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Background/Objectives: Emergency airway management is a critical skill for healthcare professionals, particularly in life-threatening situations like "cannot intubate, cannot oxygenate" (CICO) scenarios. Errors and delays in airway management can lead to adverse outcomes, including hypoxia and death. Cognitive aids, such as checklists and algorithms, have been proposed as tools to improve decision-making, procedural competency, and non-technical skills in these high-stakes environments. This systematic review aims to evaluate the effectiveness of cognitive aids in enhancing emergency airway management skills among health professionals and trainees. Methods: A systematic search of MEDLINE, Embase, CINAHL, Cochrane Library, Scopus, Web of Science, and ClinicalTrials.gov was conducted from February to March 2024. Studies examining the use of cognitive aids, such as the Vortex method, the ASA difficult airway algorithm, and visual airway aids, in emergency airway scenarios were included. Outcomes assessed included decision-making speed, procedural success rates, and non-technical skills. Data were extracted using standardized protocols, and the quality of included studies was appraised. Results: Five studies met inclusion criteria, encompassing randomized controlled trials, controlled studies, and mixed-methods research. Cognitive aids improved decision-making times (reduced by 44.6 s), increased procedural success rates, and enhanced non-technical skills such as teamwork and crisis management. Participants reported reduced anxiety and improved confidence levels (self-efficacy scores increased by 1.9 points). The Vortex method and visual cognitive aids demonstrated particular effectiveness in simulated scenarios. Conclusions: Cognitive aids significantly enhance emergency airway management skills, improving performance, reducing errors, and increasing provider confidence. Integrating cognitive aids into training programs has the potential to improve patient safety and outcomes. Further research is needed to validate these findings in clinical settings and optimize cognitive aid design and implementation. [ABSTRACT FROM AUTHOR]

Key summary points: Aim: To identify and comprehensively describe the assessment tools addressing avoidable care transitions that can support stakeholders´ decisions on older adults. Findings: All of the 48 reviewed tools are not comprehensive with respect to the dimensions covered, making them less useful in addressing avoidable care transitions. The review findings are systematically summarised in a clinically accessible website (www.decision4transition.com), which allows to instantly filter assessment tools based on their properties. Message: The review findings and the online database are now ready for use in clinical routine to support informed decision-making of stakeholders when choosing the right assessment tool addressing avoidable care transitions. Purpose: The phenomenon of avoidable care transitions has received increasing attention over the last decades due to its frequency and associated burden for the patients and the healthcare system. A number of assessment tools to identify avoidable transitions have been designed and implemented. The selection of the most appropriate tool appears to be challenging and time-consuming. This systematic review aimed to identify and comprehensively describe the assessment tools that can support stakeholders´ care transition decisions on older adults. Methods: This study was conducted as part of the TRANS-SENIOR research network. A systematic search was conducted in MEDLINE via PubMed, CINAHL, and CENTRAL. No restrictions regarding publication date and language were applied. Results: The search in three electronic databases revealed 1266 references and screening for eligibility resulted in 58 articles for inclusion. A total of 48 assessment tools were identified covering different concepts, judgement processes, and transition destinations. We found variation in the comprehensiveness of the tools with regard to dimensions used in the judgement process. Conclusion: All tools are not comprehensive with respect to the dimensions covered, as they address only one or a few perspectives. Although assessment tools can be useful in clinical practice, it is worth it to bear in mind that they are meant to support decision-making and supplement the care professional´s judgement, instead of replacing it. Our review might guide clinicians and researchers in choosing the right tool for identification of avoidable care transitions, and thus support informed decision-making. [ABSTRACT FROM AUTHOR]

Decision support tools in agriculture help farmers make science-informed decisions rather than experience-dependent ones, which is increasingly important in the context of climate change. However, these tools often fail to accommodate diverse users in terms of their contents, ease of use, and dissemination strategies. The objectives of this study are 1) to understand gender roles in potato production, 2) to understand men and women's knowledge and perceptions of pesticide application, and 3) assess the usability of a handheld decision support tool for potato late blight control from the perspective of gender in Peru. A qualitative participatory assessment was carried out in two communities in the Andes to explore not only the technical usability of the tool for men and women farmers, but also gender roles, knowledge, and perceptions of pest and disease management that determine household decisions on pesticide use. The findings confirmed that women farmers face challenges to using the tool, based on both technical and socio-cultural aspects. Furthermore, women farmers play a significant role in potato production in general, and their involvement in late blight management is likely to contribute to making appropriate household decisions on the use of pesticides, thereby mitigating overall health and environmental risks. The study concludes with recommendations for modifying the tool for women users as well as incorporating women's perspectives at the design stage. [ABSTRACT FROM AUTHOR]

