Lower Estimates Research Articles

Dairy Farmers and Veterinarians’ Agreement on Communication in Udder Health Consulting

Udder health consulting is an essential aspect of mastitis control and is based on herd-specific intervention often provided by the herd veterinarian, focusing on managing the bulk tank somatic cell count. Effective communication and alignment of expectations between dairy farmers and herd veterinarians are more critical than ever due to the substantial increase in herd sizes, which amplifies both potential gains and losses. To facilitate future cooperation between dairy farmers and herd veterinarians, it is important to understand the current level of agreement in communication, and therefore, we conducted a study to describe the agreement in the perception of their communication during udder health consulting. From August 2019 to February 2021, herds (n = 88) were selected to participate in the study, with the herd size ranging from 105 to 1291 milking cows. The dairy farmer and herd veterinarian were encouraged to answer a questionnaire to shed light on their perception of communication during udder health consulting. The agreement between the dairy farmer and herd veterinarian regarding their perception of communication during udder health consulting was initially analyzed using Cohen’s weighted kappa. The agreement between farmers and veterinarians ranged from −0.06 and 0.12, indicating that the herd veterinarians’ focus is inconsistent with the dairy farmers’ preferences. The frequency with which udder health is discussed and how potential problems are identified were associated with a significantly lower BTSCC estimate of 427 cells/mL. In contrast, general cooperation between the dairy farmer and herd veterinarian was associated with an estimated increase of 604 cells/mL in BTSCC.

Hole-Free Symmetric Complementary Sparse Array Design for High-Precision DOA Estimation

Direction of arrival (DOA) estimation plays a critical role in remote sensing, where it aids in identifying and tracking multiple targets across complex environments, from atmospheric monitoring to resource mapping. Leveraging difference covariance array (DCA) for DOA estimation has become prevalent, particularly with sparse arrays capable of resolving more targets than the number of sensors. This paper proposes a new hole-free sparse array configuration for remote sensing applications to achieve improved DOA estimation performance using DCA. By symmetrically placing a minimum redundancy array (MRA) and its complementary MRA on both sides of a sparse uniform linear array (ULA), this configuration maximizes degrees of freedom (DOFs) and minimizes mutual coupling effects. Expressions for calculating sensor positions and optimal element allocation methods to maximize DOFs are derived. Simulation experiments in various scenarios have shown the advantages of the proposed array in DOA estimation, including a strong ability to estimate multi-targets, high angular resolution, low estimation error, and strong robustness to mutual coupling.

Cardiac biomarkers for detection of coronary artery disease in the community

To investigate whether coronary artery disease (CAD) burden is associated with plasma levels of the myocardial biomarkers Troponin I (TropI) and NT-proBNP in a large population-based sample using a cross-sectional design. Coronary computerized tomography (CT) angiography was performed in 25,859 subjects without a history of atherosclerotic disease from SCAPIS study (age 50–65, 52% women). TropI and NT-proBNP were measured in plasma. Segment involvement score (SIS) was the primary exposure and TropI the primary outcome. Both SIS and coronary artery calcium score, were associated with TropI levels following adjustment for age, sex and multiple confounders (p < 0.001), with similar relationships in men and women. Proximal segments from all three coronary arteries were related to TropI levels independently of one another. Adding TropI to traditional risk factors marginally increased discrimination of atherosclerosis as compared to risk factors alone (C-statistics + 0.0005, p = 0.014). SIS was related also to NT-proBNP levels, mainly in men, but with lower estimates than TropI. The burden of CAD was related to TropI levels in both men and women. All three major coronary arteries contributed to this relationship. Adding TropI to traditional risk factors resulted in only marginally improved discrimination of coronary atherosclerosis.

