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In light of escalating biomedical demands across diverse diseases, there arises a pressing need for the development of sophisticated biocompatible materials exhibiting augmented biological functionality. Chitosan, a cationic polyelectrolyte copolymer of natural origin, distinguishes itself through its extraordinary biological properties, positioning it as a promising starting material to develop versatile biomedical materials. Tremendous attention has been directed towards the creation of high-performance biocomposites, achieved through the strategic manipulation of chitosan's structure or its derivative, along with the amalgamation of other biopolymers. This comprehensive review intricately explores recent advancements in chitosan-based biofunctional materials, delving into formulations involving various biopolymers including polysaccharides and proteins. It places specific emphasis on the progress in chitosan chemistry and materials development, encompassing particles, hydrogels, aerogels, membranes, films, and sponges. Also, this review critically evaluates the development and functional properties of biofunctional chitosan–biopolymer composite materials, spotlighting interactions, both dynamic covalent and noncovalent, and their pivotal roles in materials formation. These interactions may either be inherent or realized through chemical modification such as "Click" chemistry, polymer grafts, mussel-inspired chemistry, and selective oxidation. Furthermore, the text illustrates the current and potential biomedical applications of these biofunctional composite materials, spanning from wound dressing to tissue engineering (skin, bone, cartilage, and nerve), the controlled release and targeted delivery of drugs/bioactive compounds, biosensing, and 3D printing. Additionally, it addresses critical challenges within the field, posits potential solutions, and provides a forward-looking perspective on the future directions of functional biomaterials and design strategies. [Display omitted] • Types, fabrication, and biomedical applications of chitosan composites reviewed. • Polycation chitosan can form polyelectrolyte complexes with anionic biopolymers. • Chitosan versatile for chemical modification for improved biofunctional properties. • Functionalization of biopolymers leads to desired molecular interactions/bonding. • Chitosan composites with enhanced functional properties biomedically advantageous. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ions enhanced DPPH radical scavenging capacity of RA/BRA composite system. • Metal ions restrict the interaction between RA and BRA. • Ca2+ showed more detrimental behavior to the stability of BRA/RA composite system than Na+ at the same concentration. Thermal processing with salt ions is widely used for the production of food products (such as whole grain food) containing protein and anthocyanin. To date, it is largely unexplored how salt ion presence during thermal processing regulates the practical performance of protein/anthocyanin binary system. Here, rice albumin (RA) and black rice anthocyanins (BRA) were used to prepare RA/BRA composite systems as a function of temperature (60–100 °C) and NaCl concentration (10–40 mM) or CaCl 2 concentration (20 mM). It was revealed that the spontaneous complexing reaction between RA and BRA was driven by hydrophobic interactions and hydrogen bonds and becomes easier and more favorable at a higher temperature (≤90 °C), excessive temperature (100 °C), however, may result in the degradation of BRA. Moreover, the salt ion presence during thermal processing may bind with RA and BRA, respectively, which could restrict the interaction between BRA and RA. Additionally, the inclusion of Na+ or Ca2+ at 20 mM endowed the binary system with strengthened DPPH radical scavenging capacity (0.95 for Na+ and 0.99 for Ca2+). Notably, Ca2+ performed a greater impact on the stability of the system than Na+. [ABSTRACT FROM AUTHOR]

[Display omitted] • Edible oil alters the microstructure of cooked brown rice. • Edible oil ameliorates the texture properties of cooked brown rice. • Edible oil incorporation reduces the starch digestibility of brown rice. • The effect of edible oil on the brown rice varies in edible oil type. While brown rice (BR) has numerous nutritional properties, the consumption potential of which is seriously restricted since the poor cooking quality and undesirable flavor. Here, edible oils (pork lard and corn oil, 1–5 wt%) were incorporated during the cooking of BR following heat moisture treatment. Incorporating corn oil rather than lard significantly ameliorated the texture properties (e.g. hardness, cohesiveness, and chewiness) and sensory properties of cooked BR. Both lard- and corn oil-incorporated cooked BR showed obvious structural changes accompanied by the formation of amylose-lipid complexes during cooking. It was confirmed that the incorporation of lard and corn oil allowed a higher degree of short-range molecular order, more V-type starch crystallites, and elevated nano-structural arrangements. Additionally, a decreased hardness (from 559.04 g to 424.18 g and 385.91 g, respectively) and enriched resistant starch (RS) were also observed, the highest RS content (15.95 % and 16.32 %, respectively) was observed when 1 wt% of lard and corn oil were incorporated. [ABSTRACT FROM AUTHOR]

Transposons are mobile DNAs that can move to different locations in host genomes. The integration site selection of transposons is critical for both themselves and host cells. Studies on the integration of retrotransposons and retroviruses have focused more on the global preference than on the local preference. The local preferences of retrotransposons are usually weak and of large diversity. Here, we analyzed hundreds of thousands of independent integration events of the Tf1 retrotransposon in Schizosaccharomyces pombe. The consensus sequence at the Tf1 integration sites shows a palindromic pattern, which can be divided into four sections, each of them contains one or more CGnTA units with a period of 10 base pairs, indicating interaction with subunits of the integrase oligomer in the pre-integration complex. Moreover, the analysis on the nucleosome occupancy flanking Tf1 target sites shows that Tf1 integration favors regions with one entire nucleosome depletion. • The consensus sequence at the Tf1 transposon targeting sites shows a symmetric and palindromic pattern. • The analysis of the consensus sequence suggests an integrase oligomer in the pre-integration complex of Tf1. • Tf1 prefers local DNA sequences with one entire nucleosome depletion. [ABSTRACT FROM AUTHOR]

