Eco-friendly thick and wear-resistant nanodiamond composite hard coatings deposited on WC–Co substrates.

Nanodiamond composite (NDC) films, synthesized using an environmentally friendly PVD coaxial arc plasma deposition technique on commercial cemented carbide (Co6 wt%) substrates without the need for substrate heating, chemical etching of Co, and chemical gases. These NDC coatings, crafted under specific discharge power conditions (5.2 J/pulse, 120 V, and 1 Hz), with or without a substrate biasing (−100V, 40kHz, and 35% duty cycle), exhibit a distinctive nanostructure characterized by nanodiamond grains embedded in an amorphous carbon (a-C) matrix. Highlighting remarkable mechanical characteristics attributed to highly energetic ejected carbon ion. The coatings boast high hardness (H = 65–82 GPa), Young's modulus (E = 688–780 GPa), plasticity index (H/E = 0.094–0.105), and brittle fracture resistance (H3/E2 = 0.58–0.9 GPa). Additionally, these NDC films manifest a substantial thickness of 7 μm due to low internal stress, along with superior adhesion, anti-wear resistance, and a low friction coefficient (0.1–0.09) through dry sliding against an Al2O3 counterpart. Raman analysis substantiates the nanocomposite structure of the film, underscoring the influential role of biasing in enhancing the characteristics of these environmentally friendly and wear-resistant NDC coatings. Nevertheless, the application of a negative bias led to increased internal stress levels (1.28 to 4.53 GPa), adversely impacting the adhesion between the film and substrate, resulting in a decrease from HF3 to HF6 as per Rockwell C indentation. NDC coatings hold significant potential for extending the lifespan of cutting tools and improving overall machining performance

Hybrid renewable-Hydrogen energy systems and their role in the energy transition.

Global energy-related CO2 emissions grew by 1.1% in 2023, increasing 410 Mt to reach a new record of 37.4 Gt. Emissions from coal accounted for more than 65% of the increase in 2023. The global shortfall in hydropower generation due to droughts drove up emissions by around 170 Mt. Between 2019 and 2023, total energy-related emissions increased around 900 Mt. These emissions cause environmental concerns of air pollution (causing health issues), water contamination (affecting humans, animals and plants using it, land degradation or destruction from human activities (this lessens the quality and/or productivity of the land for agriculture, forestation, construction, etc.), climate change (destructive impacts include, but are not limited to, melting of polar ice, change in seasons, new illnesses, and change in the general climate situation), global warming (this results from the fossil fuel GHG emissions), effect on marine life (affecting shellfish and microscopic fish) and depletion of the ozone layer (loss of earth protection from the sun unsafe beams)

Comparative effect size distributions in strength and conditioning and implications for future research: a meta-analysis.

Controlled experimental designs are frequently used in strength and conditioning (S&C) to determine which interventions are most effective. The purpose of this large meta-analysis was to quantify the distribution of comparative effect sizes in S&C to determine likely magnitudes and inform future research regarding sample sizes and inference methods. Baseline and follow-up data were extracted from a large database of studies comparing at least two active S&C interventions. Pairwise comparative standardised mean difference effect sizes were calculated and categorised according to the outcome domain measured. Hierarchical Bayesian meta-analyses and meta-regressions were used to model overall comparative effect size distributions and correlations, respectively. The direction of comparative effect sizes within a study were assigned arbitrarily (e.g. A vs. B, or B vs. A), with bootstrapping performed to ensure effect size distributions were symmetric and centred on zero. The middle 25, 50, and 75% of distributions were used to define small, medium, and large thresholds, respectively. A total of 3874 pairwise effect sizes were obtained from 417 studies comprising 958 active interventions. Threshold values were estimated as: small = 0.14 [95%CrI: 0.12 to 0.15]; medium: = 0.29 [95%CrI: 0.28 to 0.30]; and large = 0.51 [95%CrI: 0.50 to 0.53]. No differences were identified in the threshold values across different outcome domains. Correlations ranged widely (0.06 ≤ r ≤0.36), but were larger when outcomes within the same outcome domain were considered. The finding that comparative effect sizes in S&C are typically below 0.30 and can be moderately correlated has important implications for future research. Sample sizes should be substantively increased to appropriately power controlled trials with pre-post intervention data. Alpha adjustment approaches used to control for multiple testing should account for correlations between outcomes and not assume independence

Advancing AI with green practices and adaptable solutions for the future. [Article summary]

Despite AI's achievements, how can its limitations be addressed to reduce computational costs, enhance transparency and pioneer eco-friendly practices

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things.

Embedded systems, including the Internet of Things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performance trade-offs and vulnerability to cyber-attacks. One approach to address these concerns is minimising computational overhead and adopting lightweight intrusion detection techniques. In this study, we propose a highly efficient model called Optimized Common Features Selection and Deep-Autoencoder (OCFSDA) for lightweight intrusion detection in IoT environments. The proposed OCFSDA model incorporates feature selection, data compression, pruning and deparameterization. We deployed the model on a Raspberry Pi4 using the TFLite interpreter by leveraging optimisation and inferencing with semi-supervised learning. Using the MQTT-IoT-IDS2020 and CICIDS2017 datasets, our experimental results demonstrate a remarkable reduction in the computation cost in terms of time and memory use. Notably, the model achieved an overall average accuracies of 99% and 97%, along with comparable performance on other important metrics such as precision, recall and F1-score. Moreover, the model accomplished the classification tasks within 0.30 and 0.12s using only 2KB of memory

A novel multi-factor fuzzy membership function - adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries.

