Analysis Of Long-term Data Research Articles

Developing economies and environmental impact: A VECM analysis of external debt, ODA, and CO2

The objective of this study is to examine the carbon dioxide (CO2) emissions in Asian economies between 1990 and 2019, with a specific focus on their relationship with net official development assistance (NODA) and external debt. Utilizing the IPAT model as a theoretical framework, we employ various econometric methodologies to estimate both long-term and short-term outcomes. These methodologies encompass panel unit-root tests, cointegration techniques, and the VECM method. The findings of the research provide additional substantiation for the presence of cointegration among all variables. The analysis of long-term data revealed a robust and statistically significant relationship between gross domestic product (GDP) and carbon dioxide (CO2) emissions. Conversely, a strong and inverse connection was identified between foreign debt and net official development assistance (NODA). The results indicated a modest but favorable relationship between the size of the Asian population and the levels of CO2 emissions. The segment of recommendations encompasses comprehensive proposals and an extensive examination of various alternatives.

Centimeter-level-precision seafloor geodetic positioning model with self-structured empirical sound speed profile

In-field Sound Speed Profile (SSP) measurement is still indispensable for achieving centimeter-level-precision Global Navigation Satellite System (GNSS)-Acoustic (GNSS-A) positioning in current state of the art. However, in-field SSP measurement on the one hand causes a huge cost and on the other hand prevents GNSS-A from global seafloor geodesy especially for real-time applications. We propose an Empirical Sound Speed Profile (ESSP) model with three unknown temperature parameters jointly estimated with the seafloor geodetic station coordinates, which is called the 1st-level optimization. Furthermore, regarding the sound speed variations of ESSP we propose a so-called 2nd-level optimization to achieve the centimeter-level-precision positioning for monitoring the seafloor tectonic movement. Long-term seafloor geodetic data analysis shows that, the proposed two-level optimization approach can achieve almost the same positioning result with that based on the in-field SSP. The influence of substituting the in-field SSP with ESSP on the horizontal coordinates is less than 3 mm, while that on the vertical coordinate is only 2–3 cm in the standard deviation sense.

Malignant Neoplasms in the Industrial City: Epidemiology, Current Trends and Forecast

Introduction: Optimization of information and analytical support for public health monitoring based on modern techniques of mathematical modeling and forecasting, multivariate statistical methods and artificial neural networks is becoming highly relevant. Objective: To conduct a retrospective and prospective epidemiological analysis of the incidence of malignant neoplasms in the city of Taganrog, Rostov Region, for 1985–2022. Materials and methods: We did a comprehensive long-term data analysis of statistical reporting forms and a personalized database of malignant neoplasms using factor analysis, hierarchical cluster analysis, real risk assessment, and artificial neural networks. We used software of our own design, as well as software packages IBM SPSS Statistics version 19.0 and Matlab R2021a with the Neural Network Toolbox. Results: Our findings indicate an unfavorable situation in the city of Taganrog, Rostov Region, with a 1.3-fold excess of the average annual cancer rate for the cities of the region and a continuous rising trend. Sex and age characteristics and priority cancer sites were determined. Based on regional criteria for assessing the real risk, we established that Taganrog ranks first in terms of cancer incidence and mortality, as well as seven cancer sites. The highest real risk has been found for breast and skin cancer. Using factor analysis and hierarchical cluster analysis, we examined the structure of risk factors for colon cancer in 1988–2019. The applied technique of artificial neural networks provided higher accuracy in the medium-term forecasting of the frequency of malignant neoplasms compared to extrapolation forecasting using theoretical trend lines. Conclusion: The use of multivariate statistical methods and artificial neural networks provides a highly informative characterization of the health status of the population.

