bluuebunny/crossref_metadata_2025 huggingface dataset:
An Overview of Anthropogenic Actions as Drivers for Emerging and Re-Emerging Zoonotic Diseases
Authors: Tazerji Sina Salajegheh , Nardini Roberto , Safdar Muhammad , Shehata Awad A. , Duarte Phelipe Magalhães
Published: November 2022

Population growth and industrialization have led to a race for greater food and supply productivity. As a result, the occupation and population of forest areas, contact with wildlife and their respective parasites and vectors, the trafficking and consumption of wildlife, the pollution of water sources, and the accumulation of waste occur more frequently. Concurrently, the agricultural and livestock production for human consumption has accelerated, often in a disorderly way, leading to the deforestation of areas that are essential for the planet’s climatic and ecological balance. The effects of human actions on other ecosystems such as the marine ecosystem cause equally serious damage, such as the pollution of this habitat, and the reduction of the supply of fish and other animals, causing the coastal population to move to the continent. The sum of these factors leads to an increase in the demands such as housing, basic sanitation, and medical assistance, making these populations underserved and vulnerable to the effects of global warming and to the emergence of emerging and re-emerging diseases. In this article, we discuss the anthropic actions such as climate changes, urbanization, deforestation, the trafficking and eating of wild animals, as well as unsustainable agricultural intensification which are drivers for emerging and re-emerging of zoonotic pathogens such as viral (Ebola virus, hantaviruses, Hendravirus, Nipah virus, rabies, and severe acute respiratory syndrome coronavirus disease-2), bacterial (leptospirosis, Lyme borreliosis, and tuberculosis), parasitic (leishmaniasis) and fungal pathogens, which pose a substantial threat to the global community. Finally, we shed light on the urgent demand for the implementation of the One Health concept as a collaborative global approach to raise awareness and educate people about the science behind and the battle against zoonotic pathogens to mitigate the threat for both humans and animals.
Lycopene Modulates Oxidative Stress and Inflammation in Hypercholesterolemic Rats
Authors: Albrahim Tarfa
Published: November 2022

The complicated disorder of hypercholesterolemia has several underlying factors, including genetic and lifestyle factors. Low LDL cholesterol and elevated serum total cholesterol are its defining features. A carotenoid with antioxidant quality is lycopene. Examining lycopene activity in an animal model of hypercholesterolemia induced using food was the aim of this investigation. Triglycerides, LDL cholesterol, HDL cholesterol, and plasma total cholesterol were all measured. Biomarkers of renal and cardiac function were also examined. Apoptotic indicators, pro-inflammatory markers, and oxidative stress were also assessed. Additionally, the mRNA expression of paraoxonase 1 (PON-1), peroxisome proliferator-activated receptor gamma (PPAR-γ), and PPAR-γ coactivator 1 alpha (PGC-1α) in cardiac and renal tissues was examined. Rats showed elevated serum lipid levels, renal and cardiac dysfunction, significant oxidative stress, and pro-inflammatory and apoptotic markers at the end of the study. Treatment with lycopene significantly corrected and restored these changes. Additionally, lycopene markedly increased the mRNA expression of PGC-1α and PON-1, and decreased PPAR-γ expression. It was determined that lycopene has the capacity to modulate the PPAR-γ and PON-1 signaling pathway in order to preserve the cellular energy metabolism of the heart and kidney, which in turn reduces tissue inflammatory response and apoptosis. According to these findings, lycopene may be utilized as a medication to treat hypercholesterolemia. However, further studies should be conducted first to determine the appropriate dose and any adverse effects that may appear after lycopene usage in humans.
Proposal for a Calculation Model of Perceived Luminance in Road Tunnel Interior Environment: A Case Study of a Tunnel in China
Authors: Qin Li , He Shiyong , Yang Deshan , Leon Arturo S.
Published: November 2022

This study describes applying the visual target color and spectra of light sources to calculate the perceived luminance in a tunnel interior lighting environment. The proposed approach aims to identify the combined effects of the light source, target surface color, and human eye on the perception of luminance in a tunnel interior lighting environment. The new method was tested in DIALux software using three light-emitting diodes (LEDs) with correlated color temperatures (CCTs) of 3000 K, 4000 K, and 6000 K, as well as four observed targets with red, yellow, blue, and green colors. Overall findings demonstrated that the yellow surface target’s mesopic luminance for the specified light source is greater than that of the other three-color surface targets. Additionally, it can be concluded that the mesopic luminance under a low CCT LED is greater than under a high CCT LED in the case of the specific color surface target.
Stimulated Emission in Vertically Aligned Hexagonal ZnO Microcrystals Synthesized by Magnetron Sputtering Method
Authors: Tarasov Andrey P. , Muslimov Arsen E. , Kanevsky Vladimir M.
Published: November 2022

