Energy Consumption and Economic Growth: Evidence from Electricity and Petroleum in Eastern Africa Region

This study investigates the distinct impacts of electricity and petroleum consumption on economic growth in Eastern Africa. Using a Panel Autoregressive Distributed Lag Model and data for a period spanning 2000 to 2021, the study examines both the short-run and long-run effects of these energy sources on Gross Domestic Product. The findings reveal that petroleum consumption has a statistically significant and positive impact on GDP in both the short run and long run. In contrast, while electricity consumption shows a positive but statistically insignificant effect on GDP in the short run, it exhibits a negative and statistically significant impact in the long run. These results suggest that policymakers in Eastern Africa should prioritize sustainable petroleum management to maximize its economic benefits while mitigating potential environmental risks. While the negative coefficient of electricity implies a corrective response of the variables to long-run equilibrium in the face of short-term shocks. As a result, it is recommended that economic shocks caused by energy consumption be considered in terms of their relationship to economic growth, whether positive or negative in the long or short term, as decision makers need to address their impact and limit such shocks on economic growth.

Analyzing Climate Dynamics and Developing Machine Learning Models for Flood Prediction in Sacramento, California

Climate change is leading to rapid environmental changes, including fluctuating precipitation and water levels, which raises the risk of flooding in coastal and riverine locations around the United States. This study focuses on Sacramento, California, a city significantly affected by these changes and recent severe flooding disasters. The ultimate goal is to understand the climate dynamics and create a more robust model to alert Sacramento and other communities to possible flooding and better prepare them for future climatic uncertainty. In this research, four classification machine learning models—Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM)—are examined for their capacity to predict the occurrence of floods using historical precipitation temperature and soil moisture data. Our results demonstrate that the LSTM model, with an accuracy of 89.99%, may provide better reliable flood predictions, possibly due to its ability to process complicated temporal data. SVM, RF, and ANN showed accuracies of 81.25%, 83.75%, and 85%, respectively. The study explores the correlation between increasing precipitation incidents and severe climate variations, such as the El Niño and La Niña cycles, which could have increased flooding risks. Significant rainfall peaks occurred in 1998 and 2007, indicating that external atmospheric circumstances might have considerably impacted local weather patterns. While LSTM models show potential, there remains room to improve their accuracy and adaptability in extreme flood scenarios. Given these findings, future research could combine multiple environmental data sources and hybrid modeling approaches to enhance predictions.

The Shennong’s Herbal Canon for Health Management of Herbal Foods

The Shennong’s Herbal Canon lays the foundation for the basic theory of herbal combination in traditional Chinese medicine (TCM). However, after the Tang dynasty, the text of this book was nearly entirely lost, with only a short preface and a catalogue of 365 herbs remaining. In Interpretation of Shennong’s Herb Canon and Catalogue of Herbal Foods, molecular anthropologist Hui Li systematically elaborated on the philosophical basis and practical application by starting from the TCM perspective and integrating multi-disciplinary scientific evidence. This book provides scholars with numerous empirical and logically-based scientific hypotheses and offers insights for daily health maintenance.

Experimental Study on Cold Plasma Jet (CPJ) Assisted Micro-Milling of 30CrMnSiNi2A

As a typical high-performance alloy, the excellent mechanical properties and stringent processing requirements of 30CrMnSiNi2A high-strength steel pose great challenges to high-quality and efficient processing. Currently, researchers have proposed methods such as improving cutting tool performance, minimal quantity lubrication (MQL), and applying external energy field to assist processing. However, due to the unregulated material properties, the further improvement of surface quality is limited, and there are problems of phase change and thermal damage in laser processing. Cold plasma jet (CPJ) is rich in active particles and has a low macroscopic temperature. It can effectively regulate material properties without causing serious surface damage. Therefore, a new 30CrMnSiNi2A machining approach adopting CPJ is proposed to improve the cutting process. The mechanism of its action on material properties and cutting process is revealed based on single-grain diamond scratching tests and micro-milling tests. The results show that CPJ can promote material fracture and improve material removal efficiency. The material removal efficiency R at 400 mN is increased from 0.433 before treatment to 0.895. Under the optimal processing parameters (feed speed V_f = 800 μm/s, spindle speed n = 40,000 rpm, and milling depth a_p = 5 μm), compared with dry micro-milling, the cutting forces F_z, F_x and F_y in CPJ-assisted micro-milling are reduced by 26.5%, 24.8% and 31.3%, respectively. The surface roughness Sa is reduced by 19.3%, and the phenomena of plastic flow and burr are suppressed. The CPJ-assisted machining process proposed in this paper can regulate the material properties to improve the cutting process without causing serious damage to the material, providing a new approach for achieving high-quality and efficient processing of 30CrMnSiNi2A.

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.