Wireless Sensor Networks (WSNs) experiences several technical challenges such as limited energy, short transmission range, limited storage capacities, and limited computational capabilities. Moreover, the sensor nodes are deployed randomly and massively over an inaccessible or hostile region. Hence, WSNs are vulnerable to adversaries and are usually operated in a dynamic and unreliable environment. Animal tracking using wireless sensors is one such application of WSN where power management plays a vital role. In this paper, an energy-efficient hybrid routing method is proposed that divides the whole network into smaller regions based on sensor location and chooses the routing scheme accordingly. The sensor network consists of a base station (BS) located at a distant place outside the network, and a relay node is placed inside the network for direct communications from nodes nearer to it. The nodes are further divided into two categories based on the supplied energy; such that the ones located far away from BS and relay have higher energy than the nodes nearer to them. The network performance of the proposed method is compared with protocols like LEACH, SEP, and SNRP, considering parameters like stability period, throughput and energy consumption. Simulation result shows that the proposed method outperforms other methods with better network performance.

Purpose: Healthcare organizations are mandated to improve quality and safety for patients while stressed with shorter lengths of stay, communication lapses between disciplines, and patient throughput issues that impede timely delivery of patient care. Nurses play a prominent role in the safe transition of patients from admission to discharge. Although nurses participate in discharge planning, limited research has addressed the role and outcomes of the registered nurse as a leader in the process. The aim of this study was determine if implementation of a nurse-driven discharge planning protocol for patients undergoing cardiac implant would result in improved organizational efficiencies, higher medication reconciliation rates, and higher patient satisfaction scores. Methods: A two-group posttest experimental design was used to conduct the study. Informed consent was obtained from 53 individuals scheduled for a cardiac implant procedure. Subjects were randomly assigned to either a nurse-driven discharge planning intervention group or a control group. Post procedure, 46 subjects met inclusion criteria with half (n=23) assigned to each group. All subjects received traditional discharge planning services. The morning after the cardiac implant procedure, a specially trained registered nurse assessed subjects in the intervention for discharge readiness. Subjects in the intervention groups were then discharged under protocol orders by the intervention nurse after targeted physical assessment, review of the post procedure chest radiograph, and examination of the cardiac implant device function. The intervention nurse also provided patient education, discharge instructions, and conducted medication reconciliation. The day after discharge the principal investigator conducted a scripted follow-up phone call to answer questions and monitor for post procedure complications. A Hospital Discharge Survey was administered during the subject's follow-up appointment. Results: The majority of subjects were men, Caucasian, insured, and educated at the high school level or higher. Their average age was 73.5+ 9.8 years. No significant differences between groups were noted for gender, type of insurance, education, or type of cardiac implant (chi-square); or age (t-test). A Mann-Whitney U test (one-tailed) found no significant difference in variable cost per case (p=.437) and actual charges (p=.403) between the intervention and control groups. Significant differences were found between groups for discharge satisfaction (p=.05) and the discharge perception of overall health (p=.02), with those in the intervention group reporting higher scores. Chi square analysis found no significant difference in 30-day readmission rates (p=.520). Using an independent samples t-test, those in the intervention group were discharged earlier (p=.000), had a lower length of stay (p=.005), and had higher rates of reconciled medications (p=.000). The odds of having all medications reconciled were significantly higher in the intervention group (odds ratio, 50.27; 95% CI, 5.62-450.2; p=.000). Discussion/Implications: This is the first study to evaluate the role of the nurse as a clinical leader in patient throughput, discharge planning, and patient safety initiatives. A nurse driven discharge planning protocol resulted in earlier discharge times which can have a dramatic impact on patient throughput. The nurse driven protocol significantly reduced the likelihood of unreconciled medications at discharge and significantly increased patient satisfaction. Follow-up research is needed to determine if a registered nurse can impact organizational efficiency and discharge safety in other patient populations.