• A new air conditioning system is explored by experiment. • The system starts mode H is the most stable, and the maximum superheat is 11°C. • A unique phenomenon occurs in the production of hot water. • The system fluctuates periodically in the refrigeration mode. • The drop in discharge pressure can be used as a parameter for defrost control. Air source heat pump (ASHP) and heat pump water heater (HPWH) have the advantages of convenience and high efficiency. The newly proposed refrigeration, heating and hot water combined air-conditioning system (RHHAC) combines traditional ASHP with HPWH. The demands of users for cooling, heating and hot water are effectively satisfied with this equipment. In this study, five groups of experiments were conducted to study the starting and operating characteristics of the system. Besides, two sets of experimental cases were carried out to explore the operating characteristics of the system under the frosting process. Results show that the maximum superheat degree is 11°C when the system starts in hot water mode, while the maximum superheat degree is more than 22°C when the system starts in the other modes. As the temperature of hot water rises when the system runs in hot water mode, the maximum value of the discharge pressure can reach 3.7MPa. Besides, the analysis parameters will have regular pulsations when the system is in cooling mode. For example, in the experimental case 5, the fluctuation range of the superheat degree is 5.1°C-16.2°C. [ABSTRACT FROM AUTHOR]

Electrospinning technology has recently attracted increased attention in the biomedical field, and preparing various cellulose nanofibril membranes for periodontal tissue regeneration has unique advantages. However, the characteristics of using a single material tend to make it challenging to satisfy the requirements for a periodontal barrier film, and the production of composite fibrous membranes frequently impacts the quality of the final fiber membrane due to the influence of miscibility between different materials. In this study, nanofibrous membranes composed of polylactic acid (PLA) and polycaprolactone (PCL) fibers were fabricated using side-by-side electrospinning. Different concentrations of gelatin were added to the fiber membranes to improve their hydrophilic properties. The morphological structure of the different films as well as their composition, wettability and mechanical characteristics were examined. The results show that PCL/PLA dual-fibrous composite membranes with an appropriate amount of gelatin ensures sufficient mechanical strength while obtaining improved hydrophilic properties. The viability of L929 fibroblasts was evaluated using CCK-8 assays, and cell adhesion on the scaffolds was confirmed by scanning electron microscopy and by immunofluorescence assays. The results demonstrated that none of the fibrous membranes were toxic to cells and the addition of gelatin improved cell adhesion to those membranes. Based on our findings, adding 30% gelatin to the membrane may be the most appropriate content for periodontal tissue regeneration, considering the scaffold's mechanical qualities, hydrophilic properties and biocompatibility. In addition, the PCL-gelatin/PLA-gelatin dual-fibrous membranes prepared using side-by-side electrospinning technology have potential applications for tissue engineering. [ABSTRACT FROM AUTHOR]

Lightweight, flexible, high-stable and ultra-efficient electromagnetic shielding materials are the extremely desired materials for electronic devices and products. In this paper, a lightweight, compressible, and stretchable ferric trichloride (FeCl 3)/carbon nanotubes (CNTs)/polyvinyl pyrrolidone (PVP)/polyurethane (PU) composite foam with ultra-efficient electromagnetic interference shielding is fabricated by the chemical foaming method and subsequent ultrasound assisted loading of conductive layer onto PU skeleton. The as-prepared FeCl 3 /CNTs/PVP/PU composite foam with a density of 40 mg/cm3 possesses ultrahigh electromagnetic interference shielding properties at X-band (∼91.5 dB at 12.4 GHz) attributed to the unique porous structure to dissipate electromagnetic wave as well as the synergistic shielding effect of FeCl 3 and CNTs. More importantly, the ultra-efficient electromagnetic shielding performance can still remain even after subjected to water-washing, long-term cyclic compression or high/low temperature treatment because the addition of PVP can greatly enhance the interaction between CNTs and PU skeleton. The current work proposes a simple and scalable method to fabricate ultra-efficient electromagnetic interference shielding materials for wearable and portable electronic devices. [Display omitted] • A composite foam possessing ultra-efficient electromagnetic shielding performance (∼91.5 dB at 12.4 GHz) was prepared. • The composite foam exhibits an ultrahigh specific shielding efficiency value of 2287.5 dB cm3 g−1. • The composite foam shows excellent absorption ability to electromagnetic waves. • The composite foam has the merits of lightweight, stretchability, compressibility, and electromagnetic shielding stability. [ABSTRACT FROM AUTHOR]

