Historical Data Research Articles

Emerging synergies between large language models and machine learning in e-commerce recommendations

This paper explores the integration of large language models (LLMs) into collaborative filtering algorithms to enhance recommendation systems in the e-commerce domain. The proposed approach combines user-based and item-based collaborative filtering with LLMs to improve recommendation accuracy and personalization. Specifically, the study introduces a novel framework called PALR, which leverages LLMs to refine user-item interactions and enrich item representations. PALR utilizes historical user behavior data, such as clicks, purchases, and ratings, to guide candidate retrieval and generate recommended items. This study highlights the importance of integrating LLMs into recommendation systems to deliver more accurate and personalized suggestions, ultimately improving user satisfaction and driving sales in e-commerce platforms.

Control-oriented dynamic modeling and GPC for single-tower double-circulation wet flue gas desulfurization system

In China, the current situation of large-scale grid integration of renewable energy generation and the government's introduction of capacity-based electricity pricing policies have compelled coal-fired power plants to gradually enhance their load adjustment capabilities and expand their load adjustment ranges. Simultaneously, the national environmental requirements for these plants have become increasingly stringent. However, the economic control of wet flue gas desulfurization (WFGD) systems is still far from meeting practical requirements. In this paper, starting from practical engineering applications and utilizing historical operational data, considering the actual system operation process, the impact of the absorption tower variable-frequency slurry circulation pump on the concentration of sulfur dioxide (SO2) in flue gas under different slurry pH values is investigated. This led to the establishment of a dynamic model for a single-tower double-circulation wet flue gas desulfurization (SD-WFGD) system. Recognizing the significant delay and inertia characteristics of pH value control in the SO2 absorption tank, a desulfurization system pH value desulfurization capacity-based optimization control scheme for the SD-WFGD system is proposed, termed as the Direct Sulfur Control (DSC) strategy. Based on the established dynamic model, a SD-WFGD system series control system is developed using a step generalized predictive control algorithm (GPC) combined with the DSC strategy. Validation results indicate that, compared to SGPC control schemes and traditional PID control schemes, this approach reduces fluctuations in slurry supply and outlet SO2 concentration, significantly enhancing the control performance of the SD-WFGD system. This ensures the safe, stable, economical, and flexible operation of the desulfurization system against the backdrop of flexible operation in coal-fired power plants.

Emerging synergies between large language models and machine learning in e-commerce recommendations

This paper explores the integration of large language models (LLMs) into collaborative filtering algorithms to enhance recommendation systems in the e-commerce domain. The proposed approach combines user-based and item-based collaborative filtering with LLMs to improve recommendation accuracy and personalization. Specifically, the study introduces a novel framework called PALR, which leverages LLMs to refine user-item interactions and enrich item representations. PALR utilizes historical user behavior data, such as clicks, purchases, and ratings, to guide candidate retrieval and generate recommended items. This study highlights the importance of integrating LLMs into recommendation systems to deliver more accurate and personalized suggestions, ultimately improving user satisfaction and driving sales in e-commerce platforms.

Alike but genetically divergent: The resurrection of Urotrygon asterias (Jordan & Gilbert, 1883) from its closest relatives, the Munda and the Blotched stingray.

The genus Urotrygon comprises small- to medium-sized endemic round rays on the American continent and has undergone several synonymization processes. Here, we used an integrative taxonomic approach, including meristic, morphometric, and mtDNA analyses, to resolve the particularly intricate relationship among Urotrygon munda Gill, 1863, Urotrygon chilensis (Günther, 1872), and Urotrygon asterias (Jordan & Gilbert, 1883). The latter species is currently a synonym of U. munda but is also considered the U. chilensis "northern morphotype." These taxonomic entities have historically been confounded, mainly due to their phenotypical resemblance along their geographic distribution in the eastern Pacific. We assessed 78 specimens (43 "northern" and 30 "southern morphotypes" of U. chilensis, as well as 5 U. munda) using 19 external variables for taxonomic and morphometric analysis. Distinct meristic patterns, including pectoral and pelvic ceratotrichia, vertebrae number, and thorn distribution along the dorsal midline, were observed in the series-type specimens of the three taxonomic entities. Our multivariate morphometric analyses consistently differentiated the three groups as distinct taxonomic entities, with an overall classification accuracy of 66.7%. The meristic results also provided reliable information distinguishing the three entities. Based on the nicotinamide adenine dinucleotide (NADH2) and cytochrome oxidase subunit I (COI) genes, our phylogenetic analysis were consistent with the morphometric and meristic data, supporting these three entities as distinct species having their own evolutionary lineages. Our comprehensive approach confidently demonstrated that the northern U. chilensis morphotype matched and corresponded to the description of the Starry round ray, U. asterias, confirming its taxonomic resurrection as a valid species distinct from U. chilensis and U. munda. The geographic distribution of U. asterias spans from the tropical west coast of Mexico (including the Gulf of California) to Costa Rica, revealing that microevolutionary processes have well-defined population clades within this range. Furthermore, U. chilensis is unequivocally established as the sole Urotrygon species occurring south of the Guayaquil marine ecoregion. In addition, the public COI and NADH2 sequences available for Urotrygon hosted in the ad hoc online databases were found to be misidentified, emphasizing the need for rigorous taxonomic scrutiny in this group. Finally, our research underscores the significance of an integrative approach that combines morphometric, meristic, and molecular techniques with historical data to disentangle the complexities of closely related taxa.

