Minimal Footprint Research Articles

Optimal One-Pass Nonparametric Estimation Under Memory Constraint

For nonparametric regression in the streaming setting, where data constantly flow in and require real-time analysis, a main challenge is that data are cleared from the computer system once processed due to limited computer memory and storage. We tackle the challenge by proposing a novel one-pass estimator based on penalized orthogonal basis expansions and developing a general framework to study the interplay between statistical efficiency and memory consumption of estimators. We show that, the proposed estimator is statistically optimal under memory constraint, and has asymptotically minimal memory footprints among all one-pass estimators of the same estimation quality. Numerical studies demonstrate that the proposed one-pass estimator is nearly as efficient as its nonstreaming counterpart that has access to all historical data.

Pulsed hydraulic-pressure-responsive self-cleaning membrane.

Pressure-driven membranes is a widely used separation technology in a range of industries, such as water purification, bioprocessing, food processing and chemical production1,2. Despite their numerous advantages, such as modular design and minimal footprint, inevitable membrane fouling is the key challenge in most practical applications3. Fouling limits membrane performance by reducing permeate flux or increasing pressure requirements, which results in higher energetic operation and maintenance costs4-7. Here we report a hydraulic-pressure-responsive membrane (PiezoMem) to transform pressure pulses into electroactive responses for in situ self-cleaning. A transient hydraulic pressure fluctuation across the membrane results in generation of current pulses and rapid voltage oscillations (peak, +5.0/-3.2 V) capable of foulant degradation and repulsion without the need for supplementary chemical cleaning agents, secondary waste disposal or further external stimuli3,8-13. PiezoMem showed broad-spectrum antifouling action towards a range of membrane foulants, including organic molecules, oil droplets, proteins, bacteria and inorganic colloids, through reactive oxygen species (ROS) production and dielectrophoretic repulsion.

Graphene‐Based Composite Foams with Tunable Porosity for Ultra‐Sensitive Low‐Pressure Applications

AbstractComposite foam sensors presenting promising applications in pressure sensors require seamless integration of conductive additives, simplicity of manufacturing with minimum footprint, and consistent performance with optimum mechanical properties. Here, a resistive‐type foam sensor based on polyvinyl alcohol (PVA) containing high loading of water‐based graphene slurry (30 vol%) developed by in situ condensation is presented. The composite foam sensor exhibits an open‐cell structure with high electrical conductivity (13.3 × 10–2 S m−1), high sensitivity (S = 8.2 ± 0.7 for pressure <1 kPa), and minimum response time (8 ms). The effect of processing on porosity and the corresponding disparity in the electromechanical performance of the foam sensors is witnessed. A systematic method to evaluate the mechanical properties of foam sensors as per foam standards is described. In addition to assessing the electromechanical properties, the graphene‐based foam sensor is calibrated for the first time. The calibration curve and accuracy of the foam sensor are correlated to their morphology. Synthesis and facile integration of graphene in composites, their mechanical, electromechanical characteristics, and calibration of sensors are demonstrated. Furthermore, it is shown that the sensors can empower human‐machine interface systems using a custom‐developed user interface.

Examining the significance of the ignition characteristics of hydrogen and liquefied-petroleum-gas on the reactivity controlled compression ignition and its interspersed profiles induced in an existing diesel engine: A comparative perspective

The present study highlights the comparative analysis of diesel-hydrogen and diesel- Liquefied-petroleum-gas bi-fuel operation. It investigates the ignition delay criterion and its effects on performance, emission, and stability characteristics under varying injection and reactivity phasings in an existing single-cylinder diesel engine. The experimental analysis-based statistical modelling approach for simultaneous identification of relative optimum parametric combination to improve the performance, emission, and stability has been incorporated in this study through response surface methodology. It is evident from the results that the different parametric combinations of input variables for both Liquefied-petroleum-gas and hydrogen-enriched diesel operation influenced the combustion parameter (ignition delay), performance, and emission significantly. Subsequently, the ignition delay corresponding to the maximum hydrogen enrichment scenario is 41.2% more than the corresponding Liquefied-petroleum-gas operational mode. Similarly, the exergetic efficiency attained with maximum hydrogen substitution is 9.54% higher than the exergetic efficiency of the corresponding Liquefied-petroleum-gas enriched operation. On the other hand, the minimum footprints of soot, total unburnt hydrocarbon, Carbon monoxide, and Carbon dioxide registered with hydrogen enrichment were 15.77%, 72.45%, 38.48%, and 2.37% lower than the corresponding Liquefied-petroleum-gas enriched operation. In contrast, the lowest recorded value of oxides of nitrogen with Liquefied-petroleum-gas enriched diesel operation was 77.93% lower than the corresponding hydrogen-enriched operation. Thus, the study presents itself as a first-of-a-kind strategy for the comparative analysis of the effect of ignition delay on the performance, emission, and stability matrices of existing infrastructure with diesel- Liquefied-petroleum-gas and diesel-hydrogen operation.