Long-term growth data of plantation-grown Eucalyptus established in south-eastern Uruguay

Behind beef production, Eucalyptus logs oriented to kraft pulp production represent Uruguay's second-largest export product. In this context, the analysis of long-term growth data of plantation-grown Eucalyptus forests acquires a material relevance. A total of 2208 permanent plots representing longitudinal growth data of Eucalyptus globulus, Eucalyptus maidenii, Eucalyptus dunnii, and Eucalyptus smithii trees were analyzed and modeled. The plots are part of a more extensive set established since 2012 on a forest state of over 22,000 ha planted. The selected plots were of variable shape, varying in size between 200 and 1000 m2. Both planted and coppiced areas were included. Tree-level data (diameter at the beast height and height) was measured on each plot over a different number of years. Plantation ages, at the time of measurement, ranged from 1 to 18 years. The forests were all planted for the first time under the context of degraded grasslands, where there was no other better alternative use for the land. Soils are not very deep, of medium-to-coarse texture, and have low natural fertility. The dominant land cover was grasslands. The results highlight the variability in site quality. Stand density varied between 100 and 2500 trees per ha (TPH), with a site index (SI) at base age 11 ranging between 12 and 38 m. A strong relationship between dominant height (DH) and standing volume (TV) is evident, reinforcing that SI is still a valuable quantity to represent site quality. A set of anamorphic SI curves fitted to the DH data showed that E. dunnii and E. smithii height far exceeds that of E. globulus and E. maidenii at the same age, with an average SI of 28 and 31 m, respectively. Similarly, a family of TV curves fitted to each species as a function of DH, TPH, basal area, and age showed that the implicit current annual growth (CAI), given the model form used, peaked between 7 and 9 years. Depending on the stocking and site index considered, the CAI varied; but, in all cases, the CAI of E. dunnii was the highest, followed by E. smithii, and then E. maidenii and E. globulus in descending order. As an example, for a fixed density of 1100 TPH, the peak CAI was almost 48.6 m3|ha|yr for E. dunnii (SI28), approximately 42.1 m3|ha|y for E. smithii (SI31), and 17.8-21.3 m3|ha|yr for E. maidenii and E. globulus (SI23).

CryoSat Long-Term Ocean Data Analysis and Validation: Final Words on GOP Baseline-C

ESA’s Earth explorer mission CryoSat-2 has an ice-monitoring objective, but it has proven to also be a valuable source of observations for measuring impacts of climate change over oceans. In this paper, we report on our long-term ocean data analysis and validation and give our final words on CryoSat-2’s Geophysical Ocean Products (GOP) Baseline-C. The validation is based on a cross comparison with concurrent altimetry and with in situ tide gauges. The highlights of our findings include GOP Baseline-C showing issues with the ionosphere and pole tide correction. The latter gives rise to an east–west pattern in range bias. Between Synthetic Aperture Radar (SAR) and Low-Resolution Mode (LRM), a 1.4 cm jump in range bias is explained by a 0.5 cm jump in sea state bias, which relates to a significant wave height SAR-LRM jump of 10.5 cm. The remaining 0.9 cm is due to a range bias between ascending and descending passes, exhibiting a clear north–south pattern and ascribed to a timing bias of +0.367 ms, affecting both time-tag and elevation. The overall range bias of GOP Baseline-C is established at −2.9 cm, referenced to all calibrated concurrent altimeter missions. The bias drift does not exceed 0.2 mm/yr, leading to the conclusion that GOP Baseline-C is substantially stable and measures up to the altimeter reference missions. This is confirmed by tide gauge comparison with a selected set of 309 PSMSL tide gauges over 2010–2022: we determined a correlation of R = 0.82, a mean standard deviation of σ=5.7 cm (common reference and GIA corrected), and a drift of 0.17 mm/yr. In conclusion, the quality, continuity, and reference of GOP Baseline-C is exceptionally good and stable over time, and no proof of any deterioration or platform aging has been found. Any improvements for the next CryoSat-2 Baselines could come from sea state bias optimization, ionosphere and pole tide correction improvement, and applying a calibrated value for any timing biases.