Unpacking the Relationship Between Empowerment Leadership and Electricity Worker’s Unsafe Behavior: A Multi-Moderated Mediation Approach

Ensuring workplace safety in high-risk sectors is critical to achieving sustainable productivity and occupational health, particularly in industries prone to unsafe practices. Drawing on social exchange theory (SET), this study examines the impact of empowerment leadership (EL) on electricity workers’ unsafe behaviors (EWUBs) in Jordan, focusing on the mediating roles of safety motivation (SM) and work engagement (WE), as well as the moderating role of the error management climate (EMC). A quantitative approach was employed, collecting data from 409 electricity workers across various regions of Jordan. The data were analyzed using structural equation modeling (SEM) employing SmartPLS 4 to assess the relationships of these variables and AMOS 24.0 to compute the study measurement model’s internal consistency and construct validity. The results demonstrate that empowerment leadership significantly reduces electricity workers’ unsafe behaviors through increased safety motivation and work engagement. Furthermore, the error management climate moderates the relationship between empowerment leadership and work engagement (Estimate = 0.238, t = 7.783, &lt;0.001) is stronger when the error management climate is high and weaker but also insignificant when the error management climate is low (Estimate = 0.045, t = 1.015, &gt;0.05). The research highlights the crucial role of empowerment leadership in promoting safety motivation and work engagement, which (Estimate = 0.238, t = 7.783, &lt;0.001) is stronger and essential for minimizing unsafe behavior in high-risk industries like electricity. The findings highlight the pivotal role of shaping employees’ unsafe behavior and offers practical implications for policymakers and institutions aiming to promote employees’ safety behavior. Future studies also emphasize fostering an error management climate to reinforce these effects and organizations should focus on leadership development and creating a supportive error management climate to maximize safety outcomes.

Estimating methane emissions from the waste sector in southern ontario using atmospheric measurements

ABSTRACT We estimate methane emissions rates for urban waste treatment facilities from mobile in situ atmospheric concentration measurements using an inverse Gaussian plume methodology at facilities in Southern Ontario, Canada. We use these estimated emissions rates to investigate, update, and improve the existing high resolution methane inventories at the facility level for waste sources throughout the Greater Toronto Area and Southwestern Ontario. Our measurements encompass tens of thousands of kilometers worth of mobile survey data collected over 7 years, encompassing more than 650 downwind transects where we surveyed 14 active landfills, 11 closed landfills, 2 organic waste processing facilities, 3 open air windrow compost facilities, and 11 water resource recovery facilities across our study region. These sources account for 77% of the active landfills within Southern Ontario, which is estimated in inventories to be the largest source of methane emissions in the region. Within the Greater Toronto Area (GTA) megacity, the measured facilities represent about 52% of the total inventoried non-wetland methane emissions. We find that emissions from closed landfills are lower than inventory estimates, with significant implications for the methane budget in the GTA. We update the Facility Level and Area Methane Emissions for the GTA inventory with our measured emissions rates, which results in a 54% decline in the solid waste emissions, effecting a 35% lower estimate for the total anthropogenic methane emissions in the region. We attribute the bulk of this difference to a single facility: the Keele Valley landfill. Our atmospheric measurements also serve as a novel metric for evaluating the discrepancies between four facility level, and two high resolution gridded methane emissions inventories. Based on linear regressions of our measured emissions versus inventoried values, we find that the facility level first order decay model maintained by Environment and Climate Change Canada (ECCC) to be the most consistent with our measured emissions rates at landfills and the self-reported emissions to the Greenhouse Gas Reporting Program of ECCC to be the least consistent with our measurements.

Survival Outcomes Following Chemotherapy for High-Grade Central Nervous System Tumors in Adolescents and Young Adults: An Exploration of Variations According to Ethnicity and Deprivation.

Introduction: Adolescents and young adults (AYA) are a unique subgroup of patients who experience cancer at the interface between pediatric and adult oncology services. Central nervous system (CNS) tumors are the leading cause of cancer-related morbidity and mortality in this group. Socioeconomic status and ethnicity are known to impact CNS tumor survival in patients of all ages. Studies reporting AYA CNS survival outcomes by ethnicity and area-based deprivation, however, are lacking in the United Kingdom (UK). Methods: Using cancer registration data for 351 patients (12-29 years) who received systemic chemotherapy for a high-grade malignant CNS tumor in England between April 2014 and December 2018, we quantified differences in survival at 1, 2, and 3 years post-diagnosis by ethnicity and area-based socioeconomic status. Results: Lower survival estimates were seen for non-White ethnicity and lower socioeconomic groups. Three-year survival was 64.4% (95% CI 58.3-69.9) for White patients but 46.6% (95% CI 29.9-61.7) for non-Whites and 64.0% (95% confidence interval [CI]49.0-75.7) and 62.9% (95% CI 50.7-72.8) in those from the two least deprived fifths compared to 50.2% (95% CI 36.1-62.7) and 56.1% (95% CI 42.4-67.7) in the two most deprived groups. Conclusion: Our study is the first to show the existence of health disparities in AYA treated with chemotherapy for a primary CNS tumor in England, where patients from ethnic minorities and deprived areas had worse survival rates than their White and socioeconomically advantaged counterparts. These findings call for further research into the underlying reasons behind survival differences between sociodemographic groups to improve survivorship outcomes.