This study is devoted to the luminescence and stimulated emission properties of the ZnO hybrid structure, which is vertically aligned microcrystals with the [0001] crystallographic orientation and a pronounced hexagonal shape formed on a continuous layer of micron thickness. These microcrystals are up to 10 µm high and up to 8 µm in diameter and form the main part of the structure’s thickness. The structure was synthesized on the M(101¯0) plane of sapphire using the magnetron sputtering method. Luminescence of the structure, represented only by conventional near-UV and green components under low-intensity continuous photoexcitation, confirms its high structural and optical quality. Under pulsed photoexcitation with relatively high intensity, stimulated emission (SE) was observed from the structure in the near-UV region at room temperature. The threshold power density for SE was 0.1–0.2 MW/cm2. Exceeding the threshold leads to a significant increase in the emission intensity compared to the control film without [0001] microcrystals, also grown on M(101¯0) sapphire. It was assumed that the optical gain is provided by the whispering gallery modes of individual [0001] microcrystals as a result of inelastic exciton–electron scattering, at least at near-threshold excitation intensities.
Genome-Wide Analysis of Cyclic Nucleotide-Gated Channel Genes Related to Pollen Development in Rice
Authors: Lee Su-Kyoung , Lee Soo-Min , Kim Myung-Hee , Park Soon-Ki , Jung Ki-Hong
Published: November 2022

In the angiosperm, pollen germinates and rapidly expands the pollen tube toward the ovule. This process is important for plant double fertilization and seed setting. It is well known that the tip-focused calcium gradient is essential for pollen germination and pollen tube growth. However, little is known about the Ca2+ channels that play a role in rice pollen germination and tube growth. Here, we divided the 16 cyclic nucleotide-gated channel (CNGC) genes from rice into five subgroups and found two subgroups (clades II and III) have pollen-preferential genes. Then, we performed a meta-expression analysis of all OsCNGC genes in anatomical samples and identified three pollen-preferred OsCNGCs (OsCNGC4, OsCNGC5, and OsCNGC8). The subcellular localization of these OsCNGC proteins is matched with their roles as ion channels on the plasma membrane. Unlike other OsCNGCs, these genes have a unique cis-acting element in the promoter. OsCNGC4 can act by forming a homomeric complex or a heteromeric complex with OsCNGC5 or OsCNGC8. In addition, it was suggested that they can form a multi-complex with Mildew Resistance Locus O (MLO) protein or other types of ion transporters, and that their expression can be modulated by Ruptured Pollen tube (RUPO) encoding receptor-like kinase. These results shed light on understanding the regulatory mechanisms of pollen germination and pollen tube growth through calcium channels in rice.
A Pan-Transcriptome Analysis Indicates Efficient Downregulation of the FIB Genes Plays a Critical Role in the Response of Alfalfa to Cold Stress
Authors: Zhang Xueqi , Yang Huanhuan , Li Manman , Bai Yan , Chen Chao , Guo Donglin , Guo Changhong , Shu Yongjun
Published: November 2022

Alfalfa (Medicago sativa L.) is a perennial forage legume that is widely distributed throughout the world, and cold stress is an important environmental factor limiting the growth and production of alfalfa in cold regions. However, little is known of the molecular mechanisms regarding cold tolerance in alfalfa. Here, we conducted physiological metabolism assays and pan-transcriptome sequencing on eight cultivars of alfalfa under cold stress conditions. The results of the RNA-seq analysis showed that the genes are “oxidoreductase activity” and “transcription regulator activity”, suggesting that genes with such functions are more likely to play important roles in the response to cold stress by alfalfa. In addition, to identify specific gene modules and hub genes in response to alfalfa cold stress, we applied weighted gene co-expression network (WGCNA) analyses to the RNA-seq data. Our results indicate that the modules of genes that focus on the ATPase complex, ribosome biogenesis, are more likely to be involved in the alfalfa response to cold stress. It is important to note that we identified two fibronectin (FIB) genes as hub genes in alfalfa in response to cold stress and that they negatively regulate alfalfa response to chilling stress, and it is possible that dormant alfalfa is more effective at down-regulating FIB expression and therefore more resistant to cold stress.
Assessment of a Diverse Array of Nitrite Scavengers in Solution and Solid State: A Study of Inhibitory Effect on the Formation of Alkyl-Aryl and Dialkyl N-Nitrosamine Derivatives
Authors: Homšak Miha , Trampuž Marko , Naveršnik Klemen , Kitanovski Zoran , Žnidarič Mateja , Kiefer Markus , Časar Zdenko
Published: November 2022