Cleaner production strategy tailored versatile biocomposites for antibacterial application and electromagnetic interference shielding

The clean and sustainable development of biomass resources has attracted tremendous attention to alleviate the increasingly serious energy depletion and environmental problems. Hence, it is urgently needed to design a green product through a cleaner production strategy to bridge the gap between materials and sustainability toward environmental protection and practicality. Herein, a bamboo fiber/polybutylene succinate (PBS) biocomposite that carries silver nanoparticles (AgNPs) with electromagnetic interference (EMI) shielding and antibacterial activity, as well as the performance of interface reinforcement, were prepared using a green, efficient, and scalable bionic strategy of PDA coupling with minimal environmental footprint. The proposed bamboo fiber/PBS biocomposites, which showed multifunctionality and environmental adaptability, revealed good antibacterial activity, where the average inhibition zone diameters against E. coli and S. aureus bacteria reached 5.37 ± 0.36 and 3.96 ± 0.74 mm, respectively. Simultaneously, gains in the EMI shielding effectiveness (∼29.72 dB) and mechanical properties (improved by 24.58% in tensile strength, 39.13% in tensile modulus, 14.60% in flexural strength, and 19.67% in flexural modulus) were realized. Overall, this work displays a novel pathway toward eco-friendly and multifunctional nature fiber/polymer composites for high-value utilization.

Bioorthogonal click and release: A general, rapid, chemically revertible bioconjugation strategy employing enamine N-oxides

A chemically revertible bioconjugation strategy featuring a new bioorthogonal dissociative reaction employing enamine N-oxides is described. The reaction is rapid, complete, directional, traceless, and displays a broad substrate scope. Reaction rates for cleavage of fluorophores from proteins are on the order of 82 M-1s-1, and the reaction is relatively insensitive to common aqueous buffers and pHs between 4 and 10. Diboron reagents with bidentate and tridentate ligands also effectively reduce the enamine N-oxide to induce dissociation and compound release. This reaction can be paired with the corresponding bioorthogonal hydroamination reaction to afford an integrated system of bioorthogonal click and release via an enamine N-oxide linchpin with a minimal footprint. The tandem associative and dissociative reactions are useful for the transient attachment of proteins and small molecules with access to a discrete, isolable intermediate. We demonstrate the effectiveness of this revertible transformation on cells using chemically cleavable antibody-drug conjugates.

Integrating multiscale modeling and optimization for sustainable process development

• A new approach for process development via integrated molecular modeling, process integration and mathematical optimization. • Integration of multiscale modeling and optimization as the cornerstone. • Incorporating fundamentals into process development for the best molecular transformation route. • Process integration for selecting fit-for-purpose technologies. • Mathematical optimization based on fundamental models for identifying technological breakthrough ideas. The new approach for process development proposed in this article is based on integrated molecular modeling, process integration and mathematical optimization. Molecular modeling is about achieving the best molecular transformation to maximize desired products and minimize by-products, which is accomplished via reaction chemistry optimization and catalyst development. Process integration is about selecting fit-for-purpose technologies for reaction, separation, and heat transfer systems, while mathematical optimization is about obtaining the optimal process flowsheeting and conditions to achieve the desired products with the lowest capital and operating costs as well as minimal footprint such as plot space, various emissions and wastes, hydrogen, water, and energy. When all these are integrated seamlessly, the true optimal process design can be achieved for practical applications, which will benefit the companies, communities, and environment at the same time. The cornerstone of this novel approach is the multiscale modeling and optimization integration via integrating fundamentals with the power of system integration and optimization, more specifically, integrating molecular analysis, quantum chemistry and transport mechanics with process integration and mathematical optimization for clean process development. By incorporating fundamentals into process development, it can identify the best molecular transformation routes by optimizing reaction pathways. Furthermore, mechanistic kinetic modeling such as microkinetic modeling and molecular-based kinetic Monte Carlo, which is enhanced by quantum chemistry, can help predict yield selectivity for different catalyst formula. By process integration , fit-for-purpose technologies are selected for developing hybrid systems. By mathematical optimization based on fundamental models, it can identify technological breakthrough ideas . For given multiple objective functions, mathematical combinatory optimization of the fundamental models not only determines the best-fit technology profile for an overall process based on techno-economic criteria, but also allow the process to deal with different feedstocks and make product shift based on market needs as well as the best environmental performance. In turn, this approach enables more effective feedback from development to research, as it integrated molecular based fundamentals.

