Fluctuations In Quality Research Articles

46 Use of soy hull as a supplement during late gestation and lactation for landrace hair sheep ewes in a forage-based system

Abstract Forage-based production systems can be limiting in providing adequate nutrition as the result of seasonal fluctuations in forage quality and quantity, especially during periods of increased nutritional demands such as late gestation and lactation. This study evaluated the use of an agro-byproduct supplementation (soy hull) to fill these demands. Barbados Blackbelly (BB) and St. Croix (STX) hair sheep ewes were bred November 1 in a 25-d breeding season in two single-sire groups to like breed sires, and initially managed as one group. Pregnant ewes (n = 36) were allocated, balanced by breed, parity and fetal numbers to either a corn/soybean (CS) or soy hull (SH) supplementation starting the last trimester of pregnancy. Ewes rotationally grazed predominantly fescue pastures subdivided into 6 sections (0.13 ha) using electro-netting (3 sections of 6 ewes per supplement type). Ewes were supplemented at either 0.75% BW (late gestation) or 1.5% BW (lactation) using a single group feeder per section. Ewes lambed on pasture and lambs were weaned at approximately 63 d. Lambs had access to the supplement provided to their dams. Data were collected on ewe BW changes and reproductive performance, and analyzed in a model with supplement type and breed as main effects. Results are presented as LSM. Supplement type had no effect on ewe prepartum (54.7 kg), postpartum (47.3 kg) and weaning BW (44.7 kg), whereas BW was consistently heavier in STX than BB ewes. Ewe late gestation (61 g/d) and lactation ADG (-51 g/d) were not affected by supplement type though breeds differed (P < 0.01) in lactation ADG (BB: -14 vs STX: -78 g/d). Lambing performance was also not affected by supplement type with 1.97 lambs born live, and 1.69 lambs weaned per ewe. There was a supplement type by breed interaction (P < 0.05) for number of lambs weaned with STX ewes receiving SH having a larger litter size at weaning than ewes receiving CS (1.40 vs. 2.10 lambs per ewe) with no difference in BB ewes (1.71 vs. 1.50 lambs per ewe, respectively). Neither litter birth (6.14 kg) and weaning weights (19.9 kg) nor litter ADG (233 g/d) were affected by supplement type. Litter BW was significantly greater (P < 0.01) in STX than BB (6.85 vs 5.18 kg), but not different at weaning. Lamb birth and weaning weights were not affected by supplement type, though preweaning ADG tended (P < 0.1) to be greater in CS than SH (162 vs. 142 g/d). Data indicate that substituting SH for a more traditional corn-based supplement in landrace hair sheep ewes managed in a pasture-based system did not affect ewe growth, reproductive performance, and lamb performance. Agro-byproduct supplementation should be explored further for use in sustainable, forage-based sheep production systems.

COMPOSITE SCORES FOR TRANSPLANT CENTER EVALUATION: A NEW INDIVIDUALIZED EMPIRICAL NULL METHOD.

Risk-adjusted quality measures are used to evaluate healthcare providers with respect to national norms while controlling for factors beyond their control. Existing healthcare provider profiling approaches typically assume that the between-provider variation in these measures is entirely due to meaningful differences in quality of care. However, in practice, much of the between-provider variation will be due to trivial fluctuations in healthcare quality, or unobservable confounding risk factors. If these additional sources of variation are not accounted for, conventional methods will disproportionately identify larger providers as outliers, even though their departures from the national norms may not be "extreme" or clinically meaningful. Motivated by efforts to evaluate the quality of care provided by transplant centers, we develop a composite evaluation score based on a novel individualized empirical null method, which robustly accounts for overdispersion due to unobserved risk factors, models the marginal variance of standardized scores as a function of the effective sample size, and only requires the use of publicly-available center-level statistics. The evaluations of United States kidney transplant centers based on the proposed composite score are substantially different from those based on conventional methods. Simulations show that the proposed empirical null approach more accurately classifies centers in terms of quality of care, compared to existing methods.

Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems

In the context of the increasing demand for Internet of Multimedia Things (IoMT) services, rate splitting multiple access (RSMA) and intelligent reflecting surface (IRS) technologies have been considered potential networking enablers to provide ultra-throughput wireless access. However, challenges arise due to the heterogeneity of IoMT devices and arbitrary network quality changes, resulting in unwanted service quality fluctuations and downgradation. This study addresses this problem by jointly optimizing wireless resource allocation and bitrate adaptation with Deep Reinforcement Learning (DRL)-based QoE management for IRS-aided RSMA-enabled IoMT streaming systems. We formulated the problem as a Markov decision process (MDP) and apply Proximal Policy Optimization (PPO) method to flexibly adjust IoMT bitrate, transmission beamforming, IRS phase shift, and RSMA parameters. As a result, our algorithm mitigates overestimation of client-side bandwidth, leading to smoother playback and reduced quality fluctuations. Simulations show that our approach outperforms baseline methods in terms of video resolution (up to 2.5 times) and achievable sum-rate (up to 50%), contributing to a superior streaming experience in IoMT systems.

Water quality management of micro swamp wetland based on the “source-transfer-sink” theory: A case study of Momoge Swamp Wetland in Songnen Plain, China

Swamp wetlands play a pivotal role in conserving water sources, regulating river runoff, and maintaining water safety and balance. The study pioneers a new research framework in the field of pollution ecology of swamp wetlands, with a focus on the complex “source-transfer-sink” processes affecting water quality in micro swamp wetlands, an area previously under-researched. Taking the Momoge Wetland in the Songnen Plain as an example, we innovatively combined the “source-transfer-sink” theory with landscape patterns, topographic factors, and their impacts on wetland water quality. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), our research quantifies and models the relationships among various environmental factors. This approach reveals how landscape patterns influence pollutant migration and water quality at a micro-scale. Our findings suggest that a 700-m buffer zone is most effective in capturing the fluctuations in water quality in arid wetlands, which is crucial for wetland management practices. We identified the main pollution sources in the Momoge Wetland, including sediment (21.60%), vegetation (24.94%), livestock and crop-related non-point sources (41.21%), and hydrodynamic factors such as water diversion (12.25%). In pollutant migration, land use emerged as the most significant factor affecting water quality (56.64%), followed by landscape patterns (25.54%). Notably, our study finds that grasslands are essential for water purification, and a uniform distribution of landscape patches significantly improves water quality. This research marks a significant stride in understanding and optimizing landscape patterns to enhance water quality in micro swamp wetlands. The insights gained are invaluable for the protection of these ecosystems and promoting their ecological value functions, providing a reference point for future wetland conservation and management strategies.

Life Cycle Assessment of Energy Storage Technologies for New Power Systems under Dual‐Carbon Target: A Review

Aiming at the grid security problem such as grid frequency, voltage, and power quality fluctuation caused by the large‐scale grid‐connected intermittent new energy, this article investigates the life cycle assessment of energy storage technologies based on the technical characteristics and performance indicators. First, the new power system under dual‐carbon target is reviewed, which is compared with the traditional power system from the generation side, grid side, and user side. Based on the power characteristics of the new power system, the energy storage mechanism and energy storage characteristics of mechanical energy storage, electrochemical energy storage, chemical energy storage, electromagnetic energy storage, and thermal energy storage are described. Then, compared with the existing research strategies, a comprehensive life cycle assessment of energy storage technologies is carried out from four dimensions: technical performance, economic cost, safety assessment, and environmental impact. Moreover, the suitable scenarios and application functions of various energy storage technologies on the power generation side, grid side, and user side are compared and analyzed from the working principle, key technologies, advantages and disadvantages, and technical difficulties of energy storage technologies. Finally, combined with the energy storage technologies in the electricity market, four profit models are analyzed.

COVID-19 on mind: Daily worry about the coronavirus is linked to negative affect experienced during mind-wandering and dreaming.

