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Sustainable supply chain management (SSCM) has been a tough challenge for developing economies like Saudi Arabia. Implementation of SSCM practices in the manufacturing industry has been prone to multiple risk factors that need to be identified, evaluated, and prioritized especially considering the dynamics of the manufacturing industry in a developing economy. Moreover, it is also imperative to trace out feasible and sustainable strategies to overcome the risks to SSCM practices adoption. This study serves this purpose and identifies, evaluates, prioritizes the risk factors, sub-factors, and strategies to overcome these risk factors in the implementation of SSCM practices in the manufacturing industry in Saudi Arabia. An integrated multi-criteria decision analysis approach by combining fuzzy AHP and fuzzy WASPAS methods is employed for the analyses. The fuzzy AHP analysis results show that economic risks are dominant risks followed by the managerial policy risks and environmental risks in implementing SSCM. Industrial emissions are the leading risk factors in the overall ranking of the sustainable supply chain sub-risk factors followed by market dynamics, management policy failures, financial constraints, and credit uncertainty. While evaluating the sustainable supply chain strategies using fuzzy WASPAS, it is concluded that commitment and support of top, middle, and lower level management is the most pivotal strategy to deal with the risks to SSCM in Saudi Arabia followed by establishing environmental policies and goals to adopt SSCM, and provision of the financial resources and subsidies.

A cohort investigation evaluated patient demographic information together with microbial detection patterns and antimicrobial drug resistance in a total group of 2,098 patients. Results showed an equally distributed sample consisting of 51.8% inpatients matched with 47.2% outpatients. In comparison, females comprised 51.8% of the total participants, and most patients fell within middle age categories and the elderly (58.5% aged 41–80 years). The patient demographic data showed that among 2,098 cases (14.7% were between 81 and 100 years old, and 1.2% reached the centenarian category thus demonstrating an increasing demand for geriatric healthcare services. Urine samples produced (72.9%) bacterial isolates above the combination of blood cultures and wound specimens (each with 9.7%). The comparison between 2022 and 2023 proved noteworthy, with healthcare facility admission rates escalating from 316 to 771 patient cases while outpatient walk-in numbers decreased from 587 to 403, along with a substantial rise in urinary tract infection isolates from 648 to 883 cases. The tests on antimicrobial resistance demonstrated that Imipenem (95%), along with Tigecycline (97%), were effective, but Norfloxacin (97.1%) and Cotrimoxazole (46%) had notable resistance patterns. The results showed an ESBL detection rate of 312 among Escherichia coli isolates, but MBL counts reached 83, and AmpC beta-lactamase production amounted to 142. blaCTX-M emerged as the leading ESBL gene type (38.1%) among a total of 142 collected E. coli strains, which also demonstrated blaOXA-48 (25.3%) and blaNDM (22.7%) as their prevalent MBL genes. Multiple resistance genes have become more prevalent in MBL isolates, as demonstrated by the simultaneous presence of blaVIM and blaNDM genes in (5.3%) of MBL isolates. The research results demonstrate the immediate requirement for better-controlling mechanisms in antimicrobial use and improved infection surveillance and control procedures following the detection of emerging resistance pathogens.

Cancer is a group of disorders characterized by uncontrolled cell growth that affects around 11 million people each year globally. Nanocarrier-based systems are extensively used in cancer imaging, diagnostics as well as therapeutics; owing to their promising features and potential to augment therapeutic efficacy. The focal point of research remains to develop new-fangled smart nanocarriers that can selectively respond to cancer-specific conditions and deliver medications to target cells efficiently. Nanocarriers deliver loaded therapeutic cargos to the tumour site either in a passive or active mode, with the least drug elimination from the drug delivery systems. This review chiefly focuses on current advances allied to smart nanocarriers such as dendrimers, liposomes, mesoporous silica nanoparticles, quantum dots, micelles, superparamagnetic iron-oxide nanoparticles, gold nanoparticles and carbon nanotubes, to list a few. Exhaustive discussion on crucial topics like drug targeting, surface decorated smart-nanocarriers and stimuli-responsive cancer nanotherapeutics responding to temperature, enzyme, pH and redox stimuli have been covered.