A wearable graphene transistor-based biosensor for monitoring IL-6 biomarker

Graphene-based field-effect transistor (GFET) is becoming an increasingly popular biosensing platform for monitoring health conditions through biomarker detection. Moreover, the graphene's 2-dimensional geometry makes it ideal for implementing flexible or wearable electronic devices. If implemented as a wearable biosensor, such technology can non-invasively monitor relevant biomarkers continuously in real-time and alert the user of possible health concerns. As a proof of feasibility, this paper presents a wearable GFET device fabricated on a flexible film that is capable of detecting interleukin-6 (IL-6) protein, a key biomarker implicated in immune responses, in the concentration range of 10 pM to 100 nM. The surface of graphene is modified with target-binding aptamers to ensure analyte selectivity. Our results show that the biosensor measurements were stable with minimum changes when the GFET was bent with a radius of curvature between 1.5 cm and 4.25 cm suggesting robustness of the flexible GFET device. We have also demonstrated continuous real-time monitoring of IL-6 with high sensitivity within the concentration range of 10 pM and 1 nM. Furthermore, a minimum footprint, battery-powered circuit board is also developed that controls the GFET and records the sensor responses in real-time demonstrating the feasibility of becoming a fully standalone and wearable biosensor. The results from this work suggest that the thin film GFET-based biosensor has the potential to be used as a wearable continuous health monitoring device.

URJA: A sustainable energy distribution and trade model for smart grids

Energy has always been one of the fundamental elements in the growth of human society. It is crucial that we improve our energy management due to the ever-growing imbalance in demand-supply. The new wave in the energy sector is characterized by the three D's of decarbonization, decentralization, and digitalization. For a sustainable life, it is necessary that energy is tapped from alternative sources that are renewable to leave a minimal carbon footprint. To integrate different sources with the main power grid, we require two-way communication among them. This makes it indispensable that consumers are empowered to protect their privacy and make them the sole owners of their data. A good access control scheme would ensure security of data, and an efficient trading platform would ensure judicious use of the resources. Here, we propose a framework URJA with an access control scheme and an energy trading platform based on blockchain for a smart grid. The locally available renewable sources of energy are connected to the grid such that demand-supply is managed effectively without loss of efficiency and privacy. A customized consensus scheme based on trust ensures a quick operation in real-time.

Accelerated Domestication of New Crops: Yield is Key

Sustainable agriculture in the future will depend on crops that are tolerant to biotic and abiotic stresses, require minimal input of water and nutrients and can be cultivated with a minimal carbon footprint. Wild plants that fulfill these requirements abound in nature but are typically low yielding. Thus, replacing current high-yielding crops with less productive but resilient species will require the intractable trade-off of increasing land area under cultivation to produce the same yield. Cultivating more land reduces natural resources, reduces biodiversity and increases our carbon footprint. Sustainable intensification can be achieved by increasing the yield of underutilized or wild plant species that are already resilient, but achieving this goal by conventional breeding programs may be a long-term prospect. De novo domestication of orphan or crop wild relatives using mutagenesis is an alternative and fast approach to achieve resilient crops with high yields. With new precise molecular techniques, it should be possible to reach economically sustainable yields in a much shorter period of time than ever before in the history of agriculture.

The regime of constructed wetlands in greywater treatment.

