Search Results (28)

In the evolving landscape of green shipping, the accurate estimation of shaft power is critical for reducing fuel consumption and greenhouse gas emissions. This study presents an explainable machine learning framework for shaft power prediction, utilising real-world Internet of Things (IoT) sensor data collected from nine (9) Very Large Crude Carriers (VLCCs) over a 36-month period. A diverse set of models—ranging from traditional algorithms such as Decision Trees and Support Vector Machines to advanced ensemble methods like XGBoost and LightGBM—were developed and evaluated. Model performance was assessed using the coefficient of determination ($R^2$) and RMSE, with XGBoost achieving the highest accuracy ($R^2=0.9490$, RMSE 888) and LightGBM being close behind ($R^2=0.9474$, RMSE 902), with both substantially exceeding the industry baseline model ($R^2=0.9028$, RMSE 1500). Explainability was integrated through SHapley Additive exPlanations (SHAP), offering detailed insights into the influence of each input variable. Features such as draft, GPS speed, and time since last dry dock consistently emerged as key predictors. The results demonstrate the robustness and interpretability of tree-based methods, offering a data-driven alternative to traditional performance estimation techniques and supporting the maritime industry’s transition toward more efficient and sustainable operations. Full article

Wildlife Rehabilitation Centres emerged with the purpose of recovering individuals, as a tool for environmental education and monitoring the balance of ecosystems. The White Stork (Ciconia ciconia) is one of the many species that are admitted to rehabilitation centres all around the world, due to traumatic amputations. This work presents the development of 3D-printed orthopedic prostheses aimed at partially restoring biomechanical function and enabling the reintegration of amputated birds into their natural habitat. Conducted at the Green Balkans Wildlife Rehabilitation and Breeding Center in Bulgaria, three prosthetic prototypes were created using epoxy resin, polylactic acid (PLA), and polyamide, based on detailed anatomical measurements. The process involved 3D Computer-Aided Design (CAD), biomechanical analysis, and performance evaluation, focusing on locomotion, feeding, and flight. Results showed improved prosthetic efficacy, with birds adapting within 1–5 days, resuming normal behaviours, and regaining flight. Of the 12 birds analyzed, 3 were released into the wild, with 1 tracked via GPS, marking the first documented case of an amputated bird with a prosthesis monitored post-release, covering over 470 km in 15 days. This study highlights the potential of 3D printing in conservation medicine, offering alternatives to euthanasia and open new perspectives in the global context of biodiversity preservation. Full article

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R² = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection. Full article

In recent years, seed priming has gained interest, with researchers aiming to enhance seed germination and early growth, especially under abiotic stress conditions. In this study, seeds from two squash landraces (Cucurbita maxima Duchesne; i.e., Galaoui large seeds (Galaoui hereafter) and Batati green (Batati hereafter)) were subjected to different priming methods ((a) 0.3% and 0.4% KNO₃ (halopriming); (b) 0.1% and 0.2% GA₃ (hormopriming); (c) inoculation with Trichoderma spp. (T. harzianum, T. viride, and T. virens), Bacillus subtilis, and Pseudomonas fluorescens (biopriming) in order to promote germination parameters and seedling growth under salinity stress (0, 100, and 200 mM of NaCl). Our findings indicate the better performance of primed seeds compared to the untreated ones in terms of germination and seedling growth traits, although a varied response depending on the priming method and the landrace was observed. The highest germination percentage (GP) and the lowest mean germination time (MGT) were observed in 0.4% KNO₃-primed seeds. The positive effects of 0.4% KNO₃ were also depicted in all traits related to seedling growth and the seedling vigor index (SVI), indicating its effectiveness as a priming agent in squash seeds. Under salinity stress conditions, priming with 0.4% KNO₃ significantly improved the germination and seedling growth traits for both landraces, while the application of 0.2% GA₃ at high salinity significantly improved photosynthetic quantum yield (F_v/F_m ratio). Regarding the effects of biopriming in germination and seedling growth traits, our results indicate that T. harzianum and B. subtilis were the most effective bioagents in promoting germination and seedling growth in Galaoui and Batati seeds, respectively. In conclusion, our findings provide important information regarding the practice of using priming and biopriming agents to enhance the germination and seedling growth capacity of squash seeds, as well to mitigate the negative effects of salinity stress at the critical stages of germination and early growth. Full article

