A User Feedback Centric Approach for Detecting and Mitigating God Class Code Smell Using Frequent Usage Patterns

Code smells are the fragments in the source code that indicates deeper problems in the underlying software design. These code smells can hinder software evolution and maintenance. Out of different code smell types, the God Class (GC) code smell is one of the many important code smells that directly affects the software evolution and maintenance. The GC is commonly defined as a much larger class in systems that either know too much or do too much as compared to other classes in the system. God Classes are generally accidentally created overtime during software evolution because of the incremental addition of functionalities to it. Generally, a GC indicates a bad design choice and it must be detected and mitigated in order to enhance the quality of the underlying software. However, sometimes the presence of a GC is also considered a good design choice, especially in compiler design, interpreter design and parser implementation. This makes the developer’s feedback important for the correct classification of a class as a GC or a normal class. Therefore, this paper proposes a new approach that detects and proposes refactoring opportunities for GC code smell. The proposed approach makes use of different code metrics in combination along with utilizing user feedback as an important aspect while correctly identifying the GC code smell. The proposed approach that considers combined use of code metrics, is based on two newly proposed code metrics in this paper. The first newly proposed metric is a new approach of measuring the connectivity of a given class with other classes in the system (also termed as coupling). The second newly proposed code metric is proposed to measure the extent to which a given classes make use of foreign member variables. Finally, the proposed approach is also empirically evaluated on two standard open-source commonly used software systems. The obtained result indicates that the proposed approach is capable of correctly identifying the GC code smell

Cross-Layer Optimization on Different Data Rates for Efficient Performance in Wireless Sensor Network

The traditional protocols used in wireless sensor networks adhere to stringent layering approaches, which decreases the performance of the quality of service (Quality of Service) metrics. As per specifications 802.15.4, wireless sensor networks are inexpensive and energy efficient. It is essential for evaluating the performance of WSNs. Researchers have looked into the fundamental aspects of a single physical layer and the medium access control (MAC) layer protocol using methodologies calculated using several mathematical models or experimental approaches, respectively. In this research, we offer an improved cross-layer analytical model that utilises a thorough combining and interacting of a Markov chain model of the MAC layer's propagation with a model of the PHY layer's propagation. This combination and interaction are described in detail. Various Quality of Service (quality of service) statistics are presented and evaluated, and a cross-layer effectiveness degradation study is conducted under different inputs of multi-parameter vectors. Other parameters, such as Average Wait Time, Reliability, Failure Probability, and Throughput, have been estimated from the simulation results and contrasted with standardised models. The cross-layer model provides a more thorough performance study with various cross-layer parameter sets, some of which comprise distance, power transmission, and offered loads, among other things

Congestion Aware WSN-IoT-Application Layer Protocols for Healthcare Services

In the healthcare industry, WSN-IoT networks can be used to gather patient data for statistical purposes. IoT-based application-level protocols do not take into account these facts while forwarding the data to the gateway or server, which may degrade the network performance if the data was collected from a patient with ordinary/critical health issues and the route was busy or congested. In this paper, we'll look at the performance of two application layer protocols (i.e. CoAP and MQTT) within the constraints of a scalable network by integrating a congestion-aware scheme with them

Energy Efficient IoT-Sensors Network for Smart Farming

The experience of smart farming can be improved using IoT-based applications. Still, the performance of IoT networks may be degraded due to different factors, i.e., the coverage area of the farm/location (surface or underwater)/environmental conditions etc. Network operations over heterogeneous environments may cause excessive resource consumption and thus may reduce the IoT sensor’s lifespan. To optimise energy consumption, in this paper, an energy-efficient method will be introduced for smart farming, and its performance will be analysed using different parameters (i.e., Throughput/energy consumption/residual energy etc.) using two different IoT standards (Long Range Low powered technology (LoRa)/SigFox)

Effect of Sertraline on biomarker alterations in patients of multidrug resistant tuberculosis with depression: a prospective clinical trial

Background: Lipid profile parameters may be used as biomarker for depression. Sertraline belongs to selective serotonin reuptake inhibitors (SSRIs), the most commonly used group to treat the depression in multidrug resistant tuberculosis patients.Methods: A prospective clinical trial was carried out in department of Psychiatry and department of Tuberculosis and Respiratory disease G.S.V.M. Medical College, Kanpur. Diagnosed MDR TB patients were screened for depression applying Hamilton Depression Rating Scale (HDRS) and these patients were referred to Psychiatrist for diagnosis of depression. Total 25 diagnosed patients of MDR TB with mild to moderate depression were selected. HDRS Score and morning blood sample of 5ml were collected to analyze biomarker for depression before intervention. Same test was repeated in 18 patients who completed the study at day 30 and 120 after administering Sertraline (50mg). Data were compiled and analyzed using SPSS 20.0 and paired t - test.Results: The mean decrease in HDRS score from base line at day 30 and 120 of administering Sertraline were 6.22 (±1.26) and 2.72 (±0.67) which were significant (p˂0.001). The mean increase in serum cholesterol at day 30 was 153.94 (±19.31) and at day 120 was 157.83 (±19.36) which were significant (p˂0.001). Rest of Tg, HDLc, LDLc and VLDL cholesterole levels were not increased significantly.Conclusions: As the depression symptoms improved by sertraline. The biomarkers of depression were also increased (within the normal range) from baseline but significant increase was observed in serum cholesterol only

Department of Pharmacology, G.S.V.M. Medical College, Kanpur, Uttar Pradesh, India

Background: Lipid profile parameters may be used as biomarker for depression. Sertraline belongs to selective serotonin reuptake inhibitors (SSRIs), the most commonly used group to treat the depression in multidrug resistant tuberculosis patients.Methods: A prospective clinical trial was carried out in department of Psychiatry and department of Tuberculosis and Respiratory disease G.S.V.M. Medical College, Kanpur. Diagnosed MDR TB patients were screened for depression applying Hamilton Depression Rating Scale (HDRS) and these patients were referred to Psychiatrist for diagnosis of depression. Total 25 diagnosed patients of MDR TB with mild to moderate depression were selected. HDRS Score and morning blood sample of 5ml were collected to analyze biomarker for depression before intervention. Same test was repeated in 18 patients who completed the study at day 30 and 120 after administering Sertraline (50mg). Data were compiled and analyzed using SPSS 20.0 and paired t - test.Results: The mean decrease in HDRS score from base line at day 30 and 120 of administering Sertraline were 6.22 (±1.26) and 2.72 (±0.67) which were significant (p˂0.001). The mean increase in serum cholesterol at day 30 was 153.94 (±19.31) and at day 120 was 157.83 (±19.36) which were significant (p˂0.001). Rest of Tg, HDLc, LDLc and VLDL cholesterole levels were not increased significantly.Conclusions: As the depression symptoms improved by sertraline. The biomarkers of depression were also increased (within the normal range) from baseline but significant increase was observed in serum cholesterol only

A Systematic Framework to Rapidly Obtain Data on Patients with Cancer and COVID-19: CCC19 Governance, Protocol, and Quality Assurance

When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments. When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments