Free water surface constructed wetlands (FWSCWs) for the treatment of various wastewater types have evolved significantly over the last few decades. With an increasing need and interest in FWSCWs applications worldwide due to their cost-effectiveness and other benefits, this paper reviews recent literature on FWSCWs\' ability to remove different types of pollutants such as nutrients (i.e., TN, TP, NH4-N), heavy metals (i.e., Fe, Zn, and Ni), antibiotics (i.e., oxytetracycline, ciprofloxacin, doxycycline, sulfamethazine, and ofloxacin), and pesticides (i.e., Atrazine, S-Metolachlor, imidacloprid, lambda-cyhalothrin, diuron 3,4-dichloroanilin, Simazine, and Atrazine) that may co-exist in wetland inflow, and discusses approaches for simulating hydraulic and pollutant removal processes. A bibliometric analysis of recent literature reveals that China has the highest number of publications, followed by the USA. The collected data show that FWSCWs can remove an average of 61.6%, 67.8%, 54.7%, and 72.85% of inflowing nutrients, heavy metals, antibiotics, and pesticides, respectively. Optimizing each pollutant removal process requires specific design parameters. Removing heavy metal requires the lowest hydraulic retention time (HRT) (average of 4.78 days), removing pesticides requires the lowest water depth (average of 0.34 m), and nutrient removal requires the largest system size. Vegetation, especially Typha spp. and Phragmites spp., play an important role in FWSCWs\' system performance, making significant contributions to the removal process. Various modeling approaches (i.e., black-box and process-based) were comprehensively reviewed, revealing the need for including the internal process mechanisms related to the biological processes along with plants spp., that supported by a further research with field study validations. This work presents a state-of-the-art, systematic, and comparative discussion on the efficiency of FWSCWs in removing different pollutants, main design factors, the vegetation, and well-described models for performance prediction.

OBJECTIVE: The aim of this review is to provide an overview of parental communication patterns during mealtimes, with a special emphasis being placed on the differences between families with and without a history of eating disorders.
METHODS: The systematic review was conducted according to the PRISMA statement. A systematic literature search was carried out in PubMed, PubPsych and PsycINFO and the results were assessed for eligibility by two independent raters using the PICOS criteria. Only studies that included a mealtime observation were considered suitable for analysis of both explicit and implicit parental communication.
RESULTS: The results of the review suggest that mothers communicate more, with more complexity, and with a greater variety of words with their children during mealtimes compared to fathers. The intention and type of communication is diverse and heterogeneous. In general, parents often tried to encourage their children to eat. Verbal modeling and co-eating appeared to be common behaviors. Mothers with a history of eating disorders expressed more negative emotions during eating than mothers without eating disorders. Findings regarding the use of positive comments and controlling speech are contradicting.
CONCLUSIONS: The review outlines major fields of parent-child communication and modeling behavior around family meals which might be relevant to investigate and integrate into models of intergenerational transmission of eating behavior and disordered eating.

Continued investment in the development and application of mathematical models of poliovirus transmission, economics, and risks leads to their use in support of polio endgame strategy development and risk management policies. This study complements an earlier review covering the period 2000-2019 and discusses the evolution of studies published since 2020 by modeling groups supported by the Global Polio Eradication Initiative (GPEI) partners and others. We systematically review modeling papers published in English in peer-reviewed journals from 2020-2024.25 that focus on poliovirus transmission and health economic analyses. In spite of the long-anticipated end of poliovirus transmission and the GPEI sunset, which would lead to the end of its support for modeling, we find that the number of modeling groups supported by GPEI partners doubled and the rate of their publications increased. Modeling continued to play a role in supporting GPEI and national/regional policies, but changes in polio eradication governance, decentralized management and decision-making, and increased heterogeneity in modeling approaches and findings decreased the overall impact of modeling results. Meanwhile, the failure of the 2016 globally coordinated cessation of type 2 oral poliovirus vaccine use for preventive immunization and the introduction of new poliovirus vaccines and formulation, increased the complexity and uncertainty of poliovirus transmission and economic models and policy recommendations during this time.