• A novel fault diagnosis strategy is proposed using the GMM method. • The principal component analysis is selected for data dimensionality reduction. • Evaluations are made using four faults data of the VRF system experiments. • The established PCA-GMM has better fault diagnosis performance and reduced runtime. The timely fault diagnosis of HVAC systems is important for building energy saving, equipment maintenance and indoor comfort. The Gaussian mixture model method has rarely been studied in the fault diagnosis application of HVAC systems. Therefore, a novel fault diagnosis strategy is proposed based on the Gaussian mixture model (GMM) method for the variable refrigerant flow air-conditioning system. To reduce excessive input variables resulting in large model complexity and long running time, the principal component analysis approach (PCA) is used to perform data dimensionality reduction. Therefore, the fault diagnosis model combining the Gaussian mixture model and principal component analysis is established, which is evaluated using the four types of faults of the variable refrigerant flow system. These faults include refrigerant undercharge, refrigerant overcharge, outdoor unit fouling and four-way reversing valve faults. Experiments are carried out under three heating conditions. Results show that the PCA-GMM approach can effectively reduce the running time. Especially for the VVV type model, the running time is reduced from 176.78 s to 15.18 s. Meanwhile, established PCA-GMMs still have good fault diagnosis correct rates when the input data dimension is reduced. Especially, some PCA-GMMs have fault diagnosis correct rates of over 99% when the number of principal components exceeds 7. [ABSTRACT FROM AUTHOR]

This work concerns the multi-scale structures and digestion behaviors of indica rice starches with apparent amylose contents (AAC s) of ca. 14%–24%. A higher AAC not always allowed a lower digestion rate (k), different from previous findings that starches containing more amylose normally show slowed digestion. Here, the proportion of stable molecular orders (melting temperature above ca. 73 °C) and AAC synergistically governed starch digestion rate. With a similar AAC , a higher amount of stable orders (R h/l , ratio of enthalpy of stable orders to that of the rest) led to a lower k. Also, with a similar R h/l , a larger AAC caused a lower k. Consistently, JPZ starch had almost the highest AAC and R h/l , showing the lowest k. Regarding this, amylose and stable orders played roles in increasing the bulk density of starch structure matrices, and eventually slow the diffusion, absorption and catalysis events of digestion enzymes within the matrices. • Starches contained varied amounts of stable molecular orders melted above 73 °C. • Stable orders and amylose content synergistically affect starch digestion rate. • With similar amylose content, more stable orders reduced starch digestion rate. • With similar stable orders, more amylose reduced starch digestion rate. [ABSTRACT FROM AUTHOR]

The fault diagnosis of air-conditioning systems is of great significance to the energy saving of buildings. This study proposes a novel fault diagnosis approach for building energy saving based on the deep learning method which is deep belief network, and its application potential in the air conditioning fault diagnosis field is investigated. Then, a parameter optimization selection strategy is developed for model optimization. Four kinds of faults of the variable flow refrigerant system under heating mode are used to evaluate the performance of the models. The fault diagnosis results show that the deep belief network model with initial parameters can be used to diagnose the faults of the variable flow refrigerant system. Through the parameter optimization selection strategy, the fault diagnosis correct rate of the optimized model is 97.7%, which is improved by 5.05% compared with the model with initial parameters. The number of hidden layers of the deep belief network model is selected to be 2 layers. This result indicates that the fault diagnosis for variable flow refrigerant systems may not require a very deep model. Additionally, the performance of the optimized deep belief network model is compared with that of the traditional back propagation neural network, and the former is better. This finding also shows that the unsupervised restricted Boltzmann machine layer for data feature reconstruction can improve the fault diagnosis performance. [ABSTRACT FROM AUTHOR]

Carbon dioxide transcritical heat pump air conditioning has an excellent performance in heating conditions, and is a very potential heating alternative, which will help to improve the energy saving level of buildings. The occurrence of the fault will lead to an increase of energy consumption, damage of system components and other consequences. However, the current research focuses on the heat pump system with Freon as refrigerant, and the experimental research on the fault of the carbon dioxide transcritical heat pump system has not been studied. Therefore, this research carried out the operation experiment of the carbon dioxide heat pump with eight fault types under the actual conditions in winter. The operation characteristics under different fault conditions are analyzed. Based on the fault characteristics, a causality table is proposed, which can quickly identify the fault type when there are at least 6 characteristic variables. Besides, it is concluded that after the fault occurs, the energy consumption will increase directly or indirectly. Most of the faults will directly cause the decrease of system heating capacity and reduce indoor thermal comfort. The research results will contribute to the early identification of carbon dioxide system faults and the improvement of building energy efficiency. • For the first time, eight kinds of fault experiments for CO2 heat pump system are carried out. • The operation characteristics of the CO2 heat pump system under fault conditions are analyzed. • A causality table that can quickly identify faults is proposed. • The effect of fault on the operating power of the carbon dioxide heat pump is discussed. [ABSTRACT FROM AUTHOR]

Energy demand prediction of building heating is conducive to optimal control, fault detection and diagnosis and building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of building heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4. [ABSTRACT FROM AUTHOR]

Sensor faults of air conditioning systems are harmful to optimal control strategies and system performance resulting in poor control of the indoor environment and waste of energy. In order to improve the fault detection and diagnosis (FDD) performance, this paper presents an enhanced sensor fault detection and diagnosis method based on the Satizky-Golay (SG) method and principal component analysis (PCA) method for the VRF system, namely SG-PCA method. Due to the volatility of the original data set of VRF system, the original data are smoothed using SG method at first. Then, the smoothed data are used for PCA model training and fault detection and diagnosis. In order to determine parameters of the SG method, an optimization index is proposed, which is calculated by the signal to noise ratio (SNR), the standard deviation (SD) and the self-detection efficiency. This SG-PCA method for VRF system sensor FDD is validated using field operation data of the VRF system. Various sensor faults at different fault levels are introduced. The results have showed that the SG-PCA method can significantly improve the fault detection and diagnosis performance compared to conventional PCA method. [ABSTRACT FROM AUTHOR]

