Sustainable Monitoring Research Articles

P-1887. Outpatient vancomycin therapy: Rates of acute kidney injury in patients receiving area-under-the-curve-guided dosing as an inpatient followed by transition to trough-guided dosing after discharge

Abstract Background Vancomycin is one of the most prescribed antibiotics for hospitalized patients. Targeting an area under the curve to a minimum inhibitory concentration (AUC/MIC) ratio of 400-600 mg*hour/L has been associated with a reduction in vancomycin-induced acute kidney injury (AKI) compared to trough-guided dosing. Data evaluating AUC-guided vancomycin dosing in outpatient parenteral antimicrobial therapy (OPAT) are limited, and resources required for AUC-guided dosing are often unavailable. However, trough-guided dosing has been associated with vancomycin overexposure and in the outpatient setting, rates of AKI as high as 28.5% have been reported. We aim to evaluate outcomes of patients discharged on at least one week of vancomycin on a dose established through AUC-guided dosing during hospitalization followed by trough-guided dosing in the outpatient setting. Methods In this retrospective single-center cohort study, we identified 120 patients admitted to the University of Alabama (UAB) Hospital or UAB Hospital-Highlands between October 1, 2021, and March 31, 2022, who met criteria for inclusion. Though we collected additional demographics and clinical information, our primary outcome was the development of AKI, as defined by the 2012 KDIGO guidelines, while receiving vancomycin via OPAT. Results Approximately 17% of patients developed an AKI using this hybrid model. The most treated infection was prosthetic joint or hardware-associated infections (40.8%), and the most targeted organism was MRSA (41.7%). Vancomycin monotherapy comprised almost half of the antibiotic regimens on discharge (48.3%); the second agent, if prescribed, was typically a beta-lactam other than piperacillin-tazobactam (36.7%). Conclusion Future directions include identifying predictors for the development of AKI while receiving vancomycin via OPAT and comparing outcomes of this hybrid model with a historical cohort of patients at our institution who received trough-guided dosing in both the inpatient and outpatient settings. Given the paucity of data on vancomycin dosing in the outpatient setting, investigating optimal and sustainable OPAT vancomycin dosing and monitoring is of the utmost importance to minimize adverse events and maximize patient safety. Disclosures All Authors: No reported disclosures

DNA barcoding reveals larval fish diversity and distribution along the Cibareno River (West Java, Indonesia)

Context There is a global problem with ongoing riverine infrastructure projects where, despite knowledge of potential environmental impacts, there is rapid development, often without appropriate environmental safeguards. This results in fragmentation of riverine fish communities, especially diadromous species. Understanding freshwater fish larval ecology is critical to provide insight into the likely impacts of these projects. Aims To assess fish larval biodiversity on the basis of DNA barcoding, abundance and its distribution pattern in the Cibareno River. Methods Fish larvae were collected at six locations in the Cibareno River. The larvae were identified by DNA barcoding. Key results A notable disparity was seen in the distribution of larval abundance in different locations. The non-native species, Poecilia reticulata, was the most abundant larval species, with an intraspecific diversity of 0.003 (99.7% similarity). The upstream area exhibited a lower level of larval species diversity than did the downstream area. Conclusions Genetic identification can reliably identify fish larvae and determine their spatial riverside distribution in the Cibareno River. The conservation of connectivity maintains fish community integrity and diversity between upstream and downstream locations in the weir building plan. Implications This discovery emphasises the relevance of larval identification in fish biodiversity assessment and sustainable fisheries resource monitoring.

Multimodal assessment predicts cognitive impairment after aneurysmal subarachnoid hemorrhage: a prospective cohort study.

