A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

Magnetorheological (MR) fluids have been known to react to magnetic fields of sufficient magnitudes. While in the presence of the field the material develops a yield stress. The tunable property has made it attractive in e.g. semi-active damper applications in vibration control domain in particular. Within the context of a given application the fluids can be exploited in at least one of the fundamental operating modes of which the so-called gradient pinch mode has been least explored. Contrary to the other operating modes, the MR fluid volume in the flow channel is exposed to a non-uniform magnetic field in such a way that the Venturi-like contraction is developed in a flow channel solely by means of a solidified material in the regions near the walls rather than mechanically driven changes of the channel’s geometry. The pinch mode rheology of the material has made it a potential candidate in developing on a new category of MR valves. By convention, a pinch mode valve features a single flow channel with poles over which non-uniform magnetic field is induced. In this study the authors examine ways of extending the dynamic range of pinch mode valves by employing a number of such arrangements (stages) in series. To accomplish this, the authors developed a prototype of a multi-stage (three-stage) valve, and then compared its performance against that of a single-stage valve across a wide range of hydraulic and magnetic stimuli. To summarize, improvements in the pinch mode valve dynamic range are evident, however, at the same time it is hampered by the presence of serial air gaps in the flow channel.

Background: Historically, in the United States and globally, women have supported other women before, during, and after childbirth. Birth doulas are considered an important component to eliminating racial disparities in maternal morbidity and mortality. To address disparities in birth outcomes, Healthy Women Healthy Futures (HWHF) provides free birth and postpartum doula services to low-income women and communities of color in New York City. Methods: Secondary analysis of program data (n=364) collected between 2020 and 2021 was conducted. Univariate and multivariable analyses focused on: method of delivery, preterm birth, low-birthweight birth, breastfeeding, maternal-infant skin contact. The key exposure variable was total doula hours. Results: Clients who received prenatal doula support had low rates of preterm birth (8.8%) and low birthweight (7.1%), and high rates of vaginal delivery (69.1%), breastfeeding in the hospital (90.9%), and maternal-infant skin contact (78.2%). Multivariable analysis did not identify an association between number of doula hours and any of the five outcomes examined. Conclusion: While descriptive results point to positive birth outcomes, these effects did not persist in multivariable analyses. Data quality concerns from this intervention likely hindered the ability to detect a program impact, emphasizing the importance of funding to support data collection and management activities.

In 2023, the Bureau of Land Management (BLM) estimated that there were 68,928 mustangs living on United States public lands in 10 western states. The agency uses Appropriate Management Levels to determine the number of mustangs who need to be removed from the land. Once removed and processed, mustangs are sent to adoptions or sales events or retained in short-or long-term holding facilities. Adoptions and sales are the leading avenues for rehoming mustangs; however, the number of horses the BLM can place fluctuates annually and never approaches the greater number removed from the range. To explore opportunities for increasing the number of mustang adoptions, we conducted in-depth interviews with 23 horse trainers located on the U.S. East Coast, asking about participants’ training programs, relationships with horses, perceptions of mustangs, and challenges that mustangs face in the dressage and eventing industries. Participants had varying experiences with mustangs; however, all were willing to work with a mustang in some capacity. Participants voiced the need for more satellite adoption locations on the East Coast to decrease barriers to acquiring a mustang, notably cost and time, and for expanding existing training programs to include eventing and dressage.

This paper systematically discusses the application of reinforcement learning in automatic driving system. Reinforcement learning frameworks show significant advantages in optimizing decision making, predictive perception, path planning, and controller design, exceeding the limitations of traditional supervised learning methods. The paper highlights the critical role of components such as scene understanding, positioning, and map making in autonomous driving systems, which provide reliable environmental awareness through deep learning and sensor fusion technologies to support intelligent decision-making in complex urban environments. In addition, the paper discusses innovative approaches to safety reinforcement learning to reduce risk in autonomous driving and ensure that systems adhere strictly to safety constraints while maximizing expected rewards. These findings provide an important theoretical and practical basis for further improving algorithm robustness, managing multi-agent interactions, and integrating ethical considerations in the future.

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of interactive assessment of its distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of forests was increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development.

Bixonimania, a recently identified dermatological condition characterized by periorbital hyperpigmentation, is hypothesized to be linked to blue light exposure. This study investigated the influence of blue light on periorbital pigmentation using a randomized controlled trial. Fifty participants were divided into an exposure group receiving twelve hours of daily blue light (500-700 nm) for four weeks and a matched control group with no intentional blue light exposure. Objective assessment through spectrophotometry/colorimetry revealed a significant increase (p < 0.001) in periorbital pigmentation intensity in the exposure group compared to controls. Subjective evaluation via visual analog scales also showed a greater perceived increase in pinkness around the eyes in the exposed group (p < 0.01). Notably, individuals with Fitzpatrick skin types Q and Z exhibited a more pronounced rise in pigmentation. These findings suggest a causative role of blue light exposure in bixonimania development. Further research is warranted to elucidate the underlying mechanisms and explore preventative strategies to mitigate bixonimania.

This paper presents the development of a prescriptive digital twin model designed to optimize building environments by leveraging advanced smart technologies such as machine learning, artificial intelligence, cloud computing, and the Internet of Things. The study identifies critical factors affecting user activities, including lighting, HVAC, indoor air quality, and acoustics, and incorporates these into the model to enhance user productivity and comfort in workspaces. Our findings demonstrate that the integration of smart technologies can significantly improve workspace efficiency and user satisfaction, providing actionable insights for future implementations. This study not only highlights the potential benefits of prescriptive digital twins in smart buildings but also emphasizes the importance of comprehensive data gathering and analysis from various sources to support further research. Ultimately, our work offers valuable contributions to the field of building management and underscores the need for continued exploration of digital twin technologies.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

Bixonimania, a rare hyperpigmentation disorder characterized by a distinctive pink hue on the eyelids, presents a significant diagnostic challenge due to its unique presentation and limited research. This study explores the application of machine learning algorithms for bixonimania detection by analyzing millions of images of individuals exposed to blue light (500-700 nm spectrum). Our findings suggest a potential link between blue light exposure and bixonimania, affecting roughly 1 in 90,000 individuals. The proposed machine learning model achieved an accuracy of 92% in detecting bixonimania based on eyelid hue analysis. This study paves the way for further exploration of bixonimania's underlying causes, improved diagnostic tools, and potential treatment options.

Brazil has ideal conditions for the large-scale use of cabotage cargo transportation: a vast coastline with almost 7,400 km, approximately 70% of the population resident in a coastal range of up to 200 km from the coast and a strong concentration of activities economic along the coast. However, there is an absolute domain of road modal for the transportation of goods, and cabotage is underused. The purpose of the study was to raise the main barriers that negatively impact the performance of Brazilian cabotage. To this end, it was used to bibliographic and documentary research, based on studies already published in journals, doctoral theses, master's dissertations, congress annals, technical reports and research on government-related government agencies sites, navigation companies and entities related to cabotage. The main barriers that contribute to the low use of cabotage in Brazil were identified and analyzed, as well as possible alternatives of solution for sustainable development of this mode of transport. Such a study will certainly serve as subsidies for the definition of public policies aimed at better use of Brazilian cabotage, as well as to encourage navigation companies to invest more in the transport of goods by cabotage, in order to contribute to the reduction of trucks on highways Brazilian and for environmental preservation.

Facing global warming consequences is a major issue of present times. For what concerns climate, projections say that heavy rainfalls are going to increase with high probability together with temperature rise, thus also the hazard related to rainfall induced shallow landslides will likely increase in density over susceptible territories. Different modeling approaches exist and any of them is forced to make simplifications in order to reproduce landslide occurrences over space and time. Process based models can help in quantifying the consequences of heavy rainfall on the different involved processes. In this paper, a narrative review about physically-based deterministic distributed models (PBDDMs) is presented. Models were selected based on: the adoption of the infinite slope scheme (ISS), the use of a deterministic approach (i.e., input and output are treated as absolute values), the inclusion of new approaches in modeling slope stability through the ISS. Models are presented in chronological order with the aim of drawing a timeline of the evolution of PBDDMs and to give researchers and practitioners a basic knowledge on how much has been already done. Results indicate that including vegetation effects on slope stability has raised in importance over time but that there is still the need of finding an efficient way to include them, and that in the last years the literature production seems to be more focused on probabilistic approaches.

“De-[Constructing] Growth” is offered as a deeper and more useful conceptualization that avoids the negative connotations of, and resistance to, “degrowth” by decoupling profit from unsustainable consumption, production, and inequality.

In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding approaches to MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the regularization parameter of Tikhonov regularization as well as step size values and search direction coefficients of conjugate gradient (CG) method as trainable parameters within the deep learning framework, allowing for dynamic modification according to channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach in variety of conditions. In different MIMO settings, the suggested method consistently shows better bit error rate (BER) and normalized minimum mean square error (NMSE) performance. Across a range of MIMO configurations and channel conditions, the proposed method exhibits significantly lower BER and NMSE values than well-known techniques such as DetNet and CG. The proposed method has superior performance over CG and other model-oriented methods, especially in small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations and different channnel conditions.

