Candidate Options Research Articles

Promoter screening and identification for metabolic regulation in Acremonium chrysogenum.

Acremonium chrysogenum is the major industrial producer of cephalosporin C (CPC), which is used as raw material for the production of significant cephalosporin antibiotics. Due to the lack of diverse promoter elements, the development of metabolic engineering transformation is relatively slow, resulting in a limited improvement on CPC production. In this study, based on the analysis of the transcriptome profile, 27 candidate promoters were selected to drive the expression of the reporter genes. The promoter activities of this library ranged from 0.0075 to 101 times of the control promoter PAngpdA . Simultaneously, a rapid screening method for potential bidirectional promoters was developed and 4 strong bidirectional promoters from 27 candidate options were identified and validated. Finally, the Golden Gate method was employed to combine promoter modules from the library with various target genes. Through a mixed transformation and screening process, high-yielding strains AG-6, AG-18, and AG-41 were identified, exhibiting an increase in CPC production of 30%, 35%, and 29%, respectively, compared to the control strain Ac-∆axl2:: eGFP. Therefore, the utilization of this promoter library offers a broader range of synthetic biology toolkits for the genetic engineering transformation of A. chrysogenum, thus establishing a solid foundation for the precise regulation of gene expression.

UNRAVELLING THE INTERACTION BETWEEN GARCINISIDONE-A AND HER2 PROTEIN IN BREAST CANCER: A COMPUTATIONAL STUDY

Objective: One substance found in the leaves of Garcinia cowa Roxb that has anticancer properties is garcinisidone-A. The study aims to simulate the docking of garcinisidone-A (Gar-A), molecular dynamics, and predict the ADME by predicting the binding of the HER2 protein in breast cancer cells and developing new drug candidate options for cancer treatment, often starting with computational analysis.
 Methods: The research method involves computational utilization of pkCSM applications, Gar-A docking simulation with the HER2 protein using Gnina software version 1.0.2, and molecular dynamics conducted with GROMACS 2022.2 and CHARMMGUI applications.
 Results: Gar-A has a molecular weight of less than 500, a Log P value of greater than 5, a limited amount of water solubility, a low level of skin permeability, good intestinal permeability, and a Convolutional Neural Network (CNN) pose score on the HER2 protein of 0.6178. It also does not readily cross the blood-brain barrier, and total clearance values indicate rapid elimination via other excretory routes or enzyme metabolism. Gar-A is thought to have interactions with HER2. There are hydrogen bond interactions with amino acids Lys753 and Asp863, carbon-hydrogen bonds with amino acids Leu785, Ser783, Thr862, and alkyl bonds with amino acids Leu726, Leu852, and Ile767. The stability of the Gar-A-substrate interaction could have been more evident during 100 ns molecular dynamics simulation.
 Conclusion: The physicochemical properties of Gar-A align with Lipinski's rule for drug candidates. ADME predictions indicate good intestinal permeability for Gar-A; however, it suggests it cannot penetrate the blood-brain barrier. The docking results reveal that Gar-A has a value close to one which indicates similar action to its natural ligand and molecular dynamics simulations that Gar-A is less stable. The results illustrate that Gar-A has the potential as a breast anticancer.

Optimization of water-energy-food nexus via an integrated system of solar-assisted desalination and farming

This paper introduces a systematic framework for water-energy-food (WEF) nexus with focus on integrating water desalination (via reverse osmosis and thermal desalination), energy generation (from fossil-based power plants and solar panels), and crop production. Specifically, the paper provides generally applicable and new contributions in formulating WEF nexus problems as multi-objective, multi-period optimization problems with data-driven models based on field work. The paper also established a systematic method for integrating and reconciling farming methods, water technologies, energy-resource selection and operation, carbon footprint, and economic factors. A superstructure representation is used to embed the candidate options and configurations. An optimization formulation is developed to determine the optimal system configuration, water and energy uses and sources, and crop mix selection. A multi-period model is developed to account for the seasonal variability. The optimization model is solved under different carbon-footprint cuts to establish a tradeoff curve between the economic and environmental objectives. To generate the necessary experimental data for a meaningful case study, a year-long field study was carried out to generate experimental data for three fields were constructed in Kuwait using open-field, agrivoltaic, and greenhouse farming methods. The field data were extracted and coupled with the optimization formulation to apply the proposed approach to a case study. First, the optimization formulation was solved to maximize the net profit with no limits on the carbon footprint. The solution of this relaxed formulation gives a maximum profit of 2.127 MM USD/y and an annual emission of 2242 tonne CO2eq. Next, the optimization formulation was solved with the objective of minimizing the total annual emissions. The result was a minimum annual emission of 363 tonne CO2eq that corresponded to 1.366 MM USD/y of profit. The ɛ-constraint method was employed to establish tradeoffs between the economic and environmental objectives. The solutions also provided valuable information on the percentage contribution of solar energy and water sources.

