Investigation of Activated Carbon Filtering Distillation System

People are concerned about contaminants in their drinking water that cannot be removed by water softeners or physical filtration. Solvents, pesticides, industrial wastes, and leaking underground storage tanks are some sources of water contamination. The water filtration system filters impurities and foreign particles in dirty water. There are three different section for this filtration system where the first, second and third section are pebbles, sand and filter paper respectively. First, the ability of the water filtration system to filter out foreign particles as well as purifying the water is examined. Dirty water with sand and dry leaves, colored water and colored water with activated carbon were three different conditions being tested. It is observed that the water filtration systems are unable to filter all impurities from the samples such as the dye in colored water; however, activated carbon is used as an agent to remove the dye from the colored water. Activated carbon filtration reduces the certain organic chemicals and chlorine in water. It can also reduce the amount of lead ad dissolved radon in water even though most lead-reducing systems use another filter medium in addition to carbon. Water is run through granular or block carbon material to reduce toxic compounds. In this study, factors (such as flow rate, temperature, pH, and molecular weight) that affect the performance of activated carbon are examined

Analytical Methods for Determining Connectivity Leakage in Collaboration With Meissner Filtration Products

The scope of our project involved choosing existing testing procedures and determining a viable sample size to determine an accurate leakage rate of single-use system connections (SUS). Another goal was to assess the effects of gamma irradiation on the material integrity of the hose barb to tubing connections. Our key customer requirements included: being able to determine an overall leakage rate and making a repeatable testing process. The importance of these requirements was determined through our house of quality and Pugh charts. To achieve the requirements, we tested the leakage in low-pressure and high-pressure scenarios with a large enough sample size to meet the requirements of the central limit theorem (n=30), in order to get an accurate estimate. For a repeatable testing process, there was nothing to be changed with the hydrostatic burst test since it already included calibrated machinery and a strict SOP the operator must follow. On the flip side, Meissner’s SOP for the low-pressure gas test was vague and unclear on instructions to “manipulate” the testing sample. We decided to define “manipulation” of the sample to strictly apply the 90-degree bend, 90-degree twist, pulling in a straight line, and holding the middle and end of the tube (opposite sides of the connection). Additionally, we implemented the alternation between operators to minimize or eliminate operator bias. For specifications we looked into decreasing the observation time for testing for the low-pressure gas test. The modifications were finalized to delegating at least 2 personnel to operate the testing which reduces testing time, and the SOP was modified to shorten the waiting time from 2 minutes down to 1.5 minutes. The result of our project was an improved SOP that made testing quicker and less susceptible to operator bias. Our testing performed with the modified SOP showed that there was no significant sign of operator bias. The data we gathered provided us with leakage rates for several hose barb connection configurations that we then compared to understand whether gamma-irradiation affects leakage. Our data indicates that gamma-irradiation negatively impacts the hose barb connection’s performance in terms of leakage and maximum allowable pressure

Boost Battery

In modern times, the problem of a depleted battery confronts us too often. Whether it appears with a smartphone or with any other electronic device, the situation frustrates all consumers. This project designs and constructs a compact portable battery charger that charges through kinetic energy. Thus, one can perform everyday activities, such as walking or running, and charge up a backup battery with no extra effort or time. Additionally, an attachable band harvests thermal energy to maximize the amount of clean power generated. Energy harvesting from kinetic motion and thermal differences provide users with power when no actual power suppliers are nearby; a crucial advantage especially for emergency situations. So much of the energy we expend throughout our day escapes into the environment. As renewable energy becomes more necessary in today’s culture, Boost provides an option for those trying to cut back on electricity costs or those trying to reduce their carbon footprint. Now, with this power bank, the heat and movement of your body directly charge your phone or personal electronic device. The user can even harness the energy of their pet by attaching it to their dog or cat if they do not feel up for running any further

Relational knowledge and representation for reinforcement learning

In reinforcement learning, an agent interacts with the environment, learns from feedback about the quality of its actions, and improves its behaviour or policy in order to maximise its expected utility. Learning efficiently in large scale problems is a major challenge. State aggregation is possible in problems with a first-order structure, allowing the agent to learn in an abstraction of the original problem which is of considerably smaller scale. One approach is to learn the Q-values of actions which are approximated by a relational function approximator. This is the basis for relational reinforcement learning (RRL). We abstract the state with first-order features which consist of only variables, thereby aggregating similar states from all problems of the same domain to abstract states. We study the limitations of RRL due to this abstraction and introduce the concepts of consistent abstraction, subsumption of problems, and abstract-equivalent problems. We propose three methods to overcome the limitations, extending the types of problems our RRL method can solve. Next, to further improve the learning efficiency, we propose to learn different types of generalised knowledge. The policy is influenced by directed exploration based on multiple types of intrinsic rewards and avoids previously encountered dead ends. In addition, we incorporate model-based techniques to provide better quality estimates of the Q-values. Transfer learning is possible by directly leveraging the generalised knowledge to accelerate learning in a new problem. Lastly, we introduce a new class of problems which considers dynamic objects and time-bounded goals. We discuss the complications these bring to RRL and present some solutions. We also propose a framework for multi-agent coordination to achieve joint goals represented by time-bounded goals by decomposing a multi-agent problem into single-agent problems. We evaluate our work empirically in six domains to demonstrate its efficacy in solving large scale problems and transfer learning