This paper proposed a universal virtual variable-speed compressor power (VVCP) sensor for VRF system based on 20-coefficient model. Compressor power can be obtained by the VVCP sensor using three input parameters (frequency, condensing temperature and evaporation temperature) which are measured by system itself. The performance of the proposed VVCP sensor is evaluated using experiments data. Experimental conditions include cooling, heating and non-standard refrigerant charge levels. The result shows that the mean square percentage errors (MSPE) are 9.9% under cooling conditions and 7.96% under heating conditions. The MSPE are 9.88% at steady state and 9.75% at dynamic state when the VVCP sensor is applied under nine different refrigerant charge levels. It demonstrated that the proposed VVCP sensor can obtain compressor power under cooling, heating and non-standard refrigerant charge levels, which could be applied to do operational monitoring and fault detection and diagnosis for VRF system at low cost. [ABSTRACT FROM AUTHOR]

Background: Transposable elements (TEs) are common constituents of centromeres. However, it is not known what causes this relationship. Schizosaccharomyces japonicus contains 10 families of Long Terminal Repeat (LTR)-retrotransposons and these elements cluster in centromeres and telomeres. In the related yeast, Schizosaccharomyces pombe LTR-retrotransposons Tf1 and Tf2 are distributed in the promoter regions of RNA pol II transcribed genes. Sequence analysis of TEs indicates that Tj1 of S. japonicus is related to Tf1 and Tf2, and uses the same mechanism of self-primed reverse transcription. Thus, we wondered why these related retrotransposons localized in different regions of the genome. Results: To characterize the integration behavior of Tj1 we expressed it in S. pombe. We found Tj1 was active and capable of generating de novo integration in the chromosomes of S. pombe. The expression of Tj1 is similar to Type C retroviruses in that a stop codon at the end of Gag must be present for efficient integration. 17 inserts were sequenced, 13 occurred within 12 bp upstream of tRNA genes and 3 occurred at other RNA pol III transcribed genes. The link between Tj1 integration and RNA pol III transcription is reminiscent of Ty3, an LTR-retrotransposon of Saccharomyces cerevisiae that interacts with TFIIIB and integrates upstream of tRNA genes. Conclusion: The integration of Tj1 upstream of tRNA genes and the centromeric clustering of tRNA genes in S. japonicus demonstrate that the clustering of this TE in centromere sequences is due to a unique pattern of integration. [ABSTRACT FROM AUTHOR]

Encapsulation systems have gained significant interest in designing innovative foods, as they allow for the protection and delivery of food ingredients that have health benefits but are unstable during processing, storage and in the upper gastrointestinal tract. Starch is widely available, cheap, biodegradable, edible, and easy to be modified, thus highly suitable for the development of encapsulants. Much efforts have been made to fabricate various types of porous starch and starch particles using different techniques (e.g. enzymatic hydrolysis, aggregation, emulsification, electrohydrodynamic process, supercritical fluid process, and post-processing drying). Such starch-based systems can load, protect, and deliver various food ingredients (e.g. fatty acids, phenolic compounds, carotenoids, flavors, essential oils, irons, vitamins, probiotics, bacteriocins, co-enzymes, and caffeine), exhibiting great potentials in developing foods with tailored flavor, nutrition, sensory properties, and shelf-life. This review surveys recent advances in different aspects of starch-based encapsulation systems including their forms, manufacturing techniques, and applications in foods. [Display omitted] • Recent progress in the types, fabrication and applications of starch-based encapsulation systems discussed • Starch modification improves amphiphilicity, film-forming and emulsifiability. • Starch can be fabricated into porous starch or particles for encapsulation. • Starch can encapsulate fatty acids, antioxidant, flavors, probiotics, etc. • The flavor, nutrition, sensory properties and shelf-life of foods improved [ABSTRACT FROM AUTHOR]

To effectively diagnose and monitor the vertical collusion in construction project bidding, this paper developed a comprehensive evaluation model with deep neural network and transfer learning. By this model, the collusion characteristics of bidders, tenderers, and bid evaluation experts were mined from limited data set hidden and collusion tendency was evaluated. Firstly, 18 evaluation indicators were established from literature review, court file summarization, typical case analysis, and expert consultation. Then, a comprehensive evaluation model was developed with the deep neural network and transfer learning. Finally, the model was trained and tested with the collected data set. The test results showed that the developed model achieved 87.3% identification accuracy in collusion tendency evaluation of different subjects. [ABSTRACT FROM AUTHOR]

