Analysis Of Impact Research Articles

Enhancing Kubernetes Observability: A Synthetic Testing Approach for Improved Impact Analysis

Kubernetes has become the cornerstone for deploying and managing containerized applications. Its dynamic and distributed nature, while offering scalability and resilience, introduces significant complexities in observability, monitoring, and impact analysis. Traditional monitoring solutions often fall short in providing visibility into the functionalities affected by interruptions in the Kubernetes platform, leading to prolonged downtime and impact analysis. This paper investigates the challenges associated with achieving effective observability for Kubernetes environments due to their distributed architecture. We highlight the limitations of relying solely on conventional metrics, logs, and events to understand the impact of system interruptions. To address these challenges, we propose an integrated approach that combines synthetic testing with existing observability tools. By executing synthetic transactions and simulating user interactions with the Kubernetes platform across various use cases, this method proactively evaluates the system's performance and detects issues that passive monitoring might overlook. This comprehensive view enables faster identification of impacted functionalities. Through practical experiments, we demonstrate how incorporating synthetic testing improves the detection of service degradations and supports more effective troubleshooting strategies.

Comparative analysis of Environmental Impact of Each Building Material using Life Cycle assessment(LCA)_Focusing on Types of Construction Wood and Concrete

Objectives:The construction sector is a very important sector in achieving carbon neutrality. In modern society, construction has grown rapidly, and basic materials such as cement and concrete have contributed greatly to the development of construction. These materials emit a large amount of global warming substances during the raw material extraction and production process, causing serious environmental pollution. In order to establish a carbon reduction strategy to slow down global warming, we compared the environmental impacts of each building material and showed the current situation.Methods:The LCI DB for the construction materials used in the two-story wooden house (Building area 128.67m2, total floor area 235.73m2) located in the National Institute of Forest Science was extracted. Then, the data for LCA was processed and performed through the life cycle assessment methodology for the resources consumed from raw material collection, material production, and transportation stages. Then, based on the environmental performance labeling impact assessment method, a study was conducted on the environmental impact categories of the actual construction materials used and comparisons with other construction materials.Results and Discussion:When analyzing the environmental impact of concrete [25-21-120] and construction wood used in wooden house, the environmental impact was found to be 98.79% higher on average in the pre-manufacturing stage than in the manufacturing stage, and construction wood was confirmed to have a reduction effect of 62.21 kg CO2-eq per 1 m3 compared to concrete. Through the scenario, there was a carbon reduction effect of up to 36% when the entire area was replaced with wood. In addition, the environmental impact by major construction material was quantitatively quantified.Conclusion:When building a wooden house, reducing the amount of concrete equivalent to the amount of wood is expected to have a greenhouse gas reduction effect, so it is time to activate construction wood from the production process to the post-production stage to replace concrete. In addition, follow-up research on the development of construction technology with low environmental impact during construction should be continued. This study is expected to be used as a useful indicator for R&D and policies in the construction field in the future, as it quantitatively quantified the amount of CO2 reduction per m3 when replacing concrete with construction wood and numerically compared and analyzed six major environmental impact categories by major construction materials.