First-in-human Evaluation of Safety and Dosimetry of [64Cu]FBP8: A fibrin-binding PET Probe.

This study presents for the first time in humans the biodistribution, clearance and dosimetry estimates of [64Cu]Fibrin Binding Probe #8 ([64Cu]FBP8) in healthy subjects. [64Cu]FBP8-PET previously demonstrated its potential in two recent applications: thrombus imaging and pulmonary fibrosis. This prospective study included 8 healthy subjects to evaluate biodistribution, safety and dosimetry estimates of [64Cu]FBP8, a fibrin-binding positron emission tomography (PET) probe. All subjects underwent up to 3 sessions of PET/Magnetic Resonance Imaging (PET/MRI) 0-2h, 4h and 24h post injection. Dosimetry estimates were obtained using OLINDA 2.2 software. Subjects were injected with 400 MBq of [64Cu]FBP8. Subjects did not experience adverse effects due to the injection of the probe. [64Cu]FBP8 PET images demonstrated fast blood clearance (half-life = 67min) and renal excretion of the probe, showing low background signal across the body. The organs with the higher doses were: the urinary bladder (0.075 vs. 0.091 mGy/MBq for males and females, respectively); the kidneys (0.050 vs. 0.056 mGy/MBq respectively); and the liver (0.027 vs. 0.035 mGy/MBq respectively). The combined mean effective dose for males and females was 0.016 ± 0.0029 mSv/MBq, lower than the widely used [18F]fluorodeoxyglucose ([18F]FDG, 0.020mSv/MBq). This study demonstrates the following properties of the [64Cu]FBP8 probe: low dosimetry estimates; fast blood clearance and renal excretion; low background signal; and whole-body acquisition within 20min in a single session. These properties provide the basis for [64Cu]FBP8 to be an excellent candidate for whole-body non-invasive imaging of fibrin, an important driver/feature in many cardiovascular, oncological and neurological conditions.

Identification of Multiple Thermal Events in High‐Grade Metacarbonate Rocks Using Carbon Isotope Thermometry: An Example From the Sør Rondane Mountains, East Antarctica

ABSTRACTNine metacarbonate layers from the regionally metamorphosed terrane of the Sør Rondane Mountains in the Eastern Dronning Maud Land in East Antarctica were examined in detail for constraining the thermal events using carbon isotope exchange between dolomite/calcite and graphite. Equilibrium carbon isotope fractionation between dolomite and graphite suggested peak metamorphic temperature conditions reaching up to 802°C ± 29°C were estimated at the Balchenfjella locality, where multiple samples from six thick layers of metacarbonate rocks were examined. However, some of the samples exhibit lower carbon isotope fractionation reflecting the possibility of ultrahigh‐temperature metamorphic conditions, which is consistent with recent reports. Furthermore, several metacarbonate rock samples display large variations in δ13CVPDB values for graphite grains, despite dolomite and calcite showing homogeneous carbon and oxygen isotopic composition indicating signatures of retrograde metamorphism and fluid infiltration events. Detailed textural observation suggested alteration of δ13CVPDB values of graphite during retrograde metamorphism might have resulted due to the overgrowth of graphite crystals by the infiltration of low δ13CVPDB‐bearing fluids, the extent of alteration being a direct function of the fluid–rock ratio. Field evidence indicates the presence of carbonate veins cutting across the metacarbonate rocks suggesting that carbon isotope thermometry can also be utilised to understand the effect of external fluid infiltration. At Perlebandet locality the metamorphic temperature conditions were estimated to be around 915°C, whereas those from Tanngarden and Menipa gave lower temperature estimates. Detailed textural analysis of graphite in combination with isotopic composition provided clear evidence for retrograde events. Thus, our results provide tight constraints of peak and post‐peak metamorphic temperature conditions and a regional thermal structure for the Sør Rondane Mountains and further testify the usefulness of carbon isotope thermometry in polymetamorphic terrains.