Current major demands in SPR sensor development are system miniaturization and throughput improvement. Structuring an array of integrated optical SPR sensor heads on a semiconductor based optoelectronic platform could be a promising solution for those issues, since integrated optical waveguides have highly miniaturized dimension and the optoelectronic platform enables on-chip optical-to-electrical signal conversion. Utilizing a semiconductor based platform to achieve optoelectronic functionality poses requirements to the senor head; the sensor head needs to have reasonably small size while it should have reasonable sensitivity and fabrication tolerance. This research proposes a novel type of SPR sensor head and demonstrates a fabricated device with an array of integrated optical SPR sensor heads endowed with optoelectronic functionality. The novel integrated optical SPR sensor head relies on mode conversion efficiency for its operational principle. The beauty of this type of sensor head is it can produce clear contrast in SPR spectrum with a highly miniaturized and simple structure, in contrast to several-millimeter-scale conventional absorption type or interferometer type sensor heads. The integrated optical SPR sensor with optoelectronic functionality has been realized by structuring a dielectric waveguide based SPR sensor head on a photodetector-integrated semiconductor substrate. A large number of unit sensors have been fabricated on a substrate with a batch fabrication process, which promises a high throughput SPR sensor system or low-priced disposable sensors.

There is a continuous need within most manufacturing environments for more flexible production equipment, particularly where customer satisfaction and responsiveness promote quality improvement. In this paper, an automated agile manufacturing system that uses high-speed computer numerically controlled (CNC) machines to make automotive cylinder heads is proposed and evaluated by means of discrete event simulation using the ARENA simulator. Two alternative agile system configurations are constructed and simulated to achieve the production target. The simulation shows some significant benefits in using the agile system and demonstrates that high-speed CNC equipment is a viable option for cylinder head manufacture at a production volume of 550 000 units per annum. It is shown that the agile system can provide more flexibility and half the throughput time of the transfer line.

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Droplet microfluidics is a high-throughput platform capable of accelerating the screening and synthesis of biological and chemical systems. However, significant challenges to microfluidic design and fabrication limit its broad use. In this dissertation, I overview and present potential solutions to challenges in droplet microfluidic fabrication and design. First, I present a method for low-cost rapid prototyping of complex droplet microfluidic devices. By combining desktop micromilling and electrode integration with conductive ink, thermoplastic microfluidic devices can be produced with features as small as 75 microns that can be connected to external sensors and actuators. Such devices can be designed, fabricated, and tested within a day and are shelf stable for months. Next, I developed a droplet microfluidic component library using micromilling and conductive ink electrodes. This library is high-throughput, biocompatible, and consists of components for droplet generation, anchoring, reinjection, coalescence, picoinjection, capacitance sensing, fluorescent sensing, and sorting. These components were combined in complex workflows, specifically, in the development of multi-color droplet pixel arrays. Finally, a series of machine learning based design automation tools for droplet microfluidics were created. These tools are capable of predicting the performance and automating the design of single emulsion and double emulsion droplet generators across any fluid combination. Furthermore, two quality metrics were developed, versatility and flow stability, that provide important context on the behaviors of the suggested designs. These tools are the first of their kind in microfluidics, and can play an important role in shifting droplet microfluidic design away from the manual and iterative process it is today. These advancements in droplet microfluidic design and fabrication can set the basis to rethink the microfluidic development cycle. Predictable and reproducible design and fabrication of sophisticated droplet microfluidic devices would provide a next-generation automation platform for the biological and chemical sciences, running experiments orders of magnitude faster and more sensitive than current methods. / 2025-01-16T00:00:00Z

Chunked DASH is getting attention for reducing otherwise high delay oflive segment streaming but there are a lot of unexplored problems associatedwith it. This master’s thesis investigates the difficulties involved withimplementing a chunked DASH player in the browser with JavaScript. A small system containing one JavaScript client and a server which simulateslive streaming by repeating VOD segments is implemented. Issuesrelated to the downloading of chunked segments are addressed and solvedsuch that chunked segments can be streamed within expected delay, andwith accurate throughput metrics. / Chunkad DASH får uppmärksamhet för sin förmåga att minska annars hög fördröjning vid segmentbaserad direktsändning, men det finns många associerade problem som inte har utforskats i någon större utsträckning. Denna masters avhandling undersöker svårigheterna med att implementera en chunkad DASH-spelare i webbläsaren med JavaScript. Ett litet system som innehåller en JavaScript-klient och en server som simulerar direktsändning genom att upprepa VOD-segment implementeras. Frågor relaterade till nedladdning av chunkar behandlas och löses så att innehållet kan sändas inom förväntad fördröjning och med pålitliga mätvärden av genomströmmning.