Aspects of Developing an Innovative, Energy-Efficient, Low-Emission Co-Generator

Purpose. To develop a model and construct an experimental, innovative co-generator with high energy efficiency and low emissions. This involves developing a system that can efficiently generate both electricity and heat simultaneously while minimizing emissions, contributing to sustainability efforts, and addressing energy demands in a more environmentally friendly manner. Methodology. To achieve the goal, a system approach is utilized, enabling the selection of modelling types for the development of an experimental, cutting-edge co-generation system capable of efficiently producing both electricity and heat with superior energy efficiency and minimal emissions. For this purpose, the following steps were completed: processing and summarizing available literature and patent sources, analysing scientific and technical papers on the selection and application of modelling types in co-generation systems, and considering principles and individual approaches to input data formation for mathematical modelling. This process enables the selection of a mechanism and the creation of simulation models for effective energy production at various enterprises. Findings. Assessment is performed of the energy efficiency of the co-generator system under various operating conditions, comparing it with existing conventional methods of electricity and heat generation. Results are presented of performance testing to determine the system’s capability to simultaneously generate electricity and heat efficiently, considering factors such as output stability, load responsiveness, and overall reliability. Identification and evaluation are performed of innovative technologies and methodologies integrated into the co-generator design, highlighting their effectiveness in enhancing energy efficiency and reducing emissions. Insights into any operational challenges encountered during the construction, testing, and optimization phases, along with proposed solutions or improvements to solve these problems. The analysis of the overall environmental impact of deploying the co-generator showed potential benefits in terms of reduced greenhouse gas emissions and local air quality improvement. Originality. Using a combination of scientific approaches encompassing physics and heat transfer engineering, in accordance with the first law of thermodynamics, such as the conservation laws of energy and entropy, and principles of heat exchange employed to transfer heat between different mediums, gas kinetics have yielded values for the energy transformation coefficient, indicating qualitative characteristics of fuel thermolysis and power generation as the final product. Practical value. The results provide for developing a comprehensive model of an advanced co-generation system capable of efficiently producing both electricity and heat with superior energy efficiency and minimal emissions. It also entails determining the types of models for mathematical modelling at all management levels and establishing a new method for input data formation for both technologies and their subsystems, incorporating additional technological implementations.

Digital tools and AI: Using technology to monitor carbon emissions and waste at each stage of the supply chain, enabling real-time adjustments for sustainability improvements

The increasing urgency of addressing climate change has necessitated the adoption of innovative technologies to monitor and mitigate carbon emissions and waste throughout the supply chain. This paper explores the role of digital tools and artificial intelligence (AI) in enabling real-time tracking and analysis of environmental impact at each stage of the supply chain. By integrating IoT devices, big data analytics, and machine learning algorithms, organizations can gain comprehensive insights into their operations, identifying key areas where emissions and waste can be reduced. The use of AI-powered predictive analytics allows companies to model various scenarios, optimizing resource allocation and operational efficiency while minimizing environmental footprints. This research also highlights successful case studies where companies have implemented these technologies, resulting in significant sustainability improvements and cost savings. Furthermore, the paper discusses the challenges associated with data integration, system interoperability, and the need for industry-wide standards to ensure effective monitoring and reporting. The importance of stakeholder collaboration is emphasized, as engaging suppliers, customers, and regulatory bodies is essential for achieving comprehensive sustainability goals. Ultimately, this paper advocates for the strategic implementation of digital tools and AI as pivotal enablers of sustainable supply chain management, providing organizations with the capability to adapt to changing environmental regulations and consumer expectations. By leveraging technology to monitor and reduce carbon emissions and waste, businesses can enhance their competitiveness while contributing to a more sustainable future.

HRMAS-NMR-Based Metabolomics Approach to Discover Key Differences in Cow and Goat Milk Yoghurt Metabolomes

This study highlights the differences in the metabolomes of cow milk yoghurt (CY) and goat milk yoghurt (GY) using a nuclear magnetic resonance (NMR)-based metabolomic approach. The 1H HRMAS-NMR spectrum displayed 21 metabolites comprising organic acids, sugars, amino acids, amino acid derivatives and phospholipids. The orthogonal partial least squares discriminant analysis model clearly separated CY and GY groups, implying differences in metabolite composition. The corresponding Variable Importance in Projection (VIP) plot revealed that choline, sn-glycero-3-phosphocholine, O-phosphocholine, fucose, citrate, sucrose, glucose and lactose mainly contributed to the group separation (VIP > 1). Hierarchical cluster analysis further confirmed the metabolome similarities and differences between CY and GY. Additionally, 12 significantly differential metabolites (with a fold change > 1.5 and p-value < 0.05) were identified, with 1 downregulated and 11 upregulated. Pathway impact analysis revealed the correlation of significant metabolites with starch and sucrose metabolism, galactose metabolism, and the citrate cycle. Furthermore, receiver operating characteristic curve analysis identified eight metabolites (choline, sn-glycero-3-phosphocholine, fucose, O-phosphocholine, glucose, citrate, 2-oxoglutarate, lactose and sucrose) as candidate biomarkers. This study represents the first utilization of HRMAS-NMR to analyze the metabolomic profiles of yoghurt made from cow and goat milk. In conclusion, these findings provide preliminary information on how NMR-based metabolomics can discriminate the metabolomes of yoghurt prepared from the milk of two different animals, which may be valuable for authenticity and adulteration assessments.