"Everything is in order, but in a different way": reinterpretation of the Areopagite concept of hierarchy

"Order", "subordination", "power", "repression" are the phenomena with which the concept of "hierarchy" is most often associated. As far as we know, the term "hierarchy" is found for the first time in the Areopagite corpus. After a hermeneutic analysis of the definition of hierarchy in its first usage, a number of some new and not so obvious meanings are revealed: hierarchy is not only an order, but also a knowledge, an action (or energy) and an imitation of God, it is associated with a difference in the nature of things, but at the same time it is dynamic, not static. In the basis of the central role the concept of "Divine action (or energy)" plays in describing hierarchy, we argue that in the Areopagitics can be traced the procedural logic of the Arab-Muslim world, rather than the substantial logic of the Greek-Western European world. An explanation of this paradox is proposed based on the commensurability of the Areopagite and Akbari traditions, which is confirmed by historical data.

Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots.

"Everything is in order, but in a different way": reinterpretation of the Areopagite concept of hierarchy

"Order", "subordination", "power", "repression" are the phenomena with which the concept of "hierarchy" is most often associated. As far as we know, the term "hierarchy" is found for the first time in the Areopagite corpus. After a hermeneutic analysis of the definition of hierarchy in its first usage, a number of some new and not so obvious meanings are revealed: hierarchy is not only an order, but also a knowledge, an action (or energy) and an imitation of God, it is associated with a difference in the nature of things, but at the same time it is dynamic, not static. In the basis of the central role the concept of "Divine action (or energy)" plays in describing hierarchy, we argue that in the Areopagitics can be traced the procedural logic of the Arab-Muslim world, rather than the substantial logic of the Greek-Western European world. An explanation of this paradox is proposed based on the commensurability of the Areopagite and Akbari traditions, which is confirmed by historical data.

Simulating Climatic Patterns and Their Impacts on the Food Security Stability System in Jammu, Kashmir and Adjoining Regions, India

This study investigated the historical climate data and future projections under the SSP5-8.5 scenario for Jammu, Kashmir (J&K), and its adjoining regions in India. Agriculture is a critical economic pillar of this region, making it highly vulnerable to climate change. This study focused on temperature and precipitation trends. Statistical analysis and modeling methods, including cloud computing, were employed to predict changes and assess their impact on agricultural productivity and water resources. The results indicated that by 2100, the mean maximum and minimum temperatures are projected to increase by approximately 2.90 °C and 2.86 °C, respectively. Precipitation variability is expected to rise, with a mean increase of 2.64 × 10−6 mm per day. These changes have significant consequences for crop yield, water stress, and ecosystem dynamics. An analysis of Gross Primary Productivity (GPP) as a proxy for agricultural productivity using linear regression revealed a concerning trend. Although the total GPP of the study area remained stable over time, it declined by −570 g yr−1 in 2010, coinciding with a 1 °C temperature rise. Projections based on the expected 3 °C temperature increase by 2100 suggest a total GPP loss of −2500 g yr−1. These findings highlight the urgent need for proactive adaptation measures, including sustainable agricultural practices, improved water management, and enhanced socioeconomic infrastructure, to mitigate the impact of climate change and ensure long-term resilience and food security in the region.

