorhynchus). Blow collection was performed from a small research vessel at a slow speed using a hand-held carbon fiber pole equipped with a sterile Petri dish. Determination of the sex was conducted using up to five PCRs of multiplexed markers from a classical methodology (SRY + ZFX/ZFY genes) and a novel protocol (SRY + FCB17) optimized for highly degraded, fragmented and/or scarce DNA. A total of 47 blow samples of free-ranging pilot whales off the Canary Islands were collected. The presence of DNA was confirmed in 98% of the blow samples, which were further processed resulting in 32 of them with positive genetic sex determination applying the novel methodology (70%), compared to only 8 (19%) with the classical method. Results confirmed the success of sampling, DNA extraction and sex determination using multiplexed markers in blow samples of odontocetes. This protocol represents an important management tool to conduct future non-invasive health assessments of small cetaceans in the wild. Full article

Background/Objectives: Fine motor movements are essential for daily activities, such as handwriting, and rely heavily on visual information to enhance motor complexity and minimize errors. Tracing tasks provide an ecological method for studying these movements and investigating sensorimotor processes. To date, our understanding of the influence of different quantities of visual information on fine motor control remains incomplete. Our study examined how variations in the amount of visual feedback affect motor performance during handwriting tasks using a graphic pen tablet projecting on a monitor. Methods: Thirty-seven right-handed young adults (20 to 35 years) performed dot-to-dot triangle tracing tasks under nine experimental conditions with varying quantities of visual cues. The conditions and triangle shape rotations were randomized to avoid motor training or learning effects. Motor performance metrics, including absolute error, time of execution, speed, smoothness, and pressure, were analyzed. Results: As visual information increased, absolute error (from 6.64 mm to 2.82 mm), speed (from 99.28 mm/s to 57.19 mm/s), and smoothness (from 4.17 mm²/s⁶ to 0.80 mm²/s⁶) decreased, while time of execution increased (from 12.68 s to 20.85 s), reflecting a trade-off between accuracy and speed. Pressure remained constant across conditions (from 70.35 a.u. to 74.39). Spearman correlation analysis demonstrated a moderate to strong correlation between absolute error and time of execution across conditions. The Friedman test showed significant effects of experimental conditions on all motor performance metrics except for pressure, with Kendall’s W values indicating a moderate to strong effect size. Conclusion: These findings deepen our understanding of sensorimotor integration processes and could potentially have implications for optimizing motor skills acquisition and training and developing effective rehabilitation strategies. Full article

In the realm of electronic health (eHealth) services powered by the Internet of Things (IoT), vast quantities of medical images and visualized electronic health records collected by IoT devices must be transmitted daily. Given the sensitive nature of medical information, ensuring the security of transmitted health data is paramount. To address this critical concern, this paper introduces a novel data hiding algorithm tailored for Absolute Moment Block Truncation Coding (AMBTC) in medical images, named HPDH-MI (High Payload Data Hiding for Medical Images). The proposed method embeds secret data into the AMBTC compression code inconspicuously to avoid detection by malicious users. It achieves this by first classifying AMBTC compressed blocks into four categories—flat, smooth, complex I, and complex II—using three predetermined thresholds. A 1-bit indicator, based on the proposed grouping strategy, facilitates efficient and effective block classification. A data embedding strategy is applied to each block type, focusing on block texture and taking into account the symmetric features of the pixels within the block. This approach achieves a balance between data hiding capacity, image quality, and embedding efficiency. Experimental evaluations highlight the superior performance of HPDH-MI. When tested on medical images from the Osirix database, the method achieves an average image quality of 31.22 dB, a payload capacity of 225,911 bits, and an embedding efficiency of 41.78%. These results demonstrate that the HPDH-MI method not only significantly increases the payload for concealing secret data in AMBTC compressed medical images but also maintains high image quality and embedding efficiency. This makes it a promising solution for secure data transmission in telemedicine, addressing the challenges of limited bandwidth while enhancing steganographic capabilities in eHealth applications. Full article