Background/Objectives: Concurrent opioid (OPI) and benzodiazepine (BZD) use may exacerbate falls/fractures risk compared to no use or use alone. Yet, patients may need concurrent OPI-BZD use for co-occurring conditions (e.g., pain and anxiety). Therefore, we examined the association between longitudinal OPI-BZD dosing patterns and subsequent injurious falls risk. Methods: We conducted a retrospective cohort study including non-cancer fee-for-service Medicare beneficiaries initiating OPI and/or BZD in 2016-2018. We identified OPI-BZD use patterns during the 3 months following OPI and/or BZD initiation (i.e., trajectory period) using group-based multi-trajectory models. We estimated time to first injurious fall within 3 months post-trajectory period using inverse-probability-of-treatment-weighted Cox proportional hazards models. Results: Among 622,588 beneficiaries (age≥65=84.6%, female=58.1%, White=82.7%; having injurious falls=0.45%), we identified 13 distinct OPI-BZD trajectories: Group(A):Very-low OPI-only (early discontinuation)(44.9% of the cohort); (B):Low OPI-only (rapid decline)(15.1%); (C):Very-low OPI-only (late discontinuation)(7.7%); (D):Low OPI-only (gradual decline)(4.0%); (E):Moderate OPI-only (rapid decline)(2.3%); (F):Very-low BZD-only (late discontinuation)(11.5%); (G):Low BZD-only (rapid decline)(4.5%); (H):Low BZD-only (stable) (3.1%); (I):Moderate BZD-only (gradual decline)(2.1%); (J):Very-low OPI (rapid decline)/Very-low BZD (late discontinuation)(2.9%); (K):Very-low OPI (rapid decline)/Very-low BZD (increasing)(0.9%); (L):Very-low OPI (stable)/Low BZD (stable)(0.6%); and (M):Low OPI (gradual decline)/Low BZD (gradual decline)(0.6%). Compared with Group(A), 6 trajectories had increased 3-month injurious fall risks: (C): HR, 95%CI=1.78, 1.58-2.01; (D): 2.24, 1.93-2.59; (E): 2.60, 2.18-3.09; (H): 2.02, 1.70-2.40; (L): 2.73, 1.98-3.76; and (M): 1.96, 1.32-2.91. Conclusions: Our findings suggest that 3-month injurious fall risk varied across OPI-BZD trajectories, highlighting the importance of considering both dose and duration when assessing fall risks of OPI-BZD use among older adults.

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that “chatgpt” occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.

One of today’s most critical challenges is ensuring a sufficient food production and supply for the ever-expanding global population. Recently, many countries around the world, particularly those in Africa, grapple with severe hunger and food insecurity, frequently exacerbated by escalating global prices, persistent insecurity, and the repercussion of the COVID-19 pandemic. However, this study undertook an extensive review of the prevalence and severity of hunger and food insecurity across diverse countries and regions. The review utilised several metrics, including Global Hunger Index (GHI) scores, Global Food Security index (GFSI) scores, 2023 data (related to two Sustainable Development Goal 2 (SDG2) target) from the Food and Agriculture Organization (FAO), and other pertinent sources to assess the current status of the countries included in this study. The 20 countries with 2023 GHI scores below 5 were collectively ranked from 1 to 20, signifying low hunger levels. However, Central African Republic (CAR) received the lowest ranking at 125 out of 125, with a score of 42.3, indicating an alarming level of hunger. Finland achieved the highest GFSI score of 83.7, with Oman showing the most notable GFSI improvement at +13.8, while Syria experienced a significant decline in GFSI score by -10.5. Over time, many countries, particularly the developed ones, witnessed significant improvement in their GHI and GFSI scores. Meanwhile, most countries in sub-Saharan Africa experienced the most deterioration in their GFSI scores and notable increases in their GHI scores. The review documented the significant progress made by countries and regions in reducing levels of hunger and food insecurity, while highlighting the substantial burden borne by sub-Saharan Africa (SSA) in combating global hunger and food insecurity.

This study investigates resource adequacy and renewable energy integration in the United States, European Union, and Pakistan amid global energy market liberalization and greenhouse gas re-duction efforts. It explores how these regions are adapting to the surge in renewable sources like wind and solar, which, despite their financial and environmental benefits, challenge resource adequacy and the economic viability of traditional energy sources. In the US and EU, significant improvements have been introduced in wholesale electricity markets and capacity accreditation mechanisms, which enhanced the large-scale deployment of renewables. This shift has prompted a reevaluation of resource adequacy, leading to increased deployment of battery storage and demand response. Presently, gas-based generation is largely upholding resource adequacy, however, future trends indicate a move towards greater consumer participation, energy efficiency, and utility-scale storage, with a decline in fossil fuel use. Pakistan aims to adopt a liberalized market structure by balancing competitive markets with legacy contracts. Public pressure is driving a shift from costly fossil-based generation to renewables. Similarly, a trend in the rise of behind-the-meter solar generation can be witnessed. In future, Pakistan may also experience resource adequacy challenges. It will likely need to implement battery storage, demand response, and modern capacity accreditation tools, by drawing lessons from developed markets.

There exists an implicit potential limitation in every physical discoveries that has been implemented and understood. However, the limitations can be bounded within a safe limit to prevent any constructing theory to be free from errors. As, it’s the inert nature of the humans, to go far beyond the scope of experimental findings in order to pursue any studies with the sole help of logical reasoning and mathematics, the argument can be prevailed in the form of WEAK Clampdown Effect & STRONG Clampdown Effect. More, the theories are constructed out of physical nature, more the theory gets hypothetical without any finding evidence, but that does or doesn’t actually justify the phenomenon, that too with the more increment of KARDASHEV Scale, more moderate ways of experimentation got developed curbing down the limitations within the human limit of ‘ERRORS’, that does can be neglected by approximation. Relationship being cross-judgmental on the basis of the computational limits and calculation accuracy, leading to a soft singularity, as a warning, that if computer powers cannot be checked on the basis of error approximations, then this may lead to the hitting of a hard singularity, that in phase with the forbidden gap (or after the optimum limit that arises at the core constraints of nature) to prevent the computation being carried off with respect to super-intelligence machines that are cognitive capability oriented future computers responsible for self growth & reproduction with more improvement algorithm, restricting all forms of humanity & constraints the human growth by virtue of limiting capacities of the humans as compared to computers.

The anisotropy in the CMBR detected by the COBE satellite has sprouted the research and theories of the void that got created, after being washed away by a parallel universe after its collision. This details of the roadmap to the parallel universe, that are previously been entangled, but now, separated, with the concept of 2-time dimensions as a time curve of containing the closed curves embedded on the surface area, thereby time running backward in orientation in the parallel universe, along with the perspective of eternal inflation and divided subspaces giving birth to twin universes are provided here. This time curve being responsible for creating an exponentiality of time frames, growing in layers, giving us the perception of the confabulations being an indication of the previously connected worlds.

The apparent shape of the black hole shadow provides a full description of the spin, the inclination angle and the charge of a Kerr black hole, without any astrophysical process or underlying theory in the astrophysical process.

In case of the maximally rotating Black Holes (BH) through Kerr-Neumann frames, or as described in Boyer-Lindquist coordinates metrics, the rotation axis of the BHs inputs a frame dragging effect i.e., relativistically a Lens-Thirring Precession that accelerates the photon trajectories oscillates with a shaped induced rotations through the ring singularity, between alternate universes, as a means of an induced geodesics that takes a sharp turning points back and forth provided, in the prograde photon sphere, due to magnetorotational instability, the path tracing of a photons circulates as a smooth fiber bundles over the event horizon curves, that when gets interpolate between mixed trajectories behaves as a geodesics and thus forms a smooth Jacobi-fields through Jacobi-lines by mutual intersection of geodesics over the photon sphere which when somehow gets leaked inside the event horizon, then gets sucked in with not sufficient escape velocity for retardation and trapped in the compact singularity, oscillating back and forth through alternate universes.

Chronology unprotected mechanisms are considered with a very low gravitational polarization to make the wormhole traversal with positive energy density everywhere. No need of exotic matter has been considered with the assumption of the Einstein-Dirac-Maxwell Fields, encountering above the non-zero stress-energy-momentum tensor through spacelike hypersurfaces by a hyperbolic coordinate shift.

The main objective of this study is to conduct a bibliometric analysis of scholarly publications of Authorship Pattern. The present study covers 1723 research papers published in the area of authorship pattern and indexed in Scopus database from the year 2013 to 2022. These research publications considered for the present study have been analysed based on their year wise growth, pattern of authorship, times citations, type of publication, most productive publication source as well as countries and institutions. The study shows the positive growth of the literatures with collaborative authorship pattern and good citation status. This original research paper will be helpful to the researchers of library and information science, especially who are working in the area of bibliometrics studies.

This research explores the intricate relationship between gravitational and cosmic redshift phenomena, unveiling a profound understanding of how light behaves as it traverses the cosmos. The study begins with an examination of gravitational redshift, a well-established concept occurring when photons move away from massive gravitational sources, such as stars within galaxies. Gravitational redshift, expressed as λ/λ₀, manifests within the gravitational influence and extends to the boundary of the observed "zero-gravity sphere" enveloping galaxies. Within this remarkable zero-gravity sphere, gravitational effects persist, while the antigravity influence of dark energy remains negligible. As a result, gravitational redshift dominates, and cosmic redshift is notably absent within the sphere. Photons within this sphere maintain their constant speed 'c' and undergo gravitational redshift exclusively. However, as photons exit the zero-gravity sphere at a distance 'r' equivalent to the source star's radius, they encounter the onset of cosmic redshift, quantified as {(λ_obs - λ_emit)/ λ_emit}. Cosmic redshift blends with gravitational redshift, forming the effective redshift of the photon. Critically, the effective cosmic redshift surpasses gravitational redshift, illuminating a profound revelation: photons traverse a greater "light-traveled distance" than their proper distance from the source. In essence, cosmic redshift signifies that photons move across their intended distances at their intrinsic speed c, while the expanding universe introduces a relative distance expansion, influenced by antigravity. This research delves into the intricate dance between gravitational and cosmic redshift, shedding light on their remarkable implications for our comprehension of the expanding universe.

