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  • 1
    Online Resource
    Online Resource
    Cubic q-Rung Orthopair Fuzzy Heronian Mean Operators and Their Applications to Multi-Attribute Group Decision Making
    MDPI AG ; 2020
    In:  Mathematics Vol. 8, No. 7 ( 2020-07-09), p. 1125-
    Details
    In: Mathematics, MDPI AG, Vol. 8, No. 7 ( 2020-07-09), p. 1125-
    Abstract: The cubic q-rung orthopair fuzzy set (Cq-ROFS) contains much more information to determine the interval valued q-rung orthopair fuzzy sets (IVq-ROFSs) and q-rung orthopair fuzzy sets (q-ROFSs) simultaneously for coping with the vagueness in information. It provides more space for decision makers (DMs) to describe their opinion in the environment of fuzzy set (FS) theory. In this paper, firstly, we introduce the conception of Cq-ROFS and their characteristics. Further, the Heronian mean (HM) operator based on Cq-ROFS, called the weighted HM operator, are explored. To overcome the deficiency of HM operator and keeping in mind the partitioned structure in real decision situations, we offer Cubic q-rung orthopair fuzzy partitioned HM operator and its weighted shape. An algorithm of the proposed operators based on multi-attribute group decision making (MAGDM) problems for the selection of best alternative among the given ones is established. Lastly, we provide an example to depict the authenticity and advantages of the exposed methods by contrasting with other existing drawbacks.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math8071125
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704244-3
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  • 2
    Online Resource
    Online Resource
    Interval Valued T-Spherical Fuzzy Information Aggregation Based on Dombi t-Norm and Dombi t-Conorm for Multi-Attribute Decision Making Problems
    MDPI AG ; 2021
    In:  Symmetry Vol. 13, No. 6 ( 2021-06-10), p. 1053-
    Details
    In: Symmetry, MDPI AG, Vol. 13, No. 6 ( 2021-06-10), p. 1053-
    Abstract: Multi-attribute decision-making (MADM) is commonly used to investigate fuzzy information effectively. However, selecting the best alternative information is not always symmetric because the alternatives do not have complete information, so asymmetric information is often involved. Expressing the information under uncertainty using closed subintervals of [0, 1] is beneficial and effective instead of using crisp numbers from [0, 1] . The goal of this paper is to enhance the notion of Dombi aggregation operators (DAOs) by introducing the DAOs in the interval-valued T-spherical fuzzy (IVTSF) environment where the uncertain and ambiguous information is described with the help of membership grade (MG), abstinence grade (AG), non-membership grade (NMG), and refusal grade (RG) using closed sub-intervals of [0, 1]. One of the key benefits of the proposed work is that in the environment of information loss is reduced to a negligible limit. We proposed concepts of IVTSF Dombi weighted averaging (IVTSFDWA) and IVTSF Dombi weighted geometric (IVTSFDWG) operators. The diversity of the IVTSF DAOs is proved and the influences of the parameters, associated with DAOs, on the ranking results are observed in a MADM problem where it is discussed how a decision can be made when there is asymmetric information about alternatives.