Does energy transformation improve green development efficiency? Evidence from Sichuan Province, China

Green sustainable development has become an important topic of concern to human society.Energy clean and low-carbon transformation is an important lever for promoting regional green development. Investigating the driving mechanisms of energy transformation is helpful to explore the effective path for accelerating the process of green development. Meanwhile, Sichuan Province is a demonstration province of clean energy and an important energy transportation hub in China, and it should play an exemplary role in the process of high-quality development. In this regard, this study uses the two-stage super-efficiency network SBM model to measure the green development efficiency based on the dual perspectives of green production and environmental governance. Based on historical data from 2006 to 2022, Technological innovation, industrial structure, and foreign direct investment are selected as transmission variables, and the fixed effects regression model is used to empirically analyse the direct and indirect impact effects of ET on GDE. The results show that the green development efficiency in Sichuan Province has gradually increased from 0.8303 in 2006 to 0.9270 in 2022, which is at a relatively high level. Energy transformation is helpful to improve the green development efficiency in Sichuan Province. However, due to insufficient complementary power sources and challenges in balancing energy supply and demand, the incentive level needs to be strengthened. Technological innovation, industrial structure, and foreign direct investment show indirect heterogeneous transmission effects in the process of influence.

Optimising fisheries monitoring for rare and protected species: A south-eastern Australian case study shows low levels of interaction with a critically endangered species

Scientific observer programs are often the only means to estimate the total number and circumstances of interactions (including the life status at the time of the interaction) between commercial fisheries and threatened, endangered and protected species (TEPS). However, without estimating the precision of predefined sampling fractions, the accuracy of estimates are often poorly understood. We present a methodology to estimate appropriate sampling fractions using historical observer data from the New South Wales (Australia) ocean trap and line fishery, and implement an on-board monitoring program to estimate contemporary rates of interactions with TEPS in the fishery to evaluate the effectiveness of management measures implemented to reduce interactions. A total of 327 fishing trips (∼61,150 hook deployments) were sampled over 31 months with a single observed interaction each with the critically endangered grey nurse shark (Carcharius taurus) and scalloped hammerhead (Sphyrna lewini) recorded along with the capture of six seabirds. Observed catches included 97 species (70 finfish, 23 elasmobranchs, 1 invertebrate and 3 birds), of which 74 and 57 species were retained and discarded, respectively. Current fishing practices, combined with declines is reported effort targeting ‘large sharks’ following management reform, spatial protection measures, and gear regulations, were found to be minimising interactions between the main fishing method and TEPS. However, regular monitoring and research may be required to continually assess the effectiveness of mitigation strategies. The logic and methodology for determining appropriate sample fractions using existing data before implementing on-board sampling is relevant to the monitoring of other commercial fisheries.

Modeling Sea Level Rise Using Ensemble Techniques: Impacts on Coastal Adaptation, Freshwater Ecosystems, Agriculture and Infrastructure

Sea level rise (SLR) is a crucial indicator of climate change, primarily driven by greenhouse gas emissions and the subsequent increase in global temperatures. The impact of SLR, however, varies regionally due to factors such as ocean bathymetry, resulting in distinct shifts across different areas compared to the global average. Understanding the complex factors influencing SLR across diverse spatial scales, along with the associated uncertainties, is essential. This study focuses on the East Coast of the United States and Gulf of Mexico, utilizing historical SLR data from 1993 to 2023. To forecast SLR trends from 2024 to 2103, a weighted ensemble model comprising SARIMAX, LSTM, and exponential smoothing models was employed. Additionally, using historical greenhouse gas data, an ensemble of LSTM models was used to predict real-time SLR values, achieving a testing loss of 0.005. Furthermore, conductance and dissolved oxygen (DO) values were assessed for the entire forecasting period, leveraging forecasted SLR trends to evaluate the impacts on marine life, agriculture, and infrastructure.

Contamination levels and ecological risk assessment of phthalate esters (PAEs) in the aquatic environment of Chaohu Lake.

