Search Results (43)

Accurate trajectory prediction of concealed targets in complex, interference-laden environments present a formidable challenge for millimeter-wave sensor tracking systems. To address this, we propose a state-of-the-art autonomous prediction framework that integrates an Improved Sequential Variational Mode Decomposition (ISVMD) algorithm with an Extreme Learning Machine (ELM), synergistically optimized by the novel Red-billed Blue Magpie Optimizer (RBMO). The ISVMD enhances signal reconstruction by transforming noisy target echo signals into robust feature sequences, effectively mitigating the impacts of environmental disturbances and intentional concealment. Subsequently, the RBMO-optimized ELM leverages these feature sequences to predict the future trajectories of concealed targets with high precision. The RBMO further refines critical parameters within the ISVMD-ELM pipeline, ensuring adaptability and computational efficiency across diverse scenarios. Experimental validation using real-world data demonstrates that the RBMO-ISVMD-ELM approach surpasses state-of-the-art algorithms in both accuracy and robustness when predicting the trajectories of concealed ground targets, achieving optimal performance metrics under demanding conditions. Full article

Sexual dimorphism in bill morphology influences foraging strategies and bird competitive interactions. This study uses geometric morphometric analyses to examine sex-based differences in the bill shape and size of Ciconia ciconia, a large wading bird. Standardized dorsal and lateral photographs of 45 white storks (24 females and 21 males) were analyzed. The mean centroid size (CS) confirmed that males have significantly larger bills than females, yet principal component analysis revealed no reliable shape-based sex differentiation. Allometric analyses indicated that larger individuals exhibit more pronounced shape variations, likely tied to functional adaptations, though these were insufficient for sex determination. While bill size correlates with sex, shape variation does not serve as a reliable criterion for sexing C. ciconia. This study contributes to the understanding of sexual dimorphism in Ciconia ciconia by quantifying bill size differences using geometric morphometrics. Full article

Energy transition is a challenge for remote northern communities mainly relying on diesel for electricity generation and space heating. Solar-assisted ground-coupled heat pump (SAGCHP) systems represent an alternative that was investigated in this study for the Kuujjuaq Forum, a multi-activity facility in Nunavik, Canada. The energy requirements of community buildings facing a subarctic climate are poorly known. Based on energy bills, technical documents, and site visits, this study provided an opportunity to better document the energy consumption of such building, especially considering the recent solar photovoltaic (PV) system installed on part of the roof. A comprehensive model was developed to analyze the building’s heating demand and simulate the performance of a ground-source heat pump (GSHP) coupled with PV panels. The air preheating load, accounting for 268,200 kWh and 47% of the total heating demand, was identified as an interesting and realistic load that could be met by SAGCHP. The GSHP system would require a total length of at least 8000 m, with boreholes at depths between 170 and 200 m to meet this demand. Additional PV panels covering the entire roof could supply 30% of the heat pump’s annual energy demand on average, with seasonal variations from 22% in winter to 53% in spring. Economic and environmental analysis suggest potential annual savings of CAD 164,960 and 176.7 tCO₂eq emissions reduction, including benefits from exporting solar energy surplus to the local grid. This study provides valuable insights on non-residential building energy consumption in subarctic conditions and demonstrates the technical viability of SAGCHP systems for large-scale applications in remote communities. Full article

This study investigated the impact of declining electricity prices on the profitability and optimal sizing of self-consumption photovoltaic (PV) systems in agro-industries with different consumption patterns. A computational approach was employed to analyze over half a million scenarios, considering variations in electricity prices (0.05 EUR/kWh to 0.30 EUR/kWh), consumption seasonality (uniform vs. seasonal), geographic location, solar panel tilt, and installed peak power. The analysis replicated real industrial electricity billing structures using a validated energy balance model that integrates quarter-hourly demand data, PV generation simulations, and economic assessments. The results demonstrate that falling electricity prices significantly reduce the economic benefits of PV investments. Many systems installed in previous years may now be oversized, leading to lower savings or even increased total costs in highly seasonal industries. Under current low-price conditions, new PV installations face limited profitability, with extended payback periods ranging from 7 to 11 years for optimally sized systems. In industries with uniform energy demands, the cost savings peaked at 19%, while seasonal industries achieved only 4% at best. Although surplus energy injection into the grid can improve financial returns, it does not fully offset the profitability decline. These findings highlight the need for adaptive PV sizing strategies and regulatory frameworks, such as dynamic tariff structures and incentive mechanisms, to sustain PV investment viability in agro-industrial contexts with fluctuating energy prices. Unlike previous research, which often assumes stable or rising energy prices, this study uniquely captures the financial risks posed by sudden price drops, particularly for industries with high seasonal consumption variability. Full article