Background/Objectives: Patient care and control of inflammatory disorders, such as psoriasis, can be improved by model-informed precision dosing (MIPD) techniques based on population pharmacokinetic/pharmacodynamic (PK/PD) models. Clinical dose selection decisions based on MIPD strategies need to take account of the uncertainty associated with the individual PK/PD model parameters, which is determined by the quantity of individual observational data collected in clinical practice. Methods: The aim of this study was to propose an approach for personalized dosage regimens of secukinumab (SCK) in 22 Spanish patients with plaque psoriasis, whose severity level was considered moderate to severe, taking into account the uncertainty associated with individual parameters in a population-based PK/PD model. Results: The link between SCK serum concentrations and Psoriasis Area and Severity Index (PASI) scores was explained using an indirect response model. A maximum inhibition (I_max) drug effect model was applied to limit the progression of psoriatic skin lesions within the turnover PD mechanism, which explains the changes in PASI scores during treatment. A first-order remission rate constant for psoriatic lesions (k_out = 0.11 day⁻¹) was estimated. Conclusions: According to the MIPD strategy, 50% of patients would require an optimized regimen and 14% would require an intensified dosage regimen in comparison to current clinical treatment. This research has shown its usefulness as a tool for choosing individualized SCK dosage regimens in patients with long-lasting plaque psoriasis to improve the probability of achieving satisfactory response levels. Full article

Built-up areas are the main gathering place for human activities. The widespread availability of various satellite sensors provides a rich data source for mapping built-up areas. Deep learning can automatically learn multi-level features of targets from sample data in an end-to-end manner, overcoming the limitations of traditional methods based on handcrafted features. However, existing deep-learning-based methods rely on the quantity and distribution of sample data, and the trained models often exhibit limited generalization ability when faced with image data from novel scenarios. To effectively tackle this issue, this study proposes an unsupervised domain adaptive transfer learning method based on adversarial machine learning. This method aims to utilize the feature information of the source domain to train a classifier suitable for target domain feature discrimination without requiring a target domain label, and achieve built-up area extraction of different sensor images. The model comprises a feature extraction module, a label classification module, and a domain discrimination module. Through adversarial training, the feature knowledge from the source domain is transferred to the target domain, achieving feature alignment and efficient discrimination of built-up areas. The Gaofen-2 (GF-2) and Sentinel-2 datasets were employed for experimental evaluation. The results show that the proposed method, trained on the GF-2 image dataset (source domain), can be transferred unsupervised to the Sentinel-2 image dataset (target domain), demonstrating robust detection performance. Further comparative experiments have also demonstrated the superiority of our method in extracting built-up areas through transfer learning. Full article

Enhancing the eco-efficiency of municipal solid waste (MSW) services is pivotal for the shift toward a circular economy. Although the Data Envelopment Analysis (DEA) method is widely used, it is susceptible to overfitting, potentially distorting eco-efficiency assessments. This study applies the efficiency analysis tree (EAT) method, which synergizes machine learning and linear programming, offering a more reliable framework for eco-efficiency evaluation in the MSW sector. This innovative approach provides deeper insights into the optimal levels of operational costs and unsorted waste. The research encompasses a case study of 98 Chilean municipalities from 2015 to 2019, uncovering significant disparities in optimal operational expenses and unsorted waste quantities, which underscores the necessity for customized waste management approaches. The average eco-efficiency scores for 2015–2019 range between 0.561 and 0.566. This means that assessed municipalities can reduce unsorted waste by amounts ranging from 1,632,409 tons/year (2016) to 1,822,663 tons/year (2018). Potential economic savings estimated are 105,973 USD/year (2019), which represents 44% of the total MSW management costs. Additionally, the investigation into the effects of external factors on eco-efficiency furnishes nuanced perspectives that can guide policymakers and municipal authorities in developing effective, context-specific waste management strategies. Beyond refining eco-efficiency evaluations, this study contributes to more informed decision-making processes, aiding the progression toward sustainable waste management practices. Full article