The ubiquitous presence of mutagenic and potentially carcinogenic N-nitrosamine impurities in medicines has become a major issue in the pharmaceutical industry in recent years. Rigorous mitigation strategies to limit their amount in drug products are, therefore, needed. The removal of nitrite, which is a prerequisite reagent for the N-nitrosation of amines, has been acknowledged as one of the most promising strategies. We have conducted an extensive literature search to identify nineteen structurally diverse nitrite scavengers and screened their activity experimentally under pharmaceutically relevant conditions. In the screening phase, we have identified six compounds that proved to have the best nitrite scavenging properties: ascorbic acid (vitamin C), sodium ascorbate, maltol, propyl gallate, para-aminobenzoic acid (PABA), and l-cysteine. These were selected for investigation as inhibitors of the formation of N-methyl-N-nitrosoaniline (NMA) from N-methylaniline and N-nitroso-N’-phenylpiperazine (NPP) from N-phenylpiperazine in both solution and model tablets. Much faster kinetics of NMA formation compared to NPP was observed, but the former was less stable at high temperatures. Vitamin C, PABA, and l-cysteine were recognized as the most effective inhibitors under most studied conditions. The nitrite scavenging activity does not directly translate into N-nitrosation inhibitory effectiveness, indicating other reaction pathways may take place. The study presents an important contribution to identifying physiologically acceptable chemicals that could be added to drugs to prevent N-nitrosation during manufacture and storage.
Chemical and Functional Characterization of Extracts from Leaves and Twigs of Acacia dealbata
Authors: Correia Ricardo , Duarte Maria Paula , Maurício Elisabete Muchagato , Brinco João , Quintela José Carlos , da Silva Marco Gomes , Gonçalves Margarida
Published: November 2022

The purpose of this work was to evaluate the recovery of bioactive extracts from Acacia dealbata leaves and twigs and to characterize their chemical composition and functional properties. Fresh and air-dried samples were extracted by maceration at room temperature and by hot extraction at 60 °C using aqueous solutions of acetone, ethanol, and methanol. The highest extraction yields (14.8 and 12.0% for dried leaves and twigs, respectively) were obtained with 70% acetone, for both extraction procedures. Extracts were characterized for total phenolics content (TPC), total flavonoid content (TFC) and total proanthocyanidin content (TPrAC). Bioactive extracts with high TPC (526.4 mg GAE/g extract), TFC (198.4 mg CatE/g extract), and TPrAC (631.3 mg PycE/g extract) were obtained using maceration, a technically simple and low-energy process. The non-polar fraction of selected extracts was characterized using gas chromatography and time of flight mass spectrometry (GC-TOFMS). The main components detected were phytol, squalene, α-tocopherol, lupenone, and lupeol. The antioxidant activity of the extracts was characterized through DPPH and FRAP assays. Antimicrobial activity of the extracts against different bacteria was also determined. The highest DPPH and FRAP activities were obtained from dried twigs from Alcobaça (1068.3 mg TE/g extract and 9194.6 mmol Fe2+/g extract, respectively). Extracts from both leaves and twigs showed antimicrobial properties against Staphylococcus aureus, Staphylococcus epidermidis, methicillin resistant Staphylococcus aureus (MRSA), Enterococcus faecalis, Bacillus cereus, Streptococcus mutans, and Streptococcus mitis. The results obtained demonstrate the feasibility of recovering valuable components from these biomass fractions that may be further valorized for energy production in a biorefinery concept.
Petroleum Geology and Exploration of Deep-Seated Volcanic Condensate Gas Reservoir around the Penyijingxi Sag in the Junggar Basin
Authors: Bian Baoli , Iming Ablimit , Gao Tianze , Liu Hailei , Jiang Wenlong , Wang Xueyong , Ding Xiujian
Published: November 2022

Many types of volcanic rock oil and gas reservoirs have been found in China, showing great petroleum exploration potential. Volcanic reservoir also is one of the key fields of exploration in the Junggar Basin and mainly concentrated in the middle and shallow layers, while the deep volcanic rock and natural gas fields have not been broken through. Based on comprehensive analysis of core observation, single well analysis, reservoir description, source rocks evaluation, combined with seismic data and time-frequency electromagnetic technology, multiple volcanic rock exploration targets were identified, and industrial oil and gas flow was obtained in the well SX 16 of the Penyijingxi Sag, western Junggar Basin. It is believed that the deep Permian source rocks have relatively higher natural gas generation potential and volcanic breccia usually have large reservoir space. And the mudstone of the Upper Wuerhe Formation played as the role of caprock. The success of exploration well SX16 has achieved a major breakthrough in natural gas exploration in the Penyijingxi Sag, which has essential guiding significance for the exploration of deep volcanic rocks and large-scale gas exploration in the Junggar Basin.
Limited Phosphorous Supply Improved Lipid Content of Chlorella vulgaris That Increased Phenol and 2-Chlorophenol Adsorption from Contaminated Water with Acid Treatment
Authors: Javed Sidra , Mirza Cyrus Raza , Khan Aqib Hassan Ali , Khalifa Walid , Achour Belkacem , Barros Rocio , Yousaf Sohail , Butt Tayyab Ashfaq , Iqbal Mazhar
Published: November 2022

