Search Results (111)

Citizens Advice on Prescription (CAP), a Liverpool (UK)-based service, provides welfare advice and link worker social prescription support to people experiencing and at risk of experiencing financial or social hardship. CAP, which receives referrals from healthcare and third-sector services, aims to improve service users’ financial security, health, and wellbeing. A mixed-methods social return on-investment (SROI) analysis was used to evaluate this service. Between May 2022 and November 2023, a subset of service users (n = 538) completed the Short Warwick–Edinburgh Mental Wellbeing Survey (SWEMWBS) at baseline and a 2-month follow-up. Supporting quantitative and qualitative economic data were also collected (February 2023–February 2024) through semi-structured interviews (n = 16). Changes in social value were determined by comparing pre- and post-SWEMWBS scores. These scores were then mapped to monetary values using the Mental Health Social Value Bank (MHSVB). SROI ratios were then calculated by dividing the change in social value by the associated service provision costs. The mean social value change per person ranged from GBP 505.70 to GBP 697.52, and the mean service provision cost was GBP 148.66 per person. The overall study reported a positive SROI return range of GBP 1: GBP 3.40–GBP 4.69. The results indicate that non-clinical support services, like CAP, may be an effective intervention for addressing the wider determinants of health and wellbeing. Full article

The current Common Agriculture Policy (CAP) foresees a reduction of 50% in the use of herbicides by 2030. This study investigates the potential of integrating remote sensing with a low-cost RGB sensor and variable-rate technology (VRT) to optimize herbicide application in a ryegrass (Lolium multiflorum Lam.) fodder crop. The trial was conducted on three 7.5-hectare plots, comparing a variable-rate application (VRA) of herbicide guided by a prescription map generated from segmented digital images, with a fixed-rate application (FRA) and a control (no herbicide applied). The weed population and crop biomass were assessed to evaluate the efficiency of the proposed method. Results revealed that the VRA method reduced herbicide usage by 30% (0.22 l ha⁻¹) compared to the FRA method, while maintaining comparable crop production. These findings demonstrate that smart weed management techniques can contribute to the CAP’s sustainability goals by reducing chemical inputs and promoting efficient crop production. Future research will focus on improving weed recognition accuracy and expanding this methodology to other cropping systems. Full article

This study explores the concept of resilience within the teachings of Popes Benedict XVI and Francis, comparing these with established psychological resilience theories by Ungar and Holling. Through a comprehensive analysis of documents sourced from the Vatican’s official website, resilience is examined across individual, community, and global dimensions. Individual resilience emphasizes spiritual resources such as faith and hope, which Pope Benedict XVI and Pope Francis underscore as essential for overcoming personal and societal crises. Community resilience is highlighted in the context of solidarity, cooperation, and cultural identity, as demonstrated in responses to natural disasters and sociopolitical challenges. At a global level, Pope Francis advocates ecological sustainability and systemic justice, tying resilience to shared responsibilities and global solidarity. Methods include textual frequency analysis and semantic mapping of resilience-related terms within papal documents, complemented by a comparative analysis with psychological resilience frameworks. Findings reveal unique contributions of papal teachings, such as the integration of spiritual, moral, and ecological dimensions, which expand traditional resilience concepts. This theological lens adds normative and prescriptive elements, offering transformative perspectives for resilience studies, emphasizing faith, values, and sustainability as pivotal components for enduring and thriving amidst adversity. Limitations of data mining methods suggest potential for further interdisciplinary research. Full article

Coffee production in mountainous regions faces significant challenges to mechanization, particularly in management and fertilization. Fertilizer application remains largely manual, reducing accuracy and failing to meet the demands of variable-rate application (VRA). This study developed a portable VRA fertilizer applicator with an embedded electronic control system. The innovation lies in its electrically driven metering mechanism integrated with an electronic control unit (ECU), enabling site-specific fertilization based on prescription maps or predefined rates while recording application coordinates. The mechanism was tested under laboratory and field conditions, evaluating its performance across four fertilizer types, varying inclination angles, and rotational speeds. Results showed a coefficient variation of 0.41% for doses above 24 g, demonstrating high consistency irrespective of fertilizer type or terrain slope. Field tests using potassium chloride (KCl) prescriptions (55, 123, and 185 g/plant; 220, 492, and 740 kg/ha) revealed minimal deviations, with the largest at 22.72 g and the smallest at 0.384 g. These findings demonstrate the applicator’s precision and efficiency, addressing the challenges of mountainous terrains. This system provides technological advancement for sustainable coffee production, enhancing resource optimization and supporting precision agriculture in challenging environments. Full article

