Input Data Research Articles

Predicting power and solar energy using neural networks and PCA with meteorological parameters from Diass and Taïba Ndiaye

The excessive reliance on conventional fossil fuel-based resources poses a significant threat to our environment. To mitigate this impact, it has become increasingly crucial to increase the integration of intermittent and non-polluting energy sources into our electrical grids. However, while this higher penetration rate brings benefits such as improved producer satisfaction and reduced fossil fuel consumption, it also presents challenges for traditional non-smart electrical networks. To promote intermittent energy sources effectively and maintain a balance between consumption and production, accurate forecasting of these energy outputs plays a vital role. This research paper focuses on studying the application of artificial neural networks for predicting the power and energy output of the Diass solar power plant in the short and medium term. The proposed approach utilizes not only the meteorological data from the city where the power plant is located but also data from a nearby city with a data acquisition station. Principal component analysis (PCA) is employed to select the relevant variables for the prediction model. Furthermore, the results obtained from our approach are compared to existing literature that solely uses meteorological data from the power plant's location. The comparison shows that our method achieves more satisfactory results, with mean absolute errors and root mean square errors of 0.0223 KWh and 0.003 KWh, respectively, and a prediction accuracy of 94.57% in terms of energy and power. It is worth noting that the computational resource requirements for our approach are higher, with simulation times ranging between 1788 seconds and 2201 seconds. By utilizing a broader range of data sources and employing advanced techniques like artificial neural networks, this research contributes to improving the accuracy of solar power generation forecasts. The findings highlight the potential of incorporating additional data inputs and advanced modeling techniques to enhance the performance of renewable energy systems, paving the way for a more sustainable and efficient energy future.

Impact of twelve immunization-preventable infectious diseases on population health using disability-adjusted life years (DALYs) in Spain

BackgroundThe objective of this study is to estimate the burden of selected immunization-preventable infectious diseases in Spain using the Burden of Communicable Diseases in Europe (BCoDE) methodology, as well as focusing on the national immunization programme and potential new inclusions.MethodsThe BCoDE methodology relies on an incidence and pathogen-based approach to calculate disease burden via disability-adjusted life year (DALY) estimates. It considers short and long-term sequelae associated to an infection via outcome trees. The BCoDE toolkit was used to populate those trees with Spanish-specific incidence estimates, and de novo outcome trees were developed for four infections (herpes zoster, rotavirus, respiratory syncytial virus [RSV], and varicella) not covered by the toolkit. Age/sex specific incidences were estimated based on data from the Spanish Network of Epidemiological Surveillance; hospitalisation and mortality rates were collected from the Minimum Basic Data Set. A literature review was performed to design the de novo models and obtain the rest of the parameters. The methodology, assumptions, data inputs and results were validated by a group of experts in epidemiology and disease modelling, immunization and public health policy.ResultsThe total burden of disease amounted to 163.54 annual DALYs/100,000 population. Among the selected twelve diseases, respiratory infections represented around 90% of the total burden. Influenza exhibited the highest burden, with 110.00 DALYs/100,000 population, followed by invasive pneumococcal disease and RSV, with 25.20 and 10.57 DALYs/100,000 population, respectively. Herpes zoster, invasive meningococcal disease, invasive Haemophilus influenza infection and hepatitis B virus infection ranked lower with fewer than 10 DALYs/100,000 population each, while the rest of the infections had a limited burden (< 1 DALY/100,000 population). A higher burden of disease was observed in the elderly (≥ 60 years) and children < 5 years, with influenza being the main cause. In infants < 1 year, RSV represented the greatest burden.ConclusionsAligned with the BCoDE study, the results of this analysis show a persisting high burden of immunization-preventable respiratory infections in Spain and, for the first time, highlight a high number of DALYs due to RSV. These estimates provide a basis to guide prevention strategies and make public health decisions to prioritise interventions and allocate healthcare resources in Spain.

