Investigation of androgen receptor antagonist compounds present in influent and effluent from a wastewater works

A wide range of synthetic chemicals and their metabolites present in the environment can antagonise the receptor activity of androgen hormones present in wildlife and humans. With increasing global production of new synthetic chemicals, little is known about their environmental fate, health consequences and end-points. This study was conducted to identify and characterise chemicals with anti-androgenic activity present in wastewater influent and effluent. This study was undertaken by applying a combination of biological and analytical chemistry techniques involving Solid Phase Extraction (SPE), High Performance Liquid Chromatography (HPLC) and an in vitro steroid receptor assay for profiling and characterising extracts of grab influent and effluent wastewater samples using a toxicity identification and evaluation (TIE) procedure. Initial work revealed variable recoveries of anti-androgenic activity from SPE of wastewaters. Therefore SPE methodology to screen wastewater samples was developed using a mixture of selected compounds which possess a range of polarities (log Kow). Their recoveries from SPE were measured by HPLC protocol and ranged from 95- 100%. The mean±SD and % RSD values of the analysed wastewater replicates were 3.20±0.03 mgFeq/L and 0.78% for influent and 0.22±0.01 mgFeq/L and 3.80% for effluent samples. The recoveries of wastewater extracts after fractionation were between 78.6% and 99.6%. Fractions containing anti-androgenic activity were analysed by Gas Chromatography Mass Spectrometry (GC-MS). A number of household chemicals were detected in both influent and effluent wastewater fractions that contained antiandrogenic activity. These included the anti-bacterial agents- triclosan, chlorophene, dichlorophene, chloroxylenol, the musk fragrance galaxolide, the flame retardantstris( 1-chloro-2-propyl)phosphate (TCPP) and tris(2-butoxyethyl)phosphate (TBEP), polymer plasticizer n-butylbenzenesulfonamide (NBBSA) and bisphenol A (BPA) which is a chemical associated with the polycarbonate usage. The anti-androgenic potency of pure contaminants compared with that of flutamide ranged from 0.04 (TCPP) to 13.40 (chlorophene). Anti-androgenic activity of 1.69 and 2.00% was recovered from the fractions of the effluent and influent samples respectively indicating that AA of about 98% are yet to be recovered. This work reveals for the first time that over 12 contaminants contribute to the total anti-androgenic activity present in wastewater effluent and that a number of compounds commonly used in household products (such as chlorophene, triclosan and NBBSA) are predominant anti-androgens in wastewater effluents

Improved Biocompatibility in Laser-Polished Implants

This research aims to enhance the surface quality, mechanical properties, and biocompatibility of PEEK (polyether–ether–ketone) biomimetic dental implants through laser polishing. The objective is to improve osseointegration and implant durability by reducing surface roughness, increasing hydrophilicity, and enhancing mechanical strength. The methodology involved fabricating PEEK implants via FDM and applying laser polishing. The significant findings showed a 66.7% reduction in surface roughness, Ra reduced from 2.4 µm to 0.8 µm, and a 25.3% improvement in hydrophilicity, water contact angle decreased from 87° to 65°. Mechanical tests revealed a 6.3% increase in tensile strength (96 MPa to 102 MPa) and a 50% improvement in fatigue resistance (100,000 to 150,000 cycles). The strength analysis result showed a 10% increase in stiffness storage modulus from 1400 MPa to 1500 MPa. Error analysis showed a standard deviation of ±3% across all tests. In conclusion, laser polishing significantly improves the surface, mechanical, and biological performance of PEEK implants, making it a promising approach for advancing biomimetic dental implant technology

Renewable Energy Credits Transforming Market Dynamics

This research uses advanced statistical methods to examine climate change mitigation policies’ economic and environmental impacts. The primary objective is to assess the effectiveness of carbon pricing, renewable energy subsidies, emission trading schemes, and regulatory standards in reducing CO 2 emissions, fostering economic growth, and promoting employment. A mixed-methods approach was employed, combining regression analysis, cost–benefit analysis (CBA), and computable general equilibrium (CGE) models. Data were collected from national and global databases, and sensitivity analyses were conducted to ensure the robustness of the findings. Key findings revealed a statistically significant reduction in CO 2 emissions by 0.45% for each unit increase in carbon pricing (p &lt; 0.01). Renewable energy subsidies were positively correlated with a 3.5% increase in employment in the green sector (p &lt; 0.05). Emission trading schemes were projected to increase GDP by 1.2% over a decade (p &lt; 0.05). However, chi-square tests indicated that carbon pricing disproportionately affects low-income households (p &lt; 0.05), highlighting the need for compensatory policies. The study concluded that a balanced policy mix, tailored to national contexts, can optimise economic and environmental outcomes while addressing social equity concerns. Error margins in GDP projections remained below ±0.3%, confirming the models’ reliability.</p

Innovative Approaches to Magnesium Batteries Optimisation and Impact on Voltage Stability and Internal Resistance