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital.MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

Bionanotechnology is a branch of science that has revolutionized modern science and technology. Nanomaterials, especially noble metals, have attracted researchers due to their size and application in different branches of sciences that benefit humanity. Metal nanoparticles can be synthesized using green methods, which are good for the environment, economically viable, and facilitate synthesis. Due to their size and form, gold nanoparticles have become significant. Plant materials are of particular interest in the synthesis and manufacture of theranostic gold nanoparticles (NPs), which have been generated using various materials. On the other hand, chemically produced nanoparticles have several drawbacks in terms of cost, toxicity, and effectiveness. A plant-mediated integration of metallic nanoparticles has been developed in the field of nanotechnology to overcome the drawbacks of traditional synthesis, such as physical and synthetic strategies. Nanomaterials′ tunable features make them sophisticated tools in the biomedical platform, especially for developing new diagnostics and therapeutics for malignancy, neurodegenerative, and other chronic disorders. Therefore, this review outlines the theranostic approach, the different plant materials utilized in theranostic applications, and future directions based on current breakthroughs in these fields.

Acacia seyal is an important source of gum Arabic. The availability, traditional, medicinal, pharmaceutical, nutritional, and cosmetic applications of gum acacia have pronounced its high economic value and attracted global attention. In addition to summarizing the inventions/patents applications related to gum A. seyal, the present review highlights recent updates regarding its phytoconstituents. Traditional, cosmetic, pharmaceutical, and medicinal uses with the possible mechanism of actions have been also reviewed. The patent search revealed the identification of 30 patents/patent applications of A. seyal. The first patent related to A. seyal was published in 1892, which was related to its use in the prophylaxis/treatment of kidney and bladder affections. The use of A. seyal to treat cancer and osteoporosis has also been patented. Some inventions provided compositions and formulations containing A. seyal or its ingredients for pharmaceutical and medical applications. The inventions related to agricultural applications, food industry, cosmetics, quality control of gum Arabic, and isolation of some chemical constituents (L-rhamnose and arabinose) from A. seyal have also been summarized. The identification of only 30 patents/patent applications from 1892 to 15 November 2021 indicates a steadily growing interest and encourages developing more inventions related to A. seyal. The authors recommend exploring these opportunities for the benefit of society.

The transportation industry is crucial to the realization of a smart city. However, the current growth in vehicle numbers is not being matched by an increase in road capacity. Congestion may boost the number of accidents, harm economic growth, and result in higher gas emissions. Currently, traffic congestion is seen as a severe threat to urban life. Suffering as a result of increased car traffic, insufficient infrastructure, and inefficient traffic management has exceeded the tolerance limit. Since route decisions are typically made in a short amount of time, the visualization of the data must be presented in a highly conceivable way. Also, the data generated by the transportation system face difficulties in processing and sometimes lack effective usage in certain fields. Hence, to overcome the challenges in computer vision, a novel computer vision-based traffic management system is proposed by integrating a wireless sensor network (WSN) and visual analytics framework. This research aimed to analyze average message delivery, average latency, average access, average energy consumption, and network performance. Wireless sensors are used in the study to collect road metrics, quantify them, and then rank them for entry. For optimization of the traffic data, improved phase timing optimization (IPTO) was used. The whole experimentation was carried out in a virtual environment. It was observed from the experimental results that the proposed approach outperformed other existing approaches.