Risk factors and mechanisms of cognitive impairment (CI) after aneurysmal subarachnoid hemorrhage (aSAH) are unclear. This study used a neuropsychological battery, MRI, ERP and CSF and plasma biomarkers to predict long-term cognitive impairment after aSAH. 214 patients hospitalized with aSAH (n=125) or unruptured intracranial aneurysms (UIA) (n=89) were included in this prospective cohort study. Neuropsychological tests were administered 7 to 24months post-discharge. MRI, ERP, and CSF and plasma biomarkers were used to predict long-term CI, and area under ROC curves were calculated. Patients with aSAH CI showed significant impairment across composite scores and cognitive domains on the neuropsychological battery vs. patients with aSAH No CI. On ALFF (MRI), the right medial orbitofrontal cortex (AUC=0.78), right inferior frontal gyrus (AUC=0.848), and right inferior parietal lobule (AUC=0.868) distinguished aSAH CI from aSAH No CI. For ERP, consistent changes were found across specific EEG electrodes (FP1, F3, CP1, FP2, F4, CP2), including increased PA, prolonged PL and decreased ITPC. ITPC showed the highest sensitivity for distinguishing aSAH CI from aSAH No CI, followed by PA. Channel F4 (ITPC, AUC=0.912, PA, AUC=0.846), corresponding to the right inferior frontal gyrus, was the most sensitive for detecting CI, followed by channel CP2 (ITPC, AUC=0.903, PA, AUC=0.806), corresponding to the right inferior parietal lobule. CSF (Aβ42, Aβ40, p-tau181/Aβ42, p-tau181/total-tau, total-tau) and plasma biomarkers (Aβ-40, p-tau181) were significantly associated with long-term CI. ALFF, ERP, and CSF and plasma Aβ and tau levels and ratios have clinical utility for evaluating and predicting long-term cognitive impairment following aSAH. MRI may reveal the pathogenesis of cognitive impairment following aSAH. ERP can be administered at the bedside offering sensitive, non-invasive, repeatable, and sustainable monitoring, which is particularly suitable for immobile coma patients. ERP may represent a promising method to monitor neural function and its outcomes.

Sustainable and Low-Cost Greenhouse Soil Moisture Monitoring Using Battery-Free RFID Sensors

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

Clean energy development and low-carbon transition in China’s power industry

IntroductionChina is the largest emitter of carbon dioxide (CO2) in the world, with its power industry being the primary source of these emissions. The high emission levels are primarily attributed to the extensive consumption of fossil resources during thermal power generation. Developing clean energy power generation to replace thermal power generation is one of the main strategies to mitigate the high level of carbon emissions in the power industry and thus promoting its low-carbon transition.MethodTo explore the relationship between China’s development level of clean electricity and carbon emissions from thermal power, this paper conducts an empirical study using provincial data from 2008 to 2021, while employing fixed-effects models, mediation effect tests, and heterogeneity tests.Results and discussionThe results indicate that an increase in the level of clean electricity development reduces CO2 emissions from thermal power, and this effect exhibits a trend of diminishing from the eastern economic zone to the western economic zone in China. In the eastern regions with higher levels of urbanization, the development of clean electricity has a more significant effect on mitigating CO2 emissions from thermal power, whereas upgrading industrial structure in central and western regions contributes more in decreasing CO2 emissions from thermal power. Research and development (R&D) investment plays a significant mediating role and thus helps to improve the level of clean electricity development and reduce carbon emissions. Finally, the policy implications are proposed to intensify R&D investment, promote crossregional cooperation in sharing of clean energy, implement differentiated clean energy development policies, and establish a sustainable monitoring and evaluation system.

Rabies transmitted from vampires to cattle: An overview.

Rabies is a zoonotic infectious disease of global distribution that impacts human and animal health. In rural Latin America, rabies negatively impacts food security and the economy due to losses in livestock production. The common vampire bat, Desmodus rotundus, is the main reservoir and transmitter of rabies virus (RABV) to domestic animals in Latin America. Desmodus rotundus RABV is known to impact the cattle industry, from small farmers to large corporations. We assessed the main patterns of rabies in cattle attributed to D. rotundus RABV across Latin America. Epidemiological data on rabies from Latin America were collected from the Pan American Health Organization spanning the 1970-2023 period. Analyses revealed an average of 450 outbreaks annually for the countries where D. rotundus is distributed, with at least 6 animals dying in each outbreak. Brazil, Colombia, Peru, and Mexico were the Latin American countries with the highest number of rabies outbreaks during the study period and are the most affected countries in recent years. Findings suggest a re-emergence of bat-borne rabies in the region with more outbreaks reported in recent years, especially during the 2003-2020 period. Rabies outbreaks in cattle in the 2000-2020 period were significantly more frequent than in previous decades, with an increase in cross-species transmission after 2002. The size of outbreaks, however, was smaller in recent years, involving lower cattle mortality. Peru, El Salvador, and Brazil showed a strong association (R = 0.73, p = 0.01) between rabies incidence in D. rotundus (rates per million humans: 1.61, 0.94, and 1.09, respectively) and rabies outbreaks in cattle (rates per million cattle: 465.85, 351.01, and 48.22, respectively). A sustained, standardized, and widespread monitoring of D. rotundus demography and health could serve to inform an early warning system for the early detection of RABV and other bat-borne pathogens in Latin America. Current data can be used to forecast when, where, and in which intensity RABV outbreaks are more likely to occur in subtropical and tropical Latin America. A decrease in the size of outbreaks could suggest that strategies for epidemic management (e.g., education, early diagnosis, vaccination) have been effective. The increase in the number of outbreaks could suggest that the factors facilitating cross-species transmission could be on the rise.

