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Soil erodibility is challenging for agricultural soils, sustainable farming systems and water-food security in developing countries. Regional scale precise estimation of soil erosion is vital to save fertile soils. For this purpose, geospatial technology is the researchers’ top choice. The Revised Universal Soil Loss Equation (RUSLE) has the potential plausible estimates of soil erosion in the region. The study area comprises parts from two basins (Peshawar and Nizampur) having distinct land surface characteristics. This difference made the applicability of RUSLE model challenging. The influencing factors were combined using weighted sum in GIS. The study area was categorized into five soil erosion zones: very low (0–0.1 t ha−1 year−1), low (0.1–0.50 t ha−1 year−1), moderate (0.50–0.7 t ha−1 year−1) high (0.7–1 t ha−1 year−1), and very high (>1 t ha−1 year−1). The 1,748 km2 (174800 hectares) study region has an annual soil loss of 0.1 to > 1.0 t ha−1 year−1. The overall accuracy of the estimated soil erosion was found to be 90% with Kappa coefficient of 87.25% and receiver operating characteristics area under the curve (ROC-AUC) score of 0.819. This study’s will help to address the climate change-induced challenges of soil erosion in agricultural lands.

Background: The development of a robust and accurate point-of-care platform for the detection of tuberculosis (TB) biomarkers is important for disease control. In the current study, the detection principle relies on the shredding of PES-modified non-specific ssDNA (Poly T) in the presence of target DNA IS6110, a reliable biomarker for TB diagnosis by the CRISPR-Cas12a mechanism. Cas protein has great potential in the detection of nucleic acids. Results: Herein, we developed a biosensing platform by utilizing the trans cleavage activity of CRISPR-Cas12a into an electrochemical biosensor. Square wave voltammetry technique is used for the analysis of the fabricated biosensing platform. In the presence of target DNA, the trans cleavage activity is observed by a nonspecific ssDNA substrate, PolyT chain. Various concentration of target DNA is tested on the constructed biosensor, the fabricated biosensor successfully detected TB target DNA by trans cleavage of PES-modified poly T. This novel biosensor was able to detect the target DNA, IS6110 with the limit of detection of 14.5 nM within 60 min by trans-cleavage activity of CRISPR-Cas12a and the results revealed the potential of Cas12a-based biosensors as a diagnostic platform. Significance: This is the first study reporting the CRISPR-Cas12a-based electrochemical sensor for TB. The developed CRISPR-Cas12a endonuclease-based electrochemical biosensor provides a potentially powerful platform for the accurate detection of Mycobacterium tuberculosis.

Microplastics (MPs) have become ubiquitous environmental pollutants, eliciting concerns about their negative impacts on terrestrial and agroecosystems. However, employing sustainable remediation approaches holds the potential to mitigate these negative impacts and bolster MPs tolerance in both the seeds and vegetative structures of plants. The current investigation explores the impact of arbuscular mycorrhizal fungi (AMF) and synthetic bacterial community (SynCom) as potential alleviators of MP-induced stress. Results exhibited that co-applied SynCom and AMF, resulted in the highest plant biomass, leaf area, pod per plant, and 100-seed weight under control (no MPs) and MPs stress. The combined application of SynCom and AMF enhanced colonization, association, and arbuscule abundance in the non-stressed plants (no MPs) as compared to stressed plants. The sole or co-application of SynCom and AMF enhanced the primary metabolites content and mitigated the MPs-induced reduction in soluble sugars, lipids, protein, and oil contents. Soybean inoculation stimulated the genes expression involved in lipid and protein biosynthesis, while a contradictory drift was noted for genes associated with lipid and protein degradation, underpinning the observed increment in protein and lipid content. Soybean inoculated with AMF exhibited the utmost α-amylase and β-amylase activities, demonstrating enhanced osmolyte (soluble sugar) production, mainly under MPs stress. Remarkably, sole or co-application further reinforces the positive effect of MPs stress on the osmoprotectants and antioxidant levels, for instance, phenol, flavonoid, glycine betaine contents, and glutathione-S-transferase (GST) activities. Subsequent to the stress release, a stress hormones known as abscisic acid (ABA) reduced in the seeds of inoculated soybean while gibberellin (GA), trans-zeatin riboside (ZR), and indole acetic acid (IAA) were increased. Therefore, it is concluded that the combined application of SynCom and AMF protected the soybean plants from MPs-induced oxidative damage by upregulating osmoprotectants and antioxidant levels. This study provides insights into the potential of sustainable remediation approaches in alleviating the complex impact of MPs on soybean, contributing to future strategies for environmental sustainability. Future studies could explore the optimization of SynCom and AMF formulations for different crops and environmental conditions, potentially leading to practical applications in sustainable agriculture and pollution management.

