Search

Accurate segmentation of anatomical structures and abnormalities in medical images is crucial for computer-aided diagnosis and analysis. While deep learning techniques excel at this task, their computational demands pose challenges. Additionally, some cutting-edge segmentation methods, though effective for general object segmentation, may not be optimised for medical images. To address these issues, we propose Mini-Net, a lightweight segmentation network specifically designed for medical images. With fewer than 38,000 parameters, Mini-Net efficiently captures both high- and low-frequency features, enabling real-time applications in various medical imaging scenarios. We evaluate Mini-Net on various datasets, including DRIVE, STARE, ISIC-2016, ISIC-2018, and MoNuSeg, demonstrating its robustness and good performance compared to state-of-the-art methods.

Pakistan is vulnerable to a range of climate hazards, including sea-level rise. The Indus Delta region, situated in the coastal Sindh province, is particularly at risk of sea-level rise due to low-lying land and fragile ecosystems. In this article, expertise is drawn together from the newly established Pakistan Sea-Level Working Group, consisting of policy experts, scientists, and practitioners, to provide recommendations for future research, investment, and coastal risk management. An assessment of the current scientific understanding of sea-level change and coastal climate risks in Pakistan highlights an urgent need to improve the availability and access to sea-level data and other coastal measurements. In addition, reflecting on the policy environment and the enablers needed to facilitate effective responses to future sea-level change, recommendations are made to integrate coastal climate services into the National Adaptation Plan and develop a National Framework for Climate Services. Such a framework, alongside collaboration, co-production, and capacity development, could help support required improvements in coastal observations and monitoring and continuously deliver useful, usable, and accessible sea-level information for use by practitioners and decision-makers.

Deep neural network (DNN) models for retinopathy have estimated predictive accuracies in the mid-to-high 90%. However, the following aspects remain unaddressed: State-of-the-art models are complex and require substantial computational infrastructure to train and deploy; The reliability of predictions can vary widely. In this paper, we focus on these aspects and propose a form of iterative knowledge distillation(IKD), called IKD+ that incorporates a tradeoff between size, accuracy and reliability. We investigate the functioning of IKD+ using two widely used techniques for estimating model calibration (Platt-scaling and temperature-scaling), using the best-performing model available, which is an ensemble of EfficientNets with approximately 100M parameters. We demonstrate that IKD+ equipped with temperature-scaling results in models that show up to approximately 500-fold decreases in the number of parameters than the original ensemble without a significant loss in accuracy. In addition, calibration scores (reliability) for the IKD+ models are as good as or better than the base mode, Comment: Submitted to IEEE International Conference on Image Processing (ICIP 2023)

Purpose: CheckMate 651 (ClinicalTrials.gov identifier: NCT02741570) evaluated first-line nivolumab plus ipilimumab versus EXTREME (cetuximab plus cisplatin/carboplatin plus fluorouracil ≤ six cycles, then cetuximab maintenance) in recurrent/metastatic squamous cell carcinoma of the head and neck (R/M SCCHN). Methods: Patients without prior systemic therapy for R/M SCCHN were randomly assigned 1:1 to nivolumab plus ipilimumab or EXTREME. Primary end points were overall survival (OS) in the all randomly assigned and programmed death-ligand 1 combined positive score (CPS) ≥ 20 populations. Secondary end points included OS in the programmed death-ligand 1 CPS ≥ 1 population, and progression-free survival, objective response rate, and duration of response in the all randomly assigned and CPS ≥ 20 populations. Results: Among 947 patients randomly assigned, 38.3% had CPS ≥ 20. There were no statistically significant differences in OS with nivolumab plus ipilimumab versus EXTREME in the all randomly assigned (median: 13.9 v 13.5 months; hazard ratio [HR], 0.95; 97.9% CI, 0.80 to 1.13; P = .4951) and CPS ≥ 20 (median: 17.6 v 14.6 months; HR, 0.78; 97.51% CI, 0.59 to 1.03; P = .0469) populations. In patients with CPS ≥ 1, the median OS was 15.7 versus 13.2 months (HR, 0.82; 95% CI, 0.69 to 0.97). Among patients with CPS ≥ 20, the median progression-free survival was 5.4 months (nivolumab plus ipilimumab) versus 7.0 months (EXTREME), objective response rate was 34.1% versus 36.0%, and median duration of response was 32.6 versus 7.0 months. Grade 3/4 treatment-related adverse events occurred in 28.2% of patients treated with nivolumab plus ipilimumab versus 70.7% treated with EXTREME. Conclusion: CheckMate 651 did not meet its primary end points of OS in the all randomly assigned or CPS ≥ 20 populations. Nivolumab plus ipilimumab showed a favorable safety profile compared with EXTREME. There continues to be a need for new therapies in patients with R/M SCCHN.

Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end, we propose a Multi-resolution Contextual Network (MRC-Net) that addresses these issues by extracting multi-scale features to learn contextual dependencies between semantically different features and using bi-directional recurrent learning to model former-latter and latter-former dependencies. Another key idea is training in adversarial settings for foreground segmentation improvement through optimization of the region-based scores. This novel strategy boosts the performance of the segmentation network in terms of the Dice score (and correspondingly Jaccard index) while keeping the number of trainable parameters comparatively low. We have evaluated our method on three benchmark datasets, including DRIVE, STARE, and CHASE, demonstrating its superior performance as compared with competitive approaches elsewhere in the literature.

Accurate lung nodule segmentation is crucial for early-stage lung cancer diagnosis, as it can substantially enhance patient survival rates. Computed tomography (CT) images are widely employed for early diagnosis in lung nodule analysis. However, the heterogeneity of lung nodules, size diversity, and the complexity of the surrounding environment pose challenges for developing robust nodule segmentation methods. In this study, we propose an efficient end-to-end framework, the multi-encoder-based self-adaptive hard attention network (MESAHA-Net), for precise lung nodule segmentation in CT scans. MESAHA-Net comprises three encoding paths, an attention block, and a decoder block, facilitating the integration of three types of inputs: CT slice patches, forward and backward maximum intensity projection (MIP) images, and region of interest (ROI) masks encompassing the nodule. By employing a novel adaptive hard attention mechanism, MESAHA-Net iteratively performs slice-by-slice 2D segmentation of lung nodules, focusing on the nodule region in each slice to generate 3D volumetric segmentation of lung nodules. The proposed framework has been comprehensively evaluated on the LIDC-IDRI dataset, the largest publicly available dataset for lung nodule segmentation. The results demonstrate that our approach is highly robust for various lung nodule types, outperforming previous state-of-the-art techniques in terms of segmentation accuracy and computational complexity, rendering it suitable for real-time clinical implementation.

Segmentation is an important task in a wide range of computer vision applications, including medical image analysis. Recent years have seen an increase in the complexity of medical image segmentation approaches based on sophisticated convolutional neural network architectures. This progress has led to incremental enhancements in performance on widely recognised benchmark datasets. However, most of the existing approaches are computationally demanding, which limits their practical applicability. This paper presents an expand-squeeze dual multiscale residual network (ESDMR-Net), which is a fully convolutional network that is particularly well-suited for resource-constrained computing hardware such as mobile devices. ESDMR-Net focuses on extracting multiscale features, enabling the learning of contextual dependencies among semantically distinct features. The ESDMR-Net architecture allows dual-stream information flow within encoder-decoder pairs. The expansion operation (depthwise separable convolution) makes all of the rich features with multiscale information available to the squeeze operation (bottleneck layer), which then extracts the necessary information for the segmentation task. The Expand-Squeeze (ES) block helps the network pay more attention to under-represented classes, which contributes to improved segmentation accuracy. To enhance the flow of information across multiple resolutions or scales, we integrated dual multiscale residual (DMR) blocks into the skip connection. This integration enables the decoder to access features from various levels of abstraction, ultimately resulting in more comprehensive feature representations. We present experiments on seven datasets from five distinct examples of applications. Our model achieved the best results despite having significantly fewer trainable parameters, with a reduction of two or even three orders of magnitude.

