Search Results (220)

Ammonia is a gas primarily produced for use in agriculture, refrigeration systems, chemical manufacturing, and power generation. Despite its benefits, improper management of ammonia poses significant risks to human health and the environment. Consequently, monitoring ammonia is essential for enhancing industrial safety and preventing leaks that can lead to environmental contamination. Given the abundance and diversity of studies on Internet of Things (IoT) systems for gas detection, the main objective of this paper is to systematically review the literature to identify emerging research trends and opportunities. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, focusing on sensor technologies, microcontrollers, communication technologies, IoT platforms, and applications. The main findings indicate that most studies employed sensors from the MQ family (particularly the MQ-135 and MQ-137), microcontrollers based on the Xtensa architecture (ESP32 and ESP8266) and ARM Cortex-A processors (Raspberry Pi 3B+/4), with Wi-Fi as the predominant communication technology, and Blynk and ThingSpeak as the primary cloud-based IoT platforms. The most frequent applications were agriculture and environmental monitoring. These findings highlight the growing maturity of IoT technologies in ammonia sensing, while also addressing challenges like sensor reliability, energy efficiency, and development of integrated solutions with Artificial Intelligence. Full article

Human action recognition (HAR) based on WiFi channel state information (CSI) has attracted growing attention due to its contactless, privacy-preserving, and cost-effective nature. Recent studies have reported promising results by leveraging deep learning and image-based representations of CSI. However, methodological flaws in experimental protocols, particularly improper dataset partitioning, can lead to data leakage and significantly overestimate model performance. In this paper, we critically analyze a recently published WiFi-CSI-based HAR approach that converts CSI measurements into images and applies deep learning for classification. We show that the original evaluation relied on random data splitting without subject separation, causing substantial data leakage and inflated results. To address this, we reimplemented the method using subject-independent partitioning, which provides a realistic assessment of generalization ability. Furthermore, we conduct a quantitative study of post-training quantization under both correct and flawed partitioning strategies, revealing that methodological errors can conceal the true performance degradation of compressed models. Our findings demonstrate that evaluation protocols strongly influence reported outcomes, not only for baseline models but also for engineering decisions regarding model optimization and deployment. Based on these insights, we provide guidelines for designing robust experimental protocols in WiFi-CSI-based HAR to ensure methodological integrity and reproducibility. Full article

Facility agriculture is essential for modernizing the production of horticultural plants, while long-standing over-fertilization and improper tillage in some vegetable facilities in northern China have resulted in reduced soil quality, increased greenhouse gas (GHG) emissions, and diminished vegetable yields and quality. This study systematically analyzed the deteriorating health of typical cucumber facility soils in Hebei Province, China, induced by long-term over-fertilization. Based on field surveys, we explored dynamic changes in soil physicochemical properties across different durations of over-fertilization. Subsequently, a series of field trials were conducted to assess whether reducing nitrogen application, either alone or when combined with microbial agents, could ameliorate soil properties, reduce greenhouse gas emissions, and enhance cucumber productivity. The initial field assessment revealed severe topsoil salt and nutrient accumulation, with water-soluble salt content in 5-year-old greenhouses from Yongqing soaring to 3.82 g·kg⁻¹, nearly eight times the level found in 1-year-old plots. Field experiments demonstrated that a 20% reduction in nitrogen application from the conventional rate of 900 kg·hm⁻² effectively mitigated salt accumulation, improved the structure of the microbial community, and maintained cucumber yield at 66,914 kg·hm⁻², an output comparable to conventional practices. More notably, integrating this 20% nitrogen reduction with an inoculation of Bacillus megaterium reduced the overall global warming potential by 26.7% and simultaneously increased cucumber yield to 72,747 kg·hm⁻². The most comprehensive strategy combined deep tillage, soybean straw incorporation, and B. megaterium application under reduced nitrogen, which boosted nitrogen use efficiency by 13.7% and achieved the highest yield among all treatments. In conclusion, our findings demonstrate that a combined approach of nitrogen reduction, microbial amendment, and straw application offers an effective strategy to restore soil health, enhance crop productivity, and mitigate environmental impacts in protective vegetable production systems. Full article

