Varying Weather Conditions Research Articles

Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions

Solar Power is one of the significant prevalent forms of clean energy due to its perceived to be pollution-free and easily accessible. The market for renewable energy was established by the rapid development in electrical energy consumption and the diminution of conventional energy resources (CER). Under varying weather condition extracted energy from solar system is not constant and maximum. This study suggests the applicability of machine learning algorithm (MLA) in Peak power point tracking (P3T) methods to maximize power of a PV arrangement under varying weather conditions. Machine learning methods optimize peak power point tracking in solar photovoltaic systems by bringing agility, data-driven decision-making, and increased accuracy. MLAs improve the overall efficiency, stability, and dependability of these systems by handling the unpredictability of solar energy production under varying weather circumstances and PSCs Because MLAs are able to learn and adjust to non-linear relationships between solar intensity and PVS output. In this study, the squared multiple squared exponential Gaussian process regression method SGPRA tested in three rapidly varying ecological conditions. The performance of ML-P3T methods is validated using Matlab/Simulink, and the simulation outcome are compared with one of the most used algorithms, the variable step size incremental conductance algorithm (VINA). The Matlab/Simulink findings show that SGPRA operates significantly better under varying weather circumstances, harnessing more peak power efficiency 90%, shorter tracking time 0.13 sec, a mean error of 0.042, and superior stability.

Robust RGB-T Tracking via Adaptive Modality Weight Correlation Filters and Cross-modality Learning

RGBT tracking is gaining popularity due to its ability to provide effective tracking results in a variety of weather conditions. However, feature specificity and complementarity have not been fully used in existing models that directly fuse the correlation filtering response, which leads to poor tracker performance. In this article, we propose correlation filters with adaptive modality weight and cross-modality learning (AWCM) ability to solve multimodality tracking tasks. First, we use weighted activation to fuse thermal infrared and visible modalities, and the fusion modality is used as an auxiliary modality to suppress noise and increase the learning ability of shared modal features. Second, we design modal weights through average peak-to-correlation energy coefficients to improve model reliability. Third, we propose consistency in using the fusion modality as an intermediate variable for joint learning consistency, thereby increasing tracker robustness via interactive cross-modal learning. Finally, we use the alternating direction method of multipliers algorithm to produce a closed solution and conduct extensive experiments on the RGBT234, VOT-TIR2019, and GTOT tracking benchmark datasets to demonstrate the superior performance of the proposed AWCM against compared to existing tracking algorithms. The code developed in this study is available at the following website. 1

The Application of Bamboo in the Railway Industry: A Sustainable Solution for Track Construction

The railway industry has shown a strong interest in utilizing sustainable materials, including recycled materials and composites, in construction. Bamboo, as a highly renewable natural resource, has been proposed as a construction material for the railway industry. This material offers several advantages, such as high strength and durability, sustainability, low embodied energy, and ease of handling. It has been used in various construction materials like plywood, scrimber, laminates, and fibers. This paper aims to review the application of bamboo as a material in the railway industry and provide suggestions for its future use as railway sleepers. The mechanical properties of bamboo and its desirable features for sleeper construction, such as versatility, durability, low embodied energy and carbon footprint, lightweight, and ease of handling, are discussed. Bamboo-based products like plywood and scrimber can offer higher mechanical properties compared to traditional timber sleepers. Moreover, due to its rapid growth rate, bamboo is considered an environmentally friendly material. However, there are certain factors that limit the widespread deployment of bamboo in the railway industry. For instance, the lightweight nature of bamboo can reduce the lateral resistance of sleepers. Additionally, long-term performance studies and its performance in regions with varying weather conditions need to be further investigated. This review paper aims to promote the increased utilization of bamboo in the railway industry, contributing to the development of sustainable railway tracks. By considering the mechanical properties and advantageous characteristics of bamboo, it is possible to explore its potential as a viable and eco-friendly material for railway sleepers.

