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Pneumonia is a disease caused by the virus (flu, respiratory Syncytial Virus) or bacteria. It can be fatal if not diagnosed and treated at an early stage. Chest X-rays have been widely utilized to diagnose such abnormalities with high exactitude and are primarily responsible for the augment real-world diagnosis process. Poor availability of authentic data and yardstick-based approaches and studies complicates the comparison process and identifying the safest recognition method. In this paper, a Dual Deterministic Model (DD-M) is proposed based on a Deep Neural network that would identify Pneumonia from chest X-ray and distinguish the cause in case of either viral or bacterial infection at an efficiency equivalent of an active radiologist. To accomplish the automated task of the proposed algorithm, an automatic computer-aided system is necessary. The proposed algorithm incorporates deep learning techniques to understand radiographic imaging better. The results were evaluated after implementing the proposed algorithm where; it reveals various aspects of the chest infected with Pneumonia compared to the healthy individual with approximately 97.45% accuracy and distinguishes between the viral and bacterial infection with the efficiency of 88.41%. The proposed algorithm with an improved image dataset will help the doctors diagnose.

As the integration of inverter-based resources (IBRs) is rapidly increasing in regard to the existing power system, switching from grid-following (GFL) to grid-forming (GFM) inverter control is the solution to maintain grid resilience. However, additional overcurrent protection, especially during fault transition, is required due to limited overcurrent capability and the high magnitude of spikes during fault recovery in IBRs, specifically in the GFM control mode. Furthermore, the power system stability should not be compromised by the employment of additional fault ride through (FRT) schemes. This article presents the design and implementation of an adoptive fault ride through (FRT) scheme for grid-forming inverters under symmetrical fault conditions. The proposed adoptive FRT scheme is comprised of two cascaded power electronic-based circuits, i.e., fault current ride through and a spikes reactor. This adoptive FRT scheme optimizes the fault variables during the fault time and suppresses the fault clearing spikes, without affecting system stability. A three-bus inverter-based grid-forming model is used in MATLAB/Simulink for the implementation of the proposed scheme. Further, a conventionally used FRT scheme, which includes fault current reactors, is simulated in the same test environment for justification of the proposed adoptive scheme. The adoptive FRT scheme is simulated for both time domain and frequency domain to analyze the response of harmonic distortion with the suppression of the fault current. Moreover, the proposed scheme is also simulated under the GFL mode of IBRs to justify the reliability of the scheme. The overall simulation results and performance evaluation indices authenticate the optimal, fault tolerant, harmonic, and spike-free behavior of the proposed scheme at both the AC and DC side of the grid-forming inverters.

Energy management strategies are crucial to efficiently scheduling appliances and preventing peak generation due to increased energy demand. It is essential to manage the demand and supply of energy based on the consumer’s consumption patterns using various heuristic optimization techniques. Additionally, the end-user is more concerned with minimizing electricity costs and reducing peak-to-average ratios (PARs). This work proposes an arithmetic Harris hawks optimization (AHHO) as a new approach for improving the Harris hawks algorithm to optimize residential demand response (DR) load management in a smart grid. Our method employs arithmetic and lightweight flight operators based on the Lévy flight distribution to generate diverse design solutions and improve the HHO’s exploration capabilities. We consider 15 smart appliances and categorize them based on how much energy they use, computing the electricity price using real-time pricing (RTP) and critical peak pricing (CPP). While maintaining user satisfaction within operational and power limits, the objective is to decrease energy costs and PAR. We evaluate the effectiveness of our proposed AHHO approach against nine cutting-edge algorithms using both RTP and CPP schemes. The findings demonstrate that our suggested approach performs better than the other algorithms because it achieved cost savings of 42.10% and 30% under RTP and CPP schemes, respectively. Meanwhile, it also reduced PAR by 55.17% and 50% under RTP and CPP schemes, respectively.

The agriculture sector plays a crucial role in supplying nutritious and high-quality food. Plant disorders significantly impact crop productivity, resulting in an annual loss of 33%. The early and accurate detection of plant disorders is a difficult task for farmers and requires specialized knowledge, significant effort, and labor. In this context, smart devices and advanced artificial intelligence techniques have significant potential to pave the way toward sustainable and smart agriculture. This paper presents a deep learning-based android system that can diagnose ginger plant disorders such as soft rot disease, pest patterns, and nutritional deficiencies. To achieve this, state-of-the-art deep learning models were trained on a real dataset of 4,394 ginger leaf images with diverse backgrounds. The trained models were then integrated into an Android-based mobile application that takes ginger leaf images as input and performs the real-time detection of crop disorders. The proposed system shows promising results in terms of accuracy, precision, recall, confusion matrices, computational cost, Matthews correlation coefficient (MCC), mAP, and F1-score.

