Collaborative Systems for Smart Environments: Trends and Challenges

Part 1: Introduction

Insights into the Fallback Path of Best-Effort Hardware Transactional Memory Systems

Current industry proposals for Hardware Transactional Memory (HTM) focus on best-effort solutions (BE-HTM) where hardware limits are imposed on transactions. These designs may show a significant performance degradation due to high contention scenarios and different hardware and operating system limitations that abort transactions, e.g. cache overflows, hardware and software exceptions, etc. To deal with these events and to ensure forward progress, BE-HTM systems usually provide a software fallback path to execute a lock-based version of the code. In this paper, we propose a hardware implementation of an irrevocability mechanism as an alternative to the software fallback path to gain insight into the hardware improvements that could enhance the execution of such a fallback. Our mechanism anticipates the abort that causes the transaction serialization, and stalls other transactions in the system so that transactional work loss is mini- mized. In addition, we evaluate the main software fallback path approaches and propose the use of ticket locks that hold precise information of the number of transactions waiting to enter the fallback. Thus, the separation of transactional and fallback execution can be achieved in a precise manner. The evaluation is carried out using the Simics/GEMS simulator and the complete range of STAMP transactional suite benchmarks. We obtain significant performance benefits of around twice the speedup and an abort reduction of 50% over the software fallback path for a number of benchmarks.

Manufacturing methods for liposome adjuvants

A wide range of studies have shown that liposomes can act as suitable adjuvants for a range of vaccine antigens. Properties such as their amphiphilic character and biphasic nature allow them to incorporate antigens within the lipid bilayer, on the surface, or encapsulated within the inner core. However, appropriate methods for the manufacture of liposomes are limited and this has resulted in issues with cost, supply, and wider scale application of these systems. Within this chapter we explore manufacturing processes that can be used for the production of liposomal adjuvants, and we outline new manufacturing methods can that offer fast, scalable, and cost-effective production of liposomal adjuvants.

Average-case analysis of power consumption in embedded systems

Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is ���power estimation���. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method.

Integration of process planning and scheduling using mobile-agent based approach in a networked manufacturing environment

Effective and efficient implementation of intelligent and/or recently emerged networked manufacturing systems require an enterprise level integration. The networked manufacturing offers several advantages in the current competitive atmosphere by way to reduce, by shortening manufacturing cycle time and maintaining the production flexibility thereby achieving several feasible process plans. The first step in this direction is to integrate manufacturing functions such as process planning and scheduling for multi-jobs in a network based manufacturing system. It is difficult to determine a proper plan that meets conflicting objectives simultaneously. This paper describes a mobile-agent based negotiation approach to integrate manufacturing functions in a distributed manner; and its fundamental framework and functions are presented. Moreover, ontology has been constructed by using the Prot��g�� software which possesses the flexibility to convert knowledge into Extensible Markup Language (XML) schema of Web Ontology Language (OWL) documents. The generated XML schemas have been used to transfer information throughout the manufacturing network for the intelligent interoperable integration of product data models and manufacturing resources. To validate the feasibility of the proposed approach, an illustrative example along with varied production environments that includes production demand fluctuations is presented and compared the proposed approach performance and its effectiveness with evolutionary algorithm based Hybrid Dynamic-DNA (HD-DNA) algorithm. The results show that the proposed scheme is very effective and reasonably acceptable for integration of manufacturing functions.

Interoperability Challenges in Internet of Things Systems: a Service-Oriented Computing Approach

Internet of Things systems are pervasive systems evolved from cyber-physical to large-scale systems. Due to the number of technologies involved, software development involves several integration challenges. Among them, the ones preventing proper integration are those related to the system heterogeneity, and thus addressing interoperability issues. From a software engineering perspective, developers mostly experience the lack of interoperability in the two phases of software development: programming and deployment. On the one hand, modern software tends to be distributed in several components, each adopting its most-appropriate technology stack, pushing programmers to code in a protocol- and data-agnostic way. On the other hand, each software component should run in the most appropriate execution environment and, as a result, system architects strive to automate the deployment in distributed infrastructures. This dissertation aims to improve the development process by introducing proper tools to handle certain aspects of the system heterogeneity. Our effort focuses on three of these aspects and, for each one of those, we propose a tool addressing the underlying challenge. The first tool aims to handle heterogeneity at the transport and application protocol level, the second to manage different data formats, while the third to obtain optimal deployment. To realize the tools, we adopted a linguistic approach, i.e.\ we provided specific linguistic abstractions that help developers to increase the expressive power of the programming language they use, writing better solutions in more straightforward ways. To validate the approach, we implemented use cases to show that the tools can be used in practice and that they help to achieve the expected level of interoperability. In conclusion, to move a step towards the realization of an integrated Internet of Things ecosystem, we target programmers and architects and propose them to use the presented tools to ease the software development process.

