Search Results (1,944)

The use of methane as a carbon source for producing bacterial single-cell protein (SCP) has been one of the most interesting developments in recent years. Most of these upcoming industries are using a methanotroph, Methylococcus capsulatus Bath, for SCP production using natural gas as the substrate. In the present study, we have explored the possibility of using an indigenously isolated methanotroph from a rice field in India, Methylomagnum ishizawai strain KRF4, for producing SCP from biogas [derived from cow dung]. The process was eco-friendly, required minimal instruments and chemicals, and was carried out under semi-sterile conditions in a tabletop fish tank. As the name suggests, Methylomagnum is a genus of large methanotrophs, and the strain KRF4 had elliptical to rectangular size and dimensions of ~4–5 µm × 1–2 µm. In static cultures, when biogas and air were supplied in the upper part of the growing tank, the culture grew as a thick pellicle/biofilm that could be easily scooped. The grown culture was mostly pure, from the microscopic observations where the large size of the cells, with rectangular-shaped cells and dark granules, could easily help identify any smaller contaminants. Additionally, the large cell size could be advantageous for separating biomass during downstream processing. The amino acid composition of the lyophilized biomass was analyzed using HPLC, and it was seen that the amino acid composition was comparable to commercial fish meal, soymeal, Pruteen, and the methanotroph-derived SCP-UniProtein^®. The only difference was that a slightly lower percentage of lysine, tryptophan, and methionine was observed in Methylomagnum-derived SCP. Methylomagnum ishizawai could be looked at as an alternative for SCP derived from methane or biogas due to the comparable SCP produced, on the qualitative level. Further intensive research is needed to develop a continuous, sustainable, and economical process to maximize biomass production and downstream processing. Full article

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article

In some languages, prosodic cues on word stems can be used to predict upcoming suffixes. Previous studies have shown that second language (L2) users can process such cues predictively in their L2 from approximately intermediate proficiency. This ability may depend on the mapping of the L2 prosody onto first language (L1) perceptual and functional prosodic categories. Taking as an example the Danish stød, a complex prosodic cue, we investigate an acquisition context of a predictive cue where L2 users are unfamiliar with both its perceptual correlates and its functionality. This differs from previous studies on predictive prosodic cues in Swedish and Spanish, where L2 users were only unfamiliar with either the perceptual make-up or functionality of the cue. In a speeded number judgement task, L2 users of Danish with German as their L1 (N = 39) and L1 users of Danish (N = 40) listened to noun stems with a prosodic feature (stød or non-stød) that either matched or mismatched the inflectional suffix (singular vs. plural). While L1 users efficiently utilised stød predictively for rapid and accurate grammatical processing, L2 users showed no such behaviour. These findings underscore the importance of mapping between L1 and L2 prosodic categories in second language acquisition. Full article

This work was conducted within the framework of French university reforms undertaken since 2022. Regardless of learning level and target audience, project-based learning has proved its effectiveness as a teaching strategy for many years. The novelty of the present contribution lies in the gamification of this learning method. A popular game, Trivial Pursuit, was adapted to enable students to acquire knowledge in a playful manner while preparing for upcoming technical challenges. Various technical subjects were chosen to create new cards for the game. A total of 180 questions and their answers were created. The colored tokens were then used to trace manufactured products. This teaching experiment was conducted as part of a project-based learning program with third-year Bachelor students (Electrical Engineering and Industrial Computing Department). The game components associated with the challenge proposed to the students comprised six key elements: objectives, challenges, mechanics, components, rules, and environment. Within the framework of the Industry 4.0 concept, this pedagogical activity focused on the knowledge, understanding, development, and application of an RFID (Radio Frequency Identification) system demonstrating the capabilities of this technology. This contribution outlines the various stages of the work assigned to the students. An industrial partner was also involved in this work. Full article

The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential to ensure the high performance and security of a network, which is necessary for advanced applications. This is particularly relevant in the Internet of Things (IoT), where resource-constrained platforms at the edge must manage tasks like traffic analysis and threat detection. In this context, balancing classification accuracy with computational efficiency is key to enabling practical, real-world deployments. Traditional payload-based and packet inspection methods are based on the identification of relevant patterns and fields in the packet content. However, such methods are nowadays limited by the rise of encrypted communications. To this end, the research community has turned its attention to statistical analysis and Machine Learning (ML). In particular, Convolutional Neural Networks (CNNs) are gaining momentum in the research community for ML-based NTC leveraging statistical analysis of flow characteristics. Therefore, this paper addresses CNN-based NTC in the presence of encrypted communications generated by the rising Quick UDP Internet Connections (QUIC) protocol. Different models are presented, and their performance is assessed to show the trade-off between classification accuracy and CNN complexity. In particular, our results show that even simple and low-complexity CNN architectures can achieve almost 92% accuracy with a very low-complexity architecture when compared to baseline architectures documented in the existing literature. Full article

