bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving the Performance of Polymer Solar Cells with Benzo[ghi]perylenetriimide-Based Small-Molecules as Interfacial Layers
Authors: Yu Yang-Yen , Chen Hung-Cheng , Shih Kai-Yu , Peng Yan-Cheng , Jiang Bing-Huang , Liu Chao-I , Hsu Ming-Wei , Kuo Chi-Ching , Chen Chih-Ping
Published: October 2022

In this study, we prepared three benzo[ghi]perylenetriimide (BPTI) conjugated molecules as electron-transporting surface-modifying layers for polymer solar cells (PSCs). These three BPTI derivatives differed in the nature of their terminal functionalities, featuring butylamine (C3NH2), propylammonium iodide (C3NH3I), and butyldimethylamine (C3DMA) units, respectively. We evaluated the optoelectronic properties of PTB7-Th: PC71BM blends modified with these interfacial layers, as well as the performance of resulting PSCs. We used UV–Vis spectroscopy, atomic force microscopy, surface energy analysis, ultraviolet photoelectron spectroscopy, and photoelectric flow measurements to examine the phenomena behind the changes in the optoelectronic behavior of these blend films. The presence of a BPTI derivative changed the energy band alignment at the ZnO–active layer interface, leading to the ZnO film behaving more efficiently as an electron-extraction electrode. Modifying the ZnO surface with the BPTI-C3NH3I derivative resulted in a best power conversion efficiency (PCE) of 10.2 ± 0.53% for the PTB7-Th:PC71BM PSC (cf. PCE of the control device of 9.1 ± 0.13%). In addition, modification of a PM6:Y6:PCBM PSC with the BPTI-C3NH3I derivative increased its PCE from 15.6 ± 0.25% to 16.5 ± 0.18%. Thus, BPTI derivatives appear to have potential as IFLs when developing high-performance PSCs, and might also be applicable in other optoelectronic devices.
Sources, Chemical Functionalization, and Commercial Applications of Nanocellulose and Nanocellulose-Based Composites: A Review
Authors: Tahir Danish , Karim Muhammad Ramzan Abdul , Hu Hong , Naseem Sufyan , Rehan Muhammad , Ahmad Mairaj , Zhang Minglonghai
Published: October 2022

Nanocellulose is the most abundant material extracted from plants, animals, and bacteria. Nanocellulose is a cellulosic material with nano-scale dimensions and exists in the form of cellulose nanocrystals (CNC), bacterial nanocellulose (BNC), and nano-fibrillated cellulose (NFC). Owing to its high surface area, non-toxic nature, good mechanical properties, low thermal expansion, and high biodegradability, it is obtaining high attraction in the fields of electronics, paper making, packaging, and filtration, as well as the biomedical industry. To obtain the full potential of nanocellulose, it is chemically modified to alter the surface, resulting in improved properties. This review covers the nanocellulose background, their extraction methods, and possible chemical treatments that can enhance the properties of nanocellulose and its composites, as well as their applications in various fields.
A Variable Stiffness Electroadhesive Gripper Based on Low Melting Point Alloys
Authors: Xiang Chaoqun , Li Wenyi , Guan Yisheng
Published: October 2022

Electroadhesive grippers can be used to pick up a wide range of materials, and those with variable stiffness functionality can increase load capacity and strength. This paper proposes an electroadhesive gripper (VSEAF) with variable stiffness function and a simple construction based on low melting point alloys (LMPAs) with active form adaptation through pneumatic driving. Resistance wires provide active changing stiffness. For a case study, a three-fingered gripper was designed with three electroadhesive fingers of varied stiffness. It is envisaged that these electroadhesive grippers with variable stiffness would extend the preparation process and boost the use of electroadhesion in soft robot applications.
Pilot Scale Hybrid Organic/Inorganic Coatings on a Polyolefin Separator to Enhance Dimensional Stability for Thermally Stable Long-Life Rechargeable Batteries
Authors: Choi Hyoungwoo , Lee Byoung-Sun
Published: October 2022