In this research, pectin extraction from grapefruit peel (GPP) was performed using a microwave-assisted high-pressure CO₂/H₂O (MW-HPCO₂) system. The Box–Behnken design of response surface methodology was applied for the optimization of MW-HPCO₂ extraction conditions to obtain the highest pectin yield. The effects of temperature, time, and liquid/solid ratio on pectin yield were examined in the range of 100–150 °C, 5–15 min, and 10–20 mL g⁻¹, respectively. Under the optimum extraction conditions (147 °C, 3 min, and 10 mL g⁻¹), pectin was obtained with a yield of 27.53%. The results obtained showed that the extraction temperature and time had a strong effect on the pectin yield, while the effect of the liquid/solid ratio was not significant, and the pectin was effectively extracted from grapefruit peel (GP) using MW-HPCO₂. Additionally, the application of GPP in apricot jam showed that MW-HPCO₂-GPP can be used as a thickener in the food industry. The yield and physicochemical properties (ash, protein, galacturonic acid, reducing sugar and methoxyl content, degree of esterification, equivalent weight, color, viscosity) of pectin extracted in the optimum conditions of the MW-HPCO₂ method were superior to pectin extracted by the traditional method. The results of this study revealed that MW-HPCO₂ could be an innovative green and rapid technique for pectin extraction. Full article

Agricultural practices such as tilling, sowing, cropping, It’s duty but arvesting, and land-use patterns in any agrarian economy depend on climate. Therefore, any adverse climatic conditions can seriously affect the production or yield of crops. Increased temperature enhances the susceptibility of crops to pests and various plant diseases. Weeds are also known to multiply rapidly and decrease the nutritive value of soil, negatively affecting crop production. Our present study is designed to address similar problems faced by the farming community in the South-24 Parganas district of West Bengal, India, and suggest several probable technological solutions. Importantly, West Bengal is included in one of the six agro-climatic zones. Major crops from this study site are rice, wheat, maize, jute, green gram, black gram, pigeon pea, lentils, sugarcane, pulses, rapeseed, mustard, sesame, linseed, and vegetables. Significantly, cultivable land area has decreased in comparison to the overall crop area in this region. Reduced interest in agriculture, irrigation problems, increased profit in the non-agricultural economy, and rapid conversion of agricultural land for commercial purposes (construction of plots, hatcheries for fishing practices), along with uncertainties associated with rainfall patterns and frequent cyclones, are matters of grave concern in this study area. Agricultural scientists, researchers, environmentalists, local bodies, and government organizations are suggesting alternatives to benefit farmers. Thus, precision agriculture or crop management is required to recognize site-specific variables within agricultural lands and formulate strategies for improving decision-making regarding crop sowing, appropriate use of herbicides, weedicides, and precision irrigation, along with innovative harvesting technologies. Thus, the present paper provides a vision for the farming community in our study area to overcome their traditional practices and adopt different techniques of precision agriculture to increase flexibility, performance, accuracy, and cost-effectiveness. Soil temperature, humidity, and moisture monitoring sensors could be beneficial. Precision soil management, precision irrigation, crop disease management, weed management, and harvesting technologies are the different modules considered for discussion in this paper. Machine learning algorithms, such as decision tree, K-nearest neighbor (KNN), Gaussian naïve Bayes (GNB), K-means clustering, artificial neural network (ANN), fuzzy logic system (FLS), and support vector machine (SVM), could prove helpful for progressive farmers. The use of AI-powered weeding machines, drones, and UAVs for rapid weed removal and the localized application of herbicides and pesticides could also improve the accuracy and efficiency of agriculture. Utilizing drones fitted with high-resolution cameras could help gather precision field images, proving to be quite helpful in crop monitoring and crop health assessment. Unmanned driverless tractors and harvesting machines using robotics integrated with data from GPS/GIS sensors or radars could also be considered an effective and time-saving option. Thus, machine learning, along with innovative agricultural technologies, could contribute to improving the livelihoods of the farming fraternity. Full article

