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"Kamran Ali"
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Capabilities for enhancing supply chain resilience and responsiveness in the COVID-19 pandemic: exploring the role of improvisation, anticipation, and data analytics capabilities
PurposeThis study aims to identify critical capabilities to address unforeseen and novel disruptions, such as those instigated by COVID-19, and explore their role as essential enablers of supply chain resilience and responsiveness, leading to improved performance.Design/methodology/approachThe structural equation modeling technique was employed for analyzing the proposed associations using survey data from 206 manufacturers operating during the COVID-19 pandemic in a developing country, Pakistan.FindingsKey findings show how improvisation and anticipation act distinctly yet jointly to facilitate supply chain resilience and responsiveness during the COVID-19 pandemic. Also, data analytics capability positively affects anticipation and improvisation, which mediate the effect of data analytics on supply chain resilience and responsiveness.Research limitations/implicationsThe findings contribute to the theoretical and empirical understanding of the existing literature, suggesting that a combination of improvisation, anticipation and data analytics capabilities is highly imperative for enhancing supply chain resilience and responsiveness in novel and unexpected disruptions.Originality/valueThis is the first study to examine the impact of data analytics on improvisation and anticipation and the latter as complementary capabilities to enhance supply chain resilience and responsiveness. The empirical investigation explores the interplay among data analytics, improvisation, and anticipation capabilities for enhancing supply chain resilience, responsiveness, and performance during the unforeseen and novel disruptions, such as brought to bear by the COVID-19 pandemic.
Journal Article
Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach
Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, e.g., urban/rural planning, disaster management, and climate change adaptation. Recently, Deep Learning (DL) has emerged as a paradigm shift for LULC classification. To date, little research has focused on using DL methods for LULC mapping in semi-arid regions, and none that we are aware of have compared the use of different Sentinel-2 image band combinations for mapping LULC in semi-arid landscapes with deep Convolutional Neural Network (CNN) models. Sentinel-2 multispectral image bands have varying spatial resolutions, and there is often high spectral similarity of different LULC features in semi-arid regions; therefore, selection of suitable Sentinel-2 bands could be an important factor for LULC mapping in these areas. Our study contributes to the remote sensing literature by testing different Sentinel-2 bands, as well as the transferability of well-optimized CNNs, for semi-arid LULC classification in semi-arid regions. We first trained a CNN model in one semi-arid study site (Gujranwala city, Gujranwala Saddar and Wazirabadtownships, Pakistan), and then applied the pre-trained model to map LULC in two additional semi-arid study sites (Lahore and Faisalabad city, Pakistan). Two different composite images were compared: (i) a four-band composite with 10 m spatial resolution image bands (Near-Infrared (NIR), green, blue, and red bands), and (ii) a ten-band composite made by adding two Short Wave Infrared (SWIR) bands and four vegetation red-edge bands to the four-band composite. Experimental results corroborate the validity of the proposed CNN architecture. Notably, the four-band CNN model has shown robustness in semi-arid regions, where spatially and spectrally confusing land-covers are present.
Journal Article
Effect of nutrition in Alzheimer’s disease: A systematic review
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by declining cognitive ability. Currently, there are no effective treatments for this condition. However, certain measures, such as nutritional interventions, can slow disease progression. Therefore, the objective of this systematic review was to identify and map the updates of the last 5 years regarding the nutritional status and nutritional interventions associated with AD patients.
A systematic review.
A search was conducted for randomized clinical trials, systematic reviews, and meta-analyses investigating the association between nutritional interventions and AD published between 2018 and 2022 in the PubMed, Web of Science, Scopus, and Cochrane Library databases. A total of 38 studies were identified, of which 17 were randomized clinical trials, and 21 were systematic reviews and/or meta-analyses.
The results show that the western diet pattern is a risk factor for developing AD. In contrast, the Mediterranean diet, ketogenic diet, and supplementation with omega-3 fatty acids and probiotics are protective factors. This effect is significant only in cases of mild-to-moderate AD.
Certain nutritional interventions may slow the progression of AD and improve cognitive function and quality of life. Further research is required to draw more definitive conclusions.
Journal Article
From silent partners to potential therapeutic targets: macrophages in colorectal cancer
Cancer cells grow and survive in the tumor microenvironment, which is a complicated process. As a key part of how colorectal cancer (CRC) progresses, tumor-associated macrophages (TAMs) exhibit a double role. Through angiogenesis, this TAM can promote the growth of cancers. Although being able to modify and adjust immune cells is a great advantage, these cells can also exhibit anti-cancer properties including direct killing of cancer cells, presenting antigens, and aiding T cell-mediated responses. The delicate regulatory mechanisms between the immune system and tumors are composed of a complex network of pathways regulated by several factors including hypoxia, metabolic reprogramming, cytokine/chemokine signaling, and cell interactions. Decoding and figuring out these complex systems become significant in building targeted treatment programs. Targeting TAMs in CRC involves disrupting chemokine signaling or adhesion molecules, reprogramming them to an anti-tumor phenotype using TLR agonists, CD40 agonists, or metabolic modulation, and selectively removing TAM subsets that promote tumor growth. Multi-drug resistance, the absence of an accurate biomarker, and drug non-specificity are also major problems. Combining macrophage-targeted therapies with chemotherapy and immunotherapy may revolutionize treatment. Macrophage studies will advance with new technology and multi-omics methodologies to help us understand CRC and build specific and efficient treatments.