The conventional model of energy production and consumption has come under severe scrutiny. Concerns related to climate change, increased energy needs and issues surrounding conventional sources of energy generation have highlighted a need to adopt sustainable means of energy generation. Renewable energy sources are believed to have the potential to meet rising energy needs in a sustainable and environmentally friendly manner. Solar energy is particularly interesting in this respect as it has the potential to be used at commercial as well as household level; however, to this end, its contribution to global energy supply has remained limited. A number of studies have explored factors influencing the adoption of solar photovoltaics (PV) at the household level and proposed measures to foster its development. This paper aims to systematically review and analyse the state of solar PV adoption by exploring “What are the key factors influencing the adoption of solar PV at household level?”. From 146 studies published in peer-reviewed scientific journals, the study identifies 127 unique factors influencing adoption behaviour, and groups these into eight categories, namely economic, environmental, social, personal, demographic, technical, regulatory and market-related factors. Based on the findings, this paper proposes guidelines for practitioners, identifies knowledge gaps and suggests directions for future research to strengthen the scholarship in this domain.

Metabolomics has emerged as a valuable tool in drug discovery and development, providing new insights into the mechanisms of action and toxicity of potential therapeutic agents. Metabolomics focuses on the comprehensive analysis of primary as well as secondary metabolites, within biological systems. Metabolomics provides a comprehensive understanding of the metabolic changes that occur within microbial pathogens when exposed to therapeutic agents, thus allowing for the identification of unique metabolic targets that can be exploited for therapeutic intervention. This approach can also uncover key metabolic pathways essential for survival, which can serve as potential targets for novel antibiotics. By analyzing the metabolites produced by diverse microbial communities, metabolomics can guide the discovery of previously unexplored sources of antibiotics. This review explores some examples that enable medicinal chemists to optimize drug structure, enhancing efficacy and minimizing toxicity via metabolomic approaches.

Abstract Key message: A harmonized, comprehensive meteorological time series for 78 German intensive forest monitoring plots (Level II) has been made available from 1961 to 2019. The used hybrid spatial interpolation routine using simple linear regression and inverse distance weighting allows for gap filling of missing data and also for extrapolation outside measurement period to analyze long-term effects of climate on forest ecosystems. The dataset is available at https://www.openagrar.de/receive/openagrar_mods_00079174 . The associated metadata are available at: https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/433a028f-dfc8-4a7c-82af-b8d7efafd724 .

Abstract We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. Through a single-shot approach, the reconstruction task is paired with energy regression. We describe the reconstruction performance in terms of efficiency as well as in terms of energy resolution. In addition, we show the jet reconstruction performance of our method and discuss its inference computational cost. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of $${\mathcal {O}}(1000)$$ O ( 1000 ) particles in high-luminosity conditions with 200 pileup.

Abstract Background Attraction and oviposition preference of the green lacewing, Chrysoperla carnea (Steph.) (Neuroptera: Chrysopidae), in response to prey availability in an ovipositional site was studied. The study aimed to investigate whether an adult attraction of C. carnea to oviposition on the substrate was affected by aphid juice (AJ) of fresh brassica aphid, Brevicoryne brassica. Result In laboratory bioassay, the number of eggs laid by female was significantly higher on AJ-treated area of substrate than control, indicating that oviposition was influenced by the AJ application. Attraction period of AJ lasted for 4 days. In Y-maze olfactometer studies, video tracking software ANY-maze® indicated that C. carnea spent significant more time in the novel arm provided with AJ, showing an attraction. In the greenhouse study, AJ spray attracted a significantly higher number of male and female and considerably increased the number of eggs laid on brassica plant. Conclusion Fresh AJ may be a potential attractant for raising population of this predatory species at a particular location without being involved in rearing and augmentation process. This finding is of special interest and may be of benefit in biological control. As it is likely that the AJ spray could be used to enhance C. carnea population attraction to the desire field.