Sustainable Environmental Analysis of Wooden Boxes for Fruit and Vegetable Packaging and Transport in Comparison with Corrugated Cardboard Boxes

This study analyses the environmental sustainability of using wooden boxes (WBs) compared to corrugated cardboard boxes (CCBs) for the transport of fruit and vegetable products, where the same box size between WB and CCB is assessed and compared. The Life Cycle Assessment (LCA) followed ISO 14040:2006 and ISO 14044:2006 standards, using the ReCiPe 2016 MidPoint methodology. The following impact categories analysed for sustainability impact, evaluation and monitoring are included: global warming potential, acidification, eutrophication, human toxicity and abiotic resource depletion. The study covered all the stages of packaging life through cradle-to-grave analysis. The study results show that CCBs have a higher environmental impact across most categories despite being single-use packaging systems. The comparison between both packaging systems shows that WBs are a more sustainable alternative, with lower overall environmental impacts in fruit and vegetable packaging and transport. As a general conclusion, WBs have a lower overall environmental impact than CCBs, especially in the key impact criteria of global warming potential, acidification, eutrophication, human non-carcinogenic toxicity, fossil resource depletion and water consumption. Due to that, the wooden box is a more sustainable material for fruit and vegetable packaging, logistics and transport than the corrugated cardboard box, considering the scope and destination analysed.

Genetic and Molecular Characterization of Avian Influenza A(H9N2) Viruses from Live Bird Markets (LBM) in Senegal.

Despite extensive experience with influenza surveillance in humans in Senegal, there is limited knowledge about the actual situation and genetic diversity of avian influenza viruses (AIVs) circulating in the country, hindering control measures and pandemic risk assessment. Therefore, as part of the "One Health" approach to influenza surveillance, we conducted active AIV surveillance in two live bird markets (LBMs) in Dakar to better understand the dynamics and diversity of influenza viruses in Senegal, obtain genetic profiles of circulating AIVs, and assess the risk of emergence of novel strains and their transmission to humans. Cloacal swabs from poultry and environmental samples collected weekly from the two LBMs were screened by RT-qPCR for H5, H7, and H9 AIVs. Subsequently, a subset of H9-positive samples was selected for whole sequencing. From December 2023 to October 2024, 499 samples were tested, and AIV was detected in 58.3% of them. Among these, A/H9N2 was the only subtype detected in both markets, with a detection rate of 47.7% (82/172) in Thiaroye and 35.3% (42/119) in Tilene, resulting in an overall positivity rate of 42.6% (124/291). Genome sequencing of 22 A/H9N2 isolates, including 11 poultry drinking water samples, 7 carcass wash water samples, 3 fecal samples, and 1 cloacal swab, yielded 7 complete and 15 partial genomic sequences. Phylogenetic analyses of the resulting sequences showed that the A/H9N2 isolates obtained in this study formed a monophyletic cluster and were closely related to the Senegalese human strain (A/Senegal/0243/2019) identified through the national influenza sentinel surveillance program. These strains were also closely related to the A/H9N2 viruses of the G1 lineage circulating in neighboring countries, suggesting cross-border transmission. The A/H9N2 strains carried the low pathogenicity RSSR/GLF motif at the HA cleavage site and possessed several key amino acid mutations, including HA-I155T and HA-Q226L, which are associated with human host adaptation, PB2-T105V, PB2-A661T, and PB2-A588V, which are linked to the human-to-human transmission and increased polymerase activity, NS2-T14M, NS2-M100I, NS1-I106M, NS1-V222M, NS1-E223A, NS1-I226V, NS1-E227G, and NS1-P228S, which are known to alter virulence (increased or reduced) in humans or mice, and M2-S31N, which promotes drug resistance. Seven potential N-glycosylation sites were predicted in the HA protein and six in the NA protein. The selection pressure analysis revealed that the A/H9N2 isolates were primarily under neutral evolution or purifying selection pressure. Overall, our findings highlight the potential for cross-species transmission of Senegalese A/H9N2 viruses, emphasizing the need for sustained monitoring of these viruses in both animal and human populations.