This study investigates the effects of an AI-supported irrigation system on the production of natural plant species and irrigation efficiency at Rize Recep Tayyip Erdoğan University. To enhance water resource efficiency while utilizing Turkey’s rich plant diversity, Geranium psilostemon Ledeb. (Black-Eyed Crane’s-Bill) was selected for cultivation. The research includes adaptation trials and growth monitoring of this perennial taxon, which naturally grows at an altitude of 2000 m. The experiments were conducted in two different environments: one utilizing an AI-supported irrigation system and the other relying on manual irrigation. The findings reveal that AI-supported irrigation systems optimize irrigation strategies, providing a more efficient and effective plant cultivation process compared to manual irrigation. The AI-supported irrigation system continuously monitors air and soil moisture levels, ensuring optimal irrigation conditions and instant adaptation to seasonal variations. This innovative approach minimizes water losses while preventing soil salinization, thereby offering a significant solution for sustainable agricultural practices. In conclusion, this study demonstrates that natural plant species can be effectively cultivated using AI-supported irrigation systems and that these systems hold great potential for water conservation and ecological balance. These findings present a crucial step toward developing effective solutions for global water challenges and promoting sustainable landscape and agricultural practices. Full article

When the total nitrogen content in water sources exceeds the standard, it can promote the rapid proliferation of algae and other plankton, leading to eutrophication of the water body and also causing damage to the ecological environment of the water source area. Therefore, making timely and accurate predictions of water quality at the source is of vital importance. Since water quality data exhibit non-stationary characteristics, predicting them is quite challenging. This study proposes a novel hybrid deep learning model based on modal decomposition, ERSCB (EMD-RBMO-SVMD-CNN-BiGRU), to enhance the accuracy of water quality forecasting. The model first employs Empirical Mode Decomposition (EMD) technology to decompose the original water quality data. Subsequently, it quantifies the complexity of the subsequences obtained from EMD using Sample Entropy (SE) and further decomposes the most complex subsequences using Sequential Variational Mode Decomposition (SVMD). To address the matter of selecting balanced parameters in SVMD, this study introduces the Red-Billed Blue Magpie Optimization (RBMO) algorithm to optimize the hyperparameters of SVMD. On this basis, a forecasting model is constructed by integrating Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (BiGRU) networks. The experimental results show that, compared to existing water quality prediction models, the ERSCB model has an improved prediction accuracy of 4.0% and 3.1% for the KaShi River and GongNaiSi River areas, respectively. Full article

Regulation and customer awareness pressurize manufacturers to use recycled plastic (RP) in the production system to reduce the negative environmental impact of plastic waste for sustainable production. Compared to virgin materials, the RP available in the market shows high variation in quality, composition, and properties, and often experiences higher variability in lead time. This renders the supply chain of RP and the production systems more vulnerable, making it difficult for material requirement planning (MRP) to decide the optimal quantity and reorder time. This paper first examines the RP supply chain and the sources of variations therein, identifies the associated uncertainties for operations management, reviews the current MRP design elements in managing supply uncertainty, and finally aligns strategies and design elements with the dimensions of the uncertainties. A set of valuable propositions is drawn for the plastic firms to manage variation from upstream suppliers and promote a high-value chain of plastic circularity. MRP practices at the operational level including safety stock, optimization techniques, and alternative bills of material are proposed to mitigate the variations in the supply chain. The work provides a conceptual foundation for the circular economy of plastic, which opens a new paradigm of future research in managing RP in the production system for sustainability. Full article

There have been several studies conducted on the economic viability of home battery systems paired with rooftop solar PV systems over the years; however, there have been far fewer studies looking into the economic viability of standalone home battery systems, which is the main area assessed in this study. Rather than being used to store excess solar generation, a home battery system is used with time-of-use tariffs to take advantage of cheaper, off-peak rates. This works by charging the battery overnight at the off-peak rate and drawing electricity from the battery during the day, rather than from the grid at the more expensive peak rate. This study found only one of the four assessed home battery systems to be economically viable for this application, with a net cash flow of GBP 1842 over its 12-year lifetime, a return on investment of 33%, and a payback period of 9 years. Beyond their potential to save money on electricity bills, this study also investigated the potential of home battery systems to prevent GHG emissions by considering the variation in the carbon intensity of the grid between peak and off-peak times. The economically viable system has a lifetime emissions prevention potential of 308 kg CO₂-e. However, when compared with the emissions associated with its manufacture based on an LCA study of home battery systems, the manufacture of the system causes more emissions than it prevents throughout its life by 1754 kg CO₂-e. This shows that the systems do not have a positive impact on the environment in this respect. However, this study also considers some additional impacts of home battery systems to discuss whether they could have an overall positive impact, such as by improving grid stability, which helps facilitate the implementation of renewable energy generation. Full article