Despite a surge of studies on the effects of COVID-19 on our well-being, we know little about how the pandemic is reflected in people's spontaneous thoughts and experiences, such as mind-wandering (or daydreaming) during wakefulness and dreaming during sleep. We investigated whether and how COVID-19-related general concern, anxiety, and daily worry are associated with the daily fluctuation of the affective quality of mind-wandering and dreaming, and to what extent these associations can be explained by poor sleep quality. We used ecological momentary assessment by asking participants to rate the affect they experienced during mind-wandering and dreaming in daily logs over a 2-week period. Our preregistered analyses based on 1,755 dream logs from 172 individuals and 1,496 mind-wandering logs from 152 individuals showed that, on days when people reported higher levels of negative affect and lower levels of positive affect during mind-wandering, they experienced more worry. Only daily sleep quality was associated with affect experienced during dreaming at the within-person level: on nights with poorer sleep quality people reported experiencing more negative and less positive affect in dreams and were more likely to experience nightmares. However, at the between-person level, individuals who experienced more daily COVID-19 worry during the study period also reported experiencing more negative affect during mind-wandering and during dreaming. As such, the continuity between daily and nightly experiences seems to rely more on stable trait-like individual differences in affective processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Channel-aware low Earth orbit satellite cluster networking for space-to-Earth laser communication: reliability and bandwidth advantages.

The reliability of the space-to-Earth laser communication plays a crucial role in providing uninterrupted real-time services in satellite optical networks. In traditional satellite optical networks, the space-to-Earth laser communication is carried out using a monolithic satellite in close proximity to the target optical ground station. However, the reliability of the communication in this approach is heavily influenced by the atmospheric environment. For instance, variations in cloud thickness can cause fluctuations in the link quality of the space-to-Earth laser communication, significantly reducing its reliability. This study proposes an innovative channel-adaptive space-to-Earth laser communication (CA-S2E-LC) architecture based on satellite cluster optical networking (SCON). SCON provides space-diversity link sets, reducing the probability of space-to-Earth laser communications affected by clouds. By leveraging the perception of link quality, the CA-S2E-LC architecture can adaptively choose the better space-to-Earth laser communication links established by member satellites within a satellite cluster under different environments, and properly schedule the resource, ensuring reliable space-to-Earth laser communication. The principles of the SCON is analyzed and the implementation of the CA-S2E-LC architecture is demonstrated through the explanation of hardware and functional modules, workflows, finite state machines, and strategies. Simulation results demonstrate that the CA-S2E-LC architecture can significantly enhance communication reliability and capacity compared with the traditional monolithic satellite. Furthermore, the workflow of the architecture is demonstrated to validate the feasibility.

A Time Series-Based Approach to Elastic Kubernetes Scaling

With the increasing popularity of cloud-native architectures and containerized applications, Kubernetes has become a critical platform for managing these applications. However, Kubernetes still faces challenges when it comes to resource management. Specifically, the platform cannot achieve timely scaling of the resources of applications when their workloads fluctuate, leading to insufficient resource allocation and potential service disruptions. To address this challenge, this study proposes a predictive auto-scaling Kubernetes Operator based on time series forecasting algorithms, aiming to dynamically adjust the number of running instances in the cluster to optimize resource management. In this study, the Holt–Winter forecasting method and the Gated Recurrent Unit (GRU) neural network, two robust time series forecasting algorithms, are employed and dynamically managed. To evaluate the effectiveness, we collected workload metrics from a deployed RESTful HTTP application, implemented predictive auto-scaling, and assessed the differences in service quality before and after the implementation. The experimental results demonstrate that the predictive auto-scaling component can accurately predict the future trend of the metrics and intelligently scale resources based on the prediction results, with a Mean Squared Error (MSE) of 0.00166. Compared to the deployment using a single algorithm, the cold start time is reduced by 1 h and 41 min, and the fluctuation in service quality is reduced by 83.3%. This process effectively enhances the quality of service and offers a novel solution for resource management in Kubernetes clusters.