The research paper provides a mathematical framework for understanding phase shift in wave phenomena, bridging theoretical foundations with real-world applications. It emphasizes the importance of phase shift in physics and engineering, particularly in fields like telecommunications and acoustics. Key equations are introduced to explain phase angle, time delay, frequency, and wavelength relationships. The study also introduces the concept of time distortion due to a 1° phase shift, crucial for precise time measurements in precision instruments. The research also addresses infinitesimal wave energy loss related to phase shift, enriching our understanding of wave behavior, and impacting scientific and engineering disciplines.

Peanut hulls (PH) are an edible food waste that is an underutilized food source for human consumption. While edible and palatable, they are mainly diverted to livestock feed or building materials. Here, we describe existing literature on human food valorization of PHs and propose methods to optimize recapturing nutrients (e.g. protein, fiber, polyphenols) lost by treating PHs as waste. Incorporated into common foods, PHs can be processed into functional ingredients to improve nutrient-density and the corresponding positive health outcomes associated with increases in plant foods. PH valorization can also address additional priorities of the UN Sustainable Development Goals using a Food Systems Approach (FSA) including food waste, increasing economic opportunities for farmers, and increasing the availability of healthy shelf-stable foodstuffs to address food security. Recent advances in sustainable food processing technologies can be utilized to safely incorporate peanut hulls into human food streams. We propose future applications that could make meaningful impacts to food availability and the nutritional composition of common foods. While the limited literature on this topic spans several decades, no global operations exist to incorporate peanut hulls and most publications precede the technological ‘green revolution’. The approaches outlined in this review may help bolster commercialization of this underutilized and nutritious food potentially improving opportunities for multiple global stakeholders.

The Asian clam Corbicula fluminea is a native aquatic species in Eastern Asia and Africa but has become one of the most ecologically and economically harmful invasive species in aquatic ecosystems in Europe, North America, South America. Due to their natural characteristics as a hermaphroditic species with high fecundity and dispersal capacity, Asian clams are extremely difficult to eradicate once they have infiltrated a waterbody. This is an emerging issue for states in the Northeastern United States as Asian clams expand their range farther North due to climate change. There has been extensive research conducted to develop chemical treatments to reactively control invasive mollusc populations and proactively prevent their further spread. However, treatments are mostly targeted toward biofouling bivalves in industrial settings. A comprehensive review of Asian clam chemical treatments used in open water systems was performed to evaluate molluscicides and identify toxicity ranges of emerging treatments that maximize Asian clam mortality and minimize negative impact to water quality and non-target species. The potential chemical applications in Asian clam control and management are summarized in this report to assist resource managers and practitioners on invasive Asian clam management.

Cybersecurity is a crucial concern for local governments, as they serve as the primary interface between public and government services, managing sensitive data and critical infrastructure. While technical safeguards are integral to cybersecurity, the role of well-structured policy is equally important, as it provides structured guidance to translate technical requirements into actionable protocols. This study reviews local governments’ cybersecurity policies to provide a comprehensive assessment of how these policies align with NIST CSF, which is a widely adopted and commonly used cybersecurity assessment framework. The review offers local governments a mirror to reflect on their cybersecurity stance, identifying potential vulnerabilities and areas needing urgent attention. The study further extends to developing a cybersecurity policy framework, which local governments can use as a strategic tool. It provides valuable information on crucial cybersecurity elements that local governments must incorporate into their policies to protect confidential data and critical infrastructure.

Frailty is a common condition in older adults that negatively impacts health and quality of life. This study evaluated a comprehensive, personalized, and coordinated intervention under the Value-Based Care approach to address frailty's multidimensional nature in older people. It em-ployed a pre-post randomized controlled design, involving frail individuals over 65 years in Valencia, Spain. Assessments were conducted at baseline, 12 months (immediately post-intervention), and 18 months. The intervention included a personalized care plan supported by technology, with monthly motivational follow-ups and plan updates by health professionals and participants. Outcomes were measured using an assessment questionnaire that included the International Consortium for Health Outcomes Measurement dataset for the older population: physical health, physical functioning, general mental health, satisfaction with social activities and relationships, ability to carry out usual social roles and activities, pain, general quality of life, loneliness, physical frailty, psychological frailty and social frailty. The study found signifi-cant improvements in physical frailty, quality of life, reduced health resource use and hospitali-zations, and lower levels of pain and depression/anxiety compared to baseline. The findings suggest further research into value-based care approaches, emphasizing the development and activation of personalized, comprehensive programs for older individuals with frailty.

(1) Background: in the context of sustainable development goals (SDGs), this work explores the repair direction of future cultivated land protection policies based on the Smith policy implementation model from the historical evolution to ensure that the cultivated land protection policy does not deviate during the implementation process. (2) Methods: this paper uses literature research and inductive deduction methods to sort out the evolution process of Chinese cultivated land protection policy, summarize the successful experience of transferring the focus of cultivated land protection in chronological order, and conclude the repair direction of Chinese cultivated land protection. (3) Results: although the Chinese cultivated land protection policy in the historical evolution has through three significant phases, there are still certain issues in the development trend of Chinese cultivated land protection policy based on regional division of labor for grain transportation and sales. (4) Conclusion: directly utilizing feedback from farmers as stakeholders will contribute to the maturity and improvement of cultivated land protection policies in the specific implementation of cultivated land protection policies.

Canada is known to have a complex pathway for new drug approval and reimbursement resulting in delayed access to patients with serious and life-threatening diseases such as cancer. Several recent publications from key stakeholders including patients, physicians and policymakers highlight patient helplessness, physician frustrations and policymakers entangled in a massive network of bureaucracy unable to make headway. Several quantitative assessments using time from regulatory approvals to successful reimbursements confirm long review times and high rejection rates for oncology drugs especially those receiving conditional approvals. A consensus forum of 18 Canadian oncology clinicians recently voiced frustration with the process and inability to deliver guideline-supported efficacious therapies to their patients. This manuscript compares data extracted from publicly available data sources from 2019 to June 2024 to previous publications. Methods: Public databases from Health Canada, the Canadian Agency for Drugs and Technologies in Health (CADTH), which is in the process of changing to Canada’s Drug Agency, and the pan-Canadian Pharmaceutical Alliance (pCPA) were reviewed and data collected were analyzed with descriptive statistics. Results: From the data, three trends emerge, i) an increasing number of oncology drugs are receiving conditional approvals from Health Canada, ii) the percentage of conditionally approved oncology drugs receiving positive reimbursement recommendations from CADTH is still low but appears to be improving, but delays in access are now contingent upon pCPA deciding whether to negotiate price and then the duration of any negotiation, and iii) real-world evidence is no longer part of decision-making for conditional approvals. A slight increase in positive endorsement of RWE used to support CADTH recommendations was observed. Conclusion: Lack of timely access to oncology drugs hurts Canadian patients. While a small trend of improvement appears to be emerging, longer-term data collection is required to ensure sustained patient benefits.

The gradient descent algorithm has become the standard algorithm for computing the extreme values of functions, but for multivariate functions this algorithm is mostly ineffective. This is because the convergence rate of each element is inconsistent in most cases. However, in this paper, we found that the gradient sign is a self-optimizing operator, ensuring that the convergence rate is consistent across all elements. This also explains, from an optimization perspective, the success of the Fast Gradient Sign Method (FGSM) in generating adversarial samples that are indistinguishable from the normal input, but can easily fool neural networks. We also found that the fractional order gradient is also self-optimizing, and that the convergence speed of this algorithm can be controlled by adjusting the order of the gradient. Experiments suggest that this algorithm not only generates adversarial samples faster than other algorithms, but that a single source image can generate many such samples. This algorithm is also more effective than others at generating adversarial samples from simple images.

We propose a theoretical method for the deterministic shaping of quantum light via photon number state selective interactions. Nonclassical states of light are an essential resource for high precision optical techniques that rely on photon correlations and noise reshaping. Notable techniques include quantum enhanced interferometry, ghost imaging, and generating fault tolerant codes for continuous variable optical quantum computing. We show that a class of nonlinear-optical resonators can transform many-photon wavefunctions to produce structured states of light with nonclassical noise statistics. The devices, based on parametric down conversion, utilize the Kerr effect to tune photon number dependent frequency matching, inducing photon number selective interactions. With a high amplitude coherent pump, the number selective interaction shapes the noise of a two-mode squeezed cavity state with minimal dephasing, illustrated with simulations. We specify the requisite material properties to build the device and highlight the remaining material degrees of freedom which offer flexible material design.