Inflation-adjusted analysis of ICER’s Unsupported Price Increase reports: contextualizing drug spending changes

Objective The Institute for Clinical and Economic Review (ICER) publishes annual Unsupported Price Increase (UPI) reports on prescription drugs, which have gained attention from policymakers and healthcare stakeholders. These reports do not adjust for inflation in their analyses of net price changes. This study aimed to evaluate the economic context of reported drug price increases in ICER’s UPI reports by applying inflation adjustments to the estimated impact on healthcare spending. Methods Separate inflation adjustments using the Medical Consumer Price Index (Medical CPI) and the Consumer Price Index for All Urban Consumers (CPI-U) were applied to unadjusted UPI findings. The study analyzed data from four most recent UPI reports spanning pricing data from 2018 to 2019 (report year 2020) to 2021 to 2022 (report year 2023), focusing on drugs classified as having “unsupported” price increases. Results Inflation-adjusted analyses showed lower estimates of real changes in spending compared to ICER’s unadjusted figures across all drugs analyzed. Across the four UPI reports, the average report-year difference in Medical CPI inflation adjustment spending versus unadjusted findings was 53%, whereas the average report-year difference in CPI-U inflation adjustment spending versus unadjusted findings was 80%. In the most recent report year of 2023, ICER’s unadjusted estimate of a $1.3 billion increase in healthcare spending transformed to a $134 million decrease when adjusted by Medical CPI and a $1.51 billion decrease when adjusted by CPI-U. Conclusion This study demonstrates that inflation adjustment can alter not only the findings but also the interpretation of drug spending changes in UPI reports. The findings highlight the importance of considering economic context in evaluating drug pricing trends and suggest that unadjusted figures may overstate the impact of real price changes. Given the influence of these reports on policy discussions, incorporating inflation adjustments into analyses is one of many steps toward informing evidence-based policy decisions.

Enhanced fault distance estimation for robust protection in unbalanced active distribution networks

Active Distribution Networks (ADN) have evolved significantly by integrating renewable energy sources. While these advancements have ushered in a more sustainable and efficient power system, these have also introduced challenges for traditional protection strategies. Conventional methods, often reliant on overcurrent relays, can struggle to adapt to the complexities of active distribution networks, where fault scenarios are diverse and dynamic. This paper identifies the limitations of conventional protection strategies and explores the redundancy concept applied to protection systems. To improve protection system reliability, an approach that overcomes these limitations using the advantages of distance protection is proposed here. The distance-based protection relay, traditionally employed in transmission networks, offers a promising redundancy solution for the challenges imposed by ADN. Considering the previous exposure, this paper introduces an algorithm for fault resistance and distance estimation designed to optimise the operation of distance relays in active distribution networks. The approach achieves remarkable accuracy with consistently low estimation errors for fault resistance and distance, regardless of fault type or distance. The proposed distance-based protection method is poised to revolutionise fault analysis in ADN, ultimately leading to faster fault isolation and more reliable and improved grid resilience.

Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network

Accurately estimating the state of charge (SOC) of lithium-ion batteries is of great significance for extending battery lifespan and enhancing the efficiency of energy management. Regarding the issue of the relatively low estimation accuracy of SOC by the backpropagation neural network (BPNN), an enhanced dung beetle optimizer (DBO) algorithm is proposed to optimize the initial weights and thresholds of the BPNN. This overcomes the drawback of a single BP neural network being prone to local optimum and accelerates the convergence rate. Simulation analyses on the experimental data of NCM and A123 lithium batteries were conducted in Matlab R2022a. The results indicate that the proposed algorithm in this paper has an average SOC estimation error of less than 1.6% and a maximum error within 2.9%, demonstrating relatively high estimation accuracy and robustness, and it holds certain theoretical research significance.