Biological pretreatment coupled gravity-driven membrane filtration for purifying decentralized domestic wastewater toward resource reuse

Membrane bioreactor (MBR) is an effective technology for the decentralized wastewater treatment; however, its high energy consumption and complex operation & maintenance limit its intensive application. This study introduced an innovative membrane-based process, gravity-driven membrane (GDM) filtration, to treat the decentralized wastewater without any cleaning procedures, and different biological pretreatments were constructed, including (I) tubular filler (TF-8), (II) tubular filler (TF-4), (III) fiber bundle filler (FBF), and (IV) sole aeration (control) to improve the filtration performance. The results indicated that the flux of GDM control can be maintained during long-term filtration in treating the real decentralized wastewater, and introducing the biological pretreatment processes could improve the stabilized flux of GDM without any influence on its stability. The average flux of TF-8/GDM was 3.69 ± 0.89 L m−2 h−1 during long-term filtration. Furthermore, coupling pre-treatment to GDM can effectively improve permeability quality and withstand the shock of water quality fluctuations. The average removal rates of chemical oxygen demand (COD) and dissolved organic carbon (DOC) by TF-8 were 59.7% and 48.3%, respectively. Besides, the organic contaminants could be further removed by the following GDM process owing to the biodegradation function of the self-formed biofilm on the membrane surface, and the DOC concentration in the GDM effluent can be reduced to as low as 4.5 mg L−1. Additionally, the elements of nitrogen (N) and phosphorus (P) could be effectively retention through the coupling process, with a retention rate of 84.3%–94.6% for total nitrogen (TN) and 99.8%–104.8% for total phosphorus (TP), respectively. Therefore, these findings can benefit to develop of new strategies to provide stable effluent quality, resource recovery, and energy consumption reduction in the membrane-based decentralized domestic wastewater treatment.

The Effects of Pregnancy and Childbirth on Women's Health-Related Quality of Life: A Scoping Review

Context: Although pregnancy and childbearing are the most natural processes for women, it is accompanied by significant life changes for the mother, who needs to adjust to parenting. Hormonal variables cause these changes, resulting in physiological and emotional consequences. Although the changes that pregnant women experience are only transitory, they might affect their quality of life.
 Aim: This scoping review highlight the effect of pregnancy and childbearing on women’s health-related quality of life.
 Methods: A scoping review was completed by searching data converging about pregnancy and childbirth's effects on women's health-related quality of life utilizing databases from 2015-2022 papers. The review was done via different database engines. The research selection procedure uses Mesh keywords to find the most relevant papers, and the PRISMA flowchart helped pick research papers. The search engines were picked from the Saudi digital library and Google Scholar. Three main themes and three subthemes were created in this scoping review. A total of 82 articles were initially retrieved. Twenty-four duplications were removed. Thirty-four articles that did not meet the aim were excluded. The remaining 26 were screened; the non-English, outdated articles, text articles, and more reasons were counted, so another ten articles were removed. So, 14 studies met the criteria and were eligible to be included in this review.
 Results: revealed a correlation between delayed first birth and better health-related quality of life. It is highlighted that physical quality of life-related to maternal age, gestational age, BMI before 12 weeks of pregnancy, mother's education and work, and spouse's education (p<0.05). Psychological quality of life-related to gestational age, mother's education and occupation, and spouse's education. The main characteristics related to greater quality of life were mean maternal age, primiparity, early gestational age, the lack of social and economic issues, having family and friends, exercising, being happy to be pregnant, and being hopeful. Early second-trimester pregnant women had the best health-related quality of life drops in the third trimester. More than half of the participants in the reviewed studies had urinary incontinence as a main pelvic floor disorder. Mothers showed lower gray matter (GM) volumes and higher white matter (WM) volumes in empathy and reward networks. Those who gave birth vaginally reported the highest HRQoL, whereas those who underwent cesareans reported the lowest.
 Conclusion: The study elucidates the fluctuations in quality of life throughout pregnancy, showing its dependence on various demographic and health-related aspects. The nuanced variations and pathological features emerging during pregnancy encompass the impact of morning sickness, sleep patterns, and pelvic floor disorders. The influence of the mode of delivery on women's health-related quality of life emphasizes differences in pain, physical functioning, and mental health between those who undergo cesarean sections and those who have vaginal births. Collectively, these findings underscore the complexity of the maternal experience, calling for a holistic approach to healthcare that addresses the diverse challenges women face during this pivotal life stage.