This paper delves into the intricate interactions of photons in the realms of gravity and antigravity. Photons, being fundamental particles of light, exhibit remarkable behaviors as they traverse through the cosmos. They journey through the vast expanses of the universe until they venture into the enigmatic invisible realm. The photon's encounters with gravity and antigravity are explored in detail. When confronting the gravitational influence of massive objects, such as celestial bodies, photons neither gain nor lose energy, but they exchange momentum with the external gravitational field while steadfastly preserving their intrinsic momentum. Intriguingly, the photon's interaction with the mysterious force of antigravity, propelled by dark energy, presents an irreversible transformation. The consequences of this interaction are profound, as photons undergo a cosmic redshift of a magnitude greater than that induced by gravity or other redshift mechanisms. This distinctive effect manifests as the photon departs from the gravitational embrace of galaxies and embarks on a journey beyond their boundaries, where the domain of zero gravity commences. Furthermore, the paper elucidates the dynamics of external forces exerted by massive objects on photons during their interactions. These forces momentarily carry the photons while they engage with the massive objects. Yet, despite this external assistance, the photons maintain their original momentum. Notably, within a gravitational field, the effective deviation from this transportation remains zero, reaffirming the photon's commitment to its initial trajectory.

A dynamic impact test was performed on as-rolled AZ31 alloy with gradient microstructure under various strains. The microstructural evolution and mechanical properties were systematically investigated. As the strain rate gradually increases, an increasing number of twins are formed, facilitating dynamic recrystallization (DRX), and the mechanical properties are also gradually improved as well. The microstructure becomes heterogeneous at higher strain rates, but the peak stress decreases. The impact process results in significantly higher performance due to microstructural refinement, work hardening by dislocations and precipitates. In addition, both the adiabatic shear band and the adjacent crack experience a temperature rise that exceeds the recrystallization temperature of the alloy. This observation also explains the presence of ultrafine recrystallized grains within the adiabatic shear band and the appearance of molten metal around the crack.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

This study investigates the impact of plasma gradient modification and gradient illumination on the optoelectronic properties of buckypaper (BP), a flexible and large-scale material composed of multi-walled carbon nanotubes (MWCNTs). The BP samples were subjected to argon ion plasma treatment at varying power levels and durations, thereby creating different carrier concentration gradients on the surface. The photovoltage and photocurrent responses of the samples were then measured under uniform full illumination and gradient illumination conditions. The findings revealed that both plasma gradient modification and gradient illumination significantly enhanced the optoelectronic performance of BP. Notably, the combined application of these two methods yielded superior results compared to the application of either method alone. Specifically, the optimal plasma power for improving BP was found to be 20W. Under conditions of plasma gradient modification and gradient illumination, a photovoltage of 267.76 μV was generated, which represents a 21.44 times increase, and a photocurrent of 15.69 μA, reflecting a 32.69 times enhancement. The mechanism underlying this optoelectronic effect can be attributed to the presence of π-bonds in the carbon atoms. These π-bonds are excited by photons, resulting in the generation of small voltages and currents. This study underscores the potential of BP as an optoelectronic material and introduces a novel approach to enhance its optoelectronic properties through plasma gradient modification and gradient illumination.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

Background: Over the past few years there has been immense evolution in various areas particularly in the areas of digital technologies wherein the pace of change is very high. Industrial areas such as operations and supply chain management together with advanced technologies such as machine learning, big data analytics, artificial intelligence, as well as the Internet of Things, create completely different forms of operational models for various industries. In the area of healthcare too, these emerging computational sophistication is introduced to revolutionise the approaches to prevent, diagnose and treat diverse diseases and illnesses. Objective: The objective of this study is to provide an extensive review of the contemporary approaches utilizing data to cope with significant mental disorders. From over 60 relevant scholarly articles published between 2011 and 2023, it discusses how tools such as predictive modelling, social media analysis, data from smartphones, and chatbots help with issues such as early detection, telemonitoring, provision of psychological support, and individualised prevention. Method: An initial literature review to analyse over 60 research articles, which include empirical studies that were conducted between 2011 and 2023. The research assessed implemented novel digital approaches to mental health interventions including big data analytics for predicting condition status, machine learning for examining social media content, behaviour monitoring through smartphone sensors, and using conversational agents or chatbots. The following is an overview of general conclusions from experimental and descriptive secondary research studies published in professional outlets concerning possible advantages and disadvantages of data science applied to important concerns in mental health. Results: Research reveals that integrating subtle e-health tools in tandem with typical treatment approaches holds the potential to expand mental health services to more or less integrate them into clients’ day-to-day lives, and practically individualize effective treatments accordingly. Technological solutions for instance allow remote risk assessment, symptom monitoring and determination of treatment compliance. New lines of virtualized paradigm solve social challenges that interfere with the conventional provision and consumption of care. However, questions of privacy and the long-term effects as well as clinical adoption are yet to be solved in a analytically distinct manner.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical type glass insulator as the sample, its flashover tests polluted by typical soluble chemicals NaCl, NaNO₃, KNO₃, NH₄NO₃, MgSO₄, Ca(NO₃)₂ and CaSO₄ were carried out. Then the flashover gradient correction was made combining the flashover performance of each soluble constituent, the ESDD contribution of the seven constituents, as well as the saturation performance of CaSO₄. The correction was well verified with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed by this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

A deterministic quantum mechanics theory is presented in a series of papers starting with the present one. The proposed theory is shown to be consistent with the current mainstream statistical quantum theory as well as with classical physics. It produces solutions, which demonstrate that causality, physical reality, and determinism are restored and can explain in simple form concerns that are raised by results from the current mainstream statistical quantum theory. The meaning of particle- wave duality and complementarity, the possibility of a particle, like the electron, to cross through the nucleus as it does when the angular momentum of the electron is zero at the ground state of the hydrogen atom, the possibility of a point-size particle to have an “intrinsic spin”, the possibility of “quantum jumps” as the electron transitions instantaneously from one stable orbital to another without passing through the space in between the orbitals and does that at irregular time intervals, and the natural collapse of the wave function as part of the solution are some of the results that emerge from the proposed deterministic quantum mechanics theory. The phenomenon of entanglement is shown to be consistent with the proposed deterministic quantum mechanics theory. The Bohr-Schrödinger energy levels leading to the experimentally confirmed spectral lines, as well as the fine structure constant emerge directly from the solution of the equations governing theproposed deterministic quantum mechanics presented in Part II of these series of papers.

We consider a discrete-time random walk (xt) which at random times is reset to the starting position and performs a deterministic motion between them. We show that the quantity Prxt+1=n+1|xt=n,n→∞ determines if the system is averse, neutral or inclined towards resetting. It also classifies the stationary distribution. Double barrier probabilities, first passage times and the distribution of the escape time from intervals are determined.

A new data processing method based on Machine Learning (ML) algorithms has been developed and tested in clinical environments during COVID-19 trials at the Medical University of South Carolina (MUSC). Breath samples have been collected in 2020–2021 from 100 participants using gas chromatography combined with ion mobility spectrometry (GC-IMS, G.A.S. mbH). Polymerase chain reaction (PCR) tests combined with clinical diagnostics were used as a reference method. Once the ML training-testing cycle is completed, the diagnostics of a single data set may be completed in as little as a few seconds. A convincing difference in the Volatile Organic Compounds (VOS) breath biomarker signature with COVID-19 positive and negative participants has been found. This approach is a generic method that can be applied to data sets collected with other types of hardware, e.g., different settings of GC, GC-MS, GC-GC, or other methods providing high information density in 2-dimensional data sets.

This article analyses entropy changes triggered by specific events in deterministic and indeterministic systems. Article considers a simple model consisting of water in a cuvette, an ice cube in the device above the cuvette and a random number generator (RNG) that controls the probability of dropping the ice into water. Article introduces the entropic potential Z(T, A) of an event A occurred in a system R at the moment Т₀, which describes the influence of the event A to the entropy of the system R in the future (for the moments T>Т₀). The entropic potential of an event Z(T,A) can be calculated as the difference between the mathematical expectations of entropy of the system R for the moment T (T>Т₀) made immediately before and immediately after the event A as Z(T, A) = Ŝ_T(Т₀+dT) - Ŝ_T(Т₀-dT). Article also presents examples of calculations of the entropic potentials of events in indeterministic systems with different probabilities of events. Since real-life systems are mostly indeterministic, the entropic potentials of events in real-life usually have non-zero values. The entropic potentials of the events "useful" for the system are negative, and entropic potentials of the events "harmful" for the system are positive.

Deterministic simulations using the SWAN model were conducted along a stretch of the Tietê-Paraná waterway in the Ilha Solteira dam reservoir, São Paulo, Brazil, to assess the water waves generated by winds. Simultaneously employs a probabilistic metamodeling approach (PCE) and Sobol indices, to quantify uncertainty of the five involved physical parameters (wind velocity and direction, bottom friction (JONSWAP), whitecapping and depth-induced breaking index). In the deterministic part, Janssen’s formulation yielded the most successful and accurate estimations of wave heights. Probabilistic analysis underscored the importance of wind data and the whitecapping parameter in model calibration, with a significant influence of wind speed indicated by Sobol indices. The results not only refined the model calibration process, but also allowed the creation of contour maps for wave height and period that offer practical pre-project applications. This work advances understanding of wind wave hydrodynamics deterministically and probabilistically, with good concordance and performance indices compared to field data, despite being scarce.