Joint research in the Baltic Sea by the Atlantic Branch of the VNIRO and the Atlantic Branch of the Shirshov Institute of Oceanology of the RAS

The aim: complex study of the Baltic Sea ecosystem, including the habitat conditions of aquatic biological resources based on the results of joint research by AtlantNIRO and the P.P. Shirshov Institute of Oceanology RAS (Atlantic Branch). Methods used: analysis of long-term data of joint expeditionary research in the open part and coastal zone of the Baltic Sea.Results: owing to the interaction of AtlantNIRO with AB IO RAS in 2018–2022, the continuity of long-term observations has been preserved and the volume of scientific information necessary for assessing the state of the habitat of aquatic biological resources in the Russian sectors of the Baltic Sea has been increased. Modern data on eutrophication and pollution, on distribution of phytoplankton, zooplankton, benthos, as well as the early ontogenetic stages of the main commercial fish have been obtained. The spatial distribution of plankton and benthic communities, including areas favorable for spawning and feeding of fish, has been detailed. A rather high contamination of bottom sediments with radionuclides in certain areas of the Baltic Sea, primarily in the Gulf of Finland, has been found.Practical significance: the implementation of the areas provided by the Agreement between Rosrybolovstvo and RAS, and above all, the participation of AtlantNIRO scientific groups in expeditions in the Baltic Sea in 2018–2022, was a significant addition to the annual research and state monitoring of aquatic biological resources and their habitats conducted by VNIRO in the Baltic Sea.

The Impact of Climate Change on the Water Systems of the Yesil River Basin in Northern Kazakhstan

The geographical location of Kazakhstan, situated in the central part of the Eurasian continent, has played a crucial role in shaping a distinctly continental climate. This positioning has led to Kazakhstan facing a significant challenge in terms of water resource availability. The country’s water resources are highly vulnerable to the dual pressures of climate change and human activities. It is noteworthy that the Yesil River basin is the sole region within Kazakhstan’s borders where water resources are predominantly generated, while all other river basins experience substantial outflows beyond the nation’s boundaries. This research undertaking involves a comprehensive analysis of long-term climatic data collected from meteorological stations located within the confines of the Yesil basin in Northern Kazakhstan. Additionally, the study encompasses the computation of water consumption and annual runoff within this region. Historical meteorological observations spanning from 1961 to 2020 reveal notable trends. Most significantly, a 1.2 °C increase in temperature is observed during the spring season. Winters have also become relatively milder and warmer, particularly towards the end of February, where temperatures have shifted from −16.2 °C in the first 30-year period to −14.6 °C in the second period. These findings underscore the ongoing climatic changes within the region, with significant implications for the management and sustainability of water resources in Kazakhstan.

Statistical analysis of equatorial electrojet responses to the transient changes of solar wind conditions

Introduction: Prior case studies have indicated that changes in solar wind conditions have a significant impact on equatorial ionospheric electrodynamics. However, there have been limited statistical studies on this topic, impairing our understanding of the coupling between solar wind, magnetosphere, and equatorial ionosphere electrodynamics.Methods: In this study, we conducted a superposed epoch analysis of long-term data from the South America equatorial electrojet (EEJ) spanning from 2001 to 2021 examining the responses of the equatorial ionospheric electric field to step-like changes in solar wind velocity, density, dynamic pressure, and interplanetary magnetic field (IMF) Bz.Result: Our study shows that step-like changes in solar wind velocity, density, and dynamic pressure can trigger changes in EEJ within ∼20–40 min. EEJ exhibits the highest sensitivity to variations in solar wind velocity while being relatively less sensitive to changes in dynamic pressure. Furthermore, the response of EEJ shows greater responsiveness to northward IMF Bz compared to southward IMF Bz.Discussion: Our work provides statistical evidence of how changes in solar wind can lead to changes in low-latitude ionospheric EEJ. We inferred that the changes in solar wind conditions cause magnetospheric deformation and changes in magnetic reconnection rates, leading to the fluctuations of the ionospheric electric field and the resultant EEJ variations.

