bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards SDG 12: Upcycling Biomass and Waste to Valuable Products
Authors: Bader Lana , Szekely Gyorgy
Published: August 2024

Sustainable Development Goal 12 (SDG 12) is a goal of the United Nations that focuses on responsible consumption and production… but what does this mean? It means that we should use the materials around us in ways that prevent them from being wasted, and that we should produce them without harming the planet. SDG 12 aims for a better world. It is important to work toward this goal because Earth’s population is growing, and more resources are being used. Eventually humans will ruin the planet if we do not start acting. We must make sure we are protecting the planet and its resources for the people born in the future. Science can help achieve this goal in many ways. In this article, you will learn about several environmentally friendly methods of producing new materials from waste, including the use of normally discarded parts of living things, like plant leaves and shrimp shells.
Case report: Evaluation of head trauma in a tawny owl (Strix aluco) with advanced imaging diagnostic, FVEP and BAER test
Authors: Vetere Alessandro , Camera Nicola Della , Cococcetta Ciro , Paoletti Carlo , Dondi Maurizio , Biaggi Fabio , Ianni Francesco Di
Published: August 2024

An adult pet tawny owl (Strix aluco) presented to a veterinary hospital at Parma University with a history of head trauma. After a critical care protocol including thermal, oxygen and fluid support aimed at stabilizing the patient, a neurological examination was performed. During neurological evaluation, marked lethargy and an inability to rise from a recumbent position was noted. Anisocoria was also present, with a mydriatic left pupil exhibiting no pupillary light response (PLR) even on direct illumination of both eyes. On ocular fundus examination, retinal hemorrhage and retinal detachment were observed. Based on these clinical findings, a complete work-up was performed, including hematological exams and total body X-ray studies followed by a computed tomography (CT) scan. Additional examinations, such as brainstem auditory evoked response (BAER) measurement and flash visual evoked potential (FVEP) recording, were performed. FVEP measurements performed on the left eye exhibited no peaks in either series of stimulations, indicating an altered functional integration within the visual pathway. A CT scan revealed a large hypoattenuating lesion within the right cerebral hemisphere, suspected to be intraparenchymal edema. The BAER test demonstrated an altered trace consistent with brainstem involvement and left hypoacusis due to cranial nerve VIII deficiency. Head trauma can result in significant neurological impairments in birds, impacting their behavior, mobility, and cognitive abilities. FVEP recordings, BAER tests and CT scans may be useful diagnostic tools in clinical practice. Understanding the causes and neurologic presentation of avian traumas is essential for effective prevention, diagnosis and treatment of affected birds.
Joint Optimization of Service Migration and Resource Allocation in Mobile Edge–Cloud Computing
Authors: He Zhenli , Li Liheng , Lin Ziqi , Dong Yunyun , Qin Jianglong , Li Keqin
Published: August 2024

In the rapidly evolving domain of mobile edge–cloud computing (MECC), the proliferation of Internet of Things (IoT) devices and mobile applications poses significant challenges, particularly in dynamically managing computational demands and user mobility. Current research has partially addressed aspects of service migration and resource allocation, yet it often falls short in thoroughly examining the nuanced interdependencies between migration strategies and resource allocation, the consequential impacts of migration delays, and the intricacies of handling incomplete tasks during migration. This study advances the discourse by introducing a sophisticated framework optimized through a deep reinforcement learning (DRL) strategy, underpinned by a Markov decision process (MDP) that dynamically adapts service migration and resource allocation strategies. This refined approach facilitates continuous system monitoring, adept decision making, and iterative policy refinement, significantly enhancing operational efficiency and reducing response times in MECC environments. By meticulously addressing these previously overlooked complexities, our research not only fills critical gaps in the literature but also enhances the practical deployment of edge computing technologies, contributing profoundly to both theoretical insights and practical implementations in contemporary digital ecosystems.
Extended General Malfatti’s Problem
Authors: Chiang Ching-Shoei
Published: August 2024

Malfatti’s problem involves three circles (called Malfatti circles) that are tangent to each other and two sides of a triangle. In this study, our objective is to extend the problem to find 6, 10, … ∑1ni (n > 2) circles inside the triangle so that the three corner circles are tangent to two sides of the triangle, the boundary circles are tangent to one side of the triangle, and four other circles (at least two of them being boundary or corner circles) and the inner circles are tangent to six other circles. We call this problem the extended general Malfatti’s problem, or the Tri(Tn) problem, where Tri means that the boundary of these circles is a triangle, and Tn is the number of circles inside the triangle. In this paper, we propose an algorithm to solve the Tri(Tn) problem.
Integrated Analysis of Multi-Parameter Precursors to the Fukushima Offshore Earthquake (Mj = 7.3) on 13 February 2021 and Lithosphere–Atmosphere–Ionosphere Coupling Channels
Authors: Hayakawa Masashi , Hobara Yasuhide
Published: August 2024