<p>The decision making processes in today's manufacturing systems represent very complex and challenging tasks. The desired flexibility in terms of the functionality of a machine adds more components to the machine. The real time monitoring and reporting generates large streams of data. However the intelligent and real time processing of this large collection of system data is at the core of the manufacturing decision support tools. </p> <p>This thesis outlines the use of Frequent Episodes in Event Sequences and Hidden Markov Modeling of throughput, quality and maintenance data to model the deterioration of performance in the components that make up the manufacturing system. The thesis also introduces the concept of decision points and outlines how to integrate the total cost function in a business model. </p> This thesis deals with the following three topics: <p>First, the component-based data structure of the manufacturing system is outlined especially throughput, quality and maintenance data. In this approach, the manufacturing system is considered as a group of components that interact with each other and with raw materials to produce the manufactured product. This interaction creates a considerable amount of data which can be associated with the relevant components of the system. The relations between the manufacturing components are established on a physical and logical basis. The components properties are clearly defined in database tables specifically created for this application. The thesis also discusses the web services in manufacturing systems and the portable technologies used in plant decision support tools. </p> <p>Second, the thesis presents a novel application of Frequent Episodes in Event Sequences to identify patterns in the deterioration of performance in a component using frequent episodes of operational failures, quality failures and maintenance activities. A Hidden Markov Model (HMM) is used to model each deterioration episode to estimate the states of performance and the transition rates between the states. The thesis compares the results generated by this model to other existing models of component performance deterioration while emphasizing the benefits ofthe proposed model through the use of the plant data.</p> <p>Finally the thesis presents a methodology usmg HMM probability distributions and Bayesian Decision theory framework to provide a set of decisions and recommendations under the condition of data uncertainty. The results of this analysis are then integrated in the plant maintenance business model.</p> <p>It is worthwhile mentioning that to develop the techniques and validate the results in this research; a Manufacturing Execution System (MES) was developed to operate in an automotive engine plant. All the data and results in this research are based on the plant data. The MES which was developed in this research provided significant benefits in the plant and was adapted by many other GM plants around the world.</p> / Thesis / Doctor of Philosophy (PhD)

Investigating novel treatment approaches to combat Clostridioides difficile Rusha Pal ABSTRACT Clostridioides difficile is the leading cause of antibiotic-induced diarrhea and colitis in hospitals and communities worldwide. The enteric pathogen, classified to be an "urgent threat" by the United States Center for Disease Control and Prevention (CDC), capitalizes on disrupted intestinal microbiome to establish infection with disease symptoms ranging from mild diarrhea to potentially fatal conditions. Disruption of the intestinal microbiome, caused mostly by antibiotic use, enables C. difficile to colonize and proliferate within the host. Paradoxically, antibiotics are used to treat C. difficile infection. These antibiotics decimate the gut microbial community further, thus priming the gastrointestinal tract to become more prone to recurrence of infection. To tackle this clinical setback, we utilized a combination of traditional and non-traditional drug discovery approaches and identified chemical entities and targeted treatment options effective against this toxin-producing intestinal pathogen. Herein, we exploited the strategy of high-throughput screening to identify leads that harbor anticlostridial activity. Our primary phenotypic screen of FDA-approved drugs and natural product libraries led to the identification of novel molecules that were further characterized for their anticlostridial efficacy both in vitro and in vivo. The most potent scaffolds identified were those of mitomycin C, mithramycin A, aureomycin, NP-003875, NAT13-338148, NAT18-355531, and NAT18-355768. Of these, mithramycin A, aureomycin, and NP-003875 were also found to harbor anti-virulence properties as they inhibited toxin production by the pathogen. Furthermore, natural product NP-003875 could confer protection to 100% of the infected mice from clinical manifestations of the disease in a primary infection model of C. difficile. Our final approach has been to develop targeted therapeutics called peptide nucleic acids (PNAs). PNAs are antisense agents capable of inhibiting gene expression in bacteria. In this study, antisense inhibition of the RNA polymerase  subunit gene (rpoA) of C. difficile was found to be bactericidal for the pathogen and could also inhibit the expression of its virulence factors. Additionally, antisense inhibition of the C. difficile rpoA gene was found to be non-deleterious for the tested commensal microflora strains. Given their intriguing anticlostridial properties, it can be concluded that our research opened exciting possibilities that can be further evaluated to uncover new treatments for CDI. / Doctor of Philosophy / Investigating novel treatment approaches to combat Clostridioides difficile Rusha Pal LAYMAN LANGUAGE ABSTRACT Clostridioides difficile is a prominent human pathogen that can colonize the gut and cause fatal infections. C. difficile is the most common cause of microbial healthcare-associated infection and results in substantial morbidity and mortality. The "most urgent worldwide public health threat" label has been assigned to C. difficile by the United States Centers for Disease Control and Prevention (CDC). There is a pressing need to develop new classes of antibiotics with improved efficacy to treat C. difficile infections (CDI). To address the need for novel strategies to combat the growing problem of CDI, we screened FDA-approved drugs and natural products library in search of novel drugs that possess potent and specific anticlostridial activity. Several promising hits were identified and evaluated successfully both in vitro and in vivo. The most potent and novel hits that displayed exceptional activity were mitomycin C, mithramycin A, aureomycin, NP-003875, NAT13-338148, NAT18-355531, and NAT18-355768. Furthermore, a murine model of C. difficile infection revealed that compound NP-003875 conferred 100% protection to the infected mice from clinical manifestations of the disease. Interestingly, these compounds were non-toxic to the gut microflora and human cells. Our final approach has been to develop non-traditional therapeutics to target specific genes in C. difficile. These novel therapeutics are called peptide nucleic acids (PNA). Herein, we designed a PNA targeting RNA polymerase  subunit gene (rpoA) of C. difficile. The designed PNA could successfully inhibit the growth of the pathogen and expression of its virulence factors. In conclusion, our research opened exciting possibilities that can be further evaluated to uncover new treatments for CDI.

