The outbreak of novel coronavirus (COVID-19) infections in 2019 is in dire need of finding potential therapeutic agents. In this study, we used molecular docking to repurpose HIV protease inhibitors and nucleoside analogues for COVID-19, with evaluations based on docking scores calculated by AutoDock Vina and RosettaCommons. Our results suggest that Indinavir and Remdesivir possess the best docking scores, and comparison of the docking sites of the two drugs reveal a near perfect dock in the overlapping region of the protein pockets. After further investigation of the functional regions inferred from the proteins of SARS coronavirus, we discovered that Indinavir does not dock on any active sites of the protease, which may give rise to concern in regards to the efficacy of Indinavir. On the other hand, the docking site of Remdesivir is not compatible with any known functional regions, including template binding motifs, polymerization motifs and nucleoside triphosphate (NTP) binding motifs. However, when we tested the active form (CHEMBL2016761) of Remdesivir, the docking site revealed a perfect dock in the overlapping region of the NTP binding motif. This result suggests that Remdesivir could be a potential therapeutic agent. Clinical trials still must be done in order to confirm the curative effect of these drugs.

With the rapid development of wind power generations, the inertial response of wind turbines (WTs) are widely concerned recently, which is important for grid frequency dynamic and stability. This paper recognizes and understands the inertial response of type-3 and type-4 WTs from the view of equivalent internal voltage, in analogy with typical synchronous generators (SGs). Due to the dynamic of the equivalent inertial voltage different from SGs, the electromechanical inertia of WTs is completely hidden. The rapid power control loop and synchronization control loop is the main reasons that the WT's inertial response is disenabled. On the basis of the equivalent internal voltage's dynamic, the existing inertia control method for WTs are reviewed and summarized as three approaches from the view of WT's control, i.e. optimizing the power control or synchronization control or both. At last, the main challenges and issues of these inertia controls are attempted to explain and address.

In 2020 the world experienced the threat of the COVID-19 epidemic, as seniors and chronic disease patients typically try to reduce their exercise and social activities to avoid increasing the risk of infection, which could lead to increased loneliness and even many diseases. This research combining golf croquet games with AIoT companion robots constructs a home-based intelligent exercise system thus uses the Technology Acceptance Model (TAM), deduces users’ intention to use this system based on perceived usefulness and perceived ease of use, and adds the needs of love and belonging, esteem, cognitive, aesthetic, and self-actualization in Maslow’s needs theory to conduct system needs analysis. The analysis results show that participants have a high level of acceptance of this system, believing that it is easy to learn and operate, can increase interaction with others, also found that this system can improve self-confirmation, satisfying the third for knowledge, feeling happy, and self-actualization needs are easier to fulfill. In the future, we shall collect and record the seniors in the process of use, so as to find out their health problems as soon as possible, expand their daily life through this exercise, and achieve the goal of happy living and health care.

High-resolution (2 km) high-frequency (hourly) SST data from 2015-2020 provided by the Advanced Himawari Imager (AHI) onboard the Japanese Himawari-8 geostationary satellite positioned over 140.7°E were used to study spatial and temporal variability of the China Coastal Front (CCF) in the East China Sea. The hourly SST data were processed with the Belkin and O’Reilly (2009) algorithm to generate long-term mean monthly maps of SST gradient magnitude (GM) and frontal frequency (FM) using a GM threshold of 0.1°C/km. Horizontal structure of SST field in the vicinity of the CCF was investigated from cross-frontal distributions of SST along 8 parallels from 31°N to 24°N. The high resolution (2 km) monthly distributions of SST along these 8 parallels were used to determine inshore and offshore boundaries of the CCF and calculate the CCF strength defined as the total cross-frontal SST step dSST=Offshore SST – Inshore SST. The CCF emerges in November, fully develops in December and peaks in strength in January-February. The front’s fragmentation and shrinking/weakening begins in February and March, respectively. In winter (December-February), the front’s strength dSST exceeds 5°C offshore the Zhejiang-Fujian coast and could be as high as 7.5°C when nearshore waters cool down to 7°C. In winter, the front’s strength decreases downstream from 31°N to 24°N. The CCF changes its physical nature as the seasons progress. In winter, the CCF is a water mass front between cold and fresh water coming from the north and warm and salty water coming from the south. In summer, the CCF becomes a coastal upwelling front maintained largely by southerly winds. In winter, the CCF’s cross-frontal structure in the SST field is ramp-shaped, with SST increasing monotonously in the offshore direction. In summer, the CCF’s cross-frontal structure in the SST field is V-shaped or U-shaped, featuring a minimum SST formed by cold upwelled water at some distance from the shore. Thus, the summer SST structure effectively consists of two fronts, a nearshore and offshore, with a minimum SST in-between. Across the inshore/offshore front, the SST decreases/increases in the offshore direction. The local bathymetry, especially the relatively steep shelf slope between 20-m and 50-m isobath, steers the front, which does not meander in winter offshore the Zhejiang-Fujian coast. As the cold season progresses, the front’s axis gradually shifts into deeper waters, from ~20 m to ~50 m.

Although oncolytic viruses are currently being evaluated for cancer treatment in clinical trials, systemic administration is hindered by many factors that prevent them from reaching the tumor cells. When administered systemically, mesenchymal stem cells (MSCs) target tumors, and therefore constitute good cell carriers for oncolytic viruses. Methods: MSCs were primed with trichostatin A under hypoxia, which upregulated the expression of CXCR4, a chemokine receptor involved in tumor tropism, and coxsackievirus and adenovirus receptor that plays an important role in adenoviral infection. After priming, MSCs were loaded with conditionally replicative adenovirus that exhibits limited proliferation in cells with a functional p53 pathway and encodes Escherichia coli nitroreductase (NTR) enzymes (CRAdNTR) for targeting tumor cells. Results: Primed MSCs increased tumor tropism and susceptibility to adenoviral infection, and successfully protected CRAdNTR from neutralization by anti-Adenovirus antibodies both in vitro and in vivo, and specifically targeted p53-deficient colorectal tumors when infused intravenously. Analyses of deproteinized tissues by UPLC-MS/QTOF revealed that these MSCs converted the co-administered prodrug CB1954 into cytotoxic metabolites, such as 4-hydroxylamine and 2-amine, inducing oncolysis and tumor growth inhibition without being toxic for the host vital organs. Conclusion: This study shows that the combination of oncolytic viruses delivered by MSCs with the activation of prodrugs is a new cancer treatment strategy that provides a new approach for the development of oncolytic viral therapy for various cancers.

This paper discusses a significant public-private partnership (PPP) formed by the government and Taiwanese Farmer Associations. Particularly, it will investigate a pattern of the PPP that has successfully promoted rural development and agricultural modernization in Taiwan since 1950s. Taiwanese Farmer Associations (hereafter TFAs), similar to agricultural cooperatives in South Korea and Japan, have played a policy agent in fostering rural development in this island state since 1950s. TFA’s performance inherently came from Japanese Cooperatives before World War II. The Performances of those farmer organizations are combinations of economic, social, and educational synergies. The rural development experiences in Taiwan demonstrate that success of rural modernization is carried out by a special public-private partnership (PPP). First, this paper discusses formation and development of farmer cooperative organizations in East Asian societies and compare the similarities and differences of practice of those organizations and their relations to the governments among Taiwan, South Korea, and Japan. Secondly, this paper examines collaborative mold and process in which both the government and Taiwan Farmer Associations have been extensively involved. A specific cooperative apparatus between the government and TFA functioning and operating as a perfect PPP has been formed under administrative guidance of the state. Thirdly, this paper looks at input and various supports in financial and policy perspective by the public sector. Fourthly, the paper discusses legal framework, administrative apparatus, and governance pattern for TFA. Fifthly, the paper discuses that a specific PPP successfully involving in rural modernization in Taiwan is significantly derived from the state’ guidance that properly regulates a collaboration between the government and TFA. So-call East Asian model of PPP in agricultural modernization and rural community development may become a valuable experience for most of developing countries.

Angiostrongylus cantonensis, a zoonotic parasite, can invade the human central nervous system (CNS) and cause acute eosinophilic meningitis or eosinophilic meningoencephalitis. Mice infected with A. cantonensis show elevated levels of pro-inflammatory cytokines, plasminogen activators, and matrix metalloproteinase-9, resulting in disruption of the blood-brain barrier (BBB) and immune cell infiltration into the CNS. Caveolin-1 (Cav-1) regulates the permeability of the BBB, which affects immune cells and cerebrospinal fluid. This intricate interaction ultimately fuels the progression of brain damage and edema. This study aims to investigate the regulatory role of Cav-1 in the pathogenesis of meningoencephalitis induced by A. cantonensis infection. We investigate pathological alterations by triphenyl-tetrazolium chloride, brain water content, BBB permeability, Western blot analysis, and gelatin zymography in BALB/c mice after A. cantonensis. The study evaluates the critical role of Cav-1 regulation through the TLR4 / MyD88 signaling pathway, modulates tight junction proteins, influences BBB permeability, and contributes to brain damage in A. cantonensis-induced meningoencephalitis.

Oil-Gan, also known as emblica, is the fruit of the genus Phyllanthus emblica L. The fruits of these trees are high in nutrients and have excellent effects on health care and development values. There are many methods for the management of diabetic nephropathy (DN). However, there is a lack of effective drugs for treating DN from throughout the disease course. The primary aim of this study was to examine the protective effects (including urine analyses, the measurement of inflammatory cytokine levels, and histological examination) and mechanisms of action of an ethyl acetate extract of P. emblica (EPE) on db/db mice, an animal model of diabetic nephropathy, as well as a secondary aim of examining the expression levels of protein kinase Cα (PKCα) in the kidney and its downregulation of vascular endothelial growth factor (VEGF) and fibrosis gene transforming growth factor-β1 (TGF-β1) by Western blot analyses. Eight db/m C57BLKS/J mice were used as the control group. Forty db/db mice were randomly divided into five groups. Treatments included vehicle, EPE1, EPE2, EPE3 (at doses of 100, 200, or 400 mg/kg EPE), or the comparative drug aminoguanidine for 8 weeks. After 8 weeks of treatment, blood samples were collected for analyses of blood glucose, insulin, and HbA1c levels, and pancreatic and kidney tissues were dissected for histological analyses. The present study indicated that the administration of EPE to db/db mice effectively controlled hyperglycemia and hyperinsulinemia by markedly lowering blood glucose, insulin, and glycosylated HbA1c levels. Administration of EPE to db/db mice decreased the levels of BUN and creatinine both in blood and urine and reduced urinary albumin excretion and the albumin creatine ratio (UACR) in urine. Moreover, EPE treatment decreased the blood levels of inflammatory cytokines, including kidney injury molecule-1 (KIM-1), C-reactive protein (CRP), and NLR family pyrin domain containing (NLRP3). Our findings showed that EPE not only had antihyperglycemic effects but also improved renal function in db/db mice. Histological examination of the kidney by immunohistochemistry indicated that EPE can improve kidney function by ameliorating glomerular morphological damage following glomerular injury; alleviating proteinuria by upregulating the expression of nephrin, a biomarker of early glomerular damage; and inhibiting glomerular expansion and tubular fibrosis. Moreover, the administration of EPE to db/db mice increased the expression levels of p- PKCα/t-PKCα but decreased the expression levels of VEGF and renal fibrosis biomarkers (TGF-β1, collagen IV, p-Smad2, p-Smad3, and Smad4), as shown by Western blot analyses. These results implied that EPE as a supplement has a protective effect against renal dysfunction through the amelioration of insulin resistance as well as the suppression of nephritis and fibrosis in a DN model.

Chronic obstructive pulmonary disease (COPD) patients are particularly susceptible to respiratory infections like influenza, which exacerbate symptoms and increase healthcare utilization. While smoking cessation and influenza vaccination are recommended preventive measures, their combined impact on healthcare resource utilization is underexplored. The Charlson Comorbidity Index (CCI) assesses comorbidity burden in COPD patients and may influence healthcare outcomes. We conducted a retrospective analysis of 357 COPD patients, evaluating smoking cessation success over one year and influenza vaccination receipt, stratifying patients by CCI scores. Healthcare utilization outcomes included emergency room visits, hospitalizations, and medical expenses. Results showed that 51.82% of patients quit smoking and 59.66% received influenza vaccination, with higher comorbidity prevalence in advanced COPD stages (p=0.002). Both smoking cessation and influenza vaccination independently correlated with decreased emergency room visits, hospital admissions, days, and costs. Patients who both quit smoking and received influenza vaccination exhibited the lowest healthcare utilization rates. In conclusion, smoking cessation and influenza vaccination significantly reduce healthcare resource utilization in COPD patients, with the combination yielding synergistic benefits, particularly in those with lower CCI scores. Integrating these interventions and comorbidity management in COPD strategies is essential for optimizing patient outcomes and healthcare efficiency.

A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3,283 abstracts). Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. It is suggested that all disci-plines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

Along with the rapid development and wide application of Artificial Intelligence (AI), discussions regarding AI Sustainability have emerged. Employing a systematic mapping study, this paper reviews research about AI Sustainability from diverse fields and viewpoints, including three dimensions economic, social, and environmental. Our analysis indicates a transformative shift in research focus, evolving from fragmented approaches to a more holistic understanding of AI Sustainability. This maturation is reflected in the interplay between multiple dimensions and the integration of both "AI as a tool for sustainability" and "Sustainability of AI" perspectives. As the field continues to mature, researchers should embrace a comprehensive and unbiased exploration of AI Sustainability across all dimensions and approaches, ensuring a well-rounded foundation for future advancements in this rapidly evolving domain.

