Emotional Dissonance Mediating Between Secondary Traumatic Stress and Burnout: Probing Postgraduate Mental Healthcare Trainees

This research endeavor intended to probe the mediating role of emotional dissonance between secondary traumatic stress and burnout in postgraduate mental health trainees. Following APA-mandated ethical guidelines; a purposive sample of 248 mental health trainees was recruited from various universities and mental health facilities in Lahore. The results established a significant positive association between secondary traumatic stress and burnout while a negative correlation between emotional dissonance and burnout. Furthermore, while secondary traumatic stress positively predicted burnout, however emotional dissonance negatively predicted burnout among mental health trainees. Lastly, findings also suggested that emotional dissonance partially mediates between secondary traumatic stress and burnout. Other than making a valuable addition to the existing research scholarship by bringing in the mediating role of emotional dissonance, these findings also have significant implications for clinical, counseling, and other public health-related settings as they highlight the psychological toll that mental health trainees go through while rending professional services in the field. Academicians and policymakers can also be engaged to develop mechanisms so that novice trainees and students can be provided with tools to effectively deal with workplace challenges

Global Aggregation Node Selection Scheme in Federated Learning for Vehicular Ad Hoc Networks (VANETs)

Federated learning allows multiple users and parties to collaborate and train machine learning models in a distributed and privacy-preserving manner in Vehicular Adhoc Networks VANETs. This computing paradigm addresses privacy concerns; however, it comes at a considerable cost of network resources. After training the machine learning models in conventional federated learning frameworks, devices share that model with a central server, mostly cloud, where the global aggregation is performed. Multiple devices communicating with a central server raise network bandwidth and congestion concerns. To solve this problem, we proposed a federated learning framework for VANETs where instead of using a fixed global aggregator, we used variable global aggregation nodes. The global aggregation node is selected based on communication delay and workload in the proposed framework. We also believe that, in a vehicular Adhoc network, all network nodes cannot participate in the learning process due to network, computation, and energy resource limitations. We Also proposed a client selection algorithm that adapts itself and selects some clients based on specific criteria. Finally, the proposed technique is compared with the hierarchical federated learning framework (HFL) and FedAvg where proposed method outperformed in terms of accuracy

Flexible Global Aggregation and Dynamic Client Selection for Federated Learning in Internet of Vehicles

Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality, and limited communication resources. By employing dynamic client selection, we can prioritize relevant and high-quality data sources, enhancing model accuracy. To address this issue, we propose an FL framework that selects global aggregation nodes dynamically rather than a single fixed aggregator. Flexible global aggregation ensures efficient utilization of limited network resources while accommodating the dynamic nature of IoV data sources. This approach optimizes both model performance and resource allocation, making FL in IoV more effective and adaptable. The selection of the global aggregation node is based on workload and communication speed considerations. Additionally, our framework overcomes the constraints associated with network, computational, and energy resources in the IoV environment by implementing a client selection algorithm that dynamically adjusts participants according to predefined parameters. Our approach surpasses Federated Averaging (FedAvg) and Hierarchical FL (HFL) regarding energy consumption, delay, and accuracy, yielding superior results

Analysis of Unsteady Axisymmetric Squeezing Fluid Flow with Slip and No-Slip Boundaries Using OHAM

In this manuscript, An unsteady axisymmetric flow of nonconducting, Newtonian fluid squeezed between two circular plates is studied with slip and no-slip boundaries. Using similarity transformation, the system of nonlinear partial differential equations is reduced to a single fourth order ordinary differential equation. The resulting boundary value problems are solved by optimal homotopy asymptotic method (OHAM) and fourth order explicit Runge-Kutta method (RK4). It is observed that the results obtained from OHAM are in good agreement with numerical results by means of residuals. Furthermore, the effects of various dimensionless parameters on the velocity profiles are investigated graphically