The preparation phase of earthquakes (EQs) has been investigated by making full use of multi-parameter and multi-layer observations of EQ precursors, in order to better understand the lithosphere–atmosphere–ionosphere coupling (LAIC) process. For this purpose, we chose a specific target EQ, the huge EQ of Fukushima-ken-oki EQ on 13 February 2021 (magnitude Mj = 7.3). We initially reported on EQ precursors in different physical parameters not only of the lithosphere, but also of the atmosphere and ionosphere (Hayakawa et al. followed by Akhoondzadeh et al. and Draz et al., both based on satellite observations). Our first two papers dealt with seven electromagnetic precursors in the three layers (with emphasis on our own ground-based observations in the atmosphere and lower ionosphere), while the second paper dealt with Swarm satellite observations of magnetic field, electron density, and GPS TEC in the ionosphere, and the third paper dealt only with climatological parameters on and above the Earth’s surface (together with GPS TEC). We have extensively reviewed all of these results, and have coordinated the temporal evolutions of various physical parameters relevant to the LAIC system; we have sought to understand which hypothesis is more plausible in explaining the LAIC process. Then, we came to a conclusion that two possible LAIC channels seem to exist simultaneously for this EQ: a fast channel (nearly simultaneous responses on the ground and ionosphere), and a slow channel (or diffusion-type), with a time delay of a few to several days, in which the agent effects in the lithosphere and lowest atmosphere seem to propagate up to the ionosphere with a definite time delay. Finally, we have suggested some research directions for the future elucidation of LAIC channels, and also made some comments on an early EQ warning system.
Slow-Scale Bifurcation Analysis of a Single-Phase Voltage Source Full-Bridge Inverter with an LCL Filter
Authors: Yang Fang , Bai Weiye , Huang Xianghui , Wang Yuanbin , Liu Jiang , Kang Zhen
Published: August 2024

In high-power photovoltaic systems, the inverter with an LCL filter is widely used to reduce the value of output inductance at which a lower switching frequency is required. However, the effect on the stability of the system caused by an LCL filter due to its resonance characteristic cannot be ignored. This paper studies the stability of a single-phase voltage source full-bridge inverter with an LCL filter through the bifurcation theory as it is a nonlinear system. The simulation results show that low-frequency oscillation appears when the proportional coefficient of the system controller increases or the damping resistance decreases to a certain extent. The average model is derived to analyze the low-frequency oscillation; the theoretical analysis demonstrates that low-frequency oscillation is essentially a period in which doubling bifurcation occurs, which indicates the intrinsic mechanism of the instability of the full-bridge inverter with an LCL filter. Additionally, the limitation of the existing damping resistor design standards, which only considers the main circuit parameters but ignores the influence of the controller on system stability, is identified. To solve this problem, the analytical expression of the system stability boundary is provided, which can not only provide convenience for engineering design to protect the system from low-frequency oscillation but also expand the selection range of damping resistance in practice. The experiments are performed to verify the results of the simulation and theoretical analysis, demonstrating that the analysis method can facilitate the design of the inverter with an LCL filter.
Use of Extended Exergy Analysis to Quantify Advantages and Drawbacks of Decentralizing Industrial Production Lines
Authors: Sciubba Enrico
Published: August 2024

In the ongoing debate about the feasibility of enforcing a transition to decentralized energy conversion systems, arguments are often presented that lack scientific rigor. Granted, the issue is multi-faceted and fundamentally multi-disciplinary, and possible solutions strongly depend on the selection of location as well as on local climate and demographics. Furthermore, decentralizing the final energy distribution leads to potential socio-economic considerations that involve value judgements. However, the most serious problem is that media have appropriated the topic and are often publishing opinion papers authored by non-specialists and even by representatives of interest groups. The present paper proposes an approach that is innovative on two counts: first, it treats “final energy” as any other commodity and therefore expands the field of investigation to the problems arising from the decentralization of a generic production line or technological chain; second, it argues that a method solidly rooted in Thermodynamics, the Extended Exergy Accounting, may be used to quantify the total amount of primary exergy resources requested by a decentralized strategy (as opposed to a centralized one), so that a comparison can be performed and discussed on a rational, unbiased and scientific basis. This is an introductory paper that reports some theoretical results of the method: realistic applications are perforce excluded because the idea is that the procedure must be drafted in such a way to be applicable to different socio-economic scenarios and locations and to remain valid under a broad range of boundary conditions.
Techno-Economic Design and Optimization of Hybrid Energy Systems
Authors: Mazibuko Thokozile , Moloi Katleho , Akindeji Kayode
Published: August 2024