An improved autoencoder for denoising acoustic emission signals in rock fracturing

ABSTRACT Rock fracture acoustic emission (AE) signals are commonly used non-destructive testing data in geological exploration, resource exploitation, and engineering fields. However, these signals are often accompanied by noise interference caused by environmental factors. In this study, we propose an enhanced model for denoising rock fracture AE signals, called simplified fully convolutional denoising autoencoder (SFCDAE). This model is based on the denoising autoencoder principle in the field of deep learning neural networks. The SFCDAE model consists of only seven layers, with minimal preprocessing of data input. By comparing denoising performance evaluation indicators, higher peak signal-to-noise ratio (PSNR) and lower root mean square error (RMSE) were achieved. On average, PSNR increased by 5.575% and RMSE decreased by 22.225%. Using simulated environmental noise to validate the model, it was found that the model has good robustness and can remove artefacts from sudden noise. The practical application value of the LSTM classification model was validated using data containing real experimental noise, resulting in a higher classification accuracy of 80.083%. These results indicate that the proposed model has better denoising performance compared to existing intelligent models and has certain practical value.

The Rhetorical Structure of Method Section of Research Articles in Reputable International Journal of ELT Published in Indonesia

This study investigated the rhetorical structure of method section in English language teaching articles. A corpus consisting of 80 research articles in reputable international journal of English language teaching published in Indonesia was systematically sampled to serve as data input for analysis. The results showed that the method section of English language teaching articles were characterized by structural complexity and rich, nuanced descriptions. English language teaching researchers generally used three rhetorical moves to realize the overall communication goal of the methods section: 1) Describing the research design, 2) Describing the data collection procedures, and 3) Describing the data analysis procedures. While Move-1 was a Conventional move and did not have constituent steps, the second and third Move have a series of constituent steps, with Move-2 was an Obligatory move and the Move-3 was a Conventional. The most hypothetical model of method section in the results of this research were Describing research design (M1), Describing the Data Collection Procedure (M2): Describing the Samples (Move 2- Step B), Describing Research Instruments (Move 2- Step C), Elaborating on Data Collection Procedure (Move 2-Step D), and Describing the Data Analysis Procedure (Move 3): Relating (or recounting) data analysis procedure/s (Move 3- Step A). These Moves and Steps provide very detailed information on various aspects of the methodology. Pedagogically, the results can provide input for lecturers and students in writing research method section of English education field.Keywords: Academic writing, Rhetorical structure, Research method, Reputable international journal

Design and Fatigue Life Analysis of the Rope-Clamping Drive Mechanism in a Knotter

A knotter is a core component for the automatic bundling of agricultural materials, and a knotter with double-fluted discs is one type. Currently, the research on knotters with double-fluted discs has gradually transitioned from structural design to reliability optimization. To address rope-clamping failures in the rope-clamping drive mechanisms in knotters, the specific failure position of the rope-clamping mechanism and the failure causes were analyzed first. The redesign of the rope-clamping drive mechanism in knotters with double synclastic fluted discs was proposed, including structure optimization and 3D modeling using the GearTrax/KISSsoft and SolidWorks software. A virtual prototype model of a knotter with a flexible rope was established by combining ANSYS with the ADAMS software. A rigid–flexible coupling dynamic simulation of the knotter was carried out using ADAMS, and the simulation results were used as the data input for the ANSYS nCode DesignLife module for the fatigue life simulation of the weak parts (the worm shaft) of the knotter. The operation test results for the rope-clamping drive mechanism indicate that the redesigned rope-clamping drive mechanism is reliable in transmission, with a rope-clamping success rate of 100%. The actual operation times for the worm shaft exceed the minimum fatigue life obtained through joint simulation. The applied joint simulation method has high simulation accuracy.

Satellite-Based PT-SinRH Evapotranspiration Model: Development and Validation from AmeriFlux Data

The Priestley–Taylor model of the Jet Propulsion Laboratory (PT-JPL) evapotranspiration (ET) model is relatively simple and has been widely used based on meteorological and satellite data. However, soil moisture (SM) constraints include a vapor pressure deficit (VPD) that causes large uncertainty. In this study, we proposed a PT-SinRH model by introducing a sine function of air relative humidity (RH) to replace RHVPD to characterize SM constraints, which can improve the accuracy of ET estimations. The PT-SinRH model is validated by eddy covariance (EC) data from 2000–2020. These data were collected by AmeriFlux at 28 sites on the conterminous United States (CONUS), and the land cover types of the sites vary from croplands to wetlands, grasslands, shrub lands and forests. The validation results from daily scale-based on-site and satellite data inputs showed that the PT-SinRH model estimates fit the observations with a coefficient of determination (R2) of 0.55, root-mean-square error (RMSE) of 17.5 W/m2, bias of −1.2 W/m2 and Kling–Gupta efficiency (KGE) of 0.70. Additionally, the PT-SinRH model based on reanalysis and satellite data inputs has an R2 of 0.49, an RMSE of 20.3 W/m2, a bias of −8.6 W/m2 and a KGE of 0.55. The PT-SinRH model showed better accuracy when using the site-measured meteorological data than when using reanalysis meteorological data as inputs. Additionally, compared with the PT-JPL model, the results demonstrate that our approach, i.e., PT-SinRH, improved ET estimates, increasing the R2 and KGE by 0.02 and decreasing the RMSE by about 0.6 W/m2. This simple but accurate method permits us to investigate the decadal variation in regional ET over the land.

Unveiling individuality in the early phase of motor learning: a machine learning approach for analysing weightlifting technique in novices

IntroductionDespite the growing body of evidence highlighting the individuality in movement techniques, predominant models of motor learning, particularly during the acquisition phase, continue to emphasise generalised, person-independent approaches. Biomechanical studies, coupled with machine learning approaches, have demonstrated the uniqueness of movement techniques exhibited by individuals. However, this evidence predominantly pertains to already stabilised movement techniques, particularly evident in cyclic daily activities such as walking, running, or cycling, as well as in expert-level sports movements. This study aims to evaluate the hypothesis of individuality in whole-body movements necessitating intricate coordination and strength among novice participants at the very beginning of an acquisition phase.Methods In a within-subject design, sixteen highly active male participants (mean age: 23.1 ± 2.1 years), all absolute novices in the learning task (i.e., power snatch of Olympic weightlifting), participated in randomised snatch learning bouts. These bouts comprised 36 trials across various motor learning models: differential learning contextual interference (serial, sCIL; and blocked, bCIL), and repetitive learning. Kinematic and kinetic data were collected from three standardised snatch trials performed following each motor learning model bout. The time-continuous data were input to a linear Support Vector Machine (SVM). We conducted analyses on two classification tasks: participant and motor learning model.Results The Support Vector Machine classification revealed a notably superior participant classification compared to the motor learning model classification, with an averaged prediction accuracy of 78% (in average ≈35 out of 45 test trials across the folds) versus 27.3% (in average ≈9 out of 36 test trials across the folds). In specific fold and input combinations, accuracies of 91% versus 38% were respectively achieved.Discussion Methodically, the crucial role of selecting appropriate data pre-processing methods and identifying the optimal combinations of SVM data inputs is discussed in the context of future research. Our findings provide initial support for a dominance of individuality over motor learning models in movement techniques during the early phase of acquisition in Olympic weightlifting power snatch.

Investigating the potential of unconventional resources in the Midland Basin: A comprehensive chemostratigraphic analysis of a Cline Shale (Wolfcamp-D) core

