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A transpiler converts code from one programming language to another. Many practical uses of transpilers require the user to be able to guide or customize the program produced from a given input program. This customizability is important for satisfying many application-specific goals for the produced code such as ensuring performance, readability, maintainability, compatibility, and so on. Conventional transpilers are deterministic rule-driven systems often written without offering customizability per user and per program. Recent advances in transpilers based on neural networks offer some customizability to users, e.g. through interactive prompts, but they are still difficult to precisely control the production of a desired output. Both conventional and neural transpilation also suffer from the "last mile" problem: they produce correct code on average, i.e., on most parts of a given program, but not necessarily for all parts of it. We propose a new transpilation approach that offers fine-grained customizability and reusability of transpilation rules created by others, without burdening the user to understand the global semantics of the given source program. Our approach is mostly automatic and incremental, i.e., constructs translation rules needed to transpile the given program as per the user's guidance piece-by-piece. We implement the transpiler as a tool called DuoGlot, which translates Python to Javascript programs, and evaluate it on the popular GeeksForGeeks benchmarks. DuoGlot achieves 90% translation accuracy and so it outperforms all existing translators, while it produces readable code. We evaluate DuoGlot on two additional benchmarks, containing more challenging and longer programs, and similarly observe improved accuracy., Comment: To be published in OOPSLA 2023

Enclaves, such as those enabled by Intel SGX, offer a hardware primitive for shielding user-level applications from the OS. While enclaves are a useful starting point, code running in the enclave requires additional checks whenever control or data is transferred to/from the untrusted OS. The enclave-OS interface on SGX, however, can be extremely large if we wish to run existing unmodified binaries inside enclaves. This paper presents Ratel, a dynamic binary translation engine running inside SGX enclaves on Linux. Ratel offers complete interposition, the ability to interpose on all executed instructions in the enclave and monitor all interactions with the OS. Instruction-level interposition offers a general foundation for implementing a large variety of inline security monitors in the future. We take a principled approach in explaining why complete interposition on SGX is challenging. We draw attention to 5 design decisions in SGX that create fundamental trade-offs between performance and ensuring complete interposition, and we explain how to resolve them in the favor of complete interposition. To illustrate the utility of the Ratel framework, we present the first attempt to offer binary compatibility with existing software on SGX. We report that Ratel offers binary compatibility with over 200 programs we tested, including micro-benchmarks and real applications such as Linux shell utilities. Runtimes for two programming languages, namely Python and R, tested with standard benchmarks work out-of-the-box on Ratel without any specialized handling., 24 pages, 11 figures, 10 tables. arXiv admin note: substantial text overlap with arXiv:2009.01144

Electric discharge machining is an essential modern manufacturing process employed to machine porous sintered metals. The sintered 316L porous stainless steel (PSS) components are widely used in diverse engineering domains, as interconnected pores are present. The PSS material has excellent lightweight and damping properties and superior mechanical and metallurgical properties. However, conventional machining techniques are not suitable for porous metals machining. Such techniques tend to block the micro-pores, resulting in a decrease in porous materials’ breathability. Thus, the EDM process is an effective technique for porous metal machining. The input process parameters selected in this study are peak current (Ip), pulse on time (Ton), voltage (V), flushing pressure (fp), and porosity. The response parameters selected are material removal rate (MRR) and tool wear rate (TWR). The present work aims to obtain optimum machining process parameters in the EDM of porous sintered SS316L using two meta-heuristic optimization techniques, i.e., Teaching Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithms, to maximize the MRR and minimize the TWR values. In the case of PSS having a 12.60% porosity value, PSO and TLBO algorithms give same optimum machining parameters. However, for PSS having an 18.85% porosity value, the PSO algorithm improves by about 5.25% in MRR and by 5.63% in TWR over the TLBO. In the case of PSS having a 31.11% porosity value, the PSO algorithm improves about 3.73% in MRR and 6.46% in TWR over the TLBO. The PSO algorithm is found to be consistent and to converge more quickly, taking minimal computational time and effort compared to the TLBO algorithm. The present study’s findings contribute valuable information in regulating the EDM performance in machining porous SS316L.

Metal membrane technology is an environmentally promising method for separation technology. This green filtration technology has gained much attention than other approaches for desalination and water purification because of its high flux, simplicity in operation, space and energy savings, and no use of chemicals. In many filtration processes, frequently organic polymer membranes are used, but they have several stability concerns at higher temperatures and organic solvents’ contacts. Hence, many efforts are put into the manufacturing of high-temperature stability and solvent resistant metallic membranes. New developments in metal membrane materials have presented significant improvements in separation technologies. These improvements have led to the next-generation metal membrane-based separation. This review paper covers the significant aspects of the current research progress in the metal membrane separation technology, focusing on their manufacturing techniques, materials used, industrial applications, and establishing a relative comparison between them in terms of benefits and constraints. Existing work is further proposed to be an update and value addition to the earlier literature reviews.

Paper-based packaging products are a cheap and sustainable alternative to their plastic counterparts. The manufacturing process of paper molding is a relatively newer concept, and the tooling aspect remains somewhat unknown. The work done in this paper establishes a tool design approach for the molding of paper products. These products are also sometime referred to as the molded paper products (MPP). Functional requirements are identified and translated to process chains. The concept of non-deterministic and deterministic tooling is introduced. Tooling process chains based on micro-metal additive manufacturing are defined and implemented. The process chain is utilized to manufacture micro features of size 300 μm in a high aspect ratio. A comparative study on validation of tooling process chains is also discussed, and the suitability of rapid tooling over sintered tools is highlighted in this work.