Co‐Generation of Electricity and Chemicals From Methane Using Direct Internal Reforming Solid Oxide Fuel Cells

AbstractThe co‐generation of electricity and chemicals via direct internal reforming solid oxide fuel cells (DIR‐SOFCs) offers a promising route to carbon‐neutral energy solutions. However, challenges such as inadequate performance and fast degradation, particularly when using hydrocarbon fuels like CH4, hinder the deployment of DIR‐SOFC technology. This study addresses three critical issues: the effect of fuel composition on electrochemical properties, the mechanisms and microstructural impacts of carbon deposition, and the practical feasibility of DIR‐SOFCs at an industrial scale. First, comprehensive polarization and impedance analyses are conducted to assess the impact of varying fuel compositions—specifically p(CH4) and p(H2O)—on DIR‐SOFC performance. Second, advanced morphological characterization, machine learning‐assisted 3D reconstructions, and numerical simulations are utilized to reveal carbon deposition behavior and its effects on anode microstructures. Quantitative analysis of carbon's impact on pores, Ni, and YSZ phases provides novel insights into carbon‐induced microstructural changes. Finally, the industrial‐scale co‐generation of electricity and chemicals is validated, emphasizing both energy efficiency and operational stability. This study enhances the understanding of electrochemical and microstructural mechanisms, offering crucial insights for optimizing DIR‐SOFC design and operation, and laying the groundwork for their broader adoption in a carbon‐neutral future.

A Brief Analysis of The Economic Impact of Major Sporting Event on The Host City

The study of sports competition is a crucial field of research that comprises subjects such as economics, sociology, and other related fields. Scholars in this endeavour to tackle paramount questions with profound practical ramifications, which includes exploring the qualities and movements that underlie sports competition. Exploring the factors that influence sports competition outcomes and the ways in which these factors shape diverse competitive landscapes is a valuable and significant research area. This research examines the economic impact of major sports events on the host city. The aim of this research is to provide a thorough analysis of the economic consequences that follow from hosting such events. Through a synthesis of existing literature and empirical analysis, the research investigates the various facets of the economic impact of major sports events, encompassing employment, local commerce, tourism, and infrastructure development. The results indicate that such events can yield significant economic gains, ultimately promoting regional growth and development. However, a thorough analysis of the available evidence emphasises the importance of adopting a multidimensional viewpoint that takes into the account the social, cultural, and environmental ramifications. Despite the intricacies and constraints in accurately measuring the magnitude of the effects, this research ascertains that organising major sports events can generate favourable economic consequences for the locality. The results emphasise the significances of thorough analyses and additional research to enhance our comprehension of the economic implications of hosting such events.

Impact analysis of ethylene antagonists, storage environments and storage periods on postharvest physiology of ‘Cripps Pink’ apple fruit

Ethylene management and manipulating storage conditions are crucial elements that impact the postharvest quality of apple fruit. This study demonstrated the optimal approach for preserving the quality of ‘Cripps Pink’ apple fruit during storage by minimizing ethylene production and action, respiration rate, physiological loss of weight (PLW), and textural changes. Ethylene antagonist treatment had the greatest impact on suppressing ethylene and respiration peaks, while storage duration notably influenced fruit firmness and PLW. Fumigation treatment with 1-methylcyclopropene (MCP), 1H-cyclopropabenzene (BC), and 1H-cyclopropa[b]naphthalene (NC) effectively reduced ethylene and respiration peaks. Storage conditions (cold, controlled atmosphere, and photocatalytic oxidation) affected ethylene production and respiration, with ozonized storage showing higher rates. Prolonged storage led to increased PLW, ethylene production, and decreased fruit firmness. The most effective treatment combination for ethylene action antagonism and maintaining fruit quality was MCP treatment in ozonized cold storage for 120 d.

Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model

Abstract Background Although statistical models have been commonly used to identify patients at risk of cardiovascular disease (CVD) for preventive therapy, these models tend to over-recommend therapy [1]. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all individuals, as modelling in this context is currently inadequate. Aims To develop and validate a novel deep learning model, the Transformer-based Risk assessment survival model (TRisk) model, for prediction of 10-year risk of CVD in both the general population and individuals with diabetes. Methods An open cohort of 3 million adults aged 25 to 84 years free of CVD and statin use was identified using linked primary and secondary electronic health records from 291 and 98 general practices in England, which were used for TRisk model development and external validation, respectively. Comparison against two commonly recommended statistical models (QRISK3 [2] and SCORE2 [3]) and a deep learning derivation of QRISK3 was conducted. Additional analyses compared discriminatory performance in other age groups, by sex, and across categories of socioeconomic status. Similar analyses were conducted in a subgroup of 59 thousand patients with diabetes. Results TRisk demonstrated superior discrimination (C index in the general population: 0.910; 95% confidence interval [CI]: 0.906 to 0.913). TRisk’s performance was found to be less sensitive to population age range than the benchmark models and outperformed other models also in all stratified analyses. All models were overall well-calibrated. In decision curve analyses, TRisk demonstrated greater net benefit than benchmark models across the range of relevant thresholds. In impact analyses, at the widely recommended 10% risk threshold and the higher 15% threshold for the general population, TRisk reduced both the total number of patients classified at high risk (by 27% and 39% respectively) and the number of false negatives as compared with currently recommended strategies. TRisk similarly outperformed other models in patients with diabetes. Compared with the widely recommended treat-all policy approach for patients with diabetes, TRisk at a 10% risk threshold would lead to deselection of 24% of individuals with a small fraction of false negatives (0.2% of cohort). Conclusion TRisk enabled a more targeted selection of individuals at risk of CVD compared to benchmark statistical and deep learning models, in both the general population and patients with diabetes. Incorporation of TRisk into routine clinical care would allow a reduction in the number of treatment-eligible patients in general population and subgroup with diabetes by approximately one-third and one-quarter respectively while preventing at least as many events as with currently adopted approaches.Models' discrimination on both cohortsImpact analyses for both cohorts

From crisis to chronic: a comparative analysis of Long COVID impact in Italy and the United States

Abstract Background The aftermath of COVID-19 is bringing about socio-economic consequences, such as diminished quality of life (QoL), decreased productivity, and considerable healthcare expenditures. Despite the emerging acknowledgment of a clinical syndrome following COVID-19 disease, known as Long COVID (LC), notable research gaps persist. This study aims to assess the epidemiological and economic impact of LC in both Italy and the United States (US). Methods This ecological analysis comprises epidemiologic and economic data from 2020 to January 2023, regarding working-age individuals (18-64 years). An Italy/US incidence rate ratio (eIRR) was estimated to compute LC cases in each country. The product between the value of a year spent in good health, individual Quality-Adjusted Life Year (QALY) loss, and LC cases yielded total lost QoL. Temporary productivity loss (TPL) was calculated through the Human Capital Approach. All estimates were deemed significant at a p-value less than 0.05. Results Up to date, the eIRR was 0.78 [0.43 - 0.91] implying 22% fewer LC cases in Italy with respect to the US. Total LC cases were 11 [8.7 - 13.5] and 1.8 [0.9 - 2.1] million in the US and Italy, respectively. The relative QALYs loss was 1.5 [0.8 - 3.1] and 0.2 [0.1 - 0.9] million. Reduced QoL cost roughly $189 [164 - 214] billion in the US and €6 [1 - 11] billion in Italy. TPL amounted to $37 [21 - 57] billion in the US and €2.8 [1.2 - 7.4] billion in Italy. Americans aged 30-39 years showed the largest income loss (26.6%), while in Italy it occurred in the age group 50-64 years (38%). Conclusions LC places a substantial burden in Italy and the US, impacting both the productivity of the working-age population and influencing the younger generation, who constitute the future workforce. Our study underpins the necessity for comprehensive health policies, programs, and interventions alongside effective tertiary prevention strategies to tackle the challenges posed by LC. Key messages • According to this analysis, both countries show significant productivity and quality-adjusted life years loss due to LC among working age individuals. • Steering the formulation of evidence-based sounding health policies to address LC burden, among working age individuals, both in Italy and in the US.