Systematic review with meta-analysis: effects of probiotic fungi on irritable bowel syndrome

Abstract Treatment of irritable bowel syndrome (IBS) remains challenging for clinicians. Probiotic fungi may act as candidate options for IBS treatment, but systematic evaluation of their clinical value remains scarce. This study is aimed to assess the efficacy and the safety of probiotic fungi for IBS treatment by means of systematic review and meta-analysis. PubMed, Embase, Web of Science, and the Cochrane Library, were searched up to June 2022. Randomised controlled trials recruited subjects with prescriptions of probiotic fungi were eligible. Efficacy and safety of probiotic fungi were re-evaluated. Continuous data were pooled to obtain standardised difference in means (SMD) with a 95% confidence interval. The search strategy identified 120 articles of which 7 trial assessing 883 subjects were included in the analysis. Systematic data support that Saccharomyces helps to relieve abdominal pain/discomfort (SMD = −0.205, ), and presented potential improvements on psychological outcomes, stool form for IBS patients. It is hard to demonstrate favourable effects on other symptoms (including distension, mucus passage, sense of incomplete evacuation, urgency, straining). The incidence of mild complications ranged from 0 to 51.4%, but no serious complications were observed in the included trials. Therefore, the partial response and the relative safe of probiotic fungi for IBS treatment have been demonstrated from the existing trials. However, it is premature to eventually declare the practical effects of probiotic fungi. Conducting more high-quality and large-scale trials and real-world studies, or even developing new fungal strains, is still necessary.

Marketplace or reseller: the channel strategy analysis for e-commerce platforms considering brand differentiation

PurposeThe paper aims to clarify the effects of brand differentiation on the platform's formulation of channel strategy and help the online platform formulate the optimal channel strategy, which involves selecting a proper selling mode for each brand.Design/methodology/approachThe paper develops a multistage game model consisting of one online platform and two competing manufacturers with differentiated brands and examines the effects of brand differentiation on these three channel members' profits under each candidate channel strategy.FindingsThe results show that the platform prefers to offer the reselling mode for both brands when the brand differentiation is low, and this preference will be enhanced by the decrease in order fulfilment cost. By contrast, when the brand differentiation is high, it will offer the reselling mode for the premium brand but the marketplace service for the economy brand if the order fulfilment cost is not high; or the marketplace mode will be offered to both brands if this cost is high.Research limitations/implicationsThis study assumes that the order fulfilment costs of platform and manufacturer are fixed and symmetric. Therefore, researchers are encouraged to consider asymmetric costs of order fulfilment.Practical implicationsThe paper guides the online platform to formulate the optimal channel strategy for differentiated brands and provides managerial insights for differentiated brands entering online markets.Originality/valueThis paper explores platforms' optimal channel strategy by jointly considering the effects of brand differentiation and investigates the impacts of brand differentiation on the optimal decision making under four candidate options. Moreover, this paper has been extended to examine the case when the manufacturers' production costs cannot be neglected.