Waterbomb structures are origami-inspired deformable structural components used in new types of robots. They have a unique radially deployable ability that enables robots to better adapt to their environment. In this paper, we propose a series of new waterbomb structures with square, rectangle, and parallelogram base units. Through quasi-static axial and radial compression experiments and numerical simulations, we prove that the parallelogram waterbomb structure has a twist displacement mode along the axial direction. Compared with the square waterbomb structure, the proposed optimal design of the parallelogram waterbomb structure reduces the critical axial buckling load-to-weight ratio by 55.4% and increases the radial stiffness-to-weight ratio by 67.6%. The significant increase in the radial stiffness-to-weight ratio of the waterbomb structure and decrease in the critical axial buckling load-to-weight ratio make the proposed origami pattern attractive for practical robotics applications.

In order to maintain the best conditions of the building from the first day of opening, explorations of office buildings in the world investigated the approaches of optimizing the building operation and property facilities, whereas building managers often have trouble prioritizing improvements toward their sustainable objectives. The challenge is due to numerous equipment types in the building, which has various durability in years and represents users’ different feelings of importance and satisfaction. When faced with multiple improvement projects, building managers are often unable to determine the order of implementation.Hence, this research applies the Analytic Hierarchy Process (AHP) using TAIPEI 101 Tower as a case study to explore the key factors affecting tenants’ willingness to lease office buildings by distributing questionnaires to real estate experts. By filtering out key indicators related to property facilities that users care about, this study used Importance and Satisfaction Analysis (IS Analysis) to identify items that need urgent improvement. The research revealed that air conditioning is the top priority for improvement through empirical evaluation and weight ranking. This study hopes to establish an evaluation model for facility improvements to help clarify the prioritization and defeat limited resources to projects effectively, which is helpful for sustainable property management.

The research aims to examine the extent to which the perceived organizational support (POS) predicted career related self and environmental exploration via the mediation effect of career adaptability. Multi-group comparisons based on the gender, majors and places of origin were also performed to evaluate the differences among these variables. An internet survey was conducted to collect empirical data from 611 Chinese undergraduates. Structural equation modeling (SEM) was conducted to demonstrate the proposed relations in the model and multiple group analysis. Clearly supported was the direct relations between POS and career related self and environmental exploration and the mediation effect of career adaptability. The results also showed that gender and major are the moderating variables for the proposed model, and no significance difference among the rural and urban subgroups was found. POS could be effectively delivered to students in general, marginalized social groups, such as females and those majored in the humanities and social sciences in particular. Four dimensions of career adaptability (concern, control, curiosity and confidence) could also be targeted at in order to promote students’ sustainable employment. The findings provide a better understanding of the career construction model by incorporating the contextual factor in a collective cultural environment.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

The oncologic merits of laparoscopic technique for colorectal cancer surgery remain debatable. Eligible patients with non-metastatic colorectal cancer who were scheduled for an elective resection by only one surgeon in a medical institution were randomized to either laparoscopic or open treatment. During this period, total 188 patients received laparoscopic surgery and other 163 patients to open approach. The primary endpoint was cancer-free 5-year survival after operative treatment and secondary endpoint was the tumor recurrence incidence. We found there was no statistically significant difference between open and laparoscopic groups regarding average number of lymph nodes dissected, overall mortality rate, cancer recurrence rate or cancer-free 5-year survival. Nevertheless, laparoscopic approach was more effective for colorectal cancer treatment with shorter hospital stay and less blood loss despite operation time was significantly longer. Meanwhile fewer patients receiving laparoscopic approach developed postoperative urinary tract infection, wound infection, pneumonia or anastomosis leakage, which reached statistical significance. For non-metastatic colorectal cancer patients, laparoscopic surgery resulted in better short-term outcomes whether in total complications and intra-operative blood loss. Though there was no significant statistical difference in terms of cancer-free 5-year survival and tumor recurrence, we favor patients receiving laparoscopic surgery if not contraindicated.

To enhance industrial competitiveness and increase productivity, every country has strived to create a smart factory by introducing technologies such as Internet of Things, big data and artificial intelligence into production line and build cyber-physical system for the purpose of promoting manufacturing efficiency. For mission assignment, production line management or manufacturing field analysis, the location information of employee, machine and material is very essential. To promote manufacturing efficiency, of course, the location information became more important. A Bluetooth low energy (BLE) positioning system for the manufacturing is developed in this research. A "Tag tracking" mechanism is addressed and adopted, which uses Beacon to catch the location information and a BLE receiver is also used to receive the broadcasting information from Beacon. The position information from the BLE receiver will be compared with the data in the database for calculating the location of the target. The status of the target may also be obtained by using the data from the BLE receiver. Comparing with the mobile device, this method can reduce energy consumption and make the maintenance simple and easy. In the real applications, the target may not be limited to human. The "Regional label positioning technology" is also investigated in this research. Defining a suitable zone location and arranging BLE receiver location, and positioning analysis theory are the key factors included in this developed technology. The developed system will be tested for real industry applications. The test results show that the feasibility of this technology.

Pyrethroid pesticides (PYRs) as the third most widely used pesticides, following organophosphorus and carbamate pesticides, which are of significant importance in the analysis and detection of vegetables. However, the current pretreatment technology for PYRs confronts challenges of difficult separation and low enrichment efficiency, resulting in a cumbersome and time-consuming pretreatment process with poor selectivity. Here, a simple and efficient magnetic solid-phase extraction (MSPE) strategy was developed to simultaneously purify and enrich five PYRs in vegetables, with the magnetic covalent organic framework nanomaterial COF-SiO2@Fe3O4 as an adsorbent. The COF-SiO2@Fe3O4 was prepared by a simple solvothermal conditions method, using Fe3O4 as magnetic core, benzidine and 3,3,5,5-tetraaldehyde biphenyl as two building units. COF-SiO2@Fe3O4 could effectively capture the targeted PYRs by virtue of its abundant π-electron system and hydroxyl groups. The impact of various experimental parameters on extraction efficiency was investigated to optimize the MSPE conditions, including adsorbent amount, extraction time, elution solvent type and elution time. Subsequently, method validation was conducted under the optimal conditions in conjunction with gas chromatography-mass spectrometry (GC-MS). Within the range of 5.00–100 μg·kg-1 (1.00–100 μg·kg-1 for bifenthrin and 2.5–100 μg·kg-1 for fenpropathrin), the five PYRs exhibited a strong linear relationship, with determination coefficients ranging from 0.9990 to 0.9997. The limits of detection (LODs) were 0.3–1.5 μg·kg-1, and the limits of quantification (LOQs) were 0.9–4.5 μg·kg-1. The recoveries were 80.2–116.7% with relative standard deviations (RSDs) below 7.0%. Finally, COF-SiO2@Fe3O4, NH2-SiO2@Fe3O4 and Fe3O4 were compared as MSPE adsorbents for PYRs. The results indicated that COF-SiO2@Fe3O4 was an efficient and rapid selective adsorbent for PYRs. This method holds promising prospects for the determination of PYRs in real samples.

Second messengers, cyclic adenosine 3'-5'-monophosphate (cAMP) and cyclic guanosine 3'-5'-monophosphate (cGMP) are playing important roles in many animal cells by regulating intracellular signaling pathways and modulating cell physiology. Environmental cues like temperature, light and chemical compounds can stimulate cell surface receptors and trigger the generation of second messengers and the following regulations. Spread of cAMP and cGMP is further shaped by cyclic nucleotide phosphodiesterases (PDEs) for orchestration of intracellular microdomain signaling. However, localized intracellular cAMP and cGMP signaling requires further investigation. Optogenetic manipulation of cAMP and cGMP offers new opportunities of spatio-temporally precise study of their signaling mechanism. Light-gated nucleotide cyclases are well developed and applied for cAMP/cGMP manipulation. Recently discovered rhodopsin phosphodiesterase gene from protists established new and direct biological connection between light and PDEs. Light-regulated PDEs are under development and of demand to complete the toolkit of cAMP/cGMP manipulation. In this review, we summarize the state of the art, pros and cons of artificial and natural light-regulated PDEs and discuss potential new strategies of developing light-gated PDEs for optogenetic manipulation.

Post-evaluation of ecological redevelopment is a good method for its achievements. The eco-engineering technologies and achievements of landscape water reconstructed from aquaculture ponds in Shanghai Chenshan Botanical Garden have been introduced in this study. The sediments and water quality were also sampled and tested for basic physicochemical parameters and heavy metal concentration. The ecological redevelopment of landscape water reconstructed from aquaculture ponds was evaluated using the Nemero comprehensive pollution index method. The results shown that nutrients including organic matter, organic nitrogen and their ratio of sediment were found to be in a state of moderate pollution, while their ecological risk of heavy metals was low. Although total nitrogen and total phosphorus of water quality was really higher than that of other indexes, the decline trends of ammonia nitrogen, total nitrogen and total phosphorus was obvious presented over time. In general, this is a good example that redevelopment of water ecosystems from aquaculture ponds using eco-engineering technologies.

Lung cancer remains a formidable global health challenge that necessitates inventive strategies to improve therapeutic outcomes. Conventional treatments, including surgery, chemotherapy, and radiation, have demonstrated limitations in achieving sustained responses. Therefore, the explora-tion of novel approaches encompasses a range of interventions that show promise in enhancing outcomes for patients with lung cancer, particularly those facing advanced or refractory cases. These groundbreaking interventions hold the potential to overcome cancer resistance and offer personalized solutions. Despite the rapid evolution of emerging lung cancer therapies, persistent challenges such as resistance, toxicity, and patient selection underscore the need for continued development. Consequently, the landscape of lung cancer therapy is transforming with the in-troduction of precision medicine, immunotherapy, and innovative therapeutic modalities. Addi-tionally, a multifaceted approach involving combination therapies through the integration of tar-geted agents, immunotherapies, or traditional cytotoxic treatments addresses the heterogeneity of lung cancer while minimizing adverse effects. This review provides a brief overview of the latest emerging therapies that are reshaping the landscape of lung cancer treatment. As these novel treatments progress through clinical trials and integrate into standard care, the potential for more effective, targeted, and personalized lung cancer therapies comes into focus, instilling renewed hope for patients facing challenging diagnoses.

With the improvement in medical quality, cold mechanical rehabilitation equipment can no longer meet the psychological needs of the physically handicapped. Therefore, design for behaviour change should be viewed as a strategy for enabling lifestyle changes as soon as possible. Although how to use equipment to assist physically disabled people in hydrotherapy rehabilitation is the primary design goal, the psychological feelings of users are also one of the focuses of this study. Besides, coupled with the large-scale spread of the Coronavirus Disease (COVID-19) in the past two years. People's living habits are also forced to change, many activities that people are used to doing outdoors are being slowly completed at home. During the epidemic, it is very dangerous and inconvenient for users who need to go to institutional rehabilitation regularly. Hence, it is urgent to change past usage habits and places. In this study, a personal multi-functional hydrotherapy bucket is designed. In addition to not having to use a public spa with other people to reduce the risk of contracting the Coronavirus Disease, it can be easily placed at home by reducing its size. According to market research, it is known that the current domestic hydrotherapy buckets on the market usually only have a single function. The large medical hydrotherapy pools are not only rare in Taiwan, but this kind of equipment also takes up a lot of space. Various factors have resulted in a relatively low market share of hydrotherapy pools compared to other rehabilitation equipment. In order to take into account, the physical and psychological needs of the physically disabled, this research focus on the design of a multi-functional but small-sized hydrotherapy bucket. Taking the convenience of entering and leaving the bucket as the starting point to improve the shortcomings of the existing products, on the other hand, because most users are inconvenient to move, safety is also the focus of the design to reduce the occurrence of accidents and injuries. This study is based on the systematic design methodology of concurrent design to obtain product design results. Firstly, conduct market research and select products from different manufacturers for analysis and comparison. Through functional analysis and sorting out the problem points, the TRIZ theory and the morphological diagram method are used to find the solution to the problem. Finally, the best design scheme is obtained by the PUGH method. Besides, construct a 3D model to present the design concept that meets the design goals. The research results are hoped to be used as a reference for designing and developing related products, thereby increasing the home's multifunctional hydrotherapy buckets' market share.

Objective: Taiwan Government’s organizations have endeavored to promote the applications of big data and open data. The “My Health Bank” is one of the measures promoted by the National Health Administration, Ministry of Health and Welfare. This study proposes the perspective of the “value co-creation” with the attempt to extend the concept of service ecosystem and apply it on the platform of My Health Bank to examine whether people (patients, families, and caregivers) can promote their health literacy? Method: This cross-sectional study, with people that have registered at “My Health Bank” as subjects. Complying with the inclusion criteria, 401 questionnaires were delivered, with 391 valid ones, excluding those incompletely and inaccurately filled. Result: That the affecting factors of the co-creation of values: age, education level, annual income, and platform operation show to be significant ( p＜0.05); and gender, occupation, and resource exchange do not reach the significant level (p＞0.1). Conclusion: We found My Health Bank changed the inertia of “value creation” in the traditional medical value, it allows the traditional medical and healthcare industry to expose to the impacts of the mega trend of the internet, the transformation of the platform in a necessary trend.

