Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.

The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting in inconsistent and imprecise evaluations. To address this issue and facilitate early intervention, there is a growing interest in utilizing objective criteria such as Electroencephalography (EEG) features analyzed through Artificial Intelligence (AI) techniques. This systematic review explores the advances in EEG-based detection of MDD using both shallow and deep learning methods, with the aim of enhancing understanding of the neural mechanisms underlying the disorder and identifying potential biomarkers for its diagnosis.

Retraction notice to "Enhanced electrochemical performance with exceptional capacitive retention in Ce-Co MOFs/TiCT nanocomposite for advanced supercapacitor applications" [Heliyon 10 (2024) e36540].

[This retracts the article DOI: 10.1016/j.heliyon.

Next-gen ferulic acid-stabilized gold nanoparticles: exploring their sensing capabilities and therapeutic efficacy.

Aim: This study focuses on the synthesis and evaluation of innovative gold nanoparticles (AuNps) stabilized by short-chain ferulic acid (FA), specifically 4-hydroxy-3-methoxy-cinnamic acid.

Methods: We analyzed the size and distribution of FA-TSC-AuNps and FA-NaBH4-AuNps, with the reduction kinetics of Au3 + to Au0. The electronic and optical properties of these AuNps were scrutinized using UV-visible, AFM, and FT-IR.

Modulated theta band frequency with binaural beat stimulation correlates with improved cognitive scores in Alzheimer's patients.

Introduction: Alzheimer's disease (AD) affects 50 million individuals worldwide, a number projected to triple by 2050. Due to discomfort through electrical and magnetic neuromodulation technologies, this is the first study to propose the potential of auditory binaural beat (BB) stimulation at an alpha frequency (10 Hz) for enhancing cognitive and neurological outcomes in AD patients.

Methods: Twenty-five patients were divided into the experimental-Group ( = 15) and control-Group ( = 10).

Rapid and Facile Organic Ion-Associate Liquid-Phase Extraction and Spectrophotometric Quantification of Nitrite in Environmental Water Samples.

Nitrite is a health and environmental hazard and pollutes water sources globally, but sensitive, rapid, and facile quantification methods are lacking. Herein, we report a method for extracting and quantifying low-concentration nitrite in surface water using minimal sample and solvent volumes. The nitrite reacted with sulfanilamide and -1-naphthylethylenediammonium dichloride (NED), yielding an azo dye for extraction into an organic ion-associate liquid phase (IALP) formed in situ using ethylhexyloxypropylammonium and dodecyl sulfate ions.

Negative Enthalpy Doping Stabilizes P2-Type Oxides Cathode for High-Performance Sodium-Ion Batteries.

P2-type NaNiMnO (NNMO) as cathode material for sodium-ion batteries (SIBs) largely suffers from continuous accumulation of local stress caused by destructive structural evolution and irreversible oxygen loss upon cycling, leading to rapid capacity degradation. Herein, a strategy of negative enthalpy doping (NED), wherein transition metal (TM) sites are substituted with 0.01 mol each Sn, Sb, Cu, Ti, Mg, and Zn to increase the stability of the TM layers, is proposed.

The compressive strength development and pH of cement mortars incorporating high volume supplementary cementitious materials under accelerated curing.

The durability and service life of concrete structures is primarily dependent on the performance of cement-based material (CBM). This performance of CBMs is linked to their pH directly. Literature suggests that when supplementary cementitious materials (SCMs) are used, the pH of CBMs decreases as a result Ca(OH)₂ consumption during the Pozzolanic reaction (PR).

Roll-to-plate printable RGB achromatic metalens for wide-field-of-view holographic near-eye displays.

Metalenses show promise for replacing conventional lenses in virtual reality systems, thereby facilitating lighter and more compact near-eye displays (NEDs). However, at the centimetre scale necessary for practical applications, previous multiwavelength achromatic metalenses have faced challenges in mass production and exhibited a low numerical aperture (NA), which limits their practical application in NEDs. Here we introduce a centimetre-scale red, green and blue achromatic metalens fabricated using a roll-to-plate technique and explore its potential for practical applications in NEDs.

Droplet-Based Triboelectric Nanogenerators with Needle Electrodes for Efficient Water Energy Harvesting.

With the advent of droplet-based triboelectric nanogenerators (D-TENGs), methods for converting raindrop kinetic energy to electrical energy have developed rapidly. However, current D-TENG designs suffer from slow solid-liquid interface separation speeds and susceptibility to liquid residues. These issues compromise the output performance of D-TENGs and limit their applications in high-power electrical appliances.

Expression profile and characterization of respiratory burst oxidase homolog genes in rice under MeJA, SA and Xoo treatments.

Respiratory burst oxidase homologs (Rboh) genes is essential for synthesizing reactive oxygen species, which play a crucial role in environmental stress response. The Rboh gene family has been studied in model plants such as Arabidopsis. Nevertheless, Rboh remained largely unexplored in Rice (Oryza sativa L.

Applications of MOF-Based Nanocomposites in Heat Exchangers: Innovations, Challenges, and Future Directions.

Metal-organic frameworks (MOFs) have garnered significant attention in recent years for their potential to revolutionize heat exchanger performance, thanks to their high surface area, tunable porosity, and exceptional adsorption capabilities. This review focuses on the integration of MOFs into heat exchangers to enhance heat transfer efficiency, improve moisture management, and reduce energy consumption in Heating, Ventilation and Air Conditioning (HVAC) and related systems. Recent studies demonstrate that MOF-based coatings can outperform traditional materials like silica gel, achieving superior water adsorption and desorption rates, which is crucial for applications in air conditioning and dehumidification.

[Important medical-dental interactions. Medication and older people with frailty in the oral healthcare practice].