The power generation capacity must be increased to accommodate population growth and address the lack of electricity access in rural areas. Traditional power plants in South Africa are unable to keep up with the growing demand for electricity. By strategically planning and building clusters of renewable energy sources like solar and wind, microgrid operators can provide a sustainable solution that boosts electricity supply while being cost-effective and environmentally friendly. Utilizing renewable energy can help alleviate strain on power plants by reducing peak demand in constrained distribution networks. The benefits of renewable energy include lower electricity expenses, enhanced system reliability, investment reallocation, and reduced environmental impact. These advantages will enhance the efficiency of the power system and contribute economic value to society. However, integrating solar power into the network infrastructure presents challenges such as fundamental changes in network structure, its intermittent nature due to unpredictability, and geographical constraints, which can complicate the task of grid operators in balancing electricity supply and demand within system limits while minimizing costs. The study employs Homer Pro 3.18.1 software to assess the economic costs and benefits of effectively integrating renewable technologies into the power grid. The aim is to evaluate the economic and technical feasibility of investing in renewable energy projects within the network. The research outcomes can guide power system operators, planners, and designers in leveraging solar energy to drive economic growth and industrial advancement, as well as assist independent power producers in making informed investment choices.
Variability and Sensitivity of Models Used to Estimate Photovoltaic Production
Authors: Araújo Nícolas M. F. T. S. , Medeiros Susane Eterna Leite , Abrahão Raphael
Published: August 2024

Using renewable energies is one of the alternatives to mitigate climate change. Among them, photovoltaic energy has shown a relevant growth of participation in the electric sector. In the backdrop of such growth, in countries such as Brazil, photovoltaic energy has surpassed the generation of electricity by petroleum derivatives since 2019. The significant growth in photovoltaic generation around the world can be attributed to several key factors, including abundant sunlight, supportive government policies, falling solar panel costs, environmental concerns, energy diversification goals, investor interest, job creation, and local manufacturing. However, photovoltaic system performance is heavily tied to weather variability. Different models are used to account for this meteorological dependence; however, there is a gap regarding the differences in the outputs of these models. The study presented here investigates the variability and sensitivity of the models used to estimate photovoltaic production (Ppv). Six models were compared by percentage difference analysis. Statistical analyses from the perspective of variability revealed that the difference between the Ppv estimated by these models reaches a 12.89% absolute power difference. Considering that temperature and solar irradiance are the meteorological variables that most influence Ppv, the sensitivity analysis focused on these. Regarding sensitivity, in the context of temperature changes, the average relative difference in Ppv between models can reach up to 5.32% for a 10 °C change, while in the context of changes in solar irradiance, the average relative difference can reach up to 19.05% for a change of 41.67 W/m2. The consideration of the variability and sensitivity of the main sets of equations used to estimate the potential of photovoltaic energy production can help refine methodologies and assumptions in future research in this area. There are variations and sensitivities, as observed, of such magnitude that, depending on the set of equations adopted in the study, they can alter the conclusion about photovoltaic energy production in a given region. Accurate estimations are pivotal not only for feasibility analyses but also for gauging economic and socio-environmental impacts. These divergences can, in turn, reformulate feasibility analyses and compromise the reliability of photovoltaic energy systems, thus leading to different economic and socio-environmental consequences.
Doing More with Less: Applying Low-Frequency Energy Data to Define Thermal Performance of House Units and Energy-Saving Opportunities
Authors: Irakoze Amina , Choi Han-Sung , Kim Kee-Han
Published: August 2024

High-frequency energy data, such as hourly and sub-hourly energy, provide various options for assessing building energy performance. However, the scarcity of such energy data is among the challenges of applying most of the existing energy analysis approaches in large-scale building energy remodeling projects. The purpose of this study is to develop a practical method to define the energy performance of residential house units using monthly energy data that are relatively easy to obtain for existing building stock. In addition, based on the defined energy use characteristics, house units are classified, and energy retrofit measures are proposed for energy-inefficient units. In this study, we applied a change-point regression model to investigate the heterogeneity in the monthly gas consumption of 200 house units sampled from four apartment complexes in Ulsan, Republic of Korea. Using a four-quadrant plane and the fitted model parameters, we identified most energy-inefficient house units and their potential energy-saving measures are assessed. The results indicate that around a 41% energy reduction through enhanced thermal properties and heating systems was achieved. The study responds to the need for a straightforward procedure for identifying and prioritizing the best targets for effective energy upgrades of existing buildings.
Medium–Long-Term PV Output Forecasting Based on the Graph Attention Network with Amplitude-Aware Permutation Entropy
Authors: Shen Shuyi , He Yingjing , Chen Gaoxuan , Ding Xu , Zheng Lingwei
Published: August 2024

