Peak Period Research Articles

From spectators to stewards: Transforming public involvement in natural history collections

A comprehensive overview of volunteer-driven public programs focused on activities to enhance natural history collections (NHCs) is provided. The initiative revolves around the WeDigBio events and the Collections Club at the Field Museum, aiming to deepen the public’s connection with scientific collections, enhance participatory science, and improve data associated with natural history specimens. The implementation and journey of these programs are outlined, including surveys conducted from 2015 through 2021 to gauge participant motivation, satisfaction, and the impact of these events on public engagement with NHCs. Results show trends in on-site and virtual volunteer participation over the years, especially during the peak period of the COVID-19 pandemic. The majority of participants expressed high satisfaction, indicating a willingness to continue participating in similar activities. The surveys revealed a shift towards more altruistic motivations for participation over time, with increased emphasis on supporting the Field Museum and contributing to the scientific community. The success of participatory science events demonstrates the potential of volunteer-driven programs to contribute meaningfully to the preservation, digitisation, and understanding of biodiversity collections, ultimately transforming spectators into stewards of natural history. From 2015 to present participants celebrate a significant milestone, with over a thousand community scientists contributing to the inventorying, collection care, curation, databasing, or transcription of 286,071 specimens, objects or records. We also discuss accuracy and quality control as well as a checklist and recommendations for similar activities.

Descriptive epidemiology of Lassa fever, its trend, seasonality, and mortality predictors in Ebonyi State, South- East, Nigeria, 2018—2022

BackgroundNigeria is an epicenter for Lassa fever. Ebonyi state is located in the South-Eastern region of Nigeria where a high burden of Lassa fever has been reported. Therefore, this study was designed to assess the epidemiology of Lassa fever, its seasonality, trend, and mortality predictors in Ebonyi state, South-East, Nigeria.MethodWe analyzed data extracted from Ebonyi State Integrated Disease Surveillance and Response (IDSR) system over five years (2018–2022). A total of 1578 reported Lassa fever cases were captured in the IDSR out of which 300 were laboratory-confirmed. Data were analyzed using descriptive statistics, additive time series model, quadratic equation, and logistic regression model (α0.05). Spatial distribution of reported Lassa fever cases was conducted using Arc G.I.S.ResultsThe mean age of the individuals with the reported cases of Lassa fever was 29.4 ± 17.8 years. Lassa fever showed a seasonal trend across the years. The quadratic model provided the best fit for predicting Lassa fever cumulative cases (R2 = 98.4%, P-value < 0.05). Projected cases of Lassa fever for the year 2023 were 123 in the 1st quarter, 23 in the 2nd quarter, 42 in the 3rd quarter, and 17 in the 4th quarter. The seasonality index was + 70.76, -28.42, -9.09, and -33.2 in the 1st, 2nd, 3rd, and 4th quarters respectively. The reported cases of Lassa fever followed a declining trend (slope = -0.1363). Farmers were 70% less likely to die from Lassa fever compared to those not working (aOR:0.3, CI: 0.17–0.83). The hot spots for Lassa fever were Abakaliki and Ezza Local Government Areas.ConclusionAlthough the reported Lassa fever cases followed a declining trend in Ebonyi state, there was a seasonality in the disease pattern. Being a farmer was protective against the risk of dying from Lassa fever. While efforts to eliminate and mitigate the spread of the disease in Ebonyi state should be strengthened, more attention should target the peak period of the disease.

Charging Strategies for Electric Vehicles Using a Machine Learning Load Forecasting Approach for Residential Buildings in Canada