Grasslands provide essential forage, fuel, and ecosystem services, underpinning regional livestock husbandry and ecological integrity. However, improper utilization drives structural degradation and functional decline of the vegetation–soil–microbe system, particularly on the ecologically sensitive and fragile Qinghai–Tibet Plateau (QTP). The differential impacts of diverse utilization practices on QTP grasslands remain inadequately understood, limiting scientific support for differentiated sustainable management. To address this, we conducted a comprehensive meta-analysis to clarify effects of grazing, enclosure, and mowing on QTP grasslands, integrating studies from Web of Science, Google Scholar, and CNKI. We constructed disturbance intensity indicators to quantify utilization pressure and used multiple ecological metrics to characterize heterogeneous responses of the vegetation–soil–microbe system. Moderate grazing enhanced vegetation coverage, biomass, diversity, soil total phosphorus, and organic matter; high-intensity grazing reduced vegetation traits, soil bulk density, moisture, nutrients, and microbial biomass/diversity, while increasing soil pH. Early enclosure mitigated anthropogenic disturbance to improve grassland functions, but long-term enclosure exacerbated nutrient/moisture competition, lowering vegetation biomass/diversity and degrading soil properties. Moderate mowing improved vegetation communities by suppressing dominant species overexpansion; excessive mowing caused vegetation homogenization, soil carbon loss, and microbial destabilization. Impacts showed environmental heterogeneity linked to climate, soil, vegetation type, and elevation. In humid and fertile alpine meadows, moderate grazing more effectively promoted vegetation diversity and soil nutrient cycling, while in arid and nutrient-poor desert grasslands, even light grazing led to visible declines in vegetation coverage and soil moisture. Low-elevation alpine grasslands exhibited stronger positive responses to moderate grazing, whereas high-elevation alpine desert grasslands showed high vulnerability even to light grazing. Based on these mechanisms, regionally tailored strategies integrating multiple practices are required to balance ecological conservation and livestock production, promoting QTP grassland sustainability. In future research, we will strengthen quantitative exploration of how specific environmental factors regulate the magnitude and direction of grassland ecosystem responses to grazing, enclosure, and mowing, thereby providing more precise scientific basis for differentiated grassland management. Full article

Improper disposal of waste tires has led to significant environmental and economic challenges, including pollution and inefficient resource utilization. The growing focus on sustainable solutions in geotechnical engineering highlights the potential of sand–rubber tire shred mixtures for applications such as soil stabilization, embankment reinforcement, seismic isolation, and drainage. This paper presents a bibliometric study analyzing research trends, methodologies, and applications of these mixtures from 2000 to 2025, based on 366 relevant publications. The findings indicate a substantial increase in publications after 2015, reflecting heightened academic and industrial interest in sustainable construction materials. Keyword co-occurrence analysis reveals key research themes, including optimization of shear strength, enhancement of compressibility, and mitigation of seismic impacts. Citation network maps illustrate influential studies and collaborative research networks that are propelling advancements in this field. Despite the advantages of sand–rubber mixtures, challenges such as compaction difficulties, variability in rubber particle size, and long-term durability remain to be addressed. Future research should focus on large-scale field applications, standardization of design methodologies, and the integration of advanced computational modeling for performance optimization. This study contributes to the development of sand–rubber mixtures, positioning them as viable and ecological solutions within the framework of circular economy principles and sustainable construction practices. Full article

