DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome.

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge.

Comprehensive Analysis of Pressure Drop Phenomena in Rotating Packed Bed Distillation: An In-Depth Investigation.

A rotating packed bed (RPB) is an innovative intensification technology that improves its separation capabilities in high-gravity conditions. This process increases efficiency with smaller equipment size and footprint than conventional packed columns. Although significant advancements have been made regarding RPBs, most studies only focused on single or dual rotor configurations in addressing dry pressure drop.

Dilute acid-assisted microbubbles-mediated ozonolysis of Eucheuma denticulatum phycocolloid for biobased L-lactic acid production.

Biobased L-lactic acid (L-LA) appeals to industries; however, existing technologies are plagued by limited productivity and high energy consumption. This study established an integrated process for producing macroalgae-based L-LA from Eucheuma denticulatum phycocolloid (EDP). Dilute acid-assisted microbubbles-mediated ozonolysis (DAMMO) was selected for the ozonolysis of EDP to optimize D-galactose recovery.

Tungsten-Doped ZnO as an Electron Transport Layer for Perovskite Solar Cells: Enhancing Efficiency and Stability.

This study delves into enhancing the efficiency and stability of perovskite solar cells (PSCs) by optimizing the surface morphologies and optoelectronic properties of the electron transport layer (ETL) using tungsten (W) doping in zinc oxide (ZnO). Through a unique green synthesis process and spin-coating technique, W-doped ZnO films were prepared, exhibiting improved electrical conductivity and reduced interface defects between the ETL and perovskite layers, thus facilitating efficient electron transfer at the interface. High-quality PSCs with superior ETL demonstrated a substantial 30% increase in power conversion efficiency (PCE) compared to those employing pristine ZnO ETL.

Synergistic adsorption of methylene blue using ternary composite of phosphoric acid geopolymer, calcium alginate, and sodium lauryl sulfate.

The removal of dyes from the aquatic ecosystem is necessary being a major threat to life. For enhanced remediation of methylene blue (MB) dye, a new ternary biopolymer-geopolymer-surfactant composite adsorbent is synthesized by combining phosphoric acid geopolymer (PAGP), calcium alginate (Alg), and sodium lauryl sulfate (SLS). During the synthesis of the composites, PAGP and SLS were mixed with the alginate matrix, producing porous hybrid beads.

Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO nanofluids.

The study investigates the heat transfer and friction factor properties of ethylene glycol and glycerol-based silicon dioxide nanofluids flowing in a circular tube under continuous heat flux circumstances. This study tackles the important requirement for effective thermal management in areas such as electronics cooling, the automobile industry, and renewable energy systems. Previous research has encountered difficulties in enhancing thermal performance while handling the increased friction factor associated with nanofluids.

Altered functional connectivity subserving expressed emotion environments in schizophrenia: An fNIRS study.

Living in high-expressed emotion (EE) environments, characterized by critical, hostile, or over-involved family attitudes, has been linked to increased relapse rates among individuals with schizophrenia (SZ). In our previous work (Wang et al., 2023), we conducted the first feasibility study of using functional near-infrared spectroscopy (fNIRS) with our developed EE stimuli to examine cortical hemodynamics in SZ.

Optimization of Operational Parameters Using Artificial Neural Network and Support Vector Machine for Bio-oil Extracted from Rice Husk.

Bio-oil production from rice husk, an abundant agricultural residue, has gained significant attention as a sustainable and renewable energy source. The current research aims to employ artificial neural network (ANN) and support vector machine (SVM) modeling techniques for the optimization of operating parameters for bio-oil extracted from rice husk ash (RHA) through pyrolysis. ANN and SVM methods are employed to model and optimize the operational conditions, including temperature, heating rate, and feedstock particle size, to enhance the yield and quality of bio-oil.

Removal of Carbon Dioxide and Hydrogen Sulfide from Natural Gas Using a Hybrid Solvent of Monoethanolamine and -Methyl 2-Pyrrolidone.

The main goal of traditional methods for sweetening natural gas (NG) is to remove hydrogen sulfide (HS) and significantly lower carbon dioxide (CO). However, when NG processes are integrated into the carbon capture and storage (CCS) framework, there is potential for synergy between these two technologies. A steady-state model utilizing a hybrid solvent consisting of -methyl-2-pyrrolidone (NMP) and monoethanolamine (MEA) has been developed to successfully anticipate the CO and HS capture process from NG.

Microplastics and Co-pollutants in soil and marine environments: Sorption and desorption dynamics in unveiling invisible danger and key to ecotoxicological risk assessment.

Microplastics (MPs) and their co-pollutants pose significant threats to soil and marine environments, necessitating understanding of their colonization processes to combat the plastic pandemic and protect ecosystems. MPs can act as invisible carriers, concentrating and transporting pollutants, leading to a more widespread and potentially toxic impact than the presence of either MPs or the pollutants alone. Analyzing the sorption and desorption dynamics of MPs is crucial for understanding pollutants amplification and predicting the fate and transport of pollutants in soil and marine environments.

A review on recent advances of cellulose acetate membranes for gas separation.

This review thoroughly investigates the wide-ranging applications of cellulose-based materials, with a particular focus on their utility in gas separation processes. By focusing on cellulose acetate (CA), the review underscores its cost-effectiveness, robust mechanical attributes, and noteworthy CO solubility, positioning it as a frontrunner among polymeric gas separation membranes. The synthesis techniques for CA membranes are meticulously examined, and the discourse extends to polymeric blend membranes, underscoring their distinct advantages in gas separation applications.