Basinal mudrocks assigned to the Wolfcamp Group in the Midland Basin are prolific producers of oil and gas. Technological advancements in horizontal drilling and hydraulic fracturing have been successful in the exploitation of oil and gas from these tight mudrocks. However, many challenges remain unsolved in capturing their macroscale compositional variations to enhance recovery factors and delineate “sweet spots”. To gain improved insights into this fine-grained system, high resolution chemostratigraphic (XRF) data was integrated with mineralogical measurements (XRD), logs, thin sections, and TOC readings, as well as hierarchical cluster analysis to: 1) develop a sequence stratigraphic framework, and 2) reconstruct paleo-redox, as well as ancient water-mass, conditions within the Cline Shale (Wolfcamp-D) from a core in the Midland Basin.This study revealed the presence of three chemo-stratigraphically distinct depositional sequences in the Cline Shale, herein termed Lower, Middle, and Upper Cline from the base upwards. All three units exhibit distinct mineralogical and petrophysical characteristics, were correlated across the basin, and interpreted as third-order sequences. Superimposed onto the three interpreted third-order sequences within the Cline, are potential higher frequency (fourth-order) sequences. These fourth-order sequences typically consist of a lower interval, interpreted as lowstand deposits and an upper interval, which is interpreted as transgressive and highstand deposits. The lower interval is interpreted to be deposited during highly reducing conditions while the upper interval is interpreted to be deposited under suboxic conditions. The highest organic matter preservation is associated with the interpreted lowstand deposits, especially in the Upper Cline. Optimal landing zones (20 ft) are recognized in the upper zone of the Cline Shale based on their high resistivity, total organic richness, elevated siliceous content, and reduced percentage of clay minerals. These zones have been correlated with several nearby wells.The current study demonstrates the wealth of information that can be gained from a single core/well when properly investigated at high resolution with measurements on a cm-scale. The observed shifts in the chemistry of the sediments character and composition are distinct, with implications for sequence stratigraphic analysis, the reconstruction of ancient water-mass conditions, and the identification of “sweet spots” for hydrocarbon resources. The provided analysis lays a foundational basis for further studies in the region, serving as critical data input for expansive regional research efforts.

Experiences of patients with endometriosis with a digital health application: a qualitative analysis.

Endometriosis is a frequent disease in women of reproductive age in which the endometrium occurs outside the uterine cavity. Multimodal treatment approaches are necessary due to loss of quality of life and the chronic nature of the disease. Digital health applications (DiGa) are becoming increasingly important. This research project investigates how a healthcare app can influence the subjective experience of illness in patients with endometriosis. Empiric data were collected through semi-structured interviews. Data analysis was carried out using qualitative focussed interview analysis. Reliability was ensured by joint interdisciplinary and interprofessional evaluation of the interviews by experts and those affected. Ten patients with endometriosis and the prescribed healthcare app Endo-App© were examined. Categories were defined from the superordinate categories "Factors influencing the experience of illness" and "Evaluation of the app". The app provided reliable information, promoted self-efficacy through exercises and strengthened the perception of the individuality of the illness. It helped to minimise nocebo effects from internet research and enabled a positive change of perspective. Patients criticised the time required for data input and had data protection concerns. The educational elements were often seen as redundant. Some patients only used the app briefly, or not at all. Once a DiGa has been prescribed, it may be useful to explain its use on an outpatient basis and validate regular use. Blind re-prescribing of DiGas should be avoided. Younger patients with a recent diagnosis or patients following rehabilitation may benefit more from prescribing.

The Weaponization of Artificial Intelligence in Cybersecurity: A Systematic Review

The weaponization of artificial intelligence (AI) and machine learning (ML) models in cybersecurity is a growing concern, with cybercriminal organizations and nation-states exploiting their weaknesses. The Microsoft "Tay" chatbot incident exemplifies the risks of weaponized AI, as it displayed sexist and racist behaviors due to malicious data inputs. The researcher examined 21 academic studies on AI weaponization and AI-driven cyberattacks in this systematic review. This work, a systematic review, concludes with an in-depth understanding of the scale and scope of using AI as a cyber weapon. The findings revealed that more research is necessary on weaponizing AI for offensive cybersecurity applications and the following key observations (a) a connection between AI weaponization and countermeasures, (b) AI’s role in enhancing cybersecurity defenses, (c) AI weaponization offering mitigation strategies for protecting digital assets and infrastructure, and (d) AI-driven attacks exploiting vulnerabilities, enabling automation and scalability, facilitating data poisoning and manipulation, improving social engineering, and augmenting evasion obfuscation. This study contributes to a better understanding of AI weaponization and aids researchers in synthesizing current literature on the topic.

