As an alternative to open surgery, minimally invasive surgery (MIS) utilizes small skin incisions as ports to insert an endoscope and surgical tools. MIS offers significant advantages, including reduced pain, shorter recovery times, and better cosmetic outcomes than classical surgeries. However, MIS procedures come at the cost of losing the "sense of touch," which surgeons rely on to feel the tissues under operation, palpate organs, and assessing their conditions. This has encouraged researchers to develop smart MIS tools that provide artificial tactile sensation, mostly using electrical- or optical-based tactile sensors. In this work, we introduce a prototype of a smart laparoscopic grasper integrated with force and angle sensing capabilities via off-the-shelf sensors. The specification and design of the smart grasper are presented, as well as a demonstration on stiffness assessment of elastomeric samples and chicken meat. Overall, our prototype exhibits great potential for MIS applications, with room for future improvements.
Emotion recognition is a fast-growing domain of research with many promising applications in the clinical and computing settings. The field of longitudinal data recordings gathered by Experience Sampling Method (ESM) is of a research interest. In this research, developing a reliable emotion recognition model starts with an understanding of emotion labels using methods from regression analysis. This was achieved by modelling the week-long emotion labels recordings of 181 participants, where different Poisson distributions were compared to determine the best fit. Negative Binomial (NB) model on high valence count data showed a best fit with Std. Err. values of 0.019444 and 0.008994 for the variables R(Success) and P(Probability) of the NB function, respectively. Best fit of NB models was also shown for the low valence, high arousal and low arousal count data. This indicates that a NB regression model can be used on emotion count datasets with physiological signals as predictors.
The conventional hydrodesulfurization method currently used for desulfurization is an energy intensive process that is ineffective in the removal of sterically hindered sulfur containing compounds present in raw fuels. In this work, the use of MOFs in adsorptive desulfurization is being investigated. Likewise, the effect of IL incorporation in enhancing the activity of MOFs is studied. A-100 was used to desulfurize model fuel consisting of dibenzothiophene (DBT) in n-hexane. The study shows the effectiveness of A-100 in reaching 100?sulfurization of 500 ppmw n-hexane after 270 min. However, at higher concentrations the ?sulfurization was reduced to 81%. Upon, IL ([Bmim][Cl]) incorporation, IL@MOF samples effectively increased the ?sulfurization from 81% to 90% indicating the benefits of ILs in enhancing the MOF's activity. However, the ?sulfurization decreased with increasing IL content. Hence, further studies are required to reach the optimum IL: MOF necessary to achieve ultra-low sulfur levels in fuels.
With rapid rise in population and economic growth, energy security has become a topic of widespread discussion. An efficient method for meeting end-use energy demand by energy storage and distribution is through thermal energy storage. Phase change materials can be utilized to achieve latent heat storage. The unique properties of ionic liquids have grabbed the attention of many. Enthalpy of fusion, which represents the amount of energy stored or dissipated during phase change, is a critical criterion for judging the effectiveness of the ionic liquid. Hence, an accurate predictive model is needed to estimate the heat of fusion of an ionic liquid while avoiding the complexity of experimental work. A group contribution model is thus being developed. To enhance the results of the predictive model and increase the accuracy, a comprehensive data analysis has been performed to include the affecting parameters into the model.
With the low-cost associate with iron and sulfide, pyrite-based photovoltaics cell can become a suitable alternative to conventional silicon based solar cell. However, despite having most suitable theoretical values of required properties, pyrite has yet to become a practical solution to world energy needs. This study aims to analyze the performance of pyrite thin films in photovoltaic applications fabricated through atomic layer deposition.
In this study two new process are studied to improve the traditional Claus process. The new processes involve the combustion of elemental sulfur to produce SO2. Elemental sulfur combustion has long been used to generate SO2 for the production of sulfuric acid, which is used in the chemical industry, but it is now also being considered as a power generation energy vector. In the first method, sulfur process generates SO2, which is combined with acid gas and sent to catalytic reactors for sulfur recovery, eliminating the need for acid gas combustion. In the second process, SO2 from sulfur combustion at high-temperature is mixed with acid gas in the furnace for sulfur production. The remaining gas is sent to catalytic reactors for further sulfur recovery.
The unprecedented rise in the development of industries has led to detection of new classes of pollutants referred to as emerging pollutants which have recently been deemed as an ecological threat. The current work discusses the potential application of enzymes to treat these emerging pollutants, among which Peroxidases namely Dye De-colorizing Peroxidases were utilized after their expression in bacterial system. To investigate their ability to treat and potentially remove emerging pollutants, 31 emerging pollutants were subjected to enzymatic treatment by bacterial Dye De-colorizing Peroxidases using LCMSMS method. Our data showed complete removal of 2-Mercapto-benzothiazole (MBT), and significant degradation of other emerging pollutants. These data provide the first instance of the potential effect of Dye De-colorizing Peroxidases in degrading emerging pollutants.
The water and sanitation technologies to be implemented in a community need to be accessible by all people living in the area. Based on a study done by the Rural Water Supply Network, 15% to 30% of water and sanitation (watsan) installed infrastructures in developing countries are not operating due to inappropriate selection of watsan technologies. This paper presents a decision framework for the selection of appropriate watsan technologies. This decision model is made up of three modules. The first component is used to assess the community's capacity to manage local watsan services, the second component is a database of available watsan technologies, classified by their capacity requirement level (CRL), and the third component is a matching model between the two first components. This paper focuses on the second component, the database of watsan technologies, and more specifically on technologies' classification. The classification method proposed is based on Machine Learning.
Early prediction of Alzheimer's disease, a neuro- logical disease with irreversible damage to the brain cells, is a vast area of research dedicated to reconciling the growing need to assess, study, and treat affected subjects. Deep learning and image processing techniques have been used to facilitate the creation of Alzheimer's disease diagnosis and prediction systems. In this paper, we propose an early prediction system for Alzheimer's Disease that relies on textural analysis, regional ensemble learning, and deep learning, while utilizing structural MRI scans and clinical data.
The security of the World Wide Web is based on Transport Layer Security (TLS) and certificates issued to web sites by Certification Authorities (CAs). This allows users to securely "talk to" web sites over HTTPS. Recent developments have enabled CAs to automatically issue Domain Validated certificates to web sites that can prove ownership of their domain names e.g. mycompany.com. However, anyone can register any domain name, so this does not guarantee that the domain name is actually owned by a genuine business and not by a fraudster wishing to cheat users. Our project will allow CAs to automatically issue Extended Validation (EV) certificates to nationally registered organizations that can prove their identities using newly standardized Verifiable Credentials. This will help to assure users that the web sites are run by genuine businesses and not by fraudsters.
