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Purpose In the era of hyper-competitiveness, firms, especially project-based management structures, have to focus on ideas for both new and existing sets of products and services, i.e. ambidextrous innovation. The ambidextrous innovation can be helpful, but achieving such a level is a problem to be solved. This study aims to yield ambidextrous innovation by using innovative culture and knowledge that has been gained from learning. Design/methodology/approach The present research collected data from Saudi Arabian public-sector firms. The data collected is analyzed using the partial least squares structural equation modeling (PLS-SEM). Findings The findings of the study suggest that a range of factors can be operationalized in project-based firms to establish organizational learning and innovation culture. These factors include agile-based project management, leveraging existing innovative capabilities and growth mindset in case of innovative organizational culture and additional factors of agile-based knowledge management along with others in case of organizational learning. The PLS-SEM further concluded that both organizational learning and innovative organizational culture, in turn, help project-based Saudi Arabian public-sector firms to develop their ambidextrous innovation capability. Originality/value The PLS-SEM further concluded that both the organizational learning and innovative organizational culture, in turn, help project-based Saudi Arabian public-sector firms to develop their ambidextrous innovation capability.

AimWe compare the incidence rates of cataract in persons with diabetes with and without diabetic retinopathy in Saudi Arabia, for the first time. In addition, we explored the role of new factor, diabetes age of onset and several other known factors.MethodsIn a community-based cross-sectional study, 334 persons with diabetes type 2 were randomly selected from a diabetic register. Detailed history and comprehensive ophthalmic examination was done at an eye clinic. Body Mass Index, blood pressure and glycosylated hemoglobin were also recorded.ResultsIn 668 eyes, cataract and diabetic retinopathy were present in 35.5% and 32.2%, respectively. Diabetic retinopathy, age, duration of diabetes and systolic BP were found to be independent risk factors for cataract. Whereas, gender, BMI, HbA1c use of insulin and diastolic BP have no significant association with cataract. Persons with cataract had significantly higher age of onset of diabetes. Most of the cataracts were cortical followed by PSC, while minority were nuclear.ConclusionDR is an independent risk factor of developing cataract in persons with diabetes. Others are age, duration of DM and hypertension. Age-of-onset of DM is a new factor we report it to be significantly associated with cataract.

Mg–Al–Zn alloys are widely preferred in many applications by considering their excellent properties of high stiffness-to-weight ratio, lightweight, high strength-to-weight ratio, low density, castability, high-temperature mechanical properties, machinability, high corrosion resistance, and great damping. Improving the properties of such alloys is challenging due to their hexagonal crystal structure and other alloying limitations. This study aims to synthesize Mg–Al–Zn alloy by incorporating the alloying elements 8.3 wt% Al, 0.35 wt% Zn on pure magnesium (Control specimen). Then synthesize Mg–Al–Zn/BN/B 4 C hybrid composite by reinforcing B 4 C at three weight proportions (3 wt%, 6 wt%, 9 wt%) along with constant solid lubricant BN (3 wt%) through a stir casting process. The hybrid composite samples were characterized and compared with the performances of the control specimen. The results reveal that 9 wt% B 4 C reinforced samples outperformed through recording the improvement of tensile strength by 28.94%, compressive strength by 37.89%, yield strength by 74.63%, and hardness by 14.91% than the control specimen. Apart from this, it has reduced the corrosion area (37.81%) and noticed negligible changes in density (increased by 0.03%) and porosity (decreased by 0.01%) than the control specimen. The samples were characterized using SEM, XRD, and EDAX apparatus.

Lubrication has become essential in enhancing engine efficiency in the era of rapid globalising. The tribological, oxidation and thermal conductivity properties of an engine oil play a vital role in improving the quality of a vehicle’s engine life. In this research, molybdenum disulfide (MoS 2 ) nanoparticle was synthesised via a microwave hydrothermal reactor. Later, the nanoparticles were dispersed in SAE 20W50 diesel engine oil to formulate the nanolubricant. The results show that nanolubricant with 0.01 wt% MoS 2 concentration showed the coefficient of friction, average wear scar diameter decreased by 19.24% and 19.52%, respectively, compared to the base oil. Furthermore, the nanolubricant with 0.01 wt% concentration of MoS 2 nanoparticle showed an enhancement of 61.15% in oxidation induction time in comparison to the base oil. Furthermore, MoS 2 addition within the base oil demonstrates a ~ 10% improvement in thermal conductivity compared to the base oil.