In view of the unmeasurable state parameters of electric-vehicle lithium-ion batteries, this paper investigates a novel multi-factor fuzzy membership function - adaptive extended Kalman filter (MFMF-AEKF) algorithm for the online joint estimation of the state of charge and energy. Strong nonlinear characteristics of model parameters are characterized by considering multiple processing factors of electrochemical and diffusion effects for lithium-ion batteries and constructing an optimized multifactor coupling model. In the proposed MFMF-AEKF method, multi-space-scale factors are introduced to realize the numerical analysis of the multi-factor coupled model parameters and state estimation under dynamic working conditions of electric-vehicle lithium-ion batteries. The proposed MFMF-AEKF algorithm estimates the state of charge (SOC) with the overall best mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and maximum error (ME) values of 1.822%, 4.322%, 1.947%, and 2.954%, respectively, under challenging working conditions. And The MAE, MAPE, RMSE, and ME values for the state of energy (SOE) are 0.617%, 1.711%, 0.695%, and 1.011%, respectively. Both state estimation results are better than the traditional method. The proposed MFMF-AEKF algorithm has higher estimation accuracy which provides a feasible estimation algorithm for the joint SOC and SOE of lithium-ion batteries

Transdisciplinary and arts-centred approaches to stewardship and sustainability of urban nature.

This paper explores case studies of how artists working with scientists and land managers affiliated with the Urban Field Station Collaborative Arts Program (UFS Arts) are fostering new relations of care with urban nature and thereby informing landscape decisions. The 'wicked' problems related to sustainability demand novel, holistic approaches to transformation that engage multiple ways of knowing. We present 4 examples from UFS Arts by triangulating data across programmatic documentation, evaluation, and ethnographic materials from 2016-present. Matthew López-Jensen's Tree Love and Nikki Lindt’s Underground Sound Project sensitise us to the capacities of trees and forests through image and sound. Mary Mattingly’s Swale is a floating food forest that enacts new forms of community stewardship. The exhibition Who Takes Care of New York? maps the stories and practices of civic environmental groups. Three themes in these works suggest opportunities for transformation throughout the knowledge production cycle: posing novel questions, engaging multiple methodologies, and communicating ideas with the public. Through these transdisciplinary works, we learn things we could not have learned via traditional disciplinary or interdisciplinary work and assert that stewardship offers a pathway towards sustainability transforming management practices and landscape decisions by reshaping our relationships to community and the land

Detection-driven exposure-correction network for nighttime drone-view object detection.

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-Correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, non-linear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a Fine-grained Parameter Predictor (FPP) to estimate pixel-wise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on non-uniform illuminations in drone-captured images. In order to learn the non-linear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a Progressive Filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset

Extraterritoriality in East Asia: extraterritorial criminal jurisdiction in China, Japan, and South Korea.

Extraterritorial criminal jurisdiction is a seemingly novel, arcane subject. Belgium's efforts in relatively recent years to try and punish persons accused of some of the most serious crimes may help create this impression. It is, however, only partially true. Jurisprudentially and academically the topic is arguably approaching its centenary, whereas the subject's practical importance and relevance have today never been greater. Together, these facts underlie Danielle Ireland-Piper's book on extraterritorial criminal jurisdiction in East Asia

Co-gasification study of blends of municipal solid waste with sugarcane bagasse and rice husk using the Coats-Redfern method.

Rapid development in the current economic situation has led to an increase in carbon emissions and to find sustainable solution to deal with this problem. Co-gasification of biomass with municipal solid waste is gaining significant importance to utilize the energy content of both raw materials judiciously and efficiently. This current work includes the study of physico-chemical characterization, thermal decomposition of MSW, sugarcane bagasse, rice husk, and their blends with 30:70, 50:50, and 70:30 ratios. Employing a thermogravimetric analyzer (TGA) under controlled conditions, the Coats-Redfern approach integrated sixteen reaction models to determine kinetic and thermodynamic parameters. This study intends to interpret the influence of mixtures on activation energy and synergy effect of mixing two different materials to check its market compatibility. The physicochemical properties of the feedstocks showed good agreement and suitability to be utilized for thermal conversion. Thermal degradation mainly appeared in the temperature range of 150–500 °C for all 99.4 % total weight loss for all parent samples as well as their blends. Linear regression coefficients (R2) were in the range of 0.90–0.99 for all sixteen calculated models. The lower activation energies were obtained from the 50:50 blend for sugarcane bagasse and MSW while 70:30 for rice husk with MSW respectively which proved a great affinity to thermal degrading under a gasification environment