Paper-based packaging products, due to their excellent sustainable properties are preferred over the glass, plastic, and metal-based packaging solutions. The paper bottle molding process is very new in this category, and the tooling aspect is not well explored. Existing tooling solutions offer certain limitations in the molding process such as clogging of channels, low open-area fraction, low fatigue life, and longer cycle times. With the utilization of additive manufacturing techniques, the tooling can not only be made more economical and produced in shorter time duration but also more efficient. VAT photopolymerization based soft tooling process chain is highlighted and discussed in this work. Design of micro-features and their fabrication is carried out using UV light mask projection method. A characterization approach for precision manufacturing of micro-features using X-ray Computed Tomography is also discussed. Validation of the soft tooling process chain and its effectiveness against the conventional tools is presented and discussed in this work. It is shown that the proposed soft tooling process chain for the manufacturing of molds for paper bottle production is a better performing and cheaper alternative to existing solutions implemented in industry.

Over the years, the innovation and development of electrical and electronic equipment have been on a steep rise. Millions of electronics are being sold or discarded every year in the form of waste. Sustainable IT (Green IT or Circular Computing) is one of the most environment-friendly methods of reusing discarded or waste user electronics. The remanufacturing of a computer refers to the disassembly, repair, and upgrade of the original computer to give it a new life, along with a warranty that is as good as a new product. The goal of this work includes studying and assessing the total environmental impact of refurbishing a computer using life cycle assessment (LCA) integrated with discrete event simulation (DES), to compare two business models: (1) a case of centralized remanufacturing where the plants are in the Middle East, which is the hub for receiving waste electronics and distributing remanufactured goods; (2) a case of decentralized remanufacturing where the plants are situated in each continent for over a range of computer models. The environmental assessment was conducted using the openLCA software in combination with the WITNESS Horizon software for the DES. The results show that decentralized remanufacturing is a much more environmentally friendly option for the remanufacturing of computers, and the decentralized remanufacturing operation has a better throughput as well as efficiency, as compared to the centralized remanufacturing operation. The centralized remanufacturing scenario has a climate change impact of 1035.19 kg of CO2-Eq, as compared to the decentralized remanufacturing scenario with an impact of 816.12 kg of CO2-Eq. In terms of the impact on the marine life, decentralized remanufacturing was found to have 0.28 kg of N-Eq impact, as compared to centralized remanufacturing (0.22 kg of N-Eq). However, this does not give us a complete picture, as the environmental impact of the computer in its previous life remains unknown. Multi life cycle assessment is the assessment process that can be used to get a clearer picture of the ecological footprint of the computer during its multiple life cycles.

Classification tasks on labeled graph-structured data have many important applications ranging from social recommendation to financial modeling. Deep neural networks are increasingly being used for node classification on graphs, wherein nodes with similar features have to be given the same label. Graph convolutional networks (GCNs) are one such widely studied neural network architecture that perform well on this task. However, powerful link-stealing attacks on GCNs have recently shown that even with black-box access to the trained model, inferring which links (or edges) are present in the training graph is practical. In this paper, we present a new neural network architecture called LPGNet for training on graphs with privacy-sensitive edges. LPGNet provides differential privacy (DP) guarantees for edges using a novel design for how graph edge structure is used during training. We empirically show that LPGNet models often lie in the sweet spot between providing privacy and utility: They can offer better utility than "trivially" private architectures which use no edge information (e.g., vanilla MLPs) and better resilience against existing link-stealing attacks than vanilla GCNs which use the full edge structure. LPGNet also offers consistently better privacy-utility tradeoffs than DPGCN, which is the state-of-the-art mechanism for retrofitting differential privacy into conventional GCNs, in most of our evaluated datasets., Comment: Accepted at CCS'22

Applying the concept of “small is beautiful” into a conservative relatively low technology manufacturing sector where the “economies of scale” argument has been used to build ever more so-called efficient process lines is a major challenge. The energy efficiency of the casting process has only been investigated in a limited fashion. The two “Small is Beautiful” projects aimed to introduce a new concept into foundries with regards to the use of their resources. The new philosophy, “small is beautiful”, starts by encouraging the use of high-quality feedstock, only melting what is required and only when it is required. Recycling of internal scrap is not necessarily acceptable but an aim for higher yields is. Applying counter gravity casting methods to improve yield and give enhanced quality is encouraged as is the recovery of low-grade heat from solidification. The present paper discusses the research undertaken and the key findings from the two projects.

The food industry is very resource-intensive and continuously under stakeholder’s scanner to address the impact of climate change, resource scarcity, ever-changing consumer demands and stringent legislations. It has resulted in the food industry adopting Industry 4.0 initiatives for changes. In this context, one of the key focus is on enhancing transparency through Augmented Reality (AR) experiences. Although the food industry has seen a rise in the adoption of Industry 4.0 technologies, the implementation of AR remains significantly low. This paper defines AR, its benefits, and challenges, and proposes a framework for AR implementation in the food industry.

