Correct Approach Research Articles

Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective

General circulation models are vital tools in climate change research, profoundly influencing numerous hydroclimatological studies, but often exhibit systematic biases due to limitations in parameterization. To correct these biases, bias correction (BC) methods, which are heavily dependent on the quality of historical reference data, are employed. Ideally, accurate data comes from a dense network of gauges, but due to their limited availability, reanalysis data is frequently used instead. Although reanalysis data provides consistent global and temporal coverage, its assimilated nature can introduce additional biases, potentially increasing error propagation particularly in the multivariate perspective. Thus, this study investigates the reliability of reanalysis data as a reference for multivariate bias correction (MBC) and utilizes a two-stage bias correction approach that integrates both gauge and reanalysis data to leverage their respective strengths. Conducting a case study in South Korea, we utilized gauge data, ERA5 reanalysis data, and outputs from 10 CMIP6 General Circulation Models. We implemented three-nested experiments to test the robustness of this two-stage approach across various dimensions. Results show that using reanalysis data as a reference can lead to bias propagation, especially misrepresenting precipitation and wind dependencies in high elevation areas. However, the two-stage approach has shown an enhancement in mitigating these limitations, even when gauge data is temporally sparse, and across different combinations of MBC methods. Finally, we highlight the current limitations and potential for future research to focus on effectively combining gauge and reanalysis data to compensate for each other’s weaknesses, thereby enhancing the accuracy and reliability in future climate realizations.

Эректильная дисфункция: современное состояние проблемы, методы диагностики и рентгенэндохирургического лечения

According to the Russian Society of Urologists, almost half of men aged 27 to 77 suffer from erectile dysfunction. ED should be considered not only as a medical nosology, but also as a socio-psychological problem with a demographic aspect. There are many methodsfor improving erectile function, including psychological consultations, drug therapy and penile prosthetics. Given the accumulated international experience,the key pointis an accurate diagnosis ofthe cause of erectile dysfunction to determine the correct approach to treatment. Among the many causes of erectile dysfunction, vascular causes are quite common. Given the rapid development of endovascular and low-traumatic approachesto vascular pathology,the technique of improving inflow and outflow obstruction in vasculogenic erectile dysfunction is becoming increasingly obvious and effective. The presented article discussesthe currentstate ofthe issue and possible prospectsfor endovascularsurgery for vascular erectile dysfunction, including significant venous leakage.

Continuity of long-term climate data series after the transition from manual to automatic weather station

In climatology, transitioning from conventional manual weather stations (MWS) to automatic weather stations (AWS) presents challenges in maintaining the homogeneity of long-term data series. At the Skalnaté Pleso Observatory (1778 m a.s.l., High Tatras), manual meteorological measurements have been conducted since 1943 using the same methods and devices at the same location. In 2014, the AWS Physicus was installed to record meteorological data parallelly with manual measurements. This study processed six years of parallel measurements (2017–2022) to derive corrections for AWS monthly atmospheric precipitation totals and maximum (Tmax), minimum (Tmin) and mean air temperatures (Tmean) measured in 2023. Two correction approaches were proposed: monthly regressions (MR) and cumulative distribution functions (CDF), including Generalized Extreme Value (GEV) distribution, normal distribution (GAUSS), and Gamma distribution (GAMMA). Analyses of monthly data revealed an underestimation of precipitation totals in AWS data, with a mean bias error (MBE) of −6.8 mm and a root mean squared error (RMSE) of 20.0 mm. The most suitable correction method was the MR, which decreased MBE to 1.5 mm and RMSE to 11.1 mm. AWS monthly air temperature data were overestimated by 0.1 °C, 0.3 °C, and 0.1 °C for Tmax, Tmin, and Tmean, respectively. For Tmax, correction using the MR and GEV methods reduced the MBE to 0.0 °C and achieved RMSE of 0.1 °C for both. After applying GAUSS, MBE and RMSE decreased to 0.1 °C. The most appropriate correction method for Tmin was the MR resulting in MBE of 0.0 °C and RMSE of 0.3 °C. For Tmean, the MR and GEV methods reduced the MBE to 0.0 °C and the RMSE to 0.1 °C for both methods. These results demonstrate that AWS monthly data, when corrected using the presented methods, have the potential to maintain the continuity of historical climate data series at Skalnaté Pleso.