The study aims to assess household energy poverty in European Union (EU) countries, comparing them based on the Objective Energy Poverty Index and the Subjective Energy Poverty Index. The Objective Energy Poverty Index is derived from indicators such as energy expenditure share, risk-of-poverty rate, and electricity prices. The Subjective Energy Poverty Index includes indicators such as the inability to keep the home adequately warm, arrears on utility bills, and bad housing conditions. Both indices aggregate the indicators mentioned above using equal and non-equal weighting approaches. The analysis uses country-level data from 2019 to 2023 sourced from Eurostat. The findings indicate considerable variation in household energy poverty across the EU, with more pronounced inequalities in subjective indicators than objective ones. Additionally, the study reveals a weak correlation between the Objective Energy Poverty Index and the Subjective Energy Poverty Index, leading to differing country rankings based on these indices. However, the choice of weights in constructing the energy poverty indices does not significantly impact a country’s energy poverty ranking. The paper also identifies countries where household energy poverty decreased in 2023 compared to 2019 and those where it increased. Regarding the Subjective Energy Poverty Index, Croatia and Hungary showed the most notable improvement in their rankings among European countries, while France, Germany, and Spain deteriorated their positions. According to the Objective Energy Poverty Index, Bulgaria, Croatia, Portugal, and Spain demonstrated the most significant improvement, whereas Greece experienced a considerable decline. Full article

In this study, we delve into the dynamics of the Sri Lankan government bond market, building upon prior research that focused on the application of principal component analysis (PCA) in modelling sovereign yield curves. Our analysis encompasses data spanning from January 2010 to August 2022. The study applied several PCA variants such as multivariate PCA, Randomized PCA, Incremental PCA, Sparse PCA, Functional PCA, and Kernel PCA on smoothed data. Kernel PCA was found to explain the majority of the variation associated with the data. Findings reveal that the first principal component accounted for a substantial 97.69% of the variations in yield curve movements, 2nd PCA accounted for 1.88%, and 3rd for 0.42%. These results align with previous research, which generally posits that the initial three principal components tend to elucidate around 95% of the fluctuations within the term structure of yields. Our results question the empirical findings, which state that the 1st PCA represents the longer tenor of the yield curve. In Sri Lanka, instead, the 1st PCA represents the 3-year bond yields. It may be because of the liquidity constraints in underdeveloped frontier markets, where longer tenor yields do not react fast enough to reflect the movement of the yield curve. The 2nd PCA represents the slope of the yield curve which is the yield difference of a 10-year T-Bond and 3 months T-Bill. The 3rd PCA which represents the curvature of the yield curve attributed to 2 × 3 years T-Bond yield—3 months T-bill10-year T-Bond. Full article

Food commodities and energy bills have experienced rapid undulating movements and hikes globally in recent times. This spurred this study to examine the possibility that the shocks that arise from fluctuations of one market spill over to the other and to determine how time-varying the spillovers were across a time. Data were daily frequency (prices of grains and energy products) from 1 July 2019 to 31 December 2022, as quoted in markets. The choice of the period was to capture the COVID pandemic and the Russian–Ukrainian war as events that could impact volatility. The returns were duly calculated using spreadsheets and subjected to ADF stationarity, co-integration, and the full BEKK-GARCH estimation. The results revealed a prolonged association between returns in the energy markets and food commodity market returns. Both markets were found to have volatility persistence individually, and time-varying bidirectional transmission of volatility across the markets was found. No lagged-effects spillover was found from one market to the other. The findings confirm that shocks that emanate from fluctuations in energy markets are impactful on the volatility of prices in food commodity markets and vice versa, but this impact occurs immediately after the shocks arise or on the same day such variation occurs. Full article

Tobacco use remains a leading preventable cause of morbidity and mortality, with pharmacotherapy and counseling recognized as effective cessation aids. Yet, the potential role of pharmacists and pharmacy technicians in tobacco cessation services is underutilized. This study explores the integration of such services in community pharmacies, identifying facilitators and barriers to their implementation. A qualitative study was conducted across seven community pharmacies in California that were affiliated with the Community Pharmacy Enhanced Services Network. Participants included 22 pharmacists and 26 pharmacy technicians/clerks who completed tobacco cessation training. Data were collected through semi-structured interviews, focusing on experiences with implementing cessation services. The analysis was guided by Rogers’ Diffusion of Innovations Theory. MAXQDA software was used for data management and thematic analysis. Sixteen pharmacy personnel participated in the study, highlighting key themes around the integration of cessation services. Compatibility with existing workflows, the importance of staff buy-in, and the crucial role of pharmacy technicians emerged as significant facilitators. Challenges included the complexity of billing for services, software limitations for documenting tobacco use and cessation interventions, and gaps in training for handling complex patient cases. Despite these barriers, pharmacies successfully initiated cessation services, with variations in service delivery and follow-up practices. Community pharmacies represent viable settings for delivering tobacco cessation services, with pharmacists and technicians playing pivotal roles. However, systemic changes are needed to address challenges related to billing, documentation, and training. Enhancing the integration of cessation services in community pharmacies could significantly impact public health by increasing access to effective cessation support. Full article