The global electric vehicle (EV) market is experiencing exponential growth, driven by technological advancements, environmental awareness, and government incentives. As EV adoption accelerates, it introduces opportunities and challenges for power systems worldwide due to the large battery capacity, uncertain charging behaviors of EV users, and seasonal variations. This could result in significant peak–valley differences in load in featured time slots, particularly during winter periods when EVs’ heating systems use increases. This paper proposes three future charging strategies, namely the Overnight, Workplace/Other Charging Sites, and Overnight Workplace/Other Charging Sites, to reduce overall charging in peak periods. The charging strategies are based on predicted load utilizing a hybrid machine learning (ML) approach to reduce overall charging in peak periods. The hybrid ML method combines similar day selection, complete ensemble empirical mode decomposition with adaptive noise, and deep neural networks. The dataset utilized in this study was gathered from 1000 EVs across nine provinces in Canada between 2017 and 2019, encompassing charging loads for thirty-five vehicle models, and charging locations and levels. The analysis revealed that the aggregated charging power of EV fleets aligns and overlaps with the peak periods of residential buildings energy consumption. The proposed Overnight Workplace/Other Charging Sites strategy can significantly reduce the Peak-to-Average Ratio (PAR) and energy cost during the day by leveraging predictions made three days in advance. It showed that the PAR values were approximately half those on the predicted load profile (50% and 51%), while charging costs were reduced by 54% and 56% in spring and winter, respectively. The proposed strategies can be implemented using incentive programs to motivate EV owners to charge in the workplace and at home during off-peak times.

Short-Term Traffic Flow Prediction: Techniques and Methods

With the development of the economy, the expansion of the city scale, the increase in the population of urban residents and the increase in the per capita car ownership, the traffic congestion problem has an increasing impact on urban operation. Especially during the peak period of commuting traffic and holidays in large cities, most cities are facing the serious problem of road congestion. In order to facilitate the travel of residents and reduce air pollution, it is very important to keep the main road of the city smooth and rapid passage of vehicles. Therefore, cities should vigorously develop short-time traffic flow prediction, and accurate prediction of short-time traffic flow is the key to realizing intelligent urban traffic operation and efficient traffic flow management improvement. This paper will talk about the time series prediction model of short-time traffic flow prediction methods and advantages and disadvantages to provide auxiliary support for solving the traffic congestion problem, provide efficient travel path decision-making guidance for urban residents, facilitate the saving of urban people's travel costs, and also provide effective police deployment decision-making information for the traffic management department.

Optimal duration of progesterone before cryopreserved embryo transfer in hormone replacement therapy cycles: A prospective pilot study.

This prospective pilot cohort study aimed to ascertain the optimal duration of progesterone supplementation prior to frozen embryo transfer (FET) in women undergoing hormone replacement therapy (HRT) cycles. A total of 127 participants were enrolled and divided into 2 cohorts. The first cohort, comprising of 39 women, was used to determine the peak period of endometrial receptivity. These participants underwent serial assessments of integrin alphavbeta3, homeobox gene A10, and leukemia inhibitory expression levels from days 3 to 7 (P + 3 to P + 7) during the mock HRT cycles. The second cohort included 88 women who embarked on their inaugural HRT-FET cycle and were monitored for pregnancy outcomes after the transfer of D3 embryos after 3 or 4 days of progesterone administration. The results indicated a significant enhancement in biochemical pregnancy and embryo implantation rates in the P + 3 group (87.18% and 74.36%, respectively) compared to those in the P + 4 group (68.42% and 65.79%; P = .047 and P = .044, respectively). These data suggest that a 3-day progesterone regimen prior to D3 embryo transfer may be more beneficial. Notably, the maximal expression of endometrial receptivity markers was observed on day P + 6 within the HRT cycles, which is consistent with previous research that correlated day 6 embryos with peak endometrial receptivity. Therefore, a 3-day progesterone lead-in may enhance synchrony between D3 embryos and the endometrium. Our findings suggest that initiating D3 embryo transfer after 3 days of progesterone administration may optimize embryo-endometrial synchronization, thereby potentially enhancing clinical outcomes in FET cycles.