Long-Term Efficacy and Safety of Obecabtagene Autoleucel (obe-cel) in Adult Patients (pts) with Relapsed/Refractory B-cell Acute Lymphoblastic Leukemia ([R/R B-ALL]; Pooled Analysis from ALLCAR19 and FELIX Phase Ib Studies) or Other B-cell Malignancies (ALLCAR19 Extension Study)

Background: Obe-cel is an autologous CD19 chimeric antigen receptor (CAR) T cell product designed to reduce toxicity and improve persistence through a fast off-rate CD19 binding domain. The clinical activity of obe-cel has been explored in adults with R/R B-ALL in a Phase I study (ALLCAR19, NCT02935257; Roddie C et al. J Clin Oncol 2021) and a Phase Ib/II study (FELIX, NCT04404660; Roddie C et al. J Clin Oncol 2023;41[16 Suppl]:7000). Additionally, obe-cel has been tested in pts with R/R B-cell chronic lymphocytic leukemia (B-CLL) and R/R B-cell non-Hodgkin lymphoma (B-NHL) (ALLCAR19 extension; Roddie C et al. Blood 2022;140[1 Suppl]:7452-3). Pts from the ALLCAR19 and FELIX Phase Ib studies are in long-term follow up (≥22 mos), and the ALLCAR19 extension has been recruiting for 3 years. We report an analysis of long-term efficacy and safety data from the ALLCAR19 and FELIX Phase Ib studies, as well as data from the ALLCAR19 extension. Methods: ALLCAR19 is a multicenter, non-randomized, open-label Phase I study in pts aged ≥16 years with B-cell malignancies. ALLCAR19 initially recruited pts with R/R B-ALL but was then amended (extension study) to also include pts with R/R B-CLL and R/R B-NHL. FELIX is a global, single-arm Phase Ib/II study enrolling pts aged ≥18 years with R/R B-ALL.Study designs have been presented previously. Obe-cel was administered as a split dose in pts with B-ALL (target dose 410 × 10 6 CAR T cells) and pts with CLL (target dose 230 × 10 6 CAR T cells), and as a single infusion in pts with B-NHL (target dose 200 × 10 6 CAR T cells); the pt populations in the two studies were similar. Pts with B-ALL from the ALLCAR19 and FELIX Phase Ib studies are being followed long term for disease progression and survival. For this analysis, data in pts with B-ALL from the ALLCAR19 and FELIX Phase Ib studies were pooled. Data in pts with CLL or B-NHL are presented from the ALLCAR19 extension study. Results: Outcomes in pts with R/R B-ALL: Data in pts with B-ALL were pooled (20 pts from ALLCAR19 [data cut-off Jun 26, 2023] and 16 from FELIX Phase Ib [data cut-off Mar 16, 2023]). The median age of the pooled cohort was 41.5 (range 18 to 74) years and pts had received a median of 3 (range 2 to 6) prior lines of treatment. Twenty-nine of the 36 pts (81%) achieved complete remission (CR)/CR with incomplete hematologic recovery post obe-cel infusions, per investigator assessment. The event-free survival rate was 64% at 6 mos and 49% at 12 mos. With a median follow up of 43 (range 19 to 62) mos, 13/36 pts (36%) remain in remission (8 from ALLCAR19; 5 from FELIX Phase Ib). Among these 13 ongoing responders, 2 (15%) had consolidation with allogeneic hematopoietic stem cell transplantation (allo-HSCT). Ten of the 11 ongoing responders (91%) who did not receive allo-HSCT still had detectable CAR T cells at the last follow up. All ongoing remissions were measurable residual disease negative at last available assessment. The estimated 2-, 3- and 4-year overall survival rates were 44%, 39% and 39%, respectively. Outcomes in pts with R/R B-CLL/B-NHL: The extension phase of the ALLCAR19 study enrolled 35 pts with B-CLL or B-NHL, of which 26 (B-CLL n=5; B-NHL n=21) received obe-cel (data cut-off Jun 26, 2023). The median age of this combined cohort was 61 (range 39 to 79) years and pts had received a median of 3 (range 2 to 8) prior lines of treatment. At a median follow up of 24 mos, the overall response rate for this cohort was 92% (n=24), and 58% of responders (n=14) were alive without disease progression at last follow up. Late toxicity: Of the 11 long-term R/R B-ALL responders who had not received consolidation allo-HSCT, 10 have ongoing B-cell aplasia. Of the 14 ongoing responders in the R/R B-CLL/B-NHL cohort, 12 have ongoing B-cell aplasia (<20 B cells/µl). Of note, ongoing B-cell aplasia did not correlate with an increased risk of late serious infection. No other long-term toxicity ascribed to obe-cel was reported. Conclusions: The combined analysis of data from the ALLCAR19 and FELIX Phase Ib studies shows long-term efficacy and safety of obe-cel in pts with R/R B-ALL, with approximately one-third of pts still in remission without consolidative allo-HSCT after a median follow up of >3 years. Durable responses of >2 years were also seen in pts with R/R B-CLL and R/R B-NHL.B-cell aplasia was commonly found in long-term follow up of obe-cel recipients, but without a corresponding rise in serious late infections. Obe-cel can effect durable long-term remissions in B-cell malignancies.