Blinding eye diseases are often correlated with altered retinal morphology, which can be clinically identified by segmenting retinal structures in fundus images. However, current methodologies often fall short in accurately segmenting delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on repeated convolution and pooling operations can hinder the representation of edge information, ultimately limiting overall segmentation accuracy. In this paper, we propose a lightweight pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an exceptionally low number of learnable parameters \textbf{(only 0.172 M)}. The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. Additionally, we have optimized the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimization significantly reduces training time and enhances computational efficiency. To assess the robustness and generalizability of LMBiS-Net, we performed comprehensive evaluations on various aspects of retinal images. Specifically, the model was subjected to rigorous tests to accurately segment retinal vessels, which play a vital role in ophthalmological diagnosis and treatment. By focusing on the retinal blood vessels, we were able to thoroughly analyze the performance and effectiveness of the LMBiS-Net model. The results of our tests demonstrate that LMBiS-Net is not only robust and generalizable but also capable of maintaining high levels of segmentation accuracy. These characteristics highlight the potential of LMBiS-Net as an efficient tool for high-speed and accurate segmentation of retinal images in various clinical applications.

Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for the tracking and diagnosis of progression. However, existing vessel segmentation methods that heavily rely on encoder-decoder structures struggle to capture contextual information about retinal vessel configurations, leading to challenges in reconciling semantic disparities between encoder and decoder features. To address this, we propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps. FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling, complemented by a shallow upsampling approach to generate a binary mask for each class. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the superior performance of FES-Net compared to other competitive approaches documented in the existing literature.

Skin cancer holds the highest incidence rate among all cancers globally. The importance of early detection cannot be overstated, as late-stage cases can be lethal. Classifying skin lesions, however, presents several challenges due to the many variations they can exhibit, such as differences in colour, shape, and size, significant variation within the same class, and notable similarities between different classes. This paper introduces a novel class-wise attention technique that equally regards each class while unearthing more specific details about skin lesions. This attention mechanism is progressively used to amalgamate discriminative feature details from multiple scales. The introduced technique demonstrated impressive performance, surpassing more than 15 cutting-edge methods including the winners of HAM1000 and ISIC 2019 leaderboards. It achieved an impressive accuracy rate of 97.40% on the HAM10000 dataset and 94.9% on the ISIC 2019 dataset.

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based computational neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation therapy, and image-guided surgery. LDMRes-Net overcomes these limitations with its remarkably low number of learnable parameters (0.072M), making it highly suitable for resource-constrained devices. The model's key innovation lies in its dual multi-residual block architecture, which enables the extraction of refined features on multiple scales, enhancing overall segmentation performance. To further optimize efficiency, the number of filters is carefully selected to prevent overlap, reduce training time, and improve computational efficiency. The study includes comprehensive evaluations, focusing on segmentation of the retinal image of vessels and hard exudates crucial for the diagnosis and treatment of ophthalmology. The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms. Such advances hold significant promise for improving healthcare outcomes and enabling real-time medical image analysis in resource-limited settings.

Supervision of the research students is not a trouble-free task. It becomes more challenging when supervisors confront the students of varying potential to carry out the research during the process of the thesis. The researchers sought to explore the supervisory experience of post-graduate (M.Phil) students who had done a thesis and those who had not done a thesis in their last degree. Eleven supervisors of different universities in Punjab, Pakistan participated in this study. The participants were aged from 40 to 60 years and most of them were working as assistant professors. The interview method was used for collecting the data and were analyzed through coding, categorizing and describing the emerging themes from the interview transcripts. The results indicated that M.Phil students with a thesis in their last academic degree were found to be comparatively ‘better’ than non-thesis students regarding the conceptual understanding of the research process, using software, confidence and communication skills and they required less supervisory support. The results were presented in the form of emergent themes that described supervisory experiences with these two types of MPhil students. The study has implications for supervisors and research students, particularly for the non-thesis students who require additional skills to pursue their research degrees.            Keywords: Thesis students, non-thesis students, higher education, supervisory experience, supervisory support

Image segmentation is an important task in medical imaging. It constitutes the backbone of a wide variety of clinical diagnostic methods, treatments, and computer-aided surgeries. In this paper, we propose a multi-kernel image segmentation net (MKIS-Net), which uses multiple kernels to create an efficient receptive field and enhance segmentation performance. As a result of its multi-kernel design, MKIS-Net is a light-weight architecture with a small number of trainable parameters. Moreover, these multi-kernel receptive fields also contribute to better segmentation results. We demonstrate the efficacy of MKIS-Net on several tasks including segmentation of retinal vessels, skin lesion segmentation, and chest X-ray segmentation. The performance of the proposed network is quite competitive, and often superior, in comparison to state-of-the-art methods. Moreover, in some cases MKIS-Net has more than an order of magnitude fewer trainable parameters than existing medical image segmentation alternatives and is at least four times smaller than other light-weight architectures.

