Beyond Shell-Filling: Strong Enhancement of Electron Affinity of Metal Clusters through a Noninvasive Oriented External Electric Field.

Traditional electron counting rules, like the Jellium model, have long been successfully utilized in designing superhalogens by modifying clusters to have one electron less than a filled electronic shell. However, this shell-filling approach, which involves altering the intrinsic properties of the clusters, can be complex and challenging to control, especially in experiments. In this letter, we theoretically establish that the oriented external electric field (OEEF) can substantially enhance the electron affinity (EA) of diverse aluminum-based metal clusters with varying valence electron configurations, leading to the creation of superhalogen species without altering their shell arrangements. This OEEF approach offers a noninvasive alternative to traditional superatom design strategies, as it does not disrupt the clusters' geometrical structures and superatomic states. These findings contribute a vital piece to the puzzle of constructing superalkalis and superhalogens, extending beyond conventional shell-filling strategies and potentially expanding the range of applications for functional clusters.

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.

Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study.

BACKGROUND: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. METHODS: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. FINDINGS: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. INTERPRETATION: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. FUNDING: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

Purifying surface water contaminated with azo dyes using nanofiltration: Interactions between dyes and dissolved organic matter.

In this research, the interactions of two azo dyes, Methyl Orange (MO) and Eriochrome Black T (EBT), with dissolved organic matter (DOM) in surface water were studied, emphasizing their removal using nano-filtration membranes (NF-270 and NF-90). High-Performance Size Exclusion Chromatography (HPSEC) findings indicated that the dyes' molecular weight in deionized (DI) water ranged from 500 to 15k Dalton (Da), adjusting peak intensities with Jingmi River (JM) water Beijing. Notably, when dyes were diluted in JM water, ultraviolet (UV533 & 466, and UV254), together with total organic carbon (TOC) parameters, revealed color removal rates of 99.49% (EBT), 94.2% (MO), 87.6% DOM removal, and 86% TOC removal for NF-90. The NF-90 membrane demonstrated a 75% flux decline for 50 mL permeate volume due to its finer pore structure and higher rejection effectiveness. In contrast, the NF-270 membrane showed a 60% decline in flux under the same conditions. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) analysis of dye-treated membranes in JM water revealed that the NF-270 showed a CC bond peak at 1660 cm-1 across various samples, while analyzing NF-90, the peaks at 1400 cm-1, 1040 cm-1, 750 cm-1, and 620 cm-1 disappeared for composite sample removal. The hydrophobicity of each membrane is measured by the contact angle (CA), which identified that initial CAs for NF-270 and NF-90 were 460 and 700, respectively, that were rapidly declined but stabilized after a few seconds of processing. Overall, this investigation shows that azo dyes interact with DOM in surface waters and enhance the removal efficiency of NF membranes.

The spatiotemporal evolution of rural landscape patterns in Chinese metropolises under rapid urbanization.

Understanding the evolution of rural landscapes in metropolises during rapid urbanization is crucial for formulating policies to protect the rural ecological environment. In this study, remote sensing and geographical information system data, as well as applied landscape index analysis, are used to examine the spatiotemporal evolution of rural landscape patterns in the Beijing-Tianjin region of China, which has experienced rapid urbanization. The relationships between land use/land cover changes and changes in rural landscape patterns are explored. The results revealed significant spatial differences in the rural landscapes in the Beijing-Tianjin region; farmland and forestland were the main types of landscapes, creating a "mountain-field-sea" natural landscape pattern. The conversion of rural landscapes in the Beijing-Tianjin region involved mainly the conversion of farmland to urban areas, with few exchanges between other landscape types. The urban areas in the Beijing-Tianjin region increased by 3% per decade; farmland decreased at the same rate. Additionally, the rural landscape patterns in the Beijing-Tianjin region were dominated by fragmentation, dispersion, and heterogeneity and moved from complex to regular. Water bodies displayed the most fragmented natural landscape; their number of patches increased by 36%, though their network characteristics were maintained. Forestland was the most concentrated natural landscape. In this study, theoretical support and a scientific reference for the optimization of rural landscape patterns and the improvement in rural living environments in rapidly urbanizing areas are provided.

Isolation and characterization of novel cadmium-resistant Escherichia fergusonii ZSF-15 from industrial effluent for flocculant production and antioxidant enzyme activity.