Vessel voyage schedule planning for maritime ore transportation

This paper introduces a vessel voyage schedule planning problem for maritime ore transportation arising from a real-world project in Boffa, Guinea. In this problem, delay of vessel arrivals at the loading port often occurs which interrupts the ore loading operation and further impacts the yearly throughput of ore transportation, which dues to variable weather conditions at sea and changing loading/discharging time at ports. Thus, factors regarding the uncertainty of vessel voyages must be considered in order to ensure continuous vessel arrivals and uninterrupted loading operations at the original port, leading to an increase in the overall productivity of the project. And at the same time, since every voyage entails an increase in costs, cost control is necessary to be accounted for, so there is also a need to consider minimizing operating costs. In this study, to address the uncertainty in voyage duration, a chance-constrained model is introduced to obtain sailing schedules as well as the associated costs within a given confidence level. And then, for the multi-objective requirements, we develop a multi-objective planning model and the actual system operating capacity limitation constraints are considered. After that, considering that the multi-objective model cannot be solved by the solver directly, the weighting method is introduced. Afterwards, since the direct solving of the model is rather time-consuming, valid cuts are designed to accelerate it. Finally, considering that the Pareto solutions of multi-objective problems are numerous, a nondominated sorting genetic algorithm II (NSGA-II) is launched with the aim of providing more solutions to the manager in a shorter time. In terms of experiments, each of the aforementioned issues and proposed remedies are verified. The results confirm the effectiveness of the valid cuts and NSGA-II, and demonstrate that the obtained vessel voyage schedule is more robust and outperforms manual decisions.

A new approach to solve the problem of partial shading in a photovoltaic system

<p>This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.</p>

Highly Antibacterial and Self-Healing Janus Fabric for Effective Body Moisture/Thermal Management and Durable Waterproof.

Despite the development of many functional fabrics, they are unable to meet practical needs due to their monolithic functions and low durability. Therefore, a multifunctional waterborne polyurethane nanodroplet containing disulfide bonds (WSPU) was synthesized using a simple and environmentally friendly approach. The functional WSPU nanodroplet coating endowed fabrics with a variety of properties, including exceptional hydrophobicity, antibacterial properties, self-healing at room temperature, directional transport, etc. The functionalized fabric demonstrated durable mechanical and chemical stabilities due to the combined effects of disulfide bond reconstruction and hydrophobic chain migration. It exhibited the ability to regain its hydrophobic properties at room temperature after 50 friction cycles were performed without requiring external stimulation. Furthermore, the fabric maintained a water contact angle above 140°, even after being subjected to washing, boiling, and immersion in acid and alkali solutions. In addition, as a result of the fabric's Janus-like wettability, it performed various functions in accordance with varying weather conditions, in terms of wearing comfort and breathability. In hot weather or during exercise, the Janus fabric with the hydrophilic side facing outward enhances the process of sweat-directed perspiration, resulting in a notable cooling effect. On rainy days, the Janus fabric, when positioned with the hydrophobic side facing outward, exhibited excellent waterproof performance. This study presents an opportunity to explore the development of multifunctional fabrics through the combined effects of several functions.

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

AbstractThe accurate prediction of aerodynamic drag on satellites orbiting in the upper atmosphere is critical to the operational success of modern space technologies, such as satellite‐based communication or navigation systems, which have become increasingly popular in the last few years due to the deployment of constellations of satellites in low‐Earth orbit. As a result, physics‐based models of the ionosphere and thermosphere have emerged as a necessary tool for the prediction of atmospheric outputs under highly variable space weather conditions. This paper proposes a high‐fidelity approach for physics‐based space weather modeling based on the solution of the Navier–Stokes equations using a high‐order discontinuous Galerkin method, combined with a matrix‐free strategy suitable for high‐performance computing on GPU architectures. The approach consists of a thermospheric model that describes a chemically frozen neutral atmosphere in nonhydrostatic equilibrium driven by the external excitation of the Sun. A novel set of variables is considered to treat the low densities present in the upper atmosphere and to accommodate the wide range of scales present in the problem. At the same time, and unlike most existing approaches, radial and angular directions are treated in a nonsegregated approach. The study presents a set of numerical examples that demonstrate the accuracy of the approximation and validate the current approach against observational data along a satellite orbit, including estimates of established empirical and physics‐based models of the ionosphere‐thermosphere system. Finally, a one‐dimensional radial derivation of the physics‐based model is presented and utilized for conducting a parametric study of the main thermal quantities under various solar conditions.