While moving towards sustainable performance, organizations try to create a win-win situation not only for the organization itself but for the planet and society as well. The main aim of this study is to examine the linkage between environmental corporate social responsibility (ECSR), green talent management (GTM), and organization’s sustainable performance. The study also investigates the impact of ECSR and GTM towards sustainable performance through transformational leadership, employees’ innovative work behavior (IWB), and green performance (GP). The results of the present study show that ECSR directly influences the sustainable performance and GTM of an organization. Similarly, green hard and soft talent management (TM) both have direct and positive links with employees’ IWB and GP. Another important finding is the significant and direct relationship of both IWB and GP of employees towards the sustainable performance of an organization. The moderating role of transformational leadership exerts a significant moderating influence between green hard TM and IWB. However, the moderating role of transformational leadership between green soft TM and IWB and the moderating role of transformational leadership between GTM and employee’s GP proves insignificant. The findings of this study can help the organizations to understand the importance of engaging in environmentally sustainable activities and to support and recognize the significance of green values and competencies within their employees. Full article

The relationship between time and energy is an important aspect related to energy savings in modern multicore architectures. In this paper, we investigated and analyzed the correlation between time and energy. We compared the execution time and energy consumption of the LU factorization algorithms (versions with and without pivoting) and Cholesky with the Math Kernel Library (MKL) on a multicore machine. To reduce the energy of these multithreaded factorizations, the Dynamic Voltage and Frequency Scaling (DVFS) technique was used. This technique allows the clock frequency to be scaled without changing the implementation. In particular, we studied the correlations between time and energy using two metrics: Energy Delay Product (EDP) and Greenup, Powerup, and Speedup (GPS-UP). An experimental evaluation was performed on an Intel Xeon Gold multicore machine as a function of the number of threads and the clock speed. Our test results showed that scalability in terms of execution time, expressed by the Speedup metric, had values close to a linear function as the number of threads increased. In contrast, the scalability in terms of energy consumption, expressed by the Greenup metric, had values close to a logarithmic function as the number of threads increased. The use of the EDP and GPS-UP metrics allowed us to evaluate the impact of the optimized code (DVFS and increase in the number of threads) on the time and energy consumption and to determine a better green category representing energy savings without losing performance. Full article

The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R² = 0.89), NB (RMSE% = 17.31% and R² = 0.93) and BB (RMSE% = 24.09% and R² = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method’s applicability in diverse forest structures and biomes is warranted. Full article

Sweet cherry (Prunus avium L.) is a popular fruit tree grown for its juicy fruit and pleasing appearance. The fruit pf the sweet cherry contains active antioxidants and other chemical compounds essential for human health. For this study, we performed the transcriptomics and metabolomics analysis using young Green Peel (GP) and mature Red Peel (RP) from sweet cherries to understand the underlying genetic mechanism regulating fruit development and ripening. Using high-throughput RNA sequencing and ultra-performance liquid chromatography, with quadrupole time-of-flight tandem mass spectrometry, respectively, metabolic and transcript profiling was obtained. Relative to GP, there were equal quantities of pronouncedly varied metabolites in RP (n = 3564). Differentially expressed genes (DEGs, n = 3564), containing 45 transcription factor (TF) families, were recorded in RP. Meanwhile, 182 differentially expressed TF (DETF) members of 37 TF families, were displayed in abundance in RP compared to GP sweet cherries. The largest quantities of DETFs were members of the ERF (25) and basic helix–loop–helix (bHLH) (19) families, followed by the MYB (18), WRKY (18), and C2H2 (12) families. Interestingly, most ERF genes were down-regulated, whereas CCCH genes were mainly up-regulated in RP. Other DETFs exhibited significant variations. In addition, RT-QPCR results and metabolomics data together with transcriptomic data revealed that the abundance of catechin, epicatechin, rhoifolin, myricetin, keracyanin, and the other six glycosyltransferase genes was highly increased in RP when compared to GP sweet cherries. The relatively higher expression of DETFs, metabolite, and flavonoid biosynthesis in RP sweet cherries suggests the accumulation of distinct metabolites that cause red coloring during fruit development and ripening. Thus, the metabolomics and transcriptomic analysis of the current study are powerful tools for providing more valuable information for the metabolic engineering of flavonoids biosynthesis in sweet cherries. They are also helpful in understanding the relationship between genotype and phenotype. Full article