Detecting sub-instability before three Mw > 6.0 earthquakes on Chinese mainland in 2020–2022 with load/unload response ratio

The sub-instability refers to a stress state that exists between the maximum stress the rock can withstand and its final failure. This stress state should be an indicator of impending large earthquakes, as it occurs in the final stage of earthquake nucleation. We apply the Load/Unload Response Ratio (LURR) method to explore sub-instability in the source media before large earthquakes. The earthquake-related observation data such as the groundwater, crustal deformation, electromagnetism and so on were adopted as data input. The load and unload phases at each observation station are discriminated by the change of Coulomb failure stress caused by earth tides along the tectonically optimal sliding direction. Retrospective studies of this approach on the 2020 Mw6.0 Jiashi, Xinjiang, 2022 Mw6.6 Menyuan, Qinghai, and Mw6.6 Luding, Sichuan earthquakes show that the LURR time series derived from the observation stations near the epicenter exceed 2 standard deviations of the mean within several days to years before the mainshocks. Additionally, as the time of mainshock approaches, the distance between the stations detecting LURR anomaly and epicenter decreases. The LURR anomalies indicate the presence of sub-instability prior to a large earthquake, and their spatio-temporal evolution may suggest the weakening processes in the source media.

Developing Water Quality Formulations for a Semi-Distributed Rainfall–Runoff Model

Hydrological modeling can be challenging due to significant data requirements and computational complexities. Hydrological models must be sufficiently complex to describe physical processes yet simple enough to use. This paper describes the development of a simplified watershed-scale input–output model to simulate runoff quantity and quality during a storm event. This work builds upon an existing semi-distributed rainfall–runoff model by adding calculations for pollutant concentrations based on simplified mass balance equations. The model was tested against various watershed examples of increasing complexity. The results show the change in peak flow and pollutant concentration in different areas of the watershed, demonstrating the model’s ability to account for the dynamics of runoff movement through the watershed. This paper advances watershed management by addressing data scarcity through the development of a simplified hydrological model that effectively incorporates spatial variability within a watershed while requiring minimal data input.

Financing and cost-effectiveness of emergency medical services in low- and middle-income countries

In 2023, the 76th World Health Assembly declared coordinated emergency, critical, and operative care services fundamental for comprehensive universal health coverage in low- and middle-income countries. With increasing mortality from noncommunicable diseases, an organized emergency care system has the capacity to treat a variety of conditions with a common set of resources, optimizing per-unit cost efficiency by applying economies of scope and increasing cost-effectiveness. However, the financing and cost-effectiveness of emergency medical services remain poorly understood despite affordability and financial barriers comprising some of the most significant obstacles to development. Cost-effectiveness analyses generate incremental cost-effectiveness ratios for comparison against per-capita gross domestic product thresholds to determine cost-effectiveness, promoted by the World Health Organization’s Choosing Interventions that are Cost-Effective program. Incremental cost-effectiveness ratios may be used as context-specific indicators of value alongside budget impact and feasibility considerations. Currently, there are few high-quality cost-effectiveness studies of emergency care in low- and middle-income countries, demonstrating significant methodologic heterogeneity, little geographic diversity, neglecting descriptions of assumptions used in cost-effectiveness calculations and comparators used, and lacking incremental cost-effectiveness ratios for comparison. The assessment of emergency care cost-effectiveness is challenging, given the significant breadth of conditions encountered and difficulty in projecting subsequent impact. Without improved epidemiologic surveillance and data-collection infrastructure, data inputs for cost-effectiveness calculations will remain limited. Future efforts should practice standard cost-effectiveness methodologies to permit comparison of incremental cost-effectiveness ratios across interventions and settings while incorporating trauma registry data to longitudinally track patient outcomes over sufficient time horizons to determine impact. New indices that expand the scope of analysis to capture broader secondary impacts of emergency care for future cost-effectiveness studies are needed. In this article, we summarize the key steps for economic evaluations for prehospital care systems and recommend considerations for future prehospital emergency care cost-effectiveness analyses, determining the optimal structure for financing mechanisms well-suited to resource-limited settings are critical for future investigation.

Implications of depth conversion on fault geometries and fault-risk assessment in the Smeaheia CO 2 storage site, northern North Sea

Assessments of fault geometries and fault-risk parameters are crucial when evaluating the integrity of a structurally controlled CO 2 storage site. To perform these assessments, seismic data, recorded in time, must be converted to depth. The velocity models used for this time-depth conversion influence the final depth image and, consequently, the geometry of the interpreted faults. Against this background, we create four velocity models for depth conversion, assess the impact on fault throw, dip, and thickness of the primary seal, and, subsequently, fault-risk assessment of the Vette Fault Zone in the Smeaheia CO 2 storage site. We find that fault throw and thickness of the primary seal are more influenced by depth conversion compared to fault dip. In contrast, the overall assessment of membrane seal presence and geomechanical integrity shows less sensitivity to the depth conversion process. Consequently, we suggest that a relatively robust fault-risk assessment can be made with a variety of velocity model designs and data input. Nevertheless, we find a mean difference of 2% shale gouge ratio, 4% slip tendency, and 9% dilation tendency for the Vette Fault Zone emphasizing the importance of accounting for the influence of depth conversion to optimize structural assessments in potential CO 2 storage sites.