Phthalate esters (PAEs), widely recognized as synthetic organic compounds with extensive production and utilization, are known to disrupt physiological processes in both animals and humans, even at low environmental concentrations. This study investigated the occurrence, distribution, and potential ecological risk of five representative PAEs (DMP, Dimethyl phthalate; DEP, Diethyl phthalate; DBP, Dibutyl phthalate; DiBP, Diisobutyl phthalate; DEHP, Bis(2-ethylhexyl) phthalate) in a typical lake (Chaohu Lake, China). It was found that PAEs were detected in both the aqueous (1.09-6.402 µg/L) and solid phases (0.827-6.602 µg/g) of Chaohu Lake. Notably, DiBP and DBP were the predominant PAEs in the water, and DiBP and DEHP were the most prevalent in the sediments. The concentrations of PAEs exhibited significant seasonal variations in the aqueous phases, with total PAEs in summer being nearly twice those in winter. Toxicity assessments revealed that DEHP, DBP, and DiBP posed high risks to the survival of three indicator organisms (algae, Daphnia, and fish) in the aqueous phase. In the solid phase, the exceeding rate of DiBP was as high as 92.9%. On the other hand, DBP and DEHP generally presenting moderate risk, although some sites were identified as high-risk. This study's analysis of PAEs concentrations in Chaohu Lake reveals a discernible increasing trend when compared with historical data. These findings underscore the urgent need for interventions to mitigate the ecological threats posed by PAEs in Chaohu Lake.

Assessment of Changes in Hydrometeorological Indicators and Intra-Annual River Runoff in the Ile River Basin

Water management strategies in the Ile River basin (Republic of Kazakhstan) have traditionally relied on historical data without fully considering the potential impacts of climate change. This gap can lead to underestimating the changes in temperature, precipitation patterns, and runoff, hindering effective water resource management. This study aimed to analyze how a changing climate is affecting the Ile River basin’s water regime. Specifically, it investigated trends in temperature, precipitation, and runoff within the basin, emphasizing the importance of incorporating these intra-annual variations when planning water management strategies and hydraulic structures. A detailed analysis of the long-term data was conducted, focusing on changes in meteorological indicators. This included average air temperatures and annual precipitation for elevations above and below 1500 m during cold and warm periods. The analysis aimed to identify and quantify trends of increase or decrease. Meteorological stations were strategically chosen to represent both arid and humid areas within the basin, accounting for the region’s significant altitude variations. The investigation revealed several key findings. Rising average annual air temperatures are leading to a larger area experiencing snowmelt and a longer warm period within the runoff formation zone. This directly impacts the water balance of the basin. Additionally, an increase in total annual precipitation, particularly during the cold season within the runoff formation zone, suggests a potential for future water resource growth, assuming that these trends persist. This study highlights the importance of considering intra-annual variations in water regimes when developing water management strategies. The observed changes in temperature and precipitation patterns within the Ile River basin necessitate adjustments to existing plans to ensure sustainable water resource management in a changing climate.

KGroot: A knowledge graph-enhanced method for root cause analysis

Fault localization in online microservices is a challenging task due to the vast amount of monitoring data, diversity of types and events, and complex interdependencies among services and components. Fault events in services are propagative and can trigger a cascade of faults in a short period of time. In the industry, fault localization is typically conducted manually by experienced personnel. This reliance on experience is unreliable and lacks automation. Different modules present information barriers during manual localization, making it difficult to quickly align during urgent faults. This inefficiency lags stability assurance to minimize fault detection and repair time. Although actionable methods aimed to automate the process, the accuracy and efficiency are less than satisfactory. The precision of fault localization results is of paramount importance as it underpins engineers’ trust in the diagnostic conclusions, which are derived from multiple perspectives and offer comprehensive insights. Therefore, a more reliable method is required to automatically identify the associative relationships among fault events and propagation paths. To achieve this, a knowledge graph-enhanced root cause analysis (KGroot) method is designed for efficient and effective diagnosis of recurring failures in complex microservices environments. As the first event-driven knowledge graph method, KGroot uses event knowledge and the correlation between events to perform root cause reasoning for Root Cause Analysis (RCA). A Fault Event Knowledge Graph (FEKG) is built based on historical data, an online graph is constructed in real-time when a failure event occurs, and the similarity between each event knowledge graph and online graph is compared using GCNs to pinpoint the fault type through a ranking strategy. Comprehensive experiments demonstrate that KGroot can locate the root cause with an accuracy of 93.5% top 3 potential causes in second-level. This performance matches the level of real-time fault diagnosis in the industrial environment and significantly surpasses state-of-the-art baselines in RCA in terms of effectiveness and efficiency. (KGroot is available at https://github.com/daixixiwang/KGroot).

Assessment of Changes in Hydrometeorological Indicators and Intra-Annual River Runoff in the Ile River Basin

Water management strategies in the Ile River basin (Republic of Kazakhstan) have traditionally relied on historical data without fully considering the potential impacts of climate change. This gap can lead to underestimating the changes in temperature, precipitation patterns, and runoff, hindering effective water resource management. This study aimed to analyze how a changing climate is affecting the Ile River basin’s water regime. Specifically, it investigated trends in temperature, precipitation, and runoff within the basin, emphasizing the importance of incorporating these intra-annual variations when planning water management strategies and hydraulic structures. A detailed analysis of the long-term data was conducted, focusing on changes in meteorological indicators. This included average air temperatures and annual precipitation for elevations above and below 1500 m during cold and warm periods. The analysis aimed to identify and quantify trends of increase or decrease. Meteorological stations were strategically chosen to represent both arid and humid areas within the basin, accounting for the region’s significant altitude variations. The investigation revealed several key findings. Rising average annual air temperatures are leading to a larger area experiencing snowmelt and a longer warm period within the runoff formation zone. This directly impacts the water balance of the basin. Additionally, an increase in total annual precipitation, particularly during the cold season within the runoff formation zone, suggests a potential for future water resource growth, assuming that these trends persist. This study highlights the importance of considering intra-annual variations in water regimes when developing water management strategies. The observed changes in temperature and precipitation patterns within the Ile River basin necessitate adjustments to existing plans to ensure sustainable water resource management in a changing climate.

The here and now of climate change: Climatic trends throughout Australian cotton regions and implications for the growing season

ContextMuch research has focused on assessing future climatic conditions on cotton production. However, understanding climatic changes that have already occurred throughout Australian cotton regions may provide insights on how existing and new regions are adapting to change. ObjectivesThis study aims to understand climatic changes that have occurred throughout Australian cotton regions, and the implications that these changes may have had for cotton growth and development. MethodsWe utilised historical weather data to undertake an analysis of regional climate using changes in: heat unit accumulation for seasonal assessment of temperature conditions; frequency of extreme weather conditions affecting cotton growth; seasonal potential evapotranspiration (ETO); and the timing of key cotton developmental stages. Nine Australian cotton regions over three time periods from 1961 to 2020 were assessed. We also used the cotton simulation model OZCOT to predict yield, crop evapotranspiration (ETC), and water use efficiency (WUE, lint yield kg/mm ETC) for a crop grown at each location. ResultsOur data shows some reductions in early season cold shocks (≤11 °C min.), and increases in minimum temperature, seasonal heat accumulation, ETO, and the frequency of extremely high temperatures (>40 °C). Overall, time to first flower (FF) has decreased across several locations which may be having implications for less leaf area prior to flowering, but it may be contributing to an opportunity for longer reproductive periods at Emerald and Moree. ConclusionsClimatic changes including decreased frequency of early season cold shocks, increased minimum temperatures and frequency of high temperatures and evapotranspiration, and an increase in season long heat accumulation have occurred across Australian cotton regions from 1961 to 2020, affecting growth and development of cotton. Overall, warmer seasonal temperatures have reduced the time to first flower, increased the time to last effective flower (LEF), and resulted in some changes to last harvestable boll (LHB). ImplicationsClimatic changes have resulted in an opportunity for longer reproductive periods and the potential for increased yields. Recognising that regions have experienced specific changes may help to guide current and future agronomic practice in the different regions.

Design of an Iterative Method for MapReduce Scheduling Using Deep Reinforcement Learning and Anomaly Detection

Due to high complexities within distributed computing environments, there's a critical need for advanced scheduling frameworks that are capable of optimizing MapReduce systems. Current approaches have static policies that limit their capability to adapt to changing system dynamics and workload variations for different cloud scenarios. To overcome these issues, this study introduces a robust MapReduce framework empowered by intelligent scheduling algorithms, tailored to enhance system efficiency and resilience levels. The framework introduces three novel scheduling models: Deep Reinforcement Learning for Dynamic Job Scheduling (DRDJS), Anomaly Detection-driven Adaptive Scheduling (ADAS), and Cluster-based Job Categorization and Scheduling (CJCS). DRDJS utilizes deep reinforcement learning to dynamically generate optimal scheduling policies based on multiple metrics that include historical job data, system metrics, and workload characteristics. This adaptive approach leads to significant reductions in job completion times, outperforming static scheduling methods by up to 30%. Next, ADAS leverages anomaly detection to prioritize critical tasks and efficiently allocate resources in response to anomalies, resulting in a notable improve scheduling speed by up to 40%. Finally, CJCS employs clustering algorithms to categorize jobs based on their resource requirements and execution profiles, enabling more accurate resource allocation and reducing average job completion times by up to 25%. By integrating these methods into a unified scheduling framework, the proposed solution addresses the variability in job characteristics and system performance, thereby enhancing the overall throughput and stability of MapReduce operations.