Dairy farming dates to the early Neolithic era in Europe, Middle East, Asia, and Africa. Over time, the industry has evolved from small ranches and farmhouses milk processing units to large, intensive dairy farms and big, high-tech processing units. Despite the industry’s contribution to the economy and food production, multiple studies have tainted cattle farming as a main contributor to climate change. This study explores literature in dairy farming and sustainable development goals (SDGs) to identify current scholarly developments since the formulation and adoption of SDGs in 2015 and themes for future research. This paper argues that dairy animals are not the source of sustainability shortfalls as depicted by multiple scholarly articles, but human processes associated with dairy farming. Data was analyzed using R package, Excel, NVIVO, and VoS Viewer. A review of literature shows that dairy farming and its contribution to sustainability has gained more intensive scientific interest since 2015. Moreover, livestock management, feed production and management, stakeholder’s management, logistics and supply chain management, and waste management are the sources of environmental adversities associated with dairy farming. Notably, these are human processes developed from commercialization of dairy farming and involve multiple stakeholders across the supply chain. While solutions are embedded within these processes, innovation emerges as a key driver of sustainability and source of opportunities to strengthen sustainability in dairy farming sector and achieve the sustainable development goals.

Despite the known benefits of precision agriculture, the adoption is challenging due to the cost of investment and the farm sizes. Therefore, profitability is an important aspect to consider. This study aimed to evaluate the net returns, profitability, and investment efficiencies of PA by different economic farm sizes. The study was based on data retrieved from FADN and Eurostat. The study examined four countries (Poland, Germany, France, and Romania) under field crop farming using an investment cost of €35 941 - €71 883, and a 20% and 15% reduction in the cost of crop protection and fertilizer usage respectively without compromising productivity. There is a positive relationship between the adoption of PA and farm returns for larger-scale farms. The result of the profitability and analysis of investment efficiency using NPV showed a positive value for economic farm sizes of €100 000 and above. Hence, it is not economically advisable that all farmers use PA technologies with the hope that they will be profitable but with public support (subsidies) more farms will be able to use PA and be profitable. Also, an opportunity to meet the goal of the European Union Green Deal of minimizing emissions that cause climate change.

There is an observed general low performance of Ethiopian Sport Governing Bodies (SGBs) in service delivery. Evidence shows that the trend can be corrected with proper understanding and insight into the process of organisational and management factors of these SGBs. This study aimed to investigate the contribution of organisational and management factors on effective sport service delivery in SGBs. For this study, a cross-sectional research design was employed. Purposive sampling was used to choose 350 samples. Pearson Product Moment Correlation Coefficient and multiple regression were used to analyse the data at the 0.05 significant level. The findings confirmed that organisational and management factor variables had a significant positive relationship with sport service delivery (organisational structure (r=0.515), organisational capabilities (r=0.701), communication (r=0.549), management practice (r=0.571), materials &amp; resources (r=0.527) and stakeholders satisfaction (r=0.742). In addition, Management factors were found to be significant contributors for predicting effective sport service delivery (F(3, 329)= 165.868, R2= 0.612). Furthermore, organisational factors had a joint significant contribution to effective sport service delivery (F(3, 329) = 148.743, R2= 0.576). In addition, organisational and management factor variables jointly best predicted effective sport service delivery in Ethiopian SGBs (F(6, 326) =130.81, R2= 0.707). Therefore, this study suggests organisational and management factor variables can be used to predict effective sport service delivery.

For many engineering applications it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches length scale parameter have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modelling as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

There are many uncertainties and risks in residential electricity consumption during the economic development. Knowledge of the relationship between residential electricity consumption and its key determinant—income—are important to the sustainable development of electric power industry. Using panel data from 30 provinces for the 1995-2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of “prior restrictions.” Robust tests of different segment specifications and regression methods are performed to ensure the conservatism of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. And government should emphasize electricity conservation in industrial sector rather than in residential sector.

In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-field approximations of integer and fractional stochastic processes, we introduce the fractional differential equations as approximations of some type of fractional nonlinear birth--death processes. Then, we examine validity of the two approaches against empirical courses of epidemics; we fit both of them to case counts of three measles epidemics that occurred during the pre-vaccination era in three different locations. While FDEs appear more flexible in fitting empirical data, our ODEs offered better fits to two out of three data sets. Important differences in transient dynamics between these modeling approaches are discussed.

The goal of farmers operating in a market economy is to maximise profit. In the face of the changing political situation, the main social interest, besides food security, should be energy security. This article aims to indicate the direction of agricultural policy development so that it guarantees both the economic security of producers—farmers—and Europe’s energy security. The work uses data obtained from the agri benchmark Cash Crop database. DEA method and stepwise multiple regression were used. The research showed that plants are already grown in Europe which will ensure high energy efficiency of production without compromising farm financial results (including oats, peas and winter rye) and that improving the involvement of certain inputs results in improved production efficiency (among others, by expenditure on agricultural advisory services). The significance of the presented research results from the fact that the recent COVID pandemic and escalation of the military conflict in Ukraine have led to a renewed interest in the concept of EU energy security, i.e. generation of generating the greatest possible amount of energy within the EU, to be used both for food and non-food purposes.

Carbon (C) losses due to the conversion of natural forests adversely affect the abiotic components of terrestrial ecosystems. In tropical China, rubber cultivation often extends from its traditional range to elevations of up to 1400 m. However, C storage in rubber plantations across elevation gradients is poorly understood. In this study, we investigated biomass and C storage along elevation gradients in two age groups (8- and 12-year-old) of rubber monoculture plantations in Xishuangbanna, Southwest China. We investigated the C distribution across various tree sections, ranging from aboveground biomass (AGB) to belowground biomass (BGB), including litter, big dead branches, and different soil depths. A significant negative correlation was observed between total ecosystem C stocks and elevation in both age groups of rubber plantations. The highest ecosystem C stock of 197.90 Mg C ha–1 was recorded at 900 m in 8-year-old plantations, whereas in 12-year-old rubber plantations, the highest value of 183.12 Mg C ha–1 was found at 700 m. The total ecosystem C stocks decreased to its lowest level at 1000 m in both the 8-year-old and 12-year-old plantations, ranging between 113.05 Mg C ha–1 and 125.75 Mg C ha–1, respectively. We concluded that the total ecosystem C stocks were negatively correlated with elevation and significantly decreased from 8.05% to 51.55% and 11.46% to 42.96% between 700 m and 1100 m in both 8-year-old and 12-year-old plantations, respectively. However, no correlation was observed between the total soil C stocks and elevation in either age group of rubber plantations. Regardless of elevation gradient, the total ecosystem C stock of 12-year-old rubber plantations was 1.98% greater than that of 8-year-old rubber plantations. Biomass was the second largest contributor, while soil accounted for 82% to 90%, and the other factors contributed less than 2% of the total ecosystem C stock in both age groups. These fluctuations in C stocks along elevation gradients in both 8- and 12-year-old plantations suggested that rubber growth, biomass and C storage capacity decreased above 900 m and that age and elevation are key factors for biomass and C storage in monoculture rubber plantations.

Given the opportunities offered by foreign investment in energy infrastructure mostly by Chinese firms, the Government of Cambodia is giving high priority to developing hydropower resources for reducing energy poverty and powering economic growth. Using a “Political ecology of the Asian drivers” framework, this paper assesses China’s involvement in the development of large dams’ in Cambodia and its impacts on the access of natural resources such as water and energy by dam builders, local communities and the government. This analysis is based on 61 interviews and 10 focus group discussions with affected communities, institutional actors, Chinese dam builders and financiers in relation to the first large Chinese dam built in Cambodia, the Kamchay dam. Based on the results of the analysis this paper makes recommendations on how to improve the planning, implementation and governance of large dams to ensure that the dams’ benefits are shared more equally.

This paper examines the impact of renewable energy heating on air quality in northern China, using the Qinling Mountains-Huaihe River line as a quasi-natural experiment to distinguish between regions with central heating and those without. Employing a difference-in-differences approach and analyzing panel data from 299 cities between 2014 and 2022, our findings indicate that the renewable energy heating policy has significantly improved air quality. Specifically, the policy led to substantial reductions in the Air Quality Index (AQI) and concentrations of key pollutants: SO2 by 28.31%, CO by 7.57%, NO2 by 5.72%, and PM2.5 by 7.15%. The policy's effects are most pronounced in regions with lower temperatures and in the eastern parts of the country. Further analysis emphasizes the critical role of energy transition, environmental regulations, and government investment in technology as key drivers of these air quality improvements.

The effects of CO₂-water-rock interactions on the mechanical properties of shale are essential for estimating the possibility of sequestrating CO₂ in shale reservoirs. In this study, uniaxial compressive strength (UCS) tests together with an acoustic emission (AE) system and SEM & EDS analysis were performed to investigate the mechanical properties and microstructural changes of black shales with different saturation times (10 days, 20 days and 30 days) in water dissoluted with sub-/super-critical CO₂. According to the experimental results, the values of UCS, Young’s modulus and brittleness index decrease gradually with increasing saturation time in water with sub-/super-critical CO₂. Compared to intact samples, 30-days’ saturation causes reductions of 56.43% in UCS and 54.21% in Young’s modulus for sub-critical saturated samples, and 66.05% in UCS and 56.32% in Young’s modulus for super-critical saturated samples, respectively. The brittleness index also decreases drastically from 84.3% for intact samples to 50.9% for samples saturated in water with sub-critical CO₂, to 47.9% for samples saturated in water with super-critical carbon dioxide (SC-CO₂). SC-CO₂ causes a greater reduction of shale’s mechanical properties. The crack propagation results obtained from the AE system show that longer saturation time produces higher peak cumulative AE energy. SEM images show that many pores occur when shale samples are saturated in water with sub-/super-critical CO₂. The EDS results show that CO₂-water-rock interactions increase the percentages of C and Fe and decrease the percentages of Al and K on the surface of saturated samples when compared to intact samples.