    Type of Medium: Online Resource
    ISSN: 2073-8994
    URL: Article
    DOI: 10.3390/sym13061053
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2518382-5
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  • 3
    Online Resource
    Online Resource
    Establishment of Crop Water Stress Index for Sustainable Wheat Production under Climate Change in a Semi-Arid Region of Pakistan
    MDPI AG ; 2022
    In:  Atmosphere Vol. 13, No. 12 ( 2022-11-29), p. 2008-
    Details
    In: Atmosphere, MDPI AG, Vol. 13, No. 12 ( 2022-11-29), p. 2008-
    Abstract: The Crop Water Stress Index (CWSI) is a useful tool for evaluating irrigation scheduling and achieving water conservation and crop yield goals. This study examined the CWSI under different water stress conditions for the scheduling of wheat crop irrigation and developed indices using the leaf canopy temperature in Faisalabad, Pakistan. The experiments were conducted using a randomized, complete block design and four irrigation treatments with deficit levels of D0%, D20%, and D40% from the field capacity (FC) and D100% (100% deficit level). The CWSI was determined at pre-heading and post-heading stages through the lower baseline (fully watered crop) and upper limit (maximum stress). These baselines were computed using the air temperature and canopy temperature of plant leaves and the vapor pressure deficit (VPD). The CWSI for each irrigation treatment was calculated and the average seasonal CWSI value for the whole season was used to develop the empirical relationships for scheduling irrigation. The relationships between the air canopy temperatures and the VPD resulted in slope (x) = −0.735 and interception (c) = −0.8731 as well as x = −0.5143 and c = −1.273 at the pre- and post-heading stages, respectively. The values of the CWSI for the treatment at deficit levels of D0%, D20%, D40%, and D100% were found to be 0.08, 0.61, 0.20, and 0.64, respectively. The CWSI values developed in this study can be effectively used to promote better the monitoring of irrigated wheat crops in the region.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    URL: Article
    DOI: 10.3390/atmos13122008
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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  • 4
    Online Resource
    Online Resource
    Molecular Characterization and Epidemiology of Antibiotic Resistance Genes of β-Lactamase Producing Bacterial Pathogens Causing Septicemia from Tertiary Care Hospitals
    MDPI AG ; 2023
    In:  Antibiotics Vol. 12, No. 3 ( 2023-03-20), p. 617-
    Details
    In: Antibiotics, MDPI AG, Vol. 12, No. 3 ( 2023-03-20), p. 617-
    Abstract: Septicemia is a systematic inflammatory response and can be a consequence of abdominal, urinary tract and lung infections. Keeping in view the importance of Gram-negative bacteria as one of the leading causes of septicemia, the current study was designed with the aim to determine the antibiotic susceptibility pattern, the molecular basis for antibiotic resistance and the mutations in selected genes of bacterial isolates. In this study, clinical samples (n = 3389) were collected from potentially infected male (n = 1898) and female (n = 1491) patients. A total of 443 (13.07%) patients were found to be positive for bacterial growth, of whom 181 (40.8%) were Gram-positive and 262 (59.1%) were Gram-negative. The infected patients included 238 males, who made up 12.5% of the total number tested, and 205 females, who made up 13.7%. The identification of bacterial isolates revealed that 184 patients (41.5%) were infected with Escherichia coli and 78 (17.6%) with Pseudomonas aeruginosa. The clinical isolates were identified using Gram staining biochemical tests and were confirmed using polymerase chain reaction (PCR), with specific primers for E. coli (USP) and P. aeruginosa (oprL). Most of the isolates were resistant to aztreonam (ATM), cefotaxime (CTX), ampicillin (AMP) and trimethoprim/sulfamethoxazole (SXT), and were sensitive to tigecycline (TGC), meropenem (MEM) and imipenem (IPM), as revealed by high minimum inhibitory concentration (MIC) values. Among the antibiotic-resistant bacteria, 126 (28.4%) samples were positive for ESBL, 105 (23.7%) for AmpC β-lactamases and 45 (10.1%) for MBL. The sequencing and mutational analysis of antibiotic resistance genes revealed mutations in TEM, SHV and AAC genes. We conclude that antibiotic resistance is increasing; this requires the attention of health authorities and clinicians for proper management of the disease burden.
