Analysis Of Impact Research Articles

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.

Chaos and feasibility analysis of a healthy lifestyle’s sustainability using fractional-order polycystic ovarian syndrome’s effect on infertility

In this study, we developed a fractional-order model for polycystic ovarian syndrome issue transmission in women through four compartments with the Mittag-Leffler kernel. For biological feasibility, we discussed the positivity and boundedness of solutions with fixed point mappings in Banach spaces. Sensitivity analysis is performed using different variables as response functions each time, employing Latin Hyper-cube Sampling (LHS) and Partial Rank Correlation Coefficient (PRCC) with 200 iterations. Further, the reproductive number is determined for the model’s biological feasibility with an analysis of parameter impact through Matlab software. The solutions are examined for global stability using the Lyapunov function with a first derivative. Chaos Control will use the regulate for linear responses approach to bring the system to stabilize according to its points of equilibrium. Numerical solutions are derived by using a Newton polynomial in the generalized form of the Mittag-Leffler kernel, which illustrates the effect of the fractional operator and parameters involved in the system. Results are also analyzed with different assumptions of parameter values and initial conditions to observe the different impacts of treatment to overcome the risk of infertility in women. Such type of investigation will be useful to investigate the spread of disease as well as helpful in developing control strategies to overcome the risk of problems and best management care pre-detection of the issue and for treatment.

A budget impact analysis of exclusive human milk diet in very low birth weight infants in United Arab Emirates

Introduction Very low birth weight (VLBW) infants have the highest rate of neonatal intensive care unit (NICU) admissions owing to the higher comorbidities associated with premature birth and long hospital stays. VLBW infants fed a bovine (BOV)-based diet have higher mortality rates, necrotizing enterocolitis (NEC), late-onset sepsis (LOS), and other comorbidities than those fed 100% human milk-based products. This study aims to evaluate the budgetary impact of adopting an exclusive human milk diet (EHMD) instead of a BOV-based diet in VLBW infants from Al-Ain Hospital, Sheikh Shakhbout Medical City, and Dubai Health Authority (DHA) in the United Arab Emirates (UAE). Methods We use a decision tree model to estimate the budget impact of adopting EHMD versus the current local practice (BOV) over five years. Patients enter the tree model and have a probability of transitioning to one of the following mutually exclusive health states: NEC, which may be treated medically or surgically; LOS, NEC, and LOS; or neither NEC nor LOS. Depending on the feeding strategy, infants in the aforementioned health states are likely to develop any of the following complications: retinopathy of prematurity, bronchopulmonary dysplasia, or short bowel syndrome. The model accounts for the costs associated with diet, management of health states and complications, and the follow-up period. Results Al-Ain Hospital saved United Arab Emirates Dirham (AED) 3.3 million ($1.4million) in the first year of the EHMD feeding arm and AED 16.6 million ($7.1million) over the course of five years. In comparison to Sheikh Shabout Medical City and DHA, AED 36.7 million ($15.8million) and AED 24 million ($10.3million) were saved over five years, respectively, with AED 7.3 million ($3.1million) and 4.8 million ($2million) saved in the first year. The estimated pooled results across the three institutions were AED 5.1 million ($2.2million) and AED 25.7 million ($11million) savings in the first year and over five years, respectively. Conclusion Implementing the EHMD feeding scheme in VLBWs that has significant clinical benefits has resulted in substantial budget savings from the payer’s perspective in the UAE owing to fewer comorbidities associated with premature birth and shorter hospital stays. It is highly recommended for the Emirati health care settings to evaluate the real-world neonatal complication rates.

Analysis of the Chinese Literature on the ESG and Financial Performance

Abstract: Based on 230 Chinese articles published on the CNKI platform from 2016 to 2024 on the research of ESG and corporate financial performance, this paper clarifies the research direction of ESG and financial performance and discusses the main points of the research with the help of CiteSpace, an information visualization software. The studies on the relationship between ESG and financial performance are all based on listed companies. The empirical analysis of listed companies shows that ESG performance has a significant positive impact on the financial performance of listed companies. The impact of ESG on financial performance varies in different industries, regions, and stages of economic development. The short-term and long-term effects are also different. In addition, media attention on ESG performance also affects financial performance. However, existing studies are analyzed from a micro perspective and lack macro background analysis and policy impact analysis.