Additive Manufacturing presents unique advantages over traditional manufacturing processes and has the potential to accelerate technical advancement across multiple sectors, permitting far greater freedom in design than conventional manufacturing. However, one barrier which blocks wide adoption is residual stresses, which could seriously affect the materials’ behaviour during and after production. Selective laser sintering (SLS), a process with high energy input to the workpiece material, induces high temperature gradients, further affecting the final residual stress distribution. Within the present paper, three different methods for the assessment of the residual stresses’ distribution are presented and compared: a non-destructive method based on neutron diffraction, a destructive method known as the contour method, and a theoretical approach based on Finite Element Analysis. The aim is to examine the suitability and reliability of the application of these methods in predicting residual stresses distribution in additive manufacturing-built parts.

A 3D simulation of optical photogenreation, electrical characteristics, and thermal/heat distribution across the structure of a perovskite solar cell with a reduced graphene oxide (RGO) contact is presented. COMSOL Multiphysics package has been used to solve the coupled optical-electrical-thermal modules for this hybrid cell where the RGO added as the bottom electrode instead of a conventional metallic contact to enhance the heat dissipation towards a higher device stability. The Wave Optic module, Semiconductor module, and Heat Transfer in Solid module were coupled and solved for the proper input parameter values taken from relevant literature. The optical photogeneration, current-voltage characteristics, electric-field and the thermal maps of the cell are presented. The RGO contact doesn’t significantly impact on the optical and electrical output of the cell, but it accelerates the heat dissipation. The heat is mainly generated across the cell from the light absorption, Shockley-Read-Hall non-radiative recombination, and Joule heating. Compared to the cell with the Au electrode, the RGO contacted cell is showing a minimized heat accumulation and gradient at the bottom junction of the RGO/Spiro interface which promises a thermal stability of the cell. The nan-radiative and joule heat distribution also show a moderated density for the RGO contacted cell which are assigned to the high heat conductivity of the RGO layer compared to traditional metallic electrodes. Our simulations results are of the rarely presented thermal simulations for such devices and prove the superiority of graphene over plane metallic contacts for heat dissipation and thermodynamic aspect of a solar cell.

The heat dissipation has been rarely investigated in solar cells although it has a significant impact on their performance and reliability. For the first time, an extended three-dimensional (3-D) simulation of heat distribution in perovskite solar cells is presented here. We use COMSOL Multiphysics to investigate the temperature distribution in conventional perovskite solar cells through a coupled optical–electrical–thermal modules. Wave optics module, semiconductor module, and heat transfer in solid module are coupled in COMSOL Multiphysics package to perform the simulation in 3-D wizard. The electrical behavior, optical absorption, and heat conduction or convection are considered to gain insight into heat dissipation across the cell. The simulation results suggest that the heat produced in the cell is best dissipated from the metallic contact where the PbI $_2$ defect forms because of oxidation or decomposition of the perovskite layer at moisture exposure. The generated heat varies significantly from the front FTO contact to bottom metallic electrode. The more heat dissipation and accumulation is observed at the junction and electrode sides too. In our simulations, we consider the Joule heating, nonradiative recombination heating, and heat flux in every layer of the cell and calculate the carrier's concentration, electric field distribution, Joule heating, Shockley–Read–Hall heating, total heat flux, and temperature distribution across the solar cell structure. The simulations reveal that the metallic contact must be selected as a highly heat conductive material in order to accelerate the heat dissipation on the bottom of the cell and to enhance the cell reliability against temperature increase under normal operation.

The advancement in plastic and plastic based products in last two decades has imposed a serious threat to the environment. This has led to an increase in the demand for sustainable and biobased products. Paper and paper products are good alternatives to their plastic counterparts. A key element of paper industry is the raw material (wood, bamboo and nonwood agricultural residues such as bagasse, straws etc.) from which pulp is produced. The pulp is then utilized for production of paper. There are many pulping processes to prepare pulp of various qualities. For fine grade pulp, chemical pulping is used. The chemical pulp is produced by soda, kraft and sulphite pulping processes. However, for newsprint grade pulp, stone ground wood (SW), thermomechanical (TMP), chemi-thermomechanical (CTMP), chemi-mechanical (CMP) or refiner mechanical (RMP) corresponding pulping processes are employed. The method of pulp preparation (pulping process) consumes large amount of energy and chemicals. A new pulping process with electron irradiation using biowaste (bagasse) as a raw material is discussed in this work. Bagasse pulping process is found to reduce about 50% of energy consumption during pulping process. The process is referred to as ‘ECTMP’ (Energy Efficient Chemi-Thermo Mechanical Pulping). The paper produced from aforesaid method also reflected no loss of strength properties with respect of TMP reference pulps. Henceforth, preliminary calculation based on experimental results shows that the proposed bagasse ECTMP process explored the scope of energy savings of the order of 50%.

Automatic vulnerability diagnosis can help security analysts identify and, therefore, quickly patch disclosed vulnerabilities. The vulnerability localization problem is to automatically find a program point at which the "root cause" of the bug can be fixed. This paper employs a statistical localization approach to analyze a given exploit. Our main technical contribution is a novel procedure to systematically construct a test-suite which enables high-fidelity localization. We build our techniques in a tool called VulnLoc which automatically pinpoints vulnerability locations, given just one exploit, with high accuracy. VulnLoc does not make any assumptions about the availability of source code, test suites, or specialized knowledge of the type of vulnerability. It identifies actionable locations in its Top-5 outputs, where a correct patch can be applied, for about 88% of 43 CVEs arising in large real-world applications we study. These include 6 different classes of security flaws. Our results highlight the under-explored power of statistical analyses, when combined with suitable test-generation techniques.