Objective: To evaluate the recurrence following open surgical excision and Seton placement in treating symptomatic wrist ganglions in Military Hospitals in a six-month follow-up study. Study Design: Quasi-experimental study Place and Duration of Study: Department of General Surgery, Combined Military Hospital, Bahawalpur Pakistan, Combined Military Hospital, Dera Nawab Sahib Pakistan and Pakistan Airforce Force Hospital, Shorkot Pakistan, from Jun 2015 to Dec 2020. Methodology: The patients were divided into two groups after informed consent. A consultant general surgeon carried out every procedure. The findings were recorded on a uniform proforma, and recurrence was recorded at ten days, six weeks and six months for both groups. Results: The mean age of the patients was 29.45±8.13 years in Group-A (Seton-Group) and 28.56±8.32 years in Group-B (OpenSurgery). There was no difference in the recurrence rates between the Seton-Group (n=7, 4.35%) and Open Surgery-Group (n=2, 4.65%, p=0.931). Conclusion: Seton insertion for treating Ganglions of the wrist is a simple, economical and cosmetic procedure which can be done in the outpatient department. It is a safe alternative to open resection for successful treatment of the wrist Ganglion. Keywords: Ganglion, Open surgery, Recurrence, Seton, Wrist.

According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.

Costing method plays a vital role in assuring the feasibility of any project. When we talk about energy systems, many factors influence the decision making process, which are directly or indirectly associated with socio-enviro-economic benefits. While talking about the associated benefits, appropriate costing approach can significantly absorb the concerned factors. In this context, economics is one of the relevant factors which comes to mind while choosing a right working fluid for thermodynamic cycles if consider this as a test case. Although a wide variety of relevant approaches is available. However, the literature points out the shortcomings, validity and limitations of available costing methods. Out of them, levelized cost of energy (LCOE) is considered a well-known costing method, which has received many criticisms in this regard. On the other hand, this work proposes the concept of employing benefit-cost ratio (BCR) analysis as an alternative costing tool to select suitable working fluids for thermodynamic cycles. This work compares and evaluates the effectiveness, openness and understanding of both methods. For in-depth analysis, a heat pump system as a case study is adopted to select the suitable working fluid. The study is further supported by considering multiple scenarios. Results of this study have shown very interesting outcomes, where one method lagging the other and vice versa. The results have shown the impact of thermophysical properties of different working fluids and their influence on overall economics of the system.

The present research was carried out to examine the health and environmental impacts associated with coal mining activities in the Makarwal coal mining area in Pakistan. To achieve the objectives, 41 soil samples were collected from the coalmine-affected areas and analyzed for major and toxic trace elements (TTEs) using atomic absorption spectroscopy. Most of the soil samples have extremely high concentrations of toxic metals such as Ni, Cd, Cr, Cu, Zn, and Pb. The positive correlations and high concentration of trace elements in the Makarwal coalmine-affected region suggest an analogous origin of soil contamination. The factor analysis (FA) showed two components, i.e., F1 (53.4%) and F (74.21%), of total variability for soil. The F2 was loaded with Pb, Zn, Ni, and Cr, which was similar to cluster 2, while the F1 was loaded with Cu and Cr, having a similar pattern to cluster 1. This proves that the contamination in the surrounding area is mostly associated with geological ore strata existing in the Makarwal coalfield. Based on geoaccumulation (Igeo), the elemental concentration in the studied soil sample could be categorized as follows: (1) Pb, Ni, and Zn are moderate to severely polluted and (2) Cr and Cu are in the moderately polluted category. The ecological risk indices (ERIs) of the single trace element contamination index highlighted that Cd, Pb, Ni, and Cr pose a high risk to humans and the ecosystem. Based on different statistical tools performed for the source identification and distribution of metal contamination, it seems that the exposed sedimentary rocks, including limestone, dolomite, sandstone, and coal, are responsible for the toxic metal contamination in the study area.