The electric vehicle and energy storage markets have grown rapidly in recent years. Thermal runaway caused by malfunctioning Li-ion batteries is an urgent issue with many causes (e.g., mechanical, electrical, and thermal abuse). The most common cause of thermal runaway is the formation of an internal short circuit because of damage to the separator. There has been significant effort to improve the design of separators, but to our knowledge, only inorganic nanoparticle coatings are used in commercial Li-ion batteries. Here, hybrid organic/inorganic coating layers are synthesized in a pilot-scale process that was developed from a crosslinkable polyamide-imide synthesis technique. The fabrication process is optimized to achieve reproducible hybrid organic/inorganic coating layers that are thin (≤4 μm), permeable (≤250 s/100 cc), and thermally stable beyond 150 °C. The hybrid coating layer is applied to mini-18650 Li-ion cells to show that the discharge capacity did not change at low discharge rates, and the retention capacity after 500 cycles was better than that of the reference cells used for comparison. This work demonstrates that a novel hybrid coating layer has the potential to improve the stability of commercial Li-ion batteries.
The Photoluminescence and Vibrational Properties of Black Phosphorous Sheets Chemically/Electrochemically Functionalized in the Presence of Diphenylamine
Authors: Baibarac Mihaela , Burlanescu Teodora , Stroe Malvina , Smaranda Ion , Negrila Catalin
Published: October 2022

In this work, new information concerning the optical properties of black phosphorus (BP) sheets chemically/electrochemically functionalized with diphenyl amine (DPA) and its macromolecular compound (poly(diphenylamine) (PDPA)) in the absence/presence of phosphotungstic acid (PTA) is reported. Raman scattering and FTIR spectroscopy studies indicate that the interaction of BP with PTA leads to the elimination of the PxOy layer onto the surface of the BP sheets. In the case of the chemical interaction of BP with DPA, the reaction product corresponds to DPA chemically functionalized BP sheets having an imino-phosphorane (IP) structure. The electrochemical oxidation of BP sheets chemically functionalized with DPA in the presence of PTA leads to an increase in the weight of P-N bonds as a consequence of the generation of PDPA doped with the PTA heteropolyanions, as shown by FTIR spectroscopy and Raman scattering. This process is evidenced by a shift of the Raman line from 362 cm−1 to 378 cm−1, assigned to the A1g mode. This change was explained by taking into account the compression of the layers containing P atoms, which is induced by PDPA macromolecular chains. The decrease in the intensity of the PL spectra of DPA as well as PDPA, in the presence of BP, indicates that BP acts as a PL quenching agent for these compounds. A preferential orientation of the PDPA doped with the PTA heteropolyanions on the surface of BP sheets is highlighted by the variation of the binding angle of the PDPA on the surface of BP sheets from 44.7° to 39.9°.
Polylactic Acid Chemical Foaming Assisted by Solid-State Processing: Solid-State Shear Pulverization and Cryogenic Milling
Authors: Onffroy Philip R. , Herrold Nathan T. , Goehrig Harrison G. , Yuen Kalie , Wakabayashi Katsuyuki
Published: October 2022

A chemical foaming process of polylactic acid (PLA) was developed via the solid-state processing methods of solid-state shear pulverization (SSSP) and cryogenic milling. Based on the ability of solid-state processing to enhance the crystallization kinetics of PLA, chemical foaming agents (CFA) are first compounded before foaming via compression molding. Specifically, the effects of the pre-foaming solid-state processing method and CFA concentration were investigated. Density reduction, mechanical properties, thermal behavior, and cell density of PLA foams are characterized. Solid-state processing of PLA before foaming greatly increases the extent of PLA foaming by achieving void fractions approximately twice that of the control foams. PLA’s improved ability to crystallize is displayed through both dynamic mechanical analysis and differential scanning calorimetry. The solid-state-processed foams display superior mechanical robustness and undergo low stress relaxation. The cell density of the PLA foams also increases with solid-state processing, especially through SSSP. Additionally, crosslinking of PLA during the pre-foaming processing step is found to result in the greatest enhancement of crystallization but decreased void fraction and foam effectiveness. Overall, SSSP and cryogenic milling show significant promise in improving chemical foaming in alternative biopolymers.
A Study on Doping and Compound of Zinc Oxide Photocatalysts
Authors: Mao Tan , Liu Mengchen , Lin Liyuan , Cheng Youliang , Fang Changqing
Published: October 2022

As an excellent semiconductor photocatalyst, zinc oxide is widely used in the field of photocatalysis and is regarded as one of the most reliable materials to solve environmental problems. However, because its band gap energy limits the absorption of visible light and reduces the efficiency of catalytic degradation, it needs to be doped with other substances or compounded with other substances and precious metal. This paper summarizes the research on this aspect at home and abroad in recent years, introduces the doping of transition metal ions by zinc oxide, the compounding of zinc oxide with precious metals or other semiconductors, and the prospect of further improving the catalytic efficiency of zno photocatalyst is also put forward.
Biopolymer-Based Films Reinforced with FexOy-Nanoparticles
Authors: Abdullah Johar Amin Ahmed , Jiménez-Rosado Mercedes , Benítez José J. , Guerrero Antonio , Romero Alberto
Published: October 2022