Phenolic compounds are toxic and ominously present in industrial effluents, which can end up in water bodies, causing potential damage to living organisms. This study employed the dried biomass of freshwater green microalgae Chlorella vulgaris to remove phenol and 2-chlorophenol from an aqueous environment. C. vulgaris was grown under different phosphorus- (P) starved conditions, and biomass was treated with sulfuric acid. It was observed that reducing the P level enhanced the lipid content by 7.8 times while decreasing protein by 7.2 times. P-starved C. vulgaris dried biomass removed phenol and 2-chlorophenol by 69 and 57%, respectively, after 180 min from the contaminated water. Acid-treated P-starved C. vulgaris dried biomass removed phenol and 2-chlorophenol by 77 and 75%, respectively, after 180 min. Thus, an economical and eco-friendly P-starved and acid treated C. vulgaris biomass has better potential to remove phenol and 2-chlorophenol from contaminated ground water and industrial wastewater.
Purity Control Based on a Type-II Fuzzy Controller for a Simulated Moving Bed
Authors: Xie Chao-Fan , Hwang Rey-Chue
Published: November 2022

The control of a simulated moving bed (SMB) is always a challenging chemical control topic due to its complexity and nonlinearity. Its mathematical model must undergo an affine transformation and digitization before it can be controlled. Basically, there are three aspects that need to be considered in the nonlinear control of an SMB. First, the nonlinear characteristics are more complicated due to the switching time parameters of discrete events. Second, the control objective is not to minimize the control output error, but to make the separated concentrations between the components of the substance reach a certain ratio. Finally, the control variables are highly coupled. So far, the vast majority of the industry still uses relatively simple PLC controls; a few use specific controllers based on materials to be separated such as model predictive controls and PID controllers. Therefore, there is no unified intelligent processing mode. In this paper, a type-II fuzzy controller is presented and used as an SMB control. The interference of the related parameters was tested to observe the stability and robustness of the controller. The type-II fuzzy control was based on type-II fuzzy sets, which resulted in the type-II fuzzy controller having more flexible attribution function values. The results showed that the type-II fuzzy controller was not only more accurate in the control, but also better for robustness and adaptability than an ordinary fuzzy controller and PID controller.
Facile Preparation and Characterization of Silica Nanoparticles from South Africa Fly Ash Using a Sol–Gel Hydrothermal Method
Authors: Imoisili Patrick Ehi , Nwanna Emeka Charles , Jen Tien-Chien
Published: November 2022

Silica nanoparticles (SNPs) consist of several applications which include lightweight aggregates, energy storage, and drug delivery. Nevertheless, the silica reagents used in SNP synthesis are both costly and hazardous. As a result, it is critical to look for other sources of silica. For this research, a simple sol–gel hydrothermal approach is used to make SNPs from South African fly ash (SAFA). SAFA is classified as fly ash class F according to X-ray fluorescence (XRF) analysis. The wide-angle X-ray diffraction (XRD) pattern reveals the structural composition of SAFA and the amorphous phase of extracted SNPs, while Fourier transform infrared (FTIR) examination reveals the presence of silanol and siloxane groups. Basic SNPs were generally spherical with diameters of about 60 nm, according to scanning electron microscopy (SEM) and transition electron microscope (TEM) studies. The presence of SiO2 is confirmed by energy-dispersive X-ray spectroscopy (EDX) spectrum analysis. Particle size assessment indicates particle sizes ranging from 48 nm to 87 nm in diameter, with a mean diameter of 67 nm. The application of SNPs in wastewater treatment demonstrated that they can be used to remove Cd2+ from an aqueous solution. This research offers new ideas for using South African fly ash in SNP manufacturing.
COD Reduction of Aeration Effluent by Utilizing Optimum Quantities of UV/H2O2/O3 in a Small-Scale Reactor
Authors: Rafiee Mehdi , Sabeti Morteza , Torabi Farshid , Rahimbakhsh Aria
Published: November 2022

Extensive research has been carried out to figure out safe means of disposing various industrial effluents. Industrial wastewaters from the aeration industry such as heavy metals and oily substances contain a high degree of contamination. The advanced oxidation process is one of the most effective and rapid methods of removing contaminations, which can lead to a high chemical oxygen demand (COD). The aim of the present study is to reduce the COD of an aeration effluent with the initial COD of 13,004 mg/L. About 20 sets of experimental tests were conducted to identify the contribution of H2O2, O3, and UV to the treatment process. The influence of the quantities of additives and the dose of the UV irradiance were, too, among the subjects of the study. These factors were altered throughout the experiments and their mutual effects were measured. To design the experiments, Minitab software 16 was utilized. The experimental conditions were set at the standard values of 25 °C and 1 bar to minimize any uncertainty. Based on the results, a correlation was derived, which was capable of expressing the effects of the input parameters (AOPs parameters) on the response (the COD level). Finally, the optimization process was conducted to find the quantities of H2O2, O3, and UV irradiance required to decrease the CODs of the effluent to their lowest possible. Based on the findings, when the doses of H2O2, O3, and UV to the treatment process were 40 mg/L, 8 mg/L and 86 mWs/cm2, respectively, the COD percent change was 51.5%.
Effect of Inclusion of Date Press Cake on Texture, Color, Sensory, Microstructure, and Functional Properties of Date Jam
Authors: Alqahtani Nashi K. , Alnemr Tareq M. , Ahmed Abdelrahman R. , Ali Salim
Published: November 2022