There is an excellent need for supply-side threats due to the enhanced degradation and reclamation of existing water bodies in the present scenario. This led to the global water crisis. One of the easiest ways to fulfil the growing need for freshwater is the recycling of wastewater. Greywater is a form of wastewater from households, industries, etc., with some less toxic materials. The recycling of this greywater has provoked the development of new and sustainable technologies to meet the growing water demand. Engineered constructed wetlands are considered one of the most economically practical processes to treat greywater due to its minimal footprint. In this case study, we summarize several categories of constructed wetlands, operating conditions, and the effects of biological, physical, and chemical aspects of greywater on their treatment performance. On the other hand, the effluent quality from diverse wetlands is also summarized. Furthermore, it would be better to consider that constructed wetlands' integrated performance with disinfection may improve the effluent quality to desirable standards.

Train Me If You Can: Decentralized Learning on the Deep Edge

The end of Moore’s Law aligned with data privacy concerns is forcing machine learning (ML) to shift from the cloud to the deep edge. In the next-generation ML systems, the inference and part of the training process will perform at the edge, while the cloud stays responsible for major updates. This new computing paradigm, called federated learning (FL), alleviates the cloud and network infrastructure while increasing data privacy. Recent advances empowered the inference pass of quantized artificial neural networks (ANNs) on Arm Cortex-M and RISC-V microcontroller units (MCUs). Nevertheless, the training remains confined to the cloud, imposing the transaction of high volumes of private data over a network and leading to unpredictable delays when ML applications attempt to adapt to adversarial environments. To fill this gap, we make the first attempt to evaluate the feasibility of ANN training in Arm Cortex-M MCUs. From the available optimization algorithms, stochastic gradient descent (SGD) has the best trade-off between accuracy, memory footprint, and latency. However, its original form and the variants available in the literature still do not fit the stringent requirements of Arm Cortex-M MCUs. We propose L-SGD, a lightweight implementation of SGD optimized for maximum speed and minimal memory footprint in this class of MCUs. We developed a floating-point version and another that operates over quantized weights. For a fully-connected ANN trained on the MNIST dataset, L-SGD (float-32) is 4.20× faster than the SGD while requiring only 2.80% of the memory with negligible accuracy loss. Results also show that quantized training is still unfeasible to train an ANN from the scratch but is a lightweight solution to perform minor model fixes and counteract the fairness problem in typical FL systems.

Sustainable Agricultural Development In Indonesia (Article Review)

ABSTRACTThe definition of sustainable agriculture is the successful management of resources for agricultural enterprises to meet changing human needs while maintaining or improving environmental quality and conserving natural resources. The true meaning of sustainable agriculture is one that is economically sustainable which is achieved by: less energy use, minimal ecological footprint, less packaged goods, widespread local purchasing with shorter food supply chains, less processed foodstuffs, community gardens and gardens more houses, and so on Sustainable agriculture relies heavily on returning nutrients to the soil by minimizing the use of non-renewable natural resources such as natural gas (which is used as a feedstock for fertilizers) and minerals (such as phosphates). The most important factors in the utilization of natural resources in a land are soil, sunlight, air, and water.Sustainable agriculture is the implementation of the concept of sustainable development in the agricultural sector. The concept of sustainable agriculture is based on three pillars: economic, social, and ecological.Keywords: Development, Agricultural, Sustainable

Строгость водородной стратегии

The main criterion for classifying hydrogen is the amount of carbon emissions generated during its production. Since only green hydrogen, received from renewable energy sources (RES), can be considered a fuel with a minimal carbon footprint, the article examines the question of to what extent the hydrogen strategies of various countries may be regarded as decarbonized. A typological approach is applied and a framework is established to define the stringency of green hydrogen regulation in national hydrogen strategies based on three parameters: fossil fuel penalties, hydrogen certification and exceptional technology development. According to these parameters, countries are classified into groups depending on the degree of regulation severity. The problems, associated with increasing the strictness of regulation for hydrogen production both at the national and international levels, are identified.

Creating Composite Vortex Beams with a Single Geometric Metasurface.