BACKGROUND: Travel-related strategies to reduce the spread of COVID-19 evolved rapidly in response to changes in the understanding of SARS-CoV-2 and newly available tools for prevention, diagnosis, and treatment. Modeling is an important methodology to investigate the range of outcomes that could occur from different disease containment strategies.
METHODS: We examined 43 articles published from December 2019 through September 2022 that used modeling to evaluate travel-related COVID-19 containment strategies. We extracted and synthesized data regarding study objectives, methods, outcomes, populations, settings, strategies, and costs. We used a standardized approach to evaluate each analysis according to 26 criteria for modeling quality and rigor.
RESULTS: The most frequent approaches included compartmental modeling to examine quarantine, isolation, or testing. Early in the pandemic, the goal was to prevent travel-related COVID-19 cases with a focus on individual-level outcomes and assessing strategies such as travel restrictions, quarantine without testing, social distancing, and on-arrival PCR testing. After the development of diagnostic tests and vaccines, modeling studies projected population-level outcomes and investigated these tools to limit COVID-19 spread. Very few published studies included rapid antigen screening strategies, costs, explicit model calibration, or critical evaluation of the modeling approaches.
CONCLUSIONS: Future modeling analyses should leverage open-source data, improve the transparency of modeling methods, incorporate newly available prevention, diagnostics, and treatments, and include costs and cost-effectiveness so that modeling analyses can be informative to address future SARS-CoV-2 variants of concern and other emerging infectious diseases (e.g., mpox and Ebola) for travel-related health policies.

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

UNASSIGNED: Significant mitigation efforts beyond the Nationally Determined Commitments (NDCs) coming out of the 2015 Paris Climate Agreement are required to avoid warming of 2°C above pre-industrial temperatures. Health co-benefits represent selected near term, positive consequences of climate policies that can offset mitigation costs in the short term before the beneficial impacts of those policies on the magnitude of climate change are evident. The diversity of approaches to modeling mitigation options and their health effects inhibits meta-analyses and syntheses of results useful in policy-making.
UNASSIGNED: We evaluated the range of methods and choices in modeling health co-benefits of climate mitigation to identify opportunities for increased consistency and collaboration that could better inform policy-making. We reviewed studies quantifying the health co-benefits of climate change mitigation related to air quality, transportation, and diet published since the 2009 Lancet Commission \'Managing the health effects of climate change\' through January 2017. We documented approaches, methods, scenarios, health-related exposures, and health outcomes.
UNASSIGNED: Forty-two studies met the inclusion criteria. Air quality, transportation, and diet scenarios ranged from specific policy proposals to hypothetical scenarios, and from global recommendations to stakeholder-informed local guidance. Geographic and temporal scope as well as validity of scenarios determined policy relevance. More recent studies tended to use more sophisticated methods to address complexity in the relevant policy system.
UNASSIGNED: Most studies indicated significant, nearer term, local ancillary health benefits providing impetus for policy uptake and net cost savings. However, studies were more suited to describing the interaction of climate policy and health and the magnitude of potential outcomes than to providing specific accurate estimates of health co-benefits. Modeling the health co-benefits of climate policy provides policy-relevant information when the scenarios are reasonable, relevant, and thorough, and the model adequately addresses complexity. Greater consistency in selected modeling choices across the health co-benefits of climate mitigation research would facilitate evaluation of mitigation options particularly as they apply to the NDCs and promote policy uptake.

UNASSIGNED: Considering users\' emotions plays an extremely crucial role in the adoption and acceptance of recent technology by the end user. User emotions can also help to identify unknown requirements, saving resources that would otherwise be wasted if discovered later. However, eliciting and modeling users\' emotional requirements in software engineering is still an open research area.
UNASSIGNED: This systematic mapping review analyzes emotional requirements (ER) practices in software engineering from two perspectives: elicitation and modeling. For elicitation techniques, we investigate the techniques, evaluation methods, limitations, and application domains. For modeling techniques, we examine the modeling languages, analyses, limitations, and domains.
UNASSIGNED: We systematically reviewed studies on emotional requirements engineering published between 1993-2023 and identified 46 relevant primary studies.
UNASSIGNED: A total of 34 studies investigated ER elicitation techniques, five examined modeling techniques, and seven covered both. Illustrative case studies were the main evaluation method for proposed elicitation techniques. Identified limitations include time consumption and extensive human involvement. The dominant application domains were healthcare and well-being, and game development.
UNASSIGNED: This review summarizes the current landscape of emotional requirements research, highlighting key elicitation and modeling techniques, evaluations, limitations, and domains. Further research can build on these findings to advance emotional requirements practices in software engineering. Future research may address (1) managing conflicting emotional requirements across users, (2) evaluating the value and impact of considering emotional requirements during the development and (3) Modeling and analyzing emotional requirements in relation to other requirements.