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.

With the rise in popularity of Internet of Things (IoT), mobile computing, and wearable devices, a huge amount of data is being generated. Running complex tasks such as that are machine learning-based with minimum energy consumption is a challenge. It requires complex algorithms to run locally such as on middleware fog within the proximity of the devices generating data, or globally in a cloud to analyze the acquired data and create robust and smart applications. Deep learning is a technique used to analyze the raw, unstructured data coming from the sensors and mobile devices. However, it depends on the type of task execution policy applied at each level; local or global, to decide on energy and performance efficiency, since deep learning algorithms are high in complexity. Hence, task execution will be hierarchically distributed among the IoT nodes, fog, and cloud. We present in this paper a three-tier IoT-fog-cloud model by augmenting CloudSim toolkit. We argue that with distributed task execution, we can achieve high scalability of IoT services, and manage the global energy consumption as well. As a proof-of-concept, we evaluate our three-tier architecture by considering computational tasks for various applications in IoT related to medical, multimedia, location-based, and text.

The study aims to explore the factors influencing the accessibility of small and medium-sized enterprises to institutional finance and moderating role of government supports in the relationship between collateral security, interest rates, financial information, business ownership, and accessibility to finance facilities in Pakistan. The moderation multiple regression analysis was applied for analysis using SMART PLS3.0 software. A fully structured questionnaire is used for gathering data or information from the respondents. The results showed that all the variables are directly and significantly influencing access to finance except interest rates, but interest rates indirectly affect the dependent. The government support does moderate in the interaction between collateral security, interest rate, and financial accessibility. On the other hand, the variable doesn't quite moderate the interaction between financial information, business ownership, and financial accessibility in Pakistan. This research aimed at making access to finance services essentially helpful to the SME industry by knowing what impacts their financial accessibility and helping lending institutions in understanding what effectively prevents the SME industry from accessing finance-related services in Pakistan.

This study primarily focuses on developing an exclusive model, generally explaining the importance of international rivers in the relationships of the nations and particularly interlinked conflict over Kashmir and the Indus River. Conflict is a fact among nations caused because of disputed lands or un-decided borders linked to valuable resources or due to political and ideological differences. Historical differences between Pakistan and India especially about water distributions can leads them towards war. So far, they had fought three wars due to Kashmir's un-resolved issue and an upcoming one could be forecasted to control water resources originating from occupied Kashmir. According to the neo-realistic point of view, this study examines the conflicting interests among India and Pakistan, which are related to Kashmir, and claims that it is not just religious but also geographical as it is connected to the control of river waters. There is very limited significant research present that primarily focuses on this facet of India and Pakistan’s relations. The current literature expresses their conflict as of ideological, cultural, and political nature. This model-based research addresses the relationship of water and other related sources to war and peace through a rational research method demonstrated by broadly observed data.

Software Defined Networks (SDNs) have dramatically simplified network management. However, enabling pure SDNs to respond in real-time while handling massive amounts of data still remains a challenging task. In contrast, fog computing has strong potential to serve large surges of data in real-time. SDN control plane enables innovation, and greatly simplifies network operations and management thereby providing a promising solution to implement energy and performance aware SDN-enabled fog computing. Besides, power efficiency and performance evaluation in SDN-enabled fog computing is an area that has not yet been fully explored by the research community. We present a novel SDN-enabled fog architecture to improve power efficacy and performance by leveraging cooperative and non-cooperative policy-based computing. Preliminary results from extensive simulation demonstrate an improvement in the power utilization as well as the overall performance (i.e., processing time, response time). Finally, we discuss several open research issues that need further investigation in the future., Comment: 7 pages

Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols.

Information and communication technology plays an important role in developing innovation at the organization level, not only for big companies but also for micro, small and medium enterprises while also facilitating the growth and advancement of organizations. The aim of this study is to examine the impact of innovation on SMEs between information and communication technologies with a sample of 364 SMEs in Pakistan. The findings indicate that information and communication technology have a positive and significant effect on the innovation activities of small and medium-sized enterprises (SMEs) in Pakistan.