The increase in renewable energy sources (RESs) in distribution grids is a major driver for achieving green energy goals worldwide. However, RES power inverters affect power quality, increase power losses, and, in certain cases, may cause power interruptions due to harmonics, deterioration of the rate of change of frequency, and inability to rapidly react in grid faults. Today, phasor measurement units (PMUs) are the ultimate tools for real-time monitoring of distribution grids’ health, and they enable several data-driven added-value services such as fast and automated fault detection, isolation, and recovery; state estimation; power quality monitoring; dynamic events analysis, etc. The present paper proposes an open hardware and software PMU platform, which is low cost, high performance, expandable, and, in general, suitable for research and innovation activities. The system is based on two processor modules (a digital signal processor from Texas Instruments TMS320c5517, and a microprocessor System-in-Package from Octavo Systems OSD3358), two local databases of 64 Gbytes each, GPS module, 5G modem interface, as well as analog and signal conditioning circuits to interface three-phase power voltage and current signals. The entire hardware design, schematics, and instrumentation components, as well as all firmware and software functions are completely open source. Pilot operation of the prototype design has been installed in three medium-/low-voltage substations in Cyprus, as well as twelve substations in Spain and Italy. Full article

The present study assessed the potential of engaging response surface analysis in the experimental design, modeling, and optimization of the strength performance of aluminum-7075 green composite. The design of the experiment was carried out via the Box–Behnken method and the independent variables are rice husk ash (RHA) at 3–12 wt.%, glass powder (GP) at 2–10 wt.%, and stirring temperature (ST) at 600–800 °C. Responses examined are yield, ultimate tensile, flexural, and impact strengths, as well as microhardness and compressive strength. ANOVA analysis revealed that the input factors had consequential contributions to each response, eventually presenting regression models statistically fit to represent the experimental data, further affirmed by the diagnostic plots. The result of the optimization envisaged an optimal combination at 7.2% RHA, 6.2 GP, and 695 °C with a desirability of 0.910. A comparison between the predicted values for the responses and the values of the validation experiment revealed an error of <5% for each response. Consequently, the models are certified adequate for response predictions at 95% confidence, and the optimum combination is adequate for the design of the composite. Full article

