PAKISTAN IN THE FATF GREY-LIST: CHALLENGES, REMEDIES AND INTERNATIONAL RESPONSE

The Financial Action Task Force (FATF) grey-listed Pakistan due to the latter’s non-compliance to the United Nations Security Council Resolution (UNSCR)-1267. The FATF also demands Pakistan to put strict controls on money laundering and financial lifelines of terrorist organizations in Pakistan. The plan of action was reached between Pakistan and FATF to ensure sufficient action to enforce anti-money laundering policies and freeze assets of designated terrorist groups in Pakistan under UNSCR-1267 and UNSCR-1373. The NACTA in collaboration with FBR, State Bank of Pakistan, FIA, and the Securities and Exchange Commission of Pakistan has mounted operations against illegal movement of money within Pakistan. It also has choked financial lifelines of terrorist organizations and curbed Hawala/Hundi methods of laundering money. Pakistan is struggling to stick to the 26-point action plan to address the necessary concerns of FATF. This paper put forth the ramifications for being blacklisted by FATF and also highlights the Trump administration’s tough stance towards Pakistan. This paper also offers concrete recommendations to exclude Pakistan’s name from the FATF grey-list.   Bibliography Entry Amin, Musarat, Muhammad Khan , and Rizwan Naseer. 2020. "Pakistan in the FATF Grey-List: Challenges, Remedies and International Response." Margalla Papers 24 (1): 31-43

Staff Development Needs In Pakistan Higher Education

Staff development is very significant for the achievement of overall goals of higher education in Pakistan. The success of innovations depends largely upon the skills of instructors; but in Pakistan, the people with a simple masters degree (without any pedagogical training) are  inducted as teaching staff at the university level, so it is time to explore whether or not the inducted teachers feel the need for training. Further to be explored are areas in which they are interested in being trained. Therefore, the objectives of study were 1) to explore the training needs for university teaching staff, 2) to identify the areas in which development is needed by the teaching staff of the universities in Pakistan, and 3) formulation of recommendations for staff development in Pakistan to improve education at the higher level.  The sample comprised of 20% randomly-selected teaching staff of ten selected universities, degree-awarding institutions from the public sector, and 40% teaching staff of ten selected universities from the private sector. A self-developed questionnaire, consisting of 41 items to be responded to on a five-point Likert scale and two open-ended questions, was used to collect data.  The principal researcher approached all the respondents personally by repeated visits and got the completed questionnaires, so this questionnaire also served the purpose of an interview. The analysis of data revealed that the university teachers need training in the following areas: philosophy of education, Islamic philosophy of education, educational psychology, research techniques, professional trends, professional competencies, professional attitude, professional ethics ,global innovations in teaching strategies, classroom management, counselling and guidance, student discipline, communication skills, learning theories, and supervision. Therefore, it is recommended that they may be included in the training curriculum of university teachers

Sustainable growth rate, corporate value of US firms within capital and labor market distortions: The moderating effect of institutional quality

Research background: Understanding how distortions in capital and labor markets affect corporate value and sustainable growth is crucial in today's economy. These distortions can disrupt resource allocation and economic sustainability. Additionally, the role of institutional quality in shaping these dynamics requires thorough exploration.Purpose of the article: We quantify the effect of capital and labor market distortions on corporate value and sustainable growth rate (SGR) and how this association is moderated by institutional quality.Methods: Stemming from the sample criteria, we calibrated a final sample of 1971 United States-listed manufacturing firms for 2012–2022. This research offers insights into market inefficiencies and institutional effects. Progressing towards objectives, we use advanced techniques like feasible generalized least squares and generalized methods of moments. These methods help us rigorously analyze complex relationships among study variables.Findings & value added: Three key findings emerge: first, capital and labor market distortions have a negative and significant influence on corporate value and sustainable growth. Our primary finding implies that increasing distortions significantly reduce sustainable growth's value and potential. Second, we find institutional quality has a positive significant effect on corporate value and sustainable growth. Third, institutional quality positively moderates the association between capital and labor market distortions, corporate value, and sustainable growth. Findings suggest that institutional quality, as a potential mechanism, improves the efficiency of resource allocation and optimizes the sustainable economic system to lessen the negative effect of factor market distortions on corporate value and SGR. Besides, we conduct robustness checks to validate our findings. Finally, we offer policymakers and stakeholders actionable insights

Analysis and Forecast of Mining Fatalities in Cherat Coal Field, Pakistan

Mineral exploitation contributes to the economic growth of developing countries. Managing mineral production brought a more disturbing environment linked to workers' causalities due to scarcities in the safety management system. One of the barriers to attaining an adequate safety management system is the unavailability of future information relating to accidents causing fatalities. Policymakers always try to manage the safety system after each accident. Therefore, a precise forecast of the number of workers fatalities can provide significant observation to strengthen the safety management system. This study involves forecasting the number of mining workers fatalities in Cherat coal mines by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Workers' fatalities information was collected over the period of 1994 to 2018 from Mine Workers Federation, Inspectorate of Mines and Minerals and company records to evaluate the long-term forecast. Various diagnostic tests were used to obtain an optimistic model. The results show that ARIMA (0, 1, 2) was the most appropriate model for workers fatalities. Based on this model, casualties from 2019 to 2025 have been forecasted. The results suggest that policymakers should take systematic consideration by evaluating possible risks associated with an increased number of fatalities and develop a safe and effective working platform