Quantifying the health impact of PM2.5 with various chemical transport models and for different years − A case study for Poland

Poland is in the group of European countries with the most severe air quality and specific emission structure (over 80 % of PM2.5 emitted in residential combustion). In this work, we quantify the health impacts of PM2.5 concentrations in Poland using two chemical transport models (CTMs), WRF-Chem and EMEP4PL. The models were run for two years (2017 and 2018), which differed in meteorological conditions. The results show that the total number of premature deaths that could be avoided if the PM2.5 concentrations do not exceed the WHO recommended value of 5 μg m−3 exceeds 22,000 deaths for all model runs and meteorological years considered, with the lowest estimate at 22,447 (WRF-Chem model run for the year 2017; 95 % confidence intervals CI (95 % CI) 17121–––25045) and the highest at 29,227 (95 % CI 22343 – 32572; EMEP4PL for the year 2018). These numbers are underestimated, as both CTMs underestimate the measured PM2.5 concentrations at site locations. The underestimation is the highest during the heating seasons and can be reduced if the emission of primary PM2.5 from residential combustion increases. The total number of estimated premature deaths is sensitive to chemical transport model selection and meteorological year. The selection of the meteorological year for the analysis may change the estimated number of premature deaths by 10 %. This change is significant compared to the estimated change in health impact due to the envisaged implementation of the Clean Air Program in Poland.

Parasiticide use and obesity in dogs with sudden acquired retinal degeneration syndrome.

To investigate parasiticide use and describe signalment features in patients with sudden acquired retinal degeneration syndrome (SARDS). Retrospective case-control study of dogs with (n = 71) and without (136) SARDS. Parasiticide use, presentation season, weight, body condition, and signalment were compared between dogs diagnosed with SARDS and the reference population by use of descriptive statistics and logistic regression. Animals with SARDS were at a 5.99 times higher odds of having previously used imidacloprid (95% CI, 1.6 to 22.2; P = .003). However, time of last imidocloprid administration was > 6 years prior to diagnosis in 6 SARDS-affected individuals and 15, 26, or 42 months before diagnosis (n = 1 each). No other class of parasiticide had a significant association with SARDS. Seasonal variation was observed with a negative association identified between incidence of SARDS and tick season (October to January; P < .001). Overweight and obese dogs were 4.42 (95% CI, 1.9 to 10.4) and 4.96 (95% CI, 2.1 to 11.6) times more likely to have SARDS (P ≤ .001). History of polyphagia or weight gain was not associated with an increased likelihood of being overweight or obese within the SARDS-affected population (P > .108). While a statistically significant association was found between imidacloprid use and SARDS, this is unlikely to be clinically significant given the lack of a temporal association, sparse exposure numbers, and low point estimate of the OR. A positive association between being overweight or obese and a diagnosis of SARDS was found independent of polyphagia and weight gain, suggesting that it may be a risk factor for the development of SARDS.

Blood parameters predicted from milk spectra are candidate indicator traits of hyperketonemia - a retrospective study in the Italian Holstein population.