Power systems are undergoing a transformative transition as consumers seek greater participation in managing electricity systems. This shift has given rise to the concept of “prosumers,” individuals who both consume and produce electricity, primarily through renewable energy sources. While renewables offer undeniable environmental benefits, they also introduce significant energy management challenges. One major concern is the variability in energy consumption patterns within households, which can lead to inefficiencies. Also, improper energy management can result in economic losses due to unbalanced energy control or inefficient systems. Home Energy Management Systems (HEMSs) have emerged as a promising solution to address these challenges. A well-designed HEMS enables users to achieve greater efficiency in managing their energy consumption, optimizing asset usage while ensuring cost savings and system reliability. This paper presents a comprehensive systematic review of optimization techniques applied to HEMS development between 2019 and 2024, focusing on key technical and computational factors influencing their advancement. The review categorizes optimization techniques into two main groups: conventional methods, emerging techniques, and machine learning methods. By analyzing recent developments, this study provides an integrated perspective on the evolving role of HEMSs in modern power systems, highlighting trends that enhance the efficiency and effectiveness of energy management in smart grids. Unifying taxonomy of HEMSs (2019–2024) and integrating mathematical, heuristic/metaheuristic, and ML/DRL approaches across horizons, controllability, and uncertainty, we assess algorithmic complexity versus tractability, benchmark comparative evidence (cost, PAR, runtime), and highlight deployment gaps (privacy, cybersecurity, AMI/HAN, and explainability), offering a novel synthesis for AI-enabled HEMS. Full article

In greenhouse and plant factory production, improper design of the ventilation system and increasing scales will lead to a stagnant airflow zone, which could inhibit plant growth and induce physiological disease, such as tipburn. To increase the airflow within the plant canopy, simplify the equipment complexity, and improve operation convenience, a cultivation system was designed to provide a constant airflow within the plant canopy by integrating ventilation ducts with cultivation tanks. A three-dimensional computational fluid dynamics (ANSYS Fluent 2021R2) model was developed and validated through simulating the airflow distribution within the plant canopy under different intake air velocities. According to the simulated results, an intake air velocity of 10 m s⁻¹ showed better airflow uniformity, and the proportion of the suitable zone reached the highest value of 83% at an intake air velocity of 20 m s⁻¹. To validate the practical effectiveness of cultivation, a cultivation experiment was conducted. Five different canopy air velocities were set at 0 (CK), 0.35 (T₁), 0.5 (T₂), 0.65 (T₃), and 0.8 (T₄) m s⁻¹, respectively. The results showed that the photosynthetic and transpiration rate, as well as the fresh and dry weights of lettuce plants (Lactuca sativa cv. ‘Tiberius’), increased by 17.8%, 21.7%, 29.6%, and 29.9%, respectively, under treatment T₄ compared to those under the control, while the canopy air temperature and relative humidity decreased by 1.3 °C and 3.2%, respectively. The above results indicate that the newly designed cultivation system can be considered an effective system for improving lettuce plant growth and its canopy environment. Full article

This study examines the challenges and opportunities of managing electronic waste (e-waste) in the Galapagos Islands, a globally significant yet vulnerable subnational insular jurisdiction (SNIJ). Drawing on theories of Circular Economy (CE) and Political Industrial Ecology (PIE), the research investigates the status of e-waste in the archipelago, the barriers to implementing CE practices, and the institutional dynamics shaping material flows. Using a mixed-methods approach—including archival analysis, participant observation, and semi-structured interviews with key informants from government, private, and nonprofit sectors—the findings presented here demonstrate that e-waste management is hindered by limited capital, infrastructure, public awareness, and fragmented governance. While some high-capital institutions can export e-waste to mainland Ecuador, most residents and low-capital entities lack viable disposal options, leading to accumulation and improper disposal. The PIE analysis yielded findings that highlight how institutional power and financial capacity dictate the sustainability of e-waste pathways, with CE loops remaining largely incomplete. Despite national policy support for CE, implementation in Galapagos remains aspirational without targeted financial and logistical support. This case contributes to broader discussions on waste governance in island settings and underscores the need for integrated, equity-focused strategies to address e-waste in small island developing states (SIDS) and SNIJs globally. Full article

