Postmortem Blood Metal Levels: Establishing Updated Reference Ranges Using ICP-OES

Postmortem testing for metals is crucial in forensic toxicology to determine whether metal exposure contributed to an individual’s death. However, current reference ranges for metal concentrations, primarily based on living individuals, fail to account for postmortem physiological changes. This study addresses this gap by analyzing thelevels of zinc and iron postmortem blood levels over various postmortem intervals (PMI) using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Fifty samples were analyzed, revealing a significant increase in metal concentrations over time, with zinc levels rising from 181 μg/dL to 24,935 μg/dL and iron levels from 155 μg/dL to 11,421 μg/dL across a 40-month PMI. These changes are attributed to the redistribution of metals from tissues into the bloodstream and decomposition processes. The study proposes postmortem-specific reference ranges, emphasizing the need for forensic pathologists and toxicologists to consider PMI in their assessments to avoid misinterpretation and inaccurate cause-of-death determinations. This research underscores the necessity of updating reference ranges for postmortem analysis, ultimately improving the accuracy of forensic toxicology reports and contributing to more reliable determinations of cause of death.

Challenges and Solutions: China’s Illegal Hunting Crime from the Perspective of Ecological Civilization Development

In recent years, the number of crimes involving illegal hunting in China’s judicial system has steadily increased, giving rise to numerous disputes. The root of these disputes lies in the fact that China’s Criminal Law lags in terms of animal protection legislation, failing to strike a balance between wildlife protection and human rights. This disconnection is particularly evident in the legislation and judicial practice regarding illegal hunting crimes and the value principles of ecological civilization strongly advocated by China. Moreover, China’s legal framework and judicial practices concerning illegal hunting crimes suffer from low thresholds for conviction and a lack of comprehensive investigations into the subjective intentions of offenders. Chinese legislators and judges should consider international experiences in combating illegal hunting crimes, elucidate the right to defend oneself against wildlife in certain dangerous situations, and thoroughly revise legal provisions, including the definition of illegal hunting and related judicial interpretations. Additionally, greater efforts should be made to disseminate public legal knowledge regarding illegal hunting crimes.

Acute Exacerbations of Interstitial Lung Disease: Evolving Perspectives on Diagnosis and Management

Interstitial lung diseases (ILDs) are a heterogeneous group of chronic lung diseases caused by several potential etiologies but for many, the cause of a given ILD remains unknown. Accurate epidemiologic data are hard to find because of varying definitions, overlapping characteristics once thought to be unique to specific diseases, and ongoing changes in how ILDs are diagnosed and managed. In addition, there are significant variations in prevalence among different geographic populations, likely reflecting a combination of genetic and environmental differences. Certain risk factors, including exposure to cigarette smoke or environmental toxicants (asbestos, silica, fracking, coal dust, and air pollution), genetic mutations, and single nucleotide polymorphisms, have all been associated with developing interstitial lung disease. Due to the availability of high-resolution computed tomography (CT) scans, earlier and broader recognition of subtle imaging changes, and an aging worldwide population, the incidence and prevalence of ILDs are increasing. While a given cause of particular interstitial lung disease may vary, patients often experience breathlessness and a non-productive cough due to impaired alveolar gas exchange. Patients with ILD are prone to the development of acute exacerbations, marked by acute or chronic respiratory failure because of an acute exacerbation of the underlying lung disease. In this review, we discuss the definition of an acute exacerbation and comment on what is known about the underlying pathophysiology in exacerbations of idiopathic pulmonary fibrosis and other ILDs. We also emphasize the similarities in the clinical presentation of the acute exacerbations regardless of the underlying ILD, highlight key prognostic features of the diagnosis, and underscore the importance of interdisciplinary management of acute interstitial lung disease exacerbations.

Supply Chain of Grey-Blue Hydrogen from Natural Gas: A Study on Energy Efficiency and Emissions of Processes

Hydrogen energy offers a significant potential for reducing carbon emissions and integrating clean energy across sectors such as heavy-duty vehicles, energy-intensive industries, and building heating. This study analyzes the energy efficiency and emissions of grey and blue hydrogen supply chains, identifying key issues such as high energy consumption and losses in transportation, steam methane reforming, and liquid hydrogen storage. Truck transportation emerges as the highest emitter, with emissions ranging from 0.140 to 0.150 kg CO_2e per kg of hydrogen. Using a bi-objective Dijkstra Algorithm, the study identifies the most energy-emissions-efficient pathways and reveals a trade-off between energy efficiency and greenhouse gas emissions. Grey hydrogen shows higher energy efficiency (38.0%) but higher emissions (0.1689 kg CO_2e per kg of hydrogen). In contrast, with 60% and 90% carbon capture and storage, blue hydrogen has slightly lower energy efficiencies (37.5% and 36.9%) but reduced emissions (0.1564 and 0.1514 kg CO_2e per kg of hydrogen). Liquefied natural gas and hydrogen offer high energy efficiency but increase emissions, while compressed natural gas and hydrogen slightly reduce efficiency but nearly halve emissions. Hence, compressed options are preferable for an energy-emissions-efficient shortest path.

Conservation and Efficiency before Transition

Federated Transfer Learning-Based Paper Breakage Fault Diagnosis

The diagnosis of paper breakage faults during the papermaking process is of great significance for improving product quality and maintaining stability in the production process. This paper develops a cross-condition transfer learning fault diagnosis model. This study proposes a fault diagnosis method based on transfer learning to address the issue of single-condition diagnostic models performing poorly when applied to different conditions..This method uses both parameter transfer and feature transfer to diagnose faults across different conditions. At the same time, in response to the issue of insufficient small sample operating data, we introduce federated learning technology to explore the impact of model compression rates on the diagnostic accuracy of the federated global model during the federated model training process. The results indicate that compared to single operating condition models, fault diagnosis performance based on transfer learning across different operating conditions has improved. The diagnostic model based on feature transfer performs even better, achieving accuracy rates of 98.31%, 94.64%, and 96.43% under different transfer tasks, allowing for accurate classification of the majority of samples. Additionally, the federated learning method provides an effective solution for fault diagnosis in small sample operating conditions, and an appropriate model compression rate can ensure diagnostic accuracy while protecting data privacy.