An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model.

Over the past few years, the widespread outbreak of COVID-19 has caused the death of millions of people worldwide. Early diagnosis of the virus is essential to control its spread and provide timely treatment. Artificial intelligence methods are often used as powerful tools to reach a COVID-19 diagnosis via computed tomography (CT) samples.

Adsorption of terbutaline β-agonists from wastewater by mechano-synthesized iron oxide nanoparticles modified copper (II) isonicotinate metal-organic framework.

Frequent detection of terbutaline in wastewater highlights its potential risks to human health associated in the environment. Exposure to terbutaline through contaminated water sources or food chain have adverse effects to human health. This work emphasized on the removal of terbutaline from wastewater using adsorption technology.

A comprehensive study on the impact of human hair fiber and millet husk ash on concrete properties: response surface modeling and optimization.

Revolutionizing construction, the concrete blend seamlessly integrates human hair (HH) fibers and millet husk ash (MHA) as a sustainable alternative. By repurposing human hair for enhanced tensile strength and utilizing millet husk ash to replace sand, these materials not only reduce waste but also create a durable, eco-friendly solution. This groundbreaking methodology not only adheres to established structural criteria but also advances the concepts of the circular economy, representing a significant advancement towards environmentally sustainable and resilient building practices.

Nickel augmented biochar for sustaining produced water treatment to decarbonize oil and gas industrial waste using anaerobic-aerobic granular cylindrical periodic discontinuous batch reactors.

This study assessed the efficacy of granular cylindrical periodic discontinuous batch reactors (GC-PDBRs) for produced water (PW) treatment by employing eggshell and waste activated sludge (WAS) derived Nickel (Ni) augmented biochar. The synthesized biochar was magnetized to further enhance its contribution towards achieving carbon neutrality due to carbon negative nature, Carbon dioxide (CO) sorption, and negative priming effects. The GC-PDBR1 and GC-PDBR2 process variables were optimized by the application of central composite design (CCD).

The effect of consumer-perceived COVID-19 ad value on health-protective behavior: Mediating role of engagement.

Though the pandemic has passed, social media-based messaging continues to exhibit COVID-19-related cues (e.g., wearing a face mask to stay safe), continuing to foster consumers' health-protective behavior.

Development of inorganic and mixed matrix membranes for application in toxic dyes-contaminated industrial effluents with in-situ treatments.

Dyes are the most ubiquitous organic pollutants in industrial effluents. They are highly toxic to both plants and animals; thus, their removal is paramount to the sustainability of ecosystem. However, they have shown resistance to photolysis and various biological, physical, and chemical wastewater remediation processes.

Deterrents to the IoT for smart buildings and infrastructure development: A partial least square modeling approach.

Implementing Internet of things (IoT) technology in the context of intelligent buildings and infrastructure development has garnered significant attention within the construction sector. Nonetheless, the implementation of IoT could be improved by assessing various barriers. The purpose of this study was to examine the obstacles related to the adaptation of IoT techniques within the construction sector, as well as the effects on the advancement of intelligent building and infrastructure systems.

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach.

Background: Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.

Enhancing the Permeate Flux Improvement of Direct Contact Membrane Distillation Modules with Inserted S-Ribs Carbon-Fiber Filaments.

Three widths of manufacturing S-ribs carbon-fiber filaments acting as turbulence promoters were implemented into the flow channel of direct contact membrane distillation (DCMD) modules to augment the permeate flux improvement in the present study. Attempts to reduce the disadvantageous temperature polarization effect were made by inserting S-ribs turbulence promoters in improving pure water productivity, in which both heat- and mass-transfer boundary layers were diminished due to creating vortices in the flow pattern and increasing turbulence intensity. The temperature polarization coefficient ttemp was studied and found to enhance device performance (less thermal resistance) under inserting various S-ribs carbon-fiber thicknesses and operating both cocurrent- and countercurrent-flow patterns.

Sand screens application and performance for sand control: A review of selection criteria, screen materials, and causes of failure.

Production of sand from oil and gas wells necessitates the application of techniques for sand control. Sand screens are widely used. However, they are susceptible to failures, often stemming from improper selection and design.

Deep deterministic policy gradient algorithm: A systematic review.

Deep Reinforcement Learning (DRL) has gained significant adoption in diverse fields and applications, mainly due to its proficiency in resolving complicated decision-making problems in spaces with high-dimensional states and actions. Deep Deterministic Policy Gradient (DDPG) is a well-known DRL algorithm that adopts an actor-critic approach, synthesizing the advantages of value-based and policy-based reinforcement learning methods. The aim of this study is to provide a thorough examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG.

Evaluation of the Oil Recovery Potential and Cost Implication Analysis of Alternative ASP Formulations for Sandstone and Carbonate Reservoirs.

This study explores alternative chemical agents to enhance oil recovery in sandstone and carbonate reservoirs, aiming to address limitations in alkali-surfactant-polymer (ASP) flooding. Existing ASP methods face technical and environmental challenges, prompting research into alternative chemical agents. However, there are limited field deployments of these alternative chemical agents due to high costs, and ternary combinations of these agents remain unexplored.