Processing Scheme Research Articles

Deep Learning–Based Channel Extrapolation and Multiuser Beamforming for RIS-aided Terahertz Massive MIMO Systems over Hybrid-Field Channels

The reconfigurable intelligent surface (RIS) is a promising technology for terahertz (THz) massive multiple-input multiple-output (MIMO) communication systems. However, acquiring high-dimensional channel state information (CSI) and realizing efficient active/passive beamforming for RIS are challenging owing to its cascaded channel structure and lack of signal processing units. To overcome these challenges, this study proposes a deep learning (DL)-based physical signal processing scheme for RIS-aided THz massive MIMO systems over hybrid far-near field channels wherein channel estimation with low pilot overhead and robust beamforming are implemented. Specifically, first, an end-to-end DL-based channel estimation framework that consists of pilot design, CSI feedback, subchannel estimation, and channel extrapolation is introduced. In this framework, only some RIS elements are first activated, a subsampling RIS channel is then estimated, and a DL-based extrapolation network is finally used to reconstruct the full-dimensional CSI. Next, to maximize the sum rate under imperfect CSI, a DL-based scheme is developed to simultaneously design hybrid active beamforming at the base station and passive beamforming at the RIS. Simulation results show that the proposed channel extrapolation scheme achieves better CSI reconstruction performance than conventional schemes while greatly reducing pilot overhead. Moreover, the proposed beamforming scheme outperforms conventional schemes in terms of robustness to imperfect CSI.

New nematode-resistant potato varieties

Goal. Creation of new varieties of potatoes of different groups of ripeness, table use, high productivity, increased level of starch and good taste qualities, resistant to the golden cyst-forming nematode, common and aggressive pathotypes of cancer, fungal and bacterial diseases.
 Methods. Selection work was carried out in accordance with the generally accepted scheme of the selection process. The resistance of breeding material against Globodera rostochiensis was evaluated in accordance with the «Regulations on the Procedure for Testing Potato Varieties and Hybrids».
 Results. The 12 best hybrids — Zlagoda, Myroslava, Knyaginya, Vzyrets, Bazaliya, Opillya, Bazhana, Fanatka, Avangard, Predslava, Oleksandrite and Rostavitsa — were selected and submitted to the State Trial as varieties. Of the 12 transferred varieties, 11 (Zlagoda, Myro slava, Knyaginya, Vzyrets, Bazaliya, Opillya, Avangard, Predslava, Oleksandrit, Fanatka, Rostavitsa) successfully passed the State variety test in 2023 and were entered into the State Register of plant varieties suitable for distribution in Ukraine.
 Conclusions. The new 11 varieties included in the State Register of plant varieties suitable for distribution in Ukraine are advisable to use as a means of controlling the harmfulness of the golden potato nematode in areas infected with Globodera rostochiensis (pathotype Ro1).

Studies on High-Resolution Airborne Synthetic Aperture Radar Image Formation with Pseudo-Random Agility of Interpulse Waveform Parameters

By means of alteration of the transmitted linear frequency modulation (LFM) signal waveform parameters, such as pulse width or chirp rate, initial phase, pulse repetition interval (PRI), and chirp rate polarity at every position of synthetic apertures, the pseudo-random agility technology of interpulse waveform parameters in airborne Synthetic Aperture Radar (SAR) actively increases the complexity and uncertainty of radar waveforms. This technology confuses jamming interception receivers, thus improving its anti-interference ability for active coherent jamming, which is one of the main research interests of airborne SAR technology. But the pseudo-random agility technology for interpulse waveform parameters faces certain challenges of large computation and complex system design, which need to be further studied and solved. To address these issues, a processing scheme of high-resolution SAR image formation which is appropriate for agile interpulse waveform parameters is proposed in this paper. This method can deal with multiple agile parameters, not only single ones as in most existing literature. Its computation load is nearly comparable to that of traditional SAR image formation with constant waveform parameters. The high-resolution SAR imaging results obtained by processing SAR raw data with agile interpulse waveform parameters demonstrate the effectiveness of the proposed method. In addition, real SAR images with resolutions of 0.5 m and 0.15 m, which are rarely found in the public literature, are shown under the circumstance of randomly changing the transmitted wideband LFM signal pulse parameters one by one.