Soil erosion is considered one of the most prevalent natural hazards in semiarid regions, leading to the instability of ecosystems and human life. The main purpose of this research was to investigate and analyze soil erosion susceptibility maps in the Medjerda basin in northern Africa. This study utilizes four ensemble models based on the analytical hierarchy process (AHP) multicriteria decision-making analysis, namely, deep learning neural network AHP (DLNN-AHP), frequency ratio AHP (FR-AHP), Monte Carlo AHP (MC-AHP), and fuzzy AHP (F-AHP). Eight predictor variables were considered as inputs to the model, namely, the slope degree, digital elevation model (DEM), topographic wetness index (TWI), distance to river (DFR), distance to road (DFRD), normalized difference vegetation index (NDVI), rainfall erosivity (R), factor and soil erodibility factor (K). Soil erosion inventory maps were developed from field surveys and satellite images. The dataset was randomly divided into 70% for training and 30% for testing. The performances of the utilized models were compared using a receiver operating characteristic (ROC) curve. The results highlighted that all the models utilized exhibited good performance, with DLNN-AHP (93.1%) exhibiting slight superiority, followed by FR-AHP (90.9%), F-AHP (88.9%), and MC-AHP (88.5%). Among the influencing factors, the distance to the river and rainfall erosivity had the most significant impacts on the incidence of soil erosion. Moreover, the current findings revealed that 38.3% of the study area is extremely highly susceptible to soil erosion. The results of this study can aid in developing decision-support tools for planners and managers aiming to mitigate the adverse effects of soil erosion. [Display omitted] • Enhanced soil erosion assessment using a hybrid MCDM model with improved accuracy. • Deep Learning with AHP boosts soil erosion prediction. • Rainfall erosivity, river proximity are key factors in erosion. • Findings provide tools for effective erosion mitigation. [ABSTRACT FROM AUTHOR]

Simple Summary: Invasive ant species are increasingly proving dangerous to native biodiversity and ecosystems, agriculture, other economic activities, and human health. Like many Mediterranean countries, Italy is witnessing a steady increase in non-native ant species of different origins and with different biological characteristics. Climate change is further posed to alter the region's suitability for non-native ants; therefore, assessing their invasion potential is a crucial step in developing management strategies. We provide risk screenings for 15 non-native ant species already established in Italy and 12 that may be established in the future using a Terrestrial Species Invasiveness Screening Kit. The results indicate the Argentine ant, Linepithema humile, and the red imported fire ant, Solenopsis invicta, to be the most threatening species, followed by the electric ant, Wasmannia auropunctata; the Asian needle ant, Brachyponera chinensis; and the tropical fire ant, Solenopsis geminata. The harmfulness of other tropical species largely varies based on climatic predictions, while most species are far less dangerous. However, the impact of many ants is still undocumented, and the future role of climate change in their invasiveness is unclear. The detection of newly established species is often late and accidental, but public engagement could be crucial as most species first establish near cities. Over five hundred non-native ant species have spread worldwide, including many that have severe effects on biodiversity, are serious economic pests, or threaten human health and agriculture. The number of species in the Mediterranean is steadily increasing, with Italy being a prominent example. We provide risk screenings for non-native ant species in Italy using a Terrestrial Species Invasiveness Screening Kit using current climate conditions and future predictions. The screened species consist of 15 established and 12 horizon taxa. The results highlight the threat posed by Linepithema humile and Solenopsis invicta, followed by Wasmannia auropunctata, Brachyponera chinensis, and Solenopsis geminata. The threat posed by other tropical invaders such as Anoplolepis gracilipes and Pheidole megacephala depends on climate change scenarios. The Palearctic non-native Lasius neglectus and Tetramorium immigrans species are recognized as intermediate threats, while most screened species are far less threatening. The biology and ecology of most non-native ant species remain scarcely documented. Among the established species, B. chinensis, L. humile, and S. invicta deserve the most attention, while W. auropunctata is rapidly spreading in neighboring countries. Detection is still often accidental and late compared to establishment. Most species first establish around urban areas, making citizen science a promising tool for biosurveillance. [ABSTRACT FROM AUTHOR]

The WHO guidelines for classifying central nervous system (CNS) tumours are changing considerably with each release. The classification of CNS tumours is uniquely complex among most other solid tumours as it incorporates not just morphology, but also genetic and epigenetic features. Keeping current with these changes across medical fields can be challenging, even for clinical specialists. Large language models (LLMs) have demonstrated their ability to parse and process complex medical text, but their utility in neuro‐oncology has not been systematically tested. We hypothesised that LLMs can effectively diagnose neuro‐oncology cases from free‐text histopathology reports according to the latest WHO guidelines. To test this hypothesis, we evaluated the performance of ChatGPT‐4o, Claude‐3.5‐sonnet, and Llama3 across 30 challenging neuropathology cases, which each presented a complex mix of morphological and genetic information relevant to the diagnosis. Furthermore, we integrated these models with the latest WHO guidelines through Retrieval‐Augmented Generation (RAG) and again assessed their diagnostic accuracy. Our data show that LLMs equipped with RAG, but not without RAG, can accurately diagnose the neuropathological tumour subtype in 90% of the tested cases. This study lays the groundwork for a new generation of computational tools that can assist neuropathologists in their daily reporting practice. [ABSTRACT FROM AUTHOR]

Elderly patients with type 2 diabetes mellitus often face the problems of multimorbidity and multiple medications, and these conditions often lead to irrational or inappropriate medication use, significantly affecting patient outcomes and quality of life, making early and effective medication decisions particularly important. As an important supplement to medication support information, decision support tools, including electronic health record-based systems, mobile applications, online health platforms, etc., provide personalized, evidence-based medical information to help patients understand medication regimens, assist patients and healthcare professionals in making more rational medication decisions, enhance the appropriateness and safety of medication, and reduce the risk of drug interactions, thereby improving the overall treatment of patients and reducing the risk of drug interactions. The application of medication management in elderly patients with type 2 diabetes mellitus has made significant progress, thus improving the overall outcome and quality of life of patients. This article summarizes the current situation of medication use in elderly patients with type 2 diabetes mellitus, the influencing factors, types, application methods and their roles of decision aids, aiming to provide reference for the development of medication decision aids for this population in China.