The purpose of interdisciplinary communication during the early architectural design stage is to achieve the early integration of knowledge in different professional fields, which can help architects to choose correct design development strategies during the early architectural design stage. However, because there is too little information at the early design stage, and design solutions are still rapidly changing and developing, the uncertainties at this stage make it difficult for consultants in other disciplines to provide their views and analysis. In spite of this situation, the emergence of generative modeling is changing design procedures and methods of communication and cooperation for architectural teams, and has brought about a shift in the way architects transmit design information from "what" (declarative information) to "how" (procedural information). Generative modeling is like an aircraft's dashboard: It can provide a basis for interdisciplinary communication, provide interdisciplinary knowledge packages, and bring about a shift in interdisciplinary communication that will reduce the architectural team's communication needs and cost. This study uses a real design case to show the feasibility of generative modeling. Employing generative modeling as a basis, architects can enhance the efficiency of design change and multi-disciplinary communication during the early design stage, integrate specialized knowledge in relevant fields, use this knowledge to formulate design criteria for the next stage, and effectively transmit design decisions. As a consequence, the changes to the cost structure of design revisions and communication between different disciplines has initiated a paradigm shift toward multi-disciplinary communication in architectural design.

Besides the Lagrangian and the Eulerian descriptions, the motion of a body can also be expressed relative to the present configuration of the body, known as the relative motion description. It is interesting to consider such a relative motion description in general to formulate the basic system of field equations for solid bodies. In doing so, when the time increment from the present state is small enough, the nonlinear constitutive equations can be linearized relative to the present state so that the resulting system becomes linear. This will be done for thermoelastic materials with a brief comment on the exploitation of entropy principle in general. Relative Lagrangian formulation is based on the well-known ``small-on-large'' idea, and can be implemented for solving problems with large deformation in successive incremental manner. Some applications of such a formulation in numerical simulations are briefly reviewed.

This study explored the effects of cryogenic treatment on the microstructure, hardness, and wear-resistance of diamond-like carbon (DLC) by cryogenically treating NAK 80 mold steel coated with DLC. Raman spectroscopy analyzed the structure of the DLC film. Nanoindenter analyzed the hardness and Young’s modulus of the film, and their relationship determined the wear resistance. Wear test assessed the wear rate and friction coefficient of the DLC film. The results showed that cryogenic treatment increased the rate of carbide precipitation and refined the grain structure. Raman spectroscopy indicated that the Raman intensity rate (I_D/I_G) of treated DLC films was smaller than those without cryogenic treatment. When the sp3 bond increased, the hardness and wear-resistance of the DLC film also increased.

Chimeric antigen receptor (CAR) T-cell therapy has greatly improved outcomes for patients with relapsed or refractory hematological malignancies. However, challenges such as treatment resistance, relapse, and severe toxicity still hinder its widespread clinical application. Traditional transcriptome analysis has provided limited insights into the complex transcriptional landscape of both leukemia cells and engineered CAR-T cells, as well as their interactions within the tumor microenvironment. However, with the advent of single-cell sequencing techniques, a paradigm shift has occurred, providing robust tools to unravel the complexities of these factors. These techniques enable unbiased analysis of cellular heterogeneity and molecular patterns. These insights are invaluable for precise receptor design, guiding gene-based T cell modification, and optimizing manufacturing conditions. Consequently, this review utilizes modern single-cell sequencing techniques to clarify the transcriptional intricacies of leukemia cells and CAR-Ts. The aim of this manuscript is to discuss the potential mechanisms that contribute to the clinical failures of CAR-T immunotherapy. We will examine the biological characteristics of CAR-Ts, the mechanisms that govern clinical responses, and the intricacies of adverse events. By exploring these aspects, we hope to gain a deeper understanding of CAR-T therapy, which will ultimately lead to improved clinical outcomes and broader therapeutic applications.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Nowadays, the increasingly serious environmental pollution and energy problems urgently require internal combustion engine-based transportation vehicles to upgrade or replace, so the new energy transportation vehicles based on hybrid power and fuel cells have gradually stepped on the stage and continue to innovate. The shipping industry is one of the main sources of global greenhouse gas emissions. The development of clean energy ships represented by fuel cells has attracted wide attention. Fuel cell has the advantages of clean, pollution-free and low noise, but it has some disadvantages such as insufficient dynamic response performance and fast performance decay. Limited by the characteristics of a single energy source, a variety of energy sources and bidirectional DC converters are usually mixed together to form a hybrid ship to improve the flexibility, stability and economy of the ship, and enhance its adaptability to complex sea conditions through the reconfiguration of power system energy. Taking fuel cell ferry "FCS Alsterwasser" as the research object, this study proposed an improved equivalent minimum hydrogen consumption energy management strategy based on fuzzy logic control. First, the power system of the modified mother ship was simulated, and a hybrid power system including fuel cell, lithium iron phosphate battery and supercapacitor was proposed. Then, the dynamic system simulation model and double closed loop PI control model are established in MATLAB/Simulink, and the reliability of the model is verified by the simulation analysis of charge and discharge characteristics. Then, the feasibility of the proposed method is demonstrated by designing simulation experiments based on typical working conditions of the mother ship. The simulation results show that under the premise of meeting the load requirements, the control strategy designed in this paper has a better optimization effect than the S-type penalty function in terms of lithium battery power, lithium battery SOC, bus voltage stability and equivalent hydrogen consumption, which improves the stability and economy of the power system, and has certain engineering practical value.

Fuzzy Rule Interpolation (FRI) offers an innovative methodology for inferring outcomes from data points that match no established rules in a sparse fuzzy rule base. Generally, a traditional fuzzy rule-based system suffers from the inability to perform inference while facing an unmatched observation, given a sparse fuzzy rule base that is derived from insufficient data or inadequate human experience and that fails to cover parts of a specific problem domain. Fortunately, starting with a sparse rule base, Fuzzy Rule Interpolation (FRI) can help to formulate interpolated rules specifically in instances where observations fail to activate any existing rule. An interpolated rule gives a result called consequent, which is related to the unmatched observation so that the inference system will not fail to generate an outcome corresponding to the observation as the output. Conventionally, these interpolated rules will be discarded after obtaining outcomes. However, valuable information may be potentially embedded within those discarded rules addressing the limitation regarding the coverage of the original sparse knowledge space. Moreover, without exploiting the potential value of the interpolated rules, the fuzzy inference system based on a sparse rule base will never improve its efficiency and robustness in the long run. Therefore, this underscores the necessity for Dynamic Fuzzy Rule Interpolation (D-FRI), which aims to collate interpolated rules that cover particular knowledge spaces absent in the sparse rule base. Intuitively, those frequently appeared interpolated rules are considered valuable because future observations are more likely to hit the respective problem areas. Promoting selected interpolated rules to the sparse rule base can enhance the overall coverage of the knowledge space and increase the inference efficiency over time. This paper introduces a framework for Dynamic Fuzzy Rule Interpolation that integrates the widely used Transformation-based Fuzzy Rule Interpolation (T-FRI) with an effective form of clustering of interpolated results. Experimental findings validate the effectiveness of this approach.

Protein phosphorylation and ADP-ribosylation (ADPr), as two types of post-translational modifications (PTM), are the process of adding phosphate group and ADP-ribose moieties to proteins, respectively. Although both PTM types can occur on many amino acid types, serine is the most common. Serine phosphorylation (pS), serine ADPr (SADPr), and their in situ crosstalks (pSADPr) play essential roles in biological processes. Although in silico classifiers have been developed for predicting pS and SADPr sites, the classifier for predicting pSADPr sites is unavailable. In this study, we developed classifiers to predict pSADPr sites. Specifically, we collected 3250 human pSADPr, 7520 SADPr, 151,227 pS and 80,096 unmodified serine sites. Based on them, we investigated the characteristics of pSADPr sites and constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. Three of them (e.g. Convolutional Neural Network with the One-Hot encoding, dubbed CNNOH) performed better than the rest two. For instance, CNNOH had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites.Therefore, it is challenging to distinguish pSADPr sites from SADPr sites compared to the other two. It is consistent with our observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Furthermore, we used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. However, none of the ensemble classifiers showed better performances, suggesting that the base classifiers have good enough performances. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/.

Study design: Retrospective observational study. Objectives: To evaluate the change in adjacent segment disease (ASD) after hybrid dynamic stabilization with Dynesys-Transition-Optima (DTO). Methods: In 2012–2020, 115 lumbar spinal stenosis patients with spondylolisthesis re-fractory to nonsurgical management received hybrid dynamic stabilization with DTO system in a single medical center from a single neurosurgeon. After excluding those who did not receive L3–L4 dynamic stabilization and L4–L5 transforaminal lumbar interbody fusion fixation and those with incomplete postoperative data (n = 84), the 31 patients studied received follow-up vis-its at 6, 12 and 24 months postoperatively. Radiological assessment applied at the L2–L3, L3–L4 and L5–S1 segments separately included disc height, listhesis distance and angular change while in motion. Implant failure and screw loosening sign were both documented. Results: In the L3–L4 segment, the listhesis distance appeared 1 year after the operation; however, alterations in other segments showed at 6 months. At 2 years, the L3–L4 segment showed significantly less increase in listhesis distance and relatively less disc height reduction than other segments. In motion an-gular change, only the L3–L4 segment had a significant decrease at 2 years. Only the L3–L4 seg-ment maintained negative growth over 2 years in time-dependent assessment of motion angular change. Conclusions: In our study, disc height decreased, listhesis distance increased less and ASD changed later at the index level than the L2–L3 and L5–S1 levels. Angular change was re-duced at the index level while in motion. Although ASD was not significantly prevented, index level degeneration was both minor

Hepatocellular carcinoma (HCC) is the fifth most common type of cancer, and is the third leading cause of cancer-related deaths each year. It involves a multi-step progression and is strongly associated with chronic inflammation induced by the intake of environmental toxins and/or viral infections (i.e., hepatitis B and C viruses). Although several genetic dysregulations are considered to be involved in disease progression, the detailed regulatory mechanisms are not well defined. Homeobox (Hox) genes that encode transcription factors with homeodomains control cell growth, differentiation, and morphogenesis in embryonic development. Recently, more aberrant expressions of Hox genes were found in a wide variety of human cancer, including HCC. In this review, we summarize the currently available evidence related to the role of Hox genes in the development of HCC. The objective is to determine the roles of this conserved transcription factor family and its potential use as a therapeutic target in future investigations.

In the study of rough sets, there are many covering approximation spaces, how to classify covering approximation spaces has become a hot issue. In this paper, we propose concepts covering approximation $T_{1}$-space, $F-$symmtry, covering rough continuous mapping, covering rough homeomorphism mapping to solve this question. We also propose a new method for constructing topology in Theorem 5.1, and get the following properties: (1) For each $x\in U$, $\{X_{i}:i\in I\}\subseteq \mathcal{P}(U)$ is all the subsets of $U$ which contains $x$ and $*$ is a reflexive relation on $U$. If $V\in \tau$ is a subset of $U$ and $x\in V$, then $\underline{*}(\bigcap \limits_{i\in I}X_{i})$ is the smallest subset of $U$ and $x \in \underline{*}(\bigcap \limits_{i\in I}X_{i})\subseteq V$. Denoted by $C(x)=\bigcap \{\underline{*}(X_{i}):x\in \underline{*}(X_{i}),i\in I\}$. (2)If $V\in \tau$ is a subset of $U$, then $V =$ $\bigcup \limits_{x\in V} C(x)$. (3) Let $\{\underline{*}(X_{i}):x\notin \underline{*}(X_{i}), i\in I\}$, then $\overline{\{x\}}$ $=$ $U \setminus \bigcup \limits_{x\notin \underline{*}(X_{i}), i\in I}\underline{*}(X_{i})$; (4) Let $*$ be a reflexive relation on $U$. For every $X\subseteq U$, we have $int(\underline{*}(X))=\underline{*}(X)$. Where $int(\underline{*}(X))$ represents the interior of $\underline{*}(X)$. (5) Let $\{\underline{*}(X_{i}):x\in \underline{*}(X_{i}),i\in I\}$ be a family subsets of $U$, then $\{\underline{*}(X_{i}):x\in \underline{*}(X_{i}),i\in I\}$ is a base for $(U,\tau)$ at the point $x$. \\

The application of Explainable AI (XAI) to improve understanding of complex non-linear SST teleconnections and gain predictive skills is at the frontier of climate research. In this study, we attempt to refine the applications of XAI advancements to ENSO prediction by including SSTA in the western North Pacific (WNP) as a precursor. Our analyses indicate that the baseline accuracy increases from the 60% threshold previously reported for ENSO to >85% for moderate warm, cold, and neutral ENSO states one year in advance when WNP SSTA is included. Precision increases to over 90% for higher magnitude El Niños and La Niñas. Experiments conducted at 4-year lead times yield a strong WNP SSTA signal. A moderate SSTA signal is observed at 3-year lead time with no predictability; however, predictability spikes to over 80% when stratospheric zonal winds are included with WNP SSTA. While additional scientific work is required to make robust connections between XAI and dynamical processes, our results demonstrate a potential to improve long term predictability of ENSO through the ability of XAI in capturing SST patterns evolving at extended spatial and temporal scales.