This study coupled precision agriculture with agroecology to improve the agricultural systems’ sustainability in a climate variability context, characterized by fewer rainy days and more extreme events. A three-year comparative analysis was carried out in a durum wheat rotation, divided into two plots of 2.5 ha each, one managed with conventional methods (CP, sunflower as intermediate crop) and another managed with an agroecological approach (AE, field bean as green manure crop), featuring prescription maps for site-specific mineral fertilization. The statistical analysis of durum wheat parameters, soil characteristics, and economic variables was conducted alongside the examination of climatic data. In AE soil, the exchangeable calcium was statistically different from CP soil (6044 mg kg⁻¹ and 5660 mg kg⁻¹, respectively). Cation exchange capacity was significantly higher in AE (32.7 meq 100 g⁻¹), compared to CP (30.9 meq 100 g⁻¹). In AE, wheat yield (2.36 t ha⁻¹) was higher than in CP (2.07 t ha⁻¹), despite extreme rainfall causing flooding in some parts of the AE plot. The economic balance was only 6% in favor of CP (EUR + 2157), confirming the AE approach’s resilience (EUR + 2027), despite the higher costs of cover cropping and site-specific fertilization. The novelty of integration between “smartish” precision agriculture and agroecology allows for sustainable management. Full article

Background: While multimodal analgesia is the standard of care for postoperative pain relief, opioid medications continue to be a part of the treatment regimen, especially for more invasive surgeries such as spinal fusion, craniofacial reconstruction, laparotomy, and others. In pediatric patients, safe usage, storage, and dosing are especially important, along with clear instructions to caregivers on how to manage their child’s pain. Combination opioids such as hydrocodone with acetaminophen and acetaminophen with codeine are the most commonly prescribed opioid medications for postoperative pain control. However, these combination products can lead to acetaminophen toxicity, limit the ability to prescribe acetaminophen or ibuprofen, and add to caregiver confusion. Administering acetaminophen and ibuprofen individually rather than in combination products allows the maximal dosing of these nonopioid medications. The primary aim of this quality improvement (QI) project was to increase the utilization of single-agent opioids for postoperative pain control, primarily oxycodone, by the various surgical groups here at Cook Children’s Medical Center (CCMC). Methods: The project setting was a tertiary-level children’s hospital with a level 2 trauma center, performing over 20,000 surgeries annually. The opioid stewardship committee (OSC) mapped the steps and overlapping activities in the intervention that led to changes in providers’ prescription practices. A Plan–Do–Study–Act continuous improvement cycle allowed for an assessment and modification of implementation strategies. Statistical control process charts were used to detect the average percentage change in surgical specialties using single-agent opioid therapy. Data were monitored for three periods: one-year pre-intervention, one-year post-intervention, and one-year sustainment periods. Results: There were 4885 (41%) pre-intervention procedures, 3973 (33%) post-intervention procedures, and 3180 (26%) sustainment period procedures that received opioids. During the pre-intervention period, the average proportion of single-agent opioids prescribed was 8%. This average shifted to 89% for the first five months of the post-intervention period, then to 91% for the remainder of the study. Conclusions: The methodical application of process improvement strategies can result in a sustained change from outpatient post-surgical combination opioid prescriptions to single-agent opioid prescriptions in multiple surgical departments. Full article

