bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of Nutrient Removal Efficiency of Marine Microalgae Dunaliella salina in Saline Food Industry Wastewater
Authors: E. Balaji  , D. Swaminathan  , V. Sudarshana Deepa  
Published: July 2024

Industrial wastewater release into the environment is a critical global challenge leading to severe water pollution, adversely impacting terrestrial and aquatic ecosystems requiring effective solutions. The treatment, disposal, and recirculation of saline wastewater from food containing nitrate and phosphate poses significant health and environmental risks. This research aims to address the challenge by investigating the potential of using the marine microalgae Dunaliella salina for treating saline food industry wastewater, both in laboratory and pilot-scale settings using raceway tanks. In the laboratory study, a mixture of wastewater and F/2 medium was inoculated with Dunaliella salina to assess its potential for growth in saline wastewater, rich in nitrate and phosphate. The study observed a cell density of 5.5 x 105 cells/ml. Subsequently, a pilot-scale study was conducted, where the maximum cell density of 6.2 x 105 cells/ml was achieved. The efficacy of Dunaliella salina in removing nitrate and phosphorus from the culture was evaluated by measuring the levels of these compounds. A significant reduction in nitrate concentration from 146 to 22.16 mg/l was observed, with a removal efficiency of 84.82%; similarly, the phosphate concentration decreased from 55.3 to 10.79 mg/l, with a removal efficiency of 80.49 %. The reductions in nitrate and phosphate concentrations, along with the demonstrated growth of Dunaliella salina in saline industrial wastewater, confirmed the feasibility of employing these microalgae for bioremediation treatment in a sustainable way.
Metal–Organic Frameworks (MOFs) for Sustainable Water Disinfection: Synthesis, Characterization, and Antimicrobial Properties
Authors: Suman Thakur  , Sharda Bharti  
Published: July 2024

Waterborne biological pollution poses a significant threat to both the environment and human health, leading to the transmission of various waterborne diseases. Ensuring the safety of drinking water is essential for safeguarding public health and reducing the prevalence of waterborne illnesses caused by microbial pollutants. Recently, there has been a notable increase in the utilization of nanoparticles for water treatment purposes. Metal-Organic Frameworks (MOFs) are emerging as promising candidates for enhancing water treatment processes due to their structural and functional adjustability and makes them stand out for environmental remediation. Notable properties include porous structures with customizable sizes, large surface areas, and internal characteristics that are variable. In this study, zeolitic-imidazolate frameworks (ZIF-8) and Ag@zeolitic-imidazolate frameworks (Ag@ZIF-8) were synthesized using 2-methylimidazole (2-MIM) as an organic linker via a facile green synthesis method and used for water disinfection. These MOFs were characterized using a range of techniques including UV-vis spectroscopy, FTIR, and X-ray diffraction, SEM/EDS. The antibacterial activity of ZIF-8 and Ag@ZIF-8 were evaluated using the disc diffusion method against Gram-negative and Gram-positive bacteria, i.e., E. coli and S. aureus, respectively. These MOFs demonstrated strong antimicrobial activity against both Gram-positive S. aureus and Gram-negative bacteria E. coli, where the Ag-ZIF-8 MOF exhibited improved antibacterial activities over ZIF-8 alone. Finally, the suggested material shows promise for reusable water filtration and disinfection against a variety of pollutants, providing a practical option to reduce environmental and public health threats.
Probabilistic Corrosion-free Service Life of the RCC Structures Made with OPC and Ultra-fine Slag-based Binders
Authors: S. Athibaranan  , P. Chandru  , K. Nandhini  , A. Dhanalaksmi  
Published: July 2024

The primary factor in the deterioration of reinforced concrete is chloride-induced corrosion. It has a direct impact on the service life of the structure and has been the subject of much research over the past 5 decades. One of the most cost-effective ways to lessen chloride-induced corrosion was identified to be the use of more supplementary cementitious material (SCM). Moreover, when SCMs are ground down to a smaller size, the resulting particles serve as microfillers, which have a significant impact on the improvement of mechanical properties and durability. This paper highlights the influence of grinding slag into an Ultra Fine Slag (UFS) on chloride ion penetration in the mortar specimen. A bulk diffusion test was performed on a mortar replaced with UFS which show a synergic reduction in chloride diffusivity in comparison with control mixes. In addition, an attempt is made on prediction of probabilistic service life estimation by Error function model.
Computational Investigation of Eco-Friendly Fiber Reinforced Composite with the Application of Ansys ACP for Automobiles
Authors: Sidhant Dubey  , Yugendra Kumar Sahu  , T. V. Arjunan  
Published: July 2024