Dry grinding technology for automotive gears manufacturing: process modeling and optimization

The following thesis focused on the dry grinding process modelling and optimization for automotive gears production. A FEM model was implemented with the aim at predicting process temperatures and preventing grinding thermal defects on the material surface. In particular, the model was conceived to facilitate the choice of the grinding parameters during the design and the execution of the dry-hard finishing process developed and patented by the company Samputensili Machine Tools (EMAG Group) on automotive gears. The proposed model allows to analyse the influence of the technological parameters, comprising the grinding wheel specifications. Automotive gears finished by dry-hard finishing process are supposed to reach the same quality target of the gears finished through the conventional wet grinding process with the advantage of reducing production costs and environmental pollution. But, the grinding process allows very high values of specific pressure and heat absorbed by the material, therefore, removing the lubricant increases the risk of thermal defects occurrence. An incorrect design of the process parameters set could cause grinding burns, which affect the mechanical performance of the ground component inevitably. Therefore, a modelling phase of the process could allow to enhance the mechanical characteristics of the components and avoid waste during production. A hierarchical FEM model was implemented to predict dry grinding temperatures and was represented by the interconnection of a microscopic and a macroscopic approach. A microscopic single grain grinding model was linked to a macroscopic thermal model to predict the dry grinding process temperatures and so to forecast the thermal cycle effect caused by the process parameters and the grinding wheel specification choice. Good agreement between the model and the experiments was achieved making the dry-hard finishing an efficient and reliable technology to implement in the gears automotive industry.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Energy consumption of parallel algorithms for solving linear systems on HPC architecture

Modern High-Performance Computing HPC systems are gradually increasing in size and complexity due to the correspondent demand of larger simulations requiring more complicated tasks and higher accuracy. However, as side effects of the Dennard���s scaling approaching its ultimate power limit, the efficiency of software plays also an important role in increasing the overall performance of a computation. Tools to measure application performance in these increasingly complex environments provide insights into the intricate ways in which software and hardware interact. The monitoring of the power consumption in order to save energy is possible through processors interfaces like Intel Running Average Power Limit RAPL. Given the low level of these interfaces, they are often paired with an application-level tool like Performance Application Programming Interface PAPI. Since several problems in many heterogeneous fields can be represented as a complex linear system, an optimized and scalable linear system solver algorithm can decrease significantly the time spent to compute its resolution. One of the most widely used algorithms deployed for the resolution of large simulation is the Gaussian Elimination, which has its most popular implementation for HPC systems in the Scalable Linear Algebra PACKage ScaLAPACK library. However, another relevant algorithm, which is increasing in popularity in the academic field, is the Inhibition Method. This thesis compares the energy consumption of the Inhibition Method and Gaussian Elimination from ScaLAPACK to profile their execution during the resolution of linear systems above the HPC architecture offered by CINECA. Moreover, it also collates the energy and power values for different ranks, nodes, and sockets configurations. The monitoring tools employed to track the energy consumption of these algorithms are PAPI and RAPL, that will be integrated with the parallel execution of the algorithms managed with the Message Passing Interface MPI.

A Microleakage Study Of Gutta-percha/ah Plus And Resilon/real Self-etch Systems After Different Irrigation Protocols.

The development and maintenance of the sealing of the root canal system is the key to the success of root canal treatment. The resin-based adhesive material has the potential to reduce the microleakage of the root canal because of its adhesive properties and penetration into dentinal walls. Moreover, the irrigation protocols may have an influence on the adhesiveness of resin-based sealers to root dentin. The objective of the present study was to evaluate the effect of different irrigant protocols on coronal bacterial microleakage of gutta-percha/AH Plus and Resilon/Real Seal Self-etch systems. One hundred ninety pre-molars were used. The teeth were divided into 18 experimental groups according to the irrigation protocols and filling materials used. The protocols used were: distilled water; sodium hypochlorite (NaOCl)+eDTA; NaOCl+H3PO4; NaOCl+eDTA+chlorhexidine (CHX); NaOCl+H3PO4+CHX; CHX+eDTA; CHX+ H3PO4; CHX+eDTA+CHX and CHX+H3PO4+CHX. Gutta-percha/AH Plus or Resilon/Real Seal Se were used as root-filling materials. The coronal microleakage was evaluated for 90 days against Enterococcus faecalis. Data were statistically analyzed using Kaplan-Meier survival test, Kruskal-Wallis and Mann-Whitney tests. No significant difference was verified in the groups using chlorhexidine or sodium hypochlorite during the chemo-mechanical preparation followed by eDTA or phosphoric acid for smear layer removal. The same results were found for filling materials. However, the statistical analyses revealed that a final flush with 2% chlorhexidine reduced significantly the coronal microleakage. A final flush with 2% chlorhexidine after smear layer removal reduces coronal microleakage of teeth filled with gutta-percha/AH Plus or Resilon/Real Seal SE.

Does The Reciproc File Remove Root Canal Bacteria And Endotoxins As Effectively As Multifile Rotary Systems?