Simulation study on surface deformation of shallow buried coal seam mining in a gully area

Coal mining subsidence leads to frequent surface ecological and environmental problems. Due to the unique topographic conditions within gully areas, the deformation and damage characteristics, along with the evolution laws of the surface, have been difficult to analyze. To explore the surface movement and deformation law of shallow-buried coal seam mining in a gully area, this study designed a three-dimensional similar-material simulation test simulating the complete movement and deformation of the ground surface under real terrain conditions and proposed a complete data acquisition and processing scheme. Combined with the numerical simulation analysis, we utilized this scheme to analyze the dynamic influence law of coal seam mining in a gully area on the distribution of ground surface stress and displacement as well as on the dynamic characteristics of the ground surface damage. The results show that the existence of gully topography affects the stress distribution of the ground surface and the raw rock of the coal seams underneath and that there exists a stress concentration in the gully area of the ground surface. Furthermore, the mining activities of the coal seams lead to an increase in the degree of stress concentration in the gully area of the ground surface, and the slopes enter into the process of energy accumulation and sudden release, which makes the region prone to the occurrence of geological disasters such as landslides and collapses. In addition, the surface movement and deformation in the gully area are more intense, exceeding the plain area, and the surface movement is affected by the spatial location relationship between the mining area and the slope body, causing the center of the mining area to show obvious eccentricity characteristics. The results of this study provide a reference basis for the safe mining of coal mines in gully topography.

A Brief Introduction to Infrastructure Planning for Next-Generation Smart Computing Data Centers

Globally, digital technology and the digital economy have propelled technological revolution and industrial change, and it has become one of the main grounds of international industrial competition. It was estimated that the scale of China’s digital economy would reach 50 trillion yuan in 2022, accounting for more than 40% of GDP, presenting great market potential and room for the growth of the digital economy. With the rapid development of the digital economy, the state attaches great importance to the construction of digital infrastructure and has introduced a series of policies to promote the systematic development and large-scale deployment of digital infrastructure. In 2022 the Chinese government planned to build 8 arithmetic hubs and 10 national data center clusters nationwide. To proactively address the future demand for AI across various scenarios, there is a need for a well-structured computing power infrastructure. The data center, serving as the pivotal hub for computing power, has evolved from the conventional cloud center to a more intelligent computing center, allowing for a diversified convergence of computing power supply. Besides, the data center accommodates a diverse array of arithmetic business forms from customers, reflecting the multi-industry developmental trend. The arithmetic service platform is consistently broadening its scope, with ongoing optimization and innovation in the design scheme of machine room processes. The widespread application of submerged phase-change liquid cooling technology and cold plate cooling technology introduces a series of new challenges to the construction of digital infrastructure. This paper delves into the design objectives, industry considerations, layout, and other dimensions of a smart computing center and proposes a new-generation data center solution that is “flexible, resilient, green, and low-carbon.”

A unified framework for backpropagation-free soft and hard gated graph neural networks

We propose a framework for the definition of neural models for graphs that do not rely on backpropagation for training, thus making learning more biologically plausible and amenable to parallel implementation. Our proposed framework is inspired by Gated Linear Networks and allows the adoption of multiple graph convolutions. Specifically, each neuron is defined as a set of graph convolution filters (weight vectors) and a gating mechanism that, given a node and its topological context, generates the weight vector to use for processing the node’s attributes. Two different graph processing schemes are studied, i.e., a message-passing aggregation scheme where the gating mechanism is embedded directly into the graph convolution, and a multi-resolution one where neighboring nodes at different topological distances are jointly processed by a single graph convolution layer. We also compare the effectiveness of different alternatives for defining the context function of a node, i.e., based on hyperplanes or on prototypes, and using a soft or hard-gating mechanism. We propose a unified theoretical framework allowing us to theoretically characterize the proposed models’ expressiveness. We experimentally evaluate our backpropagation-free graph convolutional neural models on commonly adopted node classification datasets and show competitive performances compared to the backpropagation-based counterparts.

Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization

To address the low level of intelligence and low utilization of logs in current rotary cutting equipment, this paper proposes a digital twin-based system for optimizing the rotary cutting of logs using a five-dimensional model of digital twins. The system features a log perception platform to capture three-dimensional point cloud data, outlining the logs’ contours. Utilizing the Delaunay3D algorithm, this model performs a three-dimensional reconstruction of the log point cloud, constructing a precise digital twin. Feature information is extracted from the point cloud using the least squares method. Processing parameters, determined through the kinematic model, are verified in rotary cutting simulations via Bool operations. The system’s efficacy has been substantiated through experimental validation, demonstrating its capability to output specific processing schemes for irregular logs and to verify these through simulation. This approach notably improves log recovery rates, decreasing volume error from 12.8% to 2.7% and recovery rate error from 23.5% to 5.7% The results validate the efficacy of the proposed digital twin system in optimizing the rotary cutting process, demonstrating its capability not only to enhance the utilization rate of log resources but also to improve the economic efficiency of the factory, thereby facilitating industrial development.

Electrodeposited Nanowires for High Density Interconnects of GaN-Based MicroLEDs

For next-generation applications, fully controllable gallium nitride (GaN)-based micro light emitting diode (microLED) arrays will play a crucial role. These feature high pixel densities, high intensities, and high efficiencies. Well-established top-down etching processing schemes of pristine LED wafers allow for an easily accessible and scalable fabrication approach for microLED arrays down to the single digit µm dimensions with dies featuring several 100,000 on a square centimeter. At this scale, classical bonding approaches like wire bonding fail to harvest the true potential of such an array due to inherit technological restrictions. To allow for the individual controllability of every pixel a highly scalable integration approach to a CMOS backplane is necessary. Established processes for the realization of such an integration often feature major drawbacks. Indium bumps deposited via thermal evaporation generally fulfill the scale requirements but fail to adapt to the unique challenges of such hybrid integration. High non-planarity of the substrate, high wafer bow, and mismatching thermal expansion coefficients can lead to diminishing bonding yields and hence functioning microLEDs during the high-temperature bonding step necessary for indium bumps. Additionally, Indium is an inferior electrical and thermal conductor to other metals and limits the active performance of the device while also restricting its usage area due to its low melting point. In the currently ongoing EU Project SMILE, we are developing GaN-based microLED arrays up to 512 x 512 pixels and thoroughly examine established bonding technologies as well as an in-house developed metal nanowire approach. The necessary perquisites for electrochemical deposition, namely the conductive seed and the removal thereof can easily be integrated into the device’s processing scheme. Nanowires are electrochemically deposited out of a liquid solution at room temperature onto the microLED array and the CMOS backplane. The direction, length, and diameter of the nanowires can easily be controlled to accustom different proportions and bonding geometries. Inline monitoring via the recorded amperogram allows for the differentiation of the individual stages of nanowire growth during the electrochemical deposition. Mechanically stable and electrically sufficient interconnects can then be realized by pressing the nanowires of both substrates into each other. The high aspect ratio of the wires results in a high surface area and allows for nanowires of both substrates to intertwine forming stable connections with large contact surfaces at low pressures without any additional heating steps necessary. Due to the variability of the wire length and ongoing intertwining during the bonding, non-planarities and wafer bow can be accommodated. This flexible bonding behavior is a unique advantage of bonds based on metal nanowires. The junctions approach the conductivity of bulk material and are crucial for the thermal management of the microLED array. The final LED device generates a substantial amount of heat, which cannot be cooled meaningfully via convection to the surrounding air or by its radiation. Consequently, a capable thermal conduction towards the silicon chip is necessary for heat management. Here the metal nanowires play a crucial role in providing this thermal connection via the bonding interface, which potentially allows for improved device performance over other bonding technologies.