The fate of vegetation carbon stocks of India: Insights from a remote-sensed evaluation of carbon use efficiency

Vegetation carbon use efficiency (CUE), defined as the ratio of the net primary productivity (NPP) to gross primary productivity (GPP), is a critical metric for assessing the extent of sequestration of atmospheric carbon by vegetation into biomass. Evaluating CUE is essential for policymakers to devise carbon sequestration policies and also identify hotspots of carbon storage loss. Indian ecosystems face variable weather conditions across the year. However, the roles of weather variability, spatial patterns, and temporal dynamics are poorly understood, as much as the resilience to hydroclimatic perturbations that may influence CUE in Indian terrestrial ecosystems, particularly at sub-annual scales. The lack of sub-annual NPP datasets hinders remote sensing-based sub-annual CUE assessments. However, this study derived an 8-day scale NPP dataset through an empirical formulation on moderate-resolution imaging spectroradiometer (MODIS) products. This study assessed CUE dynamics for India across its six homogeneous meteorological regions (HMRs) including the major biomes of each HMR, encompassing croplands, forests, and grasslands, through a fine-scale remote sensing approach for 2000–2021. The results show pronounced variability of CUE across spatiotemporal scales governed by the water availability and temperature interplay, with hilly 0.59±0.01 and northwestern 0.33±0.02 regions exhibiting the highest and lowest CUE on an annual scale. Distinct patterns have been observed among the biomes, depending on the HMRs dictated by the land management practices of the regions. Similar pronounced CUE variability has been observed on seasonal scales. The study also derived sustainability maps of carbon stocks for India from the trend-persistence interplay of CUE, revealing alarming concerns across most HMRs, with peninsular India exhibiting a persistent decrease in CUE for 82% of its cropland area. A widespread loss of resilience of carbon stocks was observed, primarily caused by increased precipitation and warming under meteorological drivers, along with aridity induced by soil and atmospheric conditions. Overall, this study highlights the critical role of CUE in assessing the carbon sequestration potential of Indian ecosystems and the need for a better understanding of the factors that influence it to develop effective carbon sequestration policies.

Impact of different establishment methods for summer-sown cover crops in Southwestern Germany on their weed suppression properties

Early establishment of cover crops (CCs) is essential for attaining successful weed suppression in the autumn-to-spring period. Due to reduced precipitation and extended periods of drought in the summer within temperate zones, the establishment of CCs poses challenges. Three methods of cover crop (CC) establishment were tested: 1) preharvest sowing 10 days before harvest of cash crop (PreHa); 2) no-till sowing directly after harvest of cash crop (NoTi) and 3) sowing with reduced tillage 10 days after harvest of cash crop (ReTi). The five CC species black oat (Avena strigosa Schreb.); common buckwheat (Fagopyrum esculentum Moench), phacelia (Phacelia tanacetifolia Benth.), fodder radish (Raphanus sativus var. oleiformis L.) and common vetch (Vicia sativa L.), along with a mixture comprising 20% of all five species, were included as second factor in a three-year field experiment (2020–2022) conducted in South West Germany. One treatment per establishment method was maintained without CCs as a control. Measurements of CC, volunteer cereal and weed density, soil coverage, and dry matter were carried out throughout the autumn-to-late autumn timeframe. On average across all years and CCs, the PreHa and NoTi establishment demonstrated the highest CC dry matter contents of 99 g m−2 and 119 g m−2, respectively, despite lower CC densities. This had a particularly notable impact on weed density in the ReTi variant, which, over the three-year average across all CC treatments, was 5 times higher than in NoTi and PreHa but exhibited a 18–27% smaller volunteer cereal density by comparison, thereby influencing the respective volunteer cereal and weed coverages. Meanwhile, weed and volunteer cereal dry matters were consistent across all establishment methods. The CC mixture exhibited enhanced resilience to fluctuating weather conditions, leading to an overall superior performance across the three-year experimental period and showing highest measured CC dry matters up to 272 g m−2 in 2022. However, the observed effects in the treatments varied considerably depending on the CC species, sowing date, and weather conditions. In summary, this study demonstrates that early CC sowing, whether directly after the cash crop harvest or 10 days before the cash crop is harvested, has a positive impact on CC biomass formation, especially leading to a reduced weed emergence. Additionally, under variable weather conditions, a diverse CC mixture can exhibit a more stable overall performance compared to CC monocultures.