Effects of Foot Orthoses and Kinesio Tape on Spatiotemporal and Kinetic Gait Parameters during Running in Individuals with Flatfoot

Flatfoot is a common condition that influences gait and can cause discomfort for patients under higher loaded condition, such as running. Identifying the most effective treatment necessitates a comprehensive analysis of each therapy's impact on gait. Our research delved into the effects of Foot Orthosis and Kinesio Tape on the spatiotemporal and kinetic aspects of gait in individuals with flatfoot during running. Twenty female rearfoot strike runners with flatfoot participated in the running tasks at 3.3±5% m/s on the Zebris Medical GmbH treadmill. Gait data were collected under three conditions: shoe (A), shoe with Foot Orthoses (B), and shoe with Kinesio Tape (C). A one-way repeated measures ANOVA was employed to analyzed the gait parameters during the stance phase. Under conditions B and C, the foot rotation angle significantly decreased compared to condition A. Additionally, in condition B, it was significantly lower than in condition C. Under conditions B and C, the maximum force and pressure of forefoot significantly increased, whereas it in the midfoot significantly decreased, relative to condition A. Foot Orthosis and Kinesio Tape both effectively diminished peak midfoot pressure during running. Additionally, Foot Orthosis surpassed Kinesio Tape in enhancing foot stability and function throughout the running. These findings offered valuable insights for the selection of intervention measures for patients with flat feet during running.

Physiological, biochemical, and comparative genome analysis of salt and drought stress impact on date palm (Phoenix dactylifera L.): tolerance mechanism and management

Physiological, biochemical, and comparative genome analysis of salt and drought stress impact on date palm (Phoenix dactylifera L.): tolerance mechanism and management

Risk prediction models for incident heart failure: a systematic review and meta-analysis

Abstract Background Heart failure (HF) risk prediction models combine multivariable patient data to estimate an individual's risk of developing HF. By detecting at-risk and early-stage patients, models may facilitate earlier intervention to prevent or delay HF development. Previous systematic reviews were unable to recommend any existing prediction models for clinical use due to insufficient evidence and lack of guidelines on appraising study quality at their time of publication. Purpose To summarize the performance of risk prediction models for incident HF and identify models for further validation and potential clinical use. Methods We searched MEDLINE and EMBASE in June 2021 for English-language studies developing or validating HF risk prediction models. Studies were also retrieved from two previous systematic reviews. We narratively summarized model characteristics (e.g. model type, predictors used, prediction horizon) and study methodology (e.g. validation methods). Performance was assessed among all models validated in ≥ 1 cohort. For all models validated in ≥ 2 cohorts, we pooled discrimination measures using random-effects meta-analyses. Calibration was descriptively summarized based on individual study results from statistical tests and graph digitization of calibration plots. Study quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Of 18,937 publications screened, 41 studies consisting of 120 prediction models were included. Twenty models were both derived and validated, 99 only derived, and 1 only validated. Risk of bias was rated as high in nearly all (94.7%) PROBAST assessments, mostly attributable to issues with analysis. Among 21 models validated in ≥ 1 cohort, most had moderate (61.9%, C-statistic 0.7 to <0.8) or high (23.8%, C-statistic 0.8 to <0.9) discrimination. In patients with low predicted risk (<10%), the calibration was adequate. Nine (42.9%) of these 21 models were presented as web-based calculators and five (23.8%) as points-based risk scores. Based on performance, number of validation cohorts, study risk of bias, and user friendliness, the Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study of Atherosclerosis (MESA), Pooled Cohort equations to Prevent Heart Failure (PCP-HF), and Health ABC models emerged as the most promising risk scores for clinical practice. Conclusions Given their acceptable performance but high risk of bias, future studies should focus on the external validation of these models in studies of high methodological rigor. Models should be validated in a greater diversity of patient populations, particularly with respect to race. Impact analyses assessing how the clinical implementation of these models affects patient outcomes are also required prior to their routine use. Once validated, these models may help guide clinical decision making to prevent the onset of HF with early, aggressive risk factor modification.Discrimination in 21 validated modelsCharacteristics of recommended models