Investigation of NOx and related secondary pollutants at Anand Vihar, one of the most polluted area of Delhi

This research investigates the influence of meteorological parameters on NOx concentrations, particularly NO2, and related secondary & primary pollutants at the Anand Vihar interstate bus terminal in Delhi. Statistical analysis of long-term data (2016–2021) examines seasonal, weekly, and daily variations in NOx and other pollutants. Temperature and relative humidity show a significant impact on NO2 levels. Seasonal patterns reveal lower concentrations during the monsoon season and higher concentrations in winter and post-monsoon periods, except for ozone. The year 2018 stands out as the most polluted for NO2. Vehicular activities directly influence pollution, evident through reduced NO2 and benzene levels during the COVID-19 lockdown. The Planetary Boundary Layer (PBL) height plays a critical role, with lower PBL heights associated with increased NO2 concentrations. Electric Vehicles (EVs) have a positive effect on reducing NO2 pollution. Comparison with a less transportation-impacted site highlights higher NO2 concentrations in areas with heavy vehicular traffic, emphasizing the need for pollution control measures and sustainable transportation. Correlation and regression analyses confirm the interdependence between PM and NO emissions, underscoring the significance of addressing vehicular emissions for effective pollution mitigation. This study provides valuable insights into the intricate relationship between meteorological parameters, vehicular emissions, and NOx concentrations.

Seasonal enhancement of phytoplankton biomass in the southern tropical Indian Ocean: Significance of meteorological and oceanography parameters

The present study focused on understanding the seasonality of the phytoplankton biomass (chlorophyll a) distribution in the oligotrophic, Equatorial, and Southern Tropical Indian Ocean (ESTIO; 0–30°S and 60–90°E). The long-term satellite data analyses (2003–2020) showed a strong seasonality in sea surface temperature (SST), wind, currents, mean sea level anomaly (MSLA), photosynthetically available radiation (PAR), euphotic depth (ZEU) and mixed layer depth (MLD). As a response to the hydrographical changes, the phytoplankton biomass showed noticeable seasonal variation with the highest biomass during the Austral Winter (AW; June–September; avg. 0.11 ± 0.03 mg/m³) and lowest during the Austral Summer (AS; November–February; avg. 0.07 ± 0.03 mg/m). High chlorophyll patches (>0.1 mg/m³) were found between 0°–8°S during the AS and expanded over 0°–18°S during the AW. As multi-year mean chlorophyll a was higher (>0.1 mg/m³) in the northern part of the ESTIO (north of ∼13°S; HCD: high chlorophyll a domain) than the southern side (LCD: low chlorophyll a domain), the study area was divided into two domains and all the variables were analysed. In the HCD, enhancement of chlorophyll a was positively correlated with variables such as wind speed, wind stress, Ekman pumping, stronger northward and westward winds, as well as the presence of cyclonic eddies. These features are likely to stimulate primary production by uplifting the thermocline and enhancing nutrient supply. In the LCD, mixed layer depth also showed a strong positive correlation with elevated chlorophyll a, apparently because it is deep throughout the year (thereby keeping lower biomass) and deepens more strongly in winter than in the HCD. Another contrast with the HCD is that the cyclonic eddies appear to be insufficiently abundant to influence its chlorophyll a. Pearson's multivariable correlation analysis and principle component analysis confirmed the statistical significance of the above parameters on the enhancement of chlorophyll a in the ESTIO.