Impact Analysis of the India-EU Free Trade Agreement on Indian Horticulture

Impact Analysis of the India-EU Free Trade Agreement on Indian Horticulture

The Investigation of Nonlinear Time-Fractional Models in Optical Fibers and the Impact Analysis of Fractional-Order Derivatives on Solitary Waves

In this article, we investigate a couple of nonlinear time-fractional evolution equations, namely the cubic-quintic-septic-nonic equation and the Davey–Stewartson (DS) equation, both of which have significant applications in complex physical phenomena such as fiber optical communication, optical signal processing, and nonlinear optics. Using a powerful technique named the extended generalized Kudryashov approach, we extract different rich structured soliton solutions to these models, including bell-shaped, cuspon, parabolic soliton, singular soliton, and squeezed bell-shaped soliton. We also study the impact of fractional-order derivatives on these solutions, providing new insights into the dynamics of nonlinear models. The results are compared with the existing literature, revealing novel and distinct solutions that offer a deeper understanding of these fractional models. The results show that the implemented approach is useful, reliable, and compatible for examining fractional nonlinear evolution equations in applied science and engineering.

Speed prediction and nearby road impact analysis using machine learning and ensemble of explainable AI techniques

Traffic speed prediction is an important topic in Intelligent Transport Systems (ITS). Although traffic speed prediction for real-life applications is burgeoning, the study of explaining and interpreting AI-based speed prediction is still in its initial stage. In this paper, we applied multiple advanced regression techniques, such as XGBoost and CatBoost optimized gradient boosting, Random Forest, and LASSO to predict traffic speed more accurately in the subsequent time windows. The experiment with prediction methods was conducted using the traffic speed data of the Seoul metropolitan road network. Each road segment represented as a node in the network is associated with neighboring roads within a configurable range. We picked heavily congested nodes as prediction targets. Then, we evaluated nearby road influences to determine critical contributions to the situation of the target nodes. We interpreted the model’s output and extracted the topmost influential neighboring nodes by using an ensemble of explainable artificial intelligence (XAI) techniques such as feature importance assessment using the GINI entropy function, Recursive Feature Elimination, Shapely Additive Explanation, and a method of measuring the impact of masked nodes. We validated the XAI interpretations through traffic flow simulation by tuning the topmost influential nearby roads’ speed and observing the effect on the roads’ traffic congestion relief correspondingly. We also proved our solution through local explanation techniques such as Local Interpretable Model-Agnostic Explanations. Our methods are applicable to any transport network and open the door to new strategies for controlling the specific nearby roads for effective congestion relief.

A comprehensive quantitative lifecycle cost and environmental impact analysis model for computing infrastructure

This paper presents a comprehensive quantitative model for quantitative assessment of the lifecycle costs and environmental impacts of computing infrastructure, with a focus on internet data centers (IDCs) and high-performance computing (HPC) facilities. The key innovation lies in the integration of interdisciplinary cost evaluation and carbon emission methods for the establishment of this quantitative model. This framework, which outlines key cost components and carbon emission factors, enables the calculation of total costs, electricity expenses, and greenhouse gas emissions throughout the lifecycle of infrastructure. With IDCs as a case study, the research clarifies the intricate cost structure associated with equipment procurement, energy usage, land acquisition, and operational expenses. This paper provides an in-depth understanding of the cost structure and environmental impact of computing infrastructure in support of sustainable decision-making in its development.•Based on established cost estimation methods, such as Lifecycle Cost Analysis (LCCA) and the Analogous Estimating Method, this study examines costs across construction and operation phases.•The Emission Factor Method is used to quantify environmental impact, emphasizing the significance of regional energy mix and power usage effectiveness (PUE).