Bridging the gap in sustainability measurement and reporting for agroecosystems: Overview and development of an adaptive sustainability assessment and monitoring framework

Bridging the gap in sustainability measurement and reporting for agroecosystems: Overview and development of an adaptive sustainability assessment and monitoring framework

Phlebotomus perniciosus response to volatile organic compounds of dogs and humans.

The olfactory response of insect vectors such as phlebotomine sand flies is a key facet for investigating their interactions with vertebrate hosts and associated vector-borne pathogens. Such studies are mainly performed by assessing the electrophysiological response and the olfactory behaviour of these arthropods towards volatile organic compounds (VOCs) produced by hosts. Nonetheless, few studies are available for species of the subgenera Lutzomyia and Nyssomyia in South America, leaving a void for Old World sand fly species of the genus Phlebotomus. In this study, we evaluated the olfactory responses of Phlebotomus perniciosus, one of the most important vectors of Leishmania infantum in the Old World. To test the P. perniciosus behavioural response to VOCs, 28 compounds isolated from humans and dogs were assessed using electrophysiological (i.e., electroantennogram, EAG) and behavioural assays (i.e., Y-tube olfactometer). In the EAG trials, 14 compounds (i.e., acetic acid, nonanoic acid, 2-propanol, 2-butanol, pentanal, hexanal, nonanal, trans-2-nonenal, decanal, myrcene, p-cymene, verbenone, 2-ethyl-1-hexanol, and acetonitrile) elicited high antennal responses (i.e., ≥ 0.30 mV) in female sand flies, being those VOCs selected for the behavioural assays. From the 14 compounds tested in the Y-tube olfactometer, nonanal was significantly attractive for P. perniciosus females, whereas myrcene and p-cymene were significantly repellents (p < 0.05). The attraction indexes varied from 0.53 for nonanal (i.e., most attractive) to -0.47 to p-cymene (i.e., most repellent). Overall, our results shed light on the role of olfactory cues routing host seeking behaviour in P. perniciosus, with implications to develop sustainable sand fly monitoring as well as control in leishmaniasis endemic areas.

Communities for Environmental Protection: Fostering Responsibility and Sustainability

Community participation in environmental protection is increasingly crucial in facing global environmental challenges. This article outlines the essence of community participation and its responsibilities in providing protection for the environment, from the individual to the community level. It emphasizes the importance of community awareness, knowledge, and concrete action to achieve sustainable development. The research method used is normative or doctrinal research. Normative research methods are legal research methods that involve reading documents related to the problem to be researched. This research method refers to legal standards and principles contained in statutory regulations. The essence of community participation in environmental protection is increasing community awareness and taking active action to preserve the environment, including increasing awareness, independence, empowerment, cooperation, ability and leadership, supervision, and local culture. Community responsibility in providing protection for the environment involves various very important aspects. society's primary responsibilities in terms of understanding and awareness, reduction of ecological footprint, participation in sustainable development, reporting and monitoring, education and extension, and participation in environmental movements. Community responsibility in environmental protection is implemented in the form of concrete actions.