Environmental impact analysis of ajmaline challenge protocols: a comparative assessment of carbon footprint reduction in Brugada Syndrome diagnosis

Abstract Background Climate change is an urgent public health crisis that significantly impacts disease development and health outcomes. The 2022 Lancet Countdown Report revealed that healthcare contributes with 5.2% of global emissions, prompting a call for environmentally conscious approaches. In this context, we explored the environmental impact of different ajmaline challenge protocols, aiming to contribute to a more sustainable medical landscape. Purpose This study aims to compare the carbon footprint of two ajmaline challenge protocols commonly used in diagnosing Brugada syndrome, an inherited cardiac disorder associated with sudden cardiac death. Methods We performed a retrospective and observational analysis of adult patients (pts) who underwent ajmaline testing between March 2020 and June 2023 in two hospitals. In Group A (infusion protocol), 1mg/kg of ajmaline diluted in 50 mL of 5% glucose solution was infused in 10 minutes, up to the target dose, until a positive result or termination criteria ensued. In Group B (bolus protocol), 10 mg of ajmaline were administered every 2 minutes, again up to the target dose of 1mg/kg (maximum of 100 mg) or test interruption was indicated. Greenhouse gas emissions resulting from material processing were estimated based on life cycle analyses of each product's composition. Material consumption, waste production, and direct measurement of weights were considered. Results A total of 101 pts were included, 49 pts in group A with a mean age of 47.3±14.3 years old and 52 patients in group B with a mean age of 43.9±16.6 years-old. No complications were observed in either group. Test performance was not evaluated, since both protocols have been previously validated. Group A, utilizing the infusion method, exhibited a higher environmental impact, producing 18.19 kg CO2eq, compared to 6.23 kg CO2eq (p<0.001) in Group B (bolus protocol). Group B not only demonstrated a 66% reduction in CO2eq emissions but also used significantly less plastic (16g vs 102g per test) and glass (1.58 kg vs 1,79Kg), emphasizing its environmental superiority. Conclusions Shifting from infusion to bolus in ajmaline challenge protocols results in a substantial reduction in carbon footprint. Our findings emphasize the potential impact of adopting more dematerialized approaches in cardiac testing, urging healthcare professionals to embrace sustainable practices. Despite the study's limited sample size, the global adoption of such environmentally friendly strategies promises significant positive effects on healthcare's environmental footprint.

Utilising date palm fibres as a permeable reactive barrier to remove methylene blue dye from groundwater: a batch and continuous adsorption study.