Abstract Background Multiple clinician adjustable parameters impact upon glycemia in people with type 1 diabetes (T1D) using Medtronic Mini Med 780G (MM780G) AHCL. These include glucose targets, carbohydrate ratios (CR), and active insulin time (AIT). Algorithm-based decision support advising upon potential settings adjustments may enhance clinical decision-making. Methods Single-arm, two-phase exploratory study developing decision support to commence and sustain AHCL. Participants commenced investigational MM780G, then 8 weeks Phase 1-initial optimization tool evaluation, involving algorithm-based decision support with weekly AIT and CR recommendations. Clinicians approved or rejected CR and AIT recommendations based on perceived safety per protocol. Co-design resulted in a refined algorithm evaluated in a further identically configured Phase 2. Phase 2 participants also transitioned to commercial MM780G following “Quick Start” (algorithm-derived tool determining initial AHCL settings using daily insulin dose and weight). We assessed efficacy, safety, and acceptability of decision support using glycemic metrics, and the proportion of accepted CR and AIT settings per phase. Results Fifty three participants commenced Phase 1 (mean age 24.4; Hba1c 61.5mmol/7.7%). The proportion of CR and AIT accepted by clinicians increased between Phases 1 and 2 respectively: CR 89.2% vs. 98.6%, p

Stormwater management in coastal urban cities, where drainage networks are influenced by marine dynamics and specific soil and altimetry conditions, has specific challenges that need to be addressed to ensure adequate management in such areas, which are also heavily affected by floods. Their location downstream of drainage basins and the interaction of network outfalls with current and tidal variability increases the vulnerability of populations and should therefore be the target of specific studies. This article presents a literature review, where publications that focus on stormwater management in coastal urban areas were identified and analyzed. The main objective was to present the key issues related to drainage in coastal areas, the most relevant challenges, the solutions and strategies that reveal the greater potential for application and the challenges for modeling this type of case. It is intended to provide a grounded basis for new ways of optimizing stormwater drainage in coastal areas and promote a sustainable urban water cycle. This review reveals the necessity to implement a multidisciplinary approach to minimize three main issues: urban flooding, stormwater pollution and groundwater salinization, including the adaptation of existing infrastructures, complementing them with control solutions at source, correct urban planning and the involvement of populations. For an effective management of urban stormwater drainage in coastal areas, this approach must be carried out on a watershed scale, duly supported by reliable decision support tools and monitoring systems. [ABSTRACT FROM AUTHOR]

Near infrared spectroscopy (NIRS) is an analytical technique that offers a real advantage over laboratory analysis in the food industry due to its low operating costs, rapid analysis, and non‐destructive sampling technique. Numerous studies have shown the relevance of NIR spectra analysis for assessing certain food properties with the right calibration. This makes it useful in quality control and in the continuous monitoring of food processing. However, the NIR calibration process is difficult and time‐consuming. Analysis methods and techniques vary according to the configuration of the NIR instrument, the sample to be analyzed and the attribute that is to be predicted. This makes calibration a challenge for many manufacturers. This paper aims to provide a data‐driven methodology for developing a decision support tool based on the smart selection of NIRS wavelength to assess various food properties. The decision support tool based on the methodology has been evaluated on samples of cocoa beans, grains of wheat and mangoes. Promising results were obtained for each of the selected models for the moisture and fat content of cocoa beans (R2cv: 0.90, R2test: 0.93, RMSEP: 0.354%; R2cv: 0.73, R2test: 0.79, RMSEP: 0.913%), acidity and vitamin C content of mangoes (R2cv: 0.93, R2test: 0.97, RMSEP: 17.40%; R2cv: 0.66, R2test: 0.46, RMSEP: 0.848%), and protein content of wheat—DS2 (R2cv: 0.90, R2test:0.92, RMSEP: 0.490%) respectively. Moreover, the proposed approach allows results to be obtained that are better than benchmarks for the moisture and protein content of wheat—DS1 (R2cv: 0.90, R2test: 94, RMSEP: 0.337%; R2cv: 0.99, R2test: 0.99, RMSEP: 0.177%), respectively. Practical Application: This research introduces a practical tool aimed at determining the quality of food by identifying specific light wavelengths. However, it is important to acknowledge potential challenges, such as overfitting. Before implementation, it is crucial for further research to address and mitigate the issues to ensure the reliability and accuracy of the solution. If successfully applied, this tool could significantly enhance the accuracy of near‐infrared spectroscopy in assessing food quality attributes. This advancement would provide invaluable support for decision‐making in industries involved in food production, ultimately leading to better overall product quality for consumers. [ABSTRACT FROM AUTHOR]

Clinical Bioinformatics is a knowledge framework required to interpret data of medical interest via computational methods. This area became of dramatic importance in precision oncology, fueled by cancer genomic profiling: most definitions of Molecular Tumor Boards require the presence of bioinformaticians. However, all available literature remained rather vague on what are the specific needs in terms of digital tools and expertise to tackle and interpret genomics data to assign novel targeted or biomarker-driven targeted therapies to cancer patients. To fill this gap, in this article, we present a catalog of software families and human skills required for the tumor board bioinformatician, with specific examples of real-world applications associated with each element presented. [ABSTRACT FROM AUTHOR]