Active packaging material has been used in the food industry to maintain the quality of packaged foods. The use of conventional polymers has serious environmental consequences due to improper disposal or recycling methods. Therefore, active packaging films based on biopolymers have been developed due to their excellent biocompatibility, degradability, and eco-friendliness. Amongst all essential oils, grape seed oil is considered to be a promising antimicrobial agent. It comprises large quantities of flavonoids, tocopherols, and other antimicrobial compounds. Grape seed essential oil has good antimicrobial and antioxidant activity. As a film, it is used to preserve food items such as poultry products, fish, and tomatoes. This work aimed to develop a polybutylene adipate terephthalate (PBAT) biocomposite film incorporated with natural grape seed essential oil (GEO) in addition to silica nanoparticles (SiO2 NPs) using the solution casting process. To achieve the desired packaging properties of the prepared PBAT-based film, the concentrations of grape seed essential oil as a plasticizer and nanosilica as a filler material were varied. The optical, physical, barrier, mechanical, surface hydrophobicity, and antibacterial properties of the PBAT/GEO/SiO2NP films were assessed. The FT-IR and XRD results indicated that GEO had effective miscibility with the PBAT/SiO2NP matrix. The addition of GEO increased the film flexibility, opacity, and antimicrobial activity, but the incorporation of SiO2NPs in the PBAT/GEO blend increased the tensile strength, thermal stability, and antimicrobial activities. The PBAT/GEO/SiO2NP films exhibited excellent antibacterial activity against food spoilage microorganisms. Finally, due to improved antimicrobial activities, film flexibility, optical, and heat resistance properties, the PBAT/GEO/NP nanocomposite films were found to have high potential for usage in active food packaging applications.

3D sand mould printing using binder jet technology can enable many technical improvements in casting practice, including part consolidation, design of parts to optimise the consumption of materials and hazardous chemicals, and on-demand and flexible size part manufacturing near the customer. Incorporating artificial intelligence in optimising the design of moulds, printing process parameters, and solidification processes may help automate a production facility and reduce labour time. Elimination of hazardous chemicals from industrial use may be a challenge. Therefore, an alternative technology to fuse the sand particles during printing or an environmentally friendlier alternative option for the binders and other consumables should be utilised with the 3D sand printing process. Properties of parts produced using 3D printed sand moulds have the potential to be better than the properties of parts produced using traditional casting due to this technology's benefits. This technology is an enabling technology for traditional casting processes rather than a competing technology. It is causing a paradigm shift in casting design because of the mould geometries achievable by using additive manufacturing to produce the sand mould. This paper reviews the first twenty years of research and challenges in developing 3D sand printing processes as an innovation for sustainable manufacturing.

We design and implement from scratch a new fuzzer called Sivo that refines multiple stages of grey-box fuzzing. First, Sivo refines data-flow fuzzing in two ways: (a) it provides a new taint inference engine that requires only logarithmic number of tests in the input size to infer dependency of many program branches on the input bytes, and (b) it employs a novel method for inverting branches by solving a systems of inequalities efficiently. Second, our fuzzer refines accurate tracking and detection of code coverage with simple and easily implementable methods. Finally, Sivo refines selection of parameters and strategies by parameterizing all stages of fuzzing and then dynamically selecting optimal values during fuzzing. Thus the fuzzer can easily adapt to a target program and rapidly increase coverage. We compare our fuzzer to 11 other state-of-the-art grey-box fuzzers on 27 popular benchmarks. Our evaluation shows that Sivo scores the highest both in terms of code coverage and in terms of number of found vulnerabilities.

We design and implement from scratch a new fuzzer called SIVO that refines multiple stages of grey-box fuzzing. First, SIVO refines data-flow fuzzing in two ways: (a) it provides a new taint inference engine that requires only logarithmic in the input size number of tests to infer the dependency of all program branches on the input bytes, and (b) it deploys a novel method for inverting branches by solving directly and efficiently systems of inequalities. Second, our fuzzer refines accurate tracking and detection of code coverage with simple and easily implementable methods. Finally, SIVO refines selection of parameters and strategies by parameterizing all stages of fuzzing and then dynamically selecting optimal values during fuzzing. Thus the fuzzer can easily adapt to a target program and rapidly increase coverage. We compare our fuzzer to 11 other state-of-the-art grey-box fuzzers on 27 popular benchmarks. Our evaluation shows that SIVO scores the highest both in terms of code coverage and in terms of number of found vulnerabilities.

Programming by Example (PBE) is a program synthesis paradigm in which the synthesizer creates a program that matches a set of given examples. In many applications of such synthesis (e.g., program repair or reverse engineering), we are to reconstruct a program that is close to a specific target program, not merely to produce some program that satisfies the seen examples. In such settings, we wish that the synthesized program generalizes well, i.e., has as few errors as possible on the unobserved examples capturing the target function behavior. In this paper, we propose the first framework (called SynGuar) for PBE synthesizers that guarantees to achieve low generalization error with high probability. Our main contribution is a procedure to dynamically calculate how many additional examples suffice to theoretically guarantee generalization. We show how our techniques can be used in 2 well-known synthesis approaches: PROSE and STUN (synthesis through unification), for common string-manipulation program benchmarks. We find that often a few hundred examples suffice to provably bound generalization error below $5\%$ with high ($\geq 98\%$) probability on these benchmarks. Further, we confirm this empirically: SynGuar significantly improves the accuracy of existing synthesizers in generating the right target programs. But with fewer examples chosen arbitrarily, the same baseline synthesizers (without SynGuar) overfit and lose accuracy.