Soil microbiota are significantly influenced by their microenvironments. Therefore, to understand the impacts of various land use patterns on the diversity and composition of soil bacterial communities, this study focused on three typical land use types—NF (natural forest), AF (artificial forests), and FL (farmland)—in the Heilongjiang Central Station Black-billed Capercaillie National Nature Reserve, located in the southwestern part of Heihe City, Heilongjiang Province, China. Using high-throughput sequencing of the 16S rRNA gene, we examined the soil bacterial community structures in these different land use types and explored their correlation with soil environmental factors. The following were our main observations: (1) Significant variations in soil chemical properties among different land use patterns were observed. In artificial forests, total nitrogen (TN), alkali hydrolyzed nitrogen (AN), total phosphorus (TP), and available phosphorus (AP) were higher compared to farmland and significantly higher than those in natural forests. Furthermore, the organic carbon content (SOC) in natural forests was higher than in artificial forests and significantly higher than in farmland. (2) Comparative analysis using the Shannon and Simpson indices revealed that bacterial community diversity was higher in artificial forests than in natural forests, which was significantly higher than in farmland. (3) The effect of different land use types on soil bacterial community structure was not significant. The three land types were dominated by Proteobacteria, Acidobacteria, and Actinobacteria. Proteobacteria exhibited a higher relative abundance in farmland and artificial forests compared to natural forests, whereas Actinobacteria exhibited the lowest relative abundance in natural forests. (4) Redundancy analysis (RDA) revealed that SOC, TN, AN, and AP were key environmental factors influencing the microbial communities of soil. Collectively, our findings demonstrated that land use practices can significantly alter soil nutrient levels, thereby influencing the structure of bacterial communities. Full article

Urbanization is among the main factors of ecosystem transformation and threats to global biodiversity. Urban green spaces provide multiple services, being important for biodiversity and human well-being. However, the relationship between green spaces and forest birds has been scarcely studied in the Global South. In this work, we used citizen science data (eBird) to assess the variation in the species richness and composition of forest birds in two types of public urban green spaces characterized by different vegetation composition and management: parks and reserves. In general, reserves had more native and unmanaged vegetation than parks. We selected parks and reserves located in the coastal area of the Metropolitan Area of Buenos Aires, Argentina. Sampling effort was considered as the number of checklists for each site. The database allowed information to be extracted from 12 sites and 33 species. The most common species were the Green-barred Woodpecker (Colaptes melanochloros), the Narrow-billed Woodcreeper (Lepidocolaptes angustirostris), and the White-crested Tyrannulet (Serpophaga subcristata). Bird species richness was higher in reserves than in parks and was positively related to sampling effort. The forest bird species composition varied according to the type of green area and sampling effort. Species composition showed a significant nestedness, with the least rich sites being a subset of species from the richest sites. Reserves and sites with the highest sampling effort concentrated all species. The results obtained show the importance of urban reserves in the conservation of forest birds. Full article

Soil aggregates play a crucial role in substance and energy cycles in soil systems. The fixation of soil organic carbon (SOC) is closely tied to the safeguarding mechanisms of soil aggregates. Carbon fixation involves the conversion of atmospheric carbon dioxide into organic molecules by autotrophic organisms. Soil aggregates play a significant role in carbon stabilization, allowing for the physical occlusion of SOC. This study focuses on five forest types, Betula platyphylla, Betula dahurica, Quercus mongolica, Larix gmelinii, and mixed forests comprised of Larix gmelinii and Quercus mongolica, in the Heilongjiang Central Station Black-billed Capercaillie National Nature Reserve, northeast of China. This study investigated the soil aggregate stability (SAS) (water sieving) and aggregate-associated organic carbon (AAOC) at different soil depths in five forest types. Our findings demonstrated that fine macro-aggregates (0.25–2 mm) were the main types of soil aggregates among all the forest types. The SAS gradually decreased with increasing soil depth. Notably, broad-leaved forests exhibited relatively high soil stability. The fine macro-aggregates (0.25–2 mm) had the highest AAOC content, and the AAOC level was highest in the topsoil layer. The SAS and AOCC levels of the Betula platyphylla forest and Betula dahurica forest were higher than those of other forest types and were significantly affected by the forest type, soil depth, and soil physicochemical properties. Collectively, our findings reveal the key factors influencing aggregate stability and the variations in soil organic carbon content in different forest types. These observations provide a basis for studying the mechanisms of soil aggregate carbon sequestration, as well as for the sustainable development of forest soil carbon sequestration and emission reduction. Full article