Nowadays, natural fiber reinforced composites and their application in various components in the automotive industry are one of the important issues. They are also considered an important also considered an important alternative to classical materials such as metals. This case study highlights the importance of the automotive industry turning to sustainable materials and practices to address environmental issues and achieve sustainability goals. These findings contribute to the knowledge regarding the process of carbon emission reduction strategies in the automotive industry. In this study, composites were evaluated under static loading, creating similar conditions and loads, but adjusting the lamination process and performing tensile tests of each section. Since Ansys workbench ACP-(pre) results in different distortions while analyzing data, an Ansys ACP-(post) was used to obtain more accurate results. The results show that composite 2 (bamboo–sisal-jute) of the temporary lamination has the least deformation in the Z-direction and the smallest total value 33 mm and 13987 mm. This study shows that replacing conventional materials likecomposites of steel, aluminium, carbon fiber and glass fiber with Natural Fiber Reinforced Composites (NFRCs) can yield significant reductions in carbon emissions, with potential emission reductions of up to 21.92 times depending on the material replaced. This transition to NFRCs presents a promising strategy for achieving substantial environmental benefits in the automotive industry.
Strength Characteristics of Bentonite Nano Clay Stabilized with Addition of Lime, Fly Ash, and Silica Fume for Soil Environmental Sustainability
Authors: V. Sampathkumar  , K. Raja  , K. S. Navaneethan  , N. Jothi Lakshmi  , D. Ambika  , S. Manoj  , K. Sampath Kumar  
Published: July 2024

The suboptimal engineering characteristics, including low tensile strength and significant compressibility volumetric changes, are the significant challenges faced during the construction of sub-structures in expansive Soil. The instability of the sub-structure leads to the settlement of the structure and compromises its safety and endurance. This paper explores how Bentonite nano clay (BNC) reacts with varying proportions of lime, Fly Ash, and silica fume. The ratios used for these additives were 2%, 4%, and 6%. The Compaction Characteristics and the stress-strain curve are analyzed. Incorporating silica fume into BNC leads to a decrease in both the liquid limit and the plasticity index. Concurrently, this addition increases the plastic limit of the clay. As the percentage of silica fume in the mixture rises, there is a corresponding decrease in the maximum dry unit weight and an increase in the optimum moisture content. Additionally, the introduction of silica fume contributes to an increase in the free swell but also it has an increase in the swelling pressure of clay. These results suggest that silica fume stabilization is an effective method for treating expansive clay.
Fabrication and characterization of graphene oxide and reduced graphene oxide decorated diatomite composite materials and their adsorption performance for uranium ions
Authors: Yusan Sabriye , Mumcu Burak , López-Maldonado Eduardo A. , EL Kaim Billah Rachid , Bahsis Lahoucine 
Published: July 2024

Abstract
In this study, the composite materials based on the incorporation of diatomite of graphene oxide (GO)/reduced graphene oxide (rGO/diatomite) were developed and characterized by different techniques (SEM, XRD, FTIR and BET). Developed composites were used for the first time in the removal of uranium ions from aqueous solutions. In adsorption studies, parameters affecting the adsorption efficiency such as solution pH, temperature, contact time and initial U(VI) concentration were investigated using full factorial experimental design (FFED). ANOVA (analysis of variance) analysis within the 95 % confidence interval of the model applied to control the compatibility of the model and the experimental findings was examined. Langmuir, Freundlich and Temkin isotherms were used to determine the adsorption model and related parameters were calculated. In addition, adsorption thermodynamic parameters such as enthalpy, Gibbs free energy change and entropy were calculated. The uranium removal behaviour on GO/diatomite and rGO/diatomite was better characterized by the pseudo-second order and Langmuir models, indicating that uranium ions were chemically adsorbed onto composite materials. Additionally, it was observed that higher temperatures promoted the uranium removal on GO/diatomite and rGO/diatomite, suggesting that the removal process was a spontaneous endothermic and exothermic reaction, respectively. In addition, the adsorption of U(VI) with GO/diatomite and rGO/diatomite was investigated using density functional theory (DFT) study. Configuration and adsorption energy were determined. The GO/diatomite composite materials can be a promising candidate as an adsorbent for the removal of uranium from aqueous solutions.
Preparation of MnO2 modified winter melon-derived biochar for enhanced adsorption of U(VI) from aqueous solution
Authors: Zhou Qing , Du Yanjun , Feng Zihao , Ren Qi , Wang Yang , Chen Xiaoyong , Li Yang , Wang Yun 
Published: July 2024