Cadmium (Cd) is a highly toxic metal that frequently contaminates our environment. In this study, the bioflocculant-producing, cadmium-resistant Escherichia fergusonii ZSF-15 was characterized from Paharang drain, Bawa Chak, Faisalabad, Pakistan. The Cd-resistant E. fergusonii was used to determine the bioflocculant production using yeast-peptone-glycerol medium (pH 6.5) supplemented with 50 mg L-1 of Cd. The culture was incubated for 3 days at 37 °C in a rotary shaker at 120 rpm. The fermentation broth was centrifuged at 4000 g for 10 min after the incubation period. The maximum flocculating activity by isolate ZSF-15 was found to be 71.4% after 48 h of incubation. According to the Fourier transform infrared spectroscopy analysis, the bioflocculant produced by strain ZSF-15 was comprised of typical polysaccharide and protein, i.e. hydroxyl, carboxyl, and amino groups. The strain ZSF-15 exhibited bioflocculant activity at range of pH (6-8) and temperature (35-50℃). Maximum flocculation activity (i.e. 71%) was observed at 47℃, whereas 63% flocculation production was observed at pH 8. In the present study, antioxidant enzyme profile of ZSF-15 was also evaluated under cadmium stress. A significant increase in antioxidant enzymes including superoxide dismutase (118%) and ascorbate peroxidase (28%) was observed, whereas contents of catalase (86%), glutathione transferase (13%), and peroxidase (8%) were decreased as compared to control.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions.

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Quantitative Trait Loci Mapping for Bacterial Wilt Resistance and Plant Height in Tomatoes.

Bacterial wilt (BW) of tomatoes, caused by Ralstonia solanacearum, is a devastating disease that results in large annual yield losses worldwide. Management of BW of tomatoes is difficult due to the soil-borne nature of the pathogen. One of the best ways to mitigate the losses is through breeding for disease resistance. Moreover, plant height (PH) is a crucial element related to plant architecture, which determines nutrient management and mechanical harvesting in tomatoes. An intraspecific F2 segregating population (NC 11212) of tomatoes was developed by crossing NC 84173 (tall, BW susceptible) × CLN1466EA (short, BW resistant). We performed quantitative trait loci (QTL) mapping using single nucleotide polymorphic (SNP) markers and the NC 11212 F2 segregating population. The QTL analysis for BW resistance revealed a total of three QTLs on chromosomes 1, 2, and 3, explaining phenotypic variation (R2) ranging from 3.6% to 14.9%, whereas the QTL analysis for PH also detected three QTLs on chromosomes 1, 8, and 11, explaining R2 ranging from 7.1% to 11%. This work thus provides information to improve BW resistance and plant architecture-related traits in tomatoes.

A review on mycogenic metallic nanoparticles and their potential role as antioxidant, antibiofilm and quorum quenching agents.

The emergence of antimicrobial resistance among biofilm forming pathogens aimed to search for the efficient and novel alternative strategies. Metallic nanoparticles have drawn a considerable attention because of their significant applications in various fields. Numerous methods are developed for the generation of these nanoparticles however, mycogenic (fungal-mediated) synthesis is attractive due to high yields, easier handling, eco-friendly and being energy efficient when compared with conventional physico-chemical methods. Moreover, mycogenic synthesis provides fungal derived biomolecules that coat the nanoparticles thus improving their stability. The process of mycogenic synthesis can be extracellular or intracellular depending on the fungal genera used and various factors such as temperature, pH, biomass concentration and cultivation time may influence the synthesis process. This review focuses on the synthesis of metallic nanoparticles by using fungal mycelium, mechanism of synthesis, factors affecting the mycosynthesis and also describes their potential applications as antioxidants and antibiofilm agents. Moreover, the utilization of mycogenic nanoparticles as quorum quenching agent in hampering the bacterial cell-cell communication (quorum sensing) has also been discussed.

Molecular-weight dependent promotion and competition effects of natural organic matter on dissolved black carbon removal by coagulation.

Dissolved black carbon (DBC) is the ubiquitous component of dissolved organic matter pools with the high reactivity for disinfection byproducts formation. However, it is unknown that the influence of molecular weight (MW) of natural organic matter (NOM) on the DBC removal from potable water sources. Therefore, it was studied that the DBC removal by coagulation in the presence of the NOM with various molecular weights. The DBC removal was promoted due to the presence of NOM and the promotion degree decreased with decreasing MW of NOM. Furthermore, the removal ratio of humic-like component increased as the MW of NOM decreased, suggesting that the competition between DBC and NOM increased with decreasing MW. The functional groups after coagulation were the same with that before coagulation as the MW of NOM varied, suggesting that the molecular structure was not the key factor of influencing the DBC removal. This study will give the deep insight into the prediction of the DBC removal ratio by coagulation based on the MW of NOM in water sources.