99.6% efficiency DC-DC coupling for green hydrogen production using PEM electrolyzer, photovoltaic generation and battery storage operating in an off-grid area

This paper presents a novel connection and control strategy for a hydrogen generation system using a proton exchange membrane electrolyzer powered by solar energy in an off-grid area without network backup. Given that, the proposed architecture is based on the indirect control of the Photovoltaic plant (achieved by removing a power-converter), the presented solution is more efficient and more reliable than the traditional scheme. Furthermore, with this innovation one can reach the same hydrogen production with smaller electrolyzers.The methodology includes detailed models of the photovoltaic panel and the electrolyzer, along with a control strategy that considers the degradation mechanisms of the electrolyzer to ensure reliable and prolonged operation. The results show that the proposed strategy keeps the operating power of the electrolyzer constant, even under variations in irradiance, thanks to energy storage in batteries. It is demonstrated that the proposed system offers efficiency above 99.6% during the analyzed period, with a 100% utilization rate of the electrolyzer, avoiding periods of inactivity and high current peaks. The study also includes simulations and experimental tests that confirm the feasibility and effectiveness of the presented solution, highlighting its advantages in terms of efficiency and investment costs compared to direct connections and other existing methods.

Clinical characteristics of patients admitted to Samsun Training and Research Hospital from the Turkiye-Syria earthquake region

Aims: After the two major earthquakes that occurred in the Türkiye-Syria region on February 6th, 2023, earthquake casualties presented to hospitals in various centers across the country. This study aimed to analyze the characteristics of the presentations made to the emergency department and outpatient clinics of our tertiary hospital from the earthquake region after the earthquake. Methods: A total of 1534 earthquake casualties who were admitted to the Samsun Training and Research Hospital emergency department and outpatient clinics with the diagnosis code X-34 were included in the study. The data of the patients were scanned retrospectively through the online system and age, sex, presentation date, clinic and hospitalization status were recorded. Results: The mean age of the patients was 24.6±25.9 (range: 0-98) years. Six hundred eighty-six (44.7%) patients were male, 441 (28.7%) were age under 18 years, and 1199 (78.2%) were admitted or brought to the emergency room or intensive care units (ICUs). A total of 210 (13.7%) patients were hospitalized after being admitted or brought to the hospital. No fatalities were recorded among the earthquake casualties who presented. A total of 730 (47.6%) patients were admitted to our hospital within the first 30 days after the earthquake. The highest presentation peak period was between days 5 and 9. The mean age in women, inpatients, patients presnting to outpatient clinics, and patients from Adana, Hatay, Kahramanmaraş and Osmaniye provinces was significantly higher than the other groups (p&lt;0.001 for each). The rates of hospitalized patients were found to be significantly higher in women (p=0.002), patients presenting to outpatient clinics (p&lt;0.001), and patients presenting within the first 30 days after the earthquake compared to other groups (p&lt;0.001). The rates of patients presenting to emergency rooms or ICUs were significantly higher in men (p=0.011) and in those who presented within the first 7 days after the earthquake compared with the other groups (p=0.001). Conclusion: The findings obtained from our study indicated that the prognosis was good for earthquake casualties who presented to our hospital, which is far from the center of a major earthquake, that the highest number of presentations was between the 5th and 9th days, the period between the earthquake and the hospital admission was a determining factor in terms of the clinic presented and hospitalization, and the proximity to the center of the earthquake did not affect the prognosis in presentations. Our findings also show that the age group is an important factor in terms of the clinic of presentation, hospitalization, the period after the earthquake, and the province from which the presentation is made.

Energy management strategy for food Industry amidst the COVID-19 Coronavirus pandemic: yogurt manufacturing plant

This study aims to optimize the energy efficiency of an industrial production factory, using an optimal multi-objective model for electricity management during the COVID-19 pandemic. This tool is a procedure based on the load profile, and measured data at the level of each production chain, such as hourly electricity consumption by industrial equipment, and daily production. These measured data are used to calculate and evaluate two indicators of performance: the first indicator is the specific electricity consumption cost (SECC) which informs about the financial performance. The second is the specific electricity consumption (SEC) that highlights the energy performance of the production process. A case study was carried out on a yogurt manufacturing plant in Algeria. After optimization, SEC is decreased from 0.070 to 0.068 kWh/liter, and SECC is decreased from 7.2 to 6.6 cents US$/liter. In addition, the electricity consumption is reduced at the peak period from 6 p.m. to 9 p.m. by 35%. Therefore, the monthly electricity bill is reduced by 4,089 $US/month which represents 10.2 %. It was also recorded that there is a new peak period formed between midday and 5 p.m. due to containment during this period of pandemic in Algeria.

Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach

&amp;lt;i&amp;gt;Background&amp;lt;/i&amp;gt;: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in Nigeria between 2017 and 2023, leveraging a comprehensive dataset from the Nigerian Centre for Disease Control (NCDC). &amp;lt;i&amp;gt;Objective&amp;lt;/i&amp;gt;: The goal of this study is to analyze the seasonal variations and predict future occurrences of Lassa fever outbreaks in Nigeria. By employing the Box-Jenkins time series analysis and geo-spatial analysis, we aim to: Identify temporal patterns by Examining monthly and annual trends in Lassa fever case numbers, Forecast future outbreaks by utilizing an ARIMA model to predict future incidence rates and inform public health strategies by providing evidence-based recommendations to improve Lassa fever prevention and control efforts. &amp;lt;i&amp;gt;Methods&amp;lt;/i&amp;gt;: This study utilized a secondary dataset comprising over 60 data points collected from the NCDC portal between 2017 and 2023. The Box-Jenkins time series analysis, specifically the ARIMA model, was employed to analyze the temporal patterns and forecast future trends. The model&amp;apos;s adequacy was assessed using the Ljung-Box test. Additionally, geo-spatial analysis was conducted to visualize the spatial distribution of Lassa fever cases. &amp;lt;i&amp;gt;Results:&amp;lt;/i&amp;gt; The analysis revealed distinct seasonal patterns in Lassa fever incidence, influenced by Nigeria&amp;apos;s climatic and environmental conditions. Monthly fluctuations in confirmed cases were observed, with peak periods aligning with specific seasons. The ARIMA (0, 1, 1)(0, 1, 1)&amp;lt;sub&amp;gt;12&amp;lt;/sub&amp;gt; model demonstrated a strong fit to the data, providing reliable forecasts for future outbreaks. &amp;lt;i&amp;gt;Conclusion:&amp;lt;/i&amp;gt; This study underscores the importance of strengthening surveillance systems for early detection and rapid response to Lassa fever outbreaks, particularly during peak seasons. Implementing effective rodent control measures, promoting good hygiene practices, and improving environmental sanitation are crucial for reducing the risk of Lassa fever transmission. Furthermore, enhancing collaboration between government agencies, healthcare providers, and research institutions is essential for optimizing Lassa fever prevention and control efforts.

Information technology for forecasting epidemiological threats based on the telegraphic equation

Based on the analysis, it is established that traditional approaches, such as the SIR (Susceptible-Infectious-Covered) and SEIR (Susceptible-Exposed-Infectious-Covered) models, do not provide sufficient forecasting accuracy and do not take into account the complex dynamics of the spread of infectious diseases. The need to develop a method that will improve forecasting accuracy and provide support for managerial decision-making to predict the spread of epidemiological threats based on the telegraphic equation is substantiated. The developed system allows for making effective management decisions aimed at reducing the negative impact of the epidemic on the population and medical infrastructure. The use of the telegraphic level allows us to predict the wave spread of infection, spatial and temporal delays, as well as sources of new infections, which ensures accurate forecasting of peak periods, the duration of the epidemic, and the workload of medical facilities. The developed method integrates the classical SIR model with the telegraphic level, which allows the modelling the dynamics of infection spread in a spatio-temporal environment. This method provides forecasting of the spatial and temporal dynamics of infection spread, taking into account wave effects, delays, and the influence of external factors. It provides an opportunity to accurately analyze key epidemic indicators, such as the peak of the disease, its duration, and the distribution of the burden on hospitals. The developed method and mathematical model based on the telegraphic level provided an appropriate level of accuracy in predicting the spatial and temporal dynamics of the spread of epidemiological threats. Testing the model on historical COVID-19 data showed that the average forecast error was 5...10%. This indicates the model's adequacy. In the case of high population mobility, the model accurately described the wave dynamics of the infection. The proposed decision support system includes a user-friendly interface with four tabs for entering model parameters, analyzing results, visualizing them, and generating recommendations. It allows to improve the accuracy of estimating the duration of the epidemic, peak loads, and some resources. The developed system is a tool for managers to support the adoption of governmental decisions aimed at predicting the infection of the population of regions and optimizing the use of medical resources. The results of the study can be used to plan epidemic response measures at the local, regional, and global levels. The proposed system ensures efficiency, flexibility, and accuracy, which are key to managing epidemiological situations in the face of modern challenges.