Background: Among all ambulatory antibiotic prescriptions, about 20% are non-visit-based (ordered outside of an in-person clinical encounter), and about 30% are not associated with an infection-related diagnosis code. Objective/Methods: To identify the rationale for ambulatory antibiotic prescribing, we queried the electronic health record (EHR) of a single, large health system in the Midwest United States to identify all oral antibiotics prescribed from November 2018 to February 2019 and examined visit, procedure, lab, department, and diagnosis codes. For the remaining antibiotic prescriptions—mostly non-visit-based, no-infectious-diagnosis-documented—we randomly selected and manually reviewed the EHR to identify a prescribing rationale and, if none was present, surveyed prescribers for their rationale. Results: During the study period, there were 47,619 antibiotic prescriptions from 1177 clinicians to 41,935 patients, of which 2608 (6%) were eligible non-visit-based, no-infectious-diagnosis-documented. We randomly selected 2298. There was a documented rationale for 2116 (92%) prescriptions. The most common documented reasons—not mutually exclusive—were patient-reported symptoms (71%), persistence of symptoms after initial management (18%), travel (17%), and responding to lab or imaging results (11%). We contacted 160 clinicians who did not document any prescribing rationale in the EHR and received responses from 62 (39%). Clinicians’ stated reasons included upcoming or current patient travel (19%), the antibiotic was for the prescriber’s own family member (19%), or the clinician made a diagnosis but did not document it in the EHR (18%). Conclusions: Non-visit-based, no-infectious-diagnosis-documented antibiotic prescriptions were most often in response to patient-reported symptoms, though they also occur for a variety of other reasons, some problematic, like in the absence of documentation or for a family member. Full article

We present a lightweight autonomous driving method that uses a low-cost camera, a simple end-to-end convolutional neural network architecture, and smoother driving techniques to achieve energy-efficient vehicle control. Instead of directly constructing a mapping from raw sensory input to the action, our network takes the frame-to-frame visual difference as one of the crucial inputs to produce control commands, including the steering angle and the speed value at each time step. This choice of input allows highlighting the most relevant parts on raw image pairs to decrease the unnecessary visual complexity caused by different road and weather conditions. Additionally, our network achieves the prediction of the vehicle’s upcoming control commands by incorporating a view synthesis component into the model. The view synthesis, as an auxiliary task, aims to infer a novel view for the future from the historical environment transformation cue. By combining both the current and upcoming control commands, our framework achieves driving smoothness, which is highly associated with energy efficiency. We perform experiments on benchmarks to evaluate the reliability under different driving conditions in terms of control accuracy. We deploy a mobile robot outdoors to evaluate the power consumption of different control policies. The quantitative results demonstrate that our method can achieve energy efficiency in the real world. Full article

As part of the post-conflict reconstruction and recovery, the development of Kabul New City aims to bring relief to the existing capital city, Kabul, which has experienced exponential population growth, putting heavy pressure on its existing resources. Kabul New City is divided into four subsectors, and each of them is being developed and is expected to reach a target population by 2025, as defined by the master plan. The study’s objective is to determine which of the four zones are in demand and need to be prioritized for development, as per the model results. The data collection involves an online questionnaire, and the responses are collected from residents of Kabul and Herat. Agent-based modeling (ABM) is an emerging method of simulating urban dynamics. Cities are evolving continuously and are forming unique spatial patterns that result from the movement of residents in search of new locations that accommodate their needs and preferences. An agent-based model is developed using the weighted random selection process based on household size and income levels. The agents are the residents of Kabul and Herat, and the environment is the land use classification image using the Sentinel 2 image of Kabul New City. The barren class is treated as the developable area and is divided into four sub-sectors. The model simulates three alternative growth rate scenarios, i.e., ambitious, moderate, and steady. The results of the simulation reveal that the sub-sector Dehsabz South, being closer to Kabul city, is in higher demand. Barikab is another sub-sector high in demand, which has connectivity through the highway and is an upcoming industrial hub. Full article

The surface quality of biomedical implants, such as shoulder joint caps, plays a critical role in their performance, longevity, and biocompatibility. Most biomedical shoulder joints fail to reach their optimal functionality when finished through conventional techniques like grinding and lapping due to their inability to achieve nanometer-grade smoothness, which results in greater wear and friction along with potential failure. The advanced magnetorheological finishing (MRF) approach provides enhanced surface quality through specific dimensional control material removal. This research evaluates how MRF treatment affects the surface roughness performance and microhardness properties and wear resistance behavior of cobalt–chromium alloy shoulder joint caps which have biocompatible qualities. The study implements a magnetorheological finishing system built with an electromagnetic tool to achieve the surface roughness improvements from 0.35 µm to 0.03 µm. The microhardness measurements show that MRF applications lead to a rise from HV 510 to HV 560 which boosts the wear protection of samples. After MRF finishing, the coefficient of friction demonstrates a decrease from 0.12 to 0.06 which proves improved tribological properties of these implants. The results show that MRF technology delivers superior benefits for biomedical use as it extends implant life span and decreases medical complications leading to better patient health outcomes. The purposeful evaluation of finishing techniques and their effects on implant functionality demonstrates MRF is an advanced technology for upcoming orthopedic implants while yielding high precision and enhanced durability and functional output. Full article