: Low-temperature growth of diamond films by chemical vapor deposition (CVD) method is not so widely reported and especially its initial periods of nucleation and growth phenomenon are of particular interest to the researchers. Four sets of substrates were selected for growing diamond films by linear antenna microwave plasma enhanced CVD (LA-MPCVD). Among them, silicon and sapphire substrates were pre-treated with detonation nanodiamond (DND) seeds before diamond growth for enhancement of its nucleation. On the other hand, carbon nanotube (CNT) films on Si substrates were also used as another template for LA-MPCVD diamond growth. In order to enhance the diamond nucleation during CVD growth, some of the CNT films were again pre-treated by electrophoretic deposition (EPD) of diamond nanoparticles. All these substrates were then put inside the LA-MPCVD chamber for growing diamond films under variable processing conditions. Microwave input powers (1100 – 2800 W), input power modes (pulse or continuous), antenna to stage distances (5 - 6.5 cm), process gas recipes (with or without CO2), methane gas percentages (3 - 5%), deposition times (11 – 120 minutes) were altered to investigate their effect on the growth of diamond film on the pre-treated substrates. The substrate temperatures were found to vary from as low as 170oC to a maximum of 307oC during the alteration of the different processing parameters. Contrary to the conventional MPCVD, it was observed that during the first hour of LA-MPCVD diamond growth, DND seeds or the nucleating structures, do not coalesce together to make a continuous film. Deposition time was the most critical factor in fully covering the substrate surfaces with diamond film, since the substrate temperature could not become stable during the first hour of LA-MPCVD. CNT was found to be oxidised rapidly under LA-MPCVD plasma conditions; therefore, a CO2-free process gas recipe was used to reduce CNT burning. Moreover, EPD-coated CNT was found to be less oxidised by the LACVD plasma during diamond growth.

In this study, a simple, easy and convenient fluorescent sensing system for detection of vascular endothelial growth factor (VEGF) based on VEGF aptamers, aptamer-complementary fluorescence-labeled probe and streptavidin magnetic beads was developed in only one single tube. The VEGF is the most important biomarker in cancer angiogenesis, and is investigated that serum VEGF level was various according to the different type and course of cancers. Hence, efficient quantification of VEGF is able to improve the accuracy of cancer diagnosis and precision of disease surveillance. In this research, the VEGF aptamer was designed to be able to bind with the VEGF by forming G-quadruplex secondary structures, and then the magnetic beads would capture the none-binding aptamers due to none steric interference, and finally, the fluorescence-labeled probes were hybridized with the aptamers captured by the magnetic beads. Therefore, the fluorescent intensity in the supernatant would specifically reflect the present of VEGF. After an overall optimization, the optimal conditions for detection of VEGF were as followed, KCl, 50 μM; pH 7.0; aptamer, 0.1 μM and magnetic beads, 10 μL (4 μg/μL). The VEGF could be well quantified within a range of 0.2-2.0 ng/ml in plasma, and the calibration curve possessed a good linearity (y = 1.0391x + 0.5471, r = 0.998). The detection limit (LOD) was calculated to be 0.0445 ng/mL according to the formula (LOD = 3.3×σ/S). The specificity of this method was also investigated under the appearance of many other serum proteins, and the data showed the good specificity in this aptasensor-based magnetic sensing system. This strategy provided a simple, sensitive and selective biosensing platform for detection of serum VEGF. Finally, it was expected that this detection technique can be promoted to more clinical applications.

3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in the recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundary for positioning. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude and longitude coordinate to determine the precise location of the fisheye image. The results are then compared with the conventional GNSS and advanced 3DMA GNSS positioning methods. The aims of the proposed algorithm are to increase positioning and heading accuracy in a rich urban environment.

Our study explores the role of cancer-derived extracellular exosomes (EXs), particularly focusing on collagen alpha-3 (VI; COL6A3), in facilitating tumor dissemination and metastasis in epithelial ovarian cancer (EOC). We found that COL6A3 is expressed in aggressive ES2 derivatives, SKOV3 overexpressing COL6A3 (SKOV3/COL6A3), and mesenchymal-type ovarian carcinoma stromal progenitor cells (MSC-OCSPCs), as well as their EXs, but not in less aggressive SKOV3 cells or ES2 cells with COL6A3 knockdown (ES2/shCOL6A3). High COL6A3 expression correlates with worse overall survival in EOC patients, as evidenced by TCGA and GEO data analysis. In vitro experiments showed that EXs from MSC-OCSPCs or SKOV3/COL6A3 cells significantly enhance invasion ability in ES2 or SKOV3/COL6A3 cells, respectively (both, P <0.001). In contrast, ES2 cells with ES2/shCOL6A3 EXs exhibit reduced invasion ability (p<0.001). In vivo, the average disseminated tumor numbers in the peritoneal cavity were significantly greater in mice receiving intraperitoneally injected SKOV3/COL6A3 cells than in SKOV3 cells (p<0.001). Furthermore, mice intravenously (IV) injected with SKOV3/COL6A3 cells and SKOV3/COL6A3-EXs showed increased lung colonization compared to mice injected with SKOV3 cells and PBS (p=0.007) or SKOV3/COL6A3 cells and PBS (p=0.039). Knockdown of COL6A3 or treatment with EXs inhibitor GW4869 or rapamycin-abolished COL6A3-EXs may suppress the aggressiveness in EOC.

Stable-isotope dimethyl labeling is a highly reactive and cost-effective derivatization procedure that could be utilized in proteomics analysis. In this study, a liquid chromatography-tandem mass spectrometry in multiple reaction monitoring mode (LC-MS-MRM) platform for the quantification of kiwi allergens was first developed using this strategy. Three signature peptides for target allergens Act d 1, Act d 5, and Act d 11 were determined and were derivatized with normal and deuterated formaldehyde as external calibrants and internal standards, respectively. The results showed that sample preparation with the phenol method provided comprehensive protein populations. Recoveries at four different levels ranging from 72.5–109.3% were achieved for the H-labeled signature peptides of Act d 1 (SPA1-H) and Act d 5 (SPA5-H) with precision ranging from 1.86–9.92%. The limit of quantification (LOQ) was set at 8 pg mL–1 for SPA1-H and at 4 ng mL–1 for SPA5-H. The developed procedure was utilized to analyze seven kinds of hand-made kiwi foods containing 0.0175–0.0515 mg g–1 of Act d 1 and 0.0252–0.0556 mg g–1 of Act d 5. This study extended the applicability of stable-isotope dimethyl labeling to the economical and precise determination of food allergens and peptides.

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. The study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 keypoints using an open-source tool called DeepLabCut, which helped accurately mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the Long Short-Term Memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior were over 90%.

In this study, a composite flame retardant was created by combining fish scale (FS), fishery waste, and ammonium polyphosphate (APP), a commercially available flame retardant. The composite flame retardant was added to epoxy resin (EP) to form an EP/APP/FS composite that prevents burns and is environmentally friendly. The use of FS conforms to the concept of circular economy and lowers costs by reducing the consumption of APP. Thermogravimetric analysis (TGA), integral procedural decomposition temperature (IPDT), pyrolysis kinetics, limiting oxygen index (LOI), the Underwriters Laboratories 94 (UL94) flammability test, scanning election microscopy, Raman spectroscopy, and energy-dispersive X-ray spectroscopy were used to determine the thermal properties, flame retardant properties, flame retardant mechanism, char morphology, and composition of the composites. The TGA results indicated that the addition of 40% flame retardant raised the char residue from 16.45 wt% (pure EP) to 36.07 wt%, IPDT from 685.6 °C (pure EP) to 1143.1°C, LOI from 21% (pure EP) to 30%, and UL94 classification from fail (pure EP) to V-0. These results suggest an increase in char residue, which indicates better protection of the polymer matrix material. The improvements in IPDT, LOI, and UL94 classification, which indicate greater thermal stability, lower flammability (from flammable to fireproof), and higher flammability rating (from fail to V-0), respectively, suggest that the composite material has favorable thermal properties and is less inflammable.

The increasing prevalence of obesity and sedentary lifestyles has led to an increased incidence of metabolic syndrome (MetS) worldwide. In Taiwan, middle-aged women are at a greater risk of MetS, type 2 diabetes, and cardiovascular disease than men are because they have more subcutaneous fat and larger waist circumferences compared to men with equal visceral fat levels. This study investigated the effects of calorie restriction supplemented with fish oil (CRF) in middle-aged women with MetS. For 12 weeks, 75 eligible participants were randomly assigned either calorie restriction (CR) or CRF. Both dietary intervention groups were further divided into two age groups: ≤45 and >45 years. The changes in MetS severity, inflammatory status, iron status, and red blood cell fatty acid profile were evaluated. Seventy-one participants completed the trial. Both dietary interventions significantly ameliorated MetS and improved the participants’ inflammatory status. CR significantly increased total iron binding capacity, whereas CRF increased hepcidin levels. Furthermore, CRF significantly increased the n-6/n-3 and arachidonic acid/docosahexaenoic acid ratios. In conclusion, CR and CRF improved the anthropometric and MetS characteristics of early-middle aged women, including body weight, blood glucose levels, triglyceride levels, as well as the scores for the homeostasis model assessment of insulin resistance and quantitative insulin sensitivity cheque index. Dietary intervention was more effective in >45-year-old women than ≤45-year-old women.

Municipal solid waste incineration fly ash (MSWI FA) predominantly consists of compounds comprising elements such as calcium, aluminum, silicon, sodium, and others. Additionally, it encompasses a complex mixture of heavy metals, chlorides, sulfates, organic pollutants, and other constituents. The effective and economically viable treatment of MSWI FA poses a formidable challenge for the current stage of resource cycling. In this research report, we will adopt a novel low-temperature sintering method called "Cold Sintering Process" (CSP) as a means to immobilize heavy metals within the fly ash. By utilizing a Taguchi orthogonal array, we will adjust five control factors in the CSP, including sintering temperature, uniaxial pressure, sintering time, initial water addition, and sodium carbonate dosage. The leaching of cadmium element from the fly ash, as measured by TCLP, will serve as the quality indicator. Through the application of CSP, the MSWI FA will be transformed into structurally stable ceramic blocks, effectively immobilizing the heavy metals within the blocks. The results of the experiments showed that MSWI FA under the conditions of a temperature of 300℃, uniaxial pressure of 312 MPa, sintering time in 60 minutes, 25 wt% water addition, and 9 wt% Na2CO3 addition could effectively reduce the leaching of cadmium by 77.71%, lead by 21.14%, zinc by 42.37%, and chromium by 99.99%, as compared to the original MSWI FA TCLP results.

Some researchers in Taiwan argue that the current mechanism by which government subsidies are allocated for smart city demonstration projects warrants improvement. A comprehensive literature review determined that the development potential of smart cities should be prioritized in site selection for such demonstration projects. This study developed an evaluation framework on the basis of multi-criteria evaluation methods to enable the identification of suitable smart community demonstration sites. Evaluation criteria were first identified through the Delphi method. Next, the weights of each criterion were derived through the analytic hierarchy process. Furthermore, the capability of the proposed evaluation model was determined through simulation testing.

The airport ground handling services (AGHS) equipment supplier selection problem involves a safety guarantee on the part of the AGHS company that carries out the daily work. AGHS company can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. In our research, we developed an AGHS equipment supplier selection model based on the analytic hierarchy process and an AHP weighted fuzzy linear programming approach, and we solved the AGHS equipment supplier’s selection problem. The main objective of this article is to create an AHP and AHP-FLP decision model in order to help the AGHS company authorities select the best AGHS equipment supplier. The practical application in AGHS equipment supplier selection decisions can be interpreted as demonstrating that the proposed model provides knowledge and practical value for the AGHS industry.

Abstract Introduction: Visual perceptual skills are essential for independent participation in self-care tasks, educational, work and leisure time activities. The effect of HIV on the visual perceptual skills is not well understood among children in low resource settings like Zimbabwe. Methods: A cross sectional comparative study was done with 30 children living with HIV and 30 children living without HIV residing in Harare urban area. The TVPS-3 was used to assess their visual perceptual skills. SPSS version 22, STATISTICA 13 and Microsoft 2016 were used for data analysis. Results: Both groups of children had mean percentile ranks below 50 on their TVPS-3 scores. Children without HIV generally performed better than those with HIV but the difference was not statistically significant in most cases. Through univariate analysis, only performance on Spatial Relations significantly differed between the two groups. Both groups had lowest scores in Basic visual perceptual skills. Age and school grade were the independent predictors of the children’s performances in the study. Conclusion: There is need for Occupational therapy services in public primary schools and in the pediatric Opportunistic Infections clinics in hospitals to be part of the health team which caters for children with visual perceptual challenges.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and is highly invasive and easily recurs. For HCC, chemotherapy shows a limited effect. The gold standard for HCC treatment includes curative surgical resection or liver transplantation. However, the recur-rence rate at 5 years after liver resection is estimated at approximately 70% and even at 5 years after liver transplantation, it is 20%. Therefore, improving survival outcomes after curative surgi-cal resection of liver cancer is crucial. This review highlights the importance of identifying risk factors for HCC recurrence following radical surgical resection and adjuvant therapy options that may reduce the recurrence risk and improve overall survival, including local adjuvant therapy (e.g., transcatheter arterial chemoembolization and radiotherapy), adjuvant systemic therapy (e.g., small molecule targeted therapy and immunotherapy), and other adjuvant therapies (e.g., chem-otherapy). However, further research is needed to refine the use of these therapies and optimize their effectiveness in preventing HCC recurrence.

FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The radar view geometry diversity varies across the swath, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence NOCant is applied for the wind retrieval analysis from Ku-band, C-band. An extra engineering correction of 0.15dB and 0.2dB is applied on Ku-band and C-band backscatter values, respectively, to reduce the wind speed bias without increasing the standard deviation. Overall, NOCant is the best option for both channels. Also, the instrument backscatter data stability over time is good and the retrieved winds can fulfill operational requirements.