Bottom-up effects of variable winter weather conditions on phytoplankton dynamics in an enclosed bay: implications for ecological responses to climate change

Abstract To investigate phytoplankton dynamics in response to variable winter weather conditions along the Sea of Japan, we conducted daily sampling in enclosed Maizuru Bay in five winters and found distinctive differences between 2016/2017 and 2018/2019. A clear diatom bloom occurred through the water column in moderately cold/snowy 2016/2017, whereas in exceptionally warm/snow-free 2018/2019 a small peak of dinoflagellates occurred only in the subsurface. Nutrient concentrations changed drastically depending on freshwater discharge and diatom growth in 2016/2017, showing a contrast with constant nutrient concentrations in 2018/2019. Copepod densities were ~10 times higher in 2016/2017 than in 2018/2019, indicating bottom-up effects in 2016/2017. As the absence of a diatom bloom from 2018/2019 can be attributed neither to nutrient limitation nor to grazing pressure, we hypothesize that wind mixing is a key factor triggering a diatom bloom. Within the five winters, clear diatom blooms were found only after strong winds and heavy precipitation including snowfall. Wind mixing would possibly promote the germination/rejuvenation of viable resting stage cells as inocula for a diatom bloom, while the growth and maintenance are supported by nutrient supply through freshwater discharge. In Maizuru Bay, winter production is likely activated by harsh weather conditions characteristic of this region.

Exploratory comparison of flower visiting behavior and pollination ability of mason bees, bumble bees, and honey bees.

This study explored the flower visiting behaviors and pollination abilities of mason bees (Osmia excavata Alfken (Hymenoptera: Megachilidae)), bumble bees (Bombus terrestris (Linnaeus, 1758) (Hymenoptera: Apidae)), and Italian honey bees (Apis mellifera ligustica Spinola (Hymenoptera: Apidae)) in apple orchards in early spring in Jinan (located in the central region of Shandong) and Yantai (located in the Peninsula of Shandong). We compared the pollen collection patterns, flower visiting behavior, flying speed, and effects on apple pollination of the 3 types of bees. The frequencies of flower visits were significantly higher for mason bees (12.89/min in Jinan and 10.63/min in Yantai) than bumble bees and Italian honey bees in the 2 regions. The single flower residence times were significantly higher for Italian honey bees (8.22 s in Jinan and 9.43 s in Yantai), but Italian honey bees were most affected by the climate. The 3 bees differed significantly in terms of the amount of apple pollen collected and their effects on the fruit setting rate in apples (mason bees > bumble bees > Italian honey bees). The results showed that the mason bee was the most suitable pollinating species for spring apple orchards; Bumble bees were more suitable as alternative pollinators during cloudy and low temperatures; Italian honey bees were able to take advantage of their large number of worker bees in sunny and warm weather. Compared to individual bee species, a combination of 2 or 3 species of bees might be more advantageous in dealing with complex and variable weather conditions.