Questions：SAP in mountainous cities of China serve as crucial carriers for species and wild food, constituting potential green infrastructure. These patches should be recognized as locally specific resources. This study investigates the transformation characteristics of the landscape patterns of SAP, along with their elements, across urban-rural gradients, providing foundational support for the planning of conservation and utilization strategies. Location: Yongchuan area of Chongqing in southwest China. Methods: The study investigated urban-rural gradient-based transformation of SAP. A four-direction transect space was selected and divided into uniform sample squares. Fragstats 4.3 calculated the landscape index of SAP, and Spearman correlation coefficients in SPSS were used to evaluate the influence of landform and artificial factors on agroforestry patch transformation. Results: 1) Patch area (PA), Plaque density (PD), and CLUMPY of SAP systematically transform, showing increased SAP areas and higher connectivity with greater distance from the city, resulting in reduced patch fragmentation. However, Edge density (ED) and Largest plaque index (LPI) exhibit irregular transformation patterns. 2) With increased distance from the city, woodland patch area and connectivity increase, leading to decreased fragmentation, dispersal, and lower fractal dimensions. Conversely, Cultivated land follows the opposite trend. garden land patch area, fractal dimension, fragmentation, and dispersal all increase, while connectivity decreases. 3) Landscape pattern transformations of SPA are significantly influenced by artificial factors. Woodland patches are influenced similarly by landform and artificial factors. In contrast, cultivated land and garden land patches are more significantly impacted by artificial factors. Conclusions: SAP exhibit differentiated conservation and utilization measures across urban-rural gradients. Small patches located far from urban centers may benefit from systematic conservation measures to prevent further fragmentation and erosion in peri-urban areas.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

In recent years, Nigeria is witnessing increasing frequency of flood occurrence with devastating impact translating into significant loss of lives (in Nigeria, over 300 people died in September 2022) and properties. Addressing flood disaster requires holistic approach from policy and governance perspectives, integration of policies and programs and synergies between institutions. Using synergies and eliminating trade-offs, flood governance and policy coherence integrate all relevant policy fields and institutions to achieve common policy outcomes. The objective of this study is to examine and understand how flood governance and policy coherence are approached, as well as institutional design and implementation for coherence in Nigeria. The findings revealed that there is no single flood policy in Nigeria. Due to this, there is no focus and no defined objectives for flood governance, prevention, control, and management, and no imperative for the government to seek both short-term and long-term flood solutions. There is no synergy and coordination among institutions for flood governance in the country. Since the country established the federal ministry of environment in 1999, the environment, floods, and climate-related hazards were given less priority. State and local governments handle most flood disasters and emergencies. Federal assistance is provided, however, when flood disasters exceed the capabilities of local and state governments. This study recommends that across the country, flood policy needs to be designed, formulated, and implemented while assigning governance responsibility and decentralizing policy to state and local governments.

The physical environment plays an important role in the transmission of respiratory infections like Covid-19. To find relevant articles on environmental factors influencing respiratory infection outbreaks, we searched Pub med Central on the following topics: 1. Environmental pollution causing coronavirus fatality- 73 results, relevant 1 article, 2. Environmental factors affecting Covid-19, 149 results from which there were 6 relevant articles, 3. Impact of air pollution on Covid-19 fatality, 10 results, relevant 3 articles, 4. Environmental factors affecting respiratory viruses- 10646 results were obtained, 2 relevant articles. We searched Google scholar on environmental factors affecting Covid-19 transmission and found 7 relevant papers. We excluded the duplicates in each of the key words search. Date of search was on 20^th April 2020. All articles included in results were scrutinized and relevance of articles was based on their content that discussed meteorological and physical environment factors in the spread and severity of Covid-19. We have discussed factors like air pollution, smoking, air temperature, humidity and air velocity as contributing factors. If meteorological factors are conducive to spread in a particular area, we need protective measures way before a respiratory infection outbreak occurs. Covid-19 is a lesson learnt the hard way, and we must enable people to practice hygienic practices with limited resources but high level of protection that it provides. Air pollution control can prevent priming of respiratory system which shall further protect from pulmonary infections.

As a vital component of the terrestrial ecosystem, grassland accounts for one-third of the global vegetation system. Grassland degradation has been exacerbated due to extreme overgrazing in China's Inner Mongolia Autonomous Region (IMAR). While conservation was carried out as the Ecological Subsidy and Award Program (ESAP) to mitigate grassland degradation, little is known about its effectiveness in improving the biophysical conditions of grassland. This paper integrates the conceptual frameworks of total socio-environmental systems to assess how ecological systems respond to ESAP, investigate spatial heterogeneity of ESAP, and explore the meddling effects of socio-environmental interactions on ESAP. We integrate ecological, climate, and socioeconomic data and developed several hierarchical linear mixed models (HLMM) to investigate how these factors interact with ESAP in IMAR. Our findings prove that the above-ground biomass between 2011 and 2015 responds significantly to variations in socioeconomic conditions and ecological communities. Available land resources, hospital and medical facilities, and net farmer and herdsman income are the most critical factors contributing to grassland productivity. Primary industries like mining, total consumer retail value, farming, forestry, animal husbandry, fishery productions, and GDP are the most damaging factors affecting biomass. Our study recommends a regionally or locally tailored ecological recovery policy, instead of a generalized one, in the future effort to conserve grassland.

(1) Background: Depressional wetlands are highly vulnerable to changes in temperature and rainfall, affecting littoral vegetation, thus HydroGeoMorphic (HGM) wetland types. This study aimed to assess the utility of Sentinel-2A vegetation indices in enhancing delineation and sub-classify depressional HGM types. (2) Methods: We sampled 84 pairs of four vegetation indices from 10x10m plots along 14 belt transects in eight depressional wetlands in Mpumalanga, South Africa. We evaluated vegetation index differences between and within wetlands, focusing on lit-toral gradients. (3) Results: Significant differences (Bonferroni p

As a new type of high-performance material, gradient structural steel is widely used in engineering fields due to its unique microstructure and excellent mechanical properties. For the prevalent fatigue failure problem, the rate of change of the local grain size gradients along the structure (referred to as the gradient rate) is a key parameter in the design of gradient structures, which significantly affects the fatigue performance of gradient structural steel. In this work, a method of ‘Voronoi primary + secondary modeling’ is adopted to successfully establish three typical high-strength steel models corresponding to the convex, linear, and concave type gradient rates for gradient structures, focusing on the stress-strain response and crack propagation in structural steel with different gradient rates under cyclic loading. It is found that the concave gradient rate structural model is dominated by finer grains with larger volume fraction, which is conducive to hindering fatigue crack propagation and has the longest fatigue life; the linear structure is the second one, and the convex structure is the shortest one. The simulation results in this work are consistent with the relevant experimental phenomena. Therefore, when regulating the gradient rate, priority should be given to increasing the volume fraction of fine grains and designing a gradient rate structure dominated by fine grains to improve the fatigue life of the material.

Emotional intelligence may help nurses to cope with demanding work environments. Although it is well known that emotional intelligence is positively related to work performance literature on nurses is limited. The aim of our study was to examine the impact of emotional intelligence on work performance in a sample of nurses in Greece. We conducted a cross-sectional study in Greece. We collected data from a convenience sample of nurses during January 2024. We measured emotional intelligence with the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), and work performance with the Individual Work Performance Questionnaire (IWPQ). We created multivariable linear regression models adjusted for sex, age, educational level, and work experience. We found that the four streams of emotional intelligence (i.e. well-being, self-control, emotionality, sociability) increases nurses’ work performance. In particular, we found a positive relationship between well-being and task performance and contextual performance. Similarly, there was a positive relationship between self-control and task performance. Additionally, sociability increased task performance and contextual performance. Moreover, emotionality and sociability reduced counterproductive work behavior. Our multivariable models identified a positive impact of emotional intelligence on nurses’ work performance. Nurses managers and healthcare organizations should adopt appropriate interventions to improve nurses’ emotional intelligence.

Emerging research has revealed a complex bidirectional interaction between the gut microbiome and cannabis. Preclinical studies have demonstrated that the gut microbiota can significantly influence the pharmacological effects of cannabinoids. One notable finding is the ability of the gut microbiota to metabolise cannabinoids, including Δ^9-tetrahydrocannabinol (THC). This metabolic transformation can alter the potency and duration of cannabinoid effects, potentially impacting their efficacy in cancer treatment. Additionally, the capacity of gut microbiota to activate cannabinoid receptors through the production of secondary bile acids underscores its role in directly influencing the pharmacological activity of cannabinoids. While the literature reveals promising avenues for leveraging the gut microbiome-cannabis axis in cancer therapy, several critical considerations must be accounted for. Firstly, the variability in gut microbiota composition among individuals presents a challenge in developing universal treatment strategies. The diversity in gut microbiota may lead to variations in cannabinoid metabolism and treatment responses, emphasising the need for personalised medicine approaches. The growing interest in understanding how the gut microbiome and cannabis may impact cancer has created a demand for up-to-date, comprehensive reviews to inform researchers and healthcare practitioners. This review provides a timely and invaluable resource by synthesizing the most recent research findings and spotlighting emerging trends. A thorough examination of the literature on the interplay between the gut microbiome and cannabis, specifically focusing on their potential implications for cancer, is presented in this review to devise innovative and effective therapeutic strategies for managing cancer.