Musculoskeletal injuries are a growing concern among adolescent athletes, with significant physical and psychological consequences. This integrative literature review aimed to analyze the risk factors associated with musculoskeletal injuries in adolescents engaged in sports and to explore the role of nursing interventions in their prevention. A systematic search was conducted across four databases and one gray literature source, including studies published between 2014 and 2024. Three descriptive studies were included, with evidence levels ranging from 3 to 4, according to the Joanna Briggs Institute classification. The main findings highlight that risk factors for musculoskeletal injuries include excessive training loads, inadequate sports technique, lack of professional supervision, improper use of equipment, and failure to recognize early signs of discomfort. Preventive nursing interventions were shown to be effective, particularly those focused on health education, proprioceptive training, and continuous monitoring. Multidisciplinary collaboration between nurses, coaches, and other health professionals emerged as a key strategy in creating safe sporting environments. Despite limitations such as the scarcity of studies on nursing-specific interventions in diverse sports contexts, this review supports the potential of structured, evidence-based nursing actions to reduce musculoskeletal injuries incidence, promote safer sports practices, and enhance adolescent athletes’ health outcomes. Full article

LED strips are increasingly used as lighting sources in public and private spaces. However, traditional photometric methods, such as integrating spheres, are unsuitable for measuring their light parameters, often resulting in significant errors and requiring expensive instrumentation or calibration. These errors are typically caused by non-uniform illumination of the internal surface or improper internal geometry, especially when measuring LED sources. This article presents the development of a low-cost integrating device specifically designed to measure LED strips’ light parameters. The device is a compact cube with a volume of less than 1.0 m³. It was tested against alternative methods using an integrating sphere and a goniophotometer in a professional photometric laboratory. The verification results confirmed its effectiveness. The device showed the maximum relative error of luminous flux measurement to be around 5% compared with the accurate, expensive goniophotometric method. For colorimetric measurements, the maximum Correlated Color Temperature (CCT) absolute error was about 35 K for an LED strip with a CCT of 4000 K, indicating a difference imperceptible to the human eye. These results demonstrate the device’s proper relevance in the research and development of LED strip-based lighting equipment to improve lighting equipment quality and control processes. The device is easy to replicate, significantly reducing production and transportation costs, making it an excellent solution for companies and research units seeking a cost-effective method for LED strip measurements. Additionally, the device can measure other light sources or luminaires with reasonably small sizes emitting light in only one hemisphere. The device is the basis of a patent application. Full article

In this study, a prototype system was developed as a potential alternative to lockdown measures against the spread of airborne infectious diseases such as COVID-19. The system integrates real-time estimation functions for respiratory flow and mask leakage into a low-cost powered air-purifying respirator (PAPR) designed for the general public. Using only a single differential-pressure sensor (SDP810) and a controller (Arduino UNO R4 WiFi), the respiratory flow (Q_3e) is estimated from the differential pressure (ΔP) and battery voltage (V_b), and both the wearing status and leak status are transmitted to and displayed on a smartphone application. For evaluation, a testbench called the Respiratory Airflow Testbench was constructed by connecting a cylinder–piston drive to a mannequin head to simulate realistic wearing conditions. The estimated respiratory flow Q_3e, calculated solely from ΔP and V_b, showed high agreement with the measured flow Q_3m obtained from a reference flow sensor, confirming the effectiveness of the estimation algorithm. Furthermore, an automatic leak detection method based on the time-integrated value of Q_3e was implemented, enabling the detection of improper wearing. This system thus achieves respiratory flow estimation and leakage detection based only on ΔP and V_b. In the future, it is expected to be extended to applications such as pressure control synchronized with breathing activity and health monitoring based on respiratory and coughing analysis. This platform also has the potential to serve as the foundation of a PAPR Wearing Status Network Management System, which will contribute to societal-level infection control through the networked sharing of wearing status information. Full article

For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery management system (BMS) to enhance the predictability of the vehicle’s range and avert thermal runaway due to improper charging methods. In this study, an adaptive SOC estimation methodology was developed using parameter identification with forgetting factor recursive least squares (FFRLS). These parameters are then incorporated into a dual adaptive extended Kalman filter (DAEKF) for SOC estimation under varying load conditions. DAEKF is used to dynamically adjust the covariance matrices for process and measurement noises, significantly enhancing the filter’s adaptability and precision. The integration of FFRLS and DAEKF enables a robust SOC estimation of electric vehicles, featuring rapid computation speeds, high accuracy, and excellent adaptability, positioning them as ideal candidates for enhancements in battery management system technology. Full article