Nowadays, natural polymer-based films are considered potentially environmentally friendly alternatives to conventional plastic films, due to many advantageous properties, including their easy processability, high flexibility, non-toxicity, low cost, high availability, and environmental safety. However, they are limited in their application by a number of shortcomings, including their high water solubility and vapor permeability as well as their poor opacity and low mechanical resistance. Thus, nanoparticles, such as green FexOy-NPs, can be used to overcome the drawbacks associated with these materials. Therefore, the aim of this study was to develop three different polymer-based films (gelatin-based, cellulose acetate-based and chitosan-based films) containing green synthesized FexOy-NPs (1.0% w/w of the initial polymer weight) as an additive to improve film properties. This was accomplished by preparing the different films using the casting method and examining their physicochemical, mechanical, microstructural, and functional characteristics. The results show that the incorporation of FexOy-NPs into the different films significantly enhanced their physicochemical, mechanical, and morphological properties as well as their antioxidant characteristics. Consequently, it was possible to produce suitable natural polymer-based films with potential applications across a wide range of industries, including functional packaging for food, antioxidants, and antimicrobial additives for pharmaceutical and biomedical materials as well as pesticides for agriculture.
Power Output Enhancement of Natural Rubber Based Triboelectric Nanogenerator with Cellulose Nanofibers and Activated Carbon
Authors: Mekbuntoon Pongsakorn , Kaeochana Walailak , Prada Teerayut , Appamato Intuorn , Harnchana Viyada
Published: October 2022

The growing demand for energy and environmental concern are crucial driving forces for the development of green and sustainable energy. The triboelectric nanogenerator (TENG) has emerged as a promising solution for harvesting mechanical energy from the environment. In this research, a natural rubber (NR)-based TENG has been developed with an enhanced power output from the incorporation of cellulose nanofibers (CNF) and activated carbon (AC) nanoparticles. The highest voltage output of 137 V, a current of 12.1 µA, and power density of 2.74 W/m2 were achieved from the fabricated NR–CNF–AC TENG. This is attributed to the synergistic effect of the electron-donating properties of cellulose material and the large specific surface area of AC materials. The enhancement of TENG performance paves the way for the application of natural-based materials to convert mechanical energy into electricity, as a clean and sustainable energy source.
Influence of UV Radiation on Mechanical Properties of Polymer Optical Fibers
Authors: Leal-Junior Arnaldo , Pires-Junior Robertson , Frizera Anselmo , Marques Carlos A. F.
Published: October 2022

This paper presents an analysis of the mechanical properties of different polymer optical fibers (POFs) at ultraviolet (UV) radiation conditions. Cyclic transparent optical polymer (CYTOP) and polymethyl methacrylate (PMMA) optical fibers are used in these analyses. In this case, the fiber samples are irradiated at the same wavelength, pulse time and energy conditions for different times, namely, 10 s, 1 min, 2 min and 3 min. The samples are tested in tensile tests and dynamic mechanical thermal analysis (DMTA) to infer the variation in the static and dynamic properties of such fibers as a function of the UV radiation condition. Furthermore, reference samples of each fiber (without UV radiation) are tested for comparison purposes. The results show a lower UV resistance of PMMA fibers, i.e., higher variation in the material features in static conditions (Young’s modulus variation of 0.65 GPa). In addition, CYTOP fiber (material known for its high UV resistance related to its optical properties) also presented Young’s modulus variation of around 0.38 GPa. The reason for this reduction in the moduli is related to possible localized annealing due to thermal effects when the fibers are subjected to UV radiation. The dynamic results also indicated a higher variation in the PMMA fibers storage modulus, which is around 30% higher than the variations in the CYTOP fibers when different radiation conditions are analyzed. However, CYTOP fibers show a smaller operational temperature range and higher variation in the storage modulus as a function of the temperature when compared with PMMA fibers. In contrast, PMMA fibers show higher variations in their material properties when subjected to oscillatory loads at different frequency conditions. Thus, the results obtained in this work can be used as guidelines for the influence of UV radiation in POFs not only for the material choice, but also on the limitations of UV radiation in the fabrication of the grating as well as in sensor applications at UV radiation conditions.
Plasma-Initiated Grafting of Bioactive Peptide onto Nano-CuO/Tencel Membrane
Authors: Hu Tzer-Liang , Chen Guan-Yu , Shi Shih-Chen , Yang Jason Hsiao Chun
Published: October 2022

A bioactive peptide has been successfully grafted onto nano-CuO impregnated Tencel membranes by a simple and rapid method involving a series of textile processes, and an atmospheric argon plasma treatment that requires no additional solvent or emulsifier. Surface morphology shows an apparent change from smooth, slightly roughened, and stripped with increasing plasma treatment time. The FT-IR characteristic peaks confirm the presence of the CuO nanoparticle and peptide on the extremely hydrophilic Tencel membranes that exhibit a zero-degree contact angle. Prepared nano-CuO/Tencel membranes with 90 s plasma treatment time exhibit excellent antimicrobial activity against E. coli and S. aureus, and promote fibroblast cell viability with the assistance of a grafted bioactive peptide layer on the membrane surface.
L-Glutathione-Functionalized Silica Adsorbent for the Removal of Pesticide Malathion from Aqueous Solutions
Authors: Vegas-Mendoza Sonia M. , Gutierrez-Ortega José A. , Moran-Salazar Rene G. , Cortes-Llamas Sara A. , Carbajal-Arizaga Gregorio G. , Peregrina-Lucano Alejandro A. , Shenderovich Ilya G. , Torres-Santiago Gabriela , Gómez-Salazar Sergio
Published: October 2022

An L-glutathione-functionalized silica adsorbent was applied in this study to remove malathion from aqueous media. This adsorbent has demonstrated an improved adsorption efficiency of malathion. The maximum uptake achieved was 130 mg g−1 at pH 8. Equilibrium was reached after about 90 min. A pseudo-second-order model best described the adsorption kinetics. The adsorption isotherms were best simulated by the Freundlich model. The functional groups are thermally stable up to about 150 °C. The elemental analysis results indicated high glutathione ligand densities. The results of this study show that the environmentally friendly L-glutathione functionalized silica is a promising candidate for the removal of malathion from water at the industrial level.
Straw Biochar at Different Pyrolysis Temperatures Passivates Pyrite by Promoting Electron Transfer from Biochar to Pyrite
Authors: Shu Xiaohua , Tian Wei , Xiong Shiqing , Zhang Wenlong , Zhang Qian
Published: October 2022

To control acid mine drainage (AMD) at source, biochar, a new green and environmentally friendly passivator has been introduced to passivate pyrite. However, the raw material and pyrolysis temperature largely determine the physical and chemical properties of biochar, the causal relationship between biochar and pyrite and the underlying mechanism are still unknown. Here, biochar materials (rice-straw biochar (RSB) and sugarcane bagasse biochar (SBB)) at different pyrolysis temperatures (300–600 °C) were utilized for the passivation of pyrite. The results of our investigations revealed that the passivation ability of RSB was superior to that of SBB. The addition of RSB with higher pyrolysis temperatures could greatly enhance the passivation efficiency of pyrite. RSB-500 (produced at a pyrolysis temperature of 500 °C) achieved the best passivation effect on pyrite. RSB can form Fe-O bonds through C=O bonding with pyrite. Moreover, the addition of RSB created a reducing environment in the mixture system because of its strong electron-donation capacity (EDC) and altered the energy-band structure of pyrite, which promoted the transfer of electrons from biochar to pyrite. On the contrary, the addition of SBB did not result in the formation of Fe-O bonds with pyrite. In addition, the EDC of SBB was also lower than that of RSB and it had almost no effect on the band structure of pyrite. Hence it did not alter the direction of the electron migration. These findings shed light on the mechanism of biochar passivation of pyrite and provide a theoretical foundation for selecting suitable biochar materials for AMD prevention at source.
Optimization of L-Citrulline Operon in Corynebacterium glutamicum for L-Citrulline Production
Authors: Man Zaiwei , Li Jin , Cui Huihui , Cai Zhiqiang , Guo Jing
Published: October 2022

L-citrulline plays important roles in many physiological processes, and its application range is expanding rapidly. Corynebacterium glutamicum strains have the potential to be efficient L-citrulline producers. In this study, we performed optimization of L-citrulline biosynthesis operon in C. glutamicum ATCC13032 for L-citrulline production. Chromosomal integration of the integral argBEc gene from Escherichia coli (encoding natively insensitive N-acetylglutamate kinase), the deletion of the argR gene (encoding repressor ArgR), and the deletion of the argG gene (encoding argininosuccinate synthase) were achieved simultaneously by one-step genome modification and by obtaining the L-citrulline-producing strain. Then, plasmid-based overexpression of the optimized L-citrulline operon was carried out and the L-citrulline production was further improved. In fed-batch fermentation, the L-citrulline production and yield from glucose of the final strain reached 26.7 g/L and 0.18 g/g, respectively. These results indicate that optimization of L-citrulline operon in C. glutamicum is effective to construct the L-citrulline over-producing strain.
Adsorption of Oil by 3-(Triethoxysilyl) Propyl Isocyanate-Modified Cellulose Nanocrystals
Authors: Jonoobi Mehdi , Mekonnen Tizazu H.
Published: October 2022