• Deep reinforcement learning (DRL) based models are proposed for HVAC system energy consumption prediction. • Autoencoder (AE) algorithm is used to extract the high-level features of state space of DDPG. • The DDPG based models are applied for energy consumption forecasting over short-term time horizon. • Improvement in prediction performance is observed when combine AE with DDPG. • The DDPG based prediction models, outperform common supervised models like BP neural network and support vector machine. Short-term energy consumption prediction has fundamental importance in many HVAC system management tasks, such as demand-side management, short-term maintenance, etc. Currently, the prevailing data-driven techniques, especially supervised machine learning methods, are widely applied for short-term energy consumption prediction. Deep reinforcement learning (DRL), as the state-of-the-art machine learning techniques, have been applied for HVAC system control, but rarely for energy consumption prediction. In this paper, a DRL algorithm, namely Deep Deterministic Policy Gradient (DDPG), is firstly introduced for short-term HVAC system energy consumption prediction. Moreover, Autoencoder (AE), which is powerful in processing data in their raw form, is incorporated into DDPG method to extract the high-level features of state space and optimize the prediction model. The operation data of the ground source heat pump (GSHP) system of an office building in Henan province, China is used to train and assess the proposed models. The results demonstrate that the proposed DDPG based models can achieve better prediction performance than common supervised models like BP Neural Network and Support Vector Machine. This study is an enlightening work which may inspire other researchers to tap the potential of DRL algorithms in this field. [ABSTRACT FROM AUTHOR]

The tight sandstone gas reservoir is thin, highly heterogeneous, and its spatial distribution is very complex. Its development is faced with the problems of low gas reservoir drilling rate and poor fracturing effect. In order to overcome the difficulties in the development of horizontal wells in tight gas reservoirs, the real-time steering technology of seismic geological integration horizontal wells based on 3D seismic data of "two wide and one high" is proposed in this paper. The pilot test of 12 horizontal wells has been completed using the proposed technology. The average horizontal section length is 1755 meters, the sandstone length is 1506 meters, the sandstone drilling rate is 85.7%, the effective reservoir length is 1290 meters, the effective reservoir drilling rate is 74.5%, the coincidence rate of seismic prediction is 91.7%, the success rate of one shot is 100%, and the structural prediction error is 0.45 ‰. This technology can effectively improve the drilling rate and fracturing effect of horizontal wells in tight sandstone gas reservoirs, and has guiding significance for the development of horizontal wells in tight sandstone gas reservoirs. [ABSTRACT FROM AUTHOR]

Clothing can absorb fine particulate pollutants from the atmosphere outdoors and then release them after entering the room in a form similar to three-handed smoke. In this study, the experimental site in Zhengzhou, a typical city with heavy pollution in central China, was used to investigate the adsorption and diffusion patterns of three different fabrics of polyester, polyester-cotton, and cotton clothing for fine particle pollutants, to guide people's behavior patterns and indoor air distribution, and to reduce indoor pollution exposure. The results showed that when garments of three different fabrics were exposed to moderate and heavy PM2.5 pollution for 1 h, 2 h, and 3 h, respectively, and then transferred to the diffusion chamber, polyester always released the highest PM2.5 concentration to indoor air during the same release cycle, followed by cotton and polyester-cotton. In addition, the concentration of indoor air pollutants will be periodically affected by the diffusion of fine particulate adsorbed by clothing fabrics. With the increase in outdoor pollution and exposure duration, the indoor PM2.5 concentration takes longer to stabilize at certain levels after the clothing is transferred to the indoor side. Finally, the comparison of natural ventilation experiments proved that it is not feasible to rely solely on natural ventilation to improve the influence of clothing pollution sources on indoor air quality. [ABSTRACT FROM AUTHOR]

Energy consumption models play an integral part in energy management and conservation, as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying out building commissioning, and in identifying and diagnosing building system faults. This review takes an in-depth look at energy-demand prediction models for buildings in that it delves into recent developments in building energy approaches used to predict energy usage. By enlisting current approaches to the modelling of buildings, methods for building energy simulations can be categorized into four level classes as follows: (i) data-driven approaches; (ii) physics-based approaches; (iii) large scale building energy forecasting approaches; and (iv) hybrid approaches. The focus of this review is to target the data-driven approach and large-scale building energy predicting-based approaches. Here the data driven approaches can be categorized by (1) artificial neural network based approaches; (2) clustering based approaches; (3) statistical and machine learning-based approaches; and (4) support vector machine based approaches. From there, the type of data-driven based approach is further grouped by (a) benchmarking models; (b) energy-mapping models; (c) energy forecasting models; and (d) energy profiling models. Large-scale building-energy prediction techniques is then categorized as follows: (1) white-box based approaches; (2) black-box based approaches, and (3) grey-box based approaches. The current study explores first-rate data-driven based approaches about building energy analysis for industrial, commercial, domestic, etc., within a rural and urban setting. This review paper is based on the necessity of identifying points of departure and research opportunities for urban and rural-level analyses of building level energy performance. A variety of issues are explored which include: energy performance metrics; end-use of different building types; multiple levels of granularity; and urban and rural scales. Each technique encompasses a variety of input information as well as varying calculations or simulation models along with furnishing contrasting outcomes that suggest a variety of usages. A thorough review of each technique is presented in this study. This review highlights strengths, shortcomings, and purpose of the methods of numerous data-mining based approaches. A comprehensive review of energy forecasting models that are specified in the literature part is also provided. [ABSTRACT FROM AUTHOR]