Natural gas releases 45–50% less CO2 emissions as compared with coal and the liquefaction process is employed for storage and transport of liquefied natural gas that is an energy-intensive process. Numerous scientific efforts are being directed to reduce the energy consumption in the natural gas liquefaction process and to increase the process efficiency. Thus, this study proposes a uniquely modified and energetically enhanced small-scale natural gas liquefaction process by nitrogen single expander technology with CO2 precooling. The novelty of the designed liquefaction process is that the designed configuration does not only reduces the energy consumption of the liquefaction process but also improves the process efficiency. The proposed system is simulated and optimized using ASPEN HYSYS by different design key parameters, and energy consumption has been considered as a key function for the optimization of this cycle. Detailed characterization shows the energy consumption of 9.216 kWh·kmol−1 at a liquefaction rate of 0.77. Furthermore, the effect of pressure output of the valve on unit energy consumption and liquefaction rate is also observed and discussed. It has been observed that by decreasing the valve output pressure, the liquefaction rate decreases and energy consumption increases. Besides, the versatility of this cycle under various pressures, temperatures and composition of methane and other components in the feed gas are considered and effects of these parameters variation on system performance have been observed. The exergy destruction of each equipment is evaluated and investigated in detail for the optimization of results. Results are compared and it shows a 6.90% and 9.0% decrease of unit energy consumption and exergy as compared to referenced base cycle. The performance of numerous relationships is derived from the proposed study and compared with simulated referenced published data. By using energy consumption and liquefaction rate of compared LNG cycles, the maximum error is +0.93 and the minimum error is −0.12. Due to safe and simple operation, compact device, proposed liquefaction process shows a suitable application for development of small-scale, especially for offshore reserves.

The structure-properties relationships of novel cyclic olefinic copolymer (COC)/poly(L-lactic acid) (PLLA) polymer blends are investigated by varying the PLLA content from 5–20 wt % in COC matrix, forming an immiscible blends at all concentrations. Despite immiscibility, COC favors the formation of α- crystal form of PLLA in the blends, contrary to the α’- crystal form in neat PLLA due to non-covalent interactions of the chain moieties as evident from x-ray diffraction, Fourier transform infrared spectrometer and differential scanning calorimetry. The COC/PLLA blends revealed tunable superior mechanical and barrier properties as compared to the neat counterparts. The tensile modulus, strength and toughness of COC/PLLA blends at 15 wt% are significantly enhanced ∼ 89 %, 103 % and one order of magnitude, respectively. Such reinforcement and super toughening mechanisms are pertaining to the effective dissipation of loads across the interfaces, enabling them to delay the crack growth by avoiding stress concentration sites. Likewise, the water vapor permeation (WVP) of the blends is reduced ∼ 4 times than neat COC.

The issue of climate change, greenhouse gas emissions, global warming, and their effect on nature and the ecosystem has raised serious concerns. The desire to sustain economic growth and development while keeping a check on the environmental footprints is one of the leading challenges the contemporary world is currently facing. To ensure sustained growth, there is a need for technologies and solutions that has the potential to meet industrial needs without compromising the environment. Cleantech offers a possibility to address these needs in a sustainable and environmentally friendly manner. Cleantech, being an umbrella term, is often confused and misunderstood, in terms of its definition and scope. This study seeks to explore what cleantech actually is, how this sector came into prominence, what are the driving factors behind its surge, and what kind of socio-economic, technical, and regulatory prerequisites are necessary for the advancement of this sector.

Abstract We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries. We introduce two distance-weighted graph network architectures, dubbed GarNet and GravNet layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors.

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one μs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the hls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.