Aim: Electronic prescribing has allowed for the collection of prescription data in real time in Greece for the first time. Hence, the aim of the current study was to present the characteristics of prescriptions for the Greek population during the period from 2015 to 2021. Methods: This retrospective study was based on data extracted from the nationwide Greek electronic prescription database between January 2015 and December 2021. Descriptive statistics methods were used for the needs of the study. As the basic figures examined depend on the size of the population, in order for the results to be comparable, we estimated the corresponding measures per inhabitant, using population data from the Greek Statistical Authority. Appropriate indicators for the comparison of consumption and expenditure over time were estimated. A study of the trend was also carried out using time series and linear regression models. In order to facilitate the design and implementation of specialized policies, it is useful to identify the drug categories with the highest consumption and expenditure, as well as the geographical areas that present similar characteristics. For the first, ABC analysis was used, which helps to identify the most popular categories of drugs, while for the second, cluster analysis was carried out. Agglomerative clustering was used to divide the regions into similar groups. This hierarchical clustering algorithm classifies the population into several clusters, with areas in the same cluster being more similar, and areas in different clusters being dissimilar. The Ward linkage method with Euclidean distance was used. Results: The analysis of prescription drug consumption and expenditure from 2015 to 2021 revealed significant fluctuations and trends across various drug categories, age groups, and geographical areas. Notably, the quantity of prescriptions increased by 20% since 2015, while expenditure surged by over 30%, with significant spikes following the end of the MoU in 2019 and the onset of the pandemic in 2020. In terms of expenditure, antineoplastic and immunomodulation agents (category L) held the largest share, driven by the introduction of new, costly drugs. The expenditure per inhabitant revealed gender and age disparities, with older populations, particularly women, incurring higher costs. Geographically, drug expenditure, and consumption varied significantly, with distinct regional clusters identified. These clusters, while showing some overlap in consumption and expenditure patterns, also highlighted unique regional characteristics. Conclusions: The insights into prescription drug consumption and expenditure trends offer a valuable basis for developing targeted interventions aimed at optimizing healthcare resource allocation. Moreover, the findings underscore the importance of addressing regional and demographic disparities in pharmaceutical use, thereby contributing to more equitable and cost-effective healthcare strategies. More specifically, the age distribution of prescriptions shows the increase in younger ages, which, as a result, anticipates the overall increase in prescriptions. The knowledge of the most convex categories of medicine, as well as the percentages of the use of generic drugs, shows where interventions should be made, with financial incentives and information through new information channels. The geographic disparities recorded should lead to policies that help the residents of hard-to-reach areas to access prescriptions. In addition, the present study provides a strategic framework for policymakers and healthcare managers to guide future studies and inform decision-making processes. Full article

The integration of different sources of geological and hydrogeological information and the application of interdisciplinary methods have informed the origin of the thermal springs of Alhama de Aragón and Jaraba, as well as other associated semi thermal springs (1200 L/s of combined flow, 711 L/s at over 30 °C), which is the main objective of this article. These springs come mainly from the autogenous recharge that occurs in the Cretaceous calcareous outcrops that border the Almazán Basin to the north, both in the Ebro Basin (Jalón Valley) and in the Duero Basin. The aquifer, shaped by upper Cretaceous limestones under the Palaeogene and Neogene rocks of the Almazán Basin, has extensive depths of more than 4000 m in the NE sector. This hydrostratigraphic unit has been affected by a generalized pre-Paleogene karstification that provides the main porosity to the aquifer. The underground flow moves in a NW–SE direction, crossing the Duero–Ebro divide, favoured by the topographic difference in elevation between the two basins. The regional flow is coherent with the progressive increase in temperature, infiltrating recharge water age (about 20–25 years in the semi-thermal springs, and more than 60 years in the Alhama and Jaraba springs), mineralization, and flow of the springs through which the system discharges. This issue is key to being able to design any sustainable conservation strategy in terms of quantity and quality of resources within the recharge area of the most important thermal springs in Spain. The Jaraba and Alhama de Aragón hot springs share the same or similar temperature, chemical composition, and geological contact of the spring. Their tritium isotopic composition and its evolution over time are practically the same. Their isotopic composition in D and ¹⁸O is also very similar. Both springs share the same recharge zone of similar altitude and constitute the end of flow tubes of similar length and flow rate. Full article

DC converter stations have a high voltage level, a long transmission distance, and complex internal equipment, and contain power electronic devices, which seriously endanger the stable operation of the system itself and the active distribution network at the receiving end when faults occur. Accurate fault analysis and diagnosis are critical to the safe and stable operation of power systems. Traditional fault diagnosis methods often rely on a single source of information, leading to issues such as insufficient information utilization and incomplete diagnostic scope when applied to DC transmission systems. To address these problems, a fault diagnosis method for converter stations based on preliminary identification of the fault range and the fusion of evidence information of the switch signal and electrical quantity is proposed. First, the preprocessing of converter station sequential event recording (SER) events and a statistical analysis of event characteristics are completed to initially determine the range of the fault.Then, a fuzzy Petri net model and a BP neural network model are constructed on the basis of the fault data from a real-time digital simulation system (RTDS), and the corresponding evidence information of the switch signal and electrical quantity are obtained via iterative inference and deep learning methods. Finally, on the basis of D-S evidence theory, a comprehensive diagnosis result is obtained by fusing the switch and electric evidence information. Taking the fault data of a DC converter station as an example, the proposed method is analyzed and compared with the traditional method, which is based on single information. The results show that the proposed method can reliably and accurately identify fault points in the protected area of the converter station. Full article