Medium–long-term photovoltaic (PV) output forecasting is of great significance to power grid planning, power market transactions, power dispatching operations, equipment maintenance and overhaul. However, PV output fluctuates greatly due to weather changes. Furthermore, it is frequently challenging to ensure the accuracy of forecasts for medium–long-term forecasting involving a long time span. In response to the above problems, this paper proposes a medium–long-term forecasting method for PV output based on amplitude-aware permutation entropy component reconstruction and the graph attention network. Firstly, the PV output sequence data are decomposed by ensemble empirical mode decomposition (EEMD), and the decomposed intrinsic mode function (IMF) subsequences are combined and reconstructed according to the amplitude-aware permutation entropy. Secondly, the graph node feature sequence is constructed from the reconstructed subsequences, and the mutual information of the node feature sequence is calculated to obtain the graph node adjacency matrix which is applied to generate a graph sequence. Thirdly, the graph attention network is utilized to forecast the graph sequence and separate the PV output forecasting results. Finally, an actual measurement system is used to experimentally verify the proposed method, and the outcomes indicate that the proposed method, which has certain promotion value, can improve the accuracy of medium–long-term forecasting of PV output.
Multiparametric Methods for Rapid Classification of Diesel Fuel Quality Used in Automotive Engine Systems
Authors: Borecki Michal , Geca Mateusz , Zan Li , Prus Przemysław , Korwin-Pawlowski Michael L.
Published: August 2024

Fuels should behave appropriately in all sections of the engine system: the engine, fuel delivery system, and tank. Fuel quality can be linked to the following three crucial areas: performance, fitness for current use, and stability. Classical methods of diesel fuel examination mostly rely on the absolute value measurement of one specific parameter while stabilizing outside conditions. In contrast, multiparametric methods depend on simultaneously measuring a set of parameters. Therefore, multiparametric methods open the possibility of intriguing new examinations and classifications of diesel fuel quality while raising specific issues relating to the instrumentation and construction of sensing devices. This paper presents a review, based on the published literature and the authors’ research, of the current state-of-the-art multiparametric methods for rapid diesel fuel quality classification and related instrumentation, systematizing the various types of methods from the point of view of the principles of their operation. The main conclusion is that different measuring procedures use similar methods of data processing. Moreover, the heavy, costly, and complex devices that enable standard examinations can be converted to simpler devices in the future, whose cost of use is significantly lower. However, to achieve this, progress in electronic devices is required.
Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network
Authors: You Yuwen , Wang Zhonghua , Liu Zhihao , Guo Chunmei , Yang Bin
Published: August 2024

Cogeneration is an important means for heat supply enterprises to obtain heat, and accurate load prediction is particularly crucial. The heat load of a centralized heat supply system is influenced by various factors such as outdoor meteorological parameters, the building envelope structure, and regulation control, which exhibit a strong coupling and nonlinearity. It is essential to identify the key variables affecting the heat load at different heating stages through data mining techniques and to use deep learning algorithms to precisely regulate the heating system based on load predictions. In this study, a heat station in a northern Chinese city is taken as the subject of research. We apply the Fuzzy Clustering based on Fourier distance (FCBD-FCM) algorithm to transform the factors influencing the long and short-term load prediction of heat supply from the time domain to the frequency domain. This transformation is used to analyze the degree of their impact on load changes and to extract factors with significant influence as the multifeatured input variables for the prediction model. Five neural network models for load prediction are established, namely, Backpropagation (BP), convolutional neural network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and CNN-BiLSTM. These models are compared and analyzed for their performance in long-term, short-term, and ultrashort-term heating load prediction. The findings indicate that the load prediction accuracy is high when multifeatured input variables are based on fuzzy clustering. Furthermore, the CNN-BiLSTM model notably enhances the prediction accuracy and generalization ability compared to other models, with the Mean Absolute Percentage Error (MAPE) averaging within 3%.
Extending the Lifetime of Offshore Wind Turbines: Challenges and Opportunities
Authors: Shafiee Mahmood
Published: August 2024

A significant number of first-generation offshore wind turbines (OWTs) have either reached or are approaching the end of their operational lifespan and need to be upgraded or replaced with more modern units. In response to this concern, governments, regulatory bodies and industries have initiated the development of effective end-of-life (EOL) management strategies for offshore wind infrastructure. Lifetime extension is a relatively new concept that has recently gained significant attention within the offshore wind energy community. Extending the service lifetime of OWTs can yield many benefits, such as reduced capital cost, increased return on investment (ROI), improved overall energy output, and reduced toxic gas emissions. Nevertheless, it is important to identify and prepare for the challenges that may limit the full exploitation of the potential for OWT lifetime extension projects. The objective of this paper is to present a detailed PESTLE analysis to evaluate the various political, economic, sociological, technological, legal, and environmental challenges that must be overcome to successfully implement lifetime extension projects in the offshore wind energy sector. We propose a decision framework for extending the lifetime of OWTs, involving the degradation mechanisms and failure modes of components, remaining useful life estimation processes, safety and structural integrity assessments, economic and environmental evaluations, and the selection of lifetime extension technologies among remanufacturing, retrofitting, and reconditioning. Finally, we outline some of the opportunities that lifetime extension can offer for the wind energy industry to foster a more circular and sustainable economy in the future.
Institutional Violence Perpetrated against Transgender Individuals in Health Services: A Systematic Review of Qualitative Studies
Authors: Leal Gilberto da Cruz , Júnior José Nildo de Barros Silva , Ferreira Quezia Rosa , Ballestero Jaqueline Garcia de Almeida , Palha Pedro Fredemir
Published: August 2024