Research progress of leachate treatment in waste transfer station

The garbage leachate generated by the waste transfer station has the characteristics of scattered water sources, relatively small water quantity, large fluctuations in water quality and quantity, and high concentrations of organic matter, ammonia nitrogen, suspended solids, and other pollutants. These characteristics make it prone to environmental hazards. This paper analyzes the characteristics and hazards of leachate from waste transfer stations and introduces the current research status of physico-chemical, biological, and combined treatment of leachate from waste transfer stations. Meanwhile, this paper introduces engineering practices for leachate treatment in waste transfer stations, offering a valuable reference for research and engineering practices in this field. Finally, this review discusses pretreatment, economic, low-carbon, and high-efficiency treatment methods and facilities, the recovery of valuable elements, energy recycling technologies, and the demand outlook for high-standard deep treatment processes.

Effects of essential oils on egg production and feed efficiency as influenced by laying hen breed: A meta-analysis.

Successful rearing of laying hens to achieve optimal egg production is an endeavor that often faces various constraints and challenges, such as infectious diseases, environmental stressors, and fluctuations in feed quality. The incorporation of essential oils (EOs) into the diet of laying hens has attracted considerable attention in recent years. Therefore, our study aimed to evaluate the efficacy of EO inclusion in laying hen diets by considering the effects of production phase and breed on performance, egg quality, serum biochemistry, gut health, and antioxidant activity. The articles were obtained from the Web of Science, Scopus, Science Direct, and PubMed using the search terms "essential oils," "laying hens," and "phytobiotics." Data from 27 articles and 71 experiments were grouped according to laying hen production phase and breed in the database. The EO levels ranged from 0 to 1000 mg/kg, with thymol and carvacrol being the major EOs. A mixed model was used to analyze the data. Random effects were applied to the treatment, and fixed effects were applied to EO level, production phase, and breed. Egg production, feed intake, feed efficiency, eggshell quality, villus height, crypt depth, superoxide dismutase, and glutathione peroxidase levels increased linearly (p = 0.05) and egg weight and mass increased quadratically (p < 0.05) with increasing EO concentrations. An interaction was observed between the EO level egg production and feed conversion ratio (p = 0.05). Serum glucose, cholesterol, and malondialdehyde levels decreased with increasing EO concentrations (p < 0.05). The inclusion of EOs effectively increased egg production, feed efficiency, egg weight, egg mass, eggshell quality, oxidative enzymes, and intestinal health. In addition, the proportion of dietary EOs in lightweight laying hens was higher than that in semi-heavy-weight laying hens in improving egg production and feeding efficiency.

Characteristics of Medical Quality in Tertiary Traditional Chinese Medicine Hospitals by TOPSIS and RSR Methods.