Uridine diphosphate (UDP)-glycosyltransferases (UGTs) are major phase II detoxification enzymes involved in glycosylation of lipophilic endobiotics and xenobiotics, including phytoalexins. Nicotine, one of the most abundant secondary plant metabolites, is highly toxic to herbivorous insects. Plant-herbivore competition has been the major impetus for evolution of large superfamilies of UGTs and other detoxification enzymes. However, UGT functions in Myzus persicae adaptation have remained unknown. In this study, UGT inhibitors (sulfinpyrazone and 5-nitrouracil) significantly increased nicotine toxicity in M. persicae nicotianae, suggesting UGTs may be involved in nicotine tolerance. One hundred one UGT transcripts identified from the M. persicae genome/transcriptome were renamed according to UGT Nomenclature Committee guidelines and clustered into 11 families: UGT329, UGT330, UGT339, UGT341-UGT345, and UGT348-UGT350. UGT344 contained the most UGTs (fifty-seven). Ten UGTs (UGT330A3, UGT339A2, UGT341A6, UGT342B3, UGT343C3, UGT344D5, UGT344D8, UGT348A3, UGT349A3 and UGT350A3) were overexpressed in M. persicae nicotianae compared with M. persicae sensu stricto. UGT330A3/UGT344D5/UGT348A3/UGT349A3 knockdown significantly increased M. persicae nicotianae nicotine sensitivity, suggesting UGT upregulation in this subspecies may be associated with nicotine tolerance and thus host adaptation. This study reveals possible UGTs relevant to nicotine adaptation in tobacco-consuming M. persicae nicotianae and might facilitate further validation of the roles of these UGTs in nicotine tolerance.

The aim of the present study was to assess the efficacy of All Cosmos Industries (ACI) bio-organic and bio-chemical fertilizers and ACI N-Fixer (N-Bio Booster) on the paddy yields based on the field trial plots at Langkat, Medan, Indonesia. This application of ACI bio-organic fertilizer (NPK 5/5/5) and ACI bio-chemical (NPK 15/15/15) fertilizer and ACI N-Fixer tests were conducted at the paddy farm at Langkat from May-October 2018. This study employed a factorial randomized complete block design which consisted of two factors, namely: Factor I with four types of fertilizers while Factor II consisted of two paddy varieties (Inpari 30 and Inpari 32). Overall, the filled grains in the ACI treatments are significantly (P< 0.05) higher than those in the control treatments that used Normal Chemical Compound NPK. Overall, total weight per meter² (368-617g) in ACI treatments are also significantly (P< 0.05) higher than those (319-371g) in the control treatments. At harvesting time at 105 days after transplanting, significantly higher (P< 0.05) colony counts (13-15 x 10⁶ CFU/mL) (for ACI treatments), than those (8 x 10⁶ CFU/mL) in the controls positively indicated higher total yields of paddy grains per hectare. It was found that the application of ACI bio-organic and bio-chemical fertilizers and ACI N-Fixer can improve paddy yields of the two rice varieties, between 16.4-38.2% (up to 5.75 MT/ha), in the field trial plots at Langkat. These commercial fertilizers play an imperative role in refining the soil fertility and thereby can increase the yield of rice production. Therefore, it is highly recommended that ACI bio-organic and ACI bio-chemical fertilizers and ACI N-Fixer (N-Bio Booster) can be employed to increase the paddy yield in this region.

With a high proportion of distributed photovoltaic and lower fossil energy integrated into the distribution network, it is very difficult to ensure the reliability of power supply. The distributed photovoltaic planning model based on big data is proposed. According to the impact stochastic photovoltaics and loads on reliability planning, the static and dynamic capacity-load ratios are proposed. The big data analysis model of distributed photovoltaic planning is established. The big data multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source-load big data scenario matching model is proposed. Ac-cording to the source load big data scenario, the dynamic capacity-load ratio of the distribution network is obtained. The static capacity-load ratio calculation method in distribution network planning is studied to ensure the reliability of power supply. Finally, the IEEE 33-bus system is used as an example. The results show that distributed photovoltaic planning based on big data and multi-scenario methods can improve photovoltaic utilization and power supply reliability.

In the process of deflagration of energetic materials, strong electromagnetic radiation is to be generated, which causes the surrounding electronic equipment to fail to work normally. To solve this problem, it is necessary to clarify the mechanism of electromagnetic radiation generated by energetic materials. The mechanism of plasma changed by the deflagration of energetic materials is an important topic in the aerospace and geophysics fields. The academic community holds two main viewpoints on the mechanism of electromagnetic radiation generated by energetic materials: one is that the solid material is squeezed and deformed during the deflagration of energetic materials, and the charges of different polarities rub in space to form effective electric dipoles, which eventually generate electromagnetic radiation. Another view is that the deflagration of energetic materials causes the temperature of the medium to rise sharply, and bremsstrahlung is formed during the compression and diffusion of the high-temperature wave front, resulting in the generation of electromagnetic radiation. This paper, based on theoretical analysis and experimental data, holds the view that electromagnetic radiation is generated by the high-temperature thermal effect. It studies the relationship between temperature and electromagnetic radiation and obtains quantitative analysis conclusions.

During the explosion of energetic materials, obvious electromagnetic interference will be generated, which will affect the normal operation of surrounding electronic equipment. Experiments are still an important means to study this issue. A set of electromagnetic radiation measurement device based on short-wave omnidirectional antenna and ultra-wideband omnidirectional antenna is designed to measure the electromagnetic radiation generated by the explosion of energetic materials of different masses, and the electromagnetic radiation characteristics are obtained through data processing. The results show that the mass of the energetic material has a significant effect on the time-domain characteristics of the electromagnetic radiation generated by the explosion: the higher the mass of the energetic material, the shorter the delay response of the electromagnetic signal, the longer the duration, and the earlier the peak appears. The frequency of electromagnetic radiation signals generated by the explosion of energetic materials is mainly concentrated below 100 MHz, and the energy is most concentrated in the frequency band of 0~50 MHz. The composition of energetic materials has the greatest influence on the spectral distribution, and the spectral distribution of electromagnetic radiation produced by the explosion of explosives with different compositions has obvious specificity. The electromagnetic radiation intensity generated by the explosion of energetic materials has a strong correlation with the distance from the explosion center, and it decreases with the increase of the distance, and the decreasing range is large. The structure and detonation method of energetic materials can change the geometrical motion mode during the explosion, resulting in the non-uniformity of electromagnetic radiation propagation.

Finding the key factor and possible "Newton's laws" in financial markets has remained the central issue in this area. However, with the development of information and communication technologies, financial models are becoming more and more realistic but complex which is contradictory to the objective law “Greatest truths are the simplest”. Therefore, this paper attempts to discover the most critical parameter and establishes an evolutionary model which is independent of micro features. In the model, information is the only key factor and stock price is the emergence of the collective behavior. The statistical properties of the model are significantly similar to the real market. It also explains the correlations of stocks within an industry, which provide a new idea for the study of key factors and core structures in the financial market.

The Pythagorean fuzzy set (PFS), which is characterized by a membership and a non-membership degree and the square sum of them is less or equal to one, can act as an effective tool to express decision makers’ fuzziness and uncertainty. Considering that the Heronian mean (HM) is a powerful aggregation operator which can take the interrelationship between any two arguments, we study the HM in Pythagorean fuzzy environment and propose new operators for aggregating interval-valued Pythagorean fuzzy information. First, we investigate the HM and geometric HM (GHM) under interval-valued intuitionistic fuzzy environment and develop a series of aggregation operators for interval-valued intuitionistic fuzzy numbers (IVIFNs) including interval-valued intuitionistic fuzzy Heronian mean (IVIFHM), interval-valued intuitionistic fuzzy geometric Heronian mean (IVIFGHM), interval-valued intuitionistic fuzzy weighted Heronian mean (IVIFWHM) and interval-valued intuitionistic fuzzy weighted geometric Heronian mean (IVIFWGHM). Second, some desirable and important properties of these aggregation operators are discussed. Third, based on these aggregation operators, a novel approach to multi-attribute decision making (MADM) is proposed. Finally, to demonstrate the validity of the approach, a numerical example is provided and discussed. Moreover, we discuss several real-world applications of these operators within policy-making contexts.

Whether and how P2X7 receptor knockout (KO) modulates central post-stroke pain (CPSP) induced by lesions of the ventrobasal complex (VBC) of the thalamus regarding behavioral, molecular, and electrical recording parameters remains unclear. Following the experimental protocols of a rat hemorrhage CPSP model, the VBC of the thalamus of wild-type and P2X7 receptor KO mice was injected with 10 mU/0.2 μl type IV collagenase to produce an animal model of stroke-like thalamic hemorrhage. Behavioral data showed that the CPSP group induced thermal and mechanical pain, while the P2X7 receptor KO group showed reduced thermal and mechanical pain behaviors. Molecular assessments revealed that the CPSP group had lower expression of NeuN and KCC2 and higher expression of GFAP, IBA1, and BDNF. The P2X7 KO group showed lower expression of GFAP, IBA1, and BDNF and higher expression of KCC2 than the CPSP group. The expression of NKCC1, GABAa receptor, and TrkB did not differ significantly between the control, CPSP, and P2X7 receptor KO groups. Muscimol application increased multiunit numbers and [Cl-] influx in the membrane in the CPSP group, while P2X7 receptor KO reduced multiunit activity and [Cl-] outflux. P2X4 receptor expression was significantly decreased in the 100 kb but not the 50 kb site in the P2X7 receptor KO group. Altogether, the P2X7 hypothesis of CPSP was proposed, wherein P2X7 receptor KO changed the CPSP pain behavior, numbers of astrocytes and microglia, CSD amplitude of the anterior cingulate cortex and the medial dorsal thalamus, BDNF and KCC2 expression, [Cl-], and P2X4 activation in 100 kb with P2X7 receptors. The present findings have implications for the clinical treatment of CPSP symptoms.

This paper initiates the study of the mathematical aspects of the ad-hoc Lanzhou index. If G is a graph with the vertex set {x1,⋯,xn}, then the ad-hoc Lanzhou index of G is defined by Lz¯(G)=∑i=1ndi(n−1−di)2, where di represents the degree of the vertex xi. Several identities for the ad-hoc Lanzhou index, involving some existing topological indices are established. The problems of finding graphs with the extremum values of the ad-hoc Lanzhou index from the following sets of graphs are also attacked: (i) set of all connected ξ-cyclic graphs of a fixed order, (ii) set of all connected molecular ξ-cyclic graphs of a fixed order, (iii) set of all graphs of a fixed order, (iv) set of all connected molecular graphs of a fixed order.

In this paper, we propose a new covering-based set in which the lower and the upper approximation operations are defined by neighborhood systems. We discuss this new type of covering-based set systematically in two steps. First, we study the basic properties of this covering-based set, such as the properties of normality, contraction, and monotone. Second, we discuss the relationship between the new type of covering-based set and the other ten sets proposed.

In this paper, we propose a new covering-based set in which the lower and the upper approximation operation are defined by neighborhood systems. We discuss this new type of covering-based set systematically in two steps. First, we study the basic properties of this covering-based set, such as the properties of normality, contraction, and monotone. Second, we discuss the relationship between the new type of covering-based set and the other ten sets proposed.

This study reports multivariate statistical techniques applied including cluster analysis to evaluate and classify the river pollution level in Taiwan, and principal component analysis-multiple linear regression (PCA-MLR) to identify the possible pollution source. Water quality and heavy metal monitoring data from Taiwan Environmental Protection Administration (EPA) was evaluated for 14 rivers in the four regions of Taiwan. The Erren River was classified as the most polluted River in Taiwan. Biochemical oxygen demand, ammonia, and total phosphate concentration in this river were the highest of the 14 rivers evaluated. In addition, heavy metal levels of the following rivers exceeded the Taiwan EPA standard limit: lead - in the Dongshan, Jhuoshuei, and Xinhuwei Rivers; copper - in the Dahan, Laojie, and Erren Rivers; and manganese - in all rivers. Water pollution in the Erren River was estimated to originate 72% from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research showed that PCA-MLR and the cluster analysis model accomplished our study objectives and will be helpful tools to evaluate water quality in rivers and we suggest that the continuous monitoring should be conducted to monitor water pollution from anthropogenic activities.

By constructing a static map, the library robot can navigate and access documents autonomously, which greatly improves efficiency; however, for libraries where shelves can be moved, map changes do not allow for direct navigation, and also sensors such as radar and camera are relatively expensive, so we propose a low-cost navigation algorithm based on real-time ranging data and map matrices, and designed a mobile archive access robot. In order to obtain real-time distance data, nine near/long range sensors were mounted on the chassis and a composite digital filtering algorithm was designed based on different moving area characteristics; then the access task matrix and map matrix were designed based on the archive access task and the location characteristics of the archive shelf placement, the robot can rely on the range data to update the map matrix during the moving process, complete its own positioning, and use the task matrix The robot can use the task matrix to complete autonomous navigation and access to multiple files. Experiments show that the filtered positioning accuracy can reach 1cm, while the robot can move to the target shelf autonomously, which is more practical and less costly.

Exosomes are nano-sized extracellular vesicles excreted by mammalian cells that circulate freely in the bloodstream of living organisms. Exosomes have a lipid bilayer that encloses genetic material used in intracellular communication (e.g., double-stranded DNA, micro-RNAs, and messenger RNA). Recent evidence suggests that dysregulation of this genetic content within exosomes has a major role in tumor progression and in the surrounding microenvironment. Motivated by this discovery, we focused here on using exosomal biomarkers as a diagnostic and prognostic tool for cancer. In this review, we discuss recently discovered exosome-derived proteomic and genetic biomarkers used in cancer diagnosis and prognosis. Although several genetic biomarkers have been validated for their diagnostic values, proteomic biomarkers are still being actively pursued. We discuss both commercial technologies and emerging technologies for exosome isolation and analysis.