Journal Article
Using AI-Based Virtual Companions to Assist Adolescents with Autism in Recognizing and Addressing Cyberbullying
Social media platforms and online gaming sites play a pervasive role in facilitating peer interaction and social development for adolescents, but they also pose potential threats to health and safety. It is crucial to tackle cyberbullying issues within these platforms to ensure the healthy social development of adolescents. Cyberbullying has been linked to adverse mental health outcomes among adolescents, including anxiety, depression, academic underperformance, and an increased risk of suicide. While cyberbullying is a concern for all adolescents, those with disabilities are particularly susceptible and face a higher risk of being targets of cyberbullying. Our research addresses these challenges by introducing a personalized online virtual companion guided by artificial intelligence (AI). The web-based virtual companion’s interactions aim to assist adolescents in detecting cyberbullying. More specifically, an adolescent with ASD watches a cyberbullying scenario in a virtual environment, and the AI virtual companion then asks the adolescent if he/she detected cyberbullying. To inform the virtual companion in real time to know if the adolescent has learned about detecting cyberbullying, we have implemented fast and lightweight cyberbullying detection models employing the T5-small and MobileBERT networks. Our experimental results show that we obtain comparable results to the state-of-the-art methods despite having a compact architecture.
Journal Article
Three layered sparse dictionary learning algorithm for enhancing the subject wise segregation of brain networks
Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ICA to higher and DL to lower extent may suffer from performance degradation by the presence of anomalous observations in the recovered time courses (TCs) and high overlaps among spatial maps (SMs). This paper addressed both problems using a novel three-layered sparse DL (TLSDL) algorithm that incorporated prior information in the dictionary update process and recovered full-rank outlier-free TCs from highly corrupted measurements. The associated sequential DL model involved factorizing each subject’s data into a multi-subject (MS) dictionary and MS sparse code while imposing a low-rank and a sparse matrix decomposition restriction on the dictionary matrix. It is derived by solving three layers of feature extraction and component estimation. The first and second layers captured brain regions with low and moderate spatial overlaps, respectively. The third layer that segregated regions with significant spatial overlaps solved a sequence of vector decomposition problems using the proximal alternating linearized minimization (PALM) method and solved a decomposition restriction using the alternating directions method (ALM). It learned outlier-free dynamics that integrate spatiotemporal diversities across brains and external information. It differs from existing DL methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. The TLSDL algorithm was compared with existing dictionary learning algorithms using experimental and synthetic fMRI datasets to verify its performance. Overall, the mean correlation value was found to be
26
%
higher for the TLSDL than for the state-of-the-art subject-wise sequential DL (swsDL) technique.
Journal Article
Robust Backstepping Super-Twisting MPPT Controller for Photovoltaic Systems Under Dynamic Shading Conditions
In this research article, a fast and efficient hybrid Maximum Power Point Tracking (MPPT) control technique is proposed for photovoltaic (PV) systems. The method combines two phases—offline and online—to estimate the appropriate duty cycle for operating the converter at the maximum power point (MPP). In the offline phase, temperature and irradiance inputs are used to compute the real-time reference peak power voltage through an Adaptive Neuro-Fuzzy Inference System (ANFIS). This estimated reference is then utilized in the online phase, where the Robust Backstepping Super-Twisting (RBST) controller treats it as a set-point to generate the control signal and continuously adjust the converter’s duty cycle, driving the PV system to operate near the MPP. The proposed RBST control scheme offers a fast transient response, reduced rise and settling times, low tracking error, enhanced voltage stability, and quick adaptation to changing environmental conditions. The technique is tested in MATLAB/Simulink under three different scenarios: continuous variation in meteorological parameters, sudden step changes, and partial shading. To demonstrate the superiority of the RBST method, its performance is compared with classical backstepping and integral backstepping controllers. The results show that the RBST-based MPPT controller achieves the minimum rise time of 0.018s, the lowest squared error of 0.3015V, the minimum steady-state error of 0.29%, and the highest efficiency of 99.16%.
Journal Article
Integration of haptic virtual reality simulators in undergraduate dental curricula: A survey-based study in Gulf Cooperation Council countries
The integration of haptic simulators in contemporary dental education has been reported to improve students' hand-eye coordination and fine motor skills during pre-clinical education to facilitate a smooth transition to the clinical setting. The aim of this study was to assess the integration of haptic virtual reality simulation (HVRS) in undergraduate dental curricula in the Gulf Cooperation Council countries.
All dental schools offering undergraduate dental programs in the Gulf Cooperation Council countries were invited to participate in this cross-sectional study design. Data was collected using an online survey on a voluntary basis and analyzed using Microsoft Excel.
Out of 34 dental schools, responses were received from 30 dental schools (response rate 88.2%). In terms of haptic integration, only two (6.7%) dental institutions have adopted haptic simulation in undergraduate dental education. However, a considerable proportion of schools (n = 13, 46.4%) expressed an interest in the future use of haptic technology. The key strengths of HRVS included the integration of modern technology, opportunities for self-directed learning, development and consolidation of manual skills, and boosting self-confidence amongst undergraduate dental students. Financial cost and limited patient cases in the HRVS library were regarded as the main barriers to widespread use of this technology.
Although the Gulf Cooperation Council countries have strong economies with a high gross domestic product (GDP), only a limited number of dental schools have incorporated haptic technology in their curricula. Nevertheless, a high proportion of dental schools in the region are actively considering purchasing and implementation of haptic devices in undergraduate dental programs.
Journal Article
Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks
With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency.
Journal Article