The motivation behind conducting this research is to study the association between oil prices and Islamic and conventional stock indexes’ performance in the Malaysian market during COVID-19 using the wavelet analysis technique. The daily data on selected variables were collected from 1 January 2020, to 10 June 2021. Empirical investigation was made with wavelet analysis along with the Toda-Yamamoto test. The results revealed the significant response of both indexes to the oil price. Such response was negative for the short- and medium terms; however, it became positive in the long run. Our research has several important implications and recommendations for asset managers and policymakers. Policymakers and regulators should promote awareness and adopt effective action plans to minimize the risk of change in oil prices during the COVID-19 period. This research will enable investors, scholars, and policymakers to improve their current structure and prepare them for any potential future crisis.

Wireless body area networks (WBANs) are becoming a popular and convenient mechanism for IoT-based health monitoring applications. Maintaining the energy efficiency of the nodes in WBANs without degrading network performance is one of the crucial factors for the success of this paradigm. Obtaining routes for data packets should be a dynamic decision depending on network conditions. Consequently, in this paper, a novel cost-based routing protocol ZITA has been proposed that addresses primary issues of WBAN routing, such as timeliness, link quality, temperature control, and energy efficiency while finding the next hop for data packets. Zipf’s law is applied for relay selection to ensure the distribution of forwarding load among the potential relays. ZITA controls the transmission power level adaptively in order to cope with the time-varying channel conditions following multi-hop architecture. The protocol is simulated and the results show that the protocol gives better performance in terms of data received by the sink, heat dissipation of the wearable as well as implantable sensor nodes, and load sharing among relay nodes.

This paper provides a comparison of a designed method of a fault ride through (FRT) circuit, i.e., switch-type fault current limiter (STFCL) and bridge-type fault current limiter (BFCL), to optimize the electrical parameters of grid-connected solar systems (PVSs) under asymmetric single line-to-ground fault and symmetric three-phase fault. The main differences between switch- and bridge-type fault current limiters is the electric component devices such as the bridge rectifier, snubber capacitor, energy absorption bypass and current-limiting inductors. In addition, the designed FRT performance with the inverter control are analyzed in-depth, e.g., a well-adjusted proportional integral (PI) and proposed steepest descent (SD) controller are compared in the fault condition. To compare the proposed method with the conventional method, the AC power and voltage on a common coupling point (PCC) and DC link voltage of the PV system are analyzed with a MATLAB/Simulink model of a 100 kW three-phase grid-connected photovoltaic system. The simulation results of the proposed FRT circuit and SD controller verify the stability improvement and vibration-free and fast and robust responses of electrical parameters on both PV grid sides during asymmetric disturbances.

Smart devices in various application areas are becoming increasingly prevalent for efficient handling of multiple critical activities. One such area of interest is high-security militarized environments. Due to military zones’ harsh and unpredictable nature, monitoring devices deployed in such environments must operate without power interruption for extended time periods. Therefore, it is essential to choose an appropriate application design for operating these “things” in the internet of things (IoT) environment such that energy can be conserved throughout the operating span of an application. This paper presents two application modules and analyzes their performance in terms of energy conservation considering a military-based IoT-Fog architecture. The two modules are: A sequential application module, and a master-worker application module. Experimental results show that the master-worker module incurs lower energy consumption and communication overhead than the sequential application module. Significantly, the master-worker module exhibits a lower delay in tuple execution by almost four milliseconds while also accounting for lower simulation time and higher network utilization. The module achieves significant savings in energy consumption, making it more effective in handling smart devices.

Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages–initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.

Advanced nurse practitioners (ANPs) manage minor trauma in emergency departments; however, the quality of their care has not been objectively evaluated in comparison to orthopedic senior house officers (SHOs)/junior residents. We reviewed the management of 100 patients by ANPs. SHOs were tested as comparators. ANPs were then re-evaluated after receiving an education program. No difference between ANPs and novice SHOs was observed initially. After an education program, ANPs substantially improved with no statistical difference from experienced SHOs. ANPs provide a comparable standard of care to SHOs for minor trauma once they have completed an education program.

