Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure

This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access.

Model-based hearing-restoration strategies for cochlear synaptopathy pathologies

It is well known that ageing and noise exposure are important causes of sensorineural hearing loss, and can result in damage of the outer hair cells or other structures of the inner ear, including synaptic damage to the auditory nerve (AN), i.e., cochlear synaptopathy (CS). Despite the suspected high prevalence of CS among people with self-reported hearing difficulties but seemingly normal hearing, conventional hearing-aid algorithms do not compensate for the functional deficits associated with CS. Here, we present and evaluate a number of auditory signal-processing strategies designed to maximally restore AN coding for listeners with CS pathologies. We evaluated our algorithms in subjects with and without suspected age-related CS to assess whether physiological and behavioural markers associated with CS can be improved. Our data show that after applying our algorithms, envelope-following responses and perceptual amplitude-modulation sensitivity were consistently enhanced in both young and older listeners. Speech intelligibility showed small improvements across participants, with the young group benefitting the most from processed speech. Our proposed hearing-restoration algorithms can be rapidly executed and can thus extend the application range of current hearing aids and hearables, while leaving sound amplification unaffected.

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal ‘genetic’ networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions—a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features (such as planar polarity and regenerative re-scaling capacity) for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel “causal network” analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns—an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization.

Sustainable Horticultural Waste Management: Industrial and Environmental Perspective

Horticultural crops are highly nutritious and shared lion portion of our daily diet. These items are consumed in different ways according to their nature and processing processes. These days, a crucial concerning issue is arising globally to ensure nutrition security for huge population that leads to focus on production increase, quality improvement, food safety assurance, and processing strategies. Consequently, a large amount of waste generates in the processing industries, household kitchen, and supply chain of horticultural commodities that has led to a significant nutrition and economic loss, consequently creating environment pollution with extensive burden of landfills. However, these wastes showed magnificent potentiality of re-utilization in several industries owing to as rich source of different bioactive compounds and phytochemicals. Therefore, sustainable extraction methods and utilization strategies deserve the extensive investigations. This review paper extensively illustrates the horticultural waste generation options, sustainable recycling strategies, and potentiality of recycled products in different industries for betterment in population with the assurance of green environment and sustainable ecology.

Komposit Rumput Laut Dan Surimi Lele Terhadap Mutu Bakso

Produksi rumput laut (Eucheuma cottonii) di Sulawesi Selatan meningkat signifikan dan berkontribusi terhadap total produksi di Indonesia. Namun peningkatan produksi belum dimanfaatkan secara maksimal khususnya pada bidang pangan. Fakta ini sangat berkaitan dengan strategi dalam pengololahan pasca panen. Salah satu strategi pengolahan diversifikasi produk pangan yakni membuat bakso dengan surimi ikan lele. Tujuan penelitian untuk menganalisis pengaruh komposit surimi ikan lele (Clarias) dan bubur rumput laut (Eucheuma cottonii), terhadap mutu bakso. Metode penelitian eksperimen, yang menggunakan Rancangan Acak Lengkap (RAL) dengan dua faktor dan empat perlakuan, yaitu: faktor A sebagai perlakuan persentase bubur rumput laut (30%, 20%, 10%, dan 0%), sedangkan faktor B sebagai perlakuan persentase surimi lele (45%, 55%, 65%, dan 75%) dengan tiga kali ulangan. Parameter penelitian kadar protein, dan (uji sensorik dengan skalah hedonik) terhadap warna, aroma, tekstur, dan citarasa. Hasil penelitian diperoleh kandar protein bakso terendah 13,02% dan tertinggi 19,95% sehingga semua perlakuan memenuhi Syarat Mutu Bakso Ikan SNI No. 01-3818:2014. Sedangakan perlakuan 10% bubur rumput laut dan 65% surimi uji organoleptik memiliki nilai kesukaan warna (3,95/suka), aroma (3,93/suka), tekstur/kekenyalan (3,93/suka), dan citarasa (4,12/suka). Berdasarkan rerata nilai disimpulkan bahwa uji organoleptik bakso oleh panelis berada pada level suka. The production of seaweed (Eucheuma cottonii) in South Sulawesi increased significantly and contributed to the total production in Indonesia. However, the increase in production has not been used optimally, in the food sector. This fact is closely related to strategies in post-harvest processing. One of the food product diversification processing strategies is the manufacture of meatballs with catfish surimi. The purpose of the study was to analyze the effect of the composite of catfish surimi (Clarias) and seaweed (Eucheuma cottonii), on the quality of meatballs. Experimental research method, using Completely Randomized Design (CRD) with two factors and four treatments, namely: factor A as the percentage treatment of seaweed slurry (30%, 20%, 10%, and 0%), while factor B as the percentage treatment catfish surimi (45%, 55%, 65%, and 75%) with three replications. Research parameters protein content, and (sensory test with hedonic scale) on color, aroma, texture, and taste. The results obtained that the lowest protein content of meatballs was 13.02% and the highest was 19.95% so that all treatments met the Quality Requirements for Fish Meatballs SNI No. 01-3818:2014. Meanwhile, the treatment of 10% seaweed porridge and 65% surimi organoleptic test had a preference value of color (3.95/like), aroma (3.93/like), texture/elasticity (3.93/like), and taste (4.12/like). Based on the average value that the organoleptic test of meatballs by the panelists, was at the level of liking.

Differencing strategies for SLR observations at the Wettzell observatory

The precise estimation of geodetic parameters using single- and double-differenced SLR observations is investigated. While the differencing of observables is a standard approach for the GNSS processing, double differences of simultaneous SLR observations are practically impossible to obtain due to the SLR basic principle of observing one satellite at a time. Despite this, the availability of co-located SLR telescopes and the use of the alternative concept of quasi-simultaneity allow the forming of SLR differences under certain assumptions, thus enabling the use of these processing strategies. These differences are in principle almost free of both, satellite- and station-specific error sources, and are shown to be a valuable tool to obtain relative coordinates and range biases, and to validate local ties. Tested with the two co-located SLR telescopes at the Geodetic Observatory Wettzell (Germany) using SLR observations to GLONASS and LAGEOS, the developed differencing approach shows that it is possible to obtain single- and double-difference residuals at the millimetre level, and that it is possible to estimate parameters, such as range biases at the stations and the local baseline vector with a precision at the millimetre level and an accuracy comparable to traditional terrestrial survey methods. The presented SLR differences constitute a valuable alternative for the monitoring of the local baselines and the estimation of geodetic parameters.

Oil Recovery from Dry Grind Ethanol Plant Coproducts Using Ethanol

Corn ethanol bio-refineries are seeking economic processing strategies for recovering oil from their coproducts. The addition of ethanol can be an efficient method to recover the oil from the coproducts as the industry has available ethanol. This study considered the effects of ethanol on oil recovery from distillers’ dried grains with solubles (DDGS) and oil partitioning from whole stillage (WS) on a laboratory scale. Ethanol was added with original and heavier fraction DDGS in different temperatures (room temperature ~20 °C, 30 °C, 40 °C, and 50 °C) and solids loadings (20%, 30%, and 40%), and their effects on oil recovery were evaluated. The whole stillage was incubated with ethanol at room temperature (~20 °C) and 50 °C separately to analyze WS’s oil distribution in the liquid and solid phases. The amount of recovered oil from the original and heavier fractions of DDGS varies from 25–45% and 45–70%, respectively, with an increment of temperature. Increasing solids loadings up to 30% had no effect on oil recovery from either DDGS sample. Ethanol treatment in WS resulted in 8–10% higher wet yield of liquid fraction and 17–20% of oil increase in liquid fraction than the control treatment. It is also notable that temperature positively impacted oil partitioning from WS. The results showed that ethanol could improve oil recovery from DDGS and oil partition in WS by varying different process conditions. This outcome is beneficial to ethanol plants to increase corn oil yield using their existing setup and in-situ product.