Abstract
This work synthesized MnO2 modified winter melon-derived biochar (MnO2@WBC) using potassium permanganate oxidation/hydrothermal method for uranium(VI) removal. The factors influencing uranium(VI) adsorption by MnO2@WBC, including pH, adsorbent dosage, time, temperature, and initial U(VI) concentration were explored. The material’s performance was characterized, and the underlying mechanism of U(VI) removal was analyzed using various techniques. The characterization results indicated that the MnO2@WBC exhibited excellent dispersibility, suggesting that MnO2 effectively prevented WBC aggregation and enhanced the reactive surface area, and providing more active sites that can effectively promote the complexation and adsorption of U(VI). The process suggested a dominant role for chemical adsorption. The Freundlich isotherm model demonstrated a high degree of alignment with the observed adsorption behavior, indicating a predominantly multilayer adsorption process. Thermodynamic studies indicated that adsorption was a spontaneous endothermic process. The XPS analysis demonstrated that the adsorption process was primarily due to the formation of stable complexes with C–O, Mn–O–H and C=O. The results demonstrated that MnO2@WBC effectively removed U(VI), achieving a maximum adsorption capacity of 240.86 mg g−1 at a pH of 5.0. These findings provided a theoretical basis for the treatment of wastewater contaminated with U(VI).
Audit of caesarean deliveries in a tertiary care rural hospital of Bangalore, Karnataka, India
Authors: Sarvotham Ananya , Fathima A. Arshia , Indrani Chaitanya 
Published: July 2024

Background: Due to the rise in caesarean section rates Robsons classification system was adopted by WHO as a global standard to asses and audit the caesarean section rates within different hospitals, to reduce the caesarean section rates and the associated complications and also improve patient care along with it. The aim of the present study is to audit the number of caesarean deliveries in the hospital. Objective were to audit the caesarean deliveries in the institution using Robsons classification system as the starting point to find the contributing factors responsible for the caesarean section rates.
Methods: This retrospective hospital-based study was conducted at MVJMC and RH. The study included all pregnant patients undergoing caesarean section during the period of one year from March 2023 to February 2024. All the data was entered into Microsoft excel, sheet, which was then classified according to Robson’s classification system.
Results: In our study we noted that the highest percent of caesarean deliveries was noted among multigravida which contributed about 66.5 of the total caesarean deliveries and according to classification 35% of the caesarean deliveries was noted in group 3 with multiparous women of more than 37 weeks in spontaneous labour.
Conclusions: Globally accepted Robsons classification should be used for regular audits among hospitals to curb the caesarean section and its associated maternal and perinatal complications in order to improve patient care.
Comparison of the Effect of Fenugreek and Metformin on Clinical and Metabolic Status of Cases with Polycystic Ovary Syndrome: A Randomized Trial
Authors: Mirgaloybayat Shahla , Akbari Sene Azadeh , Jayervand Fatemeh , Vazirian Mahdi , Mohazzab Arash , Kazerooni Mitra 
Published: July 2024

Background: It is hypothesized that fenugreek seeds are a rich source of fiber with anti-diabetic effects, which can help to lower blood glucose in patients with polycystic ovary syndrome (PCOS). In this study, the clinical and metabolic effects of fenugreek were compared to those of metformin in women with PCOS aged 16-40 years.
Methods: In a randomized, triple-blind, parallel clinical trial, the efficacy of fenugreek 333 mg (n=55) was compared with metformin 500 mg (n=55), both administered three times a day in women with PCOS of reproductive age. Changes in some clinical outcomes and metabolic laboratory profile outcomes were evaluated at baseline and two months after the study.
Results: By the end of the intervention period, all investigated factors improved significantly in patients of both groups (p<0.05). Reduction in biometric indices (body mass index and waist–hip ratio), fasting blood sugar (FBS), and insulin resistance was significantly higher after metformin consumption (p<0.001). Metformin also significantly improved irregular menstruation (p=0.02). In contrast, fenugreek significantly improved patients' lipid profiles, including low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride (TG) compared to metformin (p<0.001). Both interventions improved the patient's hair loss and hirsutism.
Conclusion: Fenugreek cannot substitute metformin in PCOS treatment. However, regarding its lipid-lowering ability and low frequency of adverse effects, it can be used as an adjuvant treatment in PCOS, especially in PCOS patients with hyperlipidemia and severe hair loss.
Accumulated reserves hold back age-related neural compensation in speech-in-noise perception
Authors: Alain Claude , Zhang Lei , Ross Bernhard , Du Yi 
Published: July 2024