Integration of clotting and fibrinolysis: central role of platelets and factor XIIIa.

The aim of the present study was to establish the role of platelets and activated factor XIIIa (FXIIIa) in the structuring of the fibrin network as well as to clarify the effect of network compaction on clot lysis. Turbidimetry was used for the one-stage clotting test where platelet-free plasma (PFP) is regarded as single factor-deficient plasma (platelets as lacking factor) and autologous platelet-rich plasma (PRP) as deficiency corrected plasma. Structural features of the developed and subsequently lysed fibrin network, formed under static and flow conditions, were visualized by confocal microscopy. Thrombin-initiated plasma clotting revealed changes in the shape of the absorption curve, more pronounced in the presence of platelets. These changes correlate with the transformation of the fibrin scaffold during clot maturing. With the combined action of platelets, thrombin and Ca2+, plasma clotting passes through two phases: initial formation of a platelet-fibrin network (first peak in the polymerization curve), and then the compaction of fibrin, driven by FXIIIa (the second peak) which can be further modulate by the contractile action of platelets. These structural changes, mediated by platelets and FXIIIa, have been shown to determine subsequent clot lysis. Platelet aggregates serve as organizing centers that determine the distribution of fibrin in clot volume. The openwork structure of the platelet-transformed fibrin provides the necessary prerequisites for its timely lysis. The revealed aspects of the interaction of platelets and FXIIIa, which accompanies the maturation of a fibrin clot, may lead to new approaches in the pharmacological correction of disorders associated with both thrombotic episodes and bleeding tendency.

Company reputation and dividend payout

Purpose The purpose of this paper is to examine the association between company reputation and dividend policy. Design/methodology/approach In this study, sample of 98,809 firm-year observations from 22 countries covering 2005–2016 were used. Findings Firm reputation concerns are associated with higher propensities to pay dividends and payout ratios. Further, this positive effect is more pronounced for firms with high free cash flows, high information asymmetry and low institutional monitoring. The results are robust to an instrumental variable approach, propensity score matching and the Heckman two-stage correction approach while addressing endogeneity concerns. Practical implications These findings have significant implications for various stakeholders, such as existing and potential investors, managers, policymakers and regulators, by providing insights into the relationship between corporate reputation and firm dividend payout decisions. Corporate reputation is highlighted as crucial for accessing finance, emphasizing the role of national regulators and policymakers in facilitating firms' efforts to improve their reputation. The study highlights the dynamics of corporate reputation and dividend payout, calling for proactive engagement from regulators and policymakers. Crafting policies conducive to reputation-building can enhance firms' financial prospects, indicating the need for strategic interventions at managerial, regulatory and policy levels. Understanding the influence of economic context is crucial for firms to tailor reputation management strategies and optimize funding opportunities in different economic environments. Originality/value Overall, results suggest that reputation serves as a disciplining mechanism, where firms will pay dividends to maintain their reputations.

Altered microRNA composition in the uterine lumen fluid in cattle (Bos taurus) pregnancies initiated by artificial insemination or transfer of an in vitro produced embryo

BackgroundMicroRNAs (miRNAs) are presented in the uterine lumen of many mammals, and in vitro experiments have determined that several miRNAs are important for the regulation of endometrial and trophoblast functions. Our aim was to identify and contrast the miRNAs present in extracellular vesicles (EVs) in the uterine lumen fluid (ULF) at the onset of attachment in cattle pregnancies (gestation d 18) initiated by artificial insemination (AI) or by the transfer of an in vitro-produced blastocyst (IVP-ET). A third group had no conceptus after the transfer of an IVP embryo.ResultsThe abundance of 263 annotated miRNAs was quantified in the EVs collected from ULF. There was an increase in the transcript abundance of 20 miRNAs in the ULF EVs from the AI pregnant group, while 4 miRNAs had a lower abundance relative to the group not containing a conceptus. Additionally, 4 miRNAs were more abundant in ULF EVs in the AI pregnant group relative to IVP-ET group (bta-mir-17, bta-mir-7-3, MIR7-1, MIR18A). Specific miRNAs in the ULF EVs were co-expressed with messenger RNAs expressed in extra-embryonic tissues and endometrium, including genes that are known to be their targets.ConclusionsThe results provide biological insights into the participation of miRNAs in the regulation of trophoblast proliferation and differentiation, as well as in endometrium receptivity. The knowledge that in vitro cultured embryos can contribute to the altered abundance of specific miRNAs in the uterine lumen can lead to the development of corrective approaches to reduce conceptus losses during the first month of pregnancy in cattle.