The biological impact of transposons on the physiology of the host depends greatly on the frequency and position of integration. Previous studies of Tf1, a long terminal repeat retrotransposon in Schizosaccharomyces pombe, showed that integration occurs at the promoters of RNA polymerase II (Pol II) transcribed genes. To determine whether specific promoters are preferred targets of integration, we sequenced large numbers of insertions using high-throughput pyrosequencing. In four independent experiments we identified a total of 73,125 independent integration events. These data provided strong support for the conclusion that Pol II promoters are the targets of Tf1 integration. The size and number of the integration experiments resulted in reproducible measures of integration for each intergenic region and ORF in the S. pombe genome. The reproducibility of the integration activity from experiment to experiment demonstrates that we have saturated the full set of insertion sites that are actively targeted by Tf1. We found Tf1 integration was highly biased in favor of a specific set of Pol II promoters. The overwhelming majority (76%) of the insertions were distributed in intergenic sequences that contained 31% of the promoters of S. pombe. Interestingly, there was no correlation between the amount of integration at these promoters and their level of transcription. Instead, we found Tf1 had a strong preference for promoters that are induced by conditions of stress. This targeting of stress response genes coupled with the ability of Tf1 to regulate the expression of adjacent genes suggests Tf1 may improve the survival of S. pombe when cells are exposed to environmental stress. [ABSTRACT FROM AUTHOR]

[Display omitted] To further investigate the production and diffusion laws of tunnel blasting dust, this study utilizes a railway tunnel for field measurements and numerical simulations. On-site dust levels are monitored every 10 s using a dust meter. A one-dimensional uniform turbulent diffusion theory is applied to formulate a relationship between dust concentration and space–time variables. The Origin software is employed to fit the on-site dust concentration data. The mass of the tunnel blasting dust at the measurement point is determined using the fitting formula. Characteristics of the blasting dust from the tunnel's surrounding rock are obtained from the on-site analysis of particle size and composition. Employing on-site dust monitoring data and dust characteristics, a three-stage(shock wave generation, pre-ventilation, and post-ventilation) numerical simulation is conducted. On-site monitoring of dust concentrations revealed a characteristic 'M'-shaped temporal profile at the measurement point, and the peak dust concentration reached approximately 2100 mg/m3. Numerical inversion analysis of the three-stage dust diffusion revealed the total dust mass during tunnel blasting to be approximately 178.1 kg. Numerical simulations further indicated that particles measuring below 10 μm were predominantly dispersed throughout the tunnel after 1800 s. This investigation offers a methodology for calculating the total mass of tunnel blasting dust and for its numerical simulation, providing a data reference for dust mitigation strategies. [ABSTRACT FROM AUTHOR]

Background: Long noncoding RNAs (lncRNAs) play important roles in many physiological and pathological processes, this indicates that lncRNAs can serve as potential targets for gene therapy. Stable expression is a fundamental technology in the study of lncRNAs. The lentivirus is one of the most widely used delivery systems for stable expression. However, it was initially designed for mRNAs, and the applicability of lentiviral vectors for lncRNAs is largely unknown. Results: We found that the lentiviral vector produces lncRNAs with improper termination, appending an extra fragment of ~ 2 kb to the 3ʹ-end. Consequently, the secondary structures were changed, the RNA–protein interactions were blocked, and the functions were impaired in certain lncRNAs, which indicated that lentiviral vectors are not ideal delivery systems of lncRNAs. Here, we developed a novel lncRNA delivery method called the Expression of LncRNAs with Endogenous Characteristics using the Transposon System (ELECTS). By inserting a termination signal after the lncRNA sequence, ELECTS produces transcripts without 3ʹ-flanking sequences and retains the native features and function of lncRNAs, which cannot be achieved by lentiviral vectors. Moreover, ELECTS presents no potential risk of infection for the operators and it takes much less time. ELECTS provides a reliable, convenient, safe, and efficient delivery method for stable expression of lncRNAs. Conclusions: Our study demonstrated that improper transcriptional termination from lentiviral vectors have fundamental effects on molecular action and cellular function of lncRNAs. The ELECTS system developed in this study will provide a convenient and reliable method for the lncRNA study. [ABSTRACT FROM AUTHOR]

Beta satellite DNA (satDNA), also known as Sau3A sequences, are repetitive DNA sequences reported in human and primate genomes. It is previously thought that beta satDNAs originated in old world monkeys and bursted in great apes. In this study, we searched 7821 genome assemblies of 3767 eukaryotic species and found that beta satDNAs are widely distributed across eukaryotes. The four major branches of eukaryotes, animals, fungi, plants and Harosa/SAR, all have multiple clades containing beta satDNAs. These results were also confirmed by searching whole genome sequencing data (SRA) and PCR assay. Beta satDNA sequences were found in all the primate clades, as well as in Dermoptera and Scandentia, indicating that the beta satDNAs in primates might originate in the common ancestor of Primatomorpha or Euarchonta. In contrast, the widely patchy distribution of beta satDNAs across eukaryotes presents a typical scenario of multiple horizontal transfers. • Beta satellite DNAs compose >0.1% of the human genome. • Beta satDNAs were found in all the clades of Primatomorpha. The origin of beta satDNA in primates can be traced back to ~80 MYA. • Beta satDNAs were found widely existing across eukaryotes, instead of exclusively existing in primates. • The patchy distribution of beta satDNAs in eukaryotes and even in certain prokaryotes indicates multiple horizontal transfer events. [ABSTRACT FROM AUTHOR]