This review aims to analyze the evidence related to violence perpetrated against transgender individuals in health services based on their narratives. This is a systematic literature review of qualitative studies. A search was carried out in the Scopus, Web of Science, Latin American and Caribbean Literature in Health Sciences (LILACS), Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, and MEDLINE databases using the descriptors “transgender people”, “violence”, and “health services”. The eligibility criteria included original qualitative articles addressing the research question, with fully available text, reporting violence specifically by health workers, involving trans individuals aged 18 and above, and published in Portuguese, English, or Spanish. In addition, studies were included that reported experiences of violence suffered by the trans population, through their narratives, in health services. A total of 3477 studies were found, of which 25 were included for analysis. The results highlighted situations such as refusal of service; resistance to the use of social names and pronouns; barriers to accessing health services; discrimination and stigma; insensitivity of health workers; lack of specialized care and professional preparedness; and a system focused on binarism. The analysis of the studies listed in this review highlights the multiple facets of institutional violence faced by the transgender population in health services. It is evident that the forms of violence often interlink and reinforce each other, creating a hostile environment for the transgender population in health services. Thus, there is an urgent need to create strategies that ensure access to dignified and respectful care for all individuals, regardless of their gender identity.
Cannabis Use and Associated Risk Behavior Factors among High School Students in Mississippi: Youth Risk Behavior Surveillance System 2021
Authors: Mitra Amal K. , Zhang Zhen , Schroeder Julie A.
Published: August 2024

Cannabis is the most used illicit drug among youths in the United States. The objectives of this study were to identify the association between cannabis use and other risk behaviors, including suicidality, among high school students. This is a cross-sectional study using the 2021 Mississippi Youth Risk Behavior Surveillance System (YRBS). The 2021 YRBS data sets were combined for this study. The crude odds ratio (OR) and adjusted odds ratio (AOR) with a 95% confidence interval were generated using the survey packages in R to account for weights and the complex sampling design of the YRBS data. Univariate analysis identified seven risky behaviors that were significantly associated with current cannabis use, including carrying weapons on school campuses, suicidal attempts, electronic vapor use, current smoking, current drinking, sexual behaviors, and unsupervised children. In multivariable analysis, after adjusting for gender, race, students’ grades, and other risky behaviors, statistically significant variables for cannabis use included current use of electronic vapor, current smoking, current drinking, and sexual behaviors. Cannabis use is evenly burdened between males and females and between all race categories among Mississippi high school students. The identified associations seem to indicate that electronic vapor, tobacco products, and alcohol use could be the forerunners for drug use and should be treated accordingly in drug use prevention programs.
LPBF Processability of NiTiHf Alloys: Systematic Modeling and Single-Track Studies
Authors: Dabbaghi Hediyeh , Pourshams Mohammad , Nematollahi Mohammadreza , Poorganji Behrang , Kirka Michael M. , Smith Scott , Chinnasamy Chins , Elahinia Mohammad
Published: August 2024

Research into the processability of NiTiHf high-temperature shape memory alloys (HTSMAs) via laser powder bed fusion (LPBF) is limited; nevertheless, these alloys show promise for applications in extreme environments. This study aims to address this limitation by investigating the printability of four NiTiHf alloys with varying Hf content (1, 2, 15, and 20 at. %) to assess their suitability for LPBF applications. Solidification cracking is one of the main limiting factors in LPBF processes, which occurs during the final stage of solidification. To investigate the effect of alloy composition on printability, this study focuses on this defect via a combination of computational modeling and experimental validation. To this end, solidification cracking susceptibility is calculated as Kou’s index and Scheil–Gulliver model, implemented in Thermo-Calc/2022a software. An innovative powder-free experimental method through laser remelting was conducted on bare NiTiHf ingots to validate the parameter impacts of the LPBF process. The result is the processability window with no cracking likelihood under diverse LPBF conditions, including laser power and scan speed. This comprehensive investigation enhances our understanding of the processability challenges and opportunities for NiTiHf HTSMAs in advanced engineering applications.
Molecular Dynamics Simulation Study of Aluminum–Copper Alloys’ Anisotropy under Different Loading Conditions and Different Crystal Orientations
Authors: Wu Xiaodong , Zhang Wenkang
Published: August 2024