Oil leaks into water bodies and increased organic pollutants harm the environment and ecosystem in several ways, and cleaning up oil spills from water bodies is a global challenge. This research aimed to construct modified cellulose nanocrystal (CNC) based aerogels with 3-triethoxysilyl propyl isocyanate (TEPIC) to evaluate their potential application in oil adsorption. Here, a freeze-drying method was employed to make CNC aerogels. The aerogels were characterized using scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) analysis, porosity and density measurements, Fourier transform infrared spectroscopy (FTIR), water contact angle (WCA) measurement, compressive strength, and oil adsorption capacity. SEM results confirmed that the aerogels have a largely porous structure, including a community of uniformly interconnected cellulose fibers. Moreover, the studied aerogels had a low density due to the high porosity. Also, the small pore diameter and high specific surface area were confirmed by the BET evaluation. FTIR confirmed the existence of functional groups and strong hydrogen bonding between CNC/TEPCI/Urea molecules. All TEPIC-modified CNC aerogels had water contact angle values greater than 130° indicating their hydrophobicity. The highest oil and glycerol adsorption was obtained with the use of modified CNC aerogels. Thus, the sample modified with 3 wt% TEPIC showed the highest adsorption capacities of 130 ± 7.22, 120 ± 4.75, and 95.28 ± 4.82 gg−1 for motor oil, vegetable oil and glycerol, respectively. The results of this study showed that ultra-light, hydrophobic and oil adsorbent materials based on chemically modified CNC aerogels can successfully be fabricated.
Biological Role of the 3β-Corner Structural Motif in Proteins
Authors: Rudnev Vladimir R. , Petrovsky Denis V. , Nikolsky Kirill S. , Kulikova Liudmila I. , Stepanov Alexander A. , Malsagova Kristina A. , Kaysheva Anna L. , Efimov Alexander V.
Published: October 2022

In this study, we analyze the occurrence of the unique structural motif, the 3β-corner, belonging to the Structural Classification of Proteins (SCOP) folds, in proteins of various origins. We further assess the structural and functional role of this motif as well as the clustering of the biological functions of proteins in which it occurs. It has been shown previously that the 3β-corner occurs with different probabilities in all beta proteins, alpha and beta proteins (α + β and α/β), and alpha classes occur most often in the composition of β-proteins. The 3β-corner is often found as a building block in protein structures, such as β-barrels, -sandwiches, and -sheets/-layers.
Hybrid Diesel/PV Multi-Megawatt Plant Seasonal Behavioral Model to Analyze Microgrid Effectiveness: Case Study of a Mining Site Electrification
Authors: Moussa Kadri Sani , Dakyo Brayima , Camara Mamadou , Coulibaly Yézouma
Published: October 2022

Mining sites that combine energy-intensive operations with community living in areas remote from the electricity grid are increasingly developing dedicated micro-grids. Fossil oil gensets hybridization with renewable energy resources has gained momentum. Difficulties in assessing performance are experienced by operators who wish to benefit from improved performances. The designers of such systems also need additional knowledge to anticipate the solutions of the particular problems related to the power plant’s implementation area characteristics. The proposed approach gives more suitable tools on the effectiveness evaluation of hybridized microgrid, combining a Heavy Fuel Oil (HFO) thermal power station with photovoltaic generator powering mine activities in the Sahelian area. The authors provide key analyses and improvement factors by seasonal behavioral modelling (SBM) of the fuel consumption of the gensets related to the overall irradiance dynamics of the PV array. Several years of data analysis results have been integrated for sharper considerations on the transient interactions impact of the PV/Diesel Hybrid Power Plant operation. The results of simulations carried out using the proposed new models, including the case of an extended system with storage unit, have been used to evaluate the levelized cost of energy, and to discuss competitiveness. This very relevant approach provides additional knowledge for designers and energetic effectiveness analysts.
Stochastic Allocation of Photovoltaic Energy Resources in Distribution Systems Considering Uncertainties Using New Improved Meta-Heuristic Algorithm
Authors: Alanazi Abdulaziz , Alanazi Mohana , Abdelaziz Almoataz Y. , Kotb Hossam , Milyani Ahmad H. , Azhari Abdullah Ahmed
Published: October 2022