Compression of convolutional neural network models has recently been dominated by pruning approaches. A class of previous works focuses solely on pruning the unimportant filters to achieve network compression. Another important direction is the design of sparsity-inducing constraints which has also been explored in isolation. This paper presents a novel training scheme based on composite constraints that prune redundant filters and minimize their effect on overall network learning via sparsity promotion. Also, as opposed to prior works that employ pseudo-norm-based sparsity-inducing constraints, we propose a sparse scheme based on gradient counting in our framework. Our tests on several pixel-wise segmentation benchmarks show that the number of neurons and the memory footprint of networks in the test phase are significantly reduced without affecting performance. MobileNetV3 and UNet, two well-known architectures, are used to test the proposed scheme. Our network compression method not only results in reduced parameters but also achieves improved performance compared to MobileNetv3, which is an already optimized architecture.

In this paper, a hardware architecture of a multimodal biometric system is presented that massively exploits the inherent parallelism. The proposed system is based on multiple biometric fusion that uses two biometric traits, fingerprint and iris. Each biometric trait is first optimised at the software level, by addressing some of the issues that directly affect the FAR and FRR. Then the hardware architectures for both biometric traits are presented, followed by a final multimodal hardware architecture. To the best of the author's knowledge, no other FPGA-based design exits that used these two traits.

This paper presents a hybrid approach to achieve iris localization based on a Laplacian of Gaussian (LoG) filter, region growing, and zero-crossings of the LoG filter. In the proposed method, an LoG filter with region growing is used to detect the pupil region. Subsequently, zero-crossings of the LoG filter are used to accurately mark the inner and outer circular boundaries. The use of LoG based blob detection along with zero-crossings makes the inner and outer circle detection fast and robust. The proposed method has been tested on three public databases: MMU version 1.0, CASIA-IrisV1 and CASIA-IrisV3- Lamp. The experimental results demonstrate the segmentation accuracy of the proposed method. The robustness of the proposed method is also validated in the presence of noise, such as eyelashes, a reflection of the pupil, Poisson, Gaussian, speckle and salt-and-pepper noise. The comparison with well-known methods demonstrates the superior performance of the proposed method's accuracy and speed.

Diabetic retinopathy refers to the pathology of the retina induced by diabetes and is one of the leading causes of preventable blindness in the world. Early detection of diabetic retinopathy is critical to avoid vision problem through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time-consuming and requires intense effort due to the small size of lesion and low contrast of the images. Thus, computer-assisted diagnosis of diabetic retinopathy based on the detection of red lesions is actively being explored recently. In this paper, we present a convolutional neural network with residual skip connection for the segmentation of exudates in retinal images. To improve the performance of network architecture, a suitable image augmentation technique is used. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. Comparative performance analysis of three benchmark databases: HEI-MED, E-ophtha, and DiaretDB1 is presented. It is shown that the proposed method achieves accuracy (0.98, 0.99, 0.98) and sensitivity (0.97, 0.92, and 0.95) on E-ophtha, HEI-MED, and DiaReTDB1, respectively.

Worldwide attempts are being made to harness wasted heat or optimize the power systems by achieving the theoretical efficiency of the supercritical carbon dioxide (S-CO2) power cycle. The heterogeneity and variable quality of scholarly data may challenge researchers of the field (S-CO2 power cycle) to survey all of the available information. This study is focused on scientometric analysis to provide deep insights into global research performance and the collaborative architectonical structure. It reveals the progressive research trend (2000–2019) of the Supercritical Carbon dioxide (S-CO2) power cycle and hotspot areas by considering various quantitative measures. The sophisticated altimetric model was employed to analyze scientific researches that originated from Scopus Elsevier and Web of Science. Quantitative measures include the contribution of countries, organizations, authors, funding agencies, and journals that were investigated and ranked. Moreover, a scientific mapping approach is applied to identifying the cross-connections of each quantitative measure. It is indicated that the S-CO2 power cycle focused research increased exponentially from 2010. National Natural Science Foundation of China, USA Department of Energy, and Fundamental Research Funds for the Central Universities are leading sponsor agencies. USA Department of Energy, Xian Jiao Tong University, and Korea Advance Institute of Science and Technology are the most productive organizations. Similarly, Energy, Applied Thermal Engineering, and Energy Conversion and Management are top productive journals. At the same time, the USA, China, and South Korea are leading countries, and Lee, Jeong Ik Dai, Yiping Lee, and Jekyoung are the most dominating Authors in the S-CO2 power cycle technology developmental contributions. The core study areas include layout configuration with other power cycles, especially the Brayton cycle, optimization of operating conditions, and design of heat exchangers. S-CO2 highe