The commonly used aluminum–copper alloys in industry are mainly rolled plates and extruded or drawn bars. The aluminum–copper alloys’ anisotropy generated in the manufacturing process is unfavorable for subsequent applications. Its underlying mechanism shall be interpreted from a microscopic perspective. This paper conducted the loading simulation on Al–4%Cu alloy crystals at the microscopic scale with molecular dynamics technology. Uniaxial tension and compression loading were carried out along three orientations: X-<1¯12>, Y-<11¯1>, and Z-<110>. It analyzes the micro-mechanisms that affect the performance changes of aluminum–copper alloys through the combination of stress–strain curves and different organizational analysis approaches. As shown by the results, the elastic modulus and yield strength are the highest under tension along the <11¯1> direction. Such is the case for the reasons below: The close-packed plane of atoms ensures large atomic binding forces. In addition, the Stair-rod dislocation forms a Lomer–Cottrell dislocation lock, which has a strengthening effect on the material. The elastic modulus and yield strength are the smallest under tension along the <110> direction, and the periodic arrangement of HCP atom stacking faults serves as the main deformation mechanism. This is because the atomic arrangement on the <110> plane is relatively loose, which tends to cause atomic misalignment. When compressed in different directions, the plastic deformation mechanism is mainly dominated by dislocations and stacking faults. When compressed along the <110> direction, it has a relatively high dislocation density and the maximum yield strength. That should be attributed to the facts below. As the atomic arrangement of the <110> plane itself was not dense originally, compression loading would cause an increasingly tighter arrangement. In such a case, the stress could only be released through dislocations. This research aims to provide a reference for optimizing the processing technology and preparation methods of aluminum–copper alloy materials.
Rosa sterilis Juice Alleviated Breast Cancer by Triggering the Mitochondrial Apoptosis Pathway and Suppressing the Jak2/Stat3 Pathway
Authors: Wang Wenxi , Huang Shaolin , Li Sha , Li Xingjie , Ling Yihan , Wang Xiaomeng , Zhang Shuwen , Zhou Dingzi , Yin Wenya
Published: August 2024

Rosa sterilis (RS) is a characteristic fruit in southwestern China that has numerous health benefits; however, its pharmacological effect needs further clarification, especially with respect to the exploration of its potential anti-breast-cancer effect, as there are still knowledge gaps in this regard. This study was designed to investigate the protective effects of Rosa sterilis juice (RSJ) on breast cancer (BC) through in vitro cellular experiments and by establishing mouse 4T1 breast xenograft tumors. This study also had the aim of elucidating RSJ’s underlying mechanisms. RSJ can inhibit cell proliferation, affect cell morphology, and impact the clone formation ability of BC; furthermore, it can promote apoptosis by triggering the mitochondrial apoptosis pathway. In mouse 4T1 breast xenograft tumors, RSJ markedly inhibited tumor growth, relieved the pathological lesions, lowered the expression of Ki67, and regulated the expression of the apoptosis-associated protein. Moreover, we observed that RSJ can inhibit the Jak2/Stat3 signaling pathway both in vivo and in vitro. Overall, our research reveals that RSJ can alleviate BC by triggering the mitochondrial apoptosis pathway and suppressing the Jak2/Stat3 pathway, providing new dietary intervention strategies for BC.
The Modification of Dietary Protein with Ammonium Hydroxide Enhancement Improves Longevity and Metabolic Outcomes in a Sex-Dependent Manner
Authors: Barr Benjamin , Gollahon Lauren
Published: August 2024

(1) Background: Dietary protein is a key component of all dietary patterns. It has been demonstrated that there are subtle differences in health implications associated with the source of dietary protein consumed. This study examined dietary protein sources (DPSs) in a long-term study of diet-induced obesity ± ammonium hydroxide enhancement (AHE) and its role in improving long-term health outcomes. (2) Methods: Over 18 months, 272 C3H/HeJ mice (136 male and 136 female) were monitored on high-fat diets with varying DPSs ± AHE. Mice were monitored for weekly change in total mass, as well as 6-month assessments of lean and fat mass. At each assessment, a cohort (~8 mice per diet per sex) was censored for a cross-sectional examination of organ function. (3) Results: Longevity was improved in females fed AHE diets, regardless of DPSs. Females’ measures of fat and lean mass were markedly elevated with casein protein diets compared to beef protein diets regardless of AHE. Females fed a beef protein diet + AHE demonstrated reduced fat mass and increased lean mass with aging. In males, AHE beef protein diet-fed mice showed marked improvement to longevity and increased lean mass at 6 months. (4) Conclusions: This study demonstrates that dietary protein modification by AHE attenuates the negative impacts of HF diets in both males and females in a sex-dependent manner. Furthermore, the results from this study emphasize the importance of identifying the differences in the utilization of dietary proteins in both a sex- and age-related manner and demonstrate the potential of DPS modification by AHE as a dietary intervention.
NAD+ and Niacin Supplementation as Possible Treatments for Glaucoma and Age-Related Macular Degeneration: A Narrative Review
Authors: Gemae Mohamed R. , Bassi Mario D. , Wang Patrick , Chin Eric K. , Almeida David R.P.
Published: August 2024