Bone metastases, a common and debilitating consequence of advanced cancers, involve a complex interplay between malignant cells and the bone microenvironment. Central to this interaction are interleukins (ILs), a group of cytokines with critical roles in immune modulation and inflammation. This review explores the dualistic nature of pro-inflammatory and anti-inflammatory interleukins in bone metastases, emphasizing their molecular mechanisms, pathological impacts, and therapeutic potential. Pro-inflammatory interleukins, such as IL-1, IL-6, and IL-8, have been identified as key drivers in promoting osteoclastogenesis, tumor proliferation, and angiogenesis. These cytokines create a favorable environment for cancer cell survival and bone degradation, contributing to the progression of metastatic lesions. Conversely, anti-inflammatory interleukins, including IL-4, IL-10, and IL-13, exhibit protective roles by modulating immune responses and inhibiting osteoclast activity. Understanding these opposing effects is crucial for developing targeted therapies aimed at disrupting the pathological processes in bone metastases. Key signaling pathways, including NF-κB, JAK/STAT, and MAPK, mediate the actions of these interleukins, influencing tumor cell survival, immune cell recruitment, and bone remodeling. Targeting these pathways presents promising therapeutic avenues. Current treatment strategies, such as the use of denosumab, tocilizumab, and emerging agents like bimekizumab and ANV419, highlight the potential of interleukin-targeted therapies in mitigating bone metastases. However, challenges such as therapeutic resistance, side effects, and long-term efficacy remain significant hurdles. This review also addresses the potential of interleukins as diagnostic and prognostic biomarkers, offering insights into patient stratification and personalized treatment approaches. Interleukins have multifaceted roles that depend on the context, including the environment, cell types, and cellular interactions. Despite substantial progress, gaps in research persist, particularly regarding the precise mechanisms by which interleukins influence the bone metastatic niche and their broader clinical implications. While not exhaustive, this overview underscores the critical roles of interleukins in bone metastases and highlights the need for continued research to fully elucidate their complex interactions and therapeutic potential. Addressing these gaps will be essential for advancing our understanding and treatment of bone metastases in cancer patients.

Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared (IR)/visible (VS) images. Dissimilarities in various kind of features in these images are vital to preserve in the single fused image. Hence, simultaneous preservation of both the aspects at the same time is a challenging task. However, most of the existing methods utilize the manual extraction of features; and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image. Therefore, this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images. Firstly, fuzzification of two IR/VS images has been done by feeding it to the fuzzy sets to remove the uncertainty present in the background and object of interest of the image. Secondly, images have been learned by two parallel branches of the siamese convolutional neural network (CNN) to extract prominent features from the images as well as high-frequency information to produce focus maps containing source image information. Finally, the obtained focused maps which contained the detailed integrated information are directly mapped with the source image via pixel-wise strategy to result in fused image. Different parameters have been used to evaluate the performance of the proposed image fusion by achieving 1.008 for mutual information (MI), 0.841 for entropy , 0.655 for edge information (EI), 0.652 for human perception (HP), and 0.980 for image structural similarity (ISS). Experimental results have shown that the proposed technique has attained the best qualitative and quantitative results using 78 publically available images in comparison to the existing discrete cosine transform (DCT), anisotropic diffusion & karhunen-loeve (ADKL), guided filter (GF), random walk (RW), principal component analysis (PCA), and convolutional neural network (CNN) methods.

The treatment of patients with advanced cancer poses clinical problems due to the complications that arise as the disease progresses. Bone metastases are a common problem that cancer patients may face, and currently, there are no effective drugs to treat these individuals. Prostate, breast, and lung cancers often spread to the bone, causing significant and disabling health conditions. The bone is a highly active and dynamic tissue and is considered a favorable environment for the growth of cancer. The role of osteoblasts and osteoclasts in the process of bone remodeling and the way in which their interactions change during the progression of metastasis is critical to understanding the pathophysiology of this disease. These interactions create a self-perpetuating loop that stimulates the growth of metastatic cells in the bone. The metabolic reprogramming of both cancer cells and cells in the bone microenvironment has serious implications for the development and progression of metastasis. Insight into the process of bone remodeling and the systemic elements that regulate this process, as well as the cellular changes that occur during the progression of bone metastases, is critical to the discovery of a cure for this disease. It is crucial to explore different therapeutic options that focus specifically on malignancy in the bone microenvironment in order to effectively treat this disease. This review will focus on the bone remodeling process and the effects of metabolic disorders as well as systemic factors like hormones and cytokines on the development of bone metastases. We will also examine the various therapeutic alternatives available today and the upcoming advances in novel treatments.

Proboscis lateralis is an uncommon craniofacial malformation characterized by the presence of a rudimentary tubular nasal appendage originating from the medial canthal region. The degree of severity varies according to the extent of associated structural involvement, which may include ocular and nasal abnormalities, cleft lip and/or palate, and concurrent intracranial anomalies. Multidisciplinary approach is the mainstay of definite management. We report the case of a 4-month-old infant who presented with a 3-cm tubular lesion arising from the right medial canthus. The lesion contained a central opening with intermittent cerebrospinal fluid leakage, particularly during episodes of crying or straining. The patient was initially misdiagnosed with an ophthalmic disorder and underwent multiple referrals before definitive evaluation at our unit. Computed tomography revealed herniation of a portion of the right frontal lobe through a 2 × 1 cm bony defect into the preseptal region. The lesion was excised, and the fascial defect was repaired following plastic and reconstructive surgical principles. Postoperative recovery was uneventful, and the patient was discharged in stable condition 48 h later. Corrective management of the right hypoplastic nostril was deferred until late adolescence to address potential functional or cosmetic concerns.

FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results.