To evaluate the effectiveness of Reciproc for the removal of cultivable bacteria and endotoxins from root canals in comparison with multifile rotary systems. The root canals of forty human single-rooted mandibular pre-molars were contaminated with an Escherichia coli suspension for 21��days and randomly assigned to four groups according to the instrumentation system: GI - Reciproc (VDW); GII - Mtwo (VDW); GIII - ProTaper Universal (Dentsply Maillefer); and GIV -FKG Race(���) (FKG Dentaire) (n��=��10 per group). Bacterial and endotoxin samples were taken with a sterile/apyrogenic paper point before (s1) and after instrumentation (s2). Culture techniques determined the colony-forming units (CFU) and the Limulus Amebocyte Lysate assay was used for endotoxin quantification. Results were submitted to paired t-test and anova. At s1, bacteria and endotoxins were recovered in 100% of the root canals investigated (40/40). After instrumentation, all systems were associated with a highly significant reduction of the bacterial load and endotoxin levels, respectively: GI - Reciproc (99.34% and 91.69%); GII - Mtwo (99.86% and 83.11%); GIII - ProTaper (99.93% and 78.56%) and GIV - FKG Race(���) (99.99% and 82.52%) (P��<��0.001). No statistical difference were found amongst the instrumentation systems regarding bacteria and endotoxin removal (P��>��0.01). The reciprocating single file, Reciproc, was as effective as the multifile rotary systems for the removal of bacteria and endotoxins from root canals.

Photoelastic Analysis Of Fixed Partial Prosthesis Crown Height And Implant Length On Distribution Of Stress In Two Dental Implant Systems.

The aim of this study was to evaluate by photoelastic analysis stress distribution on short and long implants of two dental implant systems with 2-unit implant-supported fixed partial prostheses of 8���mm and 13���mm heights. Sixteen photoelastic models were divided into 4 groups: I: long implant (5 �� 11���mm) (Neodent), II: long implant (5 �� 11���mm) (Bicon), III: short implant (5 �� 6���mm) (Neodent), and IV: short implants (5 �� 6���mm) (Bicon). The models were positioned in a circular polariscope associated with a cell load and static axial (0.5���Kgf) and nonaxial load (15��, 0.5���Kgf) were applied to each group for both prosthetic crown heights. Three-way ANOVA was used to compare the factors implant length, crown height, and implant system (�� = 0.05). The results showed that implant length was a statistically significant factor for both axial and nonaxial loading. The 13���mm prosthetic crown did not result in statistically significant differences in stress distribution between the implant systems and implant lengths studied, regardless of load type (P > 0.05). It can be concluded that short implants showed higher stress levels than long implants. Implant system and length was not relevant factors when prosthetic crown height were increased.

Dentine Bond Strength And Antimicrobial Activity Evaluation Of Adhesive Systems.

This study evaluated the dentine bond strength (BS) and the antibacterial activity (AA) of six adhesives against strict anaerobic and facultative bacteria. Three adhesives containing antibacterial components (Gluma 2Bond (glutaraldehyde)/G2B, Clearfil SE Protect (MDPB)/CSP and Peak Universal Bond (PUB)/chlorhexidine) and the same adhesive versions without antibacterial agents (Gluma Comfort Bond/GCB, Clearfil SE Bond/CSB and Peak LC Bond/PLB) were tested. The AA of adhesives and control groups was evaluated by direct contact method against four strict anaerobic and four facultative bacteria. After incubation, according to the appropriate periods of time for each microorganism, the time to kill microorganisms was measured. For BS, the adhesives were applied according to manufacturers' recommendations and teeth restored with composite. Teeth (n=10) were sectioned to obtain bonded beams specimens, which were tested after artificial saliva storage for one week and one year. BS data were analyzed using two-way ANOVA and Tukey test. Saliva storage for one year reduces the BS only for GCB. In general G2B and GCB required at least 24h for killing microorganisms. PUB and PLB killed only strict anaerobic microorganisms after 24h. For CSP the average time to eliminate the Streptococcus mutans and strict anaerobic oral pathogens was 30min. CSB showed no AA against facultative bacteria, but had AA against some strict anaerobic microorganisms. Storage time had no effect on the BS for most of the adhesives. The time required to kill bacteria depended on the type of adhesive and never was less than 10min. Most of the adhesives showed stable bond strength after one year and the Clearfil SE Protect may be a good alternative in restorative procedures performed on dentine, considering its adequate bond strength and better antibacterial activity.

Self-etch Adhesive Systems: A Literature Review.

This paper presents the state of the art of self-etch adhesive systems. Four topics are shown in this review and included: the historic of this category of bonding agents, bonding mechanism, characteristics/properties and the formation of acid-base resistant zone at enamel/dentin-adhesive interfaces. Also, advantages regarding etch-and-rinse systems and classifications of self-etch adhesive systems according to the number of steps and acidity are addressed. Finally, issues like the potential durability and clinical importance are discussed. Self-etch adhesive systems are promising materials because they are easy to use, bond chemically to tooth structure and maintain the dentin hydroxyapatite, which is important for the durability of the bonding.

Habilidade motora fundamental : analise comparativa entre situa����o laboratorial e "natural"

Universidade Estadual de Campinas. Faculdade de Educa����o F��sica