At the onset of lactation, high-producing dairy cows commonly face a negative energy balance and consequent metabolic disorders, such as hyperketonemia. Blood concentrations of nonesterified fatty acids (NEFA), BHB, cholesterol, glucose and urea provide valuable information about the metabolic, health, and nutritional status of lactating animals. Milk mid-infrared (MIR) spectroscopy has been successfully used for the prediction of several health traits, including concentration of blood metabolites even though the models' accuracy is moderate. In fact, MIR-predicted blood parameters are useful for population screening and may be used for selective breeding if they are heritable and genetically variable within a population. In the present study we estimated h2 and genetic correlation of MIR-predicted BHB, NEFA, glucose, cholesterol and urea and assessed their genetic correlation with milk yield and composition traits in the Italian Holstein population using phenotypes of 9,943 cows in 460 herds. Two sets were considered: early (8,277 records - 1 per cow - between 5 and 35 d in milk) and whole lactation (105,293 records - at least 5 per cow - between 5 and 305 d in milk). The h2 and genetic variability of blood traits were greater in early than whole lactation, confirming that there is room to manipulate metabolic disease incidence in the transition period through selection. Blood BHB was the most heritable trait, no matter the lactation stage (0.13 and 0.08 in early and whole lactation), while NEFA was the least heritable trait, with h2 not significantly different from zero. Blood BHB was positively genetically correlated with NEFA, whereas glucose was negatively correlated with BHB, NEFA and urea. Blood BHB, NEFA and cholesterol were generally positively correlated with milk fat-to-protein ratio; BHB was negatively correlated with lactose content and positively with SCS. Estimated breeding values of sires with at least 20 daughters with phenotypes available were extrapolated for a-posteriori evaluation of the observed performance. The progeny of the top 5 sires exhibited a lower incidence of hyperketonemia compared with the other cows, with only 2.16% of cows having BHB concentration above the conventional threshold (1.2 mmol/L). Conversely, the prevalence of hyperketonemia was 5 times higher in the offspring of the bottom 5 bulls (10.55% cows with BHB >1.2 mmol/L). These findings suggest that, despite of the low h2 estimates, there is room to identify animals with low or high genetic merit for traits linked to the cow's metabolism. Therefore, the selection toward healthier and metabolically resistant cows is pursuable, with the infrared-predicted blood traits being potential auxiliary traits.

Lower Average Estimate for the Minimum Modulus on Circles for an Entire Function of Genus Zero

Lower Average Estimate for the Minimum Modulus on Circles for an Entire Function of Genus Zero

Defining key deprescribing measures from electronic health data: A multisite data harmonization project.

Stopping or reducing risky or unneeded medications ("deprescribing") could improve older adults' health. Electronic health data can support observational and intervention studies of deprescribing, but there are no standardized measures for key variables, and healthcare systems have differing data types and availability. We developed definitions for chronic medication use and discontinuation based on electronic health data and applied them in a case study of benzodiazepines and Z-drugs in five diverse US healthcare systems. We conducted a retrospective cohort study of adults age 65+ from 2017 to 2019 with chronic benzodiazepine or Z-drug use. We determined whether sites had access to medication orders and/or dispensings. We developed definitions for chronic use and discontinuation using both data types. Discontinuation definitions were based on (1) gaps in medication availability during follow-up or (2) not having medication available at a fixed time point. We examined the impact of varying the gap length and requiring a 30-day period without orders/dispensings ("halo") around the fixed time point. We compared results derived from orders versus dispensings at one site. Approximately 1.6%-2.6% of older adults had chronic benzodiazepine/Z-drug use (total N = 6775, ranging from 431 to 2122 across sites). Depending on the definition and site, the proportion discontinuing use during 12 months ranged from 6% to 49%. Requiring a longer gap or a 30-day "halo" resulted in lower estimates. At one site, only 56% of those with chronic use defined from orders also qualified based on dispensings, and the discontinuation rate at 180 days was 20% from orders versus 32% from dispensings. Requiring a gap of ≥90 days or a "halo" around a time point may more accurately capture discontinuation than using a shorter gap or no halo. Orders data underestimate discontinuation compared to dispensings. Work is needed to adapt these definitions for other drug classes and settings.

Deep learning-enhanced atomic norm minimization for DOA estimation of coherent and incoherent sources using coprime array

Abstract In the field of array signal processing, accurate direction of arrival (DOA) estimation is crucial for handling both coherent and incoherent mixed signal sources. This paper proposes a deep learning-enhanced atomic norm minimization (DLEANM) algorithm, designed to improve DOA estimation for coprime arrays. The algorithm first models the mixed signal scenarios realistically and employs interpolation to transform coprime arrays into virtual uniform linear arrays, which facilitates the formulation of the atomic norm minimization problem. Solving this problem reconstructs an augmented Hermitian Toeplitz covariance matrix. To further enhance the accuracy of DOA estimation, we developed a multi-scale convolutional neural network with an attention mechanism, which uses the covariance matrix as input to better capture signal variations across different frequencies and spatial distributions. By processing multi-scale inputs in parallel, the model improves adaptability and robustness in estimating mixed signal sources. Simulation results show that, under equivalent SNR and snapshot conditions, the DLEANM algorithm achieves lower estimation errors, more accurate multi-target DOA estimation, and relatively faster computation speed compared to conventional methods. Field experiments further confirm the effectiveness of the proposed algorithm. Therefore, after training, the algorithm is able to accurately identify the directions of unknown signals received by sensor arrays.

Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier

The fast improvement of cyberattacks in the area of the Internet of Things (IoT) presents novel safety challenges to zero-day attacks. Intrusion detection systems (IDS) are generally focused on exact attacks to defend the use of IoT. However, the attacks were unidentified, for IDS still signifies tasks and concerns about consumers’ data privacy and safety. Anomaly-detection models are generally based on machine learning (ML) models. Conventional ML-based models have been recognized to have low estimate excellence and recognition rates. DL-based models, particularly convolutional neural networks (CNN) with regularization techniques, direct this problem, offer a superior prediction value with unidentified data, and prevent over-fitting. This manuscript presents a Binary Snake Optimizer with DL-Enabled Zero-Day Attack Detection and Classification (BSODL-ZDADC) method. The objective of the BSODL-ZDADC method is to employ metaheuristics with the DL method for enhanced recognition and classification of zero-day attacks. For data normalization, the BSODL-ZDADC method uses a Z-score normalization approach. To reduce the high dimensionality issue and improve the classification results, the BSODL-ZDADC technique designs a BSO method to choose a set of related features. Besides, the attention-based bidirectional gated recurrent unit (ABi-GRU) method helps recognize zero-day attacks. Since the hyperparameters play a vital part in the classification performance, the BSODL-ZDADC technique employs an improved sparrow search algorithm (ISSA). The experimental validation of the BSODL-ZDADC technique is verified by utilizing the ToN-IoT dataset. The performance validation of the BSODL-ZDADC technique portrayed a superior accuracy value of 98.28% over other models.

Mode shape area difference method for sparse damage detection in beam‐like structures

AbstractThis article develops the mode shape area difference (MSAD) method for detecting sparse damage in beam‐like structures under serviceability conditions. MSAD enhances the static deformation area difference (DAD) damage detection method by extending its application to the modal domain. MSAD leverages the sensitivity of mode shapes to the damage, enabling damage detection and localization through the quantification of area differences between functions of modal displacements and their derivatives. To test the applicability of the method to real modal data that is affected by measurement noise resulting in different accuracies on which the modal displacements are estimated, method is evaluated under different levels of artificial noise added to the theoretical mode shapes derived from numerical model of a simply supported reinforced concrete beam. The noise level is defined by a maximum noise amplitude representing the maximal permutation in each component of normalized mode shape. Additionally, the threshold value of noise level from within the reliable estimation of damage based on MSAD method is determined. It is confirmed that MSAD method requires mode shapes with exceptionally low noise levels and demands precise estimation of all mode shape components, thereby reducing its applicability to real experimental data.

Early-twentieth-century cold bias in ocean surface temperature observations

The observed temperature record, which combines sea surface temperatures with near-surface air temperatures over land, is crucial for understanding climate variability and change1–4. However, early records of global mean surface temperature are uncertain owing to changes in measurement technology and practice, partial documentation5–8, and incomplete spatial coverage9. Here we show that existing estimates of ocean temperatures in the early twentieth century (1900–1930) are too cold, based on independent statistical reconstructions of the global mean surface temperature from either ocean or land data. The ocean-based reconstruction is on average about 0.26 °C colder than the land-based one, despite very high agreement in all other periods. The ocean cold anomaly is unforced, and internal variability in climate models cannot explain the observed land–ocean discrepancy. Several lines of evidence based on attribution, timescale analysis, coastal grid cells and palaeoclimate data support the argument of a substantial cold bias in the observed global sea-surface-temperature record in the early twentieth century. Although estimates of global warming since the mid-nineteenth century are not affected, correcting the ocean cold bias would result in a more modest early-twentieth-century warming trend10, a lower estimate of decadal-scale variability inferred from the instrumental record3, and better agreement between simulated and observed warming than existing datasets suggest2.