This study aimed to utilise cheap and abundantly available date palm fibre (DPF) wastes for the remediation of methylene blue (MLB) dye-contaminated groundwater. The DPF adsorbents were first prepared, followed by various characterisation analyses, including surface morphology, functional groups, and material structure. Subsequently, the DPF adsorbents were applied in the batch and continuous adsorption studies to assess the MLB dye removal from aqueous environments. The batch adsorption study achieved 98% maximum removal efficiency with a contact time, adsorbents dosage, initial pH, temperature, particle size, initial dye concentration, and agitation speed of 105min, 3g/L, 7.0, 45°C, 0.075mm, 50mg/L, and 150rpm, respectively. Langmuir was the best-fitted isotherm model depending on a higher correlation coefficient (R2 = 0.985), with a maximum monolayer dye adsorption capacity (qmax) of 54.204mg/g. Additionally, the second order was the best-fitted kinetic model (R2 = 0.990), indicating that MLB dye was removed through chemisorption. Besides, the positive enthalpy change (ΔH°) and negative Gibb's free energy (ΔG°) values verified the endothermic process and spontaneous adsorption. According to the impact analysis of initial dye concentrations and flow rates on the permeable reactive barrier (PRB) performance in the continuous adsorption study using the Thomas, Belter, and Yan models, the experimental results and predicted breakthrough curves reflected an excellent agreement (R2 ≥ 0.8767) and a sum of squared errors (SSE) ≤ 0.4834. In short, the results demonstrated DPF as an effective adsorbent material in PRB technology.

Salt Tolerance in Sugar Beet: From Impact Analysis to Adaptive Mechanisms and Future Research

The continuous global escalation of soil salinization areas presents severe challenges to the stability and growth of agricultural development across the world. In-depth research on sugar beet (Beta vulgaris L.), an important economic and sugar crop with salt tolerance characteristics, is crucial for to determine its salt-tolerance mechanisms, which has important practical implications for production. This review summarizes the multifaceted effects of salt stress on sugar beet, ranging from individual plant responses to cellular and molecular adaptations. Sugar beet exhibits robust salt-tolerance mechanisms, including osmotic regulation, ion balance management, and the compartmentalization of toxic ions. Omics technologies, including genomics, transcriptomics, proteomics, post-translational modification omics and metabolomics, have played crucial roles in elucidating these mechanisms. Key genes and pathways involved in salt tolerance in sugar beet have been identified, paving the way for targeted breeding strategies and biotechnological advancements. Understanding these mechanisms not only enhances our knowledge of sugar beet’s adaptation strategies but also provides insights for improving salt tolerance in other crops. Future studies should focus on analyzing gene expression changes in sugar beet under salt stress to gain insight into the molecular aspects of its salt-tolerance mechanisms. Meanwhile, the effects of different environmental conditions on sugar beet adaptation strategies should also be investigated to improve their growth potential in salinized soils.

Budget Impact Analysis of Implementing Antenatal Care Recommendations for Positive Pregnancy Outcomes at Public Primary Facilities in Tanzania.

Tanzania recently changed its antenatal care (ANC) guidelines to reduce perinatal mortality and improve the experience of pregnancy care. The new guideline recommends increasing the number of ANC visits from four to eight and introducing one routine ultrasound scan, among other recommendations. We estimated the budget impact of implementing the new guideline compared to the previous focused ANC guideline at public dispensaries and health centers. In a dynamic Markov model, we prospectively followed annual cohorts of between 2.3 and 2.6 million pregnant women who will be attending ANC at dispensaries and health centers for 5years. We allowed a population of pregnant women into the model every year and women exit the model at delivery. We calculated the cost of medicines, medical supplies, and laboratory supplies required to produce services from a public health system perspective. Our model neither estimated condition-related costs nor health effects. The budget impact was calculated as the difference in the estimated costs between the two guidelines. We conducted scenario analyses to explore attending more visits and different assumptions to calculate the target population. We estimated that implementing the new ANC guideline would have a cumulative budget impact of around US$154 million over 5years. The budget required will increase from US$137 million under the focused ANC guideline to US$291 million under the new guideline. Laboratory supplies will consume 47% of the estimated budget under the new guideline. We expect the annual budget impact to be US$38 million in the first year of implementation and US$32 million in the fifth year. We assumed that by the fifth year, 82% of all pregnant women would have had four or more visits. The budget impact would increase to US$214 million, with the proportion of pregnant women attending four or more ANC visits reaching 90% within 5years. Scaling up the implementation of the new ANC guideline at public dispensaries and health centers may substantially increase the supplies required to produce ANC services, particularly laboratory supplies. Studies on the health impact of the new guideline are warranted to estimate the value for money.