The present study involved the bio-fabrication of silver nanoparticles (AgNPs) by using the Euphorbia helioscopia L. leaves aqueous extract to improve the production of secondary metabolites in industrially important sunflower (Helianthus annuus L.) plants. Phyto-fabrication of AgNPs was confirmed by using spectrophotometry, SEM imaging and X-ray diffraction analysis. The morphological and optical characterization manifested that the AgNPs are crystalline and exist in the size range of 30–100 nm. Various concentrations (10, 20, 40, 60, 80 and 100 mg/L) of AgNPs were applied in combinations on sunflower seeds and crop plants. The effects of biosynthesized AgNPs were evaluated for agro-morphological parameters (plant height, flowering initiation and seed weight), biochemical metabolites (chlorophyll, proline, soluble sugar, amino acid and protein contents) and enzymatic activities (superoxide dismutase and ascorbate peroxidase) in sunflower and 60 mg/L concentration of AgNPs on sunflower seeds and foliar sprays on plants in combination were found to be effective to elicit biochemical modifications to improve secondary metabolites. It was also observed experimentally that 60 mg/L concentration of AgNPs improved the biochemical, fatty acid and enzymatic attributes of sunflower plants, which in turn improved the plant agro-morphological parameters. Near-infrared spectroscopic analysis results confirmed the improvement in the seed quality, oil contents and fatty acid composition (palmitic acid, oleic acid and linoleic acid) after the applications of AgNPs. The findings of the present investigation confirm the exogenous applications of bio-fabricated AgNPs in combinations on seeds and plants to improve the plant yield, seed quality and secondary metabolite contents of the sunflower plants.

Hydro politics is an important dimension of India Pakistan relations, overshadowed mainly by strategic issues between both states. Even the discussion on water issues is more focused on technical issues. However, the main question that arises is: Is hydro politics between India and Pakistan a problem of perceptions (intentions) or it forms part of overall strategic rivalry between both states? This paper discusses India-Pakistan water relations from the theoretical perspectives of (neo) realism, (neo) liberalism, constructivism, and human security school of thought. It argues that, like in general India-Pakistan political relations, it is realism/ neo-realism which still reigns supreme in explaining India-Pakistan hydro politics as well. It argues that in the wake of the Cold War, different theories emerged which undermined the traditional approaches and perspectives of realism and liberalism. These new theoretical traditions were also employed in explaining India-Pakistan political as well water relations. However, due to the competitive security of the region of South Asia in general and India-Pakistan’s security dilemma in particular, the theoretical perspectives of (neo) liberalism, constructivism, and human security fall short in theorizing India-Pakistan water relations. To answer the question posed earlier, this paper has mostly analyzed the available literature, both theoretical and related to hydro politics, to construct the argument.  Therefore, this paper concludes that instead of employing (neo) liberalism, constructivism, and human security, it is realism/ neo-realism which still reigns supreme in explaining India-Pakistan political as well as water relations. &nbsp

Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images; however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pat

Recent progress in encoder-decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally demanding to run on affordable hardware, and thus users often resort to practical workarounds by modifying various macro-level design aspects. Two common examples are downsampling of the input images and reducing the network depth to meet computer memory constraints. In this paper we investigate the effects of these changes on segmentation performance and show that image complexity can be used as a guideline in choosing what is best for a given dataset. We consider four statistical measures to quantify image complexity and evaluate their suitability on ten different public datasets. For the purpose of our experiments we also propose two new encoder-decoder architectures representing shallow and deep networks that are more memory efficient than currently popular networks. Our results suggest that median frequency is the best complexity measure in deciding about an acceptable input downsampling factor and network depth. For high-complexity datasets, a shallow network running on the original images may yield better segmentation results than a deep network running on downsampled images, whereas the opposite may be the case for low-complexity images.

Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back to examine the real need for such complexity. We present RC-Net, a fully convolutional network, where the number of filters per layer is optimized to reduce feature overlapping and complexity. We also used skip connections to keep spatial information loss to a minimum by keeping the number of pooling operations in the network to a minimum. Two publicly available retinal vessel segmentation datasets were used in our experiments. In our experiments, RC-Net is quite competitive, outperforming alternatives vessels segmentation methods with two or even three orders of magnitude less trainable parameters.

Ilāj bit tadbīr is one of the treatment plans in the Unani system of medicine which includes the modification in asbāb-i-sitta zarooriya (six essential factors) through certain interventions. Nutūl or irrigation is a classical and effective method in regimenal therapy which refers to pouring or dripping of liquid slowly and steadily over the body part from a pre-fixed height. The benefits achieved are an amalgamation of neurological effect, psychological effect and pharmacological actions of the procedure and the drugs used. For this purpose, usually water, oil or medicated decoction is poured from a height over specific sites of body in certain diseases. Therapeutically, nutūl is effective in various ways such as dispersing the causative morbid matter from the part, normalizing the mal-temperament, relieving pain, increasing the circulation, etc. It is specifically useful as an adjuvant treatment in the management of central nervous system disorders like headache, insomnia, migraine, amnesia, melancholia, vertigo, epilepsy and also in certain other disorders like cystitis, mastitis, arthralgia, etc. These therapeutic effects are expected due to the kafiyat (quality) of liquid and the constituents of the formulation used in the procedure. Nutūl has an analgesic effect also, hence, recommended in several Musculoskeletal problems such as waja-ul-mafasil (osteoarthritis), waja-uz-zahr (low backache), waja-ul-azlat (myalgia), irq-un-nasa (sciatica), etc. The exact mechanism of action of Nutūl therapy is still not known but few preliminary reports suggested that it has an anxiolytic effect through decreasing the plasma noradrenaline and urinary serotonin excretion, decrease in rate of breathing, reduction in diastolic blood pressure and heart rate with lowered sympathetic tone. Keywords: Taḥlīl; Kafiyat; Mizāj; Tadbīr; Joshānda

The multidimensional impacts of climate change are increasing the vulnerability of Pakistan day by day. The government of Pakistan has framed polices and laws related to climate change however, the challenge is the implementation of these polices and initiatives which is directly related to climate change awareness and literacy. This study addresses the central question of what the status of climate change awareness and literacy and what role media and social actors play in this regard. To answer this question, a qualitative, descriptive, and analytical methodology has been employed. This study found that poor climate literacy and awareness among common masses is one of the key reasons that so far these initiatives are not successful. Since the real chain movers of any response and development are people not policy makers or power elites so nourishing eagerness for climate change literacy and awareness proves effective. This study recommends that as a nation, it is necessary to develop climate literacy and awareness to generate public response against the imminent threat of climate change.

The renowned water expert, John Briscoe, predicted a bleak future for India-Pakistan water relations across the Indus attributing it to Pakistan’s downstream anxieties vis-à-vis upstream regional hegemon-India. Do the other co-riparian states of India share the same bleak future across the South Asian rivers of the Ganges, Brahmaputra, and Meghna or are the water relations across these rivers peaceful as compared to the Indus? To answer this question, this study first explores India-Pakistan water disputes on the Indus and then analyses India-Bangladesh water disputes on the Ganges and Brahmaputra, India-Nepal, India-Bhutan, and Pakistan-Afghanistan water relations. The methodology adopted for this study is descriptive, historical, and analytical in its nature. The study concludes that India has not only failed to adopt a conciliatory approach towards Pakistan on the Indus but has generated mistrust amongst other neighbouring countries over water sharing due to its hegemonic hydro-behaviour. It recommends that India should adopt a conciliatory approach to have peaceful relations across the rivers of South Asia.