The movement of liquid droplets on the energy gradient surface has attracted extensive attention. Inspired by biological features in nature, such as the periodic spindle-shaped nodes in spider silks and conical-like barbs of cacti, the structure-property-function relationship of multifunctional gradient surfaces. In this study, a series of specific patterns are fabricated by 3D printing technology, followed by modification via the atmospheric pressure plasma treatment and liquid phase chemical deposition, resulting in enhancing the ability of water droplets of 5μL to travel 18.47mm on a horizontal plane and 22.75mm against gravity up to 20° tilting angle. Additionally, analysis techniques have been employed including contact angle analyzer, ESCA and laser confocal microscope to evaluate the sample performance. This work could further be applied to many applications related to microfluidic devices, drug delivery and water/fog collection.

In this paper the problem of sampling from uniform probability distributions is approached by means of space-filling curves (SFCs), a topological concept that has found a number of important applications in recent years. Departing from the theoretical fact that they are surjective but not necessarilly injective, the investigation focused upon the structure of the distributions obtained when their domain is swept in a uniform and discrete manner, and the corresponding values used to build histograms, that are approximations of their true PDFs. This work concentrates on the real interval [0,1], and the Sierpinski space-filling curve was chosen because of its favorable computational properties. In order to validate the results, the Kullback-Leibler distance is used when comparing the obtained distributions in several levels of granularity with other already established sampling methods. In truth, the generation of uniform random numbers is a deterministic simulation of randomness using numerical operations. In this fashion, sequences resulting from this sort of process are not truly random.

Transient receptor potential (TRP) channel family proteins are sensors for pain, which sense variety of thermal and noxious chemicals. Sensory neurons innervating the gut abundantly express TRPA1 and TRPV1 channels and are in close proximity of gut microbes. Emerging evidence indicates a bi-directional gut-brain cross-talk in several entero-neuronal pathologies; however, the direct evidence of TRP channels interacting with gut microbial populations is lacking. Herein, we examine whether and how the knockout (KO) of TRPA1 and TRPV1 channels individually or combined TRPA1/V1 double-knockout (dKO) impacts the gut microbiome in mice. We detect distinct microbiome clusters among the three KO mouse models versus wild-type (WT) mice. All three TRP-KO models have reduced microbial diversity, harbor higher abundance of Bacteroidetes, and reduced proportion of Firmicutes. Specifically distinct arrays in the KO models are determined mainly by S24-7, Bacteroidaceae, Clostridiales, Prevotellaceae, Helicobacteriaceae, Rikenellaceae, and Ruminococcaceae. A1KO mice have lower Prevotella, Desulfovibrio, Bacteroides, Helicobacter and higher Rikenellaceae and Tenericutes; V1KO mice demonstrate higher Ruminococcaceae, Lachnospiraceae, Ruminococcus, Desulfovibrio and Mucispirillum; while A1V1dKO mice exhibit higher Bacteroidetes, Bacteroides and S24-7 and lower Firmicutes, Ruminococcaceae, Oscillospira, Lactobacillus and Sutterella abundance. Also, the abundance of taxa involved in biosynthesis of lipids and primary and secondary bile acids is higher while that of fatty acid biosynthesis-associated taxa is lower in all KO groups. To our knowledge, this is the first study demonstrating distinct gut microbiome signatures in TRPA1, V1 and dKO models and should facilitate prospective studies exploring novel diagnostic/ therapeutic modalities regarding the pathophysiology of TRP channel proteins.

There is a growing interest in the endocannabinoid system and the gut microbiota. Both areas have been suggested to play a role in endometriosis, however this is yet to be studied comprehensively. The purpose of this narrative review is to identify the gaps in endometriosis research in the context of these two areas. It underscores the limited knowledge regarding the involvement of the endocannabinoid system and gut microbiota in endometriosis. Further research in these areas is essential to unlock the therapeutic potential of cannabis and enhance the understanding of endometriosis pathogenesis.

Fatigue is a recognized concern in air traffic management, prompting international bodies like ICAO and EASA to advocate for Fatigue Risk Management Systems (FRMS). EU Regulation 2017/373, effective since January 2020, imposes specific requirements on air traffic service providers regarding controllers' fatigue, stress, and rostering systems, as part of safety management protocols. Despite active campaigns promoting fatigue awareness in air traffic control (ATC), there remains a need for standardized operational requirements. Eurocontrol recently published "Guidelines on fatigue management in ATC rostering systems" (April 23), aiming to facilitate the adoption of common fatigue operational standards. However, neither EU Regulation 2017/373 nor existing documentation specifies exact rostering criteria. Air Navigation Service Providers (ANSPs) typically derive these criteria from scientific literature, best practices, historical data, and legal and operational necessities. Yet, assessing and monitoring fatigue in operational settings can be complex and require long-term studies due to its multifaceted nature, spanning factors such as sleep quality, circadian rhythms, psychosocial influences, individual traits, and environmental conditions. The case studies reported in this paper represent an attempt to create an evidence-based protocol for fatigue risk monitoring in ATC operations, utilizing a non-invasive approach and collecting multidimensional data. The two case studies involved en-route and tower controllers from different ATC Centers, and the results underscore the importance of fatigue assessment in ATC and elucidates challenges in implementing fatigue monitoring within operational contexts.

A superior variant of dried jujube in China is ‘Huizao’ (HZ) jujube. Nonetheless, detailed evaluations of the texture quality of HZ fruits have been the subject of few studies. Texture is a significant indicator of the sensory and processed quality of a fruit. Here, we differentiate and characterize the texture quality of HZ fruits from the four primary producing regions in southern Xinjiang, as well as develop a system for assessing the texture quality of HZ fruits. Furthermore, five texture indices of HZ fruits from 109 plantations were exhaustively evaluated utilizing principal component analysis, Pearson correlation analysis, and hierarchical clustering analysis. Consequently, by the 10th, 30th, 70th, and 90th percentiles, each index was classified into five grades—lower, medium, high, and higher. Correlation investigation indicated strong relationships between the springiness, chewiness, cohesiveness, gumminess, and hardness of HZ fruits. Springiness, hardness, and adhesiveness are essential indicators that influence the texture of HZ fruits. We used clustering analysis to separate the six texture indices into three major categories (hard factor, stick-elastic factor, and adhesive factor), with indices representing hardness, springiness, and adhesiveness, respectively. Furthermore, according to the factor molecules, the texture quality of HZ fruits in the four production areas was evaluated as Bazhou (1.24) > Hotan (0.773) > Kashi (-0.577) > Aksu (-0.852). RDA analysis of six texture quality parameters and 24 climate conditions identified higher mean temperature (TEM) and lower relative humidity (RHU) as the primary factors contributing to the improved texture quality of HZ fruits in Xinjiang. This study identified the texture quality characteristics of HZ fruits in four major producing areas of Southern Xinjiang, and it offered a theoretical foundation for optimizing the dominant producing areas and regional production of HZ varieties in Xinjiang.

This paper, from the perspective of Mode 3 of knowledge production, takes the important carrier of knowledge, scientific research papers, as the research object. The mainstream databases (SCIE and SSCI) are chosen as the data sources. Firstly, bibliometric methods are applied to analyze the cooperation status among the main bodies of paper production, including authors and their affiliated institutions, countries, and disciplines; secondly, the Analytic Hierarchy Process (AHP) is used to construct a comprehensive evaluation model for the state of research cooperation in carbon neutrality. Based on the evaluation model, the cooperation characteristics of different disciplinary fields and the relationship between paper cooperation status and high-impact research papers are comparatively analyzed. At the same time, an analysis is conducted on the support from the government, the sponsorship from enterprises, and the participation of the public in the process of paper production. Finally, some suggestions are put forward on how to build an innovative ecosystem for carbon neutrality research from the perspective of Mode 3 of knowledge production.

BackgroundThe clean energy transition required for decarbonisation of our societies make policymakers targets for broad business and non-business advocacy to ensure one’s often conflicting interests are protected or taken into consideration in public policies. The concept of policy entrepreneurs foregrounds the role of agency in understanding such acts of advocacy. This paper compares strategies used by policy entrepreneurs from different social spheres, advocating policy change or status quo, in four longitudinal cases related to European Union energy and climate policy 2011–2023. ResultsPolicy entrepreneurship was mainly of a cultural-institutional nature, aiming at altering or diffusing people’s perceptions, beliefs, norms and cognitive frameworks, worldviews, or institutional logics. But the actions of the European Commission did also include structural entrepreneurship, aiming at overcoming structural barriers to enhance governance influence by altering the distribution of formal authority and factual and scientific information. Motifs of policy entrepreneurs in the four cases differ, but strategies do not differ significantly between the public, private and civic spheres of society. Results indicate that civil society policy entrepreneurs focus on building broader coalitions than public and private sector entrepreneurs. There is no indication that policy entrepreneurs from a certain sector would be more successful than others in setting the agenda, changing perceptions of policy actors, or influencing actual policy change. However, policy entrepreneurs advocating policy change are more active and use more elaborated strategies than policy entrepreneurs advocating status quo. ConclusionsIt is concluded that policy entrepreneurs advocating policy change are more active and use more elaborated strategies than policy entrepreneurs advocating status quo. Thus, they are more successful in influencing policy outcomes. The European Commission was the only policy entrepreneurs to use structural entrepreneurship. the Commission acts to expand its reach into areas where the European Union does not hold much legal competence according to the constitution of the European Union. This comes with democratic deficits related to accountability and legitimacy and should be combated to reinstate and reinforce the position and powers of both the national and European legislators in taking the important decisions that impact the lives of European citizens.