    Type of Medium: Online Resource
    ISSN: 2079-6382
    URL: Article
    DOI: 10.3390/antibiotics12030617
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2681345-2
    SSG: 15,3
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  • 5
    Online Resource
    Online Resource
    Theoretical Modeling of B12N12 Nanocage for the Effective Removal of Paracetamol from Drinking Water
    MDPI AG ; 2023
    In:  Computation Vol. 11, No. 9 ( 2023-09-14), p. 183-
    Details
    In: Computation, MDPI AG, Vol. 11, No. 9 ( 2023-09-14), p. 183-
    Abstract: In our current investigation, we employed a B12N12 nanocage to extract paracetamol from water utilizing a DFT approach. We explored three distinct positions of paracetamol concerning its interaction with the B12N12 nanocage, designated as complex-1 (BNP-1), complex-2 (BNP-2), and complex-3 (BNP-3), under both aqueous and gaseous conditions. The optimized bond distances exhibited strong interactions between the nanocage and the paracetamol drug in BNP-1 and BNP-3. Notably, BNP-1 and BNP-3 displayed substantial chemisorption energies, measuring at −27.94 and −15.31 kcal/mol in the gas phase and −30.69 and −15.60 kcal/mol in the aqueous medium, respectively. In contrast, BNP-2 displayed a physiosorbed nature, indicating weaker interactions with values of −6.97 kcal/mol in the gas phase and −4.98 kcal/mol in the aqueous medium. Our analysis of charge transfer revealed significant charge transfer between the B12N12 nanocage and paracetamol. Additionally, a Quantum Theory of Atoms in Molecules (QTAIM) analysis confirmed that the O─B bond within BNP-1 and BNP-3 exhibited a strong covalent and partial bond, encompassing both covalent and electrostatic interactions. In contrast, the H─N bond within BNP-2 displayed a weaker hydrogen bond. Further investigation through Noncovalent Interaction (NCI) and Reduced Density Gradient (RDG) analyses reinforced the presence of strong interactions in BNP-1 and BNP-3, while indicating weaker interactions in BNP-2. The decrease in the electronic band gap (Eg) demonstrated the potential of B12N12 as a promising adsorbent for paracetamol. Examining thermodynamics, the negative values of ∆H (enthalpy change) and ∆G (Gibbs free energy change) pointed out the exothermic and spontaneous nature of the adsorption process. Overall, our study underscores the potential of B12N12 as an effective adsorbent for eliminating paracetamol from wastewater.
    Type of Medium: Online Resource
    ISSN: 2079-3197
    URL: Article
    DOI: 10.3390/computation11090183
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2723192-6
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  • 6
    Online Resource
    Online Resource
    Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring
    MDPI AG ; 2015
    In:  Sensors Vol. 15, No. 4 ( 2015-03-25), p. 7172-7205
    Details
    In: Sensors, MDPI AG, Vol. 15, No. 4 ( 2015-03-25), p. 7172-7205
    Abstract: The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s150407172
    Language: English
    Publisher: MDPI AG
    Publication Date: 2015
    detail.hit.zdb_id: 2052857-7
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  • 7
    Online Resource
    Online Resource
    A Practical Multiclass Classification Network for the Diagnosis of Alzheimer’s Disease
    MDPI AG ; 2022
    In:  Applied Sciences Vol. 12, No. 13 ( 2022-06-27), p. 6507-
    Details
    In: Applied Sciences, MDPI AG, Vol. 12, No. 13 ( 2022-06-27), p. 6507-
    Abstract: Patients who have Alzheimer’s disease (AD) pass through several irreversible stages, which ultimately result in the patient’s death. It is crucial to understand and detect AD at an early stage to slow down its progression due to the non-curable nature of the disease. Diagnostic techniques are primarily based on magnetic resonance imaging (MRI) and expensive high-dimensional 3D imaging data. Classic methods can hardly discriminate among the almost similar pixels of the brain patterns of various age groups. The recent deep learning-based methods can contribute to the detection of the various stages of AD but require large-scale datasets and face several challenges while using the 3D volumes directly. The extant deep learning-based work is mainly focused on binary classification, but it is challenging to detect multiple stages with these methods. In this work, we propose a deep learning-based multiclass classification method to distinguish amongst various stages for the early diagnosis of Alzheimer’s. The proposed method significantly handles data shortage challenges by augmentation and manages to classify the 2D images obtained after the efficient pre-processing of the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Our method achieves an accuracy of 98.9% with an F1 score of 96.3. Extensive experiments are performed, and overall results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of overall performance.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app12136507
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704225-X
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    FH Potsdam
  • 8
    Online Resource
    Online Resource
    RDS-DR: An Improved Deep Learning Model for Classifying Severity Levels of Diabetic Retinopathy
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 19 ( 2023-10-03), p. 3116-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 19 ( 2023-10-03), p. 3116-
    Abstract: A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new “DR-Insight” dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system’s goal is to augment optometrists’ expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR).
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13193116
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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    FH Potsdam
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