Combination of Terrestrial and Satellite Topography for Pipeline Engineering and Construction

A method consisting in an optimal combination of conventional topography from a terrestrial acquisition and satellite derived topography is presented. The solution recently implemented in the UAE for the engineering and the construction of a gas export pipeline allows significant cost reduction, time saving, and safety hazard reduction as fewer terrestrial operations are needed. The survey area is split into 2 sub-areas: area with infrastructures requiring a high accuracy is surveyed with terrestrial topographical acquisition methods such as GNSS receivers, the other one with desert conditions is mapped from satellite stereoscopic imagery. Stereoscopic mode refers to when the satellite sensor acquires two images of the same location taken from different angles. Using photogrammetric techniques, it produces a 3D elevation model of the area. The native satellite imagery allows a mapping of the surface features as well. Terrestrial and satellite datasets are finally merged and adjusted to provide engineering and construction contractors with a unique survey dataset. Terrestrial survey methods provide generally 5-10cm horizontal and vertical accuracies whereas satellite topography has accuracy of a few meters, so satellite topography must be controlled and adjusted from terrestrial ground control points which allow to reach an average 50cm absolute accuracy. This is good enough in desert areas with neither particular ground feature nor steep relief requiring complex design. Satellite acquisition has limitations: vegetation masking the ground, steep slopes and dense infrastructures. It is therefore necessary to combine conventional and satellite topography to meet engineering requirements. This is considered when defining the satellite and terrestrial survey areas. Beyond these limitations, this solution has strong advantages. Satellite grid resolution can be better (1-2m versus 5-10m for GNSS surveys). Acquisition and processing are faster (about 2 weeks versus a few weeks or months), and costs are from 10 to 100 times cheaper than conventional methods. No need for personnel and equipment on site, no management of logistics and permitting as well. Finally, it reduces safety hazards such as car accident, harsh weather, manual handling, etc. In addition, limiting the area to be surveyed with conventional equipment may avoid the need to mobilize Airborne photogrammetry or lidar systems usually operated by foreign companies. This limits complex Call for Tender, permitting management and give more opportunity to contract local companies. Satellite topography is widely used for preliminary studies, but the innovation here consists in an optimal combination of terrestrial and satellite datasets for engineering and construction purposes. This solution has however some limitations as it requires suitable conditions for satellite optical imagery acquisitions: no vegetation, limited cloud cover, smooth topography, and limited infrastructures. This is of interest basically in Middle east and North Africa.

Privacy-preserving energy storage sharing with blockchain and secure multi-party computation

Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a majority of dishonest users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provided empirical evaluation in this paper 1 .

Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

Industry 4.0, cyber-physical systems, and digital twins are generating ever more data. This opens new opportunities for companies, as they can monitor development and production processes, improve their products, and offer additional services. However, companies are often overwhelmed by Big Data, as they cannot handle its volume, velocity, and variety. Additionally, they mostly do not follow a strategy in the collection and usage of data, which leads to unexploited business potentials. This paper presents the implementation of a Digital Twin module in an industrial case study, applying a concept for guiding companies on their way from data to value. A standardized use case template and a procedure model support the companies in (1) formulating a value proposition, (2) analyzing the current process, and (3) conceptualizing a target process. The presented use case entails an anomaly detection algorithm based on Gaussian processes to detect defective products in real-time for the extrusion process of aluminum profiles. The module was initially tested in a relevant environment; however, full implementation is still missing. Therefore, technology readiness level 6 (TRL6) was reached. Furthermore, the effect of the target process on production efficiency is evaluated, leading to significant cost reduction, energy savings, and quality improvements.

Direct Isomaltulose Synthesis From Beet Molasses by Immobilized Sucrose Isomerase

Isomaltulose is becoming a focus as a functional sweetener for sucrose substitutes; however, isomaltulose production using sucrose as the substrate is not economical. Low-cost feedstocks are needed for their production. In this study, beet molasses (BM) was introduced as the substrate to produce isomaltulose for the first time. Immobilized sucrose isomerase (SIase) was proved as the most efficient biocatalyst for isomaltulose synthesis from sulfuric acid (H2SO4) pretreated BM followed by centrifugation for the removal of insoluble matters and reducing viscosity. The effect of different factors on isomaltulose production is investigated. The isomaltulose still achieved a high concentration of 446.4 ± 5.5 g/L (purity of 85.8%) with a yield of 0.94 ± 0.02 g/g under the best conditions (800 g/L pretreated BM, 15 U immobilized SIase/g dosage, 40°C, pH of 5.5, and 10 h) in the eighth batch. Immobilized SIase used in repeated batch reaction showed good reusability to convert pretreated BM into isomaltulose since the sucrose conversion rate remained 97.5% in the same batch and even above 94% after 11 batches. Significant cost reduction of feedstock costs was also confirmed by economic analysis. The findings indicated that this two-step process to produce isomaltulose using low-cost BM and immobilized SIase is feasible. This process has the potential to be effective and promising for industrial production and application of isomaltulose as a functional sweetener for sucrose substitute.