A study on the combination of crystallization-controllable phase change materials and solar-assisted heat pump for electricity demand shifting in space heating

Supercooled phase change materials offer a promising solution for space heating due to their ability to release latent heat upon crystallization initiation, even when stored at ambient temperatures. This unique property makes them ideal for solar-assisted space heating, where external activation enables on-demand heat release, addressing the critical need for energy-efficient heating solutions. In this study, a system promoting demand shifting is proposed, aiming to transfer energy consumption from morning and evening peak periods to daytime and high solar irradiance days, thereby enhancing the efficiency of solar heat pumps and reducing grid stress through the use of supercooled crystallization-controllable phase change materials. A model was developed, consisting of evacuated tube collectors, a buffer tank, heat storage tanks with crystallization-controllable phase change material, and a building heating demand model. The study introduces a novel system control methodology, focusing on an effective operation of tank shifting based on the heating requirement and solar energy availability. Real weather data were used to calculate system performance. With 50 m2 of collectors, a 1000-liter buffer tank, and a heat pump with a maximum output of 7 kW, the heat storage tanks are charged and discharged following the developed operational methodology. The system achieved a weekly coefficient of performance of 3.56 and successfully shifted electricity demand to solar hours, with only 28.5% of the total consumption occurring during domestic morning and evening peak times.

Unraveling depressive symptom networks: A three-year longitudinal study among Chinese junior high school adolescents.

Adolescence is the peak period for the occurrence of mental health issues, particularly in the stage of junior high school. Depressive symptoms are among the most frequently experienced psychological problems. However, little is known about the symptom-level interaction features of depressive symptoms and the roles of different symptoms across the junior high school stage. To address these gaps, this study conducted a three-year longitudinal study that recruited 1301 Chinese junior high school adolescents (48.2% females; mean age = 12.46 ± 0.62, ranging from 11 to 14 in the first year). The Center for Epidemiological Studies Depression Scale was used to assess depressive symptoms. The regularized partial correlation network and the cross-lagged panel network models were used to explore the symptom-level interaction pattern. In regularized partial correlation networks, "I felt depressed" was a stable central symptom throughout the junior high school stage. Besides, "I felt lonely" and "I felt that people disliked me" were the other central symptoms in grade 7 and grade 8, and "I felt everything I did was an effort" played a central role in grade 9. Within cross-lagged panel networks, "I felt that people disliked me" and "I felt hopeless about the future" had important effects on predicting other depressive symptoms from grade 7 to 8 and from grade 8 to 9. By investigating the longitudinal interaction patterns of depressive symptoms among junior high school adolescents, the current study identifies core symptoms that could be potential prevention or intervention targets and provides a novel insight for understanding depressive symptoms during adolescence in depth.

Evolution of coastal transgressive aeolian sand sheets over 75 years (1948-2023) at Concheiros Barrier - Southern Brazilian coast

Transgressive sand sheets (TSS) and transgressive dunefields (TDf) over time often present multiple phases of development and various morphologies controlled by biogeomorphological interactions related to abiotic processes, vegetation, and human interference. While dunefields have experienced increased vegetation cover over the last decades worldwide, a few places have shown increased dune mobility. In this study, a GIS analysis was conducted using aerial photographs and satellite imagery to investigate the evolution of a TDf, foredune collapse, new TSS formation, and vegetation cover on a Holocene coastal barrier in southern Brazil over the last 75 years (from 1948 to 2023). Wind speed (1994–2009), significant wave height (Hs) and peak wave period (Tp) (1940–2022) were analyzed to examine these potential drivers that could have caused geomorphological changes on the foredunes and dune system. Vegetation cover exhibited an increasing trend, and both an increase and decrease in TDf movement and TSS expansion were concurrent. Correlation analyses revealed a strong negative correlation between vegetation cover and TDf movement. Three transgressive sand sheet formation phases were identified between 1948 and 2023 (1964 to 1975,1996 to 2000, and from 2010 until 2023). Post-2003, a poorly vegetated nebkha-dominated foredune subsequently collapsed and was replaced by a TSS in less than a decade, being fully formed in 2010 and continuing to move inland at rapid rates. In the third phase, the highest total TSS expansion exceeded 700 m in 12 years (from 2010 to 2022). A small increase in wind velocity and subsequent decrease may possibly have contributed to the formation of the TSS second phase and the foredunes respectively. Other possible drivers for TSS formation include climatic modes, negative precipitation anomalies and groundwater level lowering, in addition to anthropogenic actions (especially water extraction) that might have caused a negative feedback in foredune sand-biding vegetation.