Despite the recognized importance of flowing waters in global greenhouse gas (GHG) budgets, riverine GHG models remain oversimplified, consequently restraining the development of effective prediction for riverine GHG emissions feedbacks. Here we elucidate the state of the art of riverine GHG models by investigating 148 models from 122 papers published from 2010 to 2021. Our findings indicate that riverine GHG models have been mostly data-driven models (83%), while mechanistic and hybrid models were uncommonly applied (12% and 5%, respectively). Overall, riverine GHG models were mainly used to explain relationships between GHG emissions and biochemical factors, while the role of hydrological, geomorphic, land use and cover factors remains missing. The development of complex and advanced models has been limited by data scarcity issues; hence, efforts should focus on developing affordable automatic monitoring methods to improve data quality and quantity. For future research, we request for basin-scale studies explaining river and land-surface interactions for which hybrid models are recommended given their flexibility. Such a holistic understanding of GHG dynamics would facilitate scaling-up efforts, thereby reducing uncertainties in global GHG estimates. Lastly, we propose an application framework for model selection based on three main criteria, including model purpose, model scale and the spatiotemporal characteristics of GHG data, by which optimal models can be applied in various study conditions.

In response to the growing demand for therapeutic biomolecules, there is a need for continuous and cost-effective bio-separation techniques to enhance extraction yield and efficiency. Aqueous biphasic extractive fermentation has emerged as an integrated downstream processing technique, offering selective partitioning, high productivity, and preservation of biomolecule integrity. However, the dynamic nature of this technique requires a comprehensive understanding of the underlying separation mechanisms. Unfortunately, the analysis of parameters influencing this dynamic behavior can be challenging due to limited resources and time. To address this, mathematical modeling approaches can be employed to minimize the tedious trial-and-error experimentation process. This review article presents mathematical modeling approaches for both upstream and downstream processing techniques, focusing on the production of biomolecules which can be used in pharmaceutical industries in a cost-effective manner. By leveraging mathematical models, researchers can optimize the production and purification processes, leading to improved efficiency and processing cost reduction in biomolecule production.

OBJECTIVE: This study aimed to provide procedure-specific estimates of the risk for symptomatic venous thromboembolism and major bleeding in noncancer gynecologic surgeries.
METHODS: We conducted comprehensive searches on Embase, MEDLINE, Web of Science, and Google Scholar. Furthermore, we performed separate searches for randomized trials that addressed the effects of thromboprophylaxis.
METHODS: Eligible studies were observational studies that enrolled ≥50 adult patients who underwent noncancer gynecologic surgery procedures and that reported the absolute incidence of at least 1 of the following: symptomatic pulmonary embolism, symptomatic deep vein thrombosis, symptomatic venous thromboembolism, bleeding that required reintervention (including re-exploration and angioembolization), bleeding that led to transfusion, or postoperative hemoglobin level <70 g/L.
METHODS: A teams of 2 reviewers independently assessed eligibility, performed data extraction, and evaluated the risk of bias of the eligible articles. We adjusted the reported estimates for thromboprophylaxis and length of follow-up and used the median value from studies to determine the cumulative incidence at 4 weeks postsurgery stratified by patient venous thromboembolism risk factors and used the Grading of Recommendations Assessment, Development and Evaluation approach to rate the evidence certainty.
RESULTS: We included 131 studies (1,741,519 patients) that reported venous thromboembolism risk estimates for 50 gynecologic noncancer procedures and bleeding requiring reintervention estimates for 35 procedures. The evidence certainty was generally moderate or low for venous thromboembolism and low or very low for bleeding requiring reintervention. The risk for symptomatic venous thromboembolism varied from a median of <0.1% for several procedures (eg, transvaginal oocyte retrieval) to 1.5% for others (eg, minimally invasive sacrocolpopexy with hysterectomy, 1.2%-4.6% across patient venous thromboembolism risk groups). Venous thromboembolism risk was <0.5% for 30 (60%) of the procedures; 0.5% to 1.0% for 10 (20%) procedures; and >1.0% for 10 (20%) procedures. The risk for bleeding the require reintervention varied from <0.1% (transvaginal oocyte retrieval) to 4.0% (open myomectomy). The bleeding requiring reintervention risk was <0.5% in 17 (49%) procedures, 0.5% to 1.0% for 12 (34%) procedures, and >1.0% in 6 (17%) procedures.
CONCLUSIONS: The risk for venous thromboembolism in gynecologic noncancer surgery varied between procedures and patients. Venous thromboembolism risks exceeded the bleeding risks only among selected patients and procedures. Although most of the evidence is of low certainty, the results nevertheless provide a compelling rationale for restricting pharmacologic thromboprophylaxis to a minority of patients who undergo gynecologic noncancer procedures.