Efficient Removal of Glyphosate from Aqueous Solution by Cerium Dioxide Loaded Biochar

Glyphosate, which is one of the most widely used organophosphorus herbicides, poses a threat to the surrounding water environment. Traditional adsorbents were depicted to have poor capacities to eliminate it. CeO₂ embraces the potential to adsorb glyphosate efficiently. However, suitable carbonaceous composites were necessary to be employed as its support. In this paper, water hyacinth was used as the precursor to prepare CeO₂-loaded biochar (CeO₂/WHBC), which was employed to remove glyphosate from the aqueous solution via adsorption. The results showed that CeO₂/WHBC-3 illustrated the best adsorption performance for glyphosate with the capacity of 126.3 mg·g, which was prepared with per mmol CeO₂ loaded of 0.2 g WHCB. Static adsorption experiments demonstrated that glyphosate adsorption at different solution pH values followed the Langmuir isotherm model and quasi-second order kinetic model, indicating that the adsorption was monolayer adsorption and that the adsorbent’s surface active sites primarily controlled the rate. Coexisting ion interference experiments showed that common cations (K⁺, Na⁺, Ca²⁺, Mg²⁺) and anions (Cl⁻, NO₃⁻, SO₄²⁻) both promoted glyphosate adsorption on the CeO₂/WHBC-3 surface. Moreover, the prepared sorbent maintained a high adsorption capacity after five adsorption-desorption cycles. Dynamic adsorption experiments showed that the CeO₂/WHBC-3 packed column could efficiently remove glyphosate from aqueous solutions, even at high concentrations and fast flow rates. Zeta potentials and XPS analysis revealed that the adsorption mechanism of CeO₂/WHBC-3 for glyphosate is mainly through electrostatic adsorption and metal complexation.

Adaptive Event-Triggered Control for Multi-Quadrotor Systems under Aperiodically Intermittent Communications

A novel adaptive event-triggered control strategy is proposed for multi-quadrotor systems under intermittent communications, addressing the leader-follower consensus-seeking problem where the leader has an unknown bounded input. Firstly, an activation time ratio condition is proposed, eliminating the reliance on the maximum time interval of intermittent communication. Secondly, a compensation term related to the leader’s unknown bounded input is designed in the controller to compensate for the error caused by intermittent communication in each period. Meanwhile, a prediction method is developed to eliminate the dependence on continuous information of neighboring quadrotors. Zeno behavior is strictly excluded, and communication among quadrotors is efficiently reduced with the designed event-triggering condition. Finally, numerical simulations verify the effectiveness and superiority of the proposed control strategy.

The Effect of Polyethylene Glycol on Cobalt Oxide Nanoparticles Prepared Using Sonochemical Synthesis

In this work, Cobalt oxide nanoparticles (Co₃O₄·NPs) were synthesized via a simple sonochemical reaction by using polyethylene glycol (PEG) as a surfactant. Structural, morphological and spectroscopic analysis of obtained powder (Co₃O₄·NPs) was investigated by X-ray diffraction, FTIR spectroscopy and scanning electron microscope (SEM). The nanocrystalline nature of the sample was confirmed by XRD, which exhibits the cubic face-centered normal spinel structure of (Co₃O₄·NPs) and the space group of Fd-3m with the average crystallite size around 15 nm. FTIR spectrum shows two strong absorption bands of (Co⁺²–O) and (Co⁺³–O) which confirm the spinel structure of Co₃O₄·NPs. Moreover, SEM micrographs showed that the agglomeration of the nanoparticles was reduced by the addition of (PEG) surfactant and UV-Vis was used to study the synthesized material’s optical properties. The Co₃O₄ band gap ranged around 2.2 and 3.5 eV.

Intelligent Rehabilitation and Engineering: Empowering the People with Disabilities

4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System

Unmanned Aerial Vehicles (UAVs) are versatile platforms with potential applications in precision agriculture, disaster management, and more. A core need across these applications is a navigation system that accurately estimates location based on environmental perception. Commercial UAVs use multiple onboard sensors whose fused data improves localization accuracy. The bioinspired Rat-Simultaneous Localization and Mapping (Rat-SLAM) system, is a promising alternative to be explored to tackle the localization and mapping problem of UAVs. Its cognitive capabilities, semi-metric map construction, and loop closure make it attractive for localization in complex environments. This work presents an improved Rat-SLAM algorithm for UAVs, focusing on three innovations. First, Spiking Neural Networks (SNNs) are incorporated into Rat-SLAM’s core modules to emulate biological processing with greater efficiency. Second, Neuromorphic Computing models the neurons of the SNNs, assessing the feasibility of implementing SNNs on specialized hardware to reduce software processing, a key advantage for UAVs with limited onboard resources. Third, SNNs are developed based on the Memristive Leaky Integrate-and-Fire model, integrating memristors into artificial neurons to leverage their low power and memory properties. Our approach was evaluated through trajectory simulations using the Hector Quadrotor UAV in the Gazebo environment within the Robot Operating System, yielding valuable insights and guiding future research directions.