Highlights • A new charge fault diagnosis method for VRF system. • The temperature difference variable is introduced to improve the diagnosis effect. • Especially increases the distinction between normal and excess charge fault. Abstract VRF (Variable Refrigerant Flow) system is an efficient and energy-saving air-conditioning system. The VRF system performance is significantly affected by the quantity of refrigerant (charge). Both insufficient and excessive refrigerant charge will lead a dramatic decline of air conditioning system performance. Accordingly, it is necessary to forecast refrigerant charge in existing systems. This study proposed a charge fault diagnosis method based on expert modification C5.0 decision tree. Initially, C5.0 decision tree is used to diagnosis VRF system charge faults in nine different conditions. Furthermore, in order to reduce error rate of the decision tree, expert experience is used to modify the decision tree. And temperature differential variables are used to improve the diagnosis result. The result s of the C5.0 decision tree using temperature differential variables shows fault diagnosis error rates under expert experience guidance are 10% lower than without expert experience. Besides, this method an increased accuracy in classifies between excessive and normal charge states. [ABSTRACT FROM AUTHOR]

This review paper focuses on the various modeling practices for the identification and apprehension of non-technical losses. The modeling practices are extremely vital to develop, upsurge energy performance, examine and foresee the performance of power transmission & distribution of the electrical system. The data mining based models are innovative and have the subsistence to examine an enormous potential of energy consumption records and performing area profile for preparing housing zone directing the electricity effective living. In this concern, support vector machine model, which classifies illegal customers is a form of advanced mix evolutionary neural network model. Optimum-path forest clustering process is activated to recognize legitimate and irregular profiles of an industry as well as commercial customers to find out theft of electricity. Real time state estimation technique determines a state approximation method in the actual stage for every conversion (transformation) point. Aforementioned allows us to regulate the parts to the maximum extent of non-technical losses through the radial distribution method. The support vector machine with genetic algorithm advances a hybrid method for the non-technical loss investigation and provide an automated assistance to dominate the electricity theft. This model is simplified version of support vector machine. Decision tree and Bayesian regularization networks are appropriated to identify the several kinds of patterns of losses in the electrical system. These practices have been accompanied concerning testing and validation for power system losses in the experimental laboratory. It operates in an influence tool intended to expedite the investigators and scientists. It assists short of spending a massive amount of money, time and energy in experimental events. Prior fabrication modeling methods are remarkably significant in replication of diverse kinds of solar electrical systems. Accordingly, this study concentrates on the base of modeling methods not only saves time but, also preserves the monetary investment in the electrical system. The benefit and imminent opportunity of modeling practices are also conferred in the review paper. [ABSTRACT FROM AUTHOR]

An intelligent fault diagnosis network for variable refrigerant flow air conditioning system is proposed in this study. The network is developed under the foundation of bayesian belief network theory, which comprises two main elements: the structure and parameters. The structure obtained by machine learning and experts’ experiences illustrates the relationships among faults and physical variables from the qualitative prospective, and its parameters (including prior probability distribution and conditional distribution) describe the uncertainty between them quantitatively. Once the structure and parameters are determined, the posterior probability distribution which can be used to complete fault diagnosis and isolation will be calculated by some algorithms. In comparison with other fault diagnosis approaches, the proposed approach can make full use of performance information. Moreover, it is more reasonable and precise to express the relationship between faults and variables rather than Boolean variables. Evaluation was conducted on a variable refrigerant flow air conditioning system, which demonstrated that this strategy is effective and efficient. [ABSTRACT FROM AUTHOR]

Analysis and application for real-time operational data of buildings is important for energy management. But original data inevitably contains a number of outliers and which usually lead to a significant negative impact on performance of data-based models. In order to eliminate the influence of outliers and improve the robustness of data-based models, this study employs three methods (Boxplot, local outlier factor (LOF) and PCOut) to identify the potential outlying observations in original data set. For purpose of evaluating the outlier detection performance of these three methods, four SVR-based electricity consumption prediction models, Original-SVR, BOX-SVR, LOF-SVR and PCO-SVR, are established. And the performance indexes (RE, RMSE and RSE) of the models are compared and analyzed. The results show that the accuracy of electricity consumption prediction is improved with the help of Boxplot and LOF methods for outlier detection, but PCOut method reduces the accuracy compared with the Original-SVR model. Further study indicates that these observations which repeatedly identified as outlying by Boxplot and LOF methods are the most likely to be abnormal, and when these samples are removed from training data set, the RMSE falls to 2.76 from 6.44 and the RSE falls to 0.11 from 0.58 during testing course. [ABSTRACT FROM AUTHOR]

The outdoor air-side fouling fault is almost inevitable for the practical variable refrigerant flow system. Fouling fault grows gradually and naturally causing increasingly energy penalty and performance degradation to the system. Implementing a proper and reliable fault detection and diagnosis strategy is crucial for practical systems keeping away from fouling fault and maintaining optimal operations. However, traditional model-based and data-driven methods cannot work in practical systems due to the lack of critical sensors and interpretation for model reliability, respectively. Therefore, this study proposes a data mining approach to identify and isolate fouling faults using only built-in sensors. Density-based spatial clustering of applications with noise(DBSCAN) is used for data pre-processing. The classification and regression tree(CART)-based classifier is employed for fault detection. Based on Pearson’s correlation analysis, a multiple linear regression(MLR)-based fault indicator is developed for fault isolation. A case study on a 29.8 kW cooling capacity R410A variable refrigerant flow system is used to validate the proposed strategy. Four levels of foulings are experimentally investigated under three cooling conditions. Results reveal that the proposed strategy correctly identifies 98% of fouling data using only three built-in sensor measurements. It also isolate fouling data from both normal and refrigerant charge fault data. [ABSTRACT FROM AUTHOR]