Mechatronic Design of a Robot for Upper Limb Rehabilitation at Home

This paper addresses the design of a novel bionic robotic device for upper limb rehabilitation tasks at home. The main goal of the design process has been to obtain a rehabilitation device, which can be easily portable and can be managed remotely by a professional therapist. This allows to treat people also in regions that are not easily reachable with a significant cost reduction. Other potential benefits can be envisaged, for instance, in the possibility to keep social distancing while allowing rehabilitation treatments even during a pandemic spread. Specific attention has been devoted to design the main mechatronic components by developing specific kinematics and dynamics models. The design process includes the implementation of a specific control hardware and software. Preliminary experimental tests are reported to show the effectiveness and feasibility of the proposed design solution.

Ternary nickel–tungsten–copper alloy rivals platinum for catalyzing alkaline hydrogen oxidation

Operating fuel cells in alkaline environments permits the use of platinum-group-metal-free (PGM-free) catalysts and inexpensive bipolar plates, leading to significant cost reduction. Of the PGM-free catalysts explored, however, only a few nickel-based materials are active for catalyzing the hydrogen oxidation reaction (HOR) in alkali; moreover, these catalysts deactivate rapidly at high anode potentials owing to nickel hydroxide formation. Here we describe that a nickel–tungsten–copper (Ni5.2WCu2.2) ternary alloy showing HOR activity rivals Pt/C benchmark in alkaline electrolyte. Importantly, we achieved a high anode potential up to 0.3 V versus reversible hydrogen electrode on this catalyst with good operational stability over 20 h. The catalyst also displays excellent CO-tolerant ability that Pt/C catalyst lacks. Experimental and theoretical studies uncover that nickel, tungsten, and copper play in synergy to create a favorable alloying surface for optimized hydrogen and hydroxyl bindings, as well as for the improved oxidation resistance, which result in the HOR enhancement.

Halomonas as a chassis

With the rapid development of systems and synthetic biology, the non-model bacteria, Halomonas spp., have been developed recently to become a cost-competitive platform for producing a variety of products including polyesters, chemicals and proteins owing to their contamination resistance and ability of high cell density growth at alkaline pH and high salt concentration. These salt-loving microbes can partially solve the challenges of current industrial biotechnology (CIB) which requires high energy-consuming sterilization to prevent contamination as CIB is based on traditional chassis, typically, Escherichia coli, Bacillus subtilis, Pseudomonas putida and Corynebacterium glutamicum. The advantages and current status of Halomonas spp. including their molecular biology and metabolic engineering approaches as well as their applications are reviewed here. Moreover, a systematic strain engineering streamline, including product-based host development, genetic parts mining, static and dynamic optimization of modularized pathways and bioprocess-inspired cell engineering are summarized. All of these developments result in the term called next-generation industrial biotechnology (NGIB). Increasing efforts are made to develop their versatile cell factories powered by synthetic biology to demonstrate a new biomanufacturing strategy under open and continuous processes with significant cost-reduction on process complexity, energy, substrates and fresh water consumption.

Population-Based COVID-19 Screening in Mexico: Assessment of Symptoms and Their Weighting in Predicting SARS-CoV-2 Infection

Background and Objectives: Sentinel surveillance in the early stage of the COVID-19 pandemic in Mexico represented a significant cost reduction and was useful in estimating the population infected with SARS-CoV-2. However, it also implied that many patients were not screened and therefore had no accurate diagnosis. In this study, we carried out a population-based SARS-CoV-2 screening in Mexico to evaluate the COVID-19-related symptoms and their weighting in predicting SARS-CoV-2 infection. We also discuss this data in the context of the operational definition of suspected cases of COVID-19 established by the Mexican Health Authority’s consensus. Materials and Methods: One thousand two hundred seventy-nine subjects were included. They were screened for SARS-CoV-2 using RT-PCR. The weighting of COVID-19 symptoms in predicting SARS-CoV-2 infection was evaluated statistically. Results: Three hundred and twenty-five patients were positive for SARS-CoV-2 and 954 were negative. Fever, asthenia, dysgeusia, and oxygen saturation predicted SARS-CoV-2 infection (odds ratios ranged from 1.74 to 4.98; p < 0.05). The percentage of asymptomatic COVID-19 patients was 36% and only 38.15% met the Mexican operational definition. Cq-values for the gene N of SARS-CoV-2 were significantly higher in asymptomatic subjects than in the groups of COVID-19 patients with neurological, respiratory, and/or musculoskeletal manifestations (p < 0.05). Conclusions: Dysgeusia, fever, and asthenia increased the odds of a positive result for COVID-19 1.74–4.98-fold among the study population. Patients with neurological, respiratory, and/or musculoskeletal manifestations had higher viral loads at COVID-19 diagnosis than those observed in asymptomatic patients. A high percentage of the participants in the study (61.85%) did not meet the operational definition for a suspected case of COVID-19 established by the Mexican Health Authority’s consensus, representing a high percentage of the population that could have remained without a COVID-19 diagnosis, so becoming a potential source of virus spread.

Holistic Approaches in Achieving Best in Class BIC Detailed Well Design and Operation Execution — The New Norm

This paper will discuss the case study of two (2) projects in Malaysia that were stretched-to-limit and optimized ‘on-paper’ all the wells’ related engineering design and construction, operation activities pre and during drilling campaign as well as logistic arrangement, which led to significant cost reduction in wells and thus improving the overall project economics. This significant savings achieved with full compliance to technical standards as well as with the betterment to improve HSE during offshore execution.