Beyond the genome: MALAT1's role in advancing urologic cancer care.

Urologic cancers (UCs), which include bladder, kidney, and prostate tumors, account for almost a quarter of all malignancies. Long non-coding RNAs (lncRNAs) are tissue-specific RNAs that influence cell growth, death, and division. LncRNAs are dysregulated in UCs, and their abnormal expression may allow them to be used in cancer detection, outlook, and therapy. With the identification of several novel lncRNAs and significant exploration of their functions in various illnesses, particularly cancer, the study of lncRNAs has evolved into a new obsession. MALAT1 is a flexible tumor regulator implicated in an array of biological activities and disorders, resulting in an important research issue. MALAT1 appears as a hotspot, having been linked to the dysregulation of cell communication, and is intimately linked to cancer genesis, advancement, and response to treatment. MALAT1 additionally operates as a competitive endogenous RNA, binding to microRNAs and resuming downstream mRNA transcription and operation. This regulatory system influences cell growth, apoptosis, motility, penetration, and cell cycle pausing. MALAT1's evaluation and prognosis significance are highlighted, with a thorough review of its manifestation levels in several UC situations and its association with clinicopathological markers. The investigation highlights MALAT1's adaptability as a possible treatment target, providing fresh ways for therapy in UCs as we integrate existing information The article not only gathers current knowledge on MALAT1's activities but also lays the groundwork for revolutionary advances in the treatment of UCs.

An integrated network pharmacology approach to discover therapeutic mechanisms of Commiphora wightii for the treatment of Bell's palsy.

Bell's palsy (BP) can result in facial paralysis. Inflammation or injury to the cranial nerves that regulate the facial muscles is primarily responsible for that disease. Commiphora wightii remains recognized as a cure for a few human ailments. This study focused on therapeutic phenomena of C. wightii for the treatment of Bell's palsy, utilizing the network drug discovery and molecular docking techniques. Active biological constituents of C. wightii were retrieved from literature and independent databases. Potential therapeutic targets (431) of 13 bioactive phytochemicals were fetched via SwissTargetPrediction tool. Putative intersecting targets (855) of Bell's palsy were computed through the DisGeNET and GeneCards datasets. Subsequently, by the analysis of potential shared targets (87) of C. wightii and Bell's palsy, a Venn diagram was drawn. DAVID database was used to evaluate gene functional annotations and enriched pathways that are involved in Bell's palsy. STRING database was used for generating the protein-protein relationship complex. Visual presentations of the interactions of potential targets to active chemical constituents were done by the Cytoscape. Whereas, the conformational research sorted out 10 key targets through the protein-protein interactions network. Moreover, the capacity of therapeutic ingredients to interact with a target inhibiting Bell's palsy was confirmed by molecular docking, which might ratify the findings of network pharmacology. In the molecular complex of AKT1-cholesterol, a 100-ns simulation unveiled a graceful stability, with a minimal 0.167 Å ligand shift and resilient hydrogen bonds (ASN54 and SER205). The final 20 ns showcased a P1 motif pirouette, gracefully forming aromatic bonds with H165 and W186, underscoring the complex's dynamic finesse. This study evaluated compound-target interactions and their impact on disease-related genes. It revealed that five genes (AKT1, TNF, MAPK3, EGFR and SRC) of C. wightii might be useful therapeutic targets for the treatment of Bell's palsy, as well as helping in lowering down the blood pressure.Communicated by Ramaswamy H. Sarma.

Community perception and utilization of services for the severe wasted children aged 6-59 months in the Forcibly Displaced Myanmar Nationals and their nearest host communities in Bangladesh: a qualitative exploration.