The ongoing COVID-19 pandemic has already taken millions of lives and damaged economies across the globe. Most COVID-19 deaths and economic losses are reported from densely crowded cities. It is comprehensible that the effective control and prevention of epidemic/pandemic infectious diseases is vital. According to WHO, testing and diagnosis is the best strategy to control pandemics. Scientists worldwide are attempting to develop various innovative and cost-efficient methods to speed up the testing process. This paper comprehensively evaluates the applicability of the recent top ten state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically detecting COVID-19 infection using chest X-ray images. Moreover, it provides a comparative analysis of these models in terms of accuracy. This study identifies the effective methodologies to control and prevent infectious respiratory diseases. Our trained models have demonstrated outstanding results in classifying the COVID-19 infected chest x-rays. In particular, our trained models MobileNet, EfficentNet, and InceptionV3 achieved a classification average accuracy of 95%, 95%, and 94% test set for COVID-19 class classification, respectively. Thus, it can be beneficial for clinical practitioners and radiologists to speed up the testing, detection, and follow-up of COVID-19 cases.

Attackers increasingly seek to compromise organizations and their critical data with advanced stealthy methods, often utilising legitimate tools. In the main, organisations employ reactive approaches for cyber security, focused on rectifying immediate incidents and preventing repeat attacks, through protections such as vulnerability assessment and penetration testing (VAPT) security information and event management (SIEM), firewalls, anti-spam/anti-malware solutions and system patches. Such system have weaknesses in addressing modern modern stealthy attacks. Proactive approaches, have been seen as part of the solution to this problem. However, approaches such as VAPT have limited scope and only works with threats that have already been discovered. Promising methods such as threat hunting are gaining momentum, enabling organisations to identify and rapidly respond to any potential attacks, though they have been criticised for their significant cost. In this paper, we present a novel hybrid model for uncovering tactics, techniques, and procedures (TTPs) through offensive security, specifically threat hunting via adversary emulation. The proposed technique is based on a novel approach of inducing adversary emulation (mapping each respective phase) model inside the threat hunting approach. The experimental results show that the proposed approach uses threat hunting via adversary emulation and has countervailing effects on hunting advance level threats. Moreover, the threat detection ability of the proposed approach utilizes minimum resources. The proposed approach can be used to develop the offensive security-aware environment for organizations to uncover advanced attack mechanisms and test their ability for attack detection.

This study aims to analyze the responses and views of Pakistani consumers to different sales-promotion techniques used by companies. The methodological contribution of the current study used the second-generation technique (Smart PLS) as compared to OLS. In addition, the instrument used in this study is a fully structured questionnaire adapted, a total of 200 questionnaires is filled from the respondents. The findings of the current study show that different sales promotions tools have different effects on consumer buying behaviour. We observed significant evidence of an association between buy one get one free, coupon discount, price discount, and Pakistani consumer buying behaviour. We further revealed an insufficient association between free samples and buying behaviour. This study will be helpful for the marketing managers in understanding what attracts consumers most? It can be an efficient source of relevant information for marketing managers about the responses of consumers towards sales promotions.

A sound knowledge of the elbow anatomy and biomechanics is critical to understanding the pathology of various elbow disorders and instigating appropriate management. The elbow joint is a trochoginglymoid joint: that is, it has flexion-extension [ginglymoid] motion at the ulnohumeral and radiocapitellar articulations and pronation and supination [trochoid] motion at the proximal radioulnar joint. Stability of the elbow joint is achieved through static and dynamic components. The aim of this article is to concisely describe the anatomy and biomechanics of the elbow joint relevant to the practice of trauma and orthopaedic surgeons.

Stored grains commodities are attacked by a variety of beetles and larvae of lepidopteron insect pets. of them,Tribolium castaneum(Herbst) is a severe insect pest of stored commodities throughout the world. This insect is widely spreading various climatic regions throughout the world. Results of bioassay with Spinetoram exposed that maximum larval mortality was detected 88.87% and 44.04% after “48 hr” and “72 hr”. Exposure time period after 0.03% concentration, correspondingly. Minimum mortality 14.17% after “24 hr” at same concentration. At lower concentration 0.02%, maximum mortality 61.96% after “72 hr”, 31.65% after “48 hr” of exposure time period. Minimum mortality of 15.02% was observed after “24 hr”. At lowest concentration 0.01%, maximum mortality 60.38% was recorded at “72 hr”, 28.22% after “48 hr”. Whereas, minimum mortality 8.41% was detected after “24 hr”. The mortality was increased by increased the concentration of spinetoram and exposure time. Repellent activities of acetone based plant extracts ofEruca sativa,Azadirachta indicaandRicinus communisagainst the insect pest of stored grain commodities,Tribolium castaneum. Various concentrations (5, 10 and 15%) of the plant extracts were applied on the filter papers in the bioassay experiments and after the release of 15d old beetles, repellency was evaluated after fixed intervals (12, 24). Increased repellency was found at increased concentrations of plants. The concentration interaction and plant extracts findings for the treatments remained momentous. The generally findings exposed raised death rate with raise the doses and time period.