Date press cake (DPC) is a by-product of the date syrup industry with a high content of dietary fibers, minerals, and antioxidant compounds. Accordingly, the present study aimed to evaluate the inclusion of DPC on the color parameters, texture profile, sensory evaluation, microstructure properties, total phenolics, and antioxidant capacity of date jam. The DPC was included in the preparation of date jam in an increasing level of 0, 3, 6, 9, and 12% (w/w). The results revealed that increasing DPC levels significantly increased the hardness, cohesiveness, adhesiveness, and chewiness properties of date jam. The increase in DPC inclusion levels significantly decreased the L* values and increased the a*, b*, and ΔE values of the jam. The DPC addition significantly increased the phenolics content and antioxidant capacity in a dose-dependent manner and the highest value reported in the jam contained 12% DPC by 40.02 and 11.50% compared to the control, respectively. The scanning electron microscopy results revealed that the addition of DPC up to 9% resulted in a jam surface with a denser network, homogeneous structure, and without cracks and pores, indicating improvement in the morphological properties. The sensory evaluation results showed that date jam prepared with 9% DPC had the highest overall acceptability score. This study revealed the potential use of DPC in date jam formulation up to 9% as a natural, functional, and low-cost ingredient to improve the jam’s textural properties, antioxidant capacity, and overall acceptability.
Implementation of Industrial Traceability Systems: A Case Study of a Luxury Metal Pieces Manufacturing Company
Authors: Fortuna Guilherme , Gaspar Pedro Dinis
Published: November 2022

Technological advances have shown an accentuated growth trend, which is directly proportional to the quality of life in today’s society. As a result, the business market is becoming increasingly competitive and customers are becoming more demanding, forcing companies to look for new tools and adopt new work methodologies to improve their flexibility, effectiveness and efficiency, ensuring a better response to market needs. In this context, the tools for tracking objects, totally or partially automatic, are considered essential technologies to all kinds of analysis and the treatment of business data, providing several benefits to companies, including waste reduction, identification of bottlenecks, cost reduction, improvement of product quality and the entire flow of business information. A case study of an industrial company specializing in machining, polishing and galvanoplasty of metallic alloys, small size pieces to be incorporated in luxury fashion accessories, is presented. Derived from the difficulties underlying the implementation of a traceability system supported by identification technologies and obtaining data in an automatic way, the focus of the study is based on the identification of a base model, with sequential steps, which allows any industrial company to adapt these types of technological tools and systems. Based on the pillars of knowledge acquired through a bibliographic review on the subject, as well as on the recognition of the whole production flow, this work makes use of an implementation model already developed, studied and tested, supported by a project viability analysis measuring the benefits obtained with the results found after the respective implementation. Production performance increased with the implementation of a traceability system, as the time worked throughout the flow decreased. Production performance prior to implementation was around 98.6%. Applying a Kaizen (continuous improvement) strategy and based on the times collected in the pilot test, this indicator rose by 0.5%, obtaining a production performance of 99.1%, corresponding to an annual increase of 99 pieces. The integration of a robust and simple traceability system supported by automatic identification and data capture (AIDC) technologies in this industrial environment allowed for automated data collection and processing. In addition to the financial and productive benefits, this Industry 4.0 implementation encompasses a huge medium- to long-term impact in functional and monitoring terms, providing enormous aid to the management of production flows.
Selective O-alkylation of Phenol Using Dimethyl Ether
Authors: Samruddhi Mane , Bhatkar Akash , Prabu Marimuthu , Mekala Siva Prasad , Gogoi Pranjal , Mohapatra Gourab , Thirumalaiswamy Raja
Published: November 2022

Anisole is a straw-colored aromatic compound mainly used in making solvents, flavoring agents, perfumes, fuel additives, and in the synthesis industries. Anisole, also known as methoxybenzene, is synthesized from sodium phenoxide or phenol using various methylating agents. The use of dimethyl ether (DME) as an alkylating agent is seldom reported in the literature. Herein, we have synthesized anisole through the O-alkylation process of phenol and DME to obtain zero discharge from this process. The thermodynamic equilibrium for the reaction of phenol and DME is simulated by using Aspen HYSYS (Hyprotech and Systems). The O-alkylation of phenol has been investigated using phosphotungstic acid (PTA) over γ-Al2O3 with appropriate acidity. Active metal loadings of various percentages were studied and the conversion was optimized at 46.57% with a selectivity of 88.22% at a temperature of 280 °C. The liquid products from the continuously stirred reactor were analyzed with liquid G.C. and the conversion and selectivity were calculated. A comparison of the O-alkylation and C-alkylation of phenol at different temperatures, reactant ratios, residence times, and recyclability was explored, as well as the impact of these factors on the yield of the desired anisole. The catalyst was characterized by XRD, BET, HR-TEM, FE-SEM, elemental mapping, XPS, and DRIFT studies.
Kinetics of Zn–C Battery Leaching with Choline Chloride/Urea Natural Deep Eutectic Solvents
Authors: Cruz-Reyes Irlanda G. , Mendoza-Pérez Jorge A. , Ruiz-Guerrero Rosario , Medina-Velázquez Dulce Y. , Zepeda-Vallejo Luis G. , Morales-Ramírez Ángel de J.
Published: November 2022