The Jurassic Park and Ata Modo in Eastern Indonesia

Famous for its distinctive Komodo dragons (Varanus Komodoensis) and features home to underwater ecosystems, the Komodo National Park (KNP) has become a prominent and favorite destination of visitors locally and globally in recent years. Situated in Labuan Bajo, East Nusa Tenggara Province, Indonesia, KNP has three prime islands: Rinca, Komodo, and Padar. The infrastructure development in KNP formulated in 2020 by the central government of Indonesia, including establishing offices, photograph areas, clinics, warehouses, public spaces, and tour guides' offices, raises controversy about this decision. While the government of Indonesia persuaded citizens that this erection was a conservation effort for protection, preservation, and utilization, UNESCO refused this plan by enacting a refusal document and assessing whether the development could affect the property's Outstanding Universal Value (OUV) before the review of the relevant environmental impact analysis by the International Union for Conservation of Nature (IUCN). Additionally, the local people argue this plan will threaten the giant lizard's habitat regarding the entry of heavy equipment and disadvantage local tourism actors. Using a systematic literature review method, this piece collects and discovers a different concept in the issue of infrastructure development in KNP instead of another scholar's perspective. Walpole and Goodwin (2000) find that economics and facilities are the prominent aspects of tourism, while UNESCO considers the environment in development. In contrast to popular belief, this paper indicates a significant role of local people, known as Ata Modo, in the KNP establishment. This paper's findings propose that Ata Modo, as local people/tribes in KNP, should not be relocated from their area and instead be involved in the decision-making process. The bottom-up approach can be executed as a practice in a democratic process. From the fiscal approach, this development will positively impact local people's economy through collaboration and stakeholder participation.

Adapting to Climate Change with Machine Learning: The Robustness of Downscaled Precipitation in Local Impact Analysis

The skill, assumptions, and uncertainty of machine learning techniques (MLTs) for downscaling global climate model’s precipitation to the local level in Bolivia were assessed. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machine (SVM) techniques, was used on four zones (highlands, Andean slopes, Amazon lowlands, and Chaco lowlands). The downscaled series’ skill was evaluated in terms of relative errors. The uncertainty was analyzed through variance decomposition. In most cases, MLTs’ skill was adequate, with relative errors less than 50%. Moreover, RF tended to outperform SVM. Robust (weak) stationary (perfect prognosis) assumptions were found in the highlands and Andean slopes. The weakness was attributed to topographical complexity. The downscaling methods were shown to be the dominant source of uncertainties. This analysis allowed the derivation of robust future projections, showing higher annual rainfall, shorter dry spell duration, and more frequent but less intense high rainfall events in the highlands. Apart from the dry spell’s duration, a similar pattern was found for the Andean slopes. A decrease in annual rainfall was projected in the Amazon lowlands and an increase in the Chaco lowlands.

The Broken Promises of Neoliberal Soccer: Illusion and Disillusion of the 2022 FIFA World Cup

The 2022 FIFA World Cup in Qatar attracted over 3.4 million spectators and more than a million international visitors. However, the tournament required a colossal investment of 220 billion US dollars for facilities whose future use remains uncertain. It also highlighted the excesses of neoliberal soccer, dominated by financial interests. Since the World Cup was awarded in December 2010, controversies have emerged, particularly regarding the working conditions of migrant workers, which have led to several thousand deaths. There have been widespread denunciations of labor and civil rights violations, and the banning of the “One Love” armband has illustrated FIFA’s censorship. Environmental criticisms were also significant, questioning the event’s claimed carbon neutrality. Extravagant spending on stadium construction and hotel infrastructures, often seen as unnecessary luxury after the tournament, has been highlighted. Qatar has not fulfilled its promises to dismantle and repurpose the stadiums, which remain underused. The 2022 FIFA World Cup exposed the tensions between sporting values and economic realities and underscored the need for a thorough analysis of the long-term impact of mega-sporting events.