Firm climate change risk and financial flexibility: Drivers of ESG performance and firm value

This study investigates how a firm's climate change risk (FCCR) and financial flexibility (FIFL) affect its value and environmental, social, and governance (ESG) performance. We use data from publicly listed US firms for 2012–2021. We employed four estimation methods: bootstrap quantile regression, feasible generalised least squares, a generalised method of moments, and fixed effects with Driscoll-Kraay standard errors. Our main findings indicate that climate change risk has a negative effect on firm value and a positive effect on ESG performance and that financial flexibility moderates these effects by reducing risk and enhancing value. These results are robust against alternative measures and estimation techniques. Our study provides novel insights into the influence of climate risk and financial flexibility on firm value and ESG performance. We also discuss the implications of our results for academics, practitioners, and policymaker

GeoChat: Grounded Large Vision-Language Model for Remote Sensing

Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However, such general-domain VLMs perform poorly for Remote Sensing (RS) scenarios, leading to inaccurate or fabricated information when presented with RS domain-specific queries. Such a behavior emerges due to the unique challenges introduced by RS imagery. For example, to handle high-resolution RS imagery with diverse scale changes across categories and many small objects, region-level reasoning is necessary alongside holistic scene interpretation. Furthermore, the lack of domain-specific multimodal instruction following data as well as strong backbone models for RS make it hard for the models to align their behavior with user queries. To address these limitations, we propose GeoChat - the first versatile remote sensing VLM that offers multitask conversational capabilities with high-resolution RS images. Specifically, GeoChat can not only answer image-level queries but also accepts region inputs to hold region-specific dialogue. Furthermore, it can visually ground objects in its responses by referring to their spatial coordinates. To address the lack of domain-specific datasets, we generate a novel RS multimodal instruction-following dataset by extending image-text pairs from existing diverse RS datasets. We establish a comprehensive benchmark for RS multitask conversations and compare with a number of baseline methods. GeoChat demonstrates robust zero-shot performance on various RS tasks, e.g., image and region captioning, visual question answering, scene classification, visually grounded conversations and referring detection. Our code is available at https://github.com/mbzuai-oryx/geochat.Comment: 10 pages, 4 figure

Should aspirin be replaced with ADP blockers or anti-GPVI to manage thrombosis?

Platelets have a pivotal role in maintaining cardiovascular homeostasis. They are kept docile by endothelial derived mediators. Aberration in haemostatic balance predisposes an individual to an elevated risk of a pro-thrombotic environment. Anti-platelet therapy has been a key component to reduce this risk. However, understanding how these medications affect the balance between activation and inhibition of platelets is critical. There is now evidence that a key antiplatelet therapy – aspirin, may not be the most efficacious medicine of choice, as it can compromise both platelet inhibition and activation pathways. In this review the rationale of aspirin as an anti-thrombotic drug has been critically discussed. This review looks at how recent published trials are asking key questions on the efficacy and safety of aspirin in countering cardiovascular diseases. There is an increasing portfolio of evidence that identifies that although aspirin is a very cheap and accessible drug, it may be used in a manner that is not always beneficial to a patient, and a more nuanced and targeted use of aspirin may increase its clinical benefit and maximize patient response. The questions around the use of aspirin raises the potential for changes in its clinical use for dual anti-platelet therapy. This highlights the need to ensure that treatment is targeted in the most effective manner, and that other anti-platelet therapies may well be more efficacious and beneficial for CVD patients in their standard and personalized approaches

Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition

Recent video recognition models utilize Transformer models for long-range spatio-temporal context modeling. Video transformer designs are based on self-attention that can model global context at a high computational cost. In comparison, convolutional designs for videos offer an efficient alternative but lack long-range dependency modeling. Towards achieving the best of both designs, this work proposes Video-FocalNet, an effective and efficient architecture for video recognition that models both local and global contexts. Video-FocalNet is based on a spatio-temporal focal modulation architecture that reverses the interaction and aggregation steps of self-attention for better efficiency. Further, the aggregation step and the interaction step are both implemented using efficient convolution and element-wise multiplication operations that are computationally less expensive than their self-attention counterparts on video representations. We extensively explore the design space of focal modulation-based spatio-temporal context modeling and demonstrate our parallel spatial and temporal encoding design to be the optimal choice. Video-FocalNets perform favorably well against the state-of-the-art transformer-based models for video recognition on five large-scale datasets (Kinetics-400, Kinetics-600, SS-v2, Diving-48, and ActivityNet-1.3) at a lower computational cost. Our code/models are released at https://github.com/TalalWasim/Video-FocalNets.Comment: Accepted to ICCV-2023. Camera-Ready version. Project page: https://TalalWasim.github.io/Video-FocalNets

Cycling vs Running – An in-depth analysis

This short letter to the editor provides ideas about exercises that improves cardiovascular fitness