Multimodality in mmWave MIMO Beam Selection Using Deep Learning: Datasets and Challenges

The increasing availability of multimodal data holds many promises for developments in millimeter-wave (mmWave) multiple-antenna systems by harnessing the potential for enhanced situational awareness. Specifically, inclusion of non-RF modalities to complement RF-only data in communications-related decisions like beam selection may speed up decision making in situations where an exhaustive search, spanning all candidate options, is required by the standard. However, to accelerate research in this topic, there is a need to collect real-world datasets in a principled manner. This article presents an experimentally obtained dataset, composed of 23 GB of data, which aids in beam selection in vehicle-to-everything mmWave bands, with the goal of facilitating machine learning (ML) in the wireless communication required for autonomous driving. Beyond this specific example, the article describes methodologies of creating such datasets that use time synchronized and heterogeneous types of LiDAR, GPS, and camera images, paired with the RF ground truth data of selected beams in the mmWave band. While we use beam selection as the primary demonstrator, we also discuss how multimodal datasets may be used in other ML-based PHY-layer optimization areas, such as beamforming and localization.

A multi-objective equilibrium optimization for optimal allocation of batteries in distribution systems with lifetime maximization

Many economic, environmental, and technical benefits have been achieved in recent years as a result of the integration of renewable energy resources (RERs) and battery energy storage units (BESUs) through the distribution network. However, unplanned allocating of the integrated BESUs and RERs leads to excessive investment costs and insecure system operation. Moreover, the charging and discharging processes have a direct impact on the degradation of the BESUs, and consequently, the stability of the BESUs will be affected as the battery ages. Therefore, this paper presents the trade-off between different conflicting objectives. In the presented multi-objective model, the battery lifetime maximization is considered in the allocation problem, for the first time, by maximizing the battery cycle to failure (CTF). On the other hand, different beneficial objectives are included by minimizing the operation and investment costs of the integrated units, the cost of energy not supplied (CENS), the cost of power loss, and the cost of carbon dioxide (CO2) emissions. For solving this model, a multi-objective equilibrium optimization technique (MOEOT) is proposed to determine the optimum sites and sizes of photovoltaic (PV) and BESUs, maximum and minimum battery state of charge, and the charge hours. The proposed approach is employed on IEEE standard 30-bus and 69-bus radial distribution networks. The proposed MOEOT successfully acquires Pareto optimal front with diverse candidate options of different CTF and corresponding costs. Also, the impacts of varying depth of discharge (DOD) and charge and discharge hours for BESUs on the system performance are analyzed. For the IEEE 30 bus system, as the hours of the battery charge and discharge are increased from 2 to 12 h, the battery CTF is increased by 1 %; the power losses costs are decreased by 8.6 %; the emission costs are decreased with 1 %, and the life cycle cost is decreased with percentage 1 %. For the IEEE 69 bus system, the battery CTF is increased by 1 %; the power loss costs are decreased by 17 %; the emission costs are decreased by 1.8 %, and the life cycle cost is decreased by 0.1 %.

Multi-criteria material selection for casing pipe in shale gas wells application

The conventional method of casing selection is based on availability and/or order placement to manufacturers based on certain design specifications to meet the anticipated downhole conditions. This traditional approach is very much dependent on experience as well as constructing oil and gas wells at minimum budget. However, this material selection approach is very limited in meeting the requirement of shale gas wells. This study utilises the material performance indices and ANSYS Granta database to examine three different casing pipe buckling scenarios including the buckling with corrosion potentials and buckling with impact and long-term service temperature conditions. Consequently, numerical evaluations of the response of the selected casing materials established the stress, deformations, and safety factor for the first scenario (shale gas well with buckling tendencies). The significance of this new method is added advantage in terms of integrating materials’ physicochemical, thermal and mechanical properties and the casing functional performance to establish ideal selection within the design space or requirements. Results obtained in this study show that there are optional materials that outperform the most common casing grades (P110 and Q125) utilised in shale gas development in terms of both safety and cost. This study established a procedure for evaluating optimum performance between cost, safety, performance indices and materials’ physical and mechanical properties for a typical well design scenario. This procedure will assist the design engineer to justify the selection of a particular material(s) safely and technically for a given shale well casing application in future. In all the 10 materials investigated, even though the P110 (API casing grade) meets the buckling design scenario and widely used in shale gas well development, there are many alternative viable material candidate options that outperform P110 Grade with the best material candidate studied in this work being BS 145.

