Dividend Policy as a Core Determinant of Earning Management: Evidence from Pakistan

The current study estimated the impact of dividend policy on earnings management for the non-financial firms of Pakistan listed at the Karachi Stock Exchange. The data covering the period from 2005 to 2017 were estimated by using a random effect-generalized least square regression. The study findings report that dividend policy is a significant determinant of earning management and limits the probability of a firm’s earning management practices. This research gives us some empirical evidence regarding the role of key contributing factors in the scope of earnings management. Regulators can implement corporate governance rules and regulations based on empirical tracts in place of motivational debates on politics. This study results offer a compact platform for investors to eradicate ambiguity by recognizing the likelihoods of resourceful goals and improving their policymaking process. The research findings will help to give investors a clear idea about the various factors that play a contributing part for making financial reporting and misreporting of profits.  These contributing factors allow investors to be careful about the ingenious purpose and effectiveness of management to obtain returns for their benefit

En bloc resection of a giant retroperitoneal liposarcoma: A surgical challenge

Liposarcomas are exceedingly rare entities that evoke malignant transformation of connective tissue and fat cells.These tumours occur throughout the soft tissues of the body, afflicting a myriad of regions. In the adult population, liposarcomas represent the most prevalent subtype of sarcomas, and often arise de novo. Retroperitoneal liposarcomas (RLS) are a ubiquitous subset of sarcomas that, due to their deep location in the hollow abdomen, can grow to astronomical proportions before manifesting any noticeable symptoms; a prompt diagnosis of RLS is therefore often rendered dilatory. We hereby delineate the case of a 43-year-old woman who presented with vague left hemiabdominal distention and discomfort. A subsequent computed tomography scan divulged a giant retroperitoneal growth impaling on and thus displacing the pancreas. A compartmental, en bloc resection was performed, with subsequent histopathology of the excised specimen revealing a well-differentiated liposarcoma. The surgical intervention was curative and led to an uneventful recovery. This paper highlights the pertinence of surgical management as an appropriate treatment modality for a complete resection of RLS

N-Benzyl-N-(2-meth­oxy­phen­yl)benzene­sulfonamide

In the title mol­ecule, C20H19NO3S, the dihedral angle between the phenyl rings is 48.93 (18)°, and they make dihedral angles of 38.37 (17) and 86.50 (19)° with the benzene ring. A weak intra­molecular C—H⋯O inter­action might stabilize the mol­ecular conformation. In the crystal, weak π–π stacking inter­actions between the benzene rings [centroid–centroid distance = 3.774 (2) Å] may help to establish the packing

Efficacy of the muscle energy technique versus the strain-counter strain technique on immediate deactivation of myofascial trigger points in upper trapezius muscle

Aims and objectives: Thisstudy aimed to check the comparative efficacy of the Muscle EnergyTechnique versus the Strain-Counter Strain technique on immediate deactivation ofmyofascial trigger points in the upper trapezius muscle. Study design: The study comprised acomparative analytical design to compare and contrast the two study interventions. Place andduration of the study: The research was conducted in the Department of Physical therapy,Allied hospital Faisalabad for 6 months. Patients and methods: Based on inclusion andexclusion criteria, 40 subjects were enrolled in the study using the convenient samplingmethod. The subjects were divided into two groups; group A (n=20) received a single sessionof baseline treatment with themuscle energy technique, while group B (n=20)received a singlesession of baseline treatment with the strain-counter strain technique. The subjects wereevaluated through the pressure-pain threshold (algometer), Numeric Pain Rating Scale, andModified Bournemouth Questionnaire as pre-intervention and post-intervention measuringtools for pain and functional status. Results: The data was analyzed using SPSS version 17.Within group analysis showed a significant difference between pre- values and post values ofpressure-pain threshold, Numeric Pain Rating Scale and Modified BournemouthQuestionnaire in both groups (P<0.05). Between group analysis was done using independentsample t test. It also showed significant difference (P<0.05) in post mean values between thetwo group subjects in all three outcomes. The post-mean values for the strain-counter straingroup were slightly more improved than the Muscle Energy Technique group. Conclusion:The strain-counter strain technique is found more effective than the Muscle Energy Techniquefor immediate deactivation of myofascial trigger points in the upper trapezius muscle

Ensemble learning-based IDS for sensors telemetry data in IoT networks

The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with other devices using different communication protocols such as CoAP, MQTT, DDS, etc. Study shows that these protocols are vulnerable to attack and prove a significant threat to IoT telemetry data. Within a network, IoT devices are interdependent, and the behaviour of one device depends on the data coming from another device. An intruder exploits vulnerabilities of a device's interdependent feature and can alter the telemetry data to indirectly control the behaviour of other dependent devices in a network. Therefore, securing IoT devices have become a significant concern in IoT networks. The research community often proposes intrusion Detection Systems (IDS) using different techniques. One of the most adopted techniques is machine learning (ML) based intrusion detection. This study suggests a stacking-based ensemble model makes IoT devices more intelligent for detecting unusual behaviour in IoT networks. The TON-IoT (2020) dataset is used to assess the effectiveness of the proposed model. The proposed model achieves significant improvements in accuracy and other evaluation measures in binary and multi-class classification scenarios for most of the sensors compared to traditional ML algorithms and other ensemble techniques

Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

Skin cancer is developed due to abnormal cell growth. These cells are grown rapidly and destroy the normal skin cells. However, it's curable at an initial stage to reduce the patient's mortality rate. In this article, the method is proposed for localization, segmentation and classification of the skin lesion at an early stage. The proposed method contains three phases. In phase I, different types of the skin lesion are localized using tinyYOLOv2 model in which open neural network (ONNX) and squeeze Net model are used as a backbone. The features are extracted from depthconcat7 layer of squeeze Net and passed as an input to the tinyYOLOv2. The propose model accurately localize the affected part of the skin. In Phase II, 13-layer 3D-semantic segmentation model (01 input, 04 convolutional, 03 batch-normalization, 03 ReLU, softmax and pixel classification) is used for segmentation. In the proposed segmentation model, pixel classification layer is used for computing the overlap region between the segmented and ground truth images. Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method. The optimized features vector is passed to the classifiers such as optimized (O)-SVM and O-NB. The proposed method is evaluated on the top MICCAI ISIC challenging 2017, 2018 and 2019 datasets. The proposed method accurately localized, segmented and classified the skin lesion at an early stage.Qatar University [IRCC-2020-009]

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer based on CNN and YOLOv2-DFU Models

Diabetes is a chronic disease, if not treated in time may lead to many complications including diabetic foot ulcers (DFU). DFU is a dangerous disease, it needs regular treatment otherwise it may lead towards foot amputation. The DFU is classified into two categories such as infection (bacteria) and the ischaemia (inadequate supply of the blood). The DFU detection at an initial phase is a tough procedure. Therefore in this research work 16 layers convolutional neural network (CNN) for example 01 input, 03 convolutional, 03 batch-normalization, 01 average pooling, 01 skips convolutional, 03 ReLU, 01 add (element-wise addition of two inputs), fully connected, softmax and classification output layers for classification and YOLOv2-DFU for localization of infection/ischaemia models are proposed. In the classification phase, deep features are extracted and supplied to the number of classifiers such as KNN, DT, Ensemble, softmax, and NB to analyze the classification results for the selection of best classifiers. After the experimentation, we observed that DT and softmax achieved consistent results for the detection of ischaemia/infection in all performance metrics such as sensitivity, specificity, and accuracy as compared with other classifiers. In addition, after the classification, the Gradient-weighted class activation mapping (Grad-Cam) model is used to visualize the high-level features of the infected region for better understanding. The classified images are passed to the YOLOv2-DFU network for infected region localization. The Shuffle network is utilized as a mainstay of the YOLOv2 model in which bottleneck extracted features through ReLU node-199 layer and passed to the YOLOv2 model. The proposed method is validated on the newly developed DFU-Part (B) dataset and the results are compared with the latest published work using the same dataset

Recognition of different types of leukocytes using YOLoV2 and optimized bag-of-features

White blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm, which contains darkNet-19 as a basenetwork of the YOLOv2 model. In this model features are extracted from LeakyReLU-18 of darkNet-19 and supplied as an input to the YOLOv2 model. The YOLOv2-Nucleus-Cytoplasm model localizes and classifies the WBCs with maximum score labels. It also localize the WBCs into the blast and non-blast cells. After localization, the bag-of-features are extracted and optimized by using particle swarm optimization(PSO). The improved feature vector is fed to classifiers i.e., optimized naïve Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset

Cardiovascular Outcomes and Trends of Transcatheter vs. Surgical Aortic Valve Replacement Among Octogenarians With Heart Failure: A Propensity Matched National Cohort Analysis

Background: Heart failure (HF) is a complex clinical syndrome with symptoms and signs that result from any structural or functional impairment of ventricular filling or ejection of blood. Limited data is available regarding the in-hospital outcomes of TAVR compared to SAVR in the octogenarian population with HF. Methods: The National Inpatient Sample (NIS) database was used to compare TAVR versus SAVR among octogenarians with HF. The primary outcome was in-hospital mortality. The secondary outcome included acute kidney injury (AKI), cerebrovascular accident (CVA), post-procedural stroke, major bleeding, blood transfusions, sudden cardiac arrest (SCA), cardiogenic shock (CS), and mechanical circulatory support (MCS). Results: A total of 74,995 octogenarian patients with HF (TAVR-HF n = 64,890 (86.5%); SAVR n = 10,105 (13.5%)) were included. The median age of patients in TAVR-HF and SAVR-HF was 86 (83-89) and 82 (81-84) respectively. TAVR-HF had lower percentage in-hospital mortality (1.8% vs. 6.9%;p \u3c 0.001), CVA (2.5% vs. 3.6%; p = 0.009), SCA (9.9% vs. 20.2%; p \u3c 0.001), AKI (17.4% vs. 40.8%); p \u3c 0.001), major transfusion (26.4% vs 67.3%; p \u3c 0.001), CS (1.8% vs 9.8%; p \u3c 0.001), and MCS (0.8% vs 7.3%; p \u3c 0.001) when compared to SAVR-HF. Additionally, post-procedural stroke and major bleeding showed no significant difference. The median unmatched total charges for TAVR-HF and SAVR-HF were 194,561$and 246,100$ respectively. Conclusion: In this nationwide observational analysis, TAVR is associated with an improved safety profile for octogenarians with heart failure (both preserved and reduced ejection fraction) compared to SAVR