A Review of Triboelectric Nanogenerators in Wind Energy Harvesting and Environmental Monitoring

With the growing demand for renewable energy and the development of portable electronic devices, there is an increasing need for self-powered systems. This paper reviews recent advancements in wind energy harvesting and environmental monitoring, with a particular focus on triboelectric nanogenerators (TENGs). TENG technology efficiently converts wind energy into electrical energy through the coupling of triboelectric effects and electrostatic induction, offering advantages such as simplicity, low cost, and high efficiency. The paper details the fundamental working principles of TENGs, their various operational modes, and the design of hybrid electromagnetic generators (TENG-EMG) that integrate TENGs with electromagnetic generators. It also discusses innovative devices such as the calliopsis biomimetic triboelectric nanogenerator (C-TENG), the polymer wind bead rolling triboelectric nanogenerator (WB-TENG), and the self-sustaining wind speed sensor system (SSWSSS). These devices not only enhance the efficiency of wind energy harvesting but also broaden the scope of wind speed and direction monitoring. Finally, the paper explores future research directions for TENG technology, including multifunctional integration, material innovation, intelligent management, and environmental impact assessment, aiming to further advance sustainable energy and environmental monitoring.

In Situ Regenerable Molecularly Imprinted Polymer Biosensor for Electrochemical Detection of Nonelectroactive Branched-Chain Amino Acids in Human Sweat.

The significant challenge in achieving in situ regeneration for conventional molecularly imprinted polymers (MIPs) restricts their promising application in continuous monitoring of biochemical molecules closely related to human health, especially nonelectroactive molecules. This is because they are either limited to a single use or require removal of imprinted templates through chemical washing steps, which is clearly impractical for sustainable monitoring. Here, a class of in situ regenerable MIP biosensors, taking nonelectroactive branched-chain amino acids (BCAAs) as templates and methyldopa as a functional monomer, was engineered to achieve repeatable in situ regeneration and in situ target recognition. The in situ regeneration was realized through an amperometric i-t technique with a negative voltage (-0.9 V) according to intrinsic isoelectric points of analytes instead of conventional wash steps. This electrochemical extraction process not only maximally repelled the imprinted templates, creating a large number of cavities (recognition sites) and significantly enhancing sensitivity, but also ensured the successful in situ regeneration of developed biosensing interfaces. The template extraction was evaluated by examining changes in the surface morphology, elemental composition, distribution, content, and interfacial properties. The developed BCAA MIP biosensors achieved sensitive target detection with the linear range from 0.001 to 10.0 μg/mL and limits of detection down to 0.45 (Leu), 0.47 (Ile), and 0.31 (Val) ng/mL. Beyond that, the biosensors demonstrated an excellent ability in decreasing biofouling, realizing repeatable in situ target detection in human sweat, and the obtained results were highly consistent with those of the enzyme-linked immunosorbent assay, indicating high feasibility, reliability, and accuracy in practical application. Meanwhile, the biosensors showed excellent specificity, selectivity, and stability.

Remote sensing approaches to monitor tropical forest restoration: Current methods and future possibilities

Abstract Tropical forests are increasingly threatened by deforestation and degradation, impacting carbon storage, climate regulations and biodiversity. Restoring these ecosystems is crucial for environmental sustainability, yet monitoring these efforts poses significant challenges. Secondary forests are in a constant state of flux, with growth depending on multiple factors. Remote sensing technologies offer cost‐effective, scalable and transferable solutions, advancing forest restoration monitoring towards more accurate, efficient and real‐time data analysis and interpretation. This review provides a comprehensive evaluation of the current state and advancements in remote sensing technologies applied to monitoring tropical forest restoration. Synthesis and applications: This review brings together the state of the art of remote sensing technologies, such as very‐high‐resolution RGB imagery, multi‐ and hyperspectral imaging, lidar, radar and thermal‐infrared technologies and their applicability in monitoring forest restoration. In conclusion, this review emphasizes the potential of remote sensing technologies, coupled with advanced computational techniques, to enhance global efforts towards effective and sustainable forest restoration monitoring.

Heavy metals concentration and human health risk assessment in selected shrimp species of Pakistan

ABSTRACT Heavy metal pollution in marine organisms poses a significant threat to both ecosystems and human health. Dumping untreated sewage into the sea pollutes coastal waters with toxic metals. This study examined the levels of chromium (Cr), lead (Pb), nickel (Ni), mercury (Hg), arsenic (As) and copper (Cu) in three shrimp species of genus Penaeus i.e. Penaeus merguiensis, Penaeus monodon, and Penaeus indicus. The findings revealed that the average levels of arsenic, lead, and mercury were considerably higher than the legislative limits. Furthermore, chromium and lead levels remained consistent across shrimp species, while other metals exhibited significant variation. Additionally, THQ and HI values were below the limit of 1, indicating low health risks from consuming shrimp species from this area. However, sustained monitoring and research are essential to protect consumer health and ensure long-term sustainability of seafood resources.