Spatiotemporal dynamics of duck harvest distributions in the Central and Mississippi flyways, 1960–2019

AbstractGeographical distributions of waterfowl exhibit annual variation in response to spatiotemporal variation in weather conditions, habitat availability, and other factors. Continuing changes in climate and land use could lead to persistent shifts of waterfowl distributions, potentially causing a mismatch with habitat conservation planning, wetland restoration efforts, and harvest management decisions informed by historical distributions. We used band recoveries and harvest records (i.e., hunter‐harvested wings) from the United States Fish and Wildlife Service Waterfowl Parts Collection Survey as indices of duck distribution in autumn and winter, and quantified intra‐annual, interannual, and interspecific variation in their geographic distributions across 6 decades (1960–2019) for 15 duck species in the Central and Mississippi flyways in North America. Specifically, we tested for annual and decadal shifts in mean latitude and longitude of recoveries for each month (Oct–Jan) by species and taxonomic guild (i.e., dabbling, diving ducks). Overall, species varied in the extent, timing, and sometimes direction, of distributional change in recoveries. From 1960–2019, mean recovery locations for dabbling ducks shifted south 105–296 km in October and 27 km in November (wings only), whereas mean latitudes shifted north 144–234 km in December and 186–301 km in January. Mean recovery locations for diving ducks shifted north 162 km in October (wings only), 84–173 km in December, and 66–120 km in January, but shifted 99–512 km south in November. Shifts in longitude were less consistent between guilds and data types. Finally, distributional change rarely accelerated during recent decades, except for southward shifts of band recoveries of diving ducks in November and northward shifts of band and wing recoveries of dabbling ducks in January. Although anecdotal accounts of large‐scale northward shifts in duck distributions are prolific in the land management and hunting communities, our data demonstrate more subtle shifts that vary considerably by species and month. Observed changes in recovery distributions could necessitate changes in timing of habitat management practices throughout the Central and Mississippi flyways and may result in fewer hunting and recreational opportunities for some species in southern states. Quantifying patterns of historical change is a necessary first step to understanding temporal and interspecific variation in waterfowl distributions, which will help with landscape‐scale conservation and management efforts in the future and enable effective communication to core constituencies regarding ongoing changes and their implications for recreational engagement.

Fifty years after deep‐ploughing: Effects on yield, roots, nutrient stocks and soil structure

AbstractDeep‐ploughing far beyond the common depth of 30 cm was used more than 50 years ago in Northern Germany with the aim to break root‐restricting layers and thereby improve access to subsoil water and nutrient resources. We hypothesized that effects of this earlier intervention on soil properties and yields prevailed after 50 years. Hence, we sampled two sandy soils and one silty soil (Cambisols and a Luvisol) of which half of the field had been deep‐ploughed 50 years ago (soils then re‐classified as Treposols). The adjacent other half was not deep‐ploughed and thus served as the control. At all the three sites, both deep‐ploughed and control parts were then conventionally managed over the last 50 years. We assessed yields during the dry year 2019 and additionally in 2020, and rooting intensity at the year of sampling (2019), as well as changes in soil structure, carbon and nutrient stocks in that year. We found that deep‐ploughing improved yields in the dry spell of 2019 at the sandy sites, which was supported by a more general pattern of higher NDVI indices in deep‐ploughed parts for the period from 2016 to 2021 across varying weather conditions. Subsoil stocks of soil organic carbon and total plant‐available phosphorus were enhanced by 21%–199% in the different sites. Root biomass in the subsoil was reduced due to deep‐ploughing at the silty site and was increased or unaffected at the sandy sites. Overall, the effects of deep‐ploughing were site‐specific, with reduced bulk density in the buried topsoil stripes in the subsoil of the sandy sites, but with elevated subsoil density in the silty site. Hence, even 50 years after deep‐ploughing, changes in soil properties are still detectable, although effect size differed among sites.