This paper aims to identify the factors responsible for reluctance of centre to decentralise power and imitation of the same trend in provinces while dealing with minority ethno-lingual identities. The paper explores questions: what is the nature of federalism in Pakistan? How federal question is dealt in the three constitutions of the republic? How differently ethnic groups responded to various federal arrangements orchestrated by the managers of the state over the years? Objectives of the study are to develop an understanding into the challenges posed to federalism in Pakistan and to develop a more inclusive approach for addressing the federal question. The discussion generated in this study is based upon qualitative analysis of existing published literature in the form of books, research articles, reports, and official documents. The narrative upon which the federation of Pakistan has been constructed is self-contradictory. The paper not only exposes the contradictions of this narrative but also includes societal perspective on the ethnic diversity and federation of Pakistan. It has been concluded that the crisis generated by over-centralisation of the state could only be resolved by ensuring the fundamental federal values like decentralization, provincial autonomy, and devolution within the provinces to the grass root level.

Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.

The performance of cross-correlation functions can decrease computational complexity under optimal fingerprint feature selection. In this paper, a technique is proposed to perform alignment of fingerprints followed by their matching in fewer computations. Minutiae points are extracted and alignment is performed on the basis of their spatial locations and orientation fields. Unlike traditional cross-correlation based matching algorithms, ridges are not included in the matching process to avoid redundant computations. However, optimal cross-correlation is chosen by correlating feature vectors accompanying x-y locations of minutiae points and their aligned orientation fields. As a result, matching time is significantly reduced with much improved accuracy.

Background: Neurosurgical task shifting and task sharing (TS/S), delegating clinical care to non-neurosurgeons, is ongoing in many hospital systems in which neurosurgeons are scarce. Although TS/S can increase access to treatment, it remains highly controversial. This survey investigated perceptions of neurosurgical TS/S to elucidate whether it is a permissible temporary solution to the global workforce deficit. Methods: The survey was distributed to a convenience sample of individuals providing neurosurgical care. A digital survey link was distributed through electronic mailing lists of continental neurosurgical societies and various collectives, conference announcements, and social media platforms (July 2018-January 2019). Data were analyzed by descriptive statistics and univariate regression of Likert Scale scores. Results: Survey respondents represented 105 of 194 World Health Organization member countries (54.1%; 391 respondents, 162 from high-income countries and 229 from low- and middle-income countries [LMICs]). The most agreed on statement was that task sharing is preferred to task shifting. There was broad consensus that both task shifting and task sharing should require competency-based evaluation, standardized training endorsed by governing organizations, and maintenance of certification. When perspectives were stratified by income class, LMICs were significantly more likely to agree that task shifting is professionally disruptive to traditional training, task sharing should be a priority where human resources are scarce, and to call for additional TS/S regulation, such as certification and formal consultation with a neurosurgeon (in person or electronic/telemedicine). Conclusions: Both LMIC and high-income countries agreed that task sharing should be prioritized over task shifting and that additional recommendations and regulations could enhance care. These data invite future discussions on policy and training programs. Keywords: Global health; Global n

Machine learning methods with quantitative imaging features integration have recently gained a lot of attention for lung nodule classification. However, there is a dearth of studies in the literature on effective features ranking methods for classification purpose. Moreover, optimal number of features required for the classification task also needs to be evaluated. In this study, we investigate the impact of supervised and unsupervised feature selection techniques on machine learning methods for nodule classification in Computed Tomography (CT) images. The research work explores the classification performance of Naive Bayes and Support Vector Machine(SVM) when trained with 2, 4, 8, 12, 16 and 20 highly ranked features from supervised and unsupervised ranking approaches. The best classification results were achieved using SVM trained with 8 radiomic features selected from supervised feature ranking methods and the accuracy was 100%. The study further revealed that very good nodule classification can be achieved by training any of the SVM or Naive Bayes with a fewer radiomic features. A periodic increment in the number of radiomic features from 2 to 20 did not improve the classification results whether the selection was made using supervised or unsupervised ranking approaches.