Threshold Switching of ALD-NbOx Films for Neuromorphic Applications

Neuromorphic architecture has been suggested as an alternative to the existing von Neumann counterpart. The neuromorphic approach allows massively parallel processing and asynchronous timing schemes with low power consumption. This work presents ALD- NbOx thin films as a potential material for neuromorphic computation. NbO2 shows metal-insulator transition which is the desired property for threshold switching (TS). However, direct forming of NbO2 is rather difficult and deposited NbOx tends predominantly to result in Nb2O5. To obtain NbO2 we used an oxygen scavenger layer of Ti to alter Nb2O5 to NbO2. After a proper electroforming process, the device with a Ti insert showed metal-insulator transition above a specific voltage, i.e., a threshold voltage, and a stable threshold switching characteristics with wide operation range, characteristics not observed in the absence of a Ti insert. The well-defined TS characteristics clearly indicated the Ti role in controlling the oxidation state of NbOx.To verify the oxidation state of NbOx, XPS depth profile analyses were used. The presence of a Ti insertion layer resulted in an increase of 10~30% in Nb4+ states, depending on the total thickness of the NbOx films, while Nb5+ states decreased. Bearing in mind practical applications, we used only CMOS-compatible materials and processes to fabricate TS devices. Our approach suggests a reliable method to fabricate NbO2 neurons.Acknowledgment: This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government under grant " Non-CMOS Neuromorphic Device Basic Technology." (22BB1110).

Enabling WiFi Sensing on New-generation WiFi Cards

The last few years have witnessed the rapid development of WiFi sensing with a large spectrum of applications enabled. However, existing works mainly leverage the obsolete 802.11n WiFi cards (i.e., Intel 5300 and Atheros AR9k series cards) for sensing. On the other hand, the mainstream WiFi protocols currently in use are 802.11ac/ax and commodity WiFi products on the market are equipped with new-generation WiFi chips such as Broadcom BCM43794 and Qualcomm QCN5054. After conducting some benchmark experiments, we find that WiFi sensing has problems working on these new cards. The new communication features (e.g., MU-MIMO) designed to facilitate data transmissions negatively impact WiFi sensing. Conventional CSI base signals such as CSI amplitude and/or CSI phase difference between antennas which worked well on Intel 5300 802.11n WiFi card may fail on new cards. In this paper, we propose delicate signal processing schemes to make wireless sensing work well on these new WiFi cards. We employ two typical sensing applications, i.e., human respiration monitoring and human trajectory tracking to demonstrate the effectiveness of the proposed schemes. We believe it is critical to ensure WiFi sensing compatible with the latest WiFi protocols and this work moves one important step towards real-life adoption of WiFi sensing.

Spreading depolarization suppression from inter-astrocytic gap junction blockade assessed with multimodal imaging and a novel wavefront detection scheme

Spreading depolarizations (SDs) are an enigmatic and ubiquitous co-morbidity of neural dysfunction. SDs are propagating waves of local field depolarization and increased extracellular potassium. They increase the metabolic demand on brain tissue, resulting in changes in tissue blood flow, and are associated with adverse neurological consequences including stroke, epilepsy, neurotrauma, and migraine. Their occurrence is associated with poor patient prognosis through mechanisms which are only partially understood. Here we show in vivo that two (structurally dissimilar) drugs, which suppress astroglial gap junctional communication, can acutely suppress SDs. We found that mefloquine hydrochloride (MQH), administered IP, slowed the propagation of the SD potassium waveform and intermittently led to its suppression. The hemodynamic response was similarly delayed and intermittently suppressed. Furthermore, in instances where SD led to transient tissue swelling, MQH reduced observable tissue displacement. Administration of meclofenamic acid (MFA) IP was found to reduce blood flow, both proximal and distal, to the site of SD induction, preceding a large reduction in the amplitude of the SD-associated potassium wave. We introduce a novel image processing scheme for SD wavefront localization under low-contrast imaging conditions permitting full-field wavefront velocity mapping and wavefront parametrization. We found that MQH administration delayed SD wavefront's optical correlates. These two clinically used drugs, both gap junctional blockers found to distinctly suppress SDs, may be of therapeutic benefit in the various brain disorders associated with recurrent SDs.