Performance evaluation is important for improving medical quality and services. But, there is a lack of research for medical quality in traditional Chinese medicine (TCM) hospitals. This study examines the medical quality and various indicators of tertiary public traditional Chinese medicine hospitals in Gansu Province, to establish a foundation for improving the medical and management standards of these hospitals. This study collected performance assessment data from 10 tertiary TCM hospitals in Gansu Province from 2019 to 2022. Thirteen indicators with TCM characteristics were selected and categorized into 3 aspects: control of medical costs, internal operational dimensions, and comprehensive management. The level of medical quality in different hospitals and in different years were determined using the TOPSIS method for ranking and the RSR method for grading. Firstly, in terms of TCM characteristic indicators, hospital H had the highest control of medical costs and comprehensive management among different hospitals, with 45.87% and 24.20% respectively. The highest values for control of medical costs and comprehensive management were observed in 2020, with 40.65% and 18.69% respectively among different years. When evaluating the medical quality of different hospitals using the TOPSIS method, it was found that hospital H had the highest ranking from 2020 to 2022, with Ci values of 0.725, 0.778, and 0.667 respectively. Additionally, the RSR method indicated that hospital H had a high level of grading from 2020 to 2022, with Pi values of 0.687, 0.690, and 0.723 respectively. These findings suggest that the medical quality of hospital H is at a high and stable level of development. Based on the TOPSIS method to evaluate the performance appraisal results and ranking of different hospitals from 2019 to 2022. The results showed that the highest ranking was hospital B(Ci = 0.913) in 2019. The highest ranking was hospital C(Ci = 0.809)in 2020. The highest ranking was hospital D(Ci = 0.689) in 2021. The highest ranking was hospital J(Ci = 0.865) in 2022. The RSR method indicated that high grading level were hospitals B(Pi = 0.899),F(Pi = 0.795) in 2019. The highest grading level was hospital C(Pi = 0.809) in 2020. The highest grading level were hospitals A(Pi = 0.868), D(Pi = 0.813), E(Pi = 0.689), G(Pi = 0.873), J(Pi = 0.813), K(Pi = 0.842) in 2022. Based on the above results indicate that there is a large variation in the medical quality profile of different hospitals from 2019 to 2022. By comparing the results of TOPSIS and RSR method from 2019 to 2022, we found that the hospitals with identical ranking were D and J, and the hospitals with ≤2 difference in ranking was A,B,C,E in 2019, the hospitals with >2 ranking was A, F in 2020, the hospitals with >2 ranking were C, G in 2021, and the hospitals with identical ranking results were B,D,E,G,J in 2022. Comparing the ranking results of TOPSIS and RSR methods, showed that the hospitals with identical rankings were B, F from 2019 to 2022. The difference in ranking results ≤2 were A, C, D, E, G, H, J, K, indicating that high consistency between TOPSIS and RSR methods and credible results. The findings reveal significant fluctuations in medical quality across different years, while the overall level of medical quality remains relatively stable among the various hospitals. It is recommended that TCM hospitals focus on improving management efficiency, optimizing hospital operations, enhancing the utilization of medical resources, and fostering the efficient development of hospitals.

Link Quality Fluctuation in Wireless Networks Deployed on the Surface of Different Water Bodies

Link Quality Fluctuation in Wireless Networks Deployed on the Surface of Different Water Bodies

Assessing Uncertainties in Mechanistic Modeling of Quality Fluctuations in Drinking Water Distribution Systems

Assessing Uncertainties in Mechanistic Modeling of Quality Fluctuations in Drinking Water Distribution Systems

QueryTrack: Joint-Modality Query Fusion Network for RGBT Tracking.

Existing RGB-Thermal trackers usually treat intra-modal feature extraction and inter-modal feature fusion as two separate processes, therefore the mutual promotion of extraction and fusion is neglected. Then, the complementary advantages of RGB-T fusion are not fully exploited, and the independent feature extraction is not adaptive to modal quality fluctuation during tracking. To address the limitations, we design a joint-modality query fusion network, in which the intra-modal feature extraction and the inter-modal fusion are coupled together and promote each other via joint-modality queries. The queries are initialized based on the multimodal features of the current frame, making the subsequent fusion adaptive to modal quality fluctuation during tracking. Then the joint-modality query fusion (JQF) utilizes the queries to interact with RGB-T features, allowing the intra-modal enhancement and the inter-modal interactions to be unified for mutual promotion. In this way, JQF can distinguish and enhance the complementary modality features, while filtering out redundant information. For real-time tracking, we propose regional cross-attention for cross-modal interactions to reduce computational cost. Our end-to-end tracker sets a new state-of-the-art performance on multiple RGBT tracking benchmarks including LasHeR, VTUAV, RGBT234 and GTOT, while running at a real-time speed.

Mycelium-based-composites – Vision for substitution of fossil-based materials

For decades, fossil-based materials have formed the basis of an almost endless range of technical products. Through variable chemical composition and several additives, especially plastics can have a wide range of properties, which form the basis for the diversity of plastic-based products. While this variability enables many sustainability strategies, such as lightweighting, it is also impeding a fully circular economy. Therefore, in recent years, a number of new raw materials have been developed, but they can only cover a very limited part of the wide range of properties of fossil-based plastics. Another promising class of materials are composites based on fungal mycelium.However, these are mainly limited to the consumer sector, e.g. vegan leather, where there is smaller demand for durability, functionality etc. The step from consumer to engineering materials require the production (or growth) process to be reproducible within the necessary quality requirements. Cyber-physical production systems have the potential to realise necessary technical qualities despite e.g. quality fluctuations of the raw material and production processes that are susceptible to interference. For this reason, this paper analyses the state of the art in production of mycelium based composites, shows the existing gaps and draws a vision to close these gaps.