Analyzing structural properties of social networks, such as identifying their clusters or finding their most central nodes, has many applications. However, these applications are not supported by federated social networks that allow users to store their social links locally on their end devices. In the federated regime, users want access to personalized services while also keeping their social links private. In this paper, we take a step towards enabling analytics on federated networks with differential privacy guarantees about protecting the user links or contacts in the network. Specifically, we present the first work to compute hierarchical cluster trees using local differential privacy. Our algorithms for computing them are novel and come with theoretical bounds on the quality of the trees learned. The private hierarchical cluster trees enable a service provider to query the community structure around a user at various granularities without the users having to share their raw contacts with the provider. We demonstrate the utility of such queries by redesigning the state-of-the-art social recommendation algorithms for the federated setup. Our recommendation algorithms significantly outperform the baselines which do not use social contacts and are on par with the non-private algorithms that use contacts., Comment: 18 pages, In Submission

Since the 1980s, the evolution of the computing power as well as the advances in numerical modeling techniques have allowed for the development of accurate casting simulation solutions. Although casting processes involve a series of diverse phenomena such as turbulent flow, porosity formation, and various time scales, modern simulation packages are capable of accurately modeling both the filling and solidification stages of the process. Thus foundry engineers can nowadays predict the characteristics of the final cast products such as residual stresses and defective locations without having to perform numerous time-consuming trial-and-error experiments. On the other hand, one of the applications undergoing intense investigation during the last few decades has been the successful implementation of integrated modeling, simulation, and optimization frameworks in manufacturing processes. Most of the case studies implemented focus on the optimization of three main process characteristics closely related to sustainability: (1) yield, (2) energy efficiency, and (3) product quality. The aim of this chapter is to provide an overview of the modeling techniques and optimization frameworks, which have been successfully applied to the simulation of casting processes with respect to the aforementioned process characteristics.

Natural Language Understanding has made recent advancements where context-aware token representation and word disambiguation have become possible to a large extent. In this scenario, comprehension of phrasal semantics particularly in the context of multi word expressions (MWE) and idioms, is the subsequent task to be addressed. Word level metaphor detection is unable to handle phrases or MWE(s) which occur in both literal and idiomatic context. State of the art transformer architectures can be useful in this context, but the absence of a large comprehensive dataset is a bottleneck. In this paper, we present a labelled EPIE dataset containing 3136 occurrences for 358 formal idioms. To prove the efficacy of our dataset, we also train a sequence classification model effectively and perform cross-dataset evaluation on three independent datasets. Our method achieves good results on all datasets with F1 score of 96% on our test data, and 82%, 74% and 76% F1 score on SemEval All Words, SemEval Lex Sample, and PIE Corpus datasets respectively.

COVID-19 has caused a global pandemic since December 2019. It has impacted not only the wellbeing of human society but also has been damaging to the global economy. This has imposed severe threats and challenges on businesses. The British government has launched aid schemes to combat the new scenarios developed as a result of the pandemic. This paper aims to assess the impact of COVID-19 on foundries in the UK. Recorded responses from a detailed survey of the British foundries were analysed and short- and long-term action plans for the foundries are suggested. The current status, challenges, and future direction of the UK foundries are discussed. An opinion for the use of additive technologies with business model innovation for the de-centralised foundries is presented.

Peer-to-peer (P2P) systems such as BitTorrent and Bitcoin are susceptible to serious attacks from byzantine nodes that join as peers. Due to well-known impossibility results for designing P2P primitives in unrestricted byzantine settings, research has explored many adversarial models with additional assumptions, ranging from mild (such as pre-established PKI) to strong (such as the existence of common random coins). One such widely-studied model is the general-omission model, which yields simple protocols with good efficiency, but has been considered impractical or unrealizable since it artificially limits the adversary only to omitting messages.In this work, we study the setting of a synchronous network wherein peer nodes have CPUs equipped with a recent trusted computing mechanism called Intel SGX. In this model, we observe that the byzantine adversary reduces to the adversary in the general-omission model. As a first result, we show that by leveraging SGX features, we eliminate any source of advantage for a byzantine adversary beyond that gained by omitting messages, making the general-omission model realizable. Our evaluation of 1000 nodes running on 40 DeterLab machines confirms theoretical efficiency claim.

The rapid technological advancements in recent years have led to developments in manufacturing and linked information technologies which have constituted the fourth industrial revolution. Industry 4.0 is a term coined for the ongoing fourth industrial revolution that focuses on revolutionising existing business models to improve manufacturing efficiency with the help of the Internet of Things. These changes can be classified with time in history as a frame of reference. This chapter aims, first, to present the evolution of the historical transition which led to Industry 4.0, along with a brief overview of earlier industrial revolutions, highlighting the fundamental differences between them. Second, explain what Industry 4.0 is, with notable definitions from the existing literature, and third, detail the integral components of Industry 4.0. Fourth and finally, to give information on its notable advantages.