Glaucoma and age-related macular degeneration (AMD) are progressive retinal diseases characterized by increased oxidative stress, inflammation, and mitochondrial dysfunction. This review investigates the potential therapeutic benefits of NAD+ and niacin supplementation in managing glaucoma and AMD. A literature search was conducted encompassing keywords such as “niacin”, “NAD”, “glaucoma”, “AMD”, and “therapeutics”. NAD+ depletion is associated with increased oxidative stress and mitochondrial dysfunction in glaucoma and AMD. Niacin, a precursor to NAD+, has shown promise in replenishing NAD+ levels, improving choroidal blood flow, and reducing oxidative damage. Animal studies in glaucoma models indicate that nicotinamide (NAM) supplementation preserves RGC density and function. Large-scale population-based studies indicate an inverse correlation between niacin intake and glaucoma prevalence, suggesting a preventative role. Randomized controlled trials assessing niacin supplementation showed significant improvements in visual field sensitivity and inner retinal function, with a dose-dependent relationship. In AMD, nicotinamide supplementation may improve rod cell function and protect against oxidative stress-induced damage. Cross-sectional studies reveal that individuals with AMD have a lower dietary intake of niacin. Further studies suggest niacin’s role in improving choroidal blood flow and dilating retinal arterioles, potentially mitigating ischemic damage and oxidative stress in AMD. Beyond current management strategies, NAD+ and niacin supplementation may offer novel therapeutic avenues for glaucoma and AMD. Further research is warranted to elucidate their efficacy and safety in clinical settings.
Targeting Divergent Pathways in the Nutritional Management of Depression
Authors: Tobin Derek , Vuckovic Alexander , Sarris Jerome
Published: August 2024

The nutritional management of depression has long been discussed, due to the perceived benefit of a nutritional product having less side effects than pharmaceutical agents. Candidate nutrients for managing depression include vitamin D, B vitamins, tryptophan, branch chain amino acids, probiotics, omega-3 fatty acids, folate/methylfolate (also known as vitamin B9), and s-adenosylmethionine. This paper provides a narrative review of three nutrients which have significant scientific support for the management of depression. A deficiency in each nutrient is associated with depression, and interventional studies indicate that the correction of the nutritional deficiency may provide clinical benefit. We present epidemiological evidence, a mechanistic explanation and a review of interventional studies for these nutrients. Finally, relevant nutritional guidelines are presented with their conclusion for the role of each nutrient in the management of depression.
In Silico Identification of Peanut Peptides Suitable for Allergy Immunotherapy in HLA-DRB1*03:01-Restricted Patients
Authors: Doytchinova Irini , Atanasova Mariyana , Sotirov Stanislav , Dimitrov Ivan
Published: August 2024

Peanut allergy, a prevalent and potentially severe condition affecting millions worldwide, has been linked to specific human leukocyte antigens (HLAs), suggesting increased susceptibility. Employing an immunoinformatic strategy, we developed a “logo model” based on amino acid frequencies in the peptide binding core and used it to predict peptides originating from 28 known peanut allergens binding to HLA-DRB1*03:01, one of the susceptibility alleles. These peptides hold promise for immunotherapy in HLA-DRB1*03:01 carriers, offering reduced allergenicity compared to whole proteins. By targeting essential epitopes, immunotherapy can modulate immune responses with minimal risk of severe reactions. This precise approach could induce immune tolerance with fewer adverse effects, presenting a safer and more effective treatment for peanut allergy and other allergic conditions.
Volume-Based Occupancy Detection for In-Cabin Applications by Millimeter Wave Radar
Authors: Gharamohammadi Ali , Dabak Anand G. , Yang Zigang , Khajepour Amir , Shaker George
Published: August 2024

In-cabin occupancy detection has become increasingly important due to incidents involving children left in vehicles under extreme temperature conditions. Frequency modulated continuous wave (FMCW) radars are widely used for non-contact monitoring and sensing applications, particularly for occupancy detection. However, the confined and metallic environment inside vehicle cabins presents significant challenges due to multipath reflections. This paper introduces a novel approach that detects the occupied space in each seat to determine occupancy, using the variance of detected points as an indicator of volume occupancy. In an experimental study involving 70 different scenarios with single and multiple subjects, we classify occupants in each seat into one of three categories: adult, baby, or empty. The proposed method achieves an overall accuracy of 96.7% using an Adaboost classifier and a miss-detection rate of 1.8% for detecting babies. This approach demonstrates superior robustness to multipath interference compared to traditional energy-based methods, offering a significant advancement in in-cabin occupancy detection technology.
Infrared Camera Array System and Self-Calibration Method for Enhanced Dim Target Perception
Authors: Zhang Yaning , Wu Tianhao , Yang Jungang , An Wei
Published: August 2024