Study of using GIS technologies in forestry cadastre and monitoring for environmental sustainability

Abstract The integration of Geographic Information Systems (GIS) technologies in the forestry cadastre and monitoring practices in Uzbekistan represents a significant advancement in sustainable forest management. This study explores the application of GIS in enhancing the accuracy, efficiency, and comprehensiveness of forestry cadastre, which is crucial for the effective management, conservation, and restoration of forest resources in Uzbekistan. Through spatial analysis, remote sensing, and data integration, GIS technologies offer detailed insights into forest cover, land use changes, and biodiversity, providing a robust framework for decision-making and policy formulation. The research highlights how GIS tools facilitate the precise mapping of forest boundaries, monitoring of illegal logging activities, and assessment of reforestation efforts. Furthermore, the study examines the challenges and opportunities in implementing GIS in Uzbekistan’s forestry sector, considering the country’s unique geographical and ecological context. The findings suggest that adopting GIS technologies can significantly contribute to achieving Uzbekistan’s environmental sustainability goals and improving the management of its forestry resources.

A designed predictive modelling strategy based on data decomposition and machine learning to forecast solar radiation

Consistent with the United Nations Sustainable Development Goal 7, the design and optimization of clean energy resources (e.g., solar energy) is a highly motivating task for all researchers globally who continue to build research synergies that can tackle the future likelihood of energy crises due to socioeconomic and strategic environmental policy. In this paper, a weekly solar radiation (SR) forecasting model is designed using a robust local mean decomposition (RLMD) technique unified with a random forest (RF) algorithm to generate a fully optimised hybridized RLD-RF model that has a promising capability to forecast the SR values. In the first stage of model design, the RLMD, a frequency resolution method, is applied to decompose the original SR time series into amplitude modulation subseries (AMs), frequency modulation subseries (FMs), and the low-frequency product functions (PFs) to reveal the internal structure of the model construction data to incrementally optimize the RLD-RF model where only PFs were used. Subsequently, the statistically significant lagged subseries at a week ahead forecasting horizon (t – 1) of the low-frequency PFs with residual components are extracted individually, via partial autocorrelation function (PACF), to capture the historical behaviour of frequency-resolved SR component in order to build a robust modelling framework. Consequently, the random forest (RF) algorithm is employed to forecast each of the subseries using PACF-based lagged inputs to construct a fully optimised hybrid RLMD-RF predictive model. RLMD-RF is benchmarked against a baseline RF, M5tree, and multiple linear regression (MLR), Artificial neural network (ANN) and Gaussian process regression (GPR) algorithms, including their hybridized counterparts (i.e., RLMD-M5tree, RLMD-MLR, RLMD-ANN, and RLMD-GPR) using statistical score metrics in the independent testing phase. The results generated at test sites in Queensland State, Australia that have high solar energy potential confirm that the RLMD-RF method can produce quality predictions of weekly solar radiation against the benchmarking comparison models. For instance, RLMD-RF for Barcaldine are higher in terms (EWI= 0.938, ENS= 0.878) against RLMD-MLR (EWI = 0.845, ENS = 0.705), the RLMD-M5tree (EWI = 0.836, ENS = 0.684), RLMD-ANN (EWI = 0.836, ENS = 0.715), RLMD-GPR (EWI = 0.839, ENS = 0.716), the RF (EWI = 0.720, ENS = 0.564), the M5tree (EWI = 0.692, ENS = 0.522), the MLR (EWI = 0.683, ENS = 0.508), the ANN (EWI = 0.708, ENS = 0.519) and the GPR (EWI = 0.708, ENS = 0.520). Similarly, the RLMD-RF also outperformed in Rockhampton, Clermont, and Lockyer Valley stations as compared to other models. This research establishes the practical usefulness of hybridised RLMD-RF modelling framework for accurate SR forecasting and advocates its possible consideration in renewable and sustainable energy production and monitoring systems that can aid in decision-making by energy utilities and stakeholders (e.g., climate and energy policy experts).