For Convolutional Neural Network (CNN), Depthwise Separable CNN (DSCNN) is a preferred architecture for Application Specific Integrated Circuit (ASIC) implementation on edge devices. It can benefit from a multi-mode approximate multiplier proposed in this work. The proposed approximate multiplier uses two 4-bit multiplication operations to implement a 12-bit multiplication operation by reusing the same multiplier array. With this approximate multiplier, sequential multiplication operations are pipelined in a modified DSCNN to fully utilize the PE array in the convolutional layer. This Approximate (A-DSCNN) was implemented on TSMC 40-nm CMOS process with a supply voltage of 0.9 V. At the clock frequency of 200 MHz, the design achieves 4.78 GOPs/mW while occupying 1.24 mm x 1.24 mm silicon area. Compared to conventional DSCNN implemented in a similar process node, the chip area and power consumption were reduced by 53% and 25%, while the throughput was improved by 17%.

In this study, a monoethanolamine aerosol growth model was developed to investigate the aerosol growth factor. Interactions among the internal conditions in an absorber were considered in this aerosol model. Additionally, an experiment was conducted to measure aerosol particle size, for collecting in-house validation data. Sucrose was used as the aerosol nuclei instead of sulfuric acid to prevent the corrosion of equipment used in the experiment. Experimental results showed that the outlet aerosol sizes increased to the same size regardless of the sucrose concentrations. The aerosol growth model was validated using the in-house experimental data. The aerosol growth model efficiently predicted the aerosol size. For investigating aerosol growth effects, particle number concentration was determined to be the primary factor affecting aerosol growth and amine emissions. When the particle number concentration increased, the aerosol size decreased, whereas the MEA emission increased.

Background: The study aimed to explore the association between the prognostic nutrition index (PNI) and in-hospital complication and mortality in patients with spontaneous intracerebral hemorrhage (ICH). Methods: We conducted a retrospective analysis using data from the Changhua Christian Hospital Clinical Research Database (CCHRD) between January 2015 and December 2022. 2402 patients were included, with the median PNI value set at 42.77. The restricted cubic spline model, Kaplan-Meier and ROC analysis for overall survival were used to evaluate the association of PNI with medical complications and mortality. Propensity score matching (PSM) analysis was performed to balance these clinical variables between groups, which could reduce potential bias to some extent. Results: PNI was significantly associated with in-hospital complications and 28-day and 90-day mortality in multivariate analysis. The main finding of our study is that a low PNI score at admission was independently associated with a poor ICH outcome. The Kaplan-Meier curves revealed higher survival rates in the high PNI group. PNI demonstrated sensitivity to predict medical complications and mortality based on ROC analysis. Conclusions Our study suggests that PNI could serve as a valuable marker for predicting medical complications and mortality in patients with spontaneous ICH.

Multi-chiplet technique is expected to be a promising solution to achieve high-density system integration with low power consumption and high usage ratio. This technique can be integrated with glass interposer to accomplish a competitive low fabrication cost compared with the silicon-based interposer architecture. In this study, process-oriented stress simulation is performed by the element activation and de activation technique in finite element analysis architecture. Submodeling technique is also utilized to mainly conquer the scale mismatch and difficulty in mesh gridding design. It is used as well to analyze the thermomechanical responses of glass interposer with chiplet arrangement and capped epoxy molding compound (EMC) during curing. A three-factor, three-level full factorial design is applied using the analysis of variance method to explore the significance of various structural design parameters on stress generation. Analytic results reveal that the maximum first principal stresses of 130.75 and 17.18 MPa are introduced on the sidewall of Cu-filled via and the bottom of glass interposer, respectively. Moreover, the EMC thickness and through glass via pitch are the dominant factors in the adopted vehicle. They significantly influence the stress magnitude during heating and cooling.

Background: Young adults, particularly university students might be at greater risk of developing psychological distress, and exhibiting symptoms of anxiety and depression during the COVID-19 pandemic. Objective: The primary objective of this study was to explore and compare the determinants and predictors of mental health (anxiety and depression) during and after COVID-19 lockdown among university students. Methods: This was an observational, cross-sectional study with a sample size of 417 students. An online survey utilizing International Physical Activity Questionnaire–Short Form (IPAQ-SF), General Anxiety Disorder–7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) was distributed to Universiti Tunku Abdul Rahman students via Google forms. Results: During lockdown, family income [χ² (1, n=124) = 5.155, p=0.023], and physical activity [χ² (1, n=134) = 6.366, p=0.012] were associated with anxiety, while depression was associated with gender [χ² (1, n=75) = 4.655, p=0.031]. After lockdown, family income was found to be associated with both anxiety [χ2 (1, n=111) = 8.089, p=0.004], and depression [χ2 (1, n=115) =9.305, p=0.002]. During lockdown, family income (OR=1.60, p=0.018), and physical activity (OR=0.59, p=0.011) were predictors for anxiety, and gender (OR=0.65, p=0.046) being the only predictor for depression. After lockdown, family income was a predictor for both anxiety (OR=1.67, p=0.011), and depression (OR=1.70, p=0.009). Conclusion: Significant negative effects attributed to the COVID-19 lockdown, and certain factors predisposed to the worsening of mental health status in university students. Family income, physical activity level, and gender were some of the major determinants that influenced the anxiety and depression.

This study integrated coastal-watershed models and combined a risk assessment method to develop a methodology to investigate the impact resulting from coastal disasters under climate change. The mid-western coast of Taiwan suffering from land subsidence was selected as the demonstrative area for the vulnerability analysis based on prediction of sea level rise (SLR), wave run-up, overtopping, and coastal flooding under the scenarios of 2020 to 2039. Database from tidal gauges and satellite images were used to analyze sea level rise using EEMD (Ensemble Empirical Mode Decomposition). Extreme wave condition and storm surge were estimated by numerical simulation using WWM (Wind Wave Model) and POM (Princeton Ocean Model). Coastal inundation was then simulated via WASH123D watershed model. The risk map of study areas based on the analyses of vulnerability and disaster were established using the AHP (Analytic Hierarchy Process) technique. Predictions of sea level rise, the maximum wave condition and storm surge under the scenarios of 2020 to 2039 are presented. The results indicate that the sea level at the mid-western coast of Taiwan will rise in an average of 5.8 cm, equivalent to a rising velocity of 2.8 mm/year. The analysis indicates that Wuqi, Lukang, Mailiao, and Taixi townships are susceptive, low resistant and low resilient, and reaches the high risk level. The assessment provides that important information for making adaption policy in the mid-western coast of Taiwan.

Taiwan is highly susceptible to global warming, experiencing a 1.4°C increase in air temperatures from 1911-2005, which is twice the average for the Northern Hemisphere. This has led to higher rates of respiratory and cardiovascular mortality. Accurately predicting maximum temperatures during the summer season is crucial, but numerical weather models become less accurate and more uncertain beyond five days. To improve forecast reliability, statistical post-processing is needed to address systematic errors. In September 2020, NOAA NCEP implemented the Global Ensemble Forecast System version 12 (GEFSv12) to help manage climate risks. This study developed a Hybrid statistical post-processing method that combines Artificial Neural Networks (ANN) and Quantile mapping (QQ) approaches to predict daily maximum temperatures and extremes in Taiwan during the summer season. The Hybrid technique, utilizing deep learning techniques, was applied to the GEFSv12 reforecast data and evaluated against ERA5 reanalysis. The Hybrid technique was the most effective among the three techniques tested. It had the lowest bias, RMSE, and highest correlation coefficient. It successfully reduced the warm bias and overestimation of Tmax extreme days. This led to improved prediction skills for all forecast lead times. Compared to ANN and QQ, the Hybrid method was more effective in predicting summer daily Tmax and its extremes on an extended-range time scale deterministic and ensemble probabilistic forecasts over Taiwan.

The cross-linkable PCL-PEG analogues block-graft copolymer was designed and synthesized, which with the copolymer of the MEO₂MA and OEGMA as graft chains to improve the mPEG-b-PCL-b-mPEG copolymer the aqueous solution properties. And successfully prepared two hydrogels via a copper-catalyzed 1, 3-dipolar azide-alkyne cycloaddition reaction of alkyne-terminated poly[glycidyl methacrylate-co-2-(2-methoxyethoxy) ethyl methacrylate-co-oligo (ethylene glycol) methacrylate] [P(GMA-co-MEO₂MA-co-OEGMA)] with azide end-functionalized PCL-PEG analogues block-graft copolymer, and tetrakis (2-propynyloxymethyl) -methane (TPOM) and with azide end-functionalized PCL-PEG analogues block-graft copolymer. The copolymer's chemical structure was characterized by proton nuclear magnetic resonance spectroscopy and fourier transform infrared spectroscopy. The molecular weights of the copolymers were decided with gel permeation chromatography. The water solubility and temperature sensitivity of the copolymers were studied by taking digital photos and transmittance change measured by UV spectrophotometer at different temperatures. Fluorescence probes, surface tension, dynamic light scattering and transmission electron microscopy were used to analyze the micelles that copolymers self-assembly in aqueous solution. The sol-gel behavior of copolymer solutions at high concentrations was explored by vial inversion experiments. Finally, the network structure of the gels was observed by scanning electron microscopy. These conclusions indicate that these hydrogels are expected to be used as a new material in the field of biomedicine.

The intestinal microbial community of aquatic animals is closely related to the environment and thus affects the health of aquatic animals. Here we investigated annual variation of intestinal bacteria community structures of Ruditapes philippinarum cultured using two different methods, bottom sown and raft hanging. Bacterial diversity of seawater and sediment in different seasons was examined as well. Correlation between R. philippinarum intestinal bacteria community structures and environmental factors was analyzed. The results showed that in the two aquculture areas where R. philippinarum was differentially cultured, bacteria community structure of seawater and sediment both altered with seasons. However, neither seasonal changes nor difference in culture methods could significantly affect the intestinal bacteria community structures of R. philippinarum. Additionally, in terms of the alpha diversity of intestinal bacteria communities, there was no significant difference in R. philippinarum cultured using the two methods. For benthic cultured clams, alpha diversity of the intestinal bacterial communities was often higher than that of the environmental bacterial communities. For raft hanging cultured calms, Alpha diversity of the intestinal bacterial communities was normally comparable with that of the environmental counterparts. Beta diversity analysis showed that the intestinal bacterial community structure of R. philippinarum was mainly determined by both deterministic and stochastic processes. Correlation analysis showed that the intestinal bacterial community structures of R. philippinarum were determined by a variety of environmental factors. For clams collected from the benthic culture area, the intestinal bacterial structure was more correlated with the physicochemical properties of sediment in comparing with those of seawater. For clams collected from the raft-culture area, the intestinal bacterial structure was mainly determined by the phsicochemical characteristics of seawater. This study helps us to better understand the bacterial community structure in complex aquaculture ecosystems, providing a reference for healthy shellfish aquaculture and ecosystem protection in aquaculture area.

In the clock network design, the trade-off between power consumption and timing closure is an important and difficult issue. To achieve this target, a hybrid clock network architecture that combines both clock tree and clock mesh seems to be a promising solution. In this paper, we propose a novel clock mesh architecture – cross-mesh, with the average dispersion of the overall driving force, our methodology creates small non-zero skew clock trees. In addition, we integrate clock gating and register clustering techniques to further reduce dynamic power consumption. The proposed algorithms have four stages: cross-mesh planning, register clustering, mesh line connecting, and load balancing. Experimental results show that our methodology and algorithms get feasible solution effectively and improve both power consumption and clock skew simultaneously.

Supplier selection (SC) continues to be a crucial component in manufacturing procurement. Using the PLCC advantages generates revenue and lowers costs more than might be anticipated. Supplier selection (SC) product life cycle cost (SCPLCC) is an important consideration among corporations, SCPLCC problem has become associated with deciding how one supplier should be selected from possible alternatives. Past literature on this topic is limited. We try to close this gap in knowledge. In this study, we attempt to apply two types of goal programming, multiobjective linear programming and revised multichoice goal programming to develop a PLCC-based concept to solve the SCPLCC problem and construct a decision-making tool for application to a case of supply chain management in a Taiwanese light-emitting diode company in the high-tech industry. Our study main contribution Company managers can easily use the two approaches of the SCPLCC model with different parameters to solve SCPLCC model problems. Finally, we are comparing five models found RMCGP with weighted linear goal programming had an adequate effect for application to the PLCC concept for high-technology Company; this cloud makes company decision–makers focus on low PLCC and select a better supplier in sustainable supply chains development.

Supplier selection (SC) product life cycle cost (SCPLCC) is an important consideration among corporations, SCPLCC problem has become associated with deciding how one supplier should be selected from possible alternatives. In this study, we applied two types of goal programming, multiobjective linear programming and revised multichoice goal programming to develop a PLCC-based concept to solve the SCPLCC problem and construct a decision-making tool for application to a case of supply chain management in a Taiwanese light-emitting diode company in the high-tech industry. Our study main contribution Company managers can easily use the two approaches of the SCPLCC model with different parameters to solve SCPLCC model problems. Finally, we comparing five models found RMCGP with weighted linear goal programming had an adequate effect for application to the PLCC concept for high-technology company; this cloud makes company decision–makers focus on low PLCC and select a better supplier in sustainable supply chains goal.