A comparison of FLC MPPT techniques and HC MPPT techniques for photovoltaic systems

This study aims to examine the behaviour of various maximum power point tracking (MPPT) methods used with PV systems. The methodologies of hill climbing (HC) and fuzzy logic controller (FLC) are assessed in this paper. PV module and DC/DC boost converter models were simulated using PSIM and Simulink software with various MPPT strategies. The PSIM and Simulink software programmes were founder throughout the FLC MPPT method’s development. To employ each software for specific system components, the co-simulation was conducted. The effectiveness of the various MPPT approaches was assessed in response to rapidly varying weather conditions. The results show that for most of the tested MPPT approaches’ usual working ranges, FLC outperforms the HC MPPT approach in transient behavior and constant.

Speed Optimization in Bulk Carriers: A Weather-Sensitive Approach for Reducing Fuel Consumption

The maritime industry faces the critical challenge of achieving net-zero greenhouse gas emissions by 2050, as mandated by the International Maritime Organization. This study introduces a novel speed optimization model, designed specifically for bulk carriers operating between two ports. Unlike conventional models that often assume static weather conditions, the proposed model incorporated variable weather conditions at different times of arrivals, as quantified by the Beaufort number (BN) and weather direction, for each leg of the voyage. Fuel consumption was estimated by applying regression to historical voyage data. This study employed a genetic algorithm (GA) to optimize vessel speed and thereby minimize fuel consumption. The model was tested by using different fuel consumption response curves relative to different BNs and weather directions. The results indicated that the proposed method could effectively reduce fuel consumption compared with the historical sailing mode by around 3%. The optimal speed recommendation indicated that the vessel should operate at a higher speed in circumstances associated with relatively low fuel consumption, such as lower BN and following sea conditions. Nonetheless, if it is possible to attain relatively low fuel consumption by adjusting the speed, the GA assesses the viability of this course of action. The study suggests that the predictive accuracy could be further enhanced by incorporating more granular, validated voyage data in future research.

Hibernation telomere dynamics in a shifting climate: insights from wild greater horseshoe bats.

Hibernation is linked with various hypotheses to explain the extended lifespan of hibernating mammals compared with their non-hibernating counterparts. Studies on telomeres, markers of ageing and somatic maintenance, suggest telomere shortening slows during hibernation, and lengthening may reflect self-maintenance with favourable conditions. Bats in temperate zones adjust body temperatures during winter torpor to conserve energy and exploit mild conditions for foraging. Climate change may impact the hibernation cycle of bats, but more research is needed regarding the role of telomeres in understanding their response to a changing climate. Here, relative telomere length (rTL) was measured in the long-lived greater horseshoe bat Rhinolophus ferrumequinum (n = 223 individuals) over three winters, considering climatic conditions. Cross-sectional analyses revealed between-individual variation in rTL with a strong year effect, likely linked to varying weather conditions and foraging success. Additionally, within-individual increases of rTL occurred in 51% of consecutive measurements, with evidence of increasing telomerase expression during hibernation in this species. These findings highlight the beneficial effects of hibernation on telomeres and potential consequences of changing climatic conditions for long-lived temperate bats. Understanding the interplay between hibernation, telomeres, and climate can provide insights into the adaptive capacity and survival of bat populations facing environmental challenges.

Sustainability of different cropping systems under varying sowing dates in Marathwada region

The field experiments were carried out at Instructional Farm, AICRP on Dry land Agriculture, Marathwada Agricultural University, Parbhani during the rainy season of 2001-2005 on eight different cropping systems consisting of important food, pulse and oilseed crops of Marathwada region under varied weather conditions. The resultsrevealed that intercropping of sorghum (CSH-9)+ pigeonpea, pear millet + pigeonpea and castor + soybean sown in 26 meteorological week (MW) produced the highest grain yield and average productivity of the system during all the years of experimentation as compared to rest of the cropping systems. Similarly, castor + soybean produced the highest sorghum grain equivalent which was at par with soybean + pigeonpea, Arborium cotton + soybean and cotton (NHH-44) + soybean. The sowing of all the crops and cropping systems on 26 MW recorded significantly highestsustainable yield index (0.52) as compared to sowing of all cropping systems on delayed sowing dates.