Vehicle Operation Status Monitoring Based on Distributed Acoustic Sensor

To develop implementation research on distributed optical fiber sensing technology, field tests were conducted on municipal roads and railways using a distributed acoustic sensor (DAS). Data were collected by the DAS during a field test for a long time period (more than 20 min), and we conducted short-term (<10 s) and long-term (≥10 s) analyses on these data separately. In the short-term data analysis, the vehicle type, vehicle length, and working status of the vehicle engine or the compressor were identified. In the long-term data analysis, the traffic flow was monitored, and the running distance, acceleration, speed, and braking distance of the vehicle were obtained. The characteristics of the vehicle operation data obtained in these field tests are important in developing the data processing method of DASs, which will help to promote the implementation of DASs.

The impact of climate change on agricultural land use in Ukraine

The article analyses the impact of climate change on agricultural land use in Ukraine. Given the growing global consumption of food and a growing population, agriculture is becoming a key element of the country’s stability and development. However, climate change, such as rising temperatures, changes in precipitation distribution, and the frequency of extreme weather events, can significantly affect the yields and efficiency of agricultural activities. Global trends in climate change, which is one of the most pressing threats with a long-term negative impact on the population, environment and economy, are investigated. The article highlights the positive (changes in the conditions and timing of harvesting, increased efficiency of fertiliser application, etc.) and negative (deterioration of grain quality, increased number of droughts, etc.) consequences of climate change on agricultural land use. The study includes an analysis of long-term climate data in Ukraine, as well as an assessment of changes in land cover, crop distribution and crop production. The study aims to determine the impact of climate change, in particular temperature rise, changes in precipitation and extreme weather events, on agricultural production and land use in Ukraine. The article analyses data from various sources, including meteorological observations and statistical data using modern geoinformation technologies. Changes in the distribution of crops, crop yields and yields depending on climate change are investigated. The study also assesses the possible consequences of these changes for Ukraine’s food security, economy and environment. The results of the study are important for decision-making in the field of agricultural policy and development of the agricultural sector, ensuring the sustainability of agricultural products and reducing the vulnerability of agriculture to climate change. They can serve as a basis for developing climate change adaptation strategies and maintaining the sustainability of the country’s food security in the face of climate change.

Genomic diversity of Vibrio spp. and metagenomic analysis of pathogens in Florida Gulf coastal waters following Hurricane Ian.

Evidence suggests warming temperatures are associated with the spread of potentially pathogenic Vibrio spp. and the emergence of human disease globally. Following Hurricane Ian, the State of Florida reported a sharp increase in the number of reported Vibrio spp. infections and deaths. Hence, monitoring of pathogens, including vibrios, and environmental parameters influencing their occurrence is critical to public health. Here, DNA sequencing was used to investigate the genomic diversity of Vibrio parahaemolyticus and Vibrio vulnificus, both potential human pathogens, in Florida coastal waters post Hurricane Ian, in October 2022. Additionally, the microbial community of water samples was profiled to detect the presence of Vibrio spp. and other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Long-term environmental data analysis showed changes in environmental parameters during and after Ian were optimal for the growth of Vibrio spp. and related pathogens. Collectively, results will be used to develop predictive risk models during climate change.

Climatic-Geomorphological Investigation of the World's Wettest Areas around Cherrapunji and Mawsynram, Meghalaya (India)