Composite vortex beams (CVBs) have attracted considerable interest recently due to the unique optical properties and potential applications. However, these beams are mainly generated using spatial light modulators, which suffer from large volume, high cost, and limited resolution. Benefiting from the ultrathin nature and unprecedented capability in light manipulation, optical metasurfaces provide a compact platform to perform this task. A metasurface approach to creating these CVBs is proposed and experimentally demonstrated. The design is based on the superposition of multiple circularly polarized vortex beams with different topological charges, which is realized based on a geometric metasurface consisting of metallic nanorods with spatially variant orientations. The effects of the initial phases, amplitude coefficients, incident polarization state, and propagation distance on the generated CVBs, which are in good agreement with the theoretical prediction, are experimentally analyzed. This work has opened a new avenue for engineering CVBs with a minimal footprint, which has promising applications ranging from multiple optical traps to quantum science.

Enhanced chloroplast-mitochondria crosstalk promotes ambient algal-H2 production

Microalgae are natural biocatalysts of hydrogen production. Their ability to convert solar energy to valuable compounds with minimal ecological footprint potentially places them as significant contributors to the clean-energy transition. Currently, algal hydrogen production, although promising, is not scalable because it is limited to oxygen-free conditions and is short-lived due to electron loss to other processes, mainly carbon fixation. Here, we show that a strain defective in thylakoid proton gradient regulation, Δ pgr5 , bypasses both challenges simultaneously, leading to a prolonged 12-day hydrogen production under ambient mixotrophic conditions in a 1-L setup. We report that Δ pgr5 possess a repressed ability to fixate carbon and that this limitation is counterbalanced by an enhanced chloroplast-mitochondrion energetic exchange. This unique physiology supports the simplistic, yet robust and scalable, hydrogen production capability of Δ pgr5. • Scalable (from 1-L cultures) algal H 2 production over 12 days is demonstrated • Continuous ambient H 2 production is possible from Δ pgr5 mutant • Δ pgr5 bypasses key challenges of H 2 production: oxygen accumulation and electron loss • The molecular mechanism behind this phenotype is enhanced mitochondrial biogenesis Hydrogen production from green algae is a fleeting process due to energy loss to competing processes and oxygen accumulation, which inactivate the enzyme catalyzing H 2 production. Elman et al. show that a single mutation in green algae bypasses both bottlenecks and allows sustained, scalable H 2 production under ambient mixotrophic conditions.

Recent Advances in Biological Recycling of Polyethylene Terephthalate (PET) Plastic Wastes

Polyethylene terephthalate (PET) is one of the most commonly used polyester plastics worldwide but is extremely difficult to be hydrolyzed in a natural environment. PET plastic is an inexpensive, lightweight, and durable material, which can readily be molded into an assortment of products that are used in a broad range of applications. Most PET is used for single-use packaging materials, such as disposable consumer items and packaging. Although PET plastics are a valuable resource in many aspects, the proliferation of plastic products in the last several decades have resulted in a negative environmental footprint. The long-term risk of released PET waste in the environment poses a serious threat to ecosystems, food safety, and even human health in modern society. Recycling is one of the most important actions currently available to reduce these impacts. Current clean-up strategies have attempted to alleviate the adverse impacts of PET pollution but are unable to compete with the increasing quantities of PET waste exposed to the environment. In this review paper, current PET recycling methods to improve life cycle and waste management are discussed, which can be further implemented to reduce plastics pollution and its impacts on health and environment. Compared with conventional mechanical and chemical recycling processes, the biotechnological recycling of PET involves enzymatic degradation of the waste PET and the followed bioconversion of degraded PET monomers into value-added chemicals. This approach creates a circular PET economy by recycling waste PET or upcycling it into more valuable products with minimal environmental footprint.

Highly selective conversion of mixed polyolefins to valuable base chemicals using phosphorus-modified and steam-treated mesoporous HZSM-5 zeolite with minimal carbon footprint

Catalytic fast pyrolysis of polyolefinic waste streams was investigated to recover valuable base chemicals at high selectivity. HZSM-5 zeolite with different properties, affected by Si/Al, mesoporosity, phosphorus stabilization, and steaming, were tested and thoroughly characterized. Different feeds, catalyst/feed ratios and reaction temperatures were evaluated in a micropyrolysis reactor coupled to two-dimensional gas chromatography. While unmodified HZSM-5 rapidly deactivated, phosphorus-modified and steamtreated HZSM-5 showed almost no deactivation due to its lower coking propensity during 130 runs with stable conversion towards C5+ aliphatics and high C2-C4 olefins selectivity (~75%) using post-consumer mixed polyolefins. The performance of this direct olefins production route with unprecedented high olefin selectivity was further evaluated in a plantwide context. It was found that it requires ~37% lower energy input than the plastics pyrolysis followed by pyrolytic oil steam cracking, while it results to at least a one order of magnitude lower environmental burden as compared to waste incineration.

Subsurface Technology Pivoting from Oil and Gas to Geothermal Resources: A Sensitivity Study of Well Parameters

Summary There is enough geothermal energy several miles underneath to supply more power globally than the entire oil and gas industry. Geothermal energy is renewable, abundant, and has a minimal carbon footprint. However, the current geothermal energy use is only 1% of the global energy production. To scale it up, several technical problems need to be solved, addressing how, where, and when to harness it safely, efficiently, and cost-effectively. In this paper, the synergies and differences between the hydrocarbon and geothermal wells are reviewed and an opportunity assessment and management framework are proposed to improve the outcomes of diverse geothermal hydrocarbon projects. Producing geothermal energy from existing hydrocarbon wells, as electricity and/or low-temperature waste heat, can yield significant advantages over traditional geothermal wells, especially in terms of reduced capital expenditure. Geothermal and hydrocarbon well studies related to geothermal hydrocarbon field development are reviewed, presenting the similarities and differences between the geothermal and hydrocarbon wells, and the challenges for economical geothermal hydrocarbon field development. To unlock the potential of geothermal hydrocarbon field development, several areas of research are identified and explored. There are many hydrocarbon wells drilled worldwide that are either abandoned or will be abandoned in the future. This inherently brings exit options repurposing these wells in terms of extracting residual value in an environmentally friendly way. There is a great potential to extract energy in the form of low-temperature waste heat and/or work with the right power fluids, including CO2. In this study, a numerical solution and several analytical solutions are presented for closed-loop wells (i.e., concentric pipes in wells). They can be extended to enhanced geothermal systems (EGSs) (i.e., injecting and producing casedhole wells with perforations along vertical, inclined, and horizontal sections) by allowing fluid inflow into the wells. The time- and space-dependent temperature solutions for all well configurations are obtained for time- and space-dependent fluid and flow properties. A sensitivity study is then performed, showing the effects of several parameters on the flowing fluid temperature for such parameters as the fluid flow rate, well length, inner tubing and annulus diameters, geothermal temperature, and overall heat transfer coefficients. Existing hydrocarbon fields produce larger volume of waste water over their lives, increasing their operating costs owing to greater water management expenditure. Conceptually, those operating costs could be reduced by electricity and/or heat coproduction. The proposed modeling approach is useful for the design of EGSs and closed-loop systems. It can also be used as an operational tool to monitor the well and reservoir performance over the life of a deep geothermal resource. Last but not least, it can be used to assess the conditions for economically converting abandoned oil and gas wells for sustainable geothermal energy production.

Efficient 3D Spatial Queries for Complex Objects.

3D spatial data has been generated at an extreme scale from many emerging applications, such as high definition maps for autonomous driving and 3D Human BioMolecular Atlas. In particular, 3D digital pathology provides a revolutionary approach to map human tissues in 3D, which is highly promising for advancing computer-aided diagnosis and understanding diseases through spatial queries and analysis. However, the exponential increase of data at 3D leads to significant I/O, communication, and computational challenges for 3D spatial queries. The complex structures of 3D objects such as bifurcated vessels make it difficult to effectively support 3D spatial queries with traditional methods. In this article, we present our work on building an efficient and scalable spatial query system, iSPEED, for large-scale 3D data with complex structures. iSPEED adopts effective progressive compression for each 3D object with successive levels of detail. Further, iSPEED exploits structural indexing for complex structured objects in distance-based queries. By querying with data represented in successive levels of details and structural indexes, iSPEED provides an option for users to balance between query efficiency and query accuracy. iSPEED builds in-memory indexes and decompresses data on-demand, which has a minimal memory footprint. iSPEED provides a 3D spatial query engine that can be invoked on-demand to run many instances in parallel implemented with, but not limited to, MapReduce. We evaluate iSPEED with three representative queries: 3D spatial joins, 3D nearest neighbor query, and 3D spatial proximity estimation. The extensive experiments demonstrate that iSPEED significantly improves the performance of existing spatial query systems.