In this paper, a stochastic-metaheuristic model is performed for multi-objective allocation of photovoltaic (PV) resources in 33-bus and 69-bus distribution systems to minimize power losses of the distribution system lines, improving the voltage profile and voltage stability of the distribution system buses, considering the uncertainty of PV units’ power and network demand. The decision-making variables, including installation location and the size of PVs, are determined optimally via an improved human learning optimization algorithm (IHLOA). The conventional human learning optimization algorithm (IHLOA) is improved based on Gaussian mutation to enhance the exploration capability and avoid getting trapped in local optimal. The methodology is implemented in two cases as deterministic and stochastic without and with uncertainties, respectively. Monte Carol Simulation (MCS) based on probability distribution function (PDF) is used for uncertainties modeling. The deterministic results proved the superiority of the IHLOA compared with conventional HLOA, particle swarm optimization (PSO), to obtain better values of the different objectives and faster convergence speed and accuracy. The results are clear that enhancing the conventional HLOA has increased the algorithm’s ability to explore and achieve the optimal global solution with higher convergence accuracy. Moreover, the stochastic results were clear that considering the uncertainties leads to correct and robust decision-making against existing uncertainties and accurate knowledge of the network operator against the exact values of various objectives compared to the deterministic case.
Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer
Authors: Neagu Anca-Narcisa , Jayathirtha Madhuri , Whitham Danielle , Mutsengi Panashe , Sullivan Isabelle , Petre Brindusa Alina , Darie Costel C.
Published: October 2022

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein–protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.
Origins of Dualism and Nondualism in the History of Religion and Spiritual Practice
Authors: Henning Hans , Henning Max
Published: October 2022

In a previous article we proposed that spiritual traditions and practices emerged to counterbalance humans inherent “dualism”, or perceived separation from the world around us, by cultivating experiences of “subject-object nonduality” more commonly referred to as “unification with the divine” or “enlightenment”. This implies that religions (social and political institutions ostensibly based in spiritual practices) similarly aim towards overcoming the separation of self and other. However, many religions include dualistic elements. In particular, many religions incorporate “ethical dualism” in which certain individuals and groups are seen as essentially “good” or “bad”, a feature not seen in nondual traditions. Here, we explore this seeming paradox, highlighting an intriguing correspondence between the degree to which religions include dualistic or nondualistic elements and, respectively, the prevalence of conflict or cooperation as the organizing principle in their associated social context. We find major “dualistic” religions to be generally traceable to pastoral societies largely organized around intergroup conflict, whereas major “nondual religions” are generally traceable to societies in which large-scale cooperation and rule-based behavior was necessary for collective survival. Finally, we apply this pattern to the modern world, speculating that large-scale cooperation and rule-based behavior in modern society may be currently encouraging the renewal of nondual practices in modern social and political institutions that is indicated by the growing popularity of “spiritual but not religious” groups, and that this renewal of nondual practices may in turn set the frame for and reinforce behavior that will be necessary to address the historic challenges of our day like climate change and democratic backsliding.
Endurance, Acceptance, or Constitutional Gratitude: Non-Theistic and Theistic Attitudes to Suffering
Authors: Cohoe Caleb M.
Published: October 2022

Against those who think that only believers in a personal God are entitled to be grateful for their existence and for reality itself (cosmic gratitude), I show that there are non-theistic views on which everything that happens is part of an overall good order, supporting gratitude toward that order’s source. However, most non-theist views that affirm reality’s goodness, including pantheism, axiarchism, and ultimism, hold that an individual’s existence has value as part of a larger whole. Some things may be bad for me but good for the whole. In such cases, acceptance is the best available positive attitude. Many versions of theism, by contrast, support constitutional gratitude, a characteristic attitude of thankfulness toward the ultimate source of goodness. Using Marilyn Adams’ distinction between global and local goods, I show how Christianity, Islam, and other theistic views that affirm a personal God who cares for the well-being of each individual as such enable constitutional gratitude. If the evils you experience will be defeated by greater goods that you personally experience, you can be grateful to God for God’s presence and plan even in suffering. Whether this attitude is more appropriate than acceptance or endurance depends on facts about reality and value.
Scenario Generation for Market Risk Models Using Generative Neural Networks
Authors: Flaig Solveig , Junike Gero
Published: October 2022