A choline chloride/urea natural deep eutectic solvent (ChCl NADES) was prepared via a green chemistry method and used to leach Zn and Mn oxides from conventional Zn–C scrap batteries. FTIR and 1H NMR spectroscopy were used to characterize the NADES. The leaching kinetics of the Zn and Mn oxides was monitored at isothermal conditions (80, 100, 125, and 150 °C) and at two solid/NADES ratios: 3.3 and 10 g dm−3. It was possible to dissolve Zn and Mn oxides under all of tested conditions, reaching more than a 95% recovery for both metals at 150 °C after 90 min, whereas, at 25 °C, it was possible to leach up to 90% of the Zn and 30% of the Mn after 4320 min (72 h). Furthermore, the leaching kinetics was controlled by the boundary layer, coincident with a shrinking core model. According to the Arrhenius plot, the activation energy for Zn ranges from 49.13 to 52.21 kJ mol−1, and that for Mn ranges from 46.97 to 66.77 kJ mol−1.
Calling the Question: The Role of Ministries of Presence and Polity Principles in the Struggle for LGBTQIA+ Inclusion, Ordination, and Marriage in the Presbyterian Church (U.S.A.) and Its Predecessor Denominations
Authors: Smith David Brandon
Published: November 2022

This article reflects upon how LGBTQIA+ Christians and their allies within the Presbyterian Church (U.S.A.) and its predecessor denominations ‘called the question’ on their right to and responsibility for membership, ordination, and marriage by simultaneously (1) practicing apologetic ‘ministries of presence’ and (2) grounding their ecclesio-juridical arguments in the church’s long-standing polity principles. It is commonly argued that advocates for full inclusion pushed the church to change historic norms, while ‘conservative’ voices called for the maintenance of time-honored principles. In an effort to problematize such reductionistic accounts, this article begins by sketching the historical trajectory of U.S. Presbyterian theology and polity, with special emphasis on the Adopting Act of 1729 and the tradition that proceeds from it. Building upon its survey of the debates that shaped the church’s history between the early eighteenth and mid-twentieth centuries, the text then shows how LGBTQIA+ Presbyterians and their allies acted within the traditional discursive patterns of their faith community when they advocated for the repeal of the exclusive policies that arose in the second half of the twentieth century. Inspired by the work of advocates and allies alike, when the PC(USA) and its predecessor denominations articulated an inclusive stance toward openly LGBTQIA+ members in 1978/1979, removed barriers to their ordination in 2011, permitted same-sex marriages within Presbyterian communities in 2015, and opened the church to receiving new theological insights from queer people via the adapted version of the ‘Apology Overture’ in 2016, the church’s collective discernment drew on historic Presbyterian principles of theology and governance to respond (often imperfectly) to contemporary challenges. The church’s multi-generational self-critique thus created a space in which queer Christians could ‘re-de-normalize’ their experiences of life and faith in ways that may open doors for post-apologetic reconstructive theological engagement in the years to come.
Religious and Secular Visions of Peace and Pacifism
Authors: Fiala Andrew
Published: November 2022

Pacifism is a complex and significant moral, political, religious, and philosophical idea. There is an evolving conversation about peace and nonviolence that occurs among secular scholars, religious figures, and activists. This paper explores this conversation, while employing a five-part thematic frame of analysis that attempts to distinguish secular and religious visions of peace and pacifism. The result of this analysis provides a ready framework for making that distinction. But it also demonstrates that the task of distinguishing secular and religious approaches is complicated and difficult. The paper also shows, through a brief genealogy of pacifism, how secular and religious voices are in conversation with one another.
Jain Medical Professionals’ “Reflexive Ethical Orientation”: Adaptive Nonviolence, Multiple Sources of Knowledge, and Concern for Five-Sensed Beings
Authors: Donaldson Brianne
Published: November 2022

It is well-documented that patients’ religious characteristics may affect their health and health care experiences, correlating with better health and psychological well-being. Likewise, health care providers are impacted by religious characteristics that affect their attitudes and behaviors in a clinical setting. However, few of these studies examine non-theist, non-Western, or Indian-based traditions, and none have examined Jainism specifically, in spite of the high representation of Jains in medicine. Drawing upon a quantitative survey conducted in 2017–2018 of Jains in medical and healthcare fields, I argue that Jains physicians and medical professionals demonstrate a “reflexive ethical orientation”, characterized by: (1) adaptive absolutes emphasizing nonviolence, a many-sided viewpoint, and compassion; (2) balancing personally mediated sources of authority that evaluate and integrate Jain insights alongside cultural and legal sources, and clinical experience; and (3) privileging the well-being of five-sensed humans and animals.
Construction of an SDE Model from Intraday Copper Futures Prices
Authors: Mastroeni Loretta , Vellucci Pierluigi
Published: November 2022