Digital Twin and Artificial Intelligence in Machining: A Bibliometric Analysis

The past decade has witnessed an exodus toward smart and lean manufacturing methods. The trend includes integrating intelligent methods into sustainable manufacturing systems purposely to improve the machining efficiency, reduce waste and also optimize productivity. Manufacturing systems have seen transformations from conventional methods, leaning towards smart manufacturing in line with the industrial revolution 4.0. Since the manufacturing process encompasses a wide range of human development capacity, it is essential to analyze its developmental trends, thereby preparing us for future uncertainties. In this work, we have used a Bibliometric analysis technique to study the developmental trends relating to machining, digital twins and artificial intelligence techniques. The review comprises the current activities in relation to the development to this area. The article comprises a Bibliometric analysis of 464 articles that were acquired from the Web of Science database, with a search period until November 2024. The method of obtaining the data includes retrieval from the database, qualitative analysis and interpreting the data via visual representation. The raw data obtained were redrawn using the origin software, and their visual interpretations were represented using the VOSviewer software (VOSviewer_1.6.19). The results obtained indicate that the number of publications related to the searched keywords has remarkably increased since the year 2018, achieving a record maximum of over 80 articles in 2024. This is indicative of its increasing popularity. The analysis of the articles was conducted based on the author countries, journal types, journal names, institutions, article types, major and micro research areas. The findings from the analysis are meant to provide a bibliometric explanation of the developmental trends in machining systems towards achieving the IR 4.0 goals. Additionally, the results would be helpful to researchers and industrialists that intend to achieve optimum and sustainable machining using digital twin technologies.

The Intersection between Immune System and Idiopathic Pulmonary Fibrosis—A Concise Review

Idiopathic pulmonary fibrosis (IPF) is marked by progressive alveolar destruction, impaired tissue regeneration, and relentless fibrogenesis, culminating in respiratory failure and death. A diverse array of resident and non-resident cells within the lung contribute to disease pathogenesis. Notably, immune cells, both resident and recruited, respond to cues from sites of lung injury by undergoing phenotypic transitions and producing a wide range of mediators that influence, initiate, or dictate the function, or dysfunction, of key effector cells in IPF pathology, such as alveolar epithelial cells, lung fibroblasts, and capillary endothelial cells. The role of the immune system in IPF has undergone an interesting evolution, oscillating from initial enthusiasm to skepticism, and now to a renewed focus. This shift reflects both the past failures of immune-targeting therapies for IPF and the unprecedented insights into immune cell heterogeneity provided by emerging technologies. In this article, we review the historical evolution of perspectives on the immune system’s role in IPF pathogenesis and examine the lessons learned from previous therapeutic failures targeting immune responses. We discuss the major immune cell types implicated in IPF progression, highlighting their phenotypic transitions and mechanisms of action. Finally, we identify key knowledge gaps and propose future directions for research on the immune system in IPF.

Are Memory Updating Tasks Valid Working Memory Measures? A Meta-Analysis

The memory updating (MU) process is a core component of working memory (WM). To systematically examine the validity of two commonly used MU tasks as WM measures, the present meta-analysis (76 studies, total N = 16,184) synthesized results on the correlation between the two MU tasks and two criterion tasks (working memory capacity (WMC) and fluid intelligence (Gf)). Results indicated a moderate correlation between running memory (RM) and WMC (r = 0.42, 95% CI = [0.37, 0.48]), a weak correlation between n-back and WMC (r = 0.23, 95% CI = [0.19, 0.28]), and moderate correlations between both RM (r = 0.40, 95% CI = [0.35, 0.46]) and n-back (r = 0.34, 95% CI = [0.32, 0.37]) and Gf. Subgroup analyses showed that memory load moderated the correlation between RM and WMC, and stimulus-onset asynchrony moderated the correlation between n-back and both WMC and Gf. The recollection and recognition nature of RM and n-back contributed to their different correlation with WMC, and the involvement of controlled attention in both tasks accounted for their association with Gf. The present meta-analysis indicated that RM is a more valid WM measure in behavioral studies on individual differences.

The Reconfiguration of Social Bonds in the Digital Age: Virtual Connections vs. Face-to-Face Relationships

The article examines how smartphones and social media are transforming human interactions, challenging traditional concepts of friendship, intimacy, and belonging. Phenomena such as “phubbing” and constant connectivity are explored, highlighting the negative impacts of hyperconnectivity on the quality of face-to-face interactions and emotional well-being. While these technologies expand the reach of connections, they often lead to more superficial relationships, altering family, educational, and professional dynamics. Anthropological analysis is emphasized as essential for understanding these changes, revealing how digital practices vary across different cultural and social contexts. Ethnographic studies and innovative methodologies are suggested to investigate how digital technologies reshape identities, communities, and social hierarchies. The importance of an interdisciplinary approach, combining anthropology, psychology, and data science, is underscored to address the emerging challenges of the digital era and foster more authentic and healthy human relationships.

Unemployment, Inequality, and Occupational Stress: Mental Health Outcomes in Brazil (2012–2022)

This study examines the relationship between occupational stress-related leaves, classified under International Classification of Diseases code F43, and socioeconomic factors such as unemployment, income inequality, and worker income in Brazil from 2012 to 2022. Work-related stress disorders, especially those involving severe stress reactions and adjustment disorders, are big problems for occupational health. Bad working conditions and differences in income can make these problems worse. This research utilized secondary data from official Brazilian databases to perform time-series analyses and structural equation modeling. Results revealed a decline in stress-related leaves during the COVID-19 pandemic, likely influenced by remote work adoption and reduced exposure to workplace hazards. Structural modeling identified key relationships: unemployment rates and occupational risk exposure were positively associated with stress-related leaves, while higher income levels were protective. Unexpectedly, income inequality influenced aggression-related leaves but had no significant direct impact on stress-related leaves. These findings underscore the multifaceted impact of socioeconomic and workplace factors on occupational health, highlighting the need for policies addressing mental health at work and fostering equitable labor conditions. The study also identifies limitations, including potential underreporting and the exclusion of demographic nuances. Future research should adopt a multidisciplinary approach and consider disaggregated data to enhance understanding and intervention strategies.