This research explores the enhancement of electrochemical performance in magnesium batteries by optimising magnesium alloy anodes, explicitly focusing on Mg-Al and Mg-Ag alloys. The study’s objective was to determine the impact of alloy composition on anode voltage stability and overall battery efficiency, particularly under extended cycling conditions. The research assessed the anodes’ voltage behaviour and internal resistance across magnesium bis(trifluoromethanesulfonyl)imide (Mg(TFSI)2) electrolyte formulations using a systematic setup involving cyclic voltammetry on the anode and electrochemical impedance spectroscopy. The Mg-Al alloy demonstrated superior performance, with minimal voltage drop and lower resistance increase than the Mg-Ag alloy. The results showed that the Mg-Al alloy maintained over 85% energy efficiency after 100 cycles, significantly outperforming the Mg-Ag alloy, which exhibited increased degradation and efficiency reduction to approximately 80%. These findings confirm that incorporating aluminium into magnesium anodes stabilises the anode voltage and enhances the overall battery efficiency by mitigating degradation mechanisms. Consequently, the Mg-Al alloy is identified as an up-and-coming candidate for use in advanced battery technologies, offering energy density and cycle life improvements. This study lays the groundwork for future research to refine magnesium alloy compositions further to boost battery performance

Renewable Energy Credits Transforming Market Dynamics

This research uses advanced statistical methods to examine climate change mitigation policies’ economic and environmental impacts. The primary objective is to assess the effectiveness of carbon pricing, renewable energy subsidies, emission trading schemes, and regulatory standards in reducing CO 2 emissions, fostering economic growth, and promoting employment. A mixed-methods approach was employed, combining regression analysis, cost–benefit analysis (CBA), and computable general equilibrium (CGE) models. Data were collected from national and global databases, and sensitivity analyses were conducted to ensure the robustness of the findings. Key findings revealed a statistically significant reduction in CO 2 emissions by 0.45% for each unit increase in carbon pricing (p &lt; 0.01). Renewable energy subsidies were positively correlated with a 3.5% increase in employment in the green sector (p &lt; 0.05). Emission trading schemes were projected to increase GDP by 1.2% over a decade (p &lt; 0.05). However, chi-square tests indicated that carbon pricing disproportionately affects low-income households (p &lt; 0.05), highlighting the need for compensatory policies. The study concluded that a balanced policy mix, tailored to national contexts, can optimise economic and environmental outcomes while addressing social equity concerns. Error margins in GDP projections remained below ±0.3%, confirming the models’ reliability.</p

Integration of sustainable and net-zero concepts in shape-memory polymer composites to enhance environmental performance

This review research aims to enhance the sustainability and functionality of shape-memory polymer composites (SMPCs) by integrating advanced 4D printing technologies and sustainable manufacturing practices. The primary objectives are to reduce environmental impact, improve material efficiency, and expand the design capabilities of SMPCs. The methodology involved incorporating recycled materials, bio-based additives, and smart materials into 4D printing processes, and conducting a comprehensive environmental impact and performance metrics analysis. Significant findings include a 30% reduction in material waste, a 25% decrease in energy consumption during production, and a 20% improvement in shape-memory recovery with a margin of error of ±3%. Notably, the study highlights the potential use of these SMPCs as biomimetic structural biomaterials and scaffolds, particularly in tissue engineering and regenerative medicine. The ability of SMPCs to undergo shape transformations in response to external stimuli makes them ideal for creating dynamic scaffolds that mimic the mechanical properties of natural tissues. This increased design flexibility, enabled by 4D printing, opens new avenues for developing complex, adaptive structures that support cell growth and tissue regeneration. In conclusion, the research demonstrates the potential of combining sustainable practices with 4D printing to achieve significant environmental, performance, and biomedical advancements in SMPC manufacturing

Machine Learning's Role in Achieving Global Net Zero Emissions

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets

Machine Learning's Role in Achieving Global Net Zero Emissions

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets

Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant

This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds’ mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R2 scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics

Health financing in Malawi: Evidence from National Health Accounts

BACKGROUND: National health accounts provide useful information to understand the functioning of a health financing system. This article attempts to present a profile of the health system financing in Malawi using data from NHA. It specifically attempts to document the health financing situation in the country and proposes recommendations relevant for developing a comprehensive health financing policy and strategic plan. METHODS: Data from three rounds of national health accounts covering the Financial Years 1998/1999 to 2005/2006 was used to describe the flow of funds and their uses in the health system. Analysis was performed in line with the various NHA entities and health system financing functions. RESULTS: The total health expenditure per capita increased from US$12 in 1998/1999 to US$25 in 2005/2006. In 2005/2006 public, external and private contributions to the total health expenditure were 21.6%, 60.7% and 18.2% respectively. The country had not met the Abuja of allocating at least 15% of national budget on health. The percentage of total health expenditure from households' direct out-of-pocket payments decreased from 26% in 1998/99 to 12.1% in 2005/2006. CONCLUSION: There is a need to increase government contribution to the total health expenditure to at least the levels of the Abuja Declaration of 15% of the national budget. In addition, the country urgently needs to develop and implement a prepaid health financing system within a comprehensive health financing policy and strategy with a view to assuring universal access to essential health services for all citizens