The era of Zulfikar Ali Bhutto is characterized by sensational and dramatic political developments. As the post-1972 developments revealed, Bhuttos style of politics was quite different from what he had been projecting ever since 1967. The PPP was lacking the collaborative working mechanism. The opposition leaders sensed undemocratic treatment all the time. The PPP victory in the firstever general elections of 1970 afforded to the party an opportunity to make a new beginning. A change in the attitude of the party leadership, however, did not allow the ship of the nation to sail smoothly. This paper evaluates the performance of the PPP government with reference regarding constitutional development when PPP was in power. The purpose is to determine whether or not the PPP style of politics was in line with the spirit of the Constitution and under the established principles of democracy.

This article demonstrates a new topology for optimization of the electrical variables and enhancement of low-voltage-ride-through (LVRT) capacity of a grid-tied photovoltaic system (PS) during asymmetrical faults. The proposed topology comprises a fuzzy-logic controller (FLC) based on gradient descent (GD) optimization in association with parallel-resonance-type fault current limiter (PRFCL) as an LVRT circuitry. Gradient descent is an iterative process to minimize the objective function by updating the variable in the opposite direction of the gradient of the objective function. The PRFCL optimizes the fault variables, specifically preventing voltage sag without any transitional spikes. A 100-kW detailed model of grid-tied PS is used in MATLAB/Simulink to analyze the response of the proposed topology at the point of common coupling (PCC) and PV side variables. A keen comparative study of the conventionally adopted proportional-integral (PI) controller in association with crowbar circuitry is carried out for the justification of the proposed topology. The simulation findings of the proposed topology authenticate the optimal response of variables in terms of stability, robustness, smoothness, and fault tolerance at both the grid side and the PV side.

This paper presents a three phase nine-level transformer based cascaded H-bridge (CHB) modular multilevel inverter. Each single-phase module consists of two cascaded H-bridge circuits, having only eight switches and a transformer for efficient and cost-effective voltage conversion. This will lead to fewer number of switches and lower filtering requirement to generate multilevel output. For the switching logic, multiple modulation techniques are reviewed, such as Level-shift, Carrier-based and Space Vector Pulse Width Modulation technique (SVPWM. An improvised switching pattern is applied and multilevel voltage output is demonstrated with the aid of mathematical model and simulations.

Suprascapular neuropathy is often overlooked as a cause for shoulder pain in overhead athletes and can be misdiagnosed for many months, even years. With recent advancements in the understanding of the condition as well as its treatment methods, suprascapular neuropathy is now being diagnosed more frequently. However with a multitude of treatment options, it can be difficult to choose the optimal management. In this systematic review, we have carried out a detailed literature search about suprascapular neuropathy looking into evidence-based diagnostic workup and treatment modalities available. Repetitive overhead activities, rotator cuff tear and direct compression of the nerve by space-occupying lesion are important etiologies for suprascapular neuropathy. Whilst MRI is widely used for the identification of space-occupying lesions and rotator cuff injury; Electromyography (EMG) and Nerve Conduction Studies (NCS) remain gold standards for confirming injury to the nerve. Conservative treatment modalities including physiotherapy and activity modification, nerve blocks, arthroscopic and open surgical interventions are the main treatment options for suprascapular neuropathy.

Osteoarthritis of the wrist and hand can cause significant functional impairment and disability. Its management is as much an art as it is science, as one has to consider increasing patient expectations, functional demands, pain relief and preserving motion. A sound knowledge of the wrist and hand anatomy, biomechanics, appreciation of the specific location and grade of wrist and hand joints arthritis and various treatment options and their pros and cons is important to successfully manage patients with this condition. Wrist osteoarthritis can be idiopathic, however more commonly it is secondary to distal radius and/or carpal fractures and ligamentous injuries. A small proportion still can occur due to avascular necrosis of carpus or congenital and developmental causes. Osteoarthritis of the hand is often idiopathic with a high familial tendency, especially in younger patients but can be secondary to other causes such as trauma, gout and ligamentous laxity. Once the non-surgical management options such as analgesia, activity modifications, steroid injections and splints have been exhausted, then there are various surgical options available for wrist and hand osteoarthritis. These are tailored to the exact location of arthritis and patient factors. For the wrist, these include anterior and posterior interosseous nerve neurectomy, arthroscopic debridement, selective bony excisions, wrist arthroplasty and focal or total fusion. Whilst for finger osteoarthritis, surgical options include arthroplasty and arthrodesis. This article aims to describe the pathology of wrist and hand arthritis and various treatment options available.

Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99% for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96%. For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (96%). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.

Stroke is the second overall driving reason for human death and disability. Strokes are categorized into Ischemic and Hemorrhagic strokes. Ischemic stroke is 85% of strokes while hemorrhagic is 15%. An exact automatic lesion segmentation of ischemic stroke remains a test to date. A few machine learning techniques are applied previously to beat manual human observers yet slacks to survive. In this paper, we propose a completely automatic lesion segmentation of ischemic stroke in view of the Convolutional Neural Network (CNN). The dataset used as a part of this study is obtained from ISLES 2015 challenge, included four MRI modalities DWI, T1, T1c, and FLAIR of 28 patients. The CNN model is trained on 25 patient’s data while tested on the remaining 3 patients. As CNN is only used for classification, we convert segmentation to the pixel-by-pixel classification tasks. Dice Coefficient (DC) is used as a performance evaluation metric for assessing the performance of the model. The experimental results show that the proposed model achieves a comparatively higher DC rate from 4–5% than the considered state-of-the-art machine learning techniques.

This paper proposed Fuzzy-Sliding Mode Controller (F-SMC) for regulation of DC-link voltage and Proportional Resonant (PR) with Resonant Harmonic Compensator (RHC) for output current control of two stages 3 kW single-phase grid-tied Photovoltaic (PV) system. The dynamics of the system are tested with and without a feed-forward PV power loop. A SOGI phase lock loop is implemented that has harmonic insusceptibility, a fast-tracking accuracy, and rapid-dynamic response. The proposed controller enhances the dynamic and steady state performance of the overall system. Furthermore, the simulation results of PI controller is also presented to show the effectiveness of the proposed control strategy. A graphical and tabulated comparative assessment with a well-tuned PI controller authenticates the effectiveness, fastness and robustness of proposed controller. MATLAB/Simulink R2017b software is used as a design and implementation platform.

Spirituality is usually understood as a way of being that flows from a certain profound experience of reality, which is known as ‘mystical’, ‘religious’, or ‘spiritual’ experience. There are numerous descriptions of this experience in the literature of the world’s religions, which tend to agree that it is a direct, non-intellectual experience of reality with some fundamental characteristics that are independent of cultural and historical contexts. Spiritual and scientific quests are two complementary inquiries into reality. Any feeling of antagonism between them is a product of a narrow vision. Science deals with what is measurable; religion is the quest for discovering and understanding the immeasurable. A scientist is not intelligent if he denies the existence of the immeasurable. There is nothing that is anti-science but there is a lot that is beyond science. The two quests have to go hand in hand. We not only need to have an understanding of the laws that govern the phenomena occurring in the external world around us but also we need to discover order and harmony in our consciousness. Human understanding is incomplete unless it covers both aspects of reality: matter as well as consciousness. Indeed the division between the scientific and spiritual quests is itself the creation of the human mind. Reality is one undivided whole which includes both matter and consciousness. Our thoughts, being limited by our experience, divide the external world from the inner world of our consciousness, in much the same way as our mind divides time from space though they are both two aspects of a single continuum۔

Background:Management of distal humerus fractures remains a challenge for trauma surgeons and advancements in treatment options continue to be made to achieve the best results for patients presenting with these complex fractures. Our aim in this article is to provide the surgeons with a detailed review of current literature to help them make an evidence based decision when faced with managing such complex injuries in their surgical practice.Methods:This is a comprehensive review of the current literature that details various aspects of distal distal humerus fractures such as classification, surgical anatomy, surgical approaches, treatment options, choices of devices, outcomes and complications.Results:With the advancements in techniques and equipment, there has been improvement in patients’ outcomes following surgical management of these fractures and a large proportion of these patients are able to achieve pre-injury level of function. The contoured locking plates have enabled successful fixation of many of these fractures that were previously considered unfixable. For those not amenable to surgical fixation, total elbow arthroplasty and elbow hemiarthroplasty are considered as good alternatives.Conclusion:Since the days where the ‘bag of bones’ technique was the preferred method of treating these complex injuries, techniques and outcomes have advanced greatly. However, they still present a significant technical challenge and need meticulous technique and experience to achieve optimal results.