This paper introduces a model for intraday copper futures prices based on a stochastic differential equation (SDE). In particular, we derive an SDE that fits the model to the data and that is based on the whitening filter approach, a method characterizing linear time-variant systems. This method is applied to construct a model able to simulate the trajectories of copper futures prices, statistically described by means of an empirical autocorrelation approach. We show that the predictability of copper futures prices is rather weak. In fact, the developed model produces trajectories close to the actual data only in the short term. Consequently, the investment risk for copper futures is high. We also show that the performance of the model improves significantly if the time series satisfy particular conditions, e.g., those with a determinism measure.
Forecasting Mortality Rates with a Two-Step LASSO Based Vector Autoregressive Model
Authors: Kularatne Thilini Dulanjali , Li Jackie , Shi Yanlin
Published: November 2022

This paper proposes a two-step LASSO based vector autoregressive (2-LVAR) model to forecast mortality rates. Within the VAR framework, recent studies have developed a spatial–temporal autoregressive (STAR) model, in which age-specific mortality rates are related to their own historical values (temporality) and the rates of the neighboring cohorts (spatiality). Despite its desirable age coherence property and the improved forecasting accuracy over the widely used Lee–Carter (LC) model, STAR employs a rather restrictive structure that only allows for non-zero cohort effects of the same cohorts and the neighboring cohorts. To address this limitation, the proposed 2-LVAR model adopts a data-driven principle, as in a sparse VAR (SVAR) model, to offer more flexibility in the parametric structure. A two-step estimation strategy is developed accordingly to resolve the challenging objective function of 2-LVAR, which consists of non-standard L2 and LASSO-type penalties with constraints. Using empirical data from Australia, the United Kingdom, France, and Switzerland, we show that the 2-LVAR model outperforms the LC, STAR, and SVAR models in most of our forecasting results. Further simulation studies confirm this outperformance, and analyses based on life expectancy at birth empirically support the existence of age coherence. The results of this paper will help researchers understand the mortality projections in the long run and improve the reserving/ratemaking accuracy for life insurers.
An Overview of Security Breach Probability Models
Authors: Mazzoccoli Alessandro , Naldi Maurizio
Published: November 2022

Cybersecurity breach probability functions describe how cybersecurity investments impact the actual vulnerability to cyberattacks through the probability of success of the attack. They essentially use mathematical models to make cyber-risk management choices. This paper provides an overview of the breach probability models that appear in the literature. For each of them, the form of the mathematical functions and their properties are described. The models exhibit a wide variety of functional relationships between breach probability and investments, including linear, concave, convex, and a mixture of the latter two. Each model describes a parametric family, with some models have a single parameter, and others have two. A sensitivity analysis completes the overview to identify the impact of the model parameters: the estimation of the parameters which have a larger influence on the breach probability is more critical and deserves greater attention.
Characterizing Spatiotemporal Patterns of Land Subsidence after the South-to-North Water Diversion Project Based on Sentinel-1 InSAR Observations in the Eastern Beijing Plain
Authors: Liu Yuanyuan , Yan Xia , Xia Yuanping , Liu Bo , Lu Zhong , Yu Mei
Published: November 2022

The eastern Beijing plain has been suffering severe subsidence for the last decades, mainly associated with the long-term excessive extraction of groundwater resource. Since the end of 2014, the annual water supply in Beijing plain has reached several hundred million cubic meters because of the South-to-North Water Diversion (SNWD) Project, which has reduced the groundwater exploitation and changed the status of land subsidence. In this work, we first obtain the current spatiotemporal variations of land subsidence in the eastern Beijing plain by using progressive small baseline subsets (SBAS) InSAR time series analysis method with Sentinel-1 SAR data acquired from July 2015 to December 2021. Then, we analyze the correlations between InSAR-derived subsidence and groundwater level change by applying the cross wavelet method. The results show that two major subsidence zones are successfully detected with the maximum deformation rate of −150 mm/yr and maximum cumulative deformation of −950 mm. Besides, the ground deformation at different stages from 2016 to 2021 reveal that the area and magnitude of major deformation significantly slow down, even in the regions with severe subsidence, especially in the year of 2017, which is about two years later than the start time of SNWD Project in Beijing. Further, we find the InSAR-derived subsidence lags groundwater level change with about 1–2-month lagging time, indicating that the dynamic variation of groundwater level fluctuation may be the main factor affecting the uneven subsidence in the severe subsiding zones. Last, differential subsidence rates are identified at both sides of geological faults, such as Nankou-Sunhe fault and Nanyuan-Tongxian fault, from the observed deformation map, which could be explained that the groundwater flow is blocked when a fault is encountered. These findings can provide significant information to reveal the deformation mechanisms of land subsidence, establish the hydrogeological models and assist decision-making, early warning and hazard relief in Beijing, China.
A Novel Fully Coupled Physical–Statistical–Deep Learning Method for Retrieving Near-Surface Air Temperature from Multisource Data
Authors: Du Baoyu , Mao Kebiao , Bateni Sayed M. , Meng Fei , Wang Xu-Ming , Guo Zhonghua , Jun Changhyun , Du Guoming
Published: November 2022