Background: Primary care providers (PCPs) are often the first point of contact for discussing lung cancer screening (LCS) with patients. While guidelines recommend against screening people with limited life expectancy (LLE) who are less likely to benefit, these patients are regularly referred for LCS. Objective: We sought to understand barriers PCPs face to incorporating life expectancy into LCS decision-making for patients who otherwise meet eligibility criteria, and how a hypothetical point-of-care tool could support patient selection. Design: Qualitative study based on semi-structured telephone interviews. Participants: Thirty-one PCPs who refer patients for LCS, from six Veterans Health Administration facilities. Approach: We thematically analyzed interviews to understand how PCPs incorporated life expectancy into LCS decision-making and PCPs' receptivity to a point-of-care tool to support patient selection. Final themes were organized according to the Cabana et al. framework Why Don't Physicians Follow Clinical Practice Guidelines, capturing the influence of clinician knowledge, attitudes, and behavior on LCS appropriateness determinations. Key Results: PCP referrals to LCS for patients with LLE were influenced by limited knowledge of the life expectancy threshold at which patients are less likely to benefit from LCS, discomfort estimating life expectancy, fear of missing cancer at the point of early detection, and prioritization of factors such as quality of life, patient values, clinician-patient relationship, and family support. PCPs were receptive to a decision support tool to inform and communicate LCS appropriateness decisions if easy to use and integrated into clinical workflows. Conclusions: Our study suggests knowledge gaps and attitudes may drive decisions to offer screening despite LLE, a behavior counter to guideline recommendations. Integrating a LCS decision support tool that incorporates life expectancy within the electronic medical record and existing clinical workflows may be one acceptable solution to improve guideline concordance and increase confidence in selecting high benefit patients for LCS. [ABSTRACT FROM AUTHOR]

Wastewater reuse is a proven strategy to mitigate water stress in drought-prone regions. However, this practice is still limited due to high implementation costs, regulatory hurdles, and limited public acceptance. In regions with low reclaim rates, a thorough evaluation of the potential for reuse is needed to support decision-making, focusing on opportunities that address both low-hanging fruit and high-leverage projects. This paper introduces a streamlined, data-centric methodology for assessing wastewater reuse potential, adaptable to various regional contexts. The methodology involves comprehensive data collection and processing to evaluate wastewater treatment plant (WWTP) capabilities and identify potential users, allowing the prioritisation of case studies based on demand alignment. Different treatment and distribution systems are analysed to match WWTP capabilities with user needs, considering volume, quality, and infrastructure requirements. Cost analysis incorporates capital expenditure (CAPEX), operational expenditure (OPEX) and unit costs using novel cost functions for treatment and distribution. Risk analysis adheres to WHO methodology to ensure safety and sustainability. A case study in the Lisbon and Oeste areas in Portugal validates this approach, revealing key insights into the potential and economic viability of water reuse. By comparing tariffs and costs associated with different reuse scenarios, this paper offers benchmarks for the economic feasibility of reuse projects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Multiple clinician adjustable parameters impact upon glycemia in people with type 1 diabetes (T1D) using Medtronic Mini Med 780G (MM780G) AHCL. These include glucose targets, carbohydrate ratios (CR), and active insulin time (AIT). Algorithm-based decision support advising upon potential settings adjustments may enhance clinical decision-making. Methods: Single-arm, two-phase exploratory study developing decision support to commence and sustain AHCL. Participants commenced investigational MM780G, then 8 weeks Phase 1-initial optimization tool evaluation, involving algorithm-based decision support with weekly AIT and CR recommendations. Clinicians approved or rejected CR and AIT recommendations based on perceived safety per protocol. Co-design resulted in a refined algorithm evaluated in a further identically configured Phase 2. Phase 2 participants also transitioned to commercial MM780G following "Quick Start" (algorithm-derived tool determining initial AHCL settings using daily insulin dose and weight). We assessed efficacy, safety, and acceptability of decision support using glycemic metrics, and the proportion of accepted CR and AIT settings per phase. Results: Fifty three participants commenced Phase 1 (mean age 24.4; Hba1c 61.5mmol/7.7%). The proportion of CR and AIT accepted by clinicians increased between Phases 1 and 2 respectively: CR 89.2% vs. 98.6%, p < 0.01; AIT 95.2% vs. 99.3%, p < 0.01. Between Phases, mean glucose percentage time < 3.9mmol (< 70mg/dl) reduced (2.1% vs. 1.4%, p = 0.04); change in mean TIR 3.9-10mmol/L (70-180mg/dl) was not statistically significant: 72.9% ± 7.8 and 73.5% ± 8.6. Quick start resulted in stable TIR, and glycemic metrics compared to international guidelines. Conclusion: The co-designed decision support tools were able to deliver safe and effective therapy. They can potentially reduce the burden of diabetes management related decision making for both health care practitioners and patients. Trial registration: Prospectively registered with Australia/New Zealand Clinical Trials Registry(ANZCTR) on 30th March 2021 as study ACTRN12621000360819. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' technology needs has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' data and technology needs to improve herd health and inform innovation development. We conducted 18 focus groups with a total of 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data were analyzed using Template Analysis, and 6 themes were generated representing the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, were particular concerns in relation to youngstock management. Overall, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs. [ABSTRACT FROM AUTHOR]

Abstract The WHO guidelines for classifying central nervous system (CNS) tumours are changing considerably with each release. The classification of CNS tumours is uniquely complex among most other solid tumours as it incorporates not just morphology, but also genetic and epigenetic features. Keeping current with these changes across medical fields can be challenging, even for clinical specialists. Large language models (LLMs) have demonstrated their ability to parse and process complex medical text, but their utility in neuro‐oncology has not been systematically tested. We hypothesised that LLMs can effectively diagnose neuro‐oncology cases from free‐text histopathology reports according to the latest WHO guidelines. To test this hypothesis, we evaluated the performance of ChatGPT‐4o, Claude‐3.5‐sonnet, and Llama3 across 30 challenging neuropathology cases, which each presented a complex mix of morphological and genetic information relevant to the diagnosis. Furthermore, we integrated these models with the latest WHO guidelines through Retrieval‐Augmented Generation (RAG) and again assessed their diagnostic accuracy. Our data show that LLMs equipped with RAG, but not without RAG, can accurately diagnose the neuropathological tumour subtype in 90% of the tested cases. This study lays the groundwork for a new generation of computational tools that can assist neuropathologists in their daily reporting practice.