Abstract

Recruitment of neural activity or functional connectivity is commonly observed in older adults but poorly understood. We measured brain activity with fMRI during speech-in-noise tasks and assessed whether accumulated reserve accrued through musical training bolsters or holds back age-related neural compensation. Older musicians exhibited less upregulation of task-induced functional connectivity than older non-musicians in dorsal regions, which predicted better behavioral performance in older musicians. The findings suggest that accumulated reserve may hold back neural recruitment. Besides functional connectivity strength, we also found that older musicians showed more youth-like fine spatial patterns of functional connectivity than older non-musicians. However, benefits from visual lip movements were not specific to either hypothesis. Aligning with enhanced benefits in behavioral performance, older musicians showed more extensive functional connectivity enhancement, specifically in the right hemisphere, than older non-musicians. Our findings enlightened the intricate interplay between accumulated reserve and age-related neural compensation during speech in noise perception.
MHD Run-up Flow between Parallel Plates in the Presence of a Transverse Magnetic Field with Dynamic Wall Slip
Authors: Jha Basant K. , Yusuf K. L , Jibril H.M. 
Published: July 2024

Abstract

In this study, we investigate the impact of a uniform transverse magnetic field on the flow between parallel plates. We analyze both impulsive and uniformly accelerated motion of the lower plate in an open circuit system. The magnetic lines of force are assumed to be fixed either relative to the fluid (MFFRF) or fixed relative to the moving lower plate (MFFRP). The Laplace transform technique is employed to obtain the velocity field and skin friction. The obtained solutions are then inverted back into the time domain using a numerical inversion technique based on the Riemann-sum approximation. The results reveal that slip relaxation time affects velocity differently for impulsive versus accelerated motion. This research contributes to our understanding of magnetohydrodynamic (MHD) flow dynamics, with potential applications in aerospace engineering and material processing.
Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review
Authors: Grothman Allison , Ma William J , Tickner Kendra G , Martin Elliot A , Southern Danielle A , Quan Hude 
Published: July 2024

Abstract

Background
Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders.

Objective
This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones.

Methods
A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

Results
A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms.

Conclusions
Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general.
Identifying Marijuana Use Behaviors Among Youth Experiencing Homelessness Using a Machine Learning–Based Framework: Development and Evaluation Study
Authors: Deng Tianjie , Urbaczewski Andrew , Lee Young Jin , Barman-Adhikari Anamika , Dewri Rinku 
Published: July 2024

Background
Youth experiencing homelessness face substance use problems disproportionately compared to other youth. A study found that 69% of youth experiencing homelessness meet the criteria for dependence on at least 1 substance, compared to 1.8% for all US adolescents. In addition, they experience major structural and social inequalities, which further undermine their ability to receive the care they need.

Objective
The goal of this study was to develop a machine learning–based framework that uses the social media content (posts and interactions) of youth experiencing homelessness to predict their substance use behaviors (ie, the probability of using marijuana). With this framework, social workers and care providers can identify and reach out to youth experiencing homelessness who are at a higher risk of substance use.

Methods
We recruited 133 young people experiencing homelessness at a nonprofit organization located in a city in the western United States. After obtaining their consent, we collected the participants’ social media conversations for the past year before they were recruited, and we asked the participants to complete a survey on their demographic information, health conditions, sexual behaviors, and substance use behaviors. Building on the social sharing of emotions theory and social support theory, we identified important features that can potentially predict substance use. Then, we used natural language processing techniques to extract such features from social media conversations and reactions and built a series of machine learning models to predict participants’ marijuana use.

Results
We evaluated our models based on their predictive performance as well as their conformity with measures of fairness. Without predictive features from survey information, which may introduce sex and racial biases, our machine learning models can reach an area under the curve of 0.72 and an accuracy of 0.81 using only social media data when predicting marijuana use. We also evaluated the false-positive rate for each sex and age segment.

Conclusions
We showed that textual interactions among youth experiencing homelessness and their friends on social media can serve as a powerful resource to predict their substance use. The framework we developed allows care providers to allocate resources efficiently to youth experiencing homelessness in the greatest need while costing minimal overhead. It can be extended to analyze and predict other health-related behaviors and conditions observed in this vulnerable community.