In this paper, we present a gradient-free approach for training multi-layered neural networks based upon quantum perceptrons. Here, we depart from the classical perceptron and the elemental operations on quantum bits, i.e. qubits, so as to formulate the problem in terms of quantum perceptrons. We then make use of measurable operators to define the states of the network in a manner consistent with a Markov process. This yields a Dirac-Von Neumann formulation consistent with quantum mechanics. Moreover, the formulation presented here has the advantage of having a computational efficiency devoid of the number of layers in the network. This, paired with the natural efficiency of quantum computing, can imply a significant improvement in efficiency, particularly for deep networks. Finally, but not least, the developments here are quite general in nature since the approach presented here can also be used for quantum-inspired neural networks implemented on conventional computers., Comment: 9 pages, 2 figures, Accepted in ICONIP 2020

Background Neurosurgical task shifting and task sharing (TS/S), delegating clinical care to non-neurosurgeons, is ongoing in many hospital systems in which neurosurgeons are scarce. Although TS/S can increase access to treatment, it remains highly controversial. This survey investigated perceptions of neurosurgical TS/S to elucidate whether it is a permissible temporary solution to the global workforce deficit. Methods The survey was distributed to a convenience sample of individuals providing neurosurgical care. A digital survey link was distributed through electronic mailing lists of continental neurosurgical societies and various collectives, conference announcements, and social media platforms (July 2018-January 2019). Data were analyzed by descriptive statistics and univariate regression of Likert Scale scores. Results Survey respondents represented 105 of 194 World Health Organization member countries (54.1%; 391 respondents, 162 from high-income countries and 229 from low- and middle-income countries [LMICs]). The most agreed on statement was that task sharing is preferred to task shifting. There was broad consensus that both task shifting and task sharing should require competency-based evaluation, standardized training endorsed by governing organizations, and maintenance of certification. When perspectives were stratified by income class, LMICs were significantly more likely to agree that task shifting is professionally disruptive to traditional training, task sharing should be a priority where human resources are scarce, and to call for additional TS/S regulation, such as certification and formal consultation with a neurosurgeon (in person or electronic/telemedicine). Conclusions Both LMIC and high-income countries agreed that task sharing should be prioritized over task shifting and that additional recommendations and regulations could enhance care. These data invite future discussions on policy and training programs.

BackgroundTwo randomised trials assessing the effectiveness of decompressive craniectomy (DC) following traumatic brain injury (TBI) were published in recent years: DECRA in 2011 and RESCUEicp in 2016. As the results have generated debate amongst clinicians and researchers working in the field of TBI worldwide, it was felt necessary to provide general guidance on the use of DC following TBI and identify areas of ongoing uncertainty via a consensus-based approach.MethodsThe International Consensus Meeting on the Role of Decompressive Craniectomy in the Management of Traumatic Brain Injury took place in Cambridge, UK, on the 28th and 29th September 2017. The meeting was jointly organised by the World Federation of Neurosurgical Societies (WFNS), AO/Global Neuro and the NIHR Global Health Research Group on Neurotrauma. Discussions and voting were organised around six pre-specified themes: (1) primary DC for mass lesions, (2) secondary DC for intracranial hypertension, (3) peri-operative care, (4) surgical technique, (5) cranial reconstruction and (6) DC in low- and middle-income countries.ResultsThe invited participants discussed existing published evidence and proposed consensus statements. Statements required an agreement threshold of more than 70% by blinded voting for approval.ConclusionsIn this manuscript, we present the final consensus-based recommendations. We have also identified areas of uncertainty, where further research is required, including the role of primary DC, the role of hinge craniotomy and the optimal timing and material for skull reconstruction.

Reliably and effectively detecting and classifying leather surface defects is of great importance to tanneries and industries that use leather as a major raw material such as leather footwear and handbag manufacturers. This paper presents a detailed and methodical review of the leather surface defects, their effects on leather quality grading and automated visual inspection methods for leather defect inspection. A detailed review of inspection methods based on leather defect detection using image analysis methods is presented, which are usually classified as heuristic or basic machine learning based methods. Due to the recent success of deep learning methods in various related fields, various architectures of deep learning are discussed that are tailored to image classification, detection, and segmentation. In general, visual inspection applications, where recent CNN architectures are classified, compared, and a detailed review is subsequently presented on the role of deep learning methods in leather defect detection. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather quality quantification, and simultaneous defect inspection methods, which need to be investigated in the future to make progress in this crucial area of research.