Analysis of geomagnetic observatory data and detection of geomagnetic jerks with the MOSFiT software package

Abstract. MOSFiT (Magnetic Observatories and Stations Filtering Tool) is a Python package to visualize and filter data from magnetic observatories and magnetometer stations. The purpose of MOSFiT is to automatically isolate and analyze the secular variation (SV) information measured by geomagnetic observatory data. External field contributions may be reduced by selecting data according to local time and geomagnetic indices and by subtracting the magnetospheric field predictions of the CHAOS-7 model. MOSFiT calculates the SV by annual differences of monthly means, and geomagnetic jerk occurrence time and amplitude are automatically calculated by fitting two straight-line segments in a user-defined time interval of the SV time series. Here, we present the new Python package, validate it against independent results from previous publications and show its application. In particular, we quantify the RMS misfit between SV derived from processing schemes and the SV predicted by CHAOS-7. Analyzing the International Real-time Magnetic Observatory Network (INTERMAGNET) quasi-definitive data with MOSFiT allows for a timely investigation of SV, such as the detection of recent geomagnetic jerks. It can also be used for data selection for, e.g., external field studies or quality control of geomagnetic observatory data.

Rapid Dilute Sulfuric Acid Hydrolysis of Soy Flour to Amino Acids for Microbial Processes and Biorefining

Amino acids have relevance in biorefining as fermentation nutrients but also as valued coproducts obtainable from plant biomass. Soy flour was studied as a representative low-cost protein source requiring hydrolysis to free primary amino acids for utilization. Within the context of biorefining, process schemes, reactant concentrations, times, and temperatures were varied to explore the efficiency of dilute sulfuric acid hydrolysis of soy flour to release amino acids. Two process strategies were optimized. Either soy flour was co-processed with switchgrass biomass using a dilute-acid pretreatment, or it was hydrolyzed alone with dilute acid. Significant improvement to hydrolysate fermentability was accomplished by adding 2.5–10 g/L soy flour to switchgrass pretreatment with dilute sulfuric acid (0.936% v/v) for 15 min at 160 °C. This practice optimized accumulation of neutral sugars and resulted in a 25% reduction in furfural while boosting xylose 7% and up to doubling primary amino nitrogen (PAN), as compared to no soy flour addition to switchgrass pretreatment. When soy flour was hydrolyzed alone, PAN titers were optimized to 1588 mg N/L (9.9 g amino acids/L) and yield to 0.0529 g PAN/g flour (61% of theoretical) using a 10% (v/v) (1.8 M) sulfuric acid hydrolysis 30 min at 160 °C.

Reconstitution of the phosphodiesterase 6 maturation process important for photoreceptor cell function

The sixth family phosphodiesterases (PDE6) are principal effector enzymes of the phototransduction cascade in rods and cones. Maturation of nascent PDE6 protein into a functional enzyme relies on a coordinated action of ubiquitous chaperone HSP90, its specialized cochaperone aryl hydrocarbon receptor-interacting protein-like 1 (AIPL1), and the regulatory Pγ-subunit of PDE6. Deficits in PDE6 maturation and function underlie severe visual disorders and blindness. Here, to elucidate the roles of HSP90, AIPL1, and Pγ in the maturation process, we developed the heterologous expression system of human cone PDE6C in insect cells allowing characterization of the purified enzyme. We demonstrate that in the absence of Pγ, HSP90, and AIPL1 convert the inactive and aggregating PDE6C species into dimeric PDE6C that is predominantly misassembled. Nonetheless, a small fraction of PDE6C is properly assembled and fully functional. From the analysis of mutant mice that lack both rod Pγ and PDE6C, we conclude that, in contrast to the cone enzyme, no maturation of rod PDE6AB occurs in the absence of Pγ. Co-expression of PDE6C with AIPL1 and Pγ in insect cells leads to a fully mature enzyme that is equivalent to retinal PDE6. Lastly, using immature PDE6C and purified chaperone components, we reconstituted the process of the client maturation invitro. Based on this analysis we propose a scheme for the PDE6 maturation process.