Since the inauguration of the United Nations Sustainable Development Goals (UNSDGs), environmental performance and sustainability have become more important to decision makers, scientists and leaders of organizations than before. In response to this, leaders of different organizations spend all endeavors conserving resources and ensuring environmental sustainability. In this context, transformational leaders have the capacity to ensure the green performance of their organization. The purpose of this study is to test the link between green transformational leadership (GTL), green innovation (GI), corporate social responsibility (CSR) and green performance (GP) in the hotel industry in the Kingdom of Saudi Arabia (KSA). The study empirically tests the mediating effect of GI and CSR on the link between GTL and GP. The study used a quantitative research method via a pre-test instrument, self-distributed and collected from employees in large hotels at different regions of the KSA. The findings from 732 valid responses, analyzed with structural equation modeling (SEM) showed that GTL had a significant effect on GI (β = +0.72, t-value = 14.603, p < 0.001), CSR (β = +0.58, t-value = 8.511, p < 0.001) and GP (β = +0.17, t-value = 2.585, p < 0.001). Moreover, GI and CSR had a direct positive effect on GP (β = +0.10, t-value = 2.866, p < 0.01 and β = +0.61, t-value = 4.358, p < 0.001, respectively). GI had a partial mediation effect (p = 0.048 < 0.05) on the link between GTL and GP. On the other hand, CSR had a perfect mediation effect (p = 0.077 > 0.05) on the link between GTL and GP. This reflects the vital part that CSR plays in this relationship, which can be changed based on the status of CSR. In addition, this reflects the value of CSR in achieving GP, which contributes to the achievement of environmental sustainability at a national level (i.e., the Green Saudi Initiative) at a regional level (i.e., the Green Middle East Initiative) and at an international level (i.e., UNSDGs). Full article

Several trials have been carried out by various authors concerning autonomous mowers, which are battery-powered machines. The effects of these machines on turfgrass quality and energy consumption have been thoroughly investigated. However, there are still some aspects that have not been studied. Among these, random trajectory overlapping is one of the most important. To investigate these aspects, two RTK-GPS devices along with the custom-built software used for previous trials has been upgraded in order to precisely calculate how many times the mower drives over the same spot using random trajectories. This parameter, the number of passages in the same position, was hypothesized to explain the autonomous mower’s overlapping and trampling action. The trial has been carried out testing a commercial autonomous mower on three areas with different levels of complexity to assess its performances. The following variables were examined: the percentage of mowed area, the distance travelled, the number of intersections, the number of passages, and the autonomous mower’s work efficiency. The average percentage of area mown (average value for the three areas) was 54.64% after one hour and 80.15% after two hours of work. Percentage of area mown was 15% higher for the area with no obstacles after two hours of work. The number of passages was slightly different among the three garden designs. The garden with no obstacles obtained the highest number of passages with an average of 37 passages. The highest working efficiency was obtained in the garden with an intermediate number of obstacles with a value of 0.40 after two hours of work. The estimated energy consumption resulted 0.31 Wh m⁻² after one hour and 0.42 Wh m⁻² after two hours of working. These results highlight how the correct settings of cutting time may be crucial to consistently save energy during the long period and may be useful for a complete automation of the maintenance of green areas. Full article

The pandemic emergency determined by the spreading worldwide of the SARS-CoV-2 virus has focused the scientific and economic efforts of the pharmaceutical industry and governments on the possibility to fight the virus by genetic immunization. The genetic material must be delivered inside the cells by means of vectors. Due to the risk of adverse or immunogenic reaction or replication connected with the more efficient viral vectors, non-viral vectors are in many cases considered as a preferred strategy for gene delivery into eukaryotic cells. This paper is devoted to the evaluation of the gene delivery ability of new synthesized gemini bis-pyridinium surfactants with six methylene spacers, both hydrogenated and fluorinated, in comparison with compounds with spacers of different lengths, previously studied. Results from MTT proliferation assay, electrophoresis mobility shift assay (EMSA), transient transfection assay tests and atomic force microscopy (AFM) imaging confirm that pyridinium gemini surfactants could be a valuable tool for gene delivery purposes, but their performance is highly dependent on the spacer length and strictly related to their structure in solution. All the fluorinated compounds are unable to transfect RD-4 cells, if used alone, but they are all able to deliver a plasmid carrying an enhanced green fluorescent protein (EGFP) expression cassette, when co-formulated with 1,2-dioleyl-sn-glycero-3-phosphoethanolamine (DOPE) in a 1:2 ratio. The fluorinated compounds with spacers formed by six (FGP6) and eight carbon atoms (FGP8) give rise to a very interesting gene delivery activity, greater to that of the commercial reagent, when formulated with DOPE. The hydrogenated compound GP16_6 is unable to sufficiently compact the DNA, as shown by AFM images. Full article