Fast fashion is a rapidly expanding sector characterized by high production volumes, low costs, and short product lifecycles. While recent efforts have focused on improving sustainability within supply chains, consumer behavior remains a critical yet underexplored driver of environmental impacts. This study presents a web-based calculator tool designed to estimate both the carbon and plastic footprints associated with individual fast fashion consumption, with a particular focus on shopping behaviors, garment disposal, and laundry habits. Adopting a geographical perspective, the analysis explicitly considers the spatial dynamics of consumption and logistics within the urban context of Milan (Italy), a dense metropolitan area representative of high fashion activity and mobility. By incorporating user-reported travel patterns, logistics routes, and localized emission factors, the tool links consumer habits to place-specific environmental impacts. By involving over 360 users, the tool not only quantifies emissions and plastic waste (including microfibers) but also serves an educational function, raising awareness about the hidden consequences of fashion-related choices. Results reveal high variability in environmental impacts depending on user profiles and behaviors, with online shopping, frequent use of private vehicles, and improper garment disposal contributing significantly to emissions and plastic pollution. Our findings highlight the importance of integrating consumer-focused educational tools into broader sustainability strategies. The tool’s dual function as both calculator and awareness-raising platform suggests its potential value for educational and policy initiatives aimed at promoting more sustainable fashion consumption patterns. Full article

Tactile pavings (Tenji Blocks) are prone to wear, obstruction, and improper installation, posing significant safety risks for visually impaired pedestrians. This system incorporates a lightweight YOLOv8 (You Only Look Once version 8) model for real-time detection using a fisheye camera to maximize field-of-view coverage, which is highly advantageous for low-altitude UAV navigation in complex urban settings. To enable lightweight deployment, a novel Lightweight Shared Detail Enhanced Oriented Bounding Box (LSDE-OBB) head module is proposed. The design rationale of LSDE-OBB leverages the consistent structural patterns of tactile pavements, enabling parameter sharing within the detection head as an effective optimization strategy without significant accuracy compromise. The feature extraction module is further optimized using StarBlock to reduce computational complexity and model size. Integrated Contextual Anchor Attention (CAA) captures long-range spatial dependencies and refines critical feature representations, achieving an optimal speed–precision balance. The framework demonstrates a 25.13% parameter reduction (2.308 M vs. 3.083 M), 46.29% lower GFLOPs, and achieves 11.97% ${\mathrm{mAP}}_{50:95}$ on tactile paving datasets, enabling real-time edge deployment. Validated through public/custom datasets and actual UAV flights, the system realizes robust tactile paving detection and stable navigation in complex urban environments via hierarchical control algorithms for dynamic trajectory planning and obstacle avoidance, providing an efficient and scalable platform for automated infrastructure inspection. Full article

Blasting operations in surface mining pose a risk of flyrock, which is a critical safety concern for both personnel and infrastructure. This study presents the use of unmanned aerial vehicles (UAVs) and photogrammetric techniques to improve the accuracy of blast design, particularly in relation to controlling burden values and reducing flyrock. The research was conducted in a basalt quarry in Lower Silesia, where high rock fracturing complicated conventional blast planning. A DJI Mavic 3 Enterprise UAV was used to capture high-resolution aerial imagery, and 3D models were created using Strayos software. These models enabled precise analysis of bench face geometry and burden distribution with centimeter-level accuracy. The results showed a significant improvement in identifying zones with improper burden values and allowed for real-time corrections in blasthole design. Despite a ten-fold reduction in the number of images used, no loss in model quality was observed. UAV-based surveys followed software-recommended flight paths, and the application of this methodology reduced the flyrock range by an average of 42% near sensitive areas. This approach demonstrates the operational benefits and enhanced safety potential of integrating UAV-based photogrammetry into blasting design workflows. Full article