Introduction: There is a paucity of data on community perception and utilization of services for wasted children in Forcibly Displaced Myanmar Nationals (FDMN) and their nearest host communities. Methods: We conducted a qualitative study to explore community perceptions and understand the utilization of services for severely wasted children among the FDMN and their nearest host communities in Teknaf, Cox's Bazar. We carried out 13 focus group discussions and 17 in-depth interviews with the caregivers of the children of 6-59 months, and 8 key informant interviews. Results: Caregivers' perceived causes of severe wasting of their children included caregivers' inattention, unhygienic practices, and inappropriate feeding practices. However, the context and settings of the FDMN camps shaped perceptions of the FDMN communities. Caregivers in both the FDMN and host communities sought care from healthcare providers for their children with severe acute malnutrition (SAM) when they were noticed and encouraged by their neighbors or community outreach workers, and when their SAM children suffered from diseases such as diarrhea and fever. Some caregivers perceived ready-to-use therapeutic food (RUTF) as a food to be shared and so they fed it to their non-SAM children. Discussion: Caregivers of the children having SAM with complications, in the FDMN and host communities, were reluctant to stay in stabilization centers or complex respectively, due to their households' chores and husbands' unwillingness to grant them to stay. The findings of this study are expected to be used to design interventions using locally produced RUTF for the management of SAM children in the FDMN, as well as to inform the health sector working on SAM child management in the host communities.

Electron transfer-mediated synergistic nonlinear optical response in the Agn@C18 (n = 4-6) complexes: A DFT study on the electronic structures and optical characteristics.

Seeking highly efficient and stable non-linear optical (NLO) materials is crucial yet challenging, given their promising applications in laser diodes and photovoltaics. In this study, we employ the excess electron and charge transfer strategies to theoretically design three novel complexes, namely Agn@C18 (n = 4-6), by adsorbing silver clusters onto the cyclo[18]carbon ring (C18). Our aim is to investigate the NLO characteristics of these complexes using density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations. The results reveal that the adsorption of Ag clusters onto C18 leads to a decrease in excitation energy and an increase in dipole moment and oscillator strengths, thereby significantly enhancing the hyperpolarizability of the complexes. Strikingly, among all these complexes, Ag6@C18 exhibits the highest first hyperpolarizability value of approximately 109496.2620 au calculated at the B3LYP/cc-PVDZ-pp level of theory, which is about 1.3 × 106 times higher than that of pure C18. This finding validates the effectiveness of the proposed strategies in enhancing the NLO response of the species. Moreover, the calculated UV-Vis absorption spectrum demonstrates that the Agn@C18 complexes with excess electrons exhibit absorption at longer wavelengths (ranging from 385 to 731 nm) compared to C18. In addition, the stability, chemical bonding, and charge transfer characteristics of the Agn@C18 (n = 4-6) complexes were also discussed. These findings highlight the potential of these complexes for the development of highly efficient NLO devices.

Synergistically enhancing the selective adsorption of cationic dyes through copper impregnation and amino functionality into iron-based metal-organic frameworks.

Dyes contaminating the sewages have seriously threatened the living beings and their separation from wastewater in terms of potential resource recovery is of high value. Herein, both of metal node doping and ligand group grafting were taken into account to enhance the adsorption selectivity of Fe-MOFs towards cationic dyes. The positive correlation between copper doping amount and selective coefficient (∂MOMB) for methylene blue (MB) over methyl orange (MO) within a certain range was mainly attributed to the increased surface negative charges via partial replacement of Fe(III) with Cu(II). Moreover, the amount of surface negative charges was further increased after amino functionalization and there was a synergism between Cu(II) and -NH2 in selectivity enhancement. As a result, Fe0.6Cu0.4-BDC-NH2 exhibited a 22.5-times increase in ∂MOMB and other cationic dyes including malachite green (MG) and rhodamine B (Rh. B) could also be selectively separated from binary and quaternary mixed dye systems. Moreover, Fe0.6Cu0.4-BDC-NH2 showed many superiorities like a wide pH range of 4.0-8.0, strong anti-interference ability over various inorganic ions, good recyclability, and stability. The adsorption kinetics and isotherm suggested that the MB adsorption process was a homogeneous single-layer chemisorption. Additionally, the thermodynamics manifested that the overall process was exothermic and spontaneous. According to the FT-IR and XPS spectra analysis, the electrostatic interaction and hydrogen bonding were determined as the main driving forces, and π-π interaction also contributed to the adsorption process.

Risk from future climate change to Pakistan's protected area network: A composite analysis for hotspot identification.