Using the PLCC advantages generates revenue and lowers costs more than might be anticipated. It ensures better decisions by using a more perfect and practical assessment of revenues and costs. Although the supplier selection (SC) product life cycle cost (SCPLCC) is an important consideration among corporations, SCPLCC problem has become associated with deciding how one supplier should be selected from possible alternatives. In this study, we applied two types of goal programming, multiobjective linear programming and revised multichoice goal programming to develop a PLCC-based concept to solve the SCPLCC problem and construct a decision-making tool for application to a case of supply chain management in a Taiwanese light-emitting diode company in the high-tech industry. Our study main contribution Company managers can easily use the two approaches of the SCPLCC model with different parameters to solve SCPLCC model problems. Finally, we comparing five models found RMCGP with weighted linear goal programming had an adequate effect for application to the PLCC concept for high-technology company; this cloud make company decision–makers focus on low PLCC and select better supplier.

Today’s purchasing companies demand an advanced buyer equal of enactment from their supplier while the two parties maintain a respectful connection. Although the supplier selection (SC) product life cycle cost (SCPLCC) is an important consideration among corporations, SCPLCC problem has become associated with deciding how one supplier should be selected from possible alternatives. In this study, we applied two types of goal programming, multiobjective linear programming and revised multichoice goal programming to develop a PLCC-based concept to solve the SCPLCC problem and construct a decision-making tool for application to a case of supply chain management in a Taiwanese light-emitting diode company in the high-tech industry. Our study main contribution Company managers can easily use the two approaches of the SCPLCC model with different parameters to solve SCPLCC model problems. Finally, we comparing five models found RMCGP with weighted linear goal programming had an adequate effect for application to the PLCC concept for high-technology comapny; this cloud make company decision–makers focus on low PLCC and select better supplier.

Proposed as a response to the increasing global need for environmental protection, a green port balances economic vibrancy and environmental protection. However, because exhaust emissions (e.g., CO2 or sulfide) are difficult to monitor in and around ports, data on such emissions are often incomplete, which hinders research on this topic. To remedy this problem, this study aimed to formulate a method for collecting CO2 emissions data at their source; this method was applied to collect real-world operating data from a large container-handling company in Taiwan. Specifically, to account for undesirable outputs, we formulated a method that combines (1) data envelopment analysis based on a modified slack-based measure and (2) a multichoice goal programming approach. We found that rubber-tired gantry cranes are the greenest and should be used. Our findings aid port managers in selecting port equipment that best balances between environmental protection and profitability.

With the rising cost of animal feed protein, finding affordable and effective substitutes is crucial. Walnut kernel cake, a polyphenols, fiber, protein and fat-rich byproduct of walnut oil extraction, has been underexplored as a potential protein replacement in pig feed. In this study, we found that feeding large Diqing Tibetan pigs walnut kernel cake promoted adipose deposition and improved pork quality during pig growth. Transcriptome analysis revealed the upregulation of genes ANGPTL8, CCNP, ETV4, and TRIB3, associated with adipose deposition. Pathway analysis highlighted enrichment in adipose-deposition-related pathways, including PPAR, Insulin, PI3K-Akt, Wnt, and MAPK signaling. Further analysis identified DEGs (differentially expressed genes) positively correlated with adipose-related traits, such as PER2 and PTGES. Single-cell transcriptome data pointed to the specific expression of CD248 and PTGES in adipocyte progenitor/stem cells (APSCs), pivotal for adipocyte differentiation and adipose deposition regulation. This study demonstrates walnut kernel cake's potential to substitute soybean cake in pig feed, providing high-quality protein and promoting adipose deposition. It offers insights into feed protein replacement, human functional food, fat metabolism, and related diseases, with marker genes and pathways supporting pig breeding and pork quality improvement.

Global agriculture has been forced to increase food production to feed the growing human population, while confronting various environmental obstacles such as global warming, resistance to pathogens, and constraints on arable land caused by soil salinity, drought, rising sea level, saltwater intrusion, and urbanization. Regarding abiotic stresses, salinity is a worldwide problem for agricultural production. Many efforts, therefore, have been made to cope with the environmental challenges, however, the progress of salinization, which is mainly caused and accelerated by anthropogenic activities, is likely faster than our progress in finding ways to deal with this problem. In addition, drought represents a global threat to the production of major crops. In addition, pests and pathogens cause significant crop losses and diminish global food security. Among the various strategies that have been investigated and applied in plant science, antimicrobial peptides derived from plants have caught widespread attention from scientists since these peptides exhibit beneficial biological activities. In agricultural science, there have been reports on the roles of antimicrobial peptides with active properties against biotic and abiotic stresses. Non-specific plant lipid transfer proteins, thionins, systemins, defensins, cyclotides, and heveins-like antimicrobial peptides are common antimicrobial peptides that have been found to be involved in the defense system against fungi and insect pests. Based on their potential ability to protect crops from pests, bacteria, and pathogenic fungi, the use of antimicrobial peptide genes in creating transgenic plants has been largely conducted during the last decades, and these studies have obtained positive results against the growth of fungi and bacteria. This review will focus on the latest progress in studies of antimicrobial peptides related to biotic and abiotic stress tolerance in plants. We will also update the current progress in the development of antimicrobial peptide-based transgenic crops.

During the launch and return of a spacecraft, the intense combustion of propellants generates strong electromagnetic radiation, which interferes with the operation of electronic equipment in the spacecraft. To improve the electromagnetic compatibility of electronic equipment in spacecraft, it is necessary to study the electromagnetic radiation characteristics of rocket fuel. An electromagnetic radiation measurement system based on antennas is designed to measure the electromagnetic radiation generated by rocket fuel, and the electromagnetic radiation characteristics are obtained through data analysis. The mechanism of the electromagnetic radiation generated by rocket fuel is comprehensively analysed through the spatial, time-domain, frequency-domain, and energy-domain characteristics. A characterization model is established to provide a reliable scheme for evaluating the influence of rocket fuel electromagnetic radiation on electronic equipment in spacecraft.

The surface enhanced fluorescence（SEF）detection bases by plasmonic nanopillars array with nanoparticles has opened up a new gate in the application of biological imaging and sensing. The fluorescence enhancement of the probe molecule depends on its position in equilibrium, which is close to the hot spot leading to the electromagnetic field enhancement, but not too close to the metal surface resulting in quenching. Here, a large scale SiO₂-Ag-cicada wing SEF substrate was fabricated by magnetron sputtering with correction enhancement factor of 797.6. Thereinto the cicada wing provides the skeleton of the nanopillars array structure, the deposited Ag constructs two kinds of hot spots, and SiO₂ forms a separation layer to prevent quenching. Moreover, the substrate exhibited good reproducibility, high sensitivity with low limits of detection (LOD) and high stability for oxidation resistance. We propose that SEF substrate with modification of SiO₂ can not only improve the enhancement performance, but also expanding its application in the biological investigations.

The undoped and tungsten (W)-doped vanadium dioxide (VO₂) thin films were prepared by electron beam evaporation associated with ion-beam assisted deposition (IAD). The influence of different W-doped contents (3%-5%) on the optical, electrical, structural, and thermos-mechanical properties of VO₂ thin films was investigated experimentally. Spectral transmittance results showed that with the increase of W-doped contents, the transmittance in the visible light range (400-750 nm) decreases from 60.2% to 53.9%, and the transmittance in the infrared wavelength range (2.5 μm to 5.5 μm) drops from 55.8% to 15.4%. As the W-doped content increases, the residual stress in the VO₂ thin film decreases from -0.276 GPa to -0.238 GPa, but the surface roughness increases. For temperature-dependent spectroscopic measurements, heating the VO2 thin films from 30°C to 100°C showed the most significant change in transmittance for the 5% W-doped VO₂ thin film. When the heating temperature exceeds 55°C, the optical transmittance drops significantly, and the visible light transmittance drops by about 11%. Finally, X-ray Diffraction (XRD) and scanning electron microscope (SEM) were used to evaluate the microstructure characteristics of VO₂ thin films.

The stability and optimal control for an SIS epidemic model with demographics and heterogeneous control strategies (immunization and quarantine) on complex networks are investigated. The epidemic threshold, disease-free and endemic equilibria are derived. Then, global asymptotical stability about the two equilibria are studied based on different methods. Furthermore, the optimal control issue about the system is also considered. We obtain the existence and uniqueness of the optimal control and optimal control tactics. Some numerical simulations were conducted to illustrate and supplement the theoretical results. Meanwhile, the effects of the combination of various immunization and quarantine schemes are studied and also compared with the optimal control strategies.

Objective: To evaluate the clinical efficacy of antibiotic bone cement plus lateral bone repositioning for severe diabetic foot and its impact on wound healing rate. Methods: Sixty patients with hemodialysis anemia admitted to the endocrinology department of our hospital from July 2020 to July 2021 were included and randomly assigned to receive antibiotic bone cement plus lateral bone repositioning (observation group) or vacuum sealing drainage (VSD) (control group). Outcome measures included skin temperature of the affected limb, ankle-brachial index, sensory threshold, foot wound healing, dorsalis pedis artery hemodynamics, lower limb function and clinical efficacy. Results: Antibiotic bone cement plus lateral bone repositioning resulted in significantly higher treatment efficiency and transcutaneous partial pressure of oxygen, smaller wound, and lower trauma pH versus VSD, suggesting that antibiotic bone cement plus lateral bone repositioning contributes to potentiating the treatment efficacy and reducing the foot ulcer trauma, thereby facilitating rapid recovery. Patients given antibiotic bone cement plus lateral bone repositioning had larger vascular internal diameter and blood flow and better sensory conduction velocity of the common peroneal nerve and the motor conduction velocity of the tibial nerve versus those given VSD, indicating its vascular and neurological benefits. Conclusion: Antibiotic bone cement plus lateral bone repositioning enhances the treatment efficiency, boosts trauma healing, and promotes postoperative functional recovery of patients.

In recent years, the treatment of locally-advanced rectal cancer (LARC) has been spotlighted in a number of landmark, practice-changing trials, heralding in the age of so-termed “total neoadjuvant therapy” (TNT). However, the lack of a common control arm, standard inclusion criteria, or, indeed, definition of TNT necessitates further study in this field, particularly using real-world data. We conducted a retrospective analysis of all patients treated for LARC at a single Canadian academic cancer centre between July 1st 2019 and July 14th 2022. This includes the June 2020 incorporation of TNT. 290 patients were included in the final analysis, and 124 variables were collected on each patient, including demographics, treatment type, adverse events, response to treatment, surgery versus watchful waiting and survival. Our findings demonstrate that both TNT and non-TNT regimens are highly toxic, with regards to both overall toxicity and grade 2+ adverse effects. 62 (96.9%) of patients on TNT experienced toxicities, compared to 186 (82.3%) of the non-TNT patients. Grade 2+ toxicities were 46 (71.9%) and 143 (63.3%), respectively. TNT, however, was associated with improved rates of complete response, compared to non-TNT treatment regimens (44% vs 24%). These findings suggest that TNT harbours both benefit and risk in a real-world population, and both of these aspects should be considered when deciding on a treatment approach for an individual patient. More work in the area is needed to determine longer term survival, as well as impact of surgery versus organ preservation approach on patient outcomes.

There is a considerable amount of research in online social networks, most of which focuses on the structural analysis of social graphs. The interpersonal relationships of social networks, especially friend circle, can solve the cold start and sparsity problems, and through the relationship between social networks can effectively recommend users' favorite items (items), such as music , videos, brands/products, preferred tags, location, services, etc. User relationships in social networks are diverse and there are many different perspectives on different social networks. Associations among users can form multi-layered composite networks, and multi-layered social networks present new challenges and opportunities. Different relationships can influence users' preferences to different degrees, which in turn affects their behavior. Therefore, fusing multiple social networks is an effective way to improve recommendation. Although some studies have started to address multiple social network recommendations, simple linear superposition cannot reflect the coupling and nonlinear association between multiple social networks. In this paper, we propose a graph neural network recommendation model under social relationships based on this background. We first propose to compute the 2nd order collaborative signals and their intensities directly from the neighboring matrix for updating the node embedding of the graph convolution layer. Secondly by embedding historical evaluations, various social networks constituting different dimensions, the attention integration of user preferences by different social networks is achieved, and its effectiveness and scalability are demonstrated in theoretical derivation and experimental validation. The theoretical derivation and experimental validation demonstrate its effectiveness and scalability.

The popularity and development of social media has made it more and more convenient to spread rumors, and it has become especially important to detect rumor information from massive amounts of information. Most of the traditional rumor detection methods use content characteristics or propagation structure to mine rumor characteristics, ignoring the fusion characteristics of content and structure and their interaction characteristics. Therefore, a novel rumor detection method based on heterogeneous convolutional networks is proposed. Firstly, this paper constructs a heterogeneous map of joint rumor content and propagation structure to explore the interaction between content and propagation structure during rumor propagation and obtain rumor representation. On that basis, this paper uses the deep residual graph convolutional neural network to construct the content and structure interaction information of the current network propagation model. Finally, this paper uses the two datasets of Twitter15 and Twitter16 to verify the proposed method. Experimental results show that the proposed method has higher detection accuracy than the traditional rumor detection method.