Optimal configuration of a three-rod ortho-bridge system in the treatment of Vancouver type B1 periprosthetic femoral fractures: A finite element analysis

Introduction and AimPeriprosthetic femoral fractures (PFF) represent an increasing clinical and economic burden. This study aims to determine the optimal configuration of a bridge-combined internal fixation system in the treatment of Vancouver type B1 PFF, using finite element analysis. Materials and methodsA three-rod ortho-bridge system (OBS) fixation model was used to evaluate the optimal configuration of four target parameters: position of the third rod; intersection angle between the proximal screws; connecting rod diameter; and number of screws used. Femoral displacement and the maximum von Mises stress of the OBS were used as the evaluation indices, to analyze the PFF and to determine the optimal use of an OBS. For each parameter, various candidate options were tested. ResultsFinite element analysis revealed that the rate of femoral displacement and the maximum von Mises stress of the OBS were at a minimum when there was a 35 mm downward movement of the third rod from the baseline. Therefore, the optimal position of third rod fixation was 35 mm below the fovea capitis of the femur. The optimal intersection angles between the proximal screws were found to be 71.92° or 84°. A 6 mm diameter connecting rod proved to be most effective. Configuration d, utilizing 7 screws, represented the most clinically appropriate screw number configuration, despite configuration f, utilizing 9 screws, eliciting the best evaluation indices. ConclusionAn OBS used in the above-described configuration is well suited to the characteristics of PFF and provides an effective and reliable means for their treatment.

SSIN: Sentence Semantic Interaction Network for Multi-Choice Reading Comprehension

Multiple-choice reading comprehension is a challenging task in natural language processing, which aims to select the correct answer from a set of candidate options when given passage and question. Previous approaches usually focus only on word vector interactions and ignore the importance of sentence semantics for reading when modeling the relationship between passage and question. However, reading is a process that includes complex interactions of various knowledge such as vocabulary, syntax and semantics. Interactions based on word vectors alone do not effectively capture the relationship between passage and question. In this work, we propose the Sentence Semantic Interaction Network (SSIN), which models the relationship among passage, question, and answer options based on sentence semantics. The experimental results show that superior results are achieved on both the RACE and MCTest datasets, confirming that the interaction based on sentence semantic vectors can effectively improve the performance of the model reading comprehension.

A Case Study of the Shortcut Effects in Visual Commonsense Reasoning

Visual reasoning and question-answering have gathered attention in recent years. Many datasets and evaluation protocols have been proposed; some have been shown to contain bias that allows models to ``cheat'' without performing true, generalizable reasoning. A well-known bias is dependence on language priors (frequency of answers) resulting in the model not looking at the image. We discover a new type of bias in the Visual Commonsense Reasoning (VCR) dataset. In particular we show that most state-of-the-art models exploit co-occurring text between input (question) and output (answer options), and rely on only a few pieces of information in the candidate options, to make a decision. Unfortunately, relying on such superficial evidence causes models to be very fragile. To measure fragility, we propose two ways to modify the validation data, in which a few words in the answer choices are modified without significant changes in meaning. We find such insignificant changes cause models' performance to degrade significantly. To resolve the issue, we propose a curriculum-based masking approach, as a mechanism to perform more robust training. Our method improves the baseline by requiring it to pay attention to the answers as a whole, and is more effective than prior masking strategies.

Reliability and Economic Evaluation of Offshore Wind Power DC Collection Systems

One key directive to realize the global transition towards net-zero emission goals is to integrate more renewable energy resources into the generation mix. Due to higher and more consistent wind speeds, offshore wind farms (OWFs) have the potential to generate more energy at a steadier rate than their onshore counterpart. However, at the collection system level, all the OWFs use alternating current (AC) technology at present. Nonetheless, with an increasing capacity of the single wind turbine (WT) and larger distances to the shore, the use of direct current (DC) technology at the collection system level is beneficial. To select a suitable DC collection system topology, this paper proposes a comprehensive analytical reliability evaluation method, based on the Universal Generating Function technique, together with associated economic factors. Four candidates DC collection system options were evaluated with different WT capacities for a 400 MW OWF. The availability indices such as Generation Ratio Availability and Expected Energy Not Supplied were used to assess their reliability levels. The results show that the radial topology with a single platform DC/DC converter is more reliable and economical than the other candidate options.

