User Transmit Power Minimization through Uplink Resource Allocation and User Association in HetNets

The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes

Hierarchical generative modelling for autonomous robots

Humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control in the setting of autonomous robotic operations. We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control. Here, temporal depth refers to the nested time scales at which successive levels of a forward or generative model unfold, for example, delivering an object requires a global plan to contextualise the fast coordination of multiple local movements of limbs. This separation of temporal scales also motivates robotics and control. Specifically, to achieve versatile sensorimotor control, it is advantageous to hierarchically structure the planning and low-level motor control of individual limbs. We use numerical and physical simulation to conduct experiments and to establish the efficacy of this formulation. Using a hierarchical generative model, we show how a humanoid robot can autonomously complete a complex task that necessitates a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities. Our findings demonstrated the effectiveness of using human-inspired motor control algorithms, and our method provides a viable hierarchical architecture for the autonomous completion of challenging goal-directed tasks

The Effects of Religion on Depression and Suicide Rates in the United States

Depression and suicide are major health problems in the United States and in the World.  According to the American Foundation for Suicide Prevention (2014), “Suicide claims more lives than war, murder, and natural disasters combined (p. 1).”  Many of the victims of suicide were also victims of depression.  Research has shown that those who are religious are less likely to suffer from depression and less likely to commit suicide.  This study shows that for the three major religious groups in the United States, evangelical Protestants, mainline Protestants, and Catholics, number of cases of depression are reduced, but the number of suicides increases for evangelical Protestants.  Depression may be reduced since religion provides many benefits to individuals including a social network whereby those in distress can seek comfort from others.  Furthermore, those who are religious are also more secure in their beliefs of life after death, personal salvation, and the forgiveness of their sins which may reduce pressures put on them by everyday life.  However, it may  be that feelings of inadequacy over not being able to live up to the strict teachings of some evangelical religious groups and the fact that anyone questioning the strict beliefs of the church should be ostracized, could be contributing factors to the stress, trauma, and lack of self worth, that lead to thoughts of suicide. Keywords: religion, evangelical Protestants, mainline Protestants, Catholics, depression, suicid

Active Inference as a Model of Agency

Is there a canonical way to think of agency beyond reward maximisation? In this paper, we show that any type of behaviour complying with physically sound assumptions about how macroscopic biological agents interact with the world canonically integrates exploration and exploitation in the sense of minimising risk and ambiguity about states of the world. This description, known as active inference, refines the free energy principle, a popular descriptive framework for action and perception originating in neuroscience. Active inference provides a normative Bayesian framework to simulate and model agency that is widely used in behavioural neuroscience, reinforcement learning (RL) and robotics. The usefulness of active inference for RL is three-fold. \emph{a}) Active inference provides a principled solution to the exploration-exploitation dilemma that usefully simulates biological agency. \emph{b}) It provides an explainable recipe to simulate behaviour, whence behaviour follows as an explainable mixture of exploration and exploitation under a generative world model, and all differences in behaviour are explicit in differences in world model. \emph{c}) This framework is universal in the sense that it is theoretically possible to rewrite any RL algorithm conforming to the descriptive assumptions of active inference as an active inference algorithm. Thus, active inference can be used as a tool to uncover and compare the commitments and assumptions of more specific models of agency.Comment: Accepted in RLDM2022 for the workshop 'RL as a model of agency

Response of Tuberose (Polianthes tuberosa) to Potassium and Planting Depth

A research work was carried to find out the effect of planting depths, potassium levels and their interaction during the year 2012. The corms of tuberose were planted at a depth of 5, 10, and 15 cm and were fertilized with four levels of potassium 0, 50, 100 and 150 Kg of K2O per hectare using K2SO4 as a source of K2O. Result of the study revealed that planting depth of 15 cm significantly increased length of spike (56.9 cm), number of florets spike-1 (54.84), and plant height (103.13) cm. Planting depth of 5cm cause decreased number of days to last floret opening (180.08). Potassium level of 150 kg of K2O ha-1 length of spike (55.24 cm), number of florets spike-1 (49.2) and plant height (100.29 cm). Planting depth of 15 cm and fertilizer application of 150 kg of K2O ha-1proved to be superior regarding length of spike opening (64.4cm), number of florets spike-1 (62.2) and plant height (106.20 cm). Hence planting tuberose at a depth of 15cm and fertilizer application of 150 kg of K2O ha-1 is recommended for commercial cultivation of tuberose. Keywords: Tuberose; Potassium; Planting Depth; Number of Florets Spike-1; Spike Length