THE ECONOMIC CONDITIONS AND SPORTS IN DEVELOPING COUNTRIES: A CASE STUDY OF PAKISTAN

The economic conditions and sports participation in developed countries has been studied extensively. For a number of countries in the developed world, it has been reported that economic conditions have a direct impact on participation and performance in various sports (Black et al., 2002). However, there has been no significant study conducted to assess the effects of economic conditions on sports participation in the developing world. The aim of this study was to fill that void and ascertain the impact of economic conditions on high performance sports in Pakistan, and the underlying reasons for the decline of sports in the country.Pakistan's participation & performance at the Asian games have been used as a basis for the study. Moreover, studies that have assessed the teaching of physical education in Pakistan have also been reviewed to identify issues with sports participation (Sarwar et al., 2010). Decline of sports participation of children in Secondary Schools, utilization of Sports Funds & availability of Sports Facilities was also considered to identify reasons for decline in performance. Data relating to available resources such as physical education teachers employed and utilization of funds for sports was taken from National Sports Policy of Pakistan 2005, and used to test and validate various hypotheses under consideration.The results show significant decline in the performance of Pakistan at the Asian games from 1950 to 2000 (p <0.05) along with negative relationship with economic conditions of Pakistan. It was noted that only 48 % schools had access to physical education teachers. Out of these schools it was found that 40% schools were not utilizing the funds fully, for physical education and 50% school did not have facilities for indoor / outdoor games / sports. The analysis of economic conditions in Pakistan and participation in sports showed that there was no significant relationship between the economic conditions and performance in sports. These results suggest that in order to stem the decline in sports performance,  focus should be on grass roots level activities in schools, implementation of systematic and scientific coaching, long term planning and upgrading our competition and monitoring system in addition to provisioning of sports facilities at all levels.

Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)

The Industrial Internet of Things (IIoTs) is an emerging area that forms the collaborative environment for devices to share resources. In IIoT, many sensors, actuators, and other devices are used to improve industrial efficiency. As most of the devices are mobile; therefore, the impact of mobility can be seen in terms of low-device utilization. Thus, most of the time, the available resources are underutilized. Therefore, the inception of the fog computing model in IIoT has reduced the communication delay in executing complex tasks. However, it is not feasible to cover the entire region through fog nodes; therefore, fog node selection and placement is still the challenging task. This paper proposes a multi-level hierarchical fog node deployment model for the industrial environment. Moreover, the scheme utilized the IoT devices as a fog node; however, the selection depends on energy, path/location, network properties, storage, and available computing resources. Therefore, the scheme used the location-aware module before engaging the device for task computation. The framework is evaluated in terms of memory, CPU, scalability, and system efficiency; also compared with the existing approach in terms of task acceptance rate. The scheme is compared with xFogSim framework that is capable to handle workload upto 1000 devices. However, the task acceptance ratio is higher in the proposed framework due to its multi-tier model. The workload acceptance ratio is 85% reported with 3000 devices; whereas, in xFogsim the ratio is reduced to approx. 68%. The primary reason for high workload acceptation is that the proposed solution utilizes the unused resources of the user devices for computations

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

N-[(E)-4-Chloro­benzyl­idene]-2,3-dimethyl­aniline

In the title compound, C15H14ClN, the conformation about the C=N bond is trans and the dihedral angle between the aromatic rings is 51.48 (4)°. In the crystal, some very weak C—H⋯π inter­actions may help to establish the packing

Storage stability of potato variety Lady Rosetta under comparative temperature regimes

Potatoes are usually stored under low temperatures for sprout prevention and to ensure their continuous supply. Low temperature sweetening in potato is the major temperature related disorder being faced by the growers and is also known to be associated with variety specific storage temperature. The present study aimed at identifying the appropriate storage temperature for the premium potato variety Lady Rosetta with special reference to the changes in its quality attributes, that is weight loss, total sugars, starch, ascorbic acids, total phenolic contents, radical scavenging activity, enzymatic activities and potato chip color. The selected potato variety was stored under different temperature (5, 15 and 25oC) regimes to identify appropriate storage temperature. Our results showed significant variations in the tested quality attributes in response to different storage temperatures. Storage at 5oC maintained tuber dormancy up to 126 days, however, found associated with increased sugar accumulation (2.32 g/100 g), rapid starch depletion (13.25 g/100 g) and poor post processing performance (L-value, 52.00). In contrast, potato storage at 15oC retained lower sugar contents (1.33 g/100g) and superior chip color (L-value, 59.33) till the end of storage. However, they were found associated with the increased polyphenol oxidase (38.47 U/g f.w) and peroxidase (15.25 U/100 g f.w) activities as compare to those potatoes stored at 5oC during the same storage period. Storage life of potato tubers at 25oC was significantly reduced due to dormancy break on 84th day and subsequent starch degradation (15.29 g/100 g) increased sugar accumulation (1.32 g/100 g) and increased polyphenol oxidase (79.89 U/g f.w) and peroxidase activities (40.69 U/100 g f.w). Our results showed that potato variety Lady Rosetta is cold sensitive and requires specific temperature for prolonged storage and best post processing performance