Design of an Iterative Method for MapReduce Scheduling Using Deep Reinforcement Learning and Anomaly Detection

Due to high complexities within distributed computing environments, there's a critical need for advanced scheduling frameworks that are capable of optimizing MapReduce systems. Current approaches have static policies that limit their capability to adapt to changing system dynamics and workload variations for different cloud scenarios. To overcome these issues, this study introduces a robust MapReduce framework empowered by intelligent scheduling algorithms, tailored to enhance system efficiency and resilience levels. The framework introduces three novel scheduling models: Deep Reinforcement Learning for Dynamic Job Scheduling (DRDJS), Anomaly Detection-driven Adaptive Scheduling (ADAS), and Cluster-based Job Categorization and Scheduling (CJCS). DRDJS utilizes deep reinforcement learning to dynamically generate optimal scheduling policies based on multiple metrics that include historical job data, system metrics, and workload characteristics. This adaptive approach leads to significant reductions in job completion times, outperforming static scheduling methods by up to 30%. Next, ADAS leverages anomaly detection to prioritize critical tasks and efficiently allocate resources in response to anomalies, resulting in a notable improve scheduling speed by up to 40%. Finally, CJCS employs clustering algorithms to categorize jobs based on their resource requirements and execution profiles, enabling more accurate resource allocation and reducing average job completion times by up to 25%. By integrating these methods into a unified scheduling framework, the proposed solution addresses the variability in job characteristics and system performance, thereby enhancing the overall throughput and stability of MapReduce operations.

Disparities in the implantation of secondary prevention implantable cardioverter defibrillator in the United States.

The annual incidence of sudden cardiac death is over 300,000 in the United States (US). Historically, inpatient implantation of secondary prevention implantable cardioverter defibrillator (ICD) has been variable and subject to healthcare disparities. To evaluate contemporary practice trends of inpatient secondary prevention ICD implants within the US on the basis of race, sex, and socioeconomic status (SES). The study is a retrospective analysis of the National Inpatient Sample from 2016 to 2020 of adult discharges with a primary diagnosis of ventricular tachycardia (VT), ventricular flutter, and fibrillation (VF). Adjusted ICD implantation rates based on race, sex, and SES and associated temporal trends were calculated using multivariate regression. A total of 193,600 primary VT/VF discharges in the NIS were included in the cohort, of which 57,895 (29.9%) had ICD placement. There was a significant racial and ethnic disparity in ICD placement for Black, Hispanic, Asian, and Native American patients as compared to White patients; adjusted odds ratio (aOR): 0.86 [p<.01], 0.90 [p = .03], 0.81[p<.01], 0.45 [p<.01], respectively. Female patients were also less likely to receive an ICD compared to male patients (aOR: 0.75, p<.01). Disparities in ICD placement remained stable over the study period (ptrend ≥.05 in all races, both sexes and income categories). Racial, sex, and SES disparities persisted for secondary prevention ICD implants in the US. An investigation into contributing factors and subsequent approaches are needed to address the modifiable causes of disparities in ICD implantation as these trends have not improved compared to historic data.

Analysing the Impact of Attacks and Vandalism on Nigerian Electricity Transmission Lines: Causes, Consequences, and Mitigation Strategies

This study meticulously examines the impacts of attacks and vandalism on Nigerian electricity transmission lines, analysing their causes, consequences, and potential mitigation strategies. Historical data reveals that socio- economic and political factors are the root causes of these destructive acts, including insurgencies and economic desperation. Recent trends show an alarming increase in the frequency and sophistication of such attacks, primarily using explosives, which pose a significant challenge to the Nigerian electricity sector. The geographical distribution of these incidents is a cause for concern, with a concentration in the Northeast and North-Central regions directly correlating with regional instability. The severe economic and social impacts are leading to prolonged power outages, substantial financial losses, and public dissatisfaction. This paper underscores the potential of a multi-faceted approach, including enhanced security measures, community engagement, and strategic policy reforms, to protect vital infrastructure and ensure a stable electricity supply. The research findings are a call to action for policymakers and stakeholders, providing a solid foundation for enhancing the resilience and reliability of Nigeria's power transmission system. Our collective responsibility is to act on these findings and contribute to the solution.