Current frugal design practice is focused on the cost reduction of the product. Despite advancements in the domain of frugal Innovation, it is not systematized to develop products for all sets of users, including marginalized society. Many design researchers and engineers now dedicate time and knowledge to producing practical solutions to enhance the quality of life of the marginal community. The approach currently being adopted restricts the development of products intended for all segments of the users. In this paper, cumulative frequency distribution analysis and the Relative Importance Index is used to identify the essential attributes, which contribute to delivering actual frugal products in terms of functionality, usability, performance, affordability, accessibility, aesthetics, and robustness. The framework is beneficial to eradicate the discriminatory effect of being labeled as &ldquo, Jugaad&rdquo, users.

Capabilities of additive manufacturing (AM) for rapid tooling are well known in recent times. Rapid sand moulds are advantageous over traditional sand moulds in terms of cost, manufacturing time, flexibility, etc. This paper identifies metrics related to mould manufacturing and categorises them into four categories (cost, time, quality and environmental sustainability). A methodology based on the deterministic Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making algorithm is used to map at high resolution the influence of such categories on to the decision-making space when comparing AM with conventional sand mould making. Results show that AM is almost always clearly advantageous overall (excluding some very limited corner cases) for the examined case.

High-performance polymer composites are being increasingly favored for structural applications. For this purpose, efforts are being focused on exploring the potential of high-performance thermoplastics and thermosets. Cyanate ester (CE) resin is a special thermoset that can be used at up to 400 &deg, C without any considerable degradation, however, its tribological properties are not at the adequate level. Hence, it is needed to use this polymer in composite form with the fibrous/particulate reinforcement to impart better tribological properties and mechanical strength via a strong fiber&ndash, matrix interface. Carbon fiber/fabrics are at the forefront as reinforcement for specialty polymers. The tribological and tensile properties of cyanate ester (CE) composites-filled graphite, polytetrafluoroethylene (PTFE), and MoS2 micron-sized fillers reinforced with carbon fibers (CF) are investigated experimentally in a block-on-ring setup at 100 N, for 10 h, and with a sliding distance of approximately 10,000 m, against a hardened polished 100Cr6 steel shaft and diamond-like-coated (DLC) 100Cr6 steel shaft. The tribological properties of the composites including the coefficient of friction and specific wear rate are enhanced especially with the incorporation of graphite fillers. The friction coefficient and wear rate of the graphite-based composite was decreased significantly at 5 wt.% of graphite concentration. Further, at the same concentration, the graphite-based composite showed superior tensile properties as compared to the reference system owing to better dispersion and adhesion between the fibers and matrix. Tensile tests are performed to characterize the fiber&ndash, matrix interfacial adhesion and other strength properties.

Shopping malls are the place where people get their basic daily requirements such as food items, clothing, fashion accessories, electrical appliances and so on. Nowadays we can find shopping malls every few meters in any developed or developing cities around the globe. Sometimes customers face the problem of having inadequate information about the products and have to lose their valuable time at the billing counters waiting for their turn to come. Constant enhancement is required in the traditional billing system to advance the quality of shopping experience for the customers. To overcome these difficulties and to advance the present existing system, we are putting up our approach on an RFID based Smart Shopping Automation System (SSAS) including automated bill calculator, this approach is carried by attaching RFID tags to the products or items and an RFID reader with a touch panel display at the EXIT gate along with some other important components. With this approach of shopping system, customers will have complete information about the price of every product that is scanned in by the reader, and the total price of the items at the end. SSAS will save the customer’s time and labor required in malls and costs related to the products. Thus, this paper presents a preliminary development of the SSAS that can be integrated into the malls with smarts facilities to make shopping an automated experience.

Digitalization of the heat transfer in shell and tube type heat exchanger is what sets the foundation of the future of thermal industry. In traditional computational model design, a simulation model is validated with the experimental results. A digital twin is a virtual representation of the real system or processes and imbibes the validation of the model. The only difference is that the exchange of information is carried out in real time and is more reliable. A digital twin is not a static representation of the real space but rather a dynamic phenomenon connecting the two spaces. In the present analysis, a comparative finite element analysis of the shell and tube heat exchanger has been performed with an intention to enhance the effectiveness of the heat exchanger process. An approach toward development of the next generation of heat exchangers is discussed in this work.

The Cu $_2$ ZnSnS $_x$ Se $_{4-x}$ (CZTSSe) thin-film solar cells have attracted the attention of researchers due to its earth-abundant composition containing Copper, Zinc, Tin and Sulfur, and Selenide with 12.6% record efficiency (2013-IBM). A 3-D simulation analysis is presented here on the optical, electrical, and thermal characteristics of CZTSSe solar cell using COMSOL multiphysics 3-D simulation package. COMSOL is capable of calculating the optical–electrical–thermal models through electromagnetic wave, semiconductor, and heat transfer modules for a finely meshed structure. Using this capability, we have calculated the optical photogeneration rate of the a Mo/Mo(S,Se) $_2$ /CZTSSe/CdS/ZnO/ITO/air structure by inserting the refractive index and extinction coefficient of every layer in Wave optic module in COMSOL. We also calculated the total optical generation rate for two structures with and without Mo(S,Se) $_2$ layer at the junction of Mo and CZTSSe layers. The current–voltage curve, electric field profile, and the recombination rate of the cell has also been calculated by Semiconductor module coupled to wave optic module. The current–voltage characteristics show an improvement in $V_{\text{oc}}$ for the cell with Mo(S,Se) $_2$ layer (0.46 to 0.513 V) which was also suggested by IBM for a record cell efficiency. Finally, the thermal maps of the cell has been calculated by heat transfer module coupled to semiconductor module considering the Shockley–Read–Hall (SRH) recombination heat, Joule Heat, and conductive heat flux. The total heat flux magnitude of the cell was also mapped as a result out of these heat generation and cooling sources. The SRH heat is maximum within the depletion width at the CZTSSe/CdS interface, whereas the Joule heating is intensive at the Mo/Mo(S,Se) $_2$ /CZTSSe side. Interesting is to see that the heat is mainly conducted to environment from Mo side presented by the conductive heat map. The total heat flux is intensive at both top and bottom interfaces which means the heat is generated at both top and bottom sides of the cells and not only from the illuminated part.