INaturalist community observations provide valuable data on human-mosquito encounters.

Mosquitoes (Diptera: Culicidae) and the pathogens they transmit represent a threat to human and animal health. Low-cost and effective surveillance methods are necessary to enable sustainable monitoring of mosquito distributions, diversity, and human interactions. This study examined the use of iNaturalist, an online, community-populated biodiversity recording database, for passive mosquito surveillance in the United Kingdom (UK) and Ireland, countries under threat from the introduction of invasive mosquitoes and emerging mosquito-borne diseases. The Mozzie Monitors UK & Ireland iNaturalist project was established to collate mosquito observations in these countries. Data were compared with existing long-term mosquito UK datasets to assess representativeness of seasonal and distribution trends in citizen scientist-recorded observations. The project collected 738 observations with the majority recorded 2020-2022. Records were primarily associated with urban areas, with the most common species Culex pipiens and Culiseta annulata significantly more likely to be observed in urban areas than other species. Analysis of images uploaded to the iNaturalist project also provided insights into human-biting behavior. Our analyses indicate that iNaturalist provides species composition, seasonal occurrence, and distribution figures consistent with existing datasets and is therefore a useful surveillance tool for recording information on human interactions with mosquitoes and monitoring species of concern.

Total Suspended Solids in Teluk Awur, Jepara using Red Reflectance from Landsat-8

The coastal waters of Teluk Awur, Jepara are affected by human activities such as aquaculture, tourism, agriculture, and settlements. The number of human activities on land will contribute to high suspended solids (TSS) entering coastal waters by run-off. The high TSS in coastal areas will affect the entry of light intensity, disrupting the photosynthesis process and ecosystem. This study compares TSS values based on Landsat images using several TSS algorithms such as Wirasatriya, Budhiman, Ajiperwata, and Parwati algorithms and the algorithm resulting from the July 2023 field data calibration against red reflectance. The algorithm's performance from calibration has a smaller error value with RMSE = 8.51, MAPE = 25.77%, and Bias = 7.52. TSS estimation from Landsat 8 satellite imagery has a range of values of 30.56 - 62.55 mg/L (average 35.60 mg/L), while field measurements of TSS values are 22.40 - 64.52 mg/L (average 32.69 mg/L). This research will be valuable information for using the right Landsat-8 algorithm for temporal and sustainable TSS monitoring. It can be used in abrasion monitoring and management in the Teluk Awur coastal waters of Jepara.

Selection of Internet of Things-enabled sustainable real-time monitoring strategies for manufacturing processes using a disc spherical fuzzy Schweizer–Sklar aggregation model

The emergence of the Internet of Things (IoT) for monitoring in real-time is geared towards sustainable energy consumption practices by taking control over energy loss. The promising potential of current IoT real-time monitoring systems paves the way for future developments in monitoring devices with eco-friendly sensing capabilities. As a result, the creation of effective IoT real-time monitoring devices targeted at decreasing energy loss becomes crucial. This modeling procedure falls under the realm of multiple-attribute group decision-making (MAGDM), aiming to integrate the Schweizer–Sklar (SS) τ-norm and τ-conorm within the disc spherical fuzzy (D-SF) framework. The objective is to enhance the flexibility of D-SF in dealing with intricate and uncertain data. The core focus of this research is on deriving SS τ-norm and τ-conorm for D-SF data, consequently introducing innovative aggregation operators. The article offers the fundamental D-SF operations using SS aggregation operators in a systematic manner, with thorough theorem justifications. A new MAGDM tool is presented, created simply to manage ambiguous and imprecise data utilizing the suggested operators. Our model is specifically designed to tackle the critical issue of reducing energy loss in IoT real-time monitoring systems. The research not only focuses on model accuracy but also emphasizes its effectiveness in solving this pressing problem, demonstrating significant advancements in sustainable energy practices. Moreover, the proposed aggregation operators are subjected to a comparative analysis. This comprehensive comparison not only enhances the operators’ efficacy but also underscores their relevance in real-world decision-making scenarios.