The effects of cutting interval on agro-qualitative traits of different millet (Pennisetum americanum L.) cultivars

AbstractA field experiment was conducted to study the forage yield and qualitative traits of different varieties of millet with different days of harvest during the summer, 2014 at Agronomic Research Area, University of Agriculture Faisalabad. Experiment was laid out in randomized complete block design (RCBD) under factorial arrangement having three replications. Pearl millet seeds of three varieties viz. BS-2011, Ghana White and MB-87 were grown in 30cm apart rows. Net plot size was 3.6m×8.0m. Three different harvesting times were adopted i.e. 55, 65 and 75days after sowing (DAS). Maximum plant height of pearl millet was recorded for cultivar BS-2011 at harvest time of 75DAS. Maximum leaf area per plant was observed for the cultivar BS-2011 when it was harvested 75DAS. Maximum dry matter percentage was also attained in cultivar BS-2011 where plots were harvested at 75DAS. The highest forage yield was obtained where variety BS-2011 was grown and harvested at 75DAS. Similarly, maximum dry matter production of BS-2011 was recorded in plots harvested at 75DAS followed by Ghana White and MB-87 harvested at 55DAS. Higher crude protein content was recorded where plots were harvested at 55DAS and cultivar BS-2011 was sown. Higher crude fiber and total ash percentage was also seen in BS-2011. Finally, cultivar BS-2011 proved best for cultivation with harvest time of 75 DAS under Faisalabad conditions to obtain higher forage yield and better quality

Reward Maximization Through Discrete Active Inference

Active inference is a probabilistic framework for modeling the behavior of biological and artificial agents, which derives from the principle of minimizing free energy. In recent years, this framework has been applied successfully to a variety of situations where the goal was to maximize reward, often offering comparable and sometimes superior performance to alternative approaches. In this article, we clarify the connection between reward maximization and active inference by demonstrating how and when active inference agents execute actions that are optimal for maximizing reward. Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation, a formulation that underlies several approaches to model-based reinforcement learning and control. On partially observed Markov decision processes, the standard active inference scheme can produce Bellman optimal actions for planning horizons of 1 but not beyond. In contrast, a recently developed recursive active inference scheme (sophisticated inference) can produce Bellman optimal actions on any finite temporal horizon. We append the analysis with a discussion of the broader relationship between active inference and reinforcement learning

The efficacy and safety of Topiramate in the Treatment of Refractory Epilepsy in children in Paediatric Neurology clinic at Izzat Ali Shah Hospital

Objectives: This study has been performed to observe the efficacy & safety of Topiramate in refractory epilepsies in children in Pakistan.Materials and Methods: A cross-sectional prospective consecutive series of children age between (1-14 years) with refractory epilepsy were recruited from the Paediatric Neurology clinic of Izzat Ali Shah Hospital. Epilepsy was classified into Generalized, Focal, and Unknown based on ILAE Classification. Topiramate was added in patients who were refractory to at least two anti-epileptic drugs. The outcome was recorded in four categories i.e total remission, >50% seizure reduction, <50% seizure reduction, and no improvement.Results: Fifty-eight patients were enrolled in the study, but 5 patients were dropped out due to significant side effects. Most of the children were in the age group ranging from 5 to 10 years with predominant boys (n=32). The major type of epilepsy was 53% (n=28) was Unknown, while 36% (n=19) children had Generalized epilepsy and 11% (n=6) had Focal epilepsy. 25% children had total remission, 38% had >50% seizure reduction, 28% had < 50% seizure reduction and 9% had no improvement. Total remission was mostly seen in Focal epilepsy (50%) and least shown in Unknown type (14%) and the difference was significant (Chi-Square test p=0.039). Adverse effects to Topiramate were found in 33 (62%) of the total enrolled patients. Common adverse effects were weight loss in 14(26%), poor appetite in 13(24.5%), and behavioral issues in 6(22.5%) children.Conclusion: Topiramate is an effective anti-epileptic drug (AED) in refractory epilepsy especially with Focal and Generalized onset but is not as effective in other types of Refractory epilepsy. Weight loss and poor appetite were the commonest adverse effects that were reversible after dose reduction