As climate change becomes a primary driver of global ecosystem deterioration and biodiversity loss, protected areas (PAs) are posed to play a crucial conservation role. At a global scale, 17 % of land is currently covered by PAs; a figure expected to reach 30 % by 2030 under the UN post-2020 global biodiversity framework. However, focusing only on the percent coverage of PAs without assessing their efficacy may not accomplish the intended conservation goals. Here, we present the first assessment of the risk from climate change to existing PAs and non-protected lands across Pakistan by combining data on the local exposure and vulnerability of 409 species of birds, mammals, reptiles and amphibians to multidimensional changes in climate by mid (2040-2060) and late (2061-2080) century under two climate emission scenarios (RCP4.5 and RCP8.5). We find that between 7 % (2050 RCP4.5) and 19 % (2080 RCP8.5) of the current network of PAs, mostly located in the eastern and southeastern parts of the country, are projected to be under future extreme risk (i.e., highly exposed areas containing highly vulnerable communities). Importantly, hotspots of risk within these PAs are projected to significantly expand over time and with increasing severity of the scenario. In contrast, PAs in the northern part of the country are projected to remain under moderate to low risk. Results are subject to variability across the country reflecting interesting differences in climate change exposure and species vulnerability between protected and non-protected lands. Importantly, significantly lower level of risks from future climate change are projected for PAs than non-protected lands across emission scenarios and periods suggesting potential candidate areas for the future expansion of the country's PA network. Our analysis provides novel insights that can help inform conservation decisions and management at a time when the country is investing in ambitious efforts to expand its network of protected areas.

Divergent Fate and Roles of Dissolved Organic Matter from Spatially Varied Grassland Soils in China During Long-Term Biogeochemical Processes.

Terrestrial dissolved organic matter (DOM) is critical to global carbon and nutrient cycling, climate change, and human health. However, how the spatial and compositional differences of soil DOM affect its dynamics and fate in water during the carbon cycle is largely unclear. Herein, the biodegradation of DOM from 14 spatially distributed grassland soils in China with diverse organic composition was investigated by 165 days of incubation experiments. The results showed that although the high humified fraction (high-HS) regions were featured by high humic-like fractions of 4-25 kDa molecular weight, especially the abundant condensed aromatics and tannins, they unexpectedly displayed greater DOM degradation during 45-165 days. In contrast, the unique proteinaceous and 25-100 kDa fractions enriched in the low humified fraction (low-HS) regions were drastically depleted and improved the decay of bulk DOM but only during 0-45 days. Together, DOM from the high-HS regions would cause lower CO2 outgassing to the atmosphere but higher organic loads for drinking water production in the short term than that from the low-HS regions. However, this would be reversed for the two regions during the long-term transformation processes. These findings highlight the importance of spatial and temporal variability of DOM biogeochemistry to mitigate the negative impacts of grassland soil DOM on climate, waters, and humans.

The impact of a blood-culture diagnostic stewardship intervention on utilization rates and antimicrobial stewardship.

Blood-culture overutilization is associated with increased cost and excessive antimicrobial use. We implemented an intervention in the adult intensive care unit (ICU), combining education based on the DISTRIBUTE algorithm and restriction to infectious diseases and ICU providers. Our intervention led to reduced blood-culture utilization without affecting safety metrics.

Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete.

Construction industry is indirectly the largest source of CO2 emissions in the atmosphere, due to the use of cement in concrete. These emissions can be reduced by using industrial waste materials in place of cement. Self-Compacting Concrete (SCC) is a promising material to enhance the use of industrial wastes in concrete. However, there are very few methods available for accurate prediction of its strength, therefore, reliable models for estimating 28-day Compressive Strength (C-S) of SCC are developed in current study by using three Machine Learning (ML) algorithms including Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Random Forest (RF). The ML models were meticulously developed using a dataset of 231 points collected from internationally published literature considering seven most influential parameters including cement content, quantities of fly ash and silica fume, water content, coarse aggregate, fine aggregate, and superplasticizer dosage to predict C-S. The developed models were evaluated using different statistical errors including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) etc. The results showed that the XGB model outperformed the MEP and RF model in terms of accuracy with a correlation R2 = 0.998 compared to 0.923 for MEP and 0.986 for RF. Similar trend was observed for other error metrices. Thus, XGB is the most accurate model for estimating C-S of SCC. However, it is pertinent to mention here that it does not give its output in the form of an empirical equation like MEP model. The construction of these empirical models will help to efficiently estimate C-S of SCC for practical purposes.