Abstract The Clustered Regularly Interspaced Short Palindromic Repeats–Cas-9 (CRISPR-Cas9) system has been a revolutionising tool in the field of molecular genetics, which provides a versatile range of editing potentials. Researchers can produce breaks or alter genomes with ease using the system. Cancer is one of the multi-gene diseases whose genes need to be studied in detail. The CRISPR-Cas9 technology may also provide a promising potential in the field of cancer genetics. The current narrative review comprised 50 research articles which were keenly analysed and the applications and outcomes of CRISPR-Cas9 system in cancer genetics were comprehensively and critically discussed. It was concluded that application of the system had great potential to help understand cancer biology of various types and could be used for its genetic modelling. However, much work is still needed to be done to apply the technology for understanding...Continuous...

Background During a preliminary study, effects of 0, 20, 40, and 60 mM NaCl salinity were assessed on germination rate in relation to electrolyte leakage (EL) in sweet pepper. Results explored significant rises in ethylene evolution from seeds having more EL. It was, therefore, hypothesized that excessive ethylene biosynthesis in plants due to salinity stress might be a root cause of low crop productivity. As salicylic acid is one of the potent ethylene inhibitors, thus SA was used to combat effects of ethylene produced under salinity stress of 60 mM NaCl on different physiological and morphological characteristics of sweet pepper. Methodology The effect of 0.05, 0.1, 0.2, 0.3, 0.4, 0.5 and 0.6 mM SA was evaluated on seed germination, growth and yield of sweet pepper cv. Yolo wonder at salinity stress on 60 mM NaCl. Seeds were primed with SA concentrations and incubated till 312 h in an incubator to study germination. Same SA concentrations were sprayed on foliage of plants grown in saline soil (60 mM NaCl). Results Seeds primed by 0.2 to 0.3 mM SA improved germination rate by 33% due to suppression of ethylene from 3.19 (control) to 2.23–2.70 mg plate−1. Electrolyte leakage reduced to 20.8–21.3% in seeds treated by 0.2–0.3 mM SA compared to 39.9% in untreated seeds. Results also explored that seed priming by 0.3 mM improved TSS, SOD and chlorophyll contents from 13.7 to 15.0 mg g−1 FW, 4.64 to 5.38 activity h−1 100 mg−1 and 89 to 102 ug g−1 compared to untreated seeds, respectively. Results also explore that SA up to 0.2 mM SA applied on plant foliage improved LAI (5–13%), photosynthesis (4–27%), WUE (11–57%), dry weight (5–20%), SOD activity (4–20%) and finally fruit yield (4–20%) compared to untreated plants by ameliorating effect of 60 mM NaCl. Foliar application of SA also caused significant increase in nutrient use efficiency due to significant variations in POD and SOD activities. Conclusion Salicylic acid suppressed ethylene evolution from germinating seeds up to 30% under stress of 60 mM NaCl due to elevated levels of TSS and SOD activity. Foliar application of SA upgraded SOD by lowering POD activity to improve NUE particularly K use efficiency at salinity stress of 60 mM NaCl. Application of 0.2 and 0.3 mM SA emerged as the most effective concentrations of SA for mitigating 60 mM NaCl stress on different physiological and morphological characteristics of sweet pepper.

Virtual reality technologies, including head-mounted displays (HMD), can provide benefits to psychological research by combining high degrees of experimental control with improved ecological validity. This is due to the strong feeling of being in the displayed environment (presence) experienced by VR users. As of yet, it is not fully explored how using HMDs impacts basic perceptual tasks, such as object perception. In traditional display setups, the congruency between background environment and object category has been shown to impact response times in object perception tasks. In this study, we investigated whether this well-established effect is comparable when using desktop and HMD devices. In the study, 21 participants used both desktop and HMD setups to perform an object identification task and, subsequently, their subjective presence while experiencing two-distinct virtual environments (a beach and a home environment) was evaluated. Participants were quicker to identify objects in the HMD condition, independent of object-environment congruency, while congruency effects were not impacted. Furthermore, participants reported significantly higher presence in the HMD condition.