Transition to chaos in extended systems and their quantum impurity models

Chaos sets a fundamental limit to quantum-information processing schemes. We study the onset of chaos in spatially extended quantum many-body systems that are relevant to quantum optical devices. We consider an extended version of the Tavis–Cummings model on a finite chain. By studying level-spacing statistics, adjacent gap ratios, and spectral form factors, we observe the transition from integrability to chaos as the hopping between the Tavis–Cummings sites is increased above a finite value. The results are obtained by means of exact numerical diagonalization which becomes notoriously hard for extended lattice geometries. In an attempt to circumvent these difficulties, we identify a minimal single-site quantum impurity model that successfully captures the spectral properties of the lattice model. This approach is intended to be adaptable to other lattice models with large local Hilbert spaces.

An updated strong-motion database of Türkiye (SMD-TR)

We updated the earthquake strong-motion database of Türkiye (SMD-TR) that is initially compiled by Akkar et al. The updated data set consists of of 9244 earthquakes of magnitudes 3.0 ≤ M ≤ 7.8 that occurred between 1976 and 2023. It includes 95,890 three-component waveforms recorded at 1022 stations (55,375 records that are processed from 6710 events and 974 stations). The database is disseminated under research-oriented website DesignSafe ( https://doi.org/10.17603/ds2-f21x-s189 ) and is compiled to provide ground-motion flatfile, which will be updated periodically until 2025. The released version includes events occurred in Türkiye until 28 February 2023, and hence, it comprises of the mainshocks and a great portion of aftershocks from the February 6th, 2023, Kahramanmaraş earthquakes. The database includes event metadata compiled from national and international seismological agencies as well as the literature. We classified events according to their tectonic environments (i.e. active crustal and subduction) and identified the aftershocks. The station information provided by AFAD1 was used to compute the major site parameters, including time-based average of shear-wave velocity of uppermost 30 m (VS30) and depth-to-rock horizon at which the shear-wave velocity (VS) attains 1 km/s (Z1). We also computed the finite-fault distance metrics and flagged the stations located on the hanging-wall side of the rupture, as well as identified pulse-like records. We developed an automatic processing algorithm to determine the waveform quality, apply appropriate filtercut-offs to remove low- and high-frequency noise, and compute ground-motion parameters. The automatic processing was applied to small-magnitude (M < 5.5) events, whereas manual processing scheme is preferred for waveforms recorded from larger events (M ≥ 5.5). The peak ground-motion values, spectral ordinates, and ground-motion duration are provided along with the key data processing parameters in the flatfile.

Operation of a mesh grid optic-fiber sensor network with self-reconfigurable function

This paper presents a mesh grid topology for an optical fiber sensor network, which has an efficient structure for intelligent monitoring and reconfigurable protection operations. The integration of light sources and detectors within the network plays a crucial role in achieving comprehensive monitoring of the entire sensor network. Moreover, we have designed multiple nodes for sensing channels with distinct function to maximize the benefits of the topology in terms of network resilience. Finally, centralized and discrete signal processing schemes for network control systems have been demonstrated to be effective under various application conditions.