Camera arrays can enhance the signal-to-noise ratio (SNR) between dim targets and backgrounds through multi-view synthesis. This is crucial for the detection of dim targets. To this end, we design and develop an infrared camera array system with a large baseline. The multi-view synthesis of camera arrays relies heavily on the calibration accuracy of relative poses in the sub-cameras. However, the sub-cameras within a camera array lack strict geometric constraints. Therefore, most current calibration methods still consider the camera array as multiple pinhole cameras for calibration. Moreover, when detecting distant targets, the camera array usually needs to adjust the focal length to maintain a larger depth of field (DoF), so that the distant targets are located on the camera’s focal plane. This means that the calibration scene should be selected within this DoF range to obtain clear images. Nevertheless, the small parallax between the distant sub-aperture views limits the calibration. To address these issues, we propose a calibration model for camera arrays in distant scenes. In this model, we first extend the parallax by employing dual-array frames (i.e., recording a scene at two spatial locations). Secondly, we investigate the linear constraints between the dual-array frames, to maintain the minimum degrees of freedom of the model. We develop a real-world light field dataset called NUDT-Dual-Array using an infrared camera array to evaluate our method. Experimental results on our self-developed datasets demonstrate the effectiveness of our method. Using the calibrated model, we improve the SNR of distant dim targets, which ultimately enhances the detection and perception of dim targets.
A New Approximation Modeling Method for the Triaxial Induction Logging in Planar-Stratified Biaxial Anisotropic Formations
Authors: Qiao Ping , Wang Lei , Yuan Xiyong , Deng Shaogui
Published: August 2024

A novel and efficient modeling approach has been developed for simulating the responses of triaxial induction logging (TIL) in layered biaxial anisotropic (BA) formations. The core of this innovative technique lies in analytically calculating the primary fields within a homogeneous medium and approximating the scattered fields within layered formations. The former involves employing a two-level subtraction technique. Initially, the first-level subtraction entails altering the direction of the Fourier transform to mitigate the integral singularity of the spectral fields, particularly in high-angle and horizontal wells. Conversely, the second-level subtraction aims to further optimize integral convergence by creating an equivalent unbounded transverse isotropic (TI) formation and eliminating the corresponding spectral fields. With the two-level subtractions, the convergence of the spectral field has been enhanced by more than six orders of magnitude. Additionally, a strict recursive algorithm and approximation method are developed to compute the scattered fields in layered biaxial anisotropic media. The rigorous algorithm is based on a modified amplitude propagator matrix (MAPM) approach and serves as the benchmark for the approximation method. In contrast, the approximation method exploits the similarity between the spectral scattered field of the TI medium and the BA medium, establishing corresponding equivalent layered TI models for each magnetic component. Since the scattered field in TI models only involves a one-dimensional semi-infinite integral, the computational complexity is significantly reduced. Numerical simulation examples demonstrate that the new simulation method is at least two orders of magnitude faster than the current modeling approach while maintaining computational precision error within 0.5%. This significantly improved simulation efficiency provides a solid foundation for expediting the logging data processing.
Low-Rank Discriminative Embedding Regression for Robust Feature Extraction of Hyperspectral Images via Weighted Schatten p-Norm Minimization
Authors: Long Chen-Feng , Li Ya-Ru , Deng Yang-Jun , Wang Wei-Ye , Zhu Xing-Hui , Du Qian
Published: August 2024

Low-rank representation (LRR) is widely utilized in image feature extraction, as it can reveal the underlying correlation structure of data. However, the subspace learning methods based on LRR suffer from the problems of lacking robustness and discriminability. To address these issues, this paper proposes a new robust feature extraction method named the weighted Schatten p-norm minimization via low-rank discriminative embedding regression (WSNM-LRDER) method. This method works by integrating weighted Schatten p-norm and linear embedding regression into the LRR model. In WSNM-LRDER, the weighted Schatten p-norm is adopted to relax the low-rank function, which can discover the underlying structural information of the image, to enhance the robustness of projection learning. In order to improve the discriminability of the learned projection, an embedding regression regularization is constructed to make full use of prior information. The experimental results on three hyperspectral images datasets show that the proposed WSNM-LRDER achieves better performance than some advanced feature extraction methods. In particular, the proposed method yielded increases of more than 1.2%, 1.1%, and 2% in the overall accuracy (OA) for the Kennedy Space Center, Salinas, and Houston datasets, respectively, when comparing with the comparative methods.