Macroalgae have been used as indicators of the health of coastal ecosystems, they function as sinks of CO2 and are essential contributors to primary production. With the increase in anthropogenic activities, it is crucial to assess the impact of such activities on these ecosystems. As traditional surveying techniques, although accurate, are time-consuming and their area coverage is limited, novel techniques are required to monitor the coverage and diversity of intertidal macroalgae. We propose a methodology using the free-source Semi-Automatic Classification Plugin from QGIS to use UAV and multispectral cameras for the spatiotemporal monitoring of intertidal macroalgae. We also compared the performance of six classifiers: Minimum Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM), for three types of macroalgae classification: general, taxonomical groups and species. As proof of concept, an intertidal rocky shore in a marine protected area (NW Spain) was studied for four months. RF and SVM achieved similar results, with both being recommended for the general (OASVM = 97.4±1.7 and OARF = 98.3±1.7) and taxonomical groups (OASVM = 91.6±1.9 and OARF = 89.2±4.5). SVM and ML were found to be more suitable for species classification (OASVM = 77.4±11.4 and OAML = 74.2±9.7). SAM and MLP provided the least performant species classifiers because of the overlap in the macroalgae spectral signatures. The plugin showed limitations when tuning the input parameters of the MLP classifier and did not let to add a validation dataset. Additionally, we present an open-access GIS web application, Alganat 2000 GIS web, to facilitate the monitoring and management of coastal areas. We conclude that the proposed methodology using the SVM or ML classifiers is an effective tool for assessing intertidal macroalgal assemblages. Its easy and rapid implementation is beneficial for researchers who are not very familiar with coding and machine learning frameworks and reduces the time and cost of fieldwork. As future work, we propose the combination of the multispectral bands with topographic and spectral indices and to research the application of deep learning models to the classification of intertidal macroalgae.

BackgroundFifty percent of people living with dementia are undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was developed to identify older adults at risk of having undiagnosed dementia using routinely collected clinical data.ObjectiveTo externally validate eRADAR in two real-world healthcare systems, including examining performance over time and by race/ethnicity.DesignRetrospective cohort study PARTICIPANTS: 129,315 members of Kaiser Permanente Washington (KPWA), an integrated health system providing insurance coverage and medical care, and 13,444 primary care patients at University of California San Francisco Health (UCSF), an academic medical system, aged 65 years or older without prior EHR documentation of dementia diagnosis or medication.Main measuresPerformance of eRADAR scores, calculated annually from EHR data (including vital signs, diagnoses, medications, and utilization in the prior 2 years), for predicting EHR documentation of incident dementia diagnosis within 12 months.Key resultsA total of 7631 dementia diagnoses were observed at KPWA (11.1 per 1000 person-years) and 216 at UCSF (4.6 per 1000 person-years). The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was similar over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (though small samples limited precision in some groups).ConclusionseRADAR showed strong external validity for detecting undiagnosed dementia in two health systems with different patient populations and differential availability of external healthcare data for risk calculations. In this study, eRADAR demonstrated generalizability from a research sample to real-world clinical populations, transportability across health systems, robustness to temporal changes in healthcare, and similar performance across larger racial/ethnic groups.

In the field of biodiversity conservation, there is a growing need for research to translate to real-world impacts. Currently there exists a gap between research outcomes and on the ground action, commonly referred to as the knowledgeaction gap. Previous research has focused on identifying the causes of the gap, but less research has focused on how to bridge it. We conducted an online survey with conservation researchers and practitioners to identify barriers in the science-to application pipeline and to understand how potential solutions would need to account for their information needs and workflows. Through a qualitative analysis of the open-ended survey responses, we found that information about tools and approaches to address conservation challenges is needed, but decision makers also need information to help them account for context specific barriers and opportunities. Solution-specific information alone, however, is often insufficient for practitioners, who also require the resource capacity and capable personnel to work with that information. Word of mouth and scholarly databases are the most common ways of learning about new tools and techniques, but lack of time, funding and personnel are barriers to implementing them. In addition, respondents identified a need for increased engagement with the conservation social sciences. We argue that a usercentered design approach should underpin any proposed solution to the gap and suggest that an online tool could be one effective solution. [ABSTRACT FROM AUTHOR]

Health Information Technology (HIT) plays a crucial role in enhancing pharmacy operations and administrative functionality within healthcare systems. By integrating advanced electronic health record (EHR) systems, pharmacies can effectively manage patient prescriptions, track drug interactions, and streamline inventory management. HIT facilitates real-time access to patient data, enabling pharmacists to provide personalized medication therapy management. Moreover, automated systems reduce the risk of errors associated with manual entry, improve communication between healthcare providers, and enhance the overall quality of patient care. The adoption of telepharmacy solutions further extends pharmacy services to rural and underserved populations, reinforcing the importance of accessible healthcare. From an administrative perspective, HIT enhances operational efficiency through improved data management and reporting capabilities. Pharmacy management systems enable effective tracking of workflows, personnel performance, and regulatory compliance, which are essential for meeting industry standards and optimizing resource allocation. Decision support tools within HIT assist pharmacy administrators in making informed choices regarding medication formulary management and cost reduction strategies. Additionally, data analytics enable pharmacies to identify trends in medication use and patient outcomes, thereby facilitating continuous improvement in practices. Overall, the integration of HIT in pharmacy and administrative functions not only streamlines operations but also contributes to better healthcare delivery. [ABSTRACT FROM AUTHOR]