The inspiration behind this specific research is based on addressing the growing need to improve road safety via the application of the Internet of Things (IoT) system. Although several investigations have discovered the possibility of IoT-based accident recognition, recent research remains fragmented, usually concentrating on outdated science or specific use cases. This study aims to fill that gap by carefully examining and conducting a comparative analysis of 101 peer-reviewed articles published between 2008 and 2025, with a focus on IoT systems for accident recognition techniques. The review categorizes approaches depending on the sensor used, incorporation frameworks, and recognition techniques. The study examines numerous sensors, such as Global System for Mobile Communications/Global Positioning System (GSM/GPS), accelerometers, vibration, and many other superior sensors. The research shows the constraints and advantages of existing techniques, concentrating on the significance of multi-sensor utilization in enhancing recognition precision and dependability. Findings indicate that, although substantial improvements have been made in the use of IoT-based systems for accident recognition, problems such as substantial implementation costs, weather conditions, and data precision issues persist. Moreover, the research acknowledges deficiencies in standardization, as well as the requirement for strong communication systems to enhance the responsiveness of emergency services. As a result, the study suggests a plan for upcoming developments, concentrating on the incorporation of IoT-enabled infrastructure, sensor fusion approaches, and artificial intelligence. This study improves knowledge by offering an extensive viewpoint on IoT-based accident recognition, providing insights for upcoming research, and suggesting policies to facilitate implementation, eventually enhancing worldwide road safety. Full article

Microgrids (MGs) technologies, with their advanced control techniques and real-time monitoring systems, provide users with attractive benefits including enhanced power quality, stability, sustainability, and environmentally friendly energy. As a result of continuous technological development, Internet of Things (IoT) architectures and technologies are becoming more and more important to the future smart grid’s creation, control, monitoring, and protection of microgrids. Since microgrids are made up of several components that can function in network distribution mode using AC, DC, and hybrid systems, an appropriate control strategy and monitoring system is necessary to ensure that the power from microgrids is delivered to sensitive loads and the main grid effectively. As a result, this article thoroughly assesses MGs’ control systems and groups them based on their degree of protection, energy conversion, integration, advantages, and disadvantages. The functions of IoT and monitoring systems for MGs’ data analytics, energy transactions, and security threats are also demonstrated in this article. This study also identifies several factors, challenges, and concerns about the long-term advancement of MGs’ control technology. This work can serve as a guide for all upcoming energy management and microgrid monitoring systems. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

The Internet of Things (IoT) is an upcoming technology that is increasingly being used for monitoring and analysing environmental parameters and supports the progress of farm machinery. Agriculture is the main source of living for many people, including, for instance, farmers, agronomists and transporters. It can raise incomes, improve food security and benefit the environment. However, food systems are responsible for many environmental problems. While the use of IoT in agriculture and environment is widely deployed in many developed countries, it is underdeveloped in Africa, particularly in West Africa. This paper aims to provide a systematic review on this technology adoption for agriculture and environment in West African countries. To achieve this goal, the analysis of scientific contributions is performed by performing first a bibliometric study, focusing on the selected articles obtained using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, and second a qualitative study. The PRISMA analysis was performed based on 226 publications recorded from one database: Web Of Science (WoS). It has been demonstrated that the annual scientific production significantly increased during this last decade. Our conclusions highlight promising directions where IoT could significantly progress sustainability. Full article

Rapid urbanization in the twenty-first century has significantly accelerated the adoption of artificial intelligence (AI) technologies to address growing challenges in governance, mobility, energy, and urban security. This paper explores how AI is transforming smart city infrastructure, analyzing more than 92 academic publications published between 2012 and 2024. Key AI applications ranging from predictive analytics in e-governance to machine learning models in renewable energy management and autonomous mobility systems are synthesized domain-wise throughout this study. This paper highlights the benefits of AI-enabled decision making, finds current implementation barriers, and discusses the associated ethical implications. Furthermore, it presents a research agenda that stresses data interoperability, transparency, and human–AI collaboration to steer upcoming advancements in smart urban ecosystems. Full article

The current level of achievement in obtaining suitable polymer gel-based materials for efficient applications in thermoelectric devices is insufficient, although a substantial amount of research has already been performed. In this context, further investigations are necessary to design and synthesize polymer gel-based materials for ionic thermoelectric device applications. Polymer gel-based materials have attracted extensive consideration because of their multiple benefits, including easy processing, eco-friendly waste, and versatility, making them excellent materials for ionic thermoelectric devices. However, the design and synthesis of suitable polymer gel-based materials for ionic thermoelectric devices are still challenging areas of research. The surface morphological topography of prepared polymer gels is an important issue in thermoelectric device applications. In this review, significant approaches for the design and synthesis of polymer gel-based materials are discussed. This review may provide an important reference for upcoming perceptions on the design and synthesis of polymer gel materials for thermoelectric devices. Full article