The expanding adoption of artificial intelligence systems across high-impact sectors has catalyzed concerns regarding inherent biases and discrimination, leading to calls for greater transparency and accountability. Algorithm auditing has emerged as a pivotal method to assess fairness and mitigate risks in applied machine learning models. This systematic literature review comprehensively analyzes contemporary techniques for auditing the biases of black-box AI systems beyond traditional software testing approaches. An extensive search across technology, law, and social sciences publications identified 22 recent studies exemplifying innovations in quantitative benchmarking, model inspections, adversarial evaluations, and participatory engagements situated in applied contexts like clinical predictions, lending decisions, and employment screenings. A rigorous analytical lens spotlighted considerable limitations in current approaches, including predominant technical orientations divorced from lived realities, lack of transparent value deliberations, overwhelming reliance on one-shot assessments, scarce participation of affected communities, and limited corrective actions instituted in response to audits. At the same time, directions like subsidiarity analyses, human-cent
The achievement of sustainable development in Kenya has been hindered by the prevalence of HIV. The effects of HIV on sustainable development have been given less academic attention. HIV prevalence prevents people from achieving good health and well-being, which then makes them unable to conduct activities that lead to sustainable economic growth. The paper found that the prevalence of HIV causes economic hardship, destroys human capital development and human resources by reducing life expectancy and increasing mortality rates. It was equally found that the prevalence of HIV undermines social stability and mobility, reduces economic investments, influences food insecurity and makes people vulnerable. The paper found that the prevalence of HIV reduces labor supply and productivity, increases the cost of health services, promote inequality and poverty. The paper found that the prevalence of HIV was caused by the failure to integrate religion, culture and science infrastructure to achieve a holistic treatment acceptance and adherence that would overcome all misconceptions people have towards the disease. The paper found that while science provides effective HIV treatments, religious and cultural perspectives often shape community attitudes toward the disease. It was found that engaging religious and cultural as well as health workers or health advocates can help reduce stigma and promote ART adherence by aligning treatment messages with faith-based principles. The paper found that the integration that incorporates religion, culture, and science into HIV interventions would promote a more inclusive healthcare system that respects diverse beliefs while ensuring evidence-based treatment is accessible and widely accepted. The study was conducted through a qualitative methodology. Data was collected from secondary sources that included published articles, books and occasional papers as well as reports. Collected data was interpreted and analyzed through document analysis techniques.
Given the increasing demand for sustainable energy sources and the challenges associated with the limited efficiency of solar cells, this review focuses on the application of gold quantum dots (AuQDs) in enhancing solar cell performance. Gold quantum dots, with their unique properties such as the ability to absorb ultraviolet light and convert it into visible light expand the utilization of the solar spectrum in solar cells. Additionally, these quantum dots, through plasmonic effects and the enhancement of localized electric fields, improve light absorption, charge carrier generation (electrons and holes), and their transfer. This study investigates the integration of quantum dots with gold plasmonic nanoparticles into the structure of solar cells. Experimental results demonstrate that using green quantum dots and gold plasmonic nanoparticles as intermediate layers leads to an increase in power conversion efficiency. This improvement highlights the significant impact of this technology on solar cell performance. Furthermore, the reduction in charge transfer resistance and the increase in short-circuit current are additional advantages of utilizing this technology. The findings of this research emphasize the high potential of gold quantum dots in advancing next-generation solar cell technology.
In this work, the structural transformations of a suboxide vacuum-deposited film of SiO1.3 composition annealed in an inert atmosphere in a wide temperature range of 100 °C–1100 °C were characterized by the reflection-transmission spectroscopy technique. The experimental spectroscopic data were used to obtain the spectra of the absorption coefficient α(hν) in the absorption edge region of the film. Based on their processing, the dependences of Urbach energy EU and optical (Tauc) bandgap Eo on the annealing temperature were obtained. An assessment of the electronic band gap (mobility gap) Eg was also carried out. Analysis of these dependences allowed us to trace dynamics of thermally stimulated disproportionation of the suboxide film and the features of the formation of nanocomposites consisting of amorphous and/or crystalline silicon nanoparticles in an oxide matrix.
This work investigated the photocatalytic properties of polymorphic nanostructures based on silica (SiO2) and magnetite (Fe3O4) for the photodegradation of tartrazine yellow dye. In this sense, a fast, easy, and cheap synthesis route was proposed that used sugarcane bagasse biomass as a precursor material for silica. The Fourier transform infrared (FTIR) spectroscopy results showed a decrease in organic content due to the chemical treatment with NaOH solution. This was confirmed through the changes promoted in the bonds of chromophores belonging to lignin, cellulose, and hemicellulose. This treated biomass was calcined at 800 ℃, and FTIR and X-ray diffraction (XRD) also confirmed the biomass ash profile. The FTIR spectrum showed the formation of silica through stretching of the chemical bonds of the silicate group (Si-O-Si), which was confirmed by DXR with the predominance of peaks associated with the quartz phase. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) confirmed the morphological and chemical changes due to the chemical and thermal treatments applied to this biomass. Using the coprecipitation method, we synthesized Fe3O4 nanoparticles (Np) in the presence of SiO2, generating the material Fe3O4/SiO2-Np. The result was the formation of nanostructures with cubic, spherical, and octahedral geometries with a size of 200 nm. The SEM images showed that the few heterojunctions formed in the mixed material increased the photocatalytic efficiency of the photodegradation of tartrazine yellow dye by more than two times. The degradation percentage reached 45% in 120 min of reaction time. This mixed material can effectively decontaminate effluents composed of organic pollutants containing azo groups.
This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.
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