Development of a Hybrid Technological Scheme for the Treatment of Anaerobically Fermented Sludge (Digestat) of Wastewater of the Food Industry

The issue of dewatering and disposal of sewage sludge is one of the most urgent due to the acute shortage of land resources, the negative ecological burden on water resources, soils and the atmosphere of these sludges in case of their accumulation without proper treatment. Anaerobic stabilization of food production wastewater sludge is one of the most widespread methods of processing this type of sludge due to the possibility of obtaining an important energy resource – biogas. The sludge obtained after anaerobic stabilization in methane tanks is called digestate, and it also needs treatment and further disposal. Digestate can be used as a fertilizer if sanitary and hygienic requirements are met. The development of a technological scheme for processing wastewater digestate is a complex issue that does not have one correct solution. A hybrid technological process for processing wastewater digestate of the food industry is proposed on the basis of experimental studies. Experimental studies include dewatering anaerobically stabilized sludge on a multi-disc dehydrator using dilution of the sludge itself due to its significant viscosity, addition of coagulant and flocculant. Cake with a moisture content of about 65–80% can be transported and used as fertilizer for agriculture. The separated filtrate from the digestate according to the technological scheme is proposed to be purified on two stages of membranes. The efficiency of cleaning the liquid fraction of the digestate on a two-stage membrane installation according to two variants showed the following results: variant 1 (VSEP + RO) – 95%, variant 2 (ZI + RO1 + RO2) – 94%. The VSEP system uses a hermetic RO membrane, so a two-pass membrane process (VSEP+RO) is suitable to achieve the end goal (permeate obtained can be reused), while a ZI membrane requires a three-pass membrane process to achieve the same goal. The achieved parameters make it possible to use the obtained permeate for the production needs of the enterprise or in the technological scheme of the digestate processing itself for dilution of the sediment or for the preparation of reagent solutions. The resulting products (cake and concentrates after membranes) can be used as fertilizers.

Adaptive design change considering making small impact on the original manufacturing process

In the development of complex products, there are various design changes from the product itself, costumers need, and manufacturing environment issues. However, few references mention the design changes caused by abnormalities during trial production or even actual manufacturing. When faced with redesign and reproduction in customized production, it is impossible to stop all equipment to solve the product problem, which will cause great losses. Considering the way to ensure the completion of design changes while minimizing the impact on the overall production has certain research significance. This paper focuses on the factors that lead to design changes during manufacturing and highlights the importance of real-time information feedback for redesign. Attentions are paid to the response of components to changes. The changing ability of product components is evaluated based on the strong correlation between components through complex network. Under different size and scope of the initial change, the completion degree of the whole product is studied, based on which, the secondary feedback from the redesign to the original manufacturing is quantified. This method provides managers with an adaptive design change scheme suitable for the current customized manufacturing state with small impact on the original process scheme. Also, the maximum change bearing capacity under the current state could be obtained.

Homomorphic Compression: Making Text Processing on Compression Unlimited

Lossless data compression is an effective way to handle the huge transmission and storage overhead of massive text data. Its utility is even more significant today when data volumes are skyrocketing. The concept of operating on compressed data infuses new blood into efficient text management by enabling mainly access-oriented text processing tasks to be done directly on compressed data without decompression. Facing limitations of the existing compressed text processing schemes such as limited types of operations supported, low efficiency, and high space occupation, we address these problems by proposing a homomorphic compression theory. It enables the generalization and characterization of algorithms with compression processing capabilities. On this basis, we develop HOCO, an efficient text data management engine that supports a variety of processing tasks on compressed text. We select three representative compression schemes and implement them combined with homomorphism in HOCO. HOCO supports the extension of homomorphic compression schemes through a modular and object-oriented design and has convenient interfaces for text processing tasks. We evaluate HOCO on six real-world datasets. The three schemes implemented in HOCO show trade-offs in terms of compression ratio, supported operation types, and efficiency. Experiments also show that HOCO can achieve higher throughput in random access and modification operations (averagely 9.18× than the state-of-the-art) and lower latency in text analytic tasks (averagely 7.16× than processing on uncompressed text) without compromising compression efficacy.