With ageing, chances of illness and physical problems increase; these are often accompanied by the need to use medication. With today s ageing of the population, oral healthcare providers will see increasing numbers of older people with multimorbidity and polypharmacy (using 5 or more different medicines) in their practice. Due to likely complications and possible interactions as a result of oral healthcare interventions, knowledge about health issues and medication used is of great importance.

Effect of polishing methods on Candida albicans adhesion and contributing factors in heat-cured acrylic dentures: an in-vitro comparative study.

Introduction: Heat-polymerized acrylic resin is widely used in prosthodontics due to its various advantages. However, its susceptibility to microbial colonization, influenced by surface properties such as roughness, water sorption, and free surface energy, poses challenges in maintaining oral health. Microbial adhesion, particularly of Candida albicans, plays a significant role in the overall microbial load on the denture surface, predisposing several oral and health related complications.

Size Distribution and Pathogenic Potential of Culturable Airborne Clostridium spp. in a Suburb of Toyama City, Japan.

Clostridium spp. are anaerobic, Gram-positive, spore-forming bacteria comprising more than 150 species, some of which are important pathogens of humans and animals. Members of this genus have been isolated from a number of environments, but are rarely found in the atmosphere.

Context aware machine learning techniques for brain tumor classification and detection - A review.

Background: Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. Malignant gliomas, due to their aggressive growth and dismal prognosis, stand out among various brain tumor types. Recent advancements in understanding the genetic abnormalities that underlie these tumors have shed light on their histo-pathological and biological characteristics, which support in better classification and prognosis.

Assessing climate sensitivity of the Upper Indus Basin using fully distributed, physically-based hydrologic modeling and multi-model climate ensemble approach.

The Upper Indus Basin (UIB) of Pakistan is home to three largest mountain ranges: Karakoram, Hindukush, and the Himalayas. There's a hovering danger of reduced water resources due to climate-induced warming, since the Indus Basin relies mostly on snow and glacier melt runoff from these mountains. In this study, the hydrology of these areas is studied to estimate variability in water resources under changing climate.

Optimized energy management and small cell activation in ultra-dense networks through a data-driven approach.

With the exponential expansion of the wireless industry, the demand for improved data network throughput, capacity, and coverage has become critical. Heterogeneous ultra-dense networks (UDNs) have emerged as a promising solution to meet these escalating requirements for high data rates and capacity. However, effectively deploying and managing small cells within UDNs presents significant challenges, particularly amidst varying traffic loads and the necessity for efficient resource utilization to minimize energy consumption, especially in environments with high interference levels.

Self-reported illnesses in Thatta: Evidence from a rural and underdeveloped district in Sindh province, Pakistan.

Introduction: Self-reported illnesses (SRI) surveys are widely used as a low-cost substitute for weak Disease Surveillance Systems in low- and low-middle-income countries. In this paper, we report findings of a district-level disease prevalence survey of all types of illnesses including chronic, infectious, injuries and accidents, and maternal and child health in a rural district in Pakistan.

Methods: A district-level survey was conducted in Thatta in 2019 with a population-representative sample of all ages (n = 7811) a.

leaves extract-mediated synthesis of ZnO nanostructures for the enhanced photocatalytic oxidation of erythrosine.

This study was focused on the development of ZnO nanostructures for the efficient oxidation of erythrosine dye and for studying the antibacterial activity of ZnO. It was observed that the phytochemicals from leaves modified the size, shape, crystalline properties and surface chemical composition of the ZnO nanostructures. ZnO nanostructures synthesized with 15 mL leaves extract (S-15) demonstrated highly efficient oxidation of erythrosine dye under the illumination of natural sunlight.

Machine learning and Fuzzy logic fusion approach for osteoporosis risk prediction.

The metabolic disorder osteoporosis has affected a humongous number of individuals globally. Its progression can be slowed down by modifying lifestyle risk factors and by following appropriate treatment. In this research work, modifiable risk factors of osteoporosis have been considered.

Circulating Adenoid Cystic Carcinoma associated MYB transcripts enable rapid and sensitive detection of metastatic disease in blood liquid biopsies.

Adenoid cystic carcinoma (ACC) is a rare and lethal malignancy that originates in secretory glands of the head and neck. A prominent molecular feature of ACC is the overexpression of the proto-oncogene MYB. ACC has a poor long-term survival due to its high propensity for recurrence and protracted metastasis.

Comprehensive Insights into Potential Metabolic Functions of Myxococcota in Activated Sludge Systems.

Myxobacteria, belonging to the phylum Myxococcota, are ubiquitous in soil, marine, and other environments. A recent metagenomic sequencing ana-lysis showed that Myxococcota are predominant in activated sludge systems; however, their metabolic traits remain unclear. In the present study, we exami-ned the potential biological functions of 46 metagenomic bins of Myxococcota reconstructed from activated sludge samples from four municipal sewage treatment plants.

A novel fuzzy three-valued logic computational framework in machine learning for medicine dataset.

For consideration of uncertainties of a medicine dataset, a new conceptual architecture fuzzy three-valued logic is introduced in this research work. The proposed concept is applied to the heart disease dataset for the assessment of heart disease risk in individuals. By comparison of three binary (0,1) input variables, the variables' uncertainties and their collective impact can be analyzed that provide complete information leading to better outcome prediction.

Modeling peak ground acceleration for earthquake hazard safety evaluation.

This paper presents a ground motion prediction (GMP) model using an artificial neural network (ANN) for shallow earthquakes, aimed at improving earthquake hazard safety evaluation. The proposed model leverages essential input variables such as moment magnitude, fault type, epicentral distance, and soil type, with the output variable being peak ground acceleration (PGA) at 5% damping. To develop this model, 885 data pairs were obtained from the Pacific Engineering Research Center, providing a robust dataset for training and validation.

Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.

Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective.