PC Components Research Articles

Environmental Impact Minimization Model for Storage Yard of In-Situ Produced PC Components: Comparison of Dung Beetle Algorithm and Improved Dung Beetle Algorithm

If PC components are produced on site under the same conditions, the quality can be secured at least equal to that of factory production. In-situ production can reduce environmental loads by 14.58% or more than factory production, and if the number of PC components produced in-situ is increased, the cost can be reduced by up to 39.4% compared to factory production. Most of the existing studies focus on optimizing the layout of logistics centers, and relatively little attention is paid to the layout of PC parts for in-situ production. PC component yard layout planning for in-situ production can effectively reduce carbon dioxide emissions and improve construction efficiency. Therefore, the purpose of this study is to develop an environmental impact minimization model for in-situ production of PC components. As a result of applying the developed model, the optimization of the improved dung beetle optimization algorithm was verified to be efficient by improving the neighboring correlation by 22.79% and reducing carbon dioxide emissions by 18.33% compared to the dung beetle optimization algorithm. The proposed environmental impact minimization model can support the construction, reconstruction, and functional upgrade of logistics centers, contributing to low carbon dioxide in the logistics industry.

The effects of different processing treatments to active ingredients, nutrients, volatile components and antioxidant activity in Poria cocos wolf

The study compared the triterpenoid ingredients, polysaccharide, protein, amino acid, and volatile components of PC. The PC was treated with traditional sweating (TST), steaming sweating for 1 to 4 h (SS1h-SS4h, Δ = 1 h) and non-sweating (NS). The results showed the content of six triterpenoids (1.03 mg/g), water-soluble protein (0.33 mg/g) and total triterpenoid (3.81 mg/g) were the highest in NS; the content of alkali-soluble polysaccharide (51.96 mg/g) was the highest in SS1h; the content of crude protein (16.18 mg/g) and total amino acid (53.07 mg/g) were highest in TST. Moreover, the predominant volatile component of SS1h was hexanal, SS2h-SS4h and TST were Linalool, NS was D-limonene. Ulteriorly, the correlation analysis showed antioxidant effect of PC may be related to the content of alkali-soluble polysaccharide, Furan-2-pentyl, and 2-Octenal, PCA result indicated the score of SS1h-treated PC was the highest. This study could provide a new solution for the industrial production of PC.

6425 Impact Of Adrenalectomy On Mood, Sleep, And Quality Of Life In Patients With Mild Autonomous Cortisol Secretion: A Prospective Longitudinal Study

Abstract Disclosure: J. Saini: None. V. Fell: None. B. Atkinson: None. S. Achenbach: None. I. Bancos: None. Background: Patients with mild autonomous cortisol secretion (MACS) have an impaired quality of life and a higher prevalence of sleep and mood disturbances as compared to patients with nonfunctioning adrenal adenomas. The impact of adrenalectomy on quality of life, mood, and sleep is unclear. Objective: To determine the impact of adrenalectomy on quality of life, sleep, and mood in patients with MACS Methods: We conducted a single-center prospective longitudinal study of adults with MACS scheduled for adrenalectomy and referent subjects evaluated between 2018 and 2023. MACS was defined as serum cortisol>1.8 mcg/dL after 1 mg dexamethasone suppression test in patients with adrenal adenomas and no features of overt hypercortisolism. Referent subjects were included if they had abdominal imaging and had no adrenal disorders. Patients and referent subjects had a baseline and a follow-up evaluation at least 10 months later. Differences were assessed using paired t-tests for (1) Quality of life using Short Form-36 (SF 36) survey (Physical and Mental component summary score), sex and age-adjusted (2) Mood using Beck’s Depression Inventory (BDI) (3) Sleep using Pittsburgh Sleep Quality Index (PSQI). Results: We evaluated 41 patients before and after adrenalectomy, and 31 referent subjects at baseline and follow-up visit, at a similar interval (median 1.6, IQR 1.2-2.0 vs 1.5 years, IQR 1.0-2.2, P=0.759). While sex distribution was similar between the groups (66% vs 84% women, P=0.086), patients with MACS were older than the referent group (median age 57.3, IQR 48.8-66.9 vs 49.0, IQR 41.7-57.9 years, P=0.005). After adrenalectomy, patients improved their mood with a mean decrease of -4.71 (SD: 9.11), P=0.002 on the BDI questionnaire. Patients also demonstrated improvement in the SF-36 mental component score by a mean of 6.48 (SD: 10.97), P<0.001. While the overall SF36 physical component score did not improve after adrenalectomy (1.953 (SD: 11.02), P=0.269), one of the PCS components, role-physical limitation, increased by a mean of 6.25 (SD: 13.03). Patients reported better sleep with PSQI score decreasing by a mean of -2.59 (SD: 3.64), P<0.001. No differences in BDI, SF36, and PSQI were observed between baseline and follow-up in referent subjects. Conclusions: After adrenalectomy, patients with MACS showed an improvement in sleep, mood, and mental component of quality of life. Presentation: 6/3/2024

The capacitated location problem of precast concrete component factory

The location and capacity of precast concrete component factories (PC component factories) are not only the key factors for manufacturers to gain competitive advantage, but also the important factors affecting the operational efficiency of the prefabricated construction supply chain. This paper takes the capacitated location problem of PC component factories as the research object. Drawing on the model of traditional capacitated plant location problem, the model of capacitated location problem of PC component factories is constructed by setting the optional production scale by stages. According to the characteristics of this model, the optimal strategy of location is determined by using the Tabu search algorithm. Taking the location problem of PC component factory in the Beijing-Tianjin-Hebei region as the object, the calculation example is designed, in which the influence of the distance parameters on the results of location problem is analyzed. The results can make the configuration of regional PC component factories more reasonable and balanced.

Network pharmacology-based identification of bioavailable anti-inflammatory agents from Psoralea corylifolia L. in an experimental colitis model

Ethnopharmacological relevanceIn traditional oriental medicine, the dried seeds of Psoralea corylifolia L. (PC) have been used to treat various diseases, including gastrointestinal, urinary, orthopedic, diarrheal, ulcer, and inflammatory disorders. Aim of the studyAlthough its various biological properties are well-known, there is no information on the therapeutic effects and bioavailable components of PC against inflammatory bowel disease. Therefore, we focused on the relationship between hydroethanolic extract of PC (EPC) that ameliorates colitis in mice and bioactive constituents of EPC that suppress pro-inflammatory cytokines in macrophages. Materials and methodsWe investigated the therapeutic effects of EPC in a dextran sulfate sodium-induced colitis mouse model and identified the orally absorbed components of EPC using UPLC-MS/MS analysis. In addition, we evaluated and validated the mechanism of action of the bioavailable constituents of EPC using network pharmacology analysis. The effects on nitric oxide (NO) and inflammatory cytokines were measured by Griess reagent and enzyme linked immunosorbent assay in lipopolysaccharide (LPS)-induced macrophages. ResultsIn experimental colitis, EPC improved body weight loss, colon length shortening, and disease activity index. Moreover, EPC reduced the serum levels of pro-inflammatory cytokines and histopathological damage to the colon. Network pharmacological analysis identified 13 phytochemicals that were bioavailable following oral administration of EPC, as well as their potential anti-inflammatory effects. 11 identified EPC constituents markedly reduced the overproduction of NO, tumor necrosis factor-α, and/or interleukin-6 in macrophages induced by LPS. The LPS-induced expression of the nuclear factor kappa-light-chain-enhancer of activated B cells reporter gene was reduced by the 4 EPC constituents. ConclusionsThe results indicate that the protective activity of EPC against colitis is a result of the additive effects of each constituent on the expression of inflammatory cytokines. Therefore, it suggests that 11 bioavailable phytochemicals of EPC could aid in the management of intestinal inflammation, and also provides useful insights into the clinical application of PC for the treatment of inflammatory bowel diseases.

Novel Hyperspectral Analysis of Thin-Layer Chromatographic Plates-An Application to Fingerprinting of 70 Polish Grasses.

The advantages of hyperspectral imaging in videodensitometry are presented and discussed with the example of extracts from 70 Polish grasses. An inexpensive microscope camera was modified to cover the infrared spectrum range, and then 11 combinations of illumination (254 nm, 366 nm, white light), together with various filters (no filter, IRCut, UV, cobalt glass, IR pass), were used to register RGB HDR images of the same plate. It was revealed that the resulting 33 channels of information could be compressed into 5-6 principal components and then visualized separately as grayscale images. We also propose a new approach called principal component artificial coloring of images (PCACI). It allows easy classification of chromatographic spots by presenting three PC components as RGB channels, providing vivid spots with artificial colors and visualizing six principal components on two color images. The infrared region brings additional information to the registered data, orthogonal to the other channels and not redundant with photos in the visible region. This is the first published attempt to use a hyperspectral camera in TLC and it can be clearly concluded that such an approach deserves routine use and further attention.

The Detection of Hot Molecular Cores in the Small Magellanic Cloud

We report the first detection of hot molecular cores in the Small Magellanic Cloud (SMC), a nearby dwarf galaxy with 0.2 solar metallicity. We observed two high-mass young stellar objects in the SMC with the Atacama Large Millimeter/submillimeter Array and detected emission lines of CO, HCO+, H13CO+, SiO, H2CO, CH3OH, SO, and SO2. Compact hot-core regions are traced by SO2, whose spatial extent is about 0.1 pc, and the gas temperature is higher than 100 K based on the rotation diagram analysis. In contrast, CH3OH, a classical hot-core tracer, is dominated by extended (∼0.2–0.3 pc) components in both sources, and the gas temperature is estimated to be K for one source. Protostellar outflows are also detected from both sources as high-velocity components of CO. The metallicity-scaled abundances of SO2 in hot cores are comparable among the SMC, Large Magellanic Cloud (LMC), and Galactic sources, suggesting that the chemical reactions leading to SO2 formation would be regulated by elemental abundances. On the other hand, CH3OH shows a large abundance variation within SMC and LMC hot cores. The diversity in the initial condition of star formation (e.g., degree of shielding, local radiation field strength) may lead to the large abundance variation of organic molecules in hot cores. This work, in conjunction with previous hot-core studies in the LMC and outer/inner Galaxy, suggests that the formation of a hot core would be a common phenomenon during high-mass star formation across the metallicity range of 0.2–1 Z⊙. High-excitation SO2 lines will be a useful hot-core tracer in the low-metallicity environments of the SMC and LMC.

Nice PC Maker: An Online Interface to build Custom PCs

A custom-built PC is assembled to cater a specific user needs. Internationally, there are many e-stores available for users which offer them to build and order their systems as per their needs. In, 2020, Pakistan observed a covid-19 lockdown and mostly international shipments are cancelled due to the pandemic. This real world situation highlighted the deficiency of local market to fulfill the demands of custom-built PC users. In this paper, we propose and design a generic architecture of an e-store (Nice PC Maker (NPM)) to facilitate users with custom-build PC option along with other added features such as pre-build system purchasing and PC components purchasing. WooCommerce product builders (wordpress) are used to implement the architectural design. We also configured this generic architecture for a local client which helpes them to increase their business and facilitates users to get their desired systems.

Modeling the occupational health risk of workers caused by environmental release during the production of PC components.

Prefabricated construction is one of the solutions to the problem of balancing environmental improvements with the new buildings in the construction industry. Some work originally done on site is transferred to the front end, and the occupational health risks to industrial workers during the production of prefabricate concrete components are thus aggravated. This study aims to propose a framework to simulate the occupational health risks of workers in prefabricate concrete component plants from the perspective of risk identification, risk assessment, and risk control. Through the following 4 steps, including environmental release monitoring, diffusion and human inhalation mechanism analysis, occupational health risk evaluation, and full-path health risk simulation, this study maps physical entities to virtual reality. The proposed method tends to address the root causes behind occupational health risks, such as the lack of measurement, assessment and prevention criteria, and providing new ideas for theoretical research and innovative practice of HSE management and risk management in the construction industry.

Investigation of humans individual differences as predictors of their animal interaction styles, focused on the domestic cat

Humans’ individual differences including their demographics, personality, attitudes and experiences are often associated with important outcomes for the animals they interact with. This is pertinent to companion animals such as cats and dogs, given their social and emotional importance to humans and degree of integration into human society. However, the mechanistic underpinnings and causal relationships that characterise links between human individual differences and companion animal behaviour and wellbeing are not well understood. In this exploratory investigation, we firstly quantified the underlying structure of, and variation in, human’s styles of behaviour during typical human-cat interactions (HCI), focusing on aspects of handling and interaction known to be preferred by cats (i.e. ‘best practice’), and their variation. We then explored the potential significance of various human individual differences as predictors of these HCI styles. Seven separate HCI styles were identified via Principal Component Analysis (PCA) from averaged observations for 119 participants, interacting with sociable domestic cats within a rehoming context. Using General Linear Models (GLMs) and an Information Theoretic (IT) approach, we found these HCI PC components were weakly to strongly predicted by factors including cat-ownership history, participant personality (measured via the Big Five Inventory, or BFI), age, work experience with animals and participants’ subjective ratings of their cat behaviour knowledge. Paradoxically, greater cat ownership experiences and self-assessed cat knowledge were not positively associated with ‘best practice’ styles of HCI, but were instead generally predictive of HCI styles known to be less preferred by cats, as was greater participant age and Neuroticism. These findings have important implications regarding the quality of human-companion animal relationships and dyadic compatibility, in addition to the role of educational interventions and their targeting for optimal efficacy. In the context of animal adoption, these results strengthen the (limited) evidence base for decision making associated with cat-adopter screening and matching. In particular, our results suggest that greater cat ownership experiences and self-reports of cat knowledge might not necessarily convey advantages for cats in the context of HCI.

Analysis on Construction Technology of PC Component Assembled Building

Analysis on Construction Technology of PC Component Assembled Building

Comparisons of Type Ia Supernova Light Curves in the UV and Optical with the Swift Ultra-violet/Optical Telescope

We examine the light-curve parameters of 97 nearby Type Ia supernovae (SN Ia) in the ultraviolet and optical using observations from the Swift Ultra-violet/Optical Telescope. Our light-curve models used linear combinations of templates, which were based on functional principal component analysis of the optical photometry of SN Ia. The weights for each principal component used in the fit capture certain aspects of the light-curve properties. We find that there is little difference in the overall variability of principal component scores across filters. We also find correlations between certain filters and PC components, indicating that the UV and optical properties seem to be tied together. This is a promising step in UV SN Ia analysis and suggests that UV light curves may be used to infer optical properties, paving the way for future cosmological studies.

The 60th Anniversary of TFTs and the Evolution of High‐Resolution Displays

The 60th Anniversary of TFTs and the Evolution of High‐Resolution Displays

Calculation Model for the Selection of Physical Dimensions of a New Type of Heating Curing Building

The spatial pattern of the new type of heating curing building directly affects the energy consumption of the PC component and the passive utilisation efficiency of solar energy. Based on the thermal design theory and building energy consumption simulation analysis method, this study constructed the heating-curing energy consumption model and determined the evaluation index of building space shape parameters. Based on the energy consumption model and Energy Plus simulation software, the calculation case—the best spatial shape characteristic parameters of the new-type heating and curing building in the Shijiazhuang area was given. Compared with the traditional steam warming curing, the carbon emission per unit building area was reduced by 33.4% each year, and the carbon emission of one ton of PC component was reduced by 0.97t.

Analysis of the effect of the Chinese herbal pair, Semen Persicae–Carthami Flos, on psoriasis using network pharmacology


 
 
 
 Purpose: To investigate the underlying effect of the Chinese herbal pair, Semen Persicae–Carthami Flos (PC), on psoriasis using network pharmacology.
 Method: The core components and targets of the Chinese herbal pair were screened using TCMSP and BATMAN databases. Psoriasis disease targets were searched in OMIM and GeneCards databases. The common targets of the herbal pair and psoriasis were identified using a Venn diagram. The component–psoriasis-target network map was constructed using Cytoscape software. Protein interaction analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed on STRING website, R Project, and DAVID 6.8 database. Molecular docking was performed using AutoDock Vina software to predict the binding of herbal pair PC to psoriasis targets.
 Results: A total of 45 components and 105 potential targets of PC, as well as 3729 psoriasis-related genes, were screened from the databases. Venn diagram shows that there were 62 common targets of PC and psoriasis. These common targets involved biological processes such as DNA transcription factor receptor binding, cytokine receptor binding, and regulation of molecular function. The targets also involved apoptosis and the advanced glycation end-products (AGE)-receptor for AGEs (RAGE), tumor necrosis factor, and phosphatidylinositol 3-kinase/protein kinase B signaling pathways. In addition, the main active components of PC spontaneously bound to the targets.
 Conclusion: The Chinese herbal pair PC provides an alternative clinical therapeutic strategy for patients with psoriasis.
 
 
 

Using Supervised Machine Learning to Predict the Status of Road Signs

There is no data collected and saved about road signs in Sweden and the status for these signs is unknown. Furthermore, the status of the sign colors, the quality of the sign, the type of the retroreflection material, and age of the road signs are unknown. Therefore, it is difficult to know the status (approved or not) of any road sign without performing a costly inspection. The aim of this study is to predict the status of the road signs mounted on the Swedish roads by using supervised machine learning. This study investigates the effect of using principal component analysis (PCA) and data scaling on the accuracy of the prediction. The data were prepared before using then scaled using two methods which are the normalization and the standardization. The three algorithms that tested in this study are Random Forest, Artificial Neural Network (ANN), and Support Vector Machines (SVM). They are invoked to predict the status of the road signs. The algorithms exhibited overall high predicting accuracy (98%), high precision (98%), high recall (98%), and high F1 scores (98%). Random forest showed the best performance with 4 PC components on the normalized data with a highest accuracy of 98%. Using PCA showed different impacts on the performance of different techniques. In the case of ANN, invoking PCA improves the accuracy, while for SVM the accuracy decreases when PCA is used. On other hand, PCA has no effect on the accuracy of the random forest model when scaling is invoked. The effect of the data scaling using normalization and standardization is also investigated in this study, and it is noticed that scaling of the data increases the accuracy of the prediction for all the three models (ANN, SVM and Random Forest). Furthermore, better accuracy is achieved when the standardization is invoked compared with normalization.

Supervised and Unsupervised Clustering Based Dimensionality Reduction of Hyperspectral Data

Nowadays, hyperspectral images (HIs) are widely used for land cover land use (LCLU) mapping. Hyperspectral sensors collect spectral data in numerous adjacent spectral bands, which are usually redundant. Hyperspectral data processing comes with important challenges such as huge processing time, difficulties in transfer, and storage. In this study, two supervised and unsupervised dimensionality reduction methods are proposed for hyperspectral feature extraction based on the band clustering technique. In the first method, the unsupervised method, after the unsupervised band clustering stage with some statistical attributes, the principal component transform is used in each cluster, and the first PC component is considered an extracted feature. In the second method, the supervised method, bands are clustered based on training samples mean vectors of each class, and the weighted mean operator is used for feature extraction in each cluster. The experiment is conducted on the classification of real famous HI named Indian Pines. Comparing the obtained results and some other state of art methods proved the proposed method's efficiency.

Security Vulnerabilities in Microprocessors

Microprocessors such as those found in PCs and smartphones are complex in their design and nature. In recent years, an increasing number of security vulnerabilities have been found within these microprocessors that can leak sensitive user data and information. This report will investigate microarchitecture vulnerabilities focusing on the Spectre and Meltdown exploits and will look at what they do, how they do it and, the real-world impact these vulnerabilities can cause. Additionally, there will be an introduction to the basic concepts of how several PC components operate to support this.

Soybean-derived phospholipids complexed poly (lactic-co-glycolic acid) nanofibrous scaffolds for tissue engineering applications

Polymeric scaffolds play a vital role in tissue engineering. Electrospun poly (lactic-co-glycolic acid) (PLGA) membranes can be a promising scaffold. However, their applications are restricted due to poor hydrophilicity and single function. In this study, we show for the first time how blending of L-α-phosphatidylcholine (PC) to PLGA to yield hybrid scaffolds alters the physical properties and wettability and consequently affects the biodegradability and biocompatibility. Soybean-derived phospholipids complexed PLGA nanofibrous scaffolds (PLGA-PC) were readily fabricated by electrospinning polymer solutions of PLGA and PC in different ratios. The obtained scaffolds exhibited a dramatic decrease in fibre diameter and a significant increase in porosity and hydrophilicity as the PC component was added. Although the mechanical strength of PLGA scaffolds decreased due to the incorporation of PC, the PLGA-PC groups showed tensile strength above 2.5 MPa in both dry and wet states. Based on in vitro degradation, we found that it was accelerating for PLGA-PC scaffolds with PC content increased. Additionally, these scaffolds presented superior blood compatibility and cytocompatibility. The explored research on phospholipids complexed scaffolds can readily extend to other polymers and provides a feasible strategy to construct optimal microenvironments for seeded cells, indicating promising use in various tissue engineering.

Component-level construction schedule optimization for hybrid concrete structures

Hybrid concrete (HC) structures are being promoted in China, as well as in other countries. The construction process of HC structures differs from that of traditional cast-in-situ (CiS) structures in two aspects: 1) the construction unit for HC structures is no longer a floor or a construction zone, but a single component, 2) both PC components and CiS components exist in HC structures, and are usually being constructed in parallel. Thus, the optimization of component-level construction schedule considering the construction sequence of different PC and CiS components is necessary for HC structures. However, existing studies about construction sequence optimization or schedule optimization have not taken such aspects into account. To fill the research gaps, this study proposes an approach to optimize the component-level construction schedule for HC structures. An optimization model for the component-level resource-constrained project scheduling problem for HC structures (C-RCPSP-HC) is formulated, involving the precedence relationships and resource constraints, with the objectives of the minimization of the construction makespan and the construction cost. To solve the C-RCPSP-HC model, a multi-objective discrete symbiotic organisms search (MODSOS) is developed to obtain the non-dominated solutions. A case study originated from a real project is conducted to verify the proposed approach. The result shows: 1) the component-level construction schedule optimized by the proposed approach is superior to the traditional construction zone-level construction schedule in eliminating construction conflicts, and contributes to shortening the makespan, decreasing the cost, and enhancing the resource utilization efficiency, 2) the proposed MODSOS has better comprehensive performance than other competitive multi-objective optimization algorithms in solving the C-RCPSP-HC model, as far as convergence, spread, and spacing are concerned. For future improvement, the estimation of the construction durations of activities from historical data will be done by using artificial intelligence methods. Besides, Building Information Modeling will be integrated to automate the data extraction.