A Wireless Network for Monitoring Pesticides in Groundwater: An Inclusive Approach for a Vulnerable Kenyan Population.

Safe drinking water is essential to a healthy lifestyle and has been recognised as a human right by numerous countries. However, the realisation of this right remains largely aspirational, particularly in impoverished nations that lack adequate resources for water quality testing. Kenya, a Sub-Saharan country, bears the brunt of this challenge. Pesticide imports in Kenya increased by 144% from 2015 to 2018, with sales data indicating that 76% of these pesticides are classified as highly hazardous. This trend continues to rise. Over 70% of Kenya's population resides in rural areas, with 75% of the rural population engaged in agriculture and using pesticides. Agriculture is the country's main economic activity, contributing over 30% of its gross domestic product (GDP). The situation is further exacerbated by the lack of monitoring for pesticide residues in surface water and groundwater, coupled with the absence of piped water infrastructure in rural areas. Consequently, contamination levels are high, as agricultural runoff is a major contaminant of surface water and groundwater. The increased use of pesticides to enhance agricultural productivity exacerbates environmental degradation and harms water ecosystems, adversely affecting public health. This study proposes the development of a wireless sensor system that utilizes radio-frequency identification (RFID), Long-range (LoRa) protocol and a global system for mobile communications (GSM) for monitoring pesticide prevalence in groundwater sources. From the system design, individuals with limited literacy skills, advanced age, or non-expert users can utilize it with ease. The reliability of the LoRa protocol in transmitting data packets is thoroughly investigated to ensure effective communication. The system features a user-friendly interface for straightforward data input and facilitates broader access to information by employing various remote wireless sensing methods.

Groundwater contamination modelling in Ayad River Basin, Udaipur

Groundwater, a vital freshwater resource catering to agricultural, domestic, and industrial needs, faces a pressing challenge of contamination due to escalating human activities. This study focuses on the Ayad River Basin in the Udaipur district of Rajasthan, employing the FEFLOW simulation code for the first time. A steady-state numerical model and a groundwater contaminant prediction model for total dissolved solids (TDS), nitrate, and fluoride were developed, simulating trends over the next five years with an accuracy exceeding 95%. The results reveal an eastward increase in TDS, nitrate, and fluoride concentrations, attributed to contamination from two waste disposal sites-Titadi and Baleecha. Titadi, operational for four decades until closure in 2010, retains residual waste over 32 thousand m2. The initiation of a new dumping ground at Baleecha by the Udaipur Municipal Corporation post-2010 exacerbates regional contamination. Nitrate contamination is particularly high in agricultural zones with excessive chemical fertilizer usage. Of the 27 scenarios tested, 23 support using the water for irrigation but would require treatment before using it for drinking. Recommendations include deploying a chemical sensor network for real-time data input into the web enabled FEFLOW model, real-time monitoring and alerts, and a mobile application providing personalized guidance on water usage and health risks in case of contamination. This study can be beneficial to decision-makers, who work on the policy and groundwater management strategies.

An iteration-based design algorithm for high frequency transformer in SSTs and its validation by finite element analysis

This paper proposes an iteration-based algorithm for the optimum design of a high frequency transformer for solid-state transformer (SST) applications. This algorithm minimizes the total owning cost (TOC) of a distribution type solid-state transformer. The unique features of this algorithm compared with the available algorithms in the literature are as follows: it iterates eight design variables, four constraints are defined for selecting the valid designs, and it works with different core materials and AC test voltages. The algorithm uses various user-defined data inputs to calculate loss capitalization values for TOC calculation. In every iteration, TOC is estimated, and calculated values of design constraints are compared with their threshold limits. A case study is conducted on a high-frequency transformer (HFT) incorporated in 1000-kVA, 11-kV/415-V, Dyn11 three-phase wound core SST. This is to determine the optimum design parameters. In this case study, the algorithm was iterated with 2,100,000 design data inputs, generating 258,272 designs that satisfied all design constraints. The optimum design with minimum TOC is selected from the generated 258,272 designs. The optimum design is validated using finite element analysis in ANSYS software. Comparing the results of both analyses, the deviation is less than 5%. Hence, the algorithm’s reliability is proved.