Solar-hydrogen power supply system and multichannel median signal selector for spacecraft miniaturization

Solar-hydrogen energy systems for spacecraft reliably provide energy for carrying out various kinds of unscheduled work on board and for eliminating emergency situations. Surplus energy is constantly accumulated on board in the form of the chemical energy of cryogenic hydrogen.The solar-hydrogen spacecraft power supply system is based on the principle of partial conversion of excess energy generated on board into gaseous and cryogenic hydrogen.The efficiency of converting sunlight into hydrogen is 14%. Solar-hydrogen energy systems are also called rechargeable hydrogen cells. They consist of solar panels that generate electricity, water tanks and a membrane that separates hydrogen and oxygen. The power supply system implements three modes in its operation. Mode A - the planned amount of electrical energy generated by solar cells is consumed to meet the needs of the spacecraft in the normal mode. Mode B - from the excess electricity in excess of the energy consumption on board, the solar panels generate electricity, with the help of which water is electrolyzed. The resulting hydrogen is stored in special tanks and can be used during the peak period of energy consumption. Mode C - part of the hydrogen can be liquefied by the onboard cryogenerator and it can be used to generate electricity in fuel cells in super-peak power consumption situations: accidents, repairs, experiments that require a lot of energy. The efficiency of converting sunlight into cryogenic hydrogen is 5%.The article considers a new version of the median signal selection element, which provides the possibility of practically unlimited scaling of the number of input signals without loss of performance, as well as the ability to monitor malfunctions or failures in the operation of the selection element itself.The experiments have shown that the implementation of a selection element for 7 inputs takes no more than 432 logical cells on the Altera Cyclone IV EP4CE115F29C7 FPGA, which is much less than the amount occupied by the analogs discussed in the article. In this case, the delay in calculating the median signal does not exceed one FPGA cycle.

Why they take the risk to perform a direct left turn at intersections: A data-driven framework for cyclist violation modeling

Bicycle crashes at intersection areas are posed a worrying traffic safety issue, and one of the main reasons for bicycle crashes is failing to avoid conflicts with motor vehicles and other bicycles. Clearly, cyclists are more exposed to risk if they perform a direct left turn (DLT) being mixed with left-turning vehicle under a left-turn phase. Owing to the lack of exposure data, the detection of DLT event and the mechanism behind the risky riding behavior have yet to be discovered. To bridge these gaps, this study proposes a DLT detection framework based on bike sharing trajectories. Moreover, this study seeks to understand the contributing factors to DLT behavior using the random parameters logit model with heterogeneity in means and variances (RPLHMV) to account for unobserved heterogeneity in the DLT cases dataset. Statistical analysis shows that DLT is most likely to occur on weekdays during peak periods under large commuting demand. As to what caused the DLT violations, law-obeying cyclists are more susceptible to external events, while risk-taking cyclists are subtly undermined by their habits. In addition, the model of RPLHMV reveals several significant contributing factors to the propensity of DLT violations, such as event time, available passing time for left-turning bicycles, and average cycling speed, whereas the indicator variables of actual waiting time, available passing space for left-turning bicycles, and preference for DLT violation become the emerging influential variables. This study is expected to help better understand DLT occurrence and propose countermeasures more efficiently for reducing cyclists’ DLT rate.