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

We aimed to improve the performance of ship detection methods in synthetic aperture radar (SAR) images by utilizing machine learning (ML) and artificial intelligence (AI) techniques. The maritime industry faces challenges in collecting precise data due to constantly changing sea conditions and weather, which can affect various maritime operations, such as maritime security, rescue missions, and real-time monitoring of water boundaries. To overcome these challenges, we present a survey of AI- and ML-based techniques for ship detection in SAR images that provide a more effective and reliable way to detect and classify ships in a variety of weather conditions, both onshore and offshore. We identified key features frequently used in the existing literature and applied the graph theory matrix approach (GTMA) to rank the available methods. This study’s findings can help users select a quick and efficient ship detection and classification method, improving the accuracy and efficiency of maritime operations. Moreover, the results of this study will contribute to advancing AI- and ML-based techniques for ship detection in SAR images, providing a valuable resource for the maritime industry.

Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0

This paper presents an innovative approach for enhancing power output forecasting of Photovoltaic (PV) power plants in dynamic environmental conditions using a Hybrid Deep Learning Model (DLM). The hybrid DLM employs a synergy of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Bidirectional LSTM (Bi-LSTM), effectively capturing spatial and temporal dependencies within weather data crucial for accurate predictions. To optimize the DLM’s performance efficiently, a unique Kepler Optimization Algorithm (KOA) is introduced for hyperparameter tuning, drawing inspiration from Kepler’s laws of planetary motion. By leveraging KOA, the DLM attains optimal hyperparameter configurations, elevating power output prediction precision. Additionally, this study integrates Transductive Transfer Learning (TTL) with the deep learning models to enhance resource efficiency. By leveraging knowledge gained from previously learned tasks, TTL enables the DLM to improve its forecasting capabilities while minimizing resource utilization. Datasets encompassing environmental parameters and PV plant-generated power across diverse sites are employed for DLM training and testing. Three hybrid models, amalgamating KOA, CNN, LSTM, and Bi-LSTM techniques, are introduced and evaluated. Comparative assessment of these models across distinct PV sites yields insightful observations. Performance evaluation, focused on short-term PV power forecasting, underscores the hybrid DLM’s superiority over individual CNN and LSTM models. This hybrid approach achieves remarkable accuracy and resilience in predicting power output under varying weather conditions, showcasing its potential for efficient PV power plant management.

Transmission of Helmithiasis in a community and treatment option

Infections caused by parasitics helminths affect more than a billion people worldwide, causing anemia and with significant social and economic impact. To alleviate this burden, affected population resorts to a massive administration of antiparasitics without worrying about improvements in the factors that aid in spreading helminthosis in the community. In this study we are proposing as objectives, that of going in search of the we used questionnaires, oral interviews and direct observations to determine risk factors and treatment options for the disease in two communities of Kolwezi in Democratic Republic of Congo.by on the one hand a household questionnaire and observation of the investigator and others by a census of antihelminths in the pharmacies of the city of Kolwezi. We received 7,014 responses from households and 283 from pharmacies. It was observed from our analysis that most of the plots were clean and dry although sometimes covered with trees and with grass. On average, four households, six children over the age of fifteen and four children under the age of five, and two toilets were observed per plot. The quality of toilets, the source of water supply, the misuse of toilet pots for children under five years old as well as variations in weather conditions were the major risk factors for parasitosis in Kolwezi Occasional stool examination revealed mainly dominated by the feeling of vomiting, fatigue and a nocturnal cough. a small portion of the population had access to laboratory tests which, moreover, were able to observe more Ascaris lumbricoides eggs. The population resorts to self-medication with Mebendazole® and sometimes herbal teas and palm kernel oils. while on medical prescriptions and on the shelves of pharmacies, Albemedazoles was more present. The elements of transmission having been well identified, the start of an education of the population is a necessity and constitutes the next step.