In this research study, we show how existing approaches of using generative adversarial networks (GANs) as economic scenario generators (ESG) can be extended to an entire internal market risk model—with enough risk factors to model the full band-width of investments for an insurance company and for a time horizon of one year, as required in Solvency 2. We demonstrate that the results of a GAN-based internal model are similar to regulatory-approved internal models in Europe. Therefore, GAN-based models can be seen as an alternative data-driven method for market risk modeling.
SUNS: A User-Friendly Scheme for Seamless and Ubiquitous Navigation Based on an Enhanced Indoor-Outdoor Environmental Awareness Approach
Authors: Mansour Ahmed , Chen Wu
Published: October 2022

Ubiquitous and seamless indoor-outdoor (I/O) localization is the primary objective for gaining more user satisfaction and sustaining the prosperity of the location-based services (LBS) market. Regular users, on the other hand, may be unaware of the impact of activating multiple localization sources on localization performance and energy consumption, or may lack experience deciding when to enable or disable localization sources in different environments. Consequently, an automatic handover mechanism that can handle these decisions on a user’s behalf can appreciably improve user satisfaction. This study introduces an enhanced I/O environmental awareness service that provides an automated handover mechanism for seamless navigation based on multi-sensory navigation integration schemes. Moreover, the proposed service utilizes low-power consumption sensor (LPCS) indicators to execute continuous detection tasks and invoke GNSS in confusion scenarios, and transition intervals to make the most firm decision on the credibility of the LPCS-triggered transition and compensate for indicator thresholds. In this manner, GNSS are used for short intervals that help reduce detection latency and power consumption. Consequently, the proposed service guarantees accurate and reliable I/O detection while preserving low power consumption. Leveraging the proposed service as an automated handover helped realize seamless indoor-outdoor localization with less switching latency, using an integrated solution based on extended Kalman filter. Furthermore, the proposed energy-efficient service was utilized to confine crowdsourced data collection to the required areas (indoors and semi-indoors) and prevent excess data collection outdoors, thereby reducing power drainage. Accordingly, the negative impact of data collection on the user’s device can be mitigated, participation can be encouraged, and crowdsourcing systems can be widely adopted.
Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)
Authors: Ahn Jihye , Lee Yangwon
Published: October 2022

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.
Exploring Spatial Network Structure of the Metropolitan Circle Based on Multi-Source Big Data: A Case Study of Hangzhou Metropolitan Circle
Authors: Zhang Jing , Hao Qi , Chen Xinming , Zhu Congmou , Zhang Ling , Hong Mengjia , Wu Jiexia , Gan Muye
Published: October 2022

The metropolitan circle is the basic unit of regional competition. Enhancing the connection between cities in the metropolitan circle and optimizing the spatial layout of the metropolitan circle is one of the goals of regional high-quality development in the new era. Therefore, it is of great significance to analyze the spatial network structure of the metropolitan circle. Taking Hangzhou metropolitan circle as an example, this study used web crawler technology to obtain data in multiple Internet big data platforms; used centrality analysis, flow data model, and social network analysis to construct the network connection matrix of human flow, goods flow, capital flow, information flow, and traffic flow; and explored the spatial network structure of the metropolitan circle. The results showed that the node intensity of the metropolitan circle presented a distribution pattern of strong in the east and weak in the west. The network connections of each county under the action of different element flows were different, and the skeleton of the integrated flow network connections showed a starfish-shaped feature. Hangzhou, Jiaxing, Huzhou, and Shaoxing cities had strong group effects in goods flow and traffic flow, while Quzhou and Huangshan cities had relatively independent cohesive subgroups in human flow and information flow. This study can provide useful references for regional development and spatial planning implementation.
YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module
Authors: Wang Simin , Gao Song , Zhou Lun , Liu Ruochen , Zhang Hengsheng , Liu Jiaming , Jia Yong , Qian Jiang
Published: October 2022

As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance.
Developing a Dual-Stream Deep-Learning Neural Network Model for Improving County-Level Winter Wheat Yield Estimates in China
Authors: Huang Hai , Huang Jianxi , Feng Quanlong , Liu Junming , Li Xuecao , Wang Xinlei , Niu Quandi
Published: October 2022