Semi-supervised learning has gained significant attention in the field of remote sensing due to its ability to effectively leverage both a limited number of labeled samples and a large quantity of unlabeled data. An effective semi-supervised learning approach utilizes unlabeled samples to enforce prediction consistency under minor perturbations, thus reducing the model’s sensitivity to noise and suppressing false positives in change-detection tasks. This principle underlies consistency regularization-based methods. However, while these methods enhance noise robustness, they also risk overlooking subtle but meaningful changes, leading to information loss and missed detections. To address this issue, we introduce a simple yet efficient method called Sample Inflation Interpolation (SII). This method leverages labeled sample pairs to mitigate the information loss caused by consistency regularization. Specifically, we propose a novel data augmentation strategy that generates additional change samples by combining existing supervised change samples with calculated proportions of change areas. This approach increases both the quantity and diversity of change samples in the training set, effectively compensating for potential information loss and reducing missed detections. Furthermore, to prevent overfitting, small perturbations are applied to the generated sample pairs and their labels. Experiments conducted on two public change detection (CD) datasets validate the effectiveness of our proposed method. Remarkably, even with only 5% of labeled training data, our method achieves performance levels that closely approach those of fully supervised learning models. Full article

We develop a method to automatically and stably anonymize and de-anonymize face images with encoder-decoder networks and provide a robust and secure solution for identity protection. Our fundamental framework is a Neural Network (NN)-based encoder-decoder pair with a dual inferencing mechanism. We denote it as the Secure Dual Network (SDN), which can simultaneously achieve multi-attribute face de-identification and re-identification without any pre-trained/auxiliary model. In more detail, the SDN can take responsibility for successfully anonymizing the face images while generating surrogate faces, satisfying the user-defined specific conditions. Meanwhile, SDN can also execute the de-anonymization procedure and visually indistinguishably reconstruct the original ones if re-identification is required. Designing and implementing the loss functions based on information theory (IT) is one of the essential parts of our work. With the aid of the well-known IT-related quantity, Mutual Information, we successfully explained the physical meaning of our trained models. Extensive experiments justify that with pre-defined multi-attribute identity features, SDN generates user-preferred and diverse appearance anonymized faces for successfully defending against attacks from hackers and, therefore, achieves the goal of privacy protection. Moreover, it can reconstruct the original image nearly perfectly if re-identification is necessary. Full article

Access to reliable water supply is critical for sustainable development and public health, yet many peri-urban and rural communities in the developing world face challenges such as poor maintenance, faulty infrastructure, and inadequate governance of water services, hindering the UN’s Sustainable Development Goals (SDG). In Emina-Boadi, a small town in Kumasi, Ghana, residents increasingly rely on boreholes due to substandard service from the Ghana Water Company Limited (GWCL). This shift highlights concerns about water reliability, quality, and affordability, impacting public health and hindering progress toward SDG 6. This case study assesses water service levels using key performance indicators (KPIs) such as quantity, quality, affordability, accessibility, and reliability, focusing on sources like boreholes, hand pumps, piped water, hand-dug wells, rainwater, and surface waters. The study aims to delineate the roles of different water providers, understand community dynamics, and evaluate contributions to rural development and SDG 6 through stakeholder interviews and questionnaires. By leveraging the WASHCost framework, which aims to improve planning methods and the provision of water and sanitation services by addressing the lack of cost information in rural and peri-urban areas, and the Joint Monitoring Programme (JMP) framework, which monitors progress toward global targets related to water, sanitation, and hygiene, the study categorizes indicators and draws connections among them. The findings reveal a significant preference for borehole services, with 87% usage and an 83% reliability rate compared to 13% for GWCL. Daily water consumption averages 60.75 L per person, with on-premises access improving water collection efficiency to 7 min. Dissatisfaction with GWCL services highlights the need for improved delivery aligned with community needs and SDG targets. Adopting sustainable management practices, enhancing infrastructure, and improving governance are crucial for ensuring safe and affordable water access for all residents of Emina-Boadi and similar communities. Full article

Background: The histopathological assessment is critical in the comprehensive treatment process for patients diagnosed with oral squamous cell carcinoma (OSCC). A detailed and precise specimen characterization is essential to facilitate effective surgeon–pathologist communication. Methods: In response to this need, a user-friendly virtual communication protocol utilizing a 3D scanner has been developed. This study involved 50 patients with OSCC, whose resected tumors were directly scanned in the operating room and subsequently annotated and characterized using available software. Results: The direct application of annotations and descriptions onto the virtual tumor specimens significantly enhanced the quantity and accuracy of information conveyed to the pathologist. Conclusions: The proposed solution’s repeatability and standardized approach make integration into routine clinical practice feasible, thereby establishing a potential new standard in the field. Full article