Sustainability is becoming more and more important as a decision attribute in the manufacturing environment. However, quantitative metrics for all the aspects of the triple bottom line are difficult to assess. Within the present paper, the sustainability metrics are considered in tandem with other traditional manufacturing metrics such as time, flexibility, and quality and a novel framework is presented that integrates information and requirements from Computer-Aided Technologies (CAx) systems. A novel tool is outlined for considering a number of key performance indicators related to the triple bottom line when deciding the most appropriate process route. The implemented system allows the assessment of alternative process plans considering the market demands and available resources.

Eco-friendly products have gained importance in recent years. The paper bottle is a sustainable packaging solution for carbonated beverages. The moulding process is a two-stage process. At first, pulp is poured in the forming mould and fibers are formed in the desired shape. Wet bottle is then transferred to the drying mould to remove bound water. The drying process makes use of an inflatable core, which not only prevents the shrinkage of fibers but also helps in attaining good fiber compaction. Preliminary investigations reported uneven fiber compaction in changing curvatures and sharp corners. A cause of uneven thickness distribution in the geometry is uneven compaction pressure during core expansion. A FEM approach is developed to predict the occurrence of non-conformities in the bottle. Hyperelastic core material is modelled using Mooney-Rivlin material model from the elastic strain density function. The model can be used to optimize the core shape, thus developing a robust tooling solution.

In the current scenario, exploration of metal membranes has highly enhanced, and more efforts have been executed in its development and characterization. These breakthroughs have accelerated its applicability in the number of relevant sectors such as waste-water treatments, dairy processing’s, wineries, and biofuel refinement. This work inspects about recent findings and progress in the field of metal membranes, including its innovative manufacturing techniques and various applications, which have been reported in multiple research papers. The sintering technique is used generally for the preparation of metal membranes. Throughout this process, the resulting pore sizes are in the range of micrometers. Till now it is used for filtration of liquids to separate the solid particles. There is intense research work required for reducing the pore size of metal membranes using some cost-effective novel techniques, which will intensify its applicability in many green filtration technologies.

Ever since the invention of plastics, packaging has become extremely cheap and efficient. In recent times, the demand for more ecological packaging is increasing leading back to the roots of using naturally available resources, which are biodegradable. The manufacturing process of the Green Fiber Bottle (GFB) is based on moulding of wood fibers. The process is still at the research stage and not commercialized. Tooling is the most critical element in moulding and should be adapted to quick water removal techniques, such as Impulse Drying Technology. In this work, functional requirements for the development of a robust tooling solution are identified. Tooling alternatives are investigated and compared with the capacity to enable water removal. Characterization and assessment of porous tool materials using computed tomography are also outlined and discussed.

Recycling metallic powders used in the additive manufacturing (AM) process is essential for reducing the process cost, manufacturing time, energy consumption, and metallic waste. In this paper, the focus is on pore formation in recycled powder particles of stainless steel 316L during the selective laser melting process. We have introduced the concept of optimizing the powder bed's printing area in order to see the extent of the affected powders during the 3D-printing process. X-ray Computed Tomography (XCT) is used to characterize the pores inside the particles. The results from image processing of the tomography (rendered in 3D format) indicate a broader pore size distribution and a higher pore density in recycled powders compared to their virgin counterparts. To elucidate on this, the Electron Dispersion spectroscopy (EDX) analysis and Synchrotron-based Hard X-ray Photoelectron Spectroscopy (HAXPES) were performed to reveal the chemical composition distribution across the pore area and bulk of the recycled powder particles. Higher concentrations of Fe, Cr, and Ni were recorded on the interior wall of the pore in recycled particles and higher Mn, S and Si concentrations were recorded in the outer layer around the pore area and on the surface of the recycled particle. The pore formation in recycled powder is attributed to out-diffusion of Mn, S and Si to the outer surface as a result of the incident laser heat during the AM process due to higher electron affinity of such metallic elements to oxygenation. HAXPES analysis shows a higher MnO concentration around the pore area which impedes the in-diffusion of other elements into the bulk and thereby helps to creates a void. The inside wall of the pore area (dendrites), has a higher concentration of Fe and Cr oxide. We believe the higher pore density in recycled powders is due, at least in part to composition redistribution, promoted by laser heat during the AM process. Nanoindentation analyses on both virgin and recycled powder particles shows a lower hardness and higher effective modulus in the recycled powder particles attributed to the higher porosity in recycled powders.