Retrieval of near-surface air temperature (NSAT) from remote sensing data is often ill-posed because of insufficient observational information. Many factors influence the NSAT, which can lead to the instability of the accuracy of traditional algorithms. To overcome this problem, in this study, a fully coupled framework was developed to robustly retrieve NSAT from thermal remote sensing data, integrating physical, statistical, and deep learning methods (PS-DL). Based on physical derivation, the optimal combinations of remote sensing bands were chosen for building the inversion equations to retrieve NSAT, and deep learning was used to optimize the calculations. Multisource data (physical model simulations, remote sensing data, and assimilation products) were used to establish the training and test databases. The NSAT retrieval accuracy was enhanced using the land surface temperature (LST) and land surface emissivity (LSE) as prior knowledge. The highest mean absolute error (MAE) and root-mean-square error (RMSE) of the retrieved NSAT data were 0.78 K and 0.89 K, respectively. In a cross-validation against the China Meteorological Forcing Dataset (CMFD), the MAE and RMSE were 1.00 K and 1.29 K, respectively. The actual inversion MAE and RMSE for the optimal band combination were 1.21 K and 1.33 K, respectively. The proposed method effectively overcomes the limitations of traditional methods as the inversion accuracy is enhanced by adding the information of atmospheric water vapor and more bands, and the applicability (portability) of the algorithm is enhanced using LST and LSE as prior knowledge. This model can become a general inversion paradigm for geophysical parameter retrieval, which is of milestone significance because of its accuracy and the ability to allow deep learning for physical interpretation.
On the Co-Selection of Vision Transformer Features and Images for Very High-Resolution Image Scene Classification
Authors: Chaib Souleyman , Mansouri Dou El Kefel , Omara Ibrahim , Hagag Ahmed , Dhelim Sahraoui , Bensaber Djamel Amar
Published: November 2022

Recent developments in remote sensing technology have allowed us to observe the Earth with very high-resolution (VHR) images. VHR imagery scene classification is a challenging problem in the field of remote sensing. Vision transformer (ViT) models have achieved breakthrough results in image recognition tasks. However, transformer–encoder layers encode different levels of features, where the latest layer represents semantic information, in contrast to the earliest layers, which contain more detailed data but ignore the semantic information of an image scene. In this paper, a new deep framework is proposed for VHR scene understanding by exploring the strengths of ViT features in a simple and effective way. First, pre-trained ViT models are used to extract informative features from the original VHR image scene, where the transformer–encoder layers are used to generate the feature descriptors of the input images. Second, we merged the obtained features as one signal data set. Third, some extracted ViT features do not describe well the image scenes, such as agriculture, meadows, and beaches, which could negatively affect the performance of the classification model. To deal with this challenge, we propose a new algorithm for feature- and image selection. Indeed, this gives us the possibility of eliminating the less important features and images, as well as those that are abnormal; based on the similarity of preserving the whole data set, we selected the most informative features and important images by dropping the irrelevant images that degraded the classification accuracy. The proposed method was tested on three VHR benchmarks. The experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.
Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data
Authors: Zhao Hongling , Li Hongyan , Xuan Yunqing , Li Changhai , Ni Heshan
Published: November 2022

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R2, and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas.
Regional Variability and Driving Forces behind Forest Fires in Sweden
Authors: Cimdins Reinis , Krasovskiy Andrey , Kraxner Florian
Published: November 2022

Extreme forest fires have been a historic concern in the forests of Canada, the Russian Federation, and the USA, and are now an increasing threat in boreal Europe, where recent fire events in 2014 and 2018 drew attention to Sweden. Our study objective was to understand the vulnerability of Swedish forests to fire by spatially analyzing historical burned areas, and to link fire events with weather, landscape, and fire-related socioeconomic factors. We developed an extensive database of 1 × 1 km2 homogenous grids, where monthly burned areas were derived from the MODIS FireCCI51 dataset. The database consists of various socio-economic, topographic-, forest-, and weather-related remote sensing products. To include new factors in the IIASA’s FLAM model, we developed a random forest model to assess the spatial probabilities of burned areas. Due to Sweden’s geographical diversity, fire dynamics vary between six biogeographical zones. Therefore, the model was applied to each zone separately. As an outcome, we obtained probabilities of burned areas in the forests across Sweden and observed burned areas were well captured by the model. The result accuracy differs with respect to zone; the area under the curve (AUC) was 0.875 and 0.94 for zones with few fires, but above 0.95 for zones with a higher number of fire events. Feature importance analysis and their variability across Sweden provide valuable information to understand the reasons behind forest fires. The Fine Fuel Moisture Code, population and road densities, slope and aspect, and forest stand volume were found to be among the key fire-related factors in Sweden. Our modeling approach can be extended to hotspot mapping in other boreal regions and thus is highly policy-relevant. Visualization of our results is available in the Google Earth Engine Application.