The effects of owning a pet on self-esteem and self-efficacy of Malaysian per owners

In this research, 200 pet owners and non-pet owners were studied to ascertain the effects of owning a pet on the self-esteem and self-efficacy of the pet owners. All the respondents completed self-reported questionnaires. While the results showed no significant differences, it was noted that there was a tendency for people with pets to generally have slightly higher self-esteem and self-efficacy as compared to people without pets. The study also showed that higher self-esteem contributed towards predicting higher self-efficacy

Relational knowledge and representation for reinforcement learning

In reinforcement learning, an agent interacts with the environment, learns from feedback about the quality of its actions, and improves its behaviour or policy in order to maximise its expected utility. Learning efficiently in large scale problems is a major challenge. State aggregation is possible in problems with a first-order structure, allowing the agent to learn in an abstraction of the original problem which is of considerably smaller scale. One approach is to learn the Q-values of actions which are approximated by a relational function approximator. This is the basis for relational reinforcement learning (RRL). We abstract the state with first-order features which consist of only variables, thereby aggregating similar states from all problems of the same domain to abstract states. We study the limitations of RRL due to this abstraction and introduce the concepts of consistent abstraction, subsumption of problems, and abstract-equivalent problems. We propose three methods to overcome the limitations, extending the types of problems our RRL method can solve. Next, to further improve the learning efficiency, we propose to learn different types of generalised knowledge. The policy is influenced by directed exploration based on multiple types of intrinsic rewards and avoids previously encountered dead ends. In addition, we incorporate model-based techniques to provide better quality estimates of the Q-values. Transfer learning is possible by directly leveraging the generalised knowledge to accelerate learning in a new problem. Lastly, we introduce a new class of problems which considers dynamic objects and time-bounded goals. We discuss the complications these bring to RRL and present some solutions. We also propose a framework for multi-agent coordination to achieve joint goals represented by time-bounded goals by decomposing a multi-agent problem into single-agent problems. We evaluate our work empirically in six domains to demonstrate its efficacy in solving large scale problems and transfer learning

The relationship between Islamic religiosity, depression and anxiety among Muslim cancer patients.

There is a growing body of evidence that religiosity and spirituality can buffer depression and anxiety and support the healing process in cancer patients. However, literature on the role of Islamic religiosity in the healing of Muslim cancer patients are few. This study aimed to examine the relationship between Islamic religiosity with depression and anxiety in Muslim cancer patients. 59 cancer patients were approached in oncology day care and ward at a Malaysian government hospital and in a cancer support group activity. Patients completed the Muslim Religiosity and Personality Inventory which assessed their Islamic religiosity scores through the constructs of Islamic beliefs and Manifestation of Islamic belief. Self-rated depression and anxiety were assessed using validated Beck Depression Inventory and Beck Anxiety Inventory in Malay. Ten of the patients were interviewed about their spiritual experiences and emotions. Questionnaire findings revealed a significant negative correlation between Islamic religiosity with depression and anxiety. Higher manifestation of Islamic belief was associated with lower depression while higher Islamic belief was associated with higher education. Higher Islamic religiosity was associated with older age, married and pensioned patients. Interview findings revealed that being ill brought the patients closer to God and many thanked God for the blessing and time spared for them to repent and do more good actions. All of them used prayers to heal their pain. Patients also reported strong feelings of anger, frustration and sadness after the initial diagnosis which slowly disappeared as they began to accept their illness as a blessing in disguise. It is concluded that there is a need to respond to the meaning and values given to human existence besides responding to physical and mental suffering in cancer patients

Program Komputer RPG Matematika Aljabar

Game edukasi matematika berjenis RPG dibuat untuk membantu siswa kelas 7 SMP dalam mempelajari dan meningkatkan motivasi mereka terhadap aljabar. Game RPG ini dibuat menggunakan RPG Maker MV

One year changes in QCT and DXA bone densities following bariatric surgery in a multiethnic Asian cohort

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Interferon Regulatory Factor 9 Structure and Regulation

Interferon regulatory factor 9 (IRF9) is an integral transcription factor in mediating the type I interferon antiviral response, as part of the interferon-stimulated gene factor 3. However, the role of IRF9 in many important non-communicable diseases has just begun to emerge. The duality of IRF9’s role in conferring protection but at the same time exacerbates diseases is certainly puzzling. The regulation of IRF9 during these conditions is not well understood. The high homology of IRF9 DNA-binding domain to other IRFs, as well as the recently resolved IRF9 IRF-associated domain structure can provide the necessary insights for progressive inroads on understanding the regulatory mechanism of IRF9. This review sought to outline the structural basis of IRF9 that guides its regulation and interaction in antiviral immunity and other diseases