Industrial Internet of Things (IIoT) is still a new research area. The main emphasis of the IIoT literature is on identifying the challenges involved in implementation of the IIoT. This paper summarizes the literature on the barriers faced by any stakeholder aiming to adopt IIoT anywhere. After reviewing 31 empirical studies, three domains of factors have been identified, individual (skills, abilities and knowledge), institutional (organizational/management-related), and structural (technical and economic infrastructures). A total of eleven factors across the three dimensions have been extracted. The most important factors were the absence of human capital (limited individual soft and technical knowledge, skills and abilities), low information security experience leading to a high probability data leaks and high management resistance from employees and leaders. To strengthen information for successful IIoT implementation, this paper proposes the mandate of Security, Education, Training, and Awareness (SETA) initiatives for any stakeholder interested in IIoT adoption. A Causal loop diagram for the IIoT implementation is also developed and discussed in this work.

Despite the functional success of deep neural networks (DNNs), their trustworthiness remains a crucial open challenge. To address this challenge, both testing and verification techniques have been proposed. But these existing techniques pro- vide either scalability to large networks or formal guarantees, not both. In this paper, we propose a scalable quantitative verification framework for deep neural networks, i.e., a test-driven approach that comes with formal guarantees that a desired probabilistic property is satisfied. Our technique performs enough tests until soundness of a formal probabilistic property can be proven. It can be used to certify properties of both deterministic and randomized DNNs. We implement our approach in a tool called PROVERO1 and apply it in the context of certifying adversarial robustness of DNNs. In this context, we first show a new attack- agnostic measure of robustness which offers an alternative to purely attack-based methodology of evaluating robustness being reported today. Second, PROVERO provides certificates of robustness for large DNNs, where existing state-of-the-art verification tools fail to produce conclusive results. Our work paves the way forward for verifying properties of distributions captured by real-world deep neural networks, with provable guarantees, even where testers only have black-box access to the neural network.

The concept of smart foundries aims at integrating and enabling smart technologies for digitalization of manufacturing operations. Foundries 4.0 refer to the inclusion of Industry 4.0 principles in traditional foundries. The casting process is known to be one of the most energy-intensive processes. Enabling sensor-based technologies, such as the Internet of Things (IoTs) for condition monitoring, can be an efficient means to bring down the energy costs. IoTs can also be implemented for real-time waste-monitoring. Embedded sensors can be utilized in determining excessive loads during the casting operation. This is very useful to identify overloading and in avoiding potential damage to the castings. Introduction of Additive Manufacturing can reduce cycle time and material consumption. This paper aims to introduce the concept of Foundries 4.0. The key focus is to highlight the integration of digital technologies in establishing foundries of the future. Challenges involved in the establishment of smart foundries are also discussed. A case-study discussing establishment of energy-efficient smart foundries is also presented in this work.

Neural networks are increasingly employed in safety-critical domains. This has prompted interest in verifying or certifying logically encoded properties of neural networks. Prior work has largely focused on checking existential properties, wherein the goal is to check whether there exists any input that violates a given property of interest. However, neural network training is a stochastic process, and many questions arising in their analysis require probabilistic and quantitative reasoning, i.e., estimating how many inputs satisfy a given property. To this end, our paper proposes a novel and principled framework to quantitative verification of logical properties specified over neural networks. Our framework is the first to provide PAC-style soundness guarantees, in that its quantitative estimates are within a controllable and bounded error from the true count. We instantiate our algorithmic framework by building a prototype tool called NPAQ that enables checking rich properties over binarized neural networks. We show how emerging security analyses can utilize our framework in 3 concrete point applications: quantifying robustness to adversarial inputs, efficacy of trojan attacks, and fairness/bias of given neural networks.

Additive Manufacturing has significantly developed over the years and is widely used in most industrial applications. Rapid Tooling refers to manufacturing the tools (moulds and dies) using Additive Manufacturing techniques. An essential application of Rapid Tooling is the 3D printing of sand moulds for castings. Metal casting is an energy-intensive process; and a lot of research has gone into the sustainability assessment of traditional sand castings. In this work, a robust decision-making approach is developed and implemented for sand mould manufacturing. Sustainability metrics for the mould production are formulated, and the conventional sand moulds are compared against the 3D printed sand moulds. A Multi-Criteria decision-making algorithm is implemented, and the effect of the batch size in the mould manufacturing is also studied. The discussed approach can help decision-makers choose the best mould manufacturing technique for the intended number of moulds to be manufactured.

Bio-engineering parts are widely used in hearing aid applications, dental applications, inhalation therapy blends, etc. Conventional tactile measurement techniques offer a limitation in the quality assessment of such parts due to the complex shape of the part and also due to their inability to measure internal features in a three-dimensional space. Industrial micro X-ray computed tomography ( μ XCT) is a robust characterization technique designed to maintain part quality, facilitate precision manufacturing, and reduce material waste due to part rejection. This paper highlights the limitations and challenges of conventional measurement techniques in the assessment of the quality of bio-engineering components. Capabilities of μ XCT are presented and its application in the bio-engineering sector is discussed in this work.

Eco-friendly packaging such as moulded pulp products have gained commercial importance in the recent years. However, it remains a greatly under-researched area, and there is an arising need to consolidate the best practices from research and industry in order to increase its implementation. The goal of this paper is to give an overview of the main aspects involved in the manufacture of moulded pulp products. This includes a classification of moulded pulp products, historical and current applications, production processes, materials, mechanical properties and environmental sustainability. Moreover, based on the latest research in the field, an innovative drying technique that utilizes concepts derived from impulse drying is presented, and the implementation of this process technology is discussed. Copyright © 2017 John Wiley & Sons, Ltd.