Litsea cubeba, an important medicinal plant, is widely used as traditional Chinese medicine and spice. Using cytotoxicity-guided fractionation, nine new lignans (1-9) and eleven known analogues (10-19) were obtained from the EtOH extract of the twigs of L. cubeba. Their structures were assigned by extensive 1D and 2D NMR experiments, and the absolute configurations were resolved by specific rotation and a combination of experimental and theoretically calculated electronic circular dichroism (ECD) spectra. In the cytotoxicity assay, 7',9-epoxylignans with feruloyl or cinnamoyl group (7-9, 13 and 14) were selectively cytotoxic against NCI-H1650 cell line, while the dibenzylbutyrolactone lignans (17-19) exerted cytotoxicities against HCT-116 and A2780 cell lines. The results highlighted the structure-activity relationship importance of a feruloyl or a cinnamoyl moiety at C-9′ or/and C-7 ketone in 7',9-epoxylignans. Furthermore, compound 11 was moderate active toward protein tyrosine phosphatase 1B (PTP1B) with an IC50 value of 13.5 μM, and compounds 4-6, 11 and 12 displayed inhibitions against LPS-induced NO production in RAW264.7 macrophages, with IC50 values of 46.8, 50.1, 58.6, 47.5, and 66.5 μM, respectively.

A novel scheme that can simultaneously measure the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals is proposed. At the signal receiving unit (SRU), two echo signals and the reference signal are modulated by a Sagnac loop structure and sent to the central station (CS) for processing. At the CS, two low-frequency electrical signals are generated after polarization control and photoelectric conversion. The DFS without direction ambiguity and wide AOA measurement can be real-time acquired by monitoring the frequency and power of the two low-frequency electrical signals. In the simulation, an unambiguous DFS measurement with errors of ±3×10-3 Hz and a -90° to 90° AOA measurement range with errors of less than ±0.5° are realized. The safety and robustness of the system to environmental disturbance are improved, and it is more suitable for the modern electronic warfare system.

The intestinal microbiota, comprised of bacteria, archaea, and phages, inhabits the gastrointestinal tract of the organism. Male reproductive sterility is currently a prominent topic in medical research. Increasing research suggests that gut microbiota dysbiosis can result in various reproductive health problems. This article specifically investigates the impact of gut microbiota dysbiosis on male reproductive infertility development. Gut microbiota imbalances can disrupt the immune system and immune cell metabolism, affecting testicular growth and sperm production. This dysfunction can compromise the levels of hormones produced and secreted by the endocrine glands, affecting male reproductive health. Furthermore, imbalance of the gut microbiota can disrupt the gut-brain-reproductive axis, resulting in male reproductive infertility. This article will explore how the imbalance of the gut microbiota impacts male reproductive infertility through immune regulation, endocrine regulation, and interactions of the gut-brain-reproductive axis, concluding with recommendations for prevention and treatment.

Background: Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in human populations worldwide. Conversely, Huanglian Decoction is one of the most important Chinese medicine formulas, with the potential to treat cancer. Methods: To identify differentially expressed genes (DEG), we herein downloaded gene expression profile data from the TCGA (TCGA-LIHC) and GEO (GSE45436) databases. We obtained phytochemicals of the four constituent herbs of Huanglian Decoction from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). We also established a regulatory network of DEG and their drug target genes and subsequently analyzed key genes using bioinformatic approaches. Furthermore, we explored the effect of Huanglian Decoction by conducting in vitro experiments so as to verify the prediction. In particular, the CCNB1 gene was knockdown to verify the primary target of this Decoction. Results: Based on the results of network pharmacological analysis, we revealed that there are 5 bioactive compounds in Huanglian Decoction acting on HCC. In addition, our findings confirmed that CCNB1 was the primary key gene, which can be highly expressed in tumors and was significantly associated with a worse prognosis (P = 0.002) according to PPI network analysis of the target genes of these five compounds, as well as expression and prognosis analyses in tumors. We also noted that CCNB1 can be used as an independent prognostic indicator of HCC (P < 0.01). Moreover, in vitro experimental results demonstrate that Huanglian Decoction can significantly inhibit the growth, migration, and invasion of HCC cells. Finally, further analysis showed that this Decoction may inhibit the growth of HCC cells by down-regulating the expression level of CCNB1.

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions on treatment strategies or the use of palliative care for patients; (2) Methods: The gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected the RNA-seq data of breast cancer patients, a total of 1187 RNA-seq data (median age 58 years), in FPKM format from the TCGA database. Among them, 144 RNA-seq data with date of death information was selected to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to transcripts per million (TPM) to build survival prediction model SaBrcada. After normalization and dimension raising, the differential gene expression data were used for testing eight different deep learning architectures. Among them, GoogLeNet performed the best. Considering the effect of age on prognosis, we examined all ages between the lower and upper quartiles of patient age for a stratified random sampling test; (3) Results: Stratifying by age based on a cut-off of 61 years of age improved the accuracy of SaBrcada compared to previous findings, resulting in an accuracy of 0.798. We also built a free website tool to provide 5 kinds of predicted survival period information for clinician reference; (4) Conclusions: We established a breast cancer survival analysis prediction model, SaBrcada, and a website tool with the same name. Through this highly reliable survival analysis model and website tool, information on survival intervals will be provided for clinicians as part of precision medicine.

In the present work, diamond films have been grown on GaN-on-Sapphire substrates by chemical vapor deposition (CVD) with the addition of nitrogen to the standard H2/CH4 precursor recipe. Diamond on GaN materials processing is not straight-forward, as GaN is susceptible to a quasi-pure hydrogen CVD plasma etching. 1%, 3% and 5% N2 gas was gradually added to the H2 gas with fixed 6% CH4 in the recipe to promote the growth of diamond nanocrystals. Different types of microstructures were produced, with addition of nitrogen to the precursor gas recipe. Nitrogen gas changes the diamond film microstructure from faceted to spherical grains, with signs of GaN etching. The sp3 bonded diamond film produced by nitrogen addition was found to be co-deposited along with a considerable amount of other non-diamond carbon phases (D - disordered graphite, G - crystalline graphite, TPA - trans-polyacetylene) and qualitatively all the diamond films were 50% pure (Raman peak ratio Isp3 / ID). The FWHM of the sp3 carbon Raman peak was calculated to be 6.5 cm-1, when there was no nitrogen gas in the precursor recipe, which deteriorates to a large extent after successive addition of N2. Fourier transform infrared spectroscopy (FTIR) showed a strong presence of the nitrogen related defects (peak positions at 1190 and 1299 cm-1) inside the nanocrystalline diamond (NCD) films grown with N2 addition. GaN layer etching from the base substrate by the CVD plasma was clearly evidenced by energy dispersive X-ray spectra (EDS) of the deposited films. Al elemental EDS peaks from the base sapphire substrate were observed for the films grown with nitrogen addition, but no Ga EDS peaks were detected. X-ray diffraction (XRD) micrographs further supported the enhanced GaN etching phenomenon. Nitrogen addition was thus found to be detrimental for growing CVD diamond films over GaN surfaces. The electrical resistivity of the uncoated GaN was initially measured to be 22.2 Ω-cm. However, the resistivity rose to 1.59×106 Ω-cm after the nitrogen assisted film deposition; due to the GaN etching - thereby exposing the underlying insulating sapphire substrate.

The initial outbreak of COVID-19 was first reported in Wuhan (China) during the latter part of December 2019. Indonesia has the fourth-largest population globally and reported the country’s first case of the virus on 2nd March 2020. The World Health Organisation (WHO) in addition to several neighbouring provinces and educational institutions within the region began questioning the Indonesian government upon the initial case reported. The objective of this study was to describe the epidemiological characteristics of the COVID-19 outbreak in Indonesia during March 2020. The data were collected from Indonesian government databases and non-government organisations (NGOs). The data were analysed using Microsoft Office 2019 (Excel) and Adobe Illustrator 2017 software, was used in drawing the map depicting the distribution of COVID-19 in Indonesia. As at 31st March 2020, a total of 1,528 people in Indonesia have been infected by COVID-19, in addition to 136 mortalities (CFR of 8.9%). Jakarta, as the principal capital of Indonesia, quickly has become the epicentre of the virus since this period. Most patient cases were attributed to those aged between 31 and 70 years (72.64%), with male patients (64.93%) representing the highest incidence of cases compared to female patients (35.07%). The number of ventilating machines was 3,326, with hospital numbers at 859. The distribution of cases depicting COVID-19 was mainly seen in urban areas compared to rural areas. Males compared to females, are at a higher risk of contracting COVID-19, including those aged below 30, between 30 and 60 or above. Indonesia also has the highest case fatality rate (CFR) with respect to mortalities in Southeast Asia and has the second-highest CFR globally. Similarly, while the number of ventilator machines as at 31st March 2020 were sufficient in meeting the growing number of COVID-19 cases in the country, it is possible that the government may need to increase the number of ventilators if the cases continue to escalate.

Incorporating AI tools like ChatGPT into higher educational settings has been beneficial for education, yet the extent of user satisfaction with the information provided by these tools, known as user information satisfaction (UIS), remains underexplored. Our study introduces a UIS model specifically designed for ChatGPT's application in the educational sector. Drawing from established UIS theory, we crafted a model centered around seven essential factors that influence the effective use of ChatGPT, aiming to guide both educators and learners in overcoming common challenges such as plagiarism and ensuring the ethical use of AI. We gathered data from Indonesian university participants and applied Structural Equation Modeling using Smart-PLS 4.0 for our data analysis. The results reveal that completeness, precision, timeliness, convenience, and the format of information as the most influential factors driving user satisfaction with ChatGPT. Interestingly, our research indicated that the accuracy and reliability of the information, typically deemed paramount, were not the primary concerns in the academic use of ChatGPT. Our findings lead to a recommendation for a cautious approach to the integration of ChatGPT in higher education. We advocate for a strategic use that recognizes its innovative potential while also acknowledging its limitations, thus ensuring a responsible and effective application in educational contexts. This balanced perspective is crucial for integrating AI tools into the academic fabric without compromising educational integrity or quality.

ABSTRACT Introduction: Traditional penile venous surgery and enhancement have been controversial. Based on De Novo penile fibrovascular assembly, we report on penile venous stripping (PVS) and factual penile girth enhancement (FPGE). Methods: From 2013 to 2023, 18 men underwent PVS and FPGE, all confirmed with veno-occlusive dysfunction (VOD). PVS involves the stripping of erection-related veins. FPGE was performed along the tunica albuginea bilaterally; then, the two tunic defects were repaired with a 70.0x30.0 mm venous stripe and covered with either autologous venous walls (AVW, n=10) or Surgiform (SF, n=8). The penile girth was measured, and radio-opaque contrast was used. The abridged 5-item version of the International Index of Erectile Function (IIEF-5) score and the Erection Hardness Scale (EHS) were used yearly during follow-up via the Internet. Results: The follow-up period was 5.3±1.5 years. Radiopacity was consistently enhanced. Although there was indifference between AVW and SF, there was a significant improvement (both P＜0.01) in IIEF-5 and EHS scores (9.7±2.8 vs. 20.8±2.3; 1.7±0.6 vs. 3.2±0.2, respectively). Both the diameters of the glans and penile shaft increased (28.0±2.3mm and 28.3±2.1mm vs 35.3±2.2mm and 36.3±2.1mm respectively). The satisfaction rate was 81.3% (13/16). Conclusions: This novel combination of PVS and FPGE is promising, although a larger sample size is needed for further validation.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

We explore the implementation of specific optical properties of armchair graphene nanoribbons (AGNRs) through edge-defect manipulation. This technique employs the tight-binding model in conjunction with the calculated absorption spectral function. Modification of the edge states gives rise to the diverse electronic structures with striking changes in the band gap and special flat bands at low energy. The optical-absorption spectra exhibit exotic excitation peaks and they strongly depend on the type and period of the edge extension. Remarkably, there exist the unusual transition channels associated with the flat bands for selected edge-modified systems. We discover the special rule governing how the edge-defect influences the electronic and optical properties in AGNRs. Our theoretical prediction demonstrates an efficient way to manipulate the optical properties of AGNRs. This might be of importance in the search for suitable materials designed to have possible technology applications in nano-optical, plasmonic and optoelectronic devices.

Osteoporosis is a common problem in orthopedic medicine, and it has become an important medical issue in orthopedics as Taiwan is gradually becoming an aging society. In the diagnosis of osteoporosis, the bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the main criterion for orthopedic diagnosis of osteoporosis, but due to the high cost of this equipment and the lower penetration rate of the equipment compared to the X-ray images, the problem of osteoporosis has not been effectively solved for many people who suffer from osteoporosis, however, at present, in clinical diagnosis, doctors are not yet able to accurately interpret X-ray images for osteoporosis manually, and must rely on the data obtained from DXA. In recent years, with the continuous development of artificial intelligence, especially in machine learning and deep learning, significant progress has been made in image recognition. Therefore, it is worthwhile to revisit whether it is possible to use a convolutional neural network model to read a hip X-ray image and then predict the patient's BMD. In this study, we proposed a hip X-ray image segmentation model and a hip X-ray image recognition classification model. First, we used the U-Net model as a framework to segment the femoral neck, greater trochanter, wards triangle, and the total hip in the hip X-ray images. We then performed image matting and data augmentation. Finally, we constructed a predictive model for osteoporosis using deep learning algorithms. In the segmentation experiments, we used intersection over union (IoU) as the evaluation metric for image segmentation, and both the U-Net and U-Net++ models achieved segmentation results greater than or equal to 0.5. In the classification experiments, using the T-score as the classification basis, the total hip using the DenseNet121 model has the highest accuracy of 74%.