Current high-throughput screening (HTS) tools (i.e., single-use 96-well filter plate) are limited to the few membrane types that are sold commercially, restricting the ability to screen membrane materials for targeted applications. In this thesis, a multi-well device capable of screening any flat-sheet membrane was designed, where multiple devices can be stacked for extensive HTS (>32 experiments). Confocal imaging of a Natrix Q cross-section – a membrane type not sold in a commercial filter plate – was carried out after 24 h in contact with green fluorescent protein to visually confirm protein-membrane interactions. The static binding capacity (SBC) of bovine serum albumin (BSA) and Herring testes DNA was found for specific parameters: membrane type (Mustang Q, Sartobind Q, Natrix Q, Durapore), salt concentration (0, 50, 100 mM NaCl), and contact time (1 min, 4 h, 8 h, 24 h). Considering solution conditions, the highest BSA SBC was observed with Natrix Q at 0 M NaCl with a contact time of 24 h. The DNA and BSA SBC values for Natrix Q were the highest among the membrane types evaluated, demonstrating consistency with literature trends. These findings suggest that SBC experiments can predict promising membrane materials for scaled-up applications. Finally, the chromatography process was replicated in this multi-well device (Natrix Q), showing 50% BSA elution from the membrane. The results of this thesis confirmed this ability to accommodate any membrane adsorber, simultaneously compare different membrane materials, and extract the membrane for post-experimental analysis. This work’s significance was emphasized in its future potential to aid with membrane material selection, particularly with exploring the properties of next-generation membrane materials (e.g., 3D-printed membranes). Three future areas for optimization with this multi-well device were highlighted: biotherapeutic purification, sequencing of membrane materials within a process, and applying it as a tool to understand ion selectivity. / Thesis / Master of Applied Science (MASc) / Membranes are used in many industries, such as water treatment, environmental remediation, and biopharmaceuticals. In the biopharmaceutical industry, high-throughput screening (HTS) tools (e.g., filter plates), which allow for miniaturized experiments, are used to perform extensive experimental analysis to determine optimal solution conditions (e.g., pH) for biomolecule binding. Unfortunately, commercial filter plates are limited in customizability for HTS of membrane materials. To address these limitations, this thesis focuses on designing and validating a multi-well device capable of incorporating any membrane adsorber. Different biomolecules (proteins, DNA), solution conditions, and membrane materials were evaluated. The results of this thesis confirmed this ability to accommodate any membrane adsorber, simultaneously compare different membrane materials, and extract the membrane for post-experimental analysis. This work also discussed using this device for future rapid membrane material selection in multiple industries (e.g., biotherapeutics, ion extraction).