Revolutionizing Air Quality Monitoring: IoT-Enabled E-Noses and Low-Power Devices

The integration of Internet of Things (IoT) technology and low-power devices presents a transformative approach to revolutionizing air quality monitoring, addressing the critical global challenge of air pollution. In this paper, we propose an innovative solution leveraging IoT-enabled e-noses, a gateway, and an IoT platform to facilitate comprehensive air quality assessment. Our system incorporates a diverse array of sensors capable of measuring 11 essential air quality parameters, including PM10, PM2.5, CO2, CO, VOCs, ozone, and nitrogen dioxide. Crucially, our solution achieves remarkable power efficiency, with the electronic noses consuming as low as 10 during deep sleep mode. Through meticulous power management strategies and optimized battery capacity calculations, we ensure sustained functionality and operational efficiency of the monitoring system. Indoor and outdoor deployment scenarios are considered, with the system exhibiting robustness and versatility across diverse environmental contexts. Real-time data collection, transmission, and analysis enable timely detection of air quality fluctuations, empowering stakeholders with actionable insights to mitigate the adverse effects of air pollution on public health and environmental sustainability.

Mitigation of Power Quality Issue in Hybrid Renewable Energy Source by UPQC with Optimization Techniques

This paper presents a comprehensive approach to mitigate power quality issues in hybrid renewable energy systems through the utilization of Unified Power Quality Conditioner (UPQC) integrated with optimization techniques. As renewable energy sources like wind and solar play an increasingly significant role in power generation, ensuring high-quality power supply becomes imperative. The UPQC, comprising series and shunt active power filters, is strategically deployed to address voltage fluctuations, harmonics, and other disturbances. To enhance its efficacy, advanced optimization techniques such as control algorithm optimization and parameter tuning are employed. These techniques enable the UPQC to adapt to varying operating conditions and optimize its performance in real-time. Integration with hybrid renewable energy sources is seamless, facilitating coordinated operation for optimal power quality enhancement. The proposed approach emphasizes real-time monitoring and control, enabling proactive response to power quality fluctuations and ensuring stable and reliable electricity supply. Through rigorous testing, validation, and maintenance protocols, the effectiveness and reliability of the integrated system are demonstrated. This holistic approach contribute to the stability and sustainability of modern power grids, fostering the widespread adoption of hybrid renewable energy systems.

Using Mobile and Fixed Internet of Things Sensing Networks, Real-Time Monitoring and Prediction of Air Quality in One\'s Immediate Vicinity

Abstract: A significant global problem has emerged in several locations as a result of air pollution and its adverse impact on human well-being. Recently, there has been a rise in the number of researchers that are keen on evaluating and forecasting the air quality in close proximity to individuals. The use of the Internet of Things (IoT) across several businesses has significantly enhanced people's quality of life by interconnecting multiple sensors in diverse locations. Moreover, the Internet of Things (IoT) has streamlined the task of monitoring air pollution. The conventional utilization of stationary sensors is inadequate for acquiring an accurate and all-encompassing representation of the air pollution levels in close proximity to people. This is due to the fact that the sensors in closest proximity to people may be located many kilometers apart from each other. The objective of our study is to construct a model that precisely depicts the air quality pattern within a certain geographic area. This objective will be accomplished by using a combination of stationary and portable Internet of Things sensors. The sensors are affixed to vehicles that are carrying out surveillance in the vicinity. Our methodology allows for a thorough examination of the whole spectrum of air quality fluctuations in neighboring regions. Through the use of many machine learning algorithms and realworld data, we showcase the efficacy of our methodology in accurately discerning and forecasting air quality without compromising on accuracy. The results of our research indicate that there is significant potential for efficiently monitoring and forecasting air quality in the context of a smart city.