California is the largest agricultural economy in the United States; however, its current and projected climate risks pose significant challenges. Farmers will need to adapt to climate change in their farming practices. The goal of this needs assessment was to understand farmers' perceptions and experiences with climate change exposures; the risk management practices they currently use; and what tools and resources would assist them in making strategic decisions. A statewide survey was conducted through Qualtrics with farmers (n = 341). Results showed that 67% of the farmers agree (agree + strongly agree) that climate change is happening, and 53.1% agreed that actions are required. Moreover, historically underrepresented farmers were very concerned about climate change-related impacts related to water, temperatures, and natural disasters. Farmers are currently implementing adaptation practices related to water management, soil health, and renewable energy and are also seeking insurance and government assistance programs to increase agricultural resilience. They also expressed interest and a high need for information on those adaptation practices to acquire skills and knowledge to manage various challenges of farming in variable climates. Also, the assessment established that farmers (47.5%) use decision-support tools, mostly weather stations (22.4%); and 51.9% indicated their interest in using online tools designed to translate climate information into forms that support production decision-making. Farmers (60.8%) responded that they would or may attend workshops to learn about adaptation practices. The findings of this needs assessment will inform the development of extension education programs on climate-smart agriculture for farmers in California and elsewhere. [ABSTRACT FROM AUTHOR]

Introduction: Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middleincome countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided. Methods: The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature. Results: A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist. Discussion: We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need. [ABSTRACT FROM AUTHOR]

Human activities impact greenhouse gas emissions through changes in land cover, land use, and land management. Conservation, restoration, and improved land use and land management are increasingly recognized as mitigation solutions. Policy instruments are crucial for addressing environmental challenges and supporting governance actors in enhancing carbon sequestration and reducing emissions in the land sector. The aim of this study was to evaluate the existing spatial decision support systems (SDSSs) for assessing land-based mitigation options and to help policymakers choose the best way to use and manage land. In order to search for tools, a systematic literature review was conducted, where 187 articles suitable for the specified criteria were found, from which 68 tools were selected. Additionally, following the application of the exclusion criteria, 18 tools were chosen for the final analysis. The tools were classified and analyzed based on various features such as type of land-use management, land use, country of application, information on carbon pools, and the number of published articles associated with each tool. Five SDSSs were found to be most suitable for policymakers seeking to implement the most effective land use and land management in order to enhance carbon sequestration. [ABSTRACT FROM AUTHOR]

Simple Summary: In response to the challenges posed by a growing global population, the livestock industry must increase food production while ensuring environmental sustainability. This paper explores how feed producers can tackle these challenges using advanced formulation techniques. By leveraging AI decision support systems, producers can optimize feed composition to promote animal health and environmental stewardship. The study's findings offer valuable insights into improving animal feed production, supporting the livestock industry in achieving sustainability goals and contributing to global environmental conservation efforts. The unprecedented challenges presented by the increase in global population have placed substantial demands on the livestock industry for human nutrition, necessitating heightened animal productivity and leading to an increased demand for natural resources to produce animal feed. Feed producers are leading the charge, consistently refining formulations to adapt to the evolving needs of livestock, driven in part by the cost of over 50% associated with feed production. This paper critically analyses the pressing issues within feed formulation, addressing the requirement for environmentally sustainable practices amidst the challenges of climate change. The exploration extends to how advanced decision support tools can enhance formulation techniques and profitability and contribute to environmental sustainability. Through an in-depth review of current feed formulation technologies, encompassing their applications and limitations, this study aims to enhance the existing knowledge base. Additionally, we examined future trends, highlighting the essential role of connecting technologies to establish a resilient and sustainable system. The emphasis is on the potential of formulation techniques to positively impact the environment and enhance the overall quality and performance of the animals. This paper provides actionable insights to improve animal production by examining feed formulation models and decision support tools. The anticipated outcome is a more informed and sustainable decision-making process, addressing the multifaceted challenges confronted by the livestock industry and making contributions to global efforts in climate change mitigation and environmental stewardship in animal production agriculture. [ABSTRACT FROM AUTHOR]

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior to harvest at the province scale for fourteen provinces in Thailand's Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and the variance inflation factor (VIF). The temperature condition index (TCI), the normalized difference vegetation index (NDVI), land surface temperature (LSTnighttime), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root-mean-square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region will be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation. [ABSTRACT FROM AUTHOR]

Objectives: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). Patients and Methods: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%–20%. Results: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate‐specific antigen (PSA) level, and PSA density (PSAD) were 68 (63–72) years, 9 (7–15) ng/mL, and 0.20 (0.13–0.32) ng/mL2, respectively. In all, 18 multivariable risk calculators (MRI‐RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI‐RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI‐findings and PSAD. Conclusions: Even in this high‐risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI‐RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD. [ABSTRACT FROM AUTHOR]

Addressing Europe's decarbonisation challenge involves widespread deployment of nearly zero-energy buildings, deep energy renovations and renewable energy integration in the building sector. Enhancing energy efficiency in public buildings necessitates tailored solutions and strategic planning involving Local Public Administration. This work focuses on advancing insights into the application of the PrioritEE Decision Support Tool in Portuguese public buildings, highlighting the energy and financial savings and carbon dioxide emission reduction potential. Using detailed building characterisation data from energy performance certificates, we applied the tool across 22 public buildings of diverse typologies in three distinct regions of Portugal, representing various public entities. Results demonstrate the tool's adaptability, enabling a comprehensive assessment of energy performance and facilitating the exploration of customised energy efficiency and renewable energy solutions. The research emphasises the critical role of user-friendly tools in aiding policymakers and local administration technicians in meeting national renovation targets and contributing to the broader energy transition objectives. [ABSTRACT FROM AUTHOR]

Recently, there has been a rapid increase in the use of Marine Spatial Planning (MSP) worldwide, partly due to the continued loss of marine biodiversity and habitat. The sustainability of marine resources is threatened in all regions of the world by major events such as climate change, marine pollution, and overfishing, as well as illegal, unreported and unregulated fishing both on the high seas and in country waters. Here, we present a comprehensive review and analysis of how economic information has been applied and used to inform decisions about MSP in British Columbia (BC), Canada, and other similar jurisdictions around the world. This focus for the paper was selected because important gaps remain in the literature in terms of incorporating economic questions into MSP. We first present different definitions of MSP, and then we extract useful lessons from MSP regimes with well-tested decision support tools (DSTs) and use this to guide MSP implementation in BC. Finally, we present and discuss case studies from Australia, South Africa, and Belgium. Our review suggests that applying economic information to support the design and implementation of MSPs would lead to better decisions. This in turn would foster livelihoods, attract finance, increase buy-in, and advance United Nations Sustainable Development Goal 14: Life Below Water, thereby achieving Infinity Fish, i.e., ensuring that ocean benefits flow to humanity forever. [ABSTRACT FROM AUTHOR]

In the field of biodiversity conservation, there is a growing need for research to translate to real-world impacts. Currently there exists a gap between research outcomes and on the ground action, commonly referred to as the knowledge-action gap. Previous research has focused on identifying the causes of the gap, but less research has focused on how to bridge it. We conducted an online survey with conservation researchers and practitioners to identify barriers in the science-to application pipeline and to understand how potential solutions would need to account for their information needs and workflows. Through a qualitative analysis of the open-ended survey responses, we found that information about tools and approaches to address conservation challenges is needed, but decision makers also need information to help them account for context specific barriers and opportunities. Solution-specific information alone, however, is often insufficient for practitioners, who also require the resource capacity and capable personnel to work with that information. Word of mouth and scholarly databases are the most common ways of learning about new tools and techniques, but lack of time, funding and personnel are barriers to implementing them. In addition, respondents identified a need for increased engagement with the conservation social sciences. We argue that a user-centered design approach should underpin any proposed solution to the gap and suggest that an online tool could be one effective solution.

IntroductionDigital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided.MethodsThe review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature.ResultsA total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist.DiscussionWe recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.

California is the largest agricultural economy in the United States; however, its current and projected climate risks pose significant challenges. Farmers will need to adapt to climate change in their farming practices. The goal of this needs assessment was to understand farmers' perceptions and experiences with climate change exposures; the risk management practices they currently use; and what tools and resources would assist them in making strategic decisions. A statewide survey was conducted through Qualtrics with farmers (n = 341). Results showed that 67% of the farmers agree (agree + strongly agree) that climate change is happening, and 53.1% agreed that actions are required. Moreover, historically underrepresented farmers were very concerned about climate change-related impacts related to water, temperatures, and natural disasters. Farmers are currently implementing adaptation practices related to water management, soil health, and renewable energy and are also seeking insurance and government assistance programs to increase agricultural resilience. They also expressed interest and a high need for information on those adaptation practices to acquire skills and knowledge to manage various challenges of farming in variable climates. Also, the assessment established that farmers (47.5%) use decision-support tools, mostly weather stations (22.4%); and 51.9% indicated their interest in using online tools designed to translate climate information into forms that support production decision-making. Farmers (60.8%) responded that they would or may attend workshops to learn about adaptation practices. The findings of this needs assessment will inform the development of extension education programs on climate-smart agriculture for farmers in California and elsewhere.

Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians’ perceptions of data and technologies on farms. The aim of this study was to understand veterinarians’ experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers’ behaviour, and ensure sustainability of the beef and dairy industries.

Simple Summary: Tail biting in pigs is an abnormal event where one pig engages in the biting, chewing, or oral manipulation of another pig's tail. The repeat biting of the wounded site can lead to pain, infection, and the possible mortality of the victim pig(s), which is why it is a serious issue in pork production. Tail biting is often difficult to prevent as there are various reasons why a particular pig may choose to exhibit this behavior. The aim of this review is to identify current decision support tools and other technological aids that can be used to predict the likelihood of a tail biting event. Additionally, we aim to understand how dependable these decision support tools are for predictive tail biting events by examining both the underlying model and data utilized for generating predictions. Tail biting (TB) in pigs is a complex issue that can be caused by multiple factors, making it difficult to determine the exact etiology on a case-by-case basis. As such, it is often difficult to pinpoint the reason, or set of reasons, for TB events, Decision Support Tools (DSTs) can be used to identify possible risk factors of TB on farms and provide suitable courses of action. The aim of this review was to identify DSTs that could be used to predict the risk of TB behavior. Additionally, technologies that can be used to support DSTs, with monitoring and tracking the prevalence of TB behaviors, are reviewed. Using the PRISMA methodology to identify sources, the applied selection process found nine DSTs related to TB in pigs. All support tools relied on secondary information, either by way of the scientific literature or expert opinions, to determine risk factors for TB predictions. Only one DST was validated by external sources, seven were self-assessed by original developers, and one presented no evidence of validation. This analysis better understands the limitations of DSTs and highlights an opportunity for the development of DSTs that rely on objective data derived from the environment, animals, and humans simultaneously to predict TB risks. Moreover, an opportunity exists for the incorporation of monitoring technologies for TB detection into a DST. [ABSTRACT FROM AUTHOR]