The Role of AI/ML in Enhancing Security and Fraud Detection in Digital Payments

As digital payment systems continue to evolve and gain widespread adoption, the need for robust security measures and effective fraud detection mechanisms has become paramount. This article explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing fraud detection and prevention within the digital payment ecosystem. Through comprehensive analysis of implementation strategies and performance metrics, we demonstrate how AI-powered solutions achieve fraud detection rates of up to 99.9% while maintaining false positive rates below 0.1%. The article reveals that modern fraud detection systems must process transaction volumes exceeding 100,000 per second during peak periods, with real-time decision-making latency under 50 milliseconds. Integration of advanced ML models, including deep learning and federated learning approaches, has shown a 50% reduction in fraud losses within the first year of deployment. Our analysis of stream processing architectures and edge computing implementations demonstrates how organizations can achieve sub-millisecond response times while maintaining regulatory compliance. As global digital transaction values are projected to reach $8.26 trillion by 2024, these AI/ML solutions prove crucial in combating sophisticated fraud attempts, which are expected to reach $38.5 billion by 2027. The study examines specific case studies, algorithms, and models, demonstrating how AI-powered solutions offer more accurate, efficient, and adaptive mechanisms to safeguard digital transactions while maintaining system performance and user experience.

Investigating DNA damage caused by COVID-19 and influenza in post COVID-19.

The SARS-CoV-2 virus (termed COVID-19) was responsible for over 34 million global deaths. Although the COVID-19 pandemic has subsided, infection by emerging mutant variants of SARS-CoV-2 poses a continuing threat to public health. COVID-19 infection has been associated with the development of cytokine storm syndrome, hypercoagulability, immunological dysregulation and direct viral invasion of organs, and the long-term consequences for the health of COVID-19 survivors are currently unknown. Our research focuses on the possible mutagenic aspects of infection by COVID-19 and measures their harmful effects on DNA composition. DNA damage was investigated, using the comet assay method, during two periods: in the epidemic peak of COVID-19 and during the post-COVID-19 period, both in patients infected with COVID-19 and in those with influenza. During the epidemic peak, the levels of DNA damage ranged from the highest to the lowest levels in the following groups, respectively: intubated-ICU, non-intubated-ICU, non-ICU, and influenza, with a discernible increase in DNA damage in ICU-treated patients. The levels of DNA damage in the post-COVID-19 period were significantly lower compared to those in the epidemic peak period but there was still a discernible increase in DNA damage in the ICU group. Our results indicate that levels of DNA damage may be an effective indicator in prognostic decision-making and may therefore help to reduce mortality. Given that DNA damage and impaired repair processes can contribute to chronic diseases like diabetes, cancer, and neurodegenerative conditions, it will be crucial to investigate potential similar effects in patients with COVID-19.

Influence of Blood Sampling Service Process Reengineering on Medical Services Supply: Quasi-Experimental Study.

Tertiary hospitals in China are confronted with significant challenges due to limited spatial capacity and workforce constraints, leading to saturated allocation of medical resources and restricted growth in medical service provision. The incorporation of digital health into medical service process reengineering (MSPR) marks a pivotal transformation and restructuring of conventional health service delivery models. Specifically, the application of MSPR to blood sampling services processes reengineering (BSSPR) holds promise for substantially enhancing the efficiency and quality of medical services through streamlining and optimizing these procedures. However, the comprehensive impact of BSSPR has been infrequently quantified in existing research. This study aims to investigate the influence of BSSPR on the efficiency and quality of medical services and to elucidate the key informative technological support points underpinning BSSPR. Data were collected from both the new and old laboratory information systems from August 1, 2019, to December 31, 2021. A combination of statistical description, chi-square test, and t test was used to compare check-in time and waiting time of outpatients before and after the implementation of BSSPR. An interrupted time-series design was used to analyze the impact of BSSPR on medical service efficiency and quality, enabling the control of confounding variables, including changes in medical human resources and both long- and short-term temporal trends. BSSPR had an impact on the efficiency and quality of medical services. Notably, there was a significant increase in the number of patients receiving blood sampling services, with a daily service volume increase of ~150 individuals (P=.04). The average waiting time for patients decreased substantially from 29 (SD 36) to 11 (SD 11) minutes, indicating a marked improvement in patient experience. During the peak period, the number of patients receiving blood sampling services per working hour statistically increased from 9.56 to 16.77 (P<.001). The interrupted time-series model results demonstrated a reduction in patients' waiting time by an average of 26.1 (SD 3.8; 95% CI -33.64 to -18.57) minutes. Although there was an initial decline in the number of outpatients admitted following BSSPR implementation, an upward trend was observed over time (β=1.13, 95% CI 0.91-1.36). BSSPR implementation for outpatients not only reduced waiting time and improved patients' experience but also augmented the hospital's capacity to provide medical services. This study's findings offer valuable insights into the potential advantages of BSSPR and underscore the significance of harnessing digital technologies to optimize medical service processes. This research serves as a foundational basis and provides scientific support for the promotion and application of BSSPR in other health care contexts. By continuing to explore and refine the integration of digital technologies in health care, we can further enhance patient outcomes and elevate the overall quality of medical services.