Accurate and timely crop yield prediction over large spatial regions is critical to national food security and sustainable agricultural development. However, designing a robust model for crop yield prediction over a large spatial region remains challenging due to inadequate surveyed samples and an under-development of deep-learning frameworks. To tackle this issue, we integrated multi-source (remote sensing, weather, and soil properties) data into a dual-stream deep-learning neural network model for winter wheat in China’s major planting regions. The model consists of two branches for robust feature learning: one for sequential data (remote sensing and weather series data) and the other for statical data (soil properties). The extracted features by both branches were aggregated through an adaptive fusion model to forecast the final wheat yield. We trained and tested the model by using official county-level statistics of historical winter wheat yields. The model achieved an average R2 of 0.79 and a root-mean-square error of 650.21 kg/ha, superior to the compared methods and outperforming traditional machine-learning methods. The dual-stream deep-learning neural network model provided decent in-season yield prediction, with an error of about 13% compared to official statistics about two months before harvest. By effectively extracting and aggregating features from multi-source datasets, the new approach provides a practical approach to predicting winter wheat yields at the county scale over large spatial regions.
Evaluating Trade-Off and Synergies of Ecosystem Services Values of a Representative Resources-Based Urban Ecosystem: A Coupled Modeling Framework Applied to Panzhihua City, China
Authors: Zeng Jianwen , Xu Jipeng , Li Wenyu , Dai Xiaoai , Zhou Jiayun , Shan Yunfeng , Zhang Junjun , Li Weile , Lu Heng , Ye Yakang , Xu Li , Liang Shuneng , Wang Youlin
Published: October 2022

Following significant urban expansion, the ecological problems of resource-based cities are gradually exposed. It is of great significance to study the ecosystem services of resource-based cities to achieve their sustainable development goals and to alleviate the conflicts between environmental protection and the utilization of the surrounding resources. However, in the current research on resource-based cities, few scholars have combined multiple minerals and multiple ecosystem services to explore the impact of mineral resources on the ecosystem. In this study, based on the historical data spanning from 2002 to 2018, we used the CA–Markov model to project the land use of Panzhihua City to 2030. Based on future land use projection, we quantified four ecosystem services (ESs) variables, including water yield, carbon storage, habitat quality, and soil conservation, using the InVEST model from the perspective of land use evolution in Panzhihua City. In addition, we explored the trade-offs and synergies of different ecosystem services and the correlations between different mineral species and ecosystem services using Spearman’s correlation coefficient. Results showed the following: (1) During 2002–2018, water yield service, habitat quality service, and carbon storage service of Panzhihua City decreased year by year, and soil conservation service showed significant fluctuations; most of the low ESs areas were distributed in the central region of Panzhihua. On the contrary, most high ESs areas were located in the forest region. (2) The trade-offs and synergistic relationships among different ecosystem services showed significant spatial variations. There were synergistic relationships among ESs and weak trade-offs between water yield services, soil conservation, and habitat quality services. There was also significant spatial variability in the trade-offs and synergies among ecosystem services, with water production services showing “east trade-offs and west synergies” with soil conservation and habitat quality services, and most of the rest showing trade-offs in urban areas. (3) ESs in mining areas showed trade-offs in general, mainly between water production services and carbon storage services, with clay as the major negative factor of mineral species, and iron ore mines that have undergone ecological protection construction showed the lowest negative impact on ecology.
Ship Classification in Synthetic Aperture Radar Images Based on Multiple Classifiers Ensemble Learning and Automatic Identification System Data Transfer Learning
Authors: Yan Zhenguo , Song Xin , Yang Lei , Wang Yitao
Published: October 2022

With the continuous development of earth observation technology, space-based synthetic aperture radar (SAR) has become an important source of information for maritime surveillance, and ship classification in SAR images has also become a hot research direction in the field of maritime ship monitoring. In recent years, the remote sensing community has proposed several solutions to the problem of ship object classification in SAR images. However, it is difficult to obtain an adequate amount of labeled SAR samples for training classifiers, which limits the application of machine learning, particularly deep learning methods, in SAR image ship object classification. In contrast, as a real-time automatic tracking system for monitoring ships at sea, a ship automatic identification system (AIS) can provide a large amount of relatively easy-to-obtain labeled ship samples. Therefore, to solve the problem of SAR image ship classification and improve the classification performance of learning models with limited samples, we proposed a SAR image ship classification method based on multiple classifiers ensemble learning (MCEL) and AIS data transfer learning. The core idea of our method is to transfer the MCEL model trained on AIS data to SAR image ship classification, which mainly includes three steps: first, we use the acquired global space-based AIS data to build a dataset for ship object classification models training; then, the ensemble learning model is constructed by combining multiple base classifiers; and finally, the trained classification model is transferred to SAR images for ship type prediction. Experiments show that the proposed method achieves a classification accuracy of 85.00% for the SAR ship classification, which is better than the performance of each base classifier. This proves that AIS data transfer learning can effectively solve the problem of SAR ship classification with limited samples, and has important application value in maritime surveillance.