Chemical Recycling and Upcycling of Waste Plastics: A Review

The indiscriminate disposal of plastic waste represents a significant environmental hazard. Conventional remediation techniques, such as landfilling and incineration, also encounter limitations and are unable to adequately address the pollution issue. Chemical recycling and upcycling represent an effective method for the degradation of plastics into oligomers and subsequent transformation into other product substances. This review provides an overview of the various chemical treatment methods currently in use, from the earliest thermal degradation techniques to the emerging strategies. The conventional techniques for thermal degradation of discarded plastics frequently encounter difficulties due to the necessity for elevated temperatures, substantial energy consumption, and the generation of a heterogeneous product mixture. Significant advances have been made in the fields of catalytic solvolysis, hydrotreating, and oxidative cleavage for the recycling and upcycling of plastics under mild conditions. This review provides a comprehensive overview of the chemical treatment methods currently employed for plastics, with a particular focus on the principles and current developments, as well as the reaction mechanisms involved. Additionally, it offers a detailed introduction to various advanced catalytic technologies and the catalysts utilized. Finally, it presents prospective outlooks for different methods, based on their current development status and the gap between actual needs.

Modeling Viscosity in Starch-Polymer Suspensions: A Comparative Analysis of Swarm Algorithm-Aided ANN Optimization

The analysis of rheological properties of suspensions requires the use of models such as Einstein’s formulation for viscosity in dilute conditions, but its effectiveness diminishes in the context of concentrated suspensions. This study investigates the rheology of suspensions containing solid particles in aqueous media thickened with starch nanoparticles (SNP). The goal is to model the viscosity of these mixtures across a range of shear rates and varying amounts of SNP and SG hollow spheres (SGHP). Artificial neural networks (ANN) combined with swarm intelligence algorithms were used for viscosity modeling, utilizing 1104 data points. Key features include SNP proportion, SGHP content, log-transformed shear rate (LogSR), and log-transformed viscosity (LogViscosity) as an output. Three swarm algorithms—AntLion Optimizer (ALO), Particle Swarm Optimizer (PSO), and Dragonfly Algorithm (DA)—were evaluated for optimizing ANN hyperparameters. The ALO algorithm proved most effective, demonstrating strong convergence, exploration, and exploitation. Comparative analysis of ANN models revealed the superior performance of ANN-ALO, with an R² of 0.9861, mean absolute error (MAE) of 0.1013, root mean absolute error (RMSE) of 0.1356, and mean absolute percentage error (MAPE) of 3.198%. While all models showed high predictive accuracy, the ANN-PSO model had more limitations. These findings enhance understanding of starch suspension rheology, offering potential applications in materials science.

The Notch3 Pathway in Organ Fibrosis

Fibrosis occurs in many organs, including the lung, heart, skin, liver or kidney, and is characterized by progressive tissue scarring in response to repetitive or chronic non-resolving injury, ultimately leading to organ failure and death. It is, in fact, a major cause of morbidity and mortality worldwide, being estimated to account for 45% of deaths in the world. Despite this fact, little progress has been made therapeutically, and fibrosis remains a major clinical and therapeutic challenge. Although significant advances in our understanding of cellular and molecular mechanisms driving tissue fibrosis have been made, the lack of an efficient treatment reflects the limited insight into the pathophysiological mechanisms underlying the initiation and progression of the fibrotic process. Thus, there is an urgent need for better understanding of tissue fibrosis and repair mechanisms that later lead to the development of new therapeutic approaches to fight fibrosis. The Notch pathway is a highly conserved signaling pathway that has been linked to tissue fibrosis in many organs and promises to open new therapeutic opportunities. This manuscript reviews the relevance of Notch signaling in the development and progression of tissue fibrosis in several organs with a special focus on the Notch3 pathway due to the unique features of this receptor.

Engineering of Cu-Based Quaternary Sulfide Nanomaterials for Photocatalytic Applications

Semiconductor nanomaterials have been widely used as light-responsive photocatalysts for solar-to-fuel conversion over the past decade. However, the wide band gap of the most reported photocatalysts stems from light absorption in the visible region and results in low solar conversion efficiency. Therefore, it is necessary to develop new semiconductor nanomaterials with high absorption coefficients over the visible region as photocatalysts. The most promising candidates include Cu-based quaternary sulfide nanomaterials (CQSNs), such as Cu-II-III-S (e.g., CuZnInS, CuZnGaS), Cu-II-IV-S (e.g., Cu₂ZnSnS₄, Cu₂ZnGeS₄), and I-III-IV-S (e.g., CuInSnS₄, Cu₃GaSnS₅). This review provides a comprehensive overview of the recent progress in developing CQSNs for various photocatalytic applications. Firstly, we present an overview of the synthesis of CQSNs with precise control over crystal phase, composition, size, and shape using solution-based methods. Then, the enhancement of photocatalytic performance was presented via the engineering of CQSNs, which included surface engineering, elemental doping, cocatalyst loading, vacancy engineering, and interface engineering. Subsequently, we further introduce the photocatalytic applications in the fields of photocatalytic and photoelectrochemical hydrogen conversion, CO₂ reduction, organic synthesis, and pollutant degradation. Lastly, this review ends with views on the current challenges and opportunities of CQSNs in future studies.