Precision agriculture techniques contribute to optimizing the use of agricultural inputs, as they consider the spatial and temporal variability in the production factors. Prescription maps of limestone and fertilizers at variable rates (VRA) can be generated using various soil sampling techniques, such as point grid sampling, cell sampling, and management zone sampling. However, low-density grid sampling often fails to capture the spatial variability in soil properties, leading to inaccurate fertilizer recommendations. Sampling techniques by cells or management zones can generate maps of better quality and at lower costs than the sampling system by degree of points with low sampling density. Thus, this study aimed to compare the accuracy of different sampling techniques for mapping soil attributes in precision agriculture. For this purpose, the following sampling techniques were used: high-density point grid sampling method, low-density point grid sampling method, cell sampling method, management zone sampling method, and conventional method (considering the mean). Six areas located in the Brazilian states of Bahia, Minas Gerais, Mato Grosso, Goias, Mato Grosso do Sul, and Sao Paulo were used. The Root-Mean-Square-Error (RMSE) method was determined for each method using cross-validation. It was concluded that the cell method generated the lowest error, followed by the high-density point grid sampling method. Management zone sampling showed a lower error compared to the low-density point grid sampling method. By comparing different sampling techniques, we demonstrate that management zone and cell grid sampling can reduce soil sampling while maintaining comparable or superior accuracy in soil attribute mapping. Full article

Sugarcane is a perennial grass species mainly for sugar production and one of the significant crops in Costa Rica, where ideal growing conditions support its cultivation. Weed control is a critical aspect of sugarcane farming, traditionally managed through preventive or corrective mechanical and chemical methods. However, these methods can be time-consuming and costly. This study aimed to develop site-specific, variable rate prescription maps for weed control using remote sensing. High-spatial-resolution images (5 cm) and Light Detection And Ranging (LiDAR) were acquired using a Micasense Rededge-P camera and a DJI L1 sensor mounted on a drone. Precise locations of weeds were collected for calibration and validation. Normalized Difference Vegetation Index derived from multispectral images separated vegetation coverage and soil. A deep learning (DL) algorithm further classified vegetation coverage into sugarcane and weeds. The DL model performed well without overfitting. The classification accuracy was 87% compared to validation samples. The density and average heights of weed patches were extracted from the canopy height model (LiDAR). They were used to derive site-specific prescription maps for weed control. This efficient and precise alternative to traditional methods could optimize weed control, reduce herbicide usage and provide more profitable yield. Full article

Digital-twin platforms are increasingly adopted in energy infrastructure management for smart grids. Novel opportunities arise from emerging artificial intelligence technologies to increase user trust by enhancing predictive and prescriptive analytics capabilities and by improving user interaction paradigms. This paper presents a novel data-driven and knowledge-based energy digital-twin framework and architecture. Data integration and mining based on machine learning are integrated into a knowledge graph annotating asset status data, prediction outcomes, and background domain knowledge in order to support a retrieval-augmented generation approach, which enhances a conversational virtual assistant based on a large language model to provide user decision support in asset management and maintenance. Components of the proposed architecture have been mapped to commercial-off-the-shelf tools to implement a prototype framework, exploited in a case study on the management of a section of the high-voltage energy infrastructure in central Italy. Full article

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R²) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations. Full article

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R² = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection. Full article

With recent advances in spray technology and rising interest in site-specific applications, it is imperative to assess the performance of the latest application technologies to ensure effective pesticide applications. Thus, a study was conducted to compare and evaluate the performance of two different flow control systems [rate controller (RC) and pulse width modulation (PWM)] on an agricultural sprayer while simulating different site-specific application scenarios. A custom data acquisition and logging system was developed to record the real-time nozzle flow and pressure across the sprayer boom. The first experiment measured the response time to achieve different target application rates in single-rate site-specific (On/Off) states at varying simulated ground speeds. The second experiment examined the response time for rate transitions in variable-rate application scenarios among different selected target rates at varying simulated ground speeds. Across all the application scenarios, the PWM system consistently outperformed the RC system in terms of response time and rate stabilization. Specifically, the PWM system exhibited significantly lower mean rate stabilization times compared to the RC system during single-rate application states. Similarly, in the variable-rate application states—where the rate transitions were evaluated—the PWM system consistently displayed shorter mean rate transition and stabilization times compared to the RC system. Overall, the findings from this study suggest PWM systems tend to be more responsive and effective, making them the preferred choice for efficient precision site-specific pesticide applications. Future research should evaluate the influence of other operational parameters such as look-ahead time and ground speed variations on the performance of both systems in actual field applications. Full article