From facial expressions to algorithms: a narrative review of animal pain recognition technologies.

Facial expressions are essential for communication and emotional expression across species. Despite the improvements brought by tools like the Horse Grimace Scale (HGS) in pain recognition in horses, their reliance on human identification of characteristic traits presents drawbacks such as subjectivity, training requirements, costs, and potential bias. Despite these challenges, the development of facial expression pain scales for animals has been making strides. To address these limitations, Automated Pain Recognition (APR) powered by Artificial Intelligence (AI) offers a promising advancement. Notably, computer vision and machine learning have revolutionized our approach to identifying and addressing pain in non-verbal patients, including animals, with profound implications for both veterinary medicine and animal welfare. By leveraging the capabilities of AI algorithms, we can construct sophisticated models capable of analyzing diverse data inputs, encompassing not only facial expressions but also body language, vocalizations, and physiological signals, to provide precise and objective evaluations of an animal's pain levels. While the advancement of APR holds great promise for improving animal welfare by enabling better pain management, it also brings forth the need to overcome data limitations, ensure ethical practices, and develop robust ground truth measures. This narrative review aimed to provide a comprehensive overview, tracing the journey from the initial application of facial expression recognition for the development of pain scales in animals to the recent application, evolution, and limitations of APR, thereby contributing to understanding this rapidly evolving field.

Tracking shifts in forest structural complexity through space and time in human‐modified tropical landscapes

Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high‐resolution, repeat‐acquisition airborne laser scanning data collected over 450 km2 of human‐modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly degraded, selectively logged and old‐growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g. higher fractal dimension in oil palm plantations than old‐growth forests), while rugosity and height range were tightly correlated (r = 0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space.Keywords: Forest disturbance, LiDAR, logging, recovery, remote sensing, structural complexity

The burden of atrial fibrillation/flutter in the Middle East and North Africa region and its associated risk factors from 1990 to 2019

BackgroundAtrial fibrillation and flutter (AFF) are the most common cardiac arrhythmias globally, contributing to substantial morbidity and mortality. The Middle East and North Africa (MENA) region face unique challenges in managing cardiovascular diseases, including AFF, due to diverse sociodemographic factors and healthcare infrastructure variability. This study aims to comprehensively evaluate the burden of AFF in MENA from 1990 to 2019.MethodsData were obtained from the Global Burden of Diseases Study 2019, a comprehensive source incorporating diverse data inputs. The study collected global, regional, and national Age-Standardized Incidence Rate (ASIR), Age-Standardized Mortality Rate (ASMR), and Age-Standardized Disability-Adjusted Rate (ASDR), Mortality across sex, age groups, and years. LOESS regression was employed to determine the relationship between age-standardized rates attributed to AFF and Socio-Demographic Index (SDI).ResultsThe study found minimal change in ASIR of AFF in MENA from 1990 to 2019, with a slight increase observed in ASMR and ASDR during the same period. Notably, AFF burden was consistently higher in females compared to males, with age showing a direct positive relationship with AFF burden. Iraq, Iran, and Turkey exhibited the highest ASIR, while Qatar, Bahrain, and Oman had the highest ASMR and ASDR in 2019. Conversely, Kuwait, Libya, and Turkey displayed the lowest ASMR and ASDR rates.ConclusionThis study underscores the persistent burden of AFF in MENA and identifies significant disparities across countries. High systolic blood pressure emerged as a prominent risk factor for mortality in AFF patients. Findings provide crucial insights for policy-making efforts, resource allocation, and intervention strategies aimed at reducing the burden of cardiovascular diseases in the MENA region.