This research paper comprehensively examines the climate and geomorphological features of Cherrapunji and Mawsynram, aiming to understand the factors and environmental implications of their extreme precipitation. The study investigates climatic patterns, identifies geomorphological characteristics, and explores the factors influencing the occurrence of heavy rainfall in these areas, and displays unique rainfall patterns with high precipitation levels and notable spatio-temporal variation influenced by topographic interactions. Trend analysis reveals stable rainfall conditions over the past 122 years. The shift of the world's wettest place from Cherrapunji to Mawsynram in recent decades have been attributed to various factors such as geographical location, geomorphology-local topography, LULC-human influence, rain shadow effect, and orographic lifting effects. Cherrapunji recorded maximum rainfall of 24.55 thousand mm, while Mawsynram received 26 thousand mm of rainfall in the last century. The analysis of long-term rainfall data indicates distinct dry and wet seasons, with recent trends (2000-2020) suggesting a decline in rainfall for both locations. Furthermore, extreme value analysis techniques are employed to estimate maximum rainfall for different return periods, offering insights into extreme rainfall events. The return period of one day's highest rainfall of 1340.82 mm is about 100 years. The findings contribute to our understanding of climate change impacts, support sustainable development practices, and inform strategies for water resource management and erosion mitigation in similar geographic contexts. This research enhances our knowledge of these unique regions and their significance within the broader context of global climate systems.

Habitats, distribution and abundance of the Amur lynx, Lynx lynx stroganovi, and the Far Eastern leopard cat, Prionailurus bengalensis euptilura, in the Lazovsky Nature Reserve and the Zov Tigra National Park (Primorsky Krai, Russian Far East)

Analysis of long-term data of the population status of large predators is the most important environmental monitoring task in all protected areas. The aim of the study was to investigate the distribution and abundance of two cohabiting feline species in protected areas in southern Sikhote- Alin: the Lazovsky Nature Reserve and the Zov Tigra National Park. GIS maps of the distribution of the lynx and the Far Eastern leopard cat in the reserve during the 20th and 21st centuries and the fi nal maps for 60 years are given. It has been established that the lynx inhabits the reserve and the national park quite evenly, while the leopard cat prefers habitats closer to the coast of the Sea of Japan, and at a distance from the coast, in river valleys. The results of winter route counts of these two species over 60 last years are analyzed. The camera trap data showing the distribution and the relative abundance index of the lynx and the Far Eastern forest cat are summarized for 2015–2021 both in the reserve and in the national park in general, and separately in the rocky forest habitats. The average density of the lynx in the reserve is estimated at 1.9 individuals per 100 km2 and 3.3 individuals per 100 km2 in the national park. The density of the cat in the reserve and the park is the same and equals to 20 individuals per 100 km2.

The national health service England diabetes prevention program—A narrative review

Abstract The National Health Service (NHS) Diabetes Prevention Program (DPP) is a comprehensive initiative in the United Kingdom aimed at preventing type 2 diabetes (T2DM) among individuals with prediabetes. The NHS DPP emphasizes early intervention and underscores the potential for preventing or delaying T2DM. Currently, it operates since 2015 at 44 geographically defined sites, serving an estimated annual volume of 200,000 patients. This narrative review evaluates the NHS DPP. A literature search, adhering to the Participants, Concept, and Context review framework, was conducted in various databases and sources, limited to the period from 2015 to 2023. Data were categorized based on publication date, study type, program indicators, and focus areas, forming the framework for the review discussion. The review search yielded a total of 18,357 articles, of which 45 were included, with a predominant focus on program effectiveness. The NHS DPP demonstrates strengths in risk reduction and cost-effectiveness but faces challenges related to fidelity and the needs of diverse populations. The review highlights the importance of evidence-based service delivery, stakeholder engagement, and digital innovation in future DPP programs. While a centralized approach provides structure, local flexibility is essential to cater to population-specific needs. This review underscores the importance of strategic planning, political support, partnerships, and robust data collection in DPP implementation. It recommends targeting high-risk individuals, intensive lifestyle interventions, and embracing digital solutions for program delivery. Ongoing research, including analysis of long-term data and user perspectives, will further enhance the evidence base for DPP programs worldwide.

Development and internal validation of a prediction model for long-term opioid use-an analysis of insurance claims data.