Background: SARS‐CoV‐2 is a positive‐sense single‐stranded RNA virus that has a propensity for infecting epithelial cells and the respiratory system. The two important proteins, structural and nonstructural proteins, make the architecture of this virus. Aim: This research aimed at studying significant mutations in spike protein of SARS‐CoV‐2 variants of concern (VoCs) and finding shared mutations among omicron and other four variants (alpha, beta, gamma, and delta). The purpose of this study was to draw structural comparisons between wild type and mutant proteins, followed by identifying potent inhibitors (ligand) that could be used against SARS‐CoV‐2 spike protein and its latest omicron VoC. Methodology: In this research, we had studied 16 major mutations as well as shared mutations (6) present in spike region of SARS‐CoV‐2. Subsequently, we determined the structure of the wild‐type SARS‐CoV‐2 protein from the Protein Data Bank (PDB) with the ID 7R4I. Furthermore, the structure of the mutant protein of SARS‐CoV‐2 omicron variant was modeled in SWISS‐MODEL. The ligand dataset for spike protein of SARS‐CoV‐2 was also collected from literature and different databases. Both wild type and mutant proteins were docked with ligand database in Molecular Operating Environment (MOE). The docking analysis was performed, and two best ligand molecules, AZ_2 and AZ_13, were finalized based on their energy values, interactions, and docking scores to be used against our wild and mutant proteins. Results: AZ_2 demonstrated a docking score of −6.1753 in MOE, with energy values of −4.3889 and −6.1753. It formed key hydrogen bond interactions. AZ_13 showed a docking score of −5.9, with energy values of −9.3 and −5.9, forming hydrogen donor and acceptor interactions with Asp950 (3.06 Å), Ile312 (3.13 Å), and Glu309 (3.27 Å). These interactions suggest strong binding affinity and potential efficacy. Thus, present research work emphasized on identification of significant mutations and finding a potent target‐based drug against SARS‐CoV‐2 and its omicron variant. Outcomes: Based on this computational analysis performed, it is suggested that proposed compound can be used as remedy against SARS‐CoV‐2 and its omicron variant.

This review tries to summarize the purpose of steadily developing surface-functionalized nanoparticles for various bio-applications and represents a fascinating and rapidly growing field of research. Due to their unique properties—such as novel optical, biodegradable, low-toxicity, biocompatibility, size, and highly catalytic features—these materials are considered superior, and it is thus vital to study these systems in a realistic and meaningful way. However, rapid aggregation, oxidation, and other problems are encountered with functionalized nanoparticles, inhibiting their subsequent utilization. Adequate surface modification of nanoparticles with organic and inorganic compounds results in improved physicochemical properties which can overcome these barriers. This review investigates and discusses the iron oxide nanoparticles, gold nanoparticles, platinum nanoparticles, silver nanoparticles, and silica-coated nanoparticles and how their unique properties after fabrication allow for their potential use in a wide range of bio-applications such as nano-based imaging, gene delivery, drug loading, and immunoassays. The different groups of nanoparticles and the advantages of surface functionalization and their applications are highlighted here. In recent years, surface-functionalized nanoparticles have become important materials for a broad range of bio-applications.

Pyrazole, an important pharmacophore and a privileged scaffold of immense significance, is a five-membered heterocyclic moiety with an extensive therapeutic profile, viz., anti-inflammatory, anti-microbial, anti-anxiety, anticancer, analgesic, antipyretic, etc. Due to the expansion of pyrazolecent red pharmacological molecules at a quicker pace, there is an urgent need to put emphasis on recent literature with hitherto available information to recognize the status of this scaffold for pharmaceutical research. The reported potential pyrazole-containing compounds are highlighted in the manuscript for the treatment of cancer and inflammation, and the results are mentioned in % inhibition of inflammation, % growth inhibition, IC50, etc. Pyrazole is an important heterocyclic moiety with a strong pharmacological profile, which may act as an important pharmacophore for the drug discovery process. In the struggle to cultivate suitable anti-inflammatory and anticancer agents, chemists have now focused on pyrazole biomolecules. This review conceals the recent expansion of pyrazole biomolecules as anti-inflammatory and anticancer agents with an aim to provide better correlation among different research going around the world.