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This paper presents a software-based Python framework for developing future AI-enhanced end-to-end Brain-Computer-Interfaces (BCI). This framework contains modules from the emulated analogue front-end and from neural signal pre-processing for invasive neural applications. These modules can be assembled into several pipeline versions for evaluation and benchmarking. The aim of this framework is to accelerate the development of BCIs due to system-wide optimizations in order to set the requirements for hardware development without prior knowledge on the basis of accuracy (recall and precision) and latency. In the next step, the pipeline can be optimised for on-chip and embedded execution.

In the backdrop of COVID19 recovery, Pakistan is still struggling to cope with the economic challenges and disruptions caused in the energy supply chain. On one hand where COVID has brought serious socio-economic costs and prolonged delays, it has also provided opportunity for developing countries such as Pakistan to “build-forward-better” their economies in a more sustainable and climate friendly manner. This study particularly highlights the impact of COVID on energy supply and demand sectors of Pakistan, its near- and long-term impacts, and what policy interventions can be adopted to put Pakistan on-track to achieve its Nationally Determined Contributions (NDCs). The economic focus in on “Green Recovery” and what key interventions will foster a rapid transition towards decarbonization in Pakistan. Low Emission Analysis Platform (LEAP) model is used to provide energy sector outlook (2020-2040) of Pakistan under different scenario i.e., Pre COVID growth, Business-as-Usual, Slow Recovery, and Green Recovery from COVID. The results obtained from the model depicts that following a green recovery scenario, Pakistan can reduce around 10 Mtoe (9%) of its total energy use, 53 TWh of electricity, 19 Mt of emissions from demand sectors, and 11 Mt of emissions from the power sector by 2030. For total levelized cost of the power sector, the green recovery scenario represents a generation cost of $13 billion by 2030 which further highlights that energy efficiency could lead to cost savings of approximately $3 billion each year by 2030. Green recovery is however still a daunting task as it would require economic stimulus of $8 billion only to recover to its pre COVID scenario and total investments of $120 billion by 2030.

Despite global calls for climate change and its impacts in past decade, energy sector of Pakistan has remained highly dominated by high-cost carbon-intensive resources. Although a significant number of policies have been put forward by both provincial and federal government in last three years, the ground-level implementation of these policies is non-existent, and Pakistan’s progress is still far behind the developed countries. This study therefore performs a socio-economic analysis of solar PV potential in Pakistan and how recent policies can be mobilized to upscale the utilization of solar PV both as an on-grid and off-grid generation source. This also links to solar potential for corporate sector engagements in their Net-Zero Pathways. The methodological approach uses a Low Emission Analysis Platform (LEAP) model designed for Pakistan’s Power System supplies under three different scenarios i.e., Energy Transition Scenario, Conventional Generation Scenarios, and Business as Usual Scenario. Indicative Generation Capacity Expansion Plan (IGCEP 2021) along with recent policies is used as the leading data source for driving the capacity additions. The results obtained from the model indicates that despite having a large potential, under currently policies the share of solar in total grid power generation will remain under 2% by 2030. Under Energy Transition Scenario, the model runs under a least cost optimization plan leading to a higher uptake of solar power. As per this scenario, the share of renewable increase beyond 2030 to achieve a share of around 50% by 2045. This can lead to cumulative carbon reductions of around 2000 Mt by 2030 and economic savings of around $ 5 billion. Based on the model results, this study also identifies the possible pathways for upcoming iterations of Pakistan IGCEP plan that builds around solar PV

IntroductionNorovirus, mainly associated with acute gastroenteritis, is very contagious and can affect a vast range of species ranging from cattle, pigs, dogs, mice, cats, sheep, and lions to humans. It is a foodborne pathogen that mainly transmits through the fecal–oral route.MethodsThis is the first-ever study conducted in Lahore and Sheikhupura districts of Punjab, Pakistan, to investigate noroviruses through the One Health approach. From January 2020 to September 2021, 200 fecal samples were collected from clinical cases of hospitalized patients and 200 fecal samples from sick animals at veterinary hospitals and local farms. In addition, 500 food and beverage samples were collected from street vendors and retail stores. A predesigned questionnaire was used to assess the risk factors and clinical characteristics of sick people and animals.Results and discussionOverall, 14% of the human clinical samples were positive by RT-PCR for genogroup GII. All bovine samples were negative. Food and beverage samples were tested in pools, resulting in sugarcane juice samples positive for genogroup GII. Previous contact with acute gastroenteritis patients, sex, and presence of vomiting were found to be significant risk factors (p ≤ 0.05). The substantial number of diarrhea cases associated with noroviruses calls for additional studies to investigate the epidemiology and transmission and to improve surveillance.