CNN-based approach for 3D artifact correction of intensity diffraction tomography images

CNN-based approach for 3D artifact correction of intensity diffraction tomography images

Attenuated color channel adaptive correction and bilateral weight fusion for underwater image enhancement

Due to the absorption and scattering of light and the influence of suspended particles, underwater images commonly exhibit color distortions, reduced contrast, and diminished details. This paper proposes an attenuated color channel adaptive correction and bilateral weight fusion approach called WLAB to address the aforementioned degradation issues. Specifically, a novel white balance method is first applied to balance the color channel of the input image. Moreover, a local-block-based fast non-local means method is proposed to obtain a denoised version of the color-corrected image. Then, an adaptive stretching method that considers the histogram's local features to get a contrast-enhanced version of the color-corrected image. Finally, a bilateral weight fusion method is proposed to fuse the above two image versions to obtain an output image with complementary advantages. Experimental studies are conducted on three benchmark underwater image datasets and compared with ten state-of-the-art methods. The results show that WLAB has a significant advantage over the comparative methods. Notably, WLAB exhibits a degree of independence from camera settings and enhances the precision of various image processing applications, including key points and saliency detection. Additionally, it demonstrates commendable adaptability in improving low-light and foggy images.

Genomics Insights into Mycolicibacterium Hassiacum Causing Infection in a Cat with Pyogranulomatous Dermatitis and Panniculitis.

Mycolicibacterium hassiacum (homotypic synonym: Mycobacterium hassiacum) represents an ungrouped thermotolerant rapidly growing mycobacteria (RGM) species occasionally associated with infections and disease in humans. In this report, we describe a case of pyogranulomatous dermatitis and panniculitis due to M. hassiacum in an immunocompetent adult cat. To the best of our knowledge, this represents the first report of M. hassiacum infection in animals. We also report the results of the in-depth genome characterization of the isolate using a combined short- and long-read whole-genome sequencing (WGS) approach. We observed the lack of acquired-resistance genes and no evidence of mutations in housekeeping genes associated with resistance to rifampicin and isoniazid. We detected some virulence factors in our isolate, such as some associated with the interaction of mycobacteria with host cells, and the presence of multiple copies of heavy metal resistance genes (arsB, arsR, and arsL/cadL). In conclusion, M. hassiacum should be included among the RGM species associated with feline subcutaneous atypical mycobacteriosis (SAM). A reliable and fast RGM laboratory identification and characterization is important not only for an accurate etiological diagnosis but also for a correct approach to SAM treatment options.

Evaluation of Evidence-Based Practice Competency among Greek Undergraduate Nursing Students.

For several years, the global scientific community has accepted and recognized the importance of evidence-based practice for nursing science. The main factor for the implementation of evidence-based practice is the competence of undergraduate nursing students towards evidence-based practice, so that they as active nurses are ready for its application in their clinical practice, in order to provide better care for their patients. The aim of the present study is to examine the level of evidence-based practice competency evaluated with the self-reported Evidence-Based Practice Competence Questionnaire. It is important to mention that such a study has not been conducted on nursing students in Greece, but only on registered nurses. A quantitative study based on cross-sectional design was conducted from February to June 2022 in a convenience sample of Greek undergraduate nursing students. The SPSS 26.0 program was used to perform descriptive, bivariate, and multivariate analyses. A total of 175 undergraduate students participated at the Hellenic Mediterranean University Department of Nursing (Greece), specifically from the 2nd and 3rd academic years. The mean score of the Evidence-Based Practice Competence Questionnaire for nursing undergraduate students was 3.03 ± 0.26, indicating moderate evidence-based practice. Among the Evidence-Based Practice Competence Questionnaire dimensions, mean scores of 3.03 ± 0.32 for attitude toward evidence-based practice, 3.01 ± 0.49 for skills in evidence-based practice, and 3.03 ± 0.49 for knowledge in evidence-based practice were obtained. Significant differences among participants' mean Evidence-Based Practice Competence Questionnaire scores regarding gender (p = 0.766), age (p = 0.400), academic year (p = 0.153), and training in the field of research methodology (p = 0.538) were not found. It appears that the level of readiness towards evidence-based practice is mediocre among undergraduate nursing students at a university in Greece. Therefore, it is necessary to carry out new studies in the future, so that there is a correct approach of all the elements that contribute to the readiness of nursing undergraduate students regarding evidence-based practice.