Sulfur deficiency arising due to intensive cultivation, use of sulfur free fertilizers and reduction in atmospheric sulfur depositions has become a major issue limiting crop production in many parts of the world. Elemental sulfur could be a good source of available S, but its slow oxidation is a problem for its efficient use as a sulfur fertilizer. Main objective of the study was to assess the effect of organic amendments (OA) and elemental sulfur (ES) on microbial activities, sulfur oxidation and availability in soil. A laboratory incubation experiment was carried out for a 56 days period using two sulfur deficient alkaline soils. Organic amendments (OA), i.e., farmyard manure (FYM), poultry litter (PL) and sugarcane filter cake (SF), were applied (1% w/w) with or without elemental sulfur (ES) at 50 mg kg−1. Application of ES alone or in combination with OA significantly increasedCO2-C evolution, microbial biomass, and enzyme activities in the soils, except dehydrogenase activity (DHA) which was not affected by ES application. Combined application of OA and ES had a more pronounced effect on microbial parameters compared to ES or OA applied alone. Ratios of dehydrogenase activity-to-microbial biomass C and arylsulfatase activity-to-microbial biomass C were high in ES+FYM and ES+SF treatments, respectively. Elemental sulfur got sufficiently oxidized resulting in significant improvement in plant available S. Relatively more ES was distributed into C-bonded-S than ester bonded-S. Increase in sulfur availability in ES+OA amended soils was the combined function of sulfur oxidation and mineralization processes through improved microbial activity.

Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

The integration of commercial onshore large-scale wind farms into a national grid comes with several technical issues that predominately ensure power quality in accordance with respective grid codes. The resulting impacts are complemented with the absorption of larger amounts of reactive power by wind generators. In addition, seasonal variations and inter-farm wake effects further deteriorate the overall system performance and restrict the optimal use of available wind resources. This paper presented an assessment framework to address the power quality issues that have arisen after integrating large-scale wind farms into weak transmission grids, especially considering inter-farm wake effect, seasonal variations, reactive power depletion, and compensation with a variety of voltage-ampere reactive (Var) devices. Herein, we also proposed a recovery of significant active power deficits caused by the wake effect via increasing hub height of wind turbines. For large-scale wind energy penetration, a real case study was considered for three wind farms with a cumulative capacity of 154.4 MW integrated at a Nooriabad Grid in Pakistan to analyze their overall impacts. An actual test system was modeled in MATLAB Simulink for a composite analysis. Simulations were performed for various scenarios to consider wind intermittency, seasonal variations across four seasons, and wake effect. The capacitor banks and various flexible alternating current transmission systems (FACTS) devices were employed for a comparative analysis with and without considering the inter-farm wake effect. The power system parameters along with active and reactive power deficits were considered for comprehensive analysis. Unified power flow controller (UPFC) was found to be the best compensation device through comparative analysis, as it maintained voltage at nearly 1.002 pu, suppressed frequency transient in a range of 49.88–50.17 Hz, and avoided any resonance while maintaining power factors in an allowable range. Moreover, it also enhanced the power handling capability of the power system. The 20 m increase in hub height assisted the recovery of the active power deficit to 48%, which thus minimized the influence of the wake effect.

Understanding of soil potassium (K) dynamics is essential for sustainable crop production. Bioavailability of K depends on forms and distribution within the soil profile. The objectives of this research were to determine which soil K forms control the maize (Zea mays) K contents and compare the extracting capability of sodium tetraphenylborate (NaTPB) with ammonium acetate (NH4OAc) method. Nine soils representing three different parent materials, i.e. loess, sandstone and shale were sampled at three surface genetic horizons. Within each parent material, three soils at varying level of development were selected. Besides basic soil parameters, K was fractioned into water soluble K, exchangeable K, non-exchangeable K, and NaTPB-extracted K. The maize was sown in pots having 2 kg soil from each genetic horizon. Crop was harvested at seven weeks and plant was analysed for K contents. Results show that NaTPB-extracted K gave best correlation as compared to NH4OAc method. This conveys that a non-exchangeable K portion that becomes available to plants can be better estimated by NaTPB method than NH4OAc extraction.