Optimizing shared charging services at sustainable bus charging hubs: A queue theory integration approach

Integrating solar photovoltaic (PV) systems into bus charging infrastructure offers a promising solution to mitigate carbon emissions and reduce grid loads. However, a mismatch between the output of solar PV energy and the electric bus (EB) charging demand can arise due to the instability and intermittency of solar PV energy. To address this issue, this study introduces a novel shared charging business mode that allocates charging facilities to private electric vehicles (PEVs), leveraging idle infrastructure to maximize solar PV utilization. We develop an optimization framework to jointly determine the EB charging schedules and the allocation of charging facilities between EBs and PEVs within an EB network integrated with solar PV systems. Additionally, a loss queue model with time-dependent arrival rates is proposed to model the stochastic nature of PEV charging demand. A case study is conducted on a bus network in Beijing to validate our proposed operational strategy. The findings demonstrate that integrating shared charging in bus depots equipped with solar PV systems improves solar PV utilization from 79.30 % to 92.91 %. This integration reduces CO2 emissions and overall daily system costs by 7.93 % and 14.82 %, respectively, without negatively impacting the grid load during peak periods.

Evaluation of Rabi Maize-based Intercropping System for Augmenting the Productivity and Profitability of Rabi Maize under Irrigated Conditions of Bihar

Background: A combination of maize and legume, cabbage and potato in intercropping benefits the agricultural production system due to different peak period of growth and synergistic effects, which reflected into yield advantage. In order to reduce nutritional competition and the yield gaps between actual production and production potential for both crops in the intercropping system, more information is required for the optimization of maize-based intercropping systems. So, keeping above fact in mind, the present study was undertaken to find out the suitable rabi maize based intercropping system for augmenting productivity and profitability of maize. Methods: A field experiment was conducted on the farmer’s field of selected villages of KVK Bhagalpur (on-farm trials) during the winter (rabi) season of 2020-21 and 2021-22 on rabi maize to validate, refine and popularize the technology developed at Bihar Agricultural University, Sabour, Bhagalpur (Bihar) and ICAR-Indian Institute of Maize Research (IIMR), Ludhiana, for enhancing the productivity and profitability of rabi maize. The experiment, comprising four treatments, viz., sole rabi maize (farmer’s practice), maize + potato (1:1 row ratio), maize + vegetable pea (1:2 row ratio) and maize + cabbage (1:1 row ratio), was arranged in a randomized block design (RBD) with five replications. All five farmers selected for experimentation were treated as replications. Result: Experimental results revealed that all three intercropping associations of maize + vegetable pea (1:2 row ratio), maize + potato (1:1 row ratio), maize + vegetable pea (1:2 row ratio) and maize + cabbage (1:1 row ratio) produced significantly higher maize equivalent yields than the sole crop of rabi maize. However, among the intercropping systems, maize + vegetable pea (1:2 row ratio) produced the highest maize equivalent yield (241.0 q/ha) and significantly incurred the highest net return (322093 ₹ /ha), as well as a higher B: C ratio (6.04) than the rest of the intercropping systems as well as sole crop of rabi maize.