Omocisteina alta? Quello che il pediatra deve sapere

Hyperhomocysteinemia may cause concern to the paediatrician due to its correlation with increased cardiovascular risk. In paediatric age, the most common cause is a nutritional vitamin deficiency, particularly vitamin B12 or folate, which can be found in subjects who follow a lacto-ovo-vegetarian diet or one devoid of animal-derived foods for socioeconomic, cultural, or religious reasons. Hyperhomocysteinemia is not uncommon in infants born to mothers with vitamin deficiency and such condition can lead to their neurodevelopmental impairment. Genetic causes are rare. Therefore, a correct diagnostic and therapeutic approach is essential, so that to allow the paediatrician to identify and manage most cases of hyperhomocysteinemia in the outpatient setting, as well as to recognize the red flags for severe disease that require a specialist evaluation.

Values of Coexistence in Islamic Civilization: The Relationship with Others and the Challenge of Extremism

This study employs a descriptive-analytical method to emphasize the genuineness and importance of peaceful coexistence values among religions and cultures in Islamic civilization. It specifically focuses on the interaction with others, living together, acknowledging, respecting, and collaborating with them in different aspects of shared humanity. This research examines the language of extremism by studying and analyzing it, seeing it as the primary obstacle to the principles of civic coexistence and tolerance. The research findings indicate that the key factor that sets apart the principles of peaceful coexistence among individuals, irrespective of their religious and racial backgrounds, and that necessitates the acceptance of others in Islamic ideology, is the perception of such acceptance as legitimate obligations. An individual is recognized as a Muslim only when they openly acknowledge their adherence to the Islamic faith. Through the analysis of holy writings and the Prophet's biography, the researcher concludes that any radical and divisive rhetoric that promotes the rejection of peaceful coexistence cannot be regarded as Islamic speech, and does not accurately reflect Islam and Muslims. Instead, it is an extreme rhetoric that only represents its advocates, a rhetoric that Islam disavows, just as it disavows other forms of hate speech. The research urges the official religious authorities in Arab and Islamic countries to take on the legitimate, ethical, and scientific duty of freeing religious matters from ideological manipulation and restoring the correct approach to interpreting religious texts, while avoiding extremist ideological exploitation. This proposal advocates for the promotion of harmony, resolution of misunderstandings, fostering love and unity, prevention of excommunication, defamation, and conflict. It suggests the establishment of educational programs focused on promoting peace and coexistence values, as well as encouraging research in the field of tolerance education and acceptance of others. The objective is to cultivate an intellectual atmosphere in our universities that prioritizes wisdom, reason, logic, and evidence over emotional slogans. This will result in universities producing graduates who possess values of tolerance and coexistence, equipped to challenge and dismantle hateful and extremist ideologies by exposing their flaws and removing the religious facade from their harmful ideas.

Multivariate bias correction and downscaling of climate models with trend-preserving deep learning

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

Evaluation of the prostate cancer and its metastases in the [ 68 Ga]Ga-PSMA PET/CT images: deep learning method vs. conventional PET/CT processing.