Assessment of Materials Selection Problem in Cryogenic Tank Using COPRAS Method

Materials are well-known for their function in product creation and development. There are numerous materials with various qualities on the market right now. Before deciding on the best material for a type of product, the architect must give it some thought. Research Significance: Before making a final decision, the design engineer must also consider a wide range of material eligibility requirements; otherwise, the product could fail before it should while in use. The choice to choose a material for a particular product is an example of a "multi-criteria decision-making" dilemma because it involves several competing criteria and a limited number of candidate options. Research Methodology: In this study, the "COPRAS (COmplex PRoportional ASsessment) technique" is used to address some typical issues with material choice. " Al2024-T6, Al5052-O, SS301-FH, SS310-3AH, Inconel718 and 70Cu-30Zn" are further materials chosen for the cryogenic storage tank. "Toughness Index, Elastic Modulus, Yield Strength, Specific Heat, Thermal Conductivi-ty, and Density" are the material properties used as evaluation criteria. Result: The rank of alternatives using the COP-RAS method for Al2024-T6 is fifth, Al5052-O is sixth, SS301-FH is first, SS310-3AH is second, Inconel718 is third and 70Cu-30Zn is fourth. Conclusion: The outcome demonstrates that "301 Full Hard Tempered Stainless Steel" and "Stainless Steel 310" are the best materials for cryogenic tanks.

Thermal-Hydraulic and Engineering Evaluations of New LOCA Testing Methods in TREAT

Design evaluations and thermal-hydraulic predictions were undertaken to compare three candidate options for loss-of-coolant accident (LOCA)–testing capabilities at the Transient Reactor Test facility (TREAT). These options included a small water capsule, a large natural circulation capsule, and a forced-convection water loop. All options were found to create reasonable approximations of radial temperature gradients in fuel specimens prior to blowdown to a secondary tank. Given its minimalist form factor, the small water capsule was predicted to perform remarkably well in mimicking the thermal response of a 20-pellet fuel rodlet during a postulated LOCA event, while sensitivity studies showed versatility in adjusting conditions for specific test phenomena priorities. The large natural circulation capsule predictions showed representative capsule pressure evolution but indicated it would likely struggle to capture the desired fuel temperature response in the blowdown transition, primarily due to nonrepresentative cooling as its tall water column was discharged. These results were compared to previous water loop evaluations that had shown a capability for representing ideal test conditions, especially for longer rodlet test specimens in small bundles, while being achievable in a self-contained form factor to facilitate test logistics. Considering the broader cost and capability spectrum needed to support fuel safety research, these efforts resulted in a recommendation to pursue the small water capsule for near-term data needs specific to high-burnup fuel fragmentation, while continuing detailed development of the water loop to address ultimate data needs.

Interface design of the blanket system for CFETR

The China Fusion Engineering Test Reactor (CFETR) aims to bridge the gaps between fusion experimental reactor ITER and demonstration fusion power reactor. Blanket, one of the key components of the CFETR, is of great importance to achieve the functions of tritium breeding, heat extraction and radiation shielding. As for the next device for fusion energy research in China, two blanket candidate options have been developing in parallel to decrease the risk of both physical and technical issues. Apart from the research and design work on the breeding blanket module itself, the interfaces definition and integration of the blanket system is vital, particularly due to the system interdependencies. The interface design of the blanket system is a challenge due to several factors, such as the thermal expansion, the electromagnetic loads, the feasibility of remote maintenance. In this paper, a preliminary interface definition and integration design of the CFETR blanket system has been presented, based on the design with R = 7.2 m and a = 2.2 m. The purpose is to complete the interface definition and integrated design of the blanket and other components or systems. The vacuum vessel has 16 sectors, and each sector has two inboard and three outboard blanket segments. The blanket segment will be installed and removed by remote maintenance tools through the upper ports. The work addresses the interfaces between the blanket and other system, such as diagnostic system and heating system. Preliminary assessment of the impact on tritium breeding ratio (TBR) for the candidate water cooled ceramic breeder blanket integration scheme has been presented highlighting the influence of the auxiliary interfaces system to TBR.