In the United States, a public-health crisis of opioid overuse has been observed, and in Europe, prescriptions of opioids are strongly increasing over time. The objective was to develop and validate a multivariable prognostic model to be used at the beginning of an opioid prescription episode, aiming to identify individual patients at high risk for long-term opioid use based on routinely collected data. Predictors including demographics, comorbid diseases, comedication, morphine dose at episode initiation, and prescription practice were collected. The primary outcome was long-term opioid use, defined as opioid use of either >90 days duration and ≥10 claims or >120 days, independent of the number of claims. Traditional generalized linear statistical regression models and machine learning approaches were applied. The area under the curve, calibration plots, and the scaled Brier score assessed model performance. More than four hundred thousand opioid episodes were included. The final risk prediction model had an area under the curve of 0.927 (95% confidence interval 0.924-0.931) in the validation set, and this model had a scaled Brier score of 48.5%. Using a threshold of 10% predicted probability to identify patients at high risk, the overall accuracy of this risk prediction model was 81.6% (95% confidence interval 81.2% to 82.0%). Our study demonstrated that long-term opioid use can be predicted at the initiation of an opioid prescription episode, with satisfactory accuracy using data routinely collected at a large health insurance company. Traditional statistical methods resulted in higher discriminative ability and similarly good calibration as compared with machine learning approaches.

Prediction of Sensor Data in a Greenhouse for Cultivation of Paprika Plants Using a Stacking Ensemble for Smart Farms

Ensuring food security has become of paramount importance due to the rising global population. In particular, the agriculture sector in South Korea faces several challenges such as an aging farming population and a decline in the labor force. These issues have led to the recognition of smart farms as a potential solution. In South Korea, the smart farm is divided into three generations. The first generation primarily concentrates on monitoring and controlling precise cultivation environments by leveraging information and communication technologies (ICT). This is aimed at enhancing convenience for farmers. Moving on to the second generation, it takes advantage of big data and artificial intelligence (AI) to achieve improved productivity. This is achieved through precise cultivation management and automated control of various farming processes. The most advanced level is the 3rd generation, which represents an intelligent robotic farm. In this stage, the entire farming process is autonomously managed without the need for human intervention. This is made possible through energy management systems and the use of robots for various farm operations. However, in the current Korean context, the adoption of smart farms is primarily limited to the first generation, resulting in the limited utilization of advanced technologies such as AI, big data, and cloud computing. Therefore, this research aims to develop the second generation of smart farms within the first generation smart farm environment. To accomplish this, data was collected from nine sensors spanning the period between 20 June to 30 September. Following that, we conducted kernel density estimation analysis, data analysis, and correlation heatmap analysis based on the collected data. Subsequently, we utilized LSTM, BI-LSTM, and GRU as base models to construct a stacking ensemble model. To assess the performance of the proposed model based on the analyzed results, we utilized LSTM, BI-LSTM, and GRU as the existing models. As a result, the stacking ensemble model outperformed LSTM, BI-LSTM, and GRU in all performance metrics for predicting one of the sensor data variables, air temperature. However, this study collected nine sensor data over a relatively short period of three months. Therefore, there is a limitation in terms of considering the long-term data collection and analysis that accounts for the unique seasonal characteristics of Korea. Additionally, the challenge of including various environmental factors influencing crops beyond the nine sensors and conducting experiments in diverse cultivation environments with different crops for model generalization remains. In the future, we plan to address these limitations by extending the data collection period, acquiring diverse additional sensor data, and conducting further research that considers various environmental variables.

Long-Term Tropospheric Ozone Data Analysis 1997–2019 at Giordan Lighthouse, Gozo, Malta

Long-term data analysis of the hourly ozone volume fractions in the middle of the Mediterranean Seawas carried out covering a period of 22 years. It was noticed that the amount of ozone during this period very rarely exceeded the recommended upper limit value of 80 ppb and that the amount of tropospheric ozone in the area is rather low. Fourier data analysis shows the presence of only a seasonal cycle in ozone concentrations. Statistical analysis of the data is showing a slightly negative trend in ozone concentrations of −0.46 ± 0.08 ppb/year for average values and a slightly higher negative trend of −0.54 ± 0.11 ppb/year for the 95th percentile values. These results obtained through simple linear regression were confirmed using the more appropriate Mann–Kendall test. The possible quadratic trend was not observed for the whole series of data. Air mass trajectories were calculated for those days in the year with the highest pollution, indicating that during those days horizontal air transfer, in most cases, brings the air mass from the North and from Sicily in Southern Italy.