This study demonstrates the feasibility and benefits of using a deep learning-based approach for attenuation correction in [ 68 Ga]Ga-PSMA PET scans. A dataset of 700 prostate cancer patients (mean age: 67.6 ± 5.9 years, range: 45-85 years) who underwent [ 68 Ga]Ga-PSMA PET/computed tomography was collected. A deep learning model was trained to perform attenuation correction on these images. Quantitative accuracy was assessed using clinical data from 92 patients, comparing the deep learning-based attenuation correction (DLAC) to computed tomography-based PET attenuation correction (PET-CTAC) using mean error, mean absolute error, and root mean square error based on standard uptake value. Clinical evaluation was conducted by three specialists who performed a blinded assessment of lesion detectability and overall image quality in a subset of 50 subjects, comparing DLAC and PET-CTAC images. The DLAC model yielded mean error, mean absolute error, and root mean square error values of -0.007 ± 0.032, 0.08 ± 0.033, and 0.252 ± 125 standard uptake value, respectively. Regarding lesion detection and image quality, DLAC showed superior performance in 16 of the 50 cases, while in 56% of the cases, the images generated by DLAC and PET-CTAC were found to have closely comparable quality and lesion detectability. This study highlights significant improvements in image quality and lesion detection capabilities through the integration of DLAC in [ 68 Ga]Ga-PSMA PET imaging. This innovative approach not only addresses challenges such as bladder radioactivity but also represents a promising method to minimize patient radiation exposure by integrating low-dose computed tomography and DLAC, ultimately improving diagnostic accuracy and patient outcomes.

The Wheel–Rail Contact Force for a Heavy-Load Train Can Be Measured Using a Collaborative Calibration Algorithm

The wheel–rail contact force is a crucial indicator for ensuring the secure operation of a heavy-load train. However, obtaining the real-time wheel–rail contact force of a heavy-load train is a challenging task as, due to safety considerations, it is not possible to install instrumented wheelsets on heavy-load trains. This work presents a novel approach to quantify the wheel–rail contact force of a heavy-load train, utilizing a cooperative calibration methodology. First, a ground measurement platform for the wheel–rail contact force of a heavy-load train is constructed on a selected rail section. The railway inspection car’s wheel–rail contact force measurement system is fine-tuned using a multilayer perceptron calibration approach, and the ground platform then uses the results to fine-tune the railway inspection car’s examined wheelset. Second, based on actual measured data on the wheel–rail contact force of a heavy-load train, and using the golden jackal optimization algorithm, the multilayer perceptron correction approach is employed to create a data relationship mapping model. This model correlates the corrected data on the wheel–rail contact force obtained from the railway inspection car with the wheel–rail contact force of a heavy-haul train with an axle load of 25 tons, and the precision of the mapping is guaranteed. Finally, by combining the wheel–rail contact force correction method for the railway inspection car and the contact force mapping model between the railway inspection car and the heavy-load train, collaborative calibration of the wheel–rail contact force of the heavy-load train is realized. The experimental results under two working conditions show that this method can realize high-precision, real-time measurement of the wheel–rail contact force of a heavy-load train. For the working condition of a straight-line section, the calibration error was within 1.593 kN, and the MAPE was 0.105%; for the working condition of a curved-line section, the calibration error was 2.344 kN, and the RMSE was 184.72 N.

Refinement of Modified Endoscopic Lateral Soft-Tissue Procedure for Correction of Hallux Valgus Deformity

Hallux valgus is a common orthopaedic problem, and surgical correction frequently is indicated for symptomatic deformity. The modified endoscopic distal soft-tissue procedure has been reported to provide a minimally invasive approach of hallux valgus correction with augmentation of the medial metatarsosesamoid suspensory ligament and intermetatarsal ligament. The purpose of this Technical Note is to describe the refinements of the modified endoscopic distal soft-tissue procedure to achieve more anatomical augmentation of the medial metatarsosesamoid ligament and intermetatarsal ligament, which can provide more stable reconstruction and prevent stiffness of the first metatarsophalangeal joint.

Correction‐enabled reversible data hiding with pixel repetition for high embedding rate and quality preservation

AbstractA novel correction‐enabled Pixel Repetition (PR)‐based Reversible Data Hiding (RDH) framework, featuring a new embedding scheme is presented. The proposed RDH scheme uses contextually redundant block pixels, generated via PR, in a two‐phase adaptive embedding process, enhancing both image quality and data embedding rates. Specifically, each block encodes 4 bits of data using new mapping conditions that facilitate seed pixel reconstruction from remaining block pixels and provide additional embedding opportunities. Additionally, an innovative post‐embedding error correction technique, based on ‐bit error‐correction, minimises post‐embedding distortion, further improving image quality. This error correction approach augments data embedding robustness, vital for applications like medical imaging, telemedicine, and digital watermarking that requires high embedding capacity with minimum possible distortion. The proposed scheme surpasses existing state‐of‐the‐art methods in embedding rate‐distortion performance, validated through subjective and objective analyses. Furthermore, statistical analysis, including histogram and fragility testing, confirms the scheme's potential for image authentication across diverse multimedia applications. The correction‐enabled RDH with PR offers enhanced embedding capacity and image quality preservation, making it particularly advantageous for applications requiring robust data hiding while maintaining visual fidelity.