The priority problem in intensive viticulture is reducing pesticides, and fertilizers, and improving water-use efficiency. This is driven by global and EU regulatory efforts. This review, systematically examines 92 papers, focusing on progress in satellite solutions over time, and (pre)processing improvements of spatio-temporal and spectral resolution. The importance of the integration of satellites with ground truth data is highlighted. The results provide precise on-field adaptation strategies through the generation of prescription maps and variable rate application. This enhances sustainability and efficiency in vineyard management and reduces the environmental footprint of vineyard techniques. The effectiveness of different vegetation indices in capturing spatial and temporal variations in vine health, water content, chlorophyll levels, and overall vigor is discussed. The challenges in the use of satellite data in viticulture are addressed. Advanced satellite technologies provide detailed vineyard monitoring, offering insights into spatio-temporal variability, soil moisture, and vine health. These are crucial for optimizing water-use efficiency and targeted management practices. By integrating satellite data with ground-based measurements, viticulturists can enhance precision viticulture, reduce reliance on chemical interventions, and improve overall vineyard sustainability and productivity. Full article

A precision agriculture approach that uses drones for crop protection and variable rate application has become the main method of rice weed control, but it suffers from excessive spraying issues, which can pollute soil and water environments and harm ecosystems. This study proposes a method to generate variable spray prescription maps based on the actual distribution of weeds in rice fields and utilize DJI plant protection UAVs to perform automatic variable spraying operations according to the prescription maps, achieving precise pesticide application. We first construct the YOLOv8n DT model by transferring the “knowledge features” learned by the larger YOLOv8l model with strong feature extraction capabilities to the smaller YOLOv8n model through knowledge distillation. We use this model to identify weeds in the field and generate an actual distribution map of rice field weeds based on the recognition results. The number of weeds in each experimental plot is counted, and the specific amount of pesticide for each plot is determined based on the amount of weeds and the spraying strategy proposed in this study. Variable spray prescription maps are then generated accordingly. DJI plant protection UAVs are used to perform automatic variable spraying operations based on prescription maps. Water-sensitive papers are used to collect droplets during the automatic variable operation process of UAVs, and the variable spraying effect is evaluated through droplet analysis. YOLOv8n-DT improved the accuracy of the model by 3.1% while keeping the model parameters constant, and the accuracy of identifying weeds in rice fields reached 0.82, which is close to the accuracy of the teacher network. Compared to the traditional extensive spraying method, the approach in this study saves approximately 15.28% of herbicides. This study demonstrates a complete workflow from UAV image acquisition to the evaluation of the variable spraying effect of plant protection UAVs. The method proposed in this research may provide an effective solution to balance the use of chemical herbicides and protect ecological safety. Full article

The antenna field of view, the angle range that can be accessed by scanning the main beam of a phased array, is one of the key performance prescriptions especially for space-borne aerials. The classical example of the full Earth, continental and subcontinental field of view of the geosynchronous satellite is indicative, and it extends to the medium and lower orbit multibeam telecommunication systems. There, a high-gain, very small beamwidth pencil beam should scan a given service area. At the same time, it should exhibit extremely low sidelobes in order not to present interference to adjacent geographical areas, served by neighboring beams, and keep its grating lobes out of the Earth’s surface. High-throughput telecommunication satellites should comply with those prescriptions to be given permission for placement in orbit. Thus, the motivation for delivering solid methods for the design of limited-field-of-view array antennas is high. A proposal in this direction is presented in the work at hand. Indeed, in the present study a scaling transformation is used to map a wide-angle scanning array to a limited-field-of-view one. We start the design from a Full-Field-of-View array with the appropriate half-power beamwidth, sidelobe level, and directivity index, and then we enlarge it to attain the desired one with the limited-field-of-view pattern characteristics. The potential of the method is solid since it augments the limited-field-of-view design methods using the excellent performance of the respective full-field-of-view ones. As a result, the synthesis of a limited-field-of-view array can use any of the well-known array synthesis methods in conjunction with the right scaling. Additionally, one can employ design methods that rely on sampling of planar aperture distributions. Various design examples, employing both sampling of continuous apertures and utilizing classical full-field-of-view array synthesis methods, are included and presented in detail, verifying the merit of our approach. Full article