Plant type [2Fe-2S] ferredoxins function primarily as electron transfer proteins in photosynthesis. Studying the unfolding-folding of ferredoxins in vitro is challenging, because the unfolding of ferredoxin is often irreversible due to the loss or disintegration of the iron-sulfur cluster. Additionally, the in vivo folding of holo-ferredoxin requires ferredoxin biogenesis proteins. Here, we employed atomic force microscopy-based single-molecule force microscopy and protein engineering techniques to directly study the mechanical unfolding and refolding of a plant type [2Fe-2S] ferredoxin from cyanobacteria Anabaena. Our results indicate that upon stretching, ferredoxin unfolds in a three-state mechanism. The first step is the unfolding of the protein sequence that is outside and not sequestered by the [2Fe-2S] center, and the second one relates to the force-induced rupture of the [2Fe-2S] metal center and subsequent unraveling of the protein structure shielded by the [2Fe-2S] center. During repeated stretching and relaxation of a single polyprotein, we observed that the completely unfolded ferredoxin can refold to its native holo-form with a fully reconstituted [2Fe-2S] center. These results demonstrate that the unfolding-refolding of individual ferredoxin is reversible at the single-molecule level, enabling new avenues of studying both folding-unfolding mechanisms, as well as the reactivity of the metal center of metalloproteins in vitro. [ABSTRACT FROM AUTHOR]

Variable refrigerant flow systems account for a considerable portion of energy consumption in buildings. In order to improve the energy efficiency and estimate the energy saving potentials of variable refrigerant flow systems this study proposes a data mining based method to identify and interpret the power consumption patterns and associations. Two descriptive data mining algorithms, clustering analysis and association rules mining, are used for data partitioning and association mining. The proposed method consists of four phases: data pre-processing, data partitioning, data association mining and knowledge interpretation. Experimental data collected from a tested variable refrigerant flow system in the standard psychrometer testing room are pre-processed and prepared to examine the proposed method. Three time independent influential factors: part load ratio, refrigerant charge level and cooling condition are analyzed. Results show that the method is able to help identify energy consumption patterns and extract energy consumption rules in variable refrigerant flow systems. Three distinct energy consumption patterns are identified: undercharge fault, low and high part load ratio conditions. For compressor operation frequency switch control and refrigerant undercharge patterns, the energy saving potentials could be estimated by making comparisons between energy patterns and rules in a top-down way. [ABSTRACT FROM AUTHOR]

This paper presents an entire sensor fault detection and diagnosis (FDD) for screw chiller system using the support vector data description (SVDD) algorithm. It has advantages of solving problems on describing non-linear and non-Gaussian distributed data. A distance-based D -statistic plot is employed to detect sensor faults. Based on the distance transformation in mathematical way, a new distance variation-based DV -contribution plot is proposed to diagnose the sensor fault. The screw chiller field measured data is utilized to train the SVDD model via a hybrid parameter tuning approach combined the grid search and the 10-fold cross validation. Six typical sensor faults are introduced for validation, i.e. positive and negative biases, positive and negative drifts, precision degradation and complete failure. Test results of both D -statistic and DV -contribution plots show that the SVDD-based method has good FDD results for the six sensor faults. Furthermore, the proposed DV -contribution plot shows more accurate fault diagnosis results compared with the principal component analysis (PCA)-based Q -contribution plot. [ABSTRACT FROM AUTHOR]

The original article [1] contained an error whereby author Dong Yin's name was mistakenly inverted. This error has now been corrected. [ABSTRACT FROM AUTHOR]

Background: Similar to retro−/lenti- virus system, DNA transposons are useful tools for stable expression of exogenous genes in mammalian cells. Sleeping Beauty (SB) transposon has adopted for integrating genes into host genomes in recent studies. However, SB-derived vector system for proteins purifying/tracking and gene knockout are still not available. Results: In this study, we generated a series of vectors (termed as pSB vectors) containing Sleeping Beauty IRDR-L/R that can be transposed by SB transposase. Gateway cassette was combined to the pSB vectors to facilitate the cloning. Vectors with various tags, Flag, Myc, HA, V5 and SFB, were generated for multiple options. Moreover, we incorporated the CRISPR-Cas9 cassette into the pSB plasmids for gene knockout. Indeed, using one of these vectors (pSB-SFB-GFP), we performed Tandem Affinity Purification and identified that NFATc1 is a novel binding partner of FBW7. We also knocked out RCC2 and BRD7 using pSB-CRISPR vector respectively, and revealed the novel roles of these two proteins in mitosis. Conclusion: Our study demonstrated that the pSB series vectors are convenient and powerful tools for gene overexpression and knockout in mammalian cells, providing a new alternative approach for molecular cell biology research. [ABSTRACT FROM AUTHOR]