Multiscale Approach for Variational Problem Joint Diffeomorphic Image Registration and Intensity Correction: Theory and Application

Multiscale Approach for Variational Problem Joint Diffeomorphic Image Registration and Intensity Correction: Theory and Application

Automated External Bone Fixation Robot Design and Orthopedic Analysis

Background: Bone external fixation technology is an important therapeutic approach for limb correction, primarily involving a class of external fixation correction mechanisms. It has achieved good results in limb lengthening and correcting deformities to restore limb function. In clinical practice, existing correction mechanisms face challenges such as high resistance between components, complex installation procedures, and high precision requirements for installation, requiring manual adjustment by physicians, which limits precise control and quantification of correction parameters. Objective: To address these issues, an automated correction bone external fixation robot was designed based on traditional Ilizarov external fixation devices(TIEFD). Methods: By increasing the number of unidirectional hinge connections, the robot can effectively reduce motion resistance. Subsequently, the robot was combined with the tibia for correction motion simulation, obtaining the motion parameters θ of the deformed bones during the correction process based on the trajectory of the tibia's point mass. Finally, correction motion experiments and finite element analysis(FEA) were conducted on the robot combined with a control system. Results: The results showed that the robot could precisely control correction parameters, with the error range for rotational correction not exceeding 0.5 radians and the error range for traction correction not exceeding 0.2 mm. FAE, based on corrective force data, concluded that compared to (TIEFD), the robot could reduce bone stress by 16 MPa during the correction process. Conclusion: The robot effectively reduces patient discomfort, this provides a reliable theoretical basis for bone external fixation technology and holds positive significance for the treatment of skeletal deformities.

Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery

Generating granular-scale surface albedo data is extremely important for solar photovoltaic site planning and to optimise renewable energy yield of bifacial panel installations. The albedo effect brings about a significant increase in power in bifacial photovoltaic systems, compared to their mono-facial counterparts, since the spectral response of bifacial solar panels correlates with the incident solar radiation wavelength on the back of the panel, to provide additional power generation capacity. Thus, harnessing the albedo data at relatively local scales is critical towards boosting solar power generation and providing greater power density in local electricity grids. This paper develops novel modelling approaches to produce high-resolution spectral albedo imagery across the Visible and Near Infrared (VNIR) bands, using the Wavelet-Fusion super-resolution model (i.e., Wavelet-FusionSR) trained with the Learned Gamma Correction approach by applying satellite image enhancement methodology. The proposed Wavelet-FusionSR model utilises the low-resolution moderate-resolution Imaging Spectroradiometer (MODIS) as well as high-resolution multi-spectral Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) satellite images, as critical inputs and ground-truth imagery, respectively, in order to perform sensor-to-sensor deep downscaling, without employing any synthetic or low-resolution satellite imagery data pairs. To augment the proposed deep learning algorithm across the decomposed sub-images of low-resolution inputs, we integrate local and global feature representation learning to train the proposed Wavelet-FusionSR model with Cauchy loss functions. In comparison with five competing benchmark models, the proposed Wavelet-FusionSR model demonstrates performance superiority using quantitative image downscaling metrics and visual assessments of the downscaled images for the visible band of solar radiation. The proposed Wavelet-FusionSR model yielded a Mean Square Error (MSE) of 0.00017, Signal-to-noise-ratio (PSNR) of 37.80, Structural Similarity Index (SSIM) of 0.999 and combined loss, MS-SSIMLoss, based on Multi Structural Similarity and Mean Absolute Error of 2.354 for the Visible Band images, and an MSE of 0.0014, PSNR of 28.43, SSIM of 0.999 and MS-SSIMLoss of 7.426 for the NIR spectral bands, demonstrating high efficacy of the proposed Wavelet-FusionSR method. The Wavelet-FusionSR method therefore attains high-resolution spectral albedo imagery outputs.