Robust parallel decision-making in neural circuits with nonlinear inhibition

An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes [Formula: see text] time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick's law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick's law may be a symptom of near-optimal parallel decision-making with noisy input.

Optimal offshore wind farms connection considering the wind capacity maximisation – a Benders decomposition approach

This study presents a new methodology to determine the optimal Offshore Wind Farms connection point in a power system, with the purpose of maximising the wind power penetration, considering a Benders decomposition approach to represent the security constraints related to the ‘’ contingency criterion. The proposed algorithm uses a mixed-integer non-linear programming approach associated with a non-linear programming, and it is evaluated in a tutorial system (IEEE 14-Bus) and two networks from the literature: a small case (IEEE RTS-79), and a medium and real case (Brazilian Birds System). In each case, three points geographically close to each other are selected as candidates in the onshore grid for the wind power injection. The experiments conducted clearly show that the connection decision from the proposed approach led to the maximum safe wind power penetration among the candidate options, which proves its applicability and effectiveness, even for more complex networks, and considering all single transmission line contingencies for safe operation.

Design and pricing of extended warranty menus based on the multinomial logit choice model

This paper studies the design and pricing of an extended warranty menu, which offers multiple options with differentiated lengths and prices. The power law process is used to model product failures and evaluate warranty costs. The multinomial logit model is adopted to describe customer choice behaviors. From a warrantor’s perspective, the design and pricing problem is to determine which candidate options to offer and the associated prices so as to maximize the expected warranty profit. We show that the optimal strategy is to offer all candidate options associated with a cost-plus-margin pricing policy, with the same profit margins for all options. If only a limited number of options can be offered, then the options with the highest valuation margins should be selected. In addition, we present three extended models by incorporating heterogeneous warranty breadths, free preventive maintenance programs, and customers with heterogeneous perceptions of product failure probability. Major findings in the extended models include: (i) a free preventive maintenance program should be attached to a warranty option only when it is feasible for that option; and (ii) when the customer population is heterogeneous, the equal-margin pricing policy is no longer optimal. Overall, this work will equip practitioners with a quantitative tool to design and price extended warranty menus in various practical scenarios.

DCMN+: Dual Co-Matching Network for Multi-Choice Reading Comprehension

Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question. Previous approaches usually only calculate question-aware passage representation and ignore passage-aware question representation when modeling the relationship between passage and question, which cannot effectively capture the relationship between passage and question. In this work, we propose dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally. Besides, inspired by how humans solve multi-choice questions, we integrate two reading strategies into our model: (i) passage sentence selection that finds the most salient supporting sentences to answer the question, (ii) answer option interaction that encodes the comparison information between answer options. DCMN equipped with the two strategies (DCMN+) obtains state-of-the-art results on five multi-choice reading comprehension datasets from different domains: RACE, SemEval-2018 Task 11, ROCStories, COIN, MCTest.

Perturbative countersurveillance metaoptics with compound nanosieves

The progress of metaoptics relies on identifying photonic materials and geometries, the combination of which represents a promising approach to complex and desired optical functionalities. Material candidate options are primarily limited by natural availability. Thus, the search for meta-atom geometries, by either forward or inverse means, plays a pivotal role in achieving more sophisticated phenomena. Past efforts mainly focused on building the geometric library of individual meta-atoms and synthesizing various ones into a design. However, those efforts neglected the powerfulness of perturbative metaoptics due to the perception that perturbations are usually regarded as adverse and in need of being suppressed. Here, we report a perturbation-induced countersurveillance strategy using compound nanosieves mediated by structural and thermal perturbations. Private information can be almost perfectly concealed and camouflaged by the induced thermal-spectral drifts, enabling information storage and exchange in a covert way. This perturbative metaoptics can self-indicate whether the hidden information has been attacked during delivery. Our results establish a perturbative paradigm of securing a safer world of information and internet of things.