Environmental decision-making benefits from considering ecosystem services to ensure that aspects of the environment that people rely upon are fully evaluated. By focusing consideration of ecosystem services on final ecosystem goods and services (FEGS), the aspects of the environment directly enjoyed, used, or consumed by humans, these analyses can be more streamlined and effective. The U.S. Environmental Protection Agency has developed a set of tools to facilitate this consideration. The central feature of FEGS is that ecosystems are viewed through the diverse ways people directly benefit from them. The National Ecosystem Services Classification System (NESCS) Plus provides a framework for describing and identifying FEGS consistently. The standardization made available by NESCS Plus allows other tools and databases to interact using the NESCS Plus architecture and taxonomy, providing diverse insights for decision makers. Here, we examine the synergy of using the following four tools together: (1) the FEGS Scoping Tool; (2) the FEGS Metrics Report; (3) the EnviroAtlas; and (4) the EcoService Models Library. The FEGS Scoping Tool helps users determine what ecosystem services are relevant to a decision by harnessing FEGS understanding to enable communities to identify the relative importance of beneficiaries relevant to a decision and biophysical aspects of the environment of direct relevance to those beneficiaries. The FEGS Metrics Report can guide which metrics to monitor or model to represent those priority services. The EnviroAtlas, a powerful tool containing geospatial data and other resources related to ecosystem services, chemical and non-chemical stressors, and human health, and the EcoService Models Library, a database of ecosystem models, are two tools that support users in mapping and modeling endpoints relevant to priority services. While each of these tools is valuable on its own, together, they provide a powerful approach to easily incorporate and operationalize ecosystem services efforts into different parts of decision-making processes across different types of decisions. We illustrate how these integrated tools can be used together with a hypothetical example of a complex environmental management case study and the combined benefit of using the FEGS tools together. [ABSTRACT FROM AUTHOR]

Demands of domestic and foreign market specifications of carcass weight and fat cover, of beef cattle, have led to the development of cattle growth models that predict fat cover to assist on-farm managers make management decisions. The objectives of this paper are 4-fold: 1) conduct a brief review of the biological basis of adipose tissue accretion, 2) briefly review live and carcass assessments of beef cattle, and carcass grading systems used to develop quantitative compositional and quality indices, 3) review fat deposition models: Davis growth model (DGM), French National Institute for Agricultural Research growth model (IGM), Cornell Value Discovery System (CVDS), and BeefSpecs drafting tool (BeefSpecsDT), and 4) appraise the process of translating science and practical skills into research/decision support tools that assist the Beef industry improve profitability. The r 2 for live and carcass animal assessments, using several techniques across a range of species and traits, ranged from 0.61 to 0.99 and from 0.52 to 0.99, respectively. Model evaluations of DGM and IGM were conducted using Salers heifers (n = 24) and Angus-Hereford steers (n = 15) from an existing publication and model evaluations of CVDS and BeefSpecsDT were conducted using Angus steers (n = 33) from a research trial where steers were grain finished for 101 d in a commercial feedlot. Evaluating the observed and predicted fat mass (FM) is the focus of this review. The FM mean bias for Salers heifers were 7.5 and 1.3 kg and the root mean square error of prediction (RMSEP) were 31.2 and 27.8 kg and for Angus-Hereford steers the mean bias were −4.0 and −10.5 kg and the RMSEP were 9.14 and 21.5 kg for DGM and IGM, respectively. The FM mean bias for Angus steers were −5.61 and −2.93 kg and the RMSEP were 12.3 and 13.4 kg for CVDS and BeefSpecsDT, respectively. The decomposition for bias, slope, and deviance were 21%, 12%, and 68% and 5%, 4%, and 91% for CVDS and BeefSpecsDT, respectively. The modeling efficiencies were 0.38 and 0.27 and the models were within a 20 kg level of tolerance 91% and 88% for CVDS and BeefSpecsDT, respectively. Fat deposition models reported in this review have the potential to assist the beef industry make on-farm management decisions on live cattle before slaughter and improve profitability. Modelers need to continually assess and improve their models but with a caveat of 1) striving to minimize inputs, and 2) choosing on-farm inputs that are readily available. [ABSTRACT FROM AUTHOR]

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In this age of data, digital tools are widely promoted as having tremendous potential for enhancing food safety. However, the potential of these digital tools depends on the availability and quality of data, and a number of obstacles need to be overcome to achieve the goal of digitally enabled "smarter food safety" approaches. One key obstacle is that participants in the food system and in food safety often lack the willingness to share data, due to fears of data abuse, bad publicity, liability, and the need to keep certain data (e.g., human illness data) confidential. As these multifaceted concerns lead to tension between data utility and privacy, the solutions to these challenges need to be multifaceted. This review outlines the data needs in digital food safety systems, exemplified in different data categories and model types, and key concerns associated with sharing of food safety data, including confidentiality and privacy of shared data. To address the data privacy issue a combination of innovative strategies to protect privacy as well as legal protection against data abuse need to be pursued. Existing solutions for maximizing data utility, while not compromising data privacy, are discussed, most notably differential privacy and federated learning. [ABSTRACT FROM AUTHOR]