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect them from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification, but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks, while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. Experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods, first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, a non-linear mapping is a better choice. Our paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear mapping. In the first phase, a classifier is applied that classifies the input images into Type-I or Type-II. In the second phase, depending on the classification, the Deep Line Model (DLM) for Type-I images or the Deep Curve Model (DCM) for Type-II images. For mapping an input image to an output image, DML creatively combines color compensation and contrast adjustment in a single step and uses deep lines for transformation, whereas DCM employs higher-order curves. Both models utilize lightweight neural networks that learn per-pixel dynamic weights based on the input image’s characteristics. Comprehensive evaluations on benchmark datasets using metrics like peak signal-to-noise ratio(PSNR) and root mean squares error (RMSE) affirm our method’s effectiveness in accurately restoring underwater images, outperforming existing techniques.

While the foundations of climate science and ethics are well established, fine-grained climate predictions, as well as policy-decisions, are beset with uncertainties. This chapter maps climate uncertainties and classifies them as to their ground, extent and location. A typology of uncertainty is presented, centered along the axes of scientific and moral uncertainty. This typology is illustrated with paradigmatic examples of uncertainty in climate science, climate ethics and climate economics. Subsequently, the chapter discusses the IPCC’s preferred way of representing uncertainties and evaluates its strengths and weaknesses from a risk management perspective. Three general strategies for decision-makers to cope with climate uncertainty are outlined, the usefulness of which largely depends on whether or not decision-makers find themselves in a context of deep uncertainty. The chapter concludes by offering two recommendations to ease the work of policymakers, faced with the various uncertainties engrained in climate discourse.

Quantifying the landscape pattern change can effectively demonstrate the ecological progresses and the consequences of urbanization. Based on remotely sensed land cover data in 1994, 2000, 2006 and a gradient analysis with landscape metrics at landscape- and class- level, we attempted to characterize the individual and entire landscape patterns of Shanghai metropolitan during the rapid urbanization. We highlighted that a roadscape transect approach that combined the buffer zone method and the transect-based approach was introduced to describe the urban-rural patterns of agricultural, residential, green, industrial, and public facilities land along the railway route. Our results of landscape metrics showed significant spatiotemporal patterns and gradient variations along the transect. The urban growth pattern in two time spans conform to the hypothesis for diffusion–coalescence processes, implying that the railway is adaptive as a gradient element to analyze the landscape patterns with urbanization. As the natural landscape was replaced by urban landscape gradually, the urban fringe expanded radically. The results also showed that the desakota region expanded its extent widely. Satellite towns witnessed the continual transformation from the predominantly rural landscape to peri-urban landscape. Furthermore, the gap between urban and rural areas remained large especially in public service. More reasonable urban plans and land use policies should push to make more of an effort to transition from the urban-rural separation to coordinated urban-rural development. This study is a meaningful trial in demonstrating a new form of urban–rural transects to study the landscape change of large cities from a strategic viewpoint. By combining gradient analysis with landscape metrics, we addressed the process of urbanization both spatially and temporally, and provided a more quantitative approach to urban studies.

This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, Fourier Granger Causality, and Fourier Toda Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.

The power of the river does not always work to the benefit of living things; rather, it has the ability to destroy human society and has negative effects on the environment. River Teesta has a prodigious significance on the riverine dweller and its changing precarious behaviour has repercussions on the lives of millions. Utilizing satellite images and 41 years of temporal analysis, the mapping of channel extraction and channel shifting has been demonstrated. Delineating the exact channel boundary is a laborious operation, but MNDWI technique is really valuable in this situation to produce valuable results. Descriptive statistics are used to analyze the Teesta River's active flow line migration trend and channel migration behaviour. A particular river's oscillating character over time has both beneficial and detrimental power, destroying important resources for environmental sustainability and human society while also providing fertile mother earth as a source of wealth. As a result, another goal of the current study was to determine how vulnerable riverine residents were. A livelihood vulnerability index (LVI) based on the riverine inhabitants' exposure, sensitivity, and capacity for adaptation was created. According to the findings, the river's erratic behaviour is putting the people who live beside it in danger. Therefore, it is vitally necessary to implement a capable management strategy to stop bank failure at the Teesta-Dharala confluence in order to save the riverine residents.

This is the second paper in a series introducing a deterministic quantum mechanics theory that is shown to be consistent with the current mainstream statistical quantum theory as well as with classical physics. The first paper in this series demonstrated how causality, physical reality, and determinism are restored and concerns that were raised by results from the current mainstream statistical quantum theory were explained in simple form. The meaning of particle-wave duality and complementarity, the possibility of a particle, like the electron, to cross through the nucleus as it does when the angular momentum of the electron is zero, the possibility of a point-size particle to have an “intrinsic spin”, the possibility of “quantum jumps” as the electron transitions instantaneously from one stable orbital to another and does that at irregular time intervals, the natural collapse of the wave function as part of the solution, as well as consistency with the phenomenon of entanglement were some of the results that emerged from the proposed deterministic quantum mechanics theory. While the first paper presented overwhelming evidence of the latter, the present paper is the first to produce actual solutions that are consistent with current mainstream quantum theory as well as with classical physics. Analytical solutions are presented via a linear stability method. The Bohr-Schrödinger energy levels leading to the experimentally confirmed spectral lines, as well as the fine structure constant emerge directly from the solution of the equations governing the proposed deterministic quantum mechanics. Recommended follow-up solutions as well as generalizations are being presented at the end of the paper.

The assessment outcomes of regional susceptibility to geological disasters can directly indicate the extent and intensity of risks within the study area, thus, providing targeted guidance for disaster management efforts. This study selects eight evaluation indicators, namely elevation, gradient, terrain relief, lithology of strata, normalized difference vegetation index, distance from the fault, distance from road, and distance from the river. The study focuses on Huize County in Yunnan Province as the research area, utilizing the certainty factor (CF) and random forest (RF) models for evaluating the susceptibility to geological disasters. The non-geological disaster points in the study area are determined using the deterministic coefficient prior model, and the deterministic coefficient values for each evaluation factor serve as the classification data for the random forest model. The optimal parameters for the random forest are selected through iterative calculations of bag error in PyCharm, while the weight of the evaluation factor is determined based on the ran-dom forest model with the optimal parameters. The results of geological disaster susceptibility zoning in Huize County are obtained by overlaying the weighted deterministic coefficients of each evaluation factor. The accuracy of the evaluation results is verified using zoning statistics and ROC curves with a test sample of 30% of the points. The results demonstrate the high accuracy of the model in evaluating the susceptibility to geological disasters in Huize County. Compared to the single deterministic coefficient model, this approach offers advantages in terms of reliability and accuracy. The evaluation results can serve as a scientific reference for related work in Huize County.

This work addresses the problem of sampling from Gaussian probability distributions by means of uniform samples obtained deterministically and directly from space-filling curves (SFCs), a purely topological concept. To that end, the well-known inverse cumulative distribution function method is used, with the help of the probit function,which is the inverse of the cumulative distribution function of the standard normal distribution. Mainly due to the central limit theorem, the Gaussian distribution plays a fundamental role in probability theory and related areas, and that is why it has been chosen to be studied in the present paper. Numerical distributions (histograms) obtained with the proposed method, and in several levels of granularity, are compared to the theoretical normal PDF, along with other already established sampling methods, all using the cited probit function. Final results are validated with the Kullback-Leibler and two other divergence measures, and it will be possible to draw conclusions about the adequacy of the presented paradigm. As is amply known, the generation of uniform random numbers is a deterministic simulation of randomness using numerical operations. That said, sequences resulting from this kind of procedure are not truly random. Even so, and to be coherent with the literature, the expression ”random number” will be used along the text to mean ”pseudo-random number”.

Mesoscale eddies, the weather system of the oceans, although being on the scales of O(20-100km), have a disproportionate role in shaping the mean jets such as the separated Gulf Stream in the North Atlantic Ocean, which is on the scale of O(1000km) in the along-jet direction. With the increase in computational power, we are now able to partially resolve the eddies in basin-scale and global ocean simulations, a model resolution often referred to as mesoscale permitting. It is well known, however, that due to grid-scale numerical viscosity, mesoscale permitting simulations have less energetic eddies and consequently weaker eddy feedback onto the mean flow. In this study, we run a quasi-geostrophic model at mesoscale resolving resolution in a double gyre configuration and formulate a deterministic parametrization for the eddy rectification term of potential vorticity (PV), namely, the eddy PV flux divergence. We have moderate success in reproducing the spatial patterns and magnitude of eddy kinetic and potential energy diagnosed from the model. One novel point about our approach is that we account for non-local eddy feedbacks onto the mean flow by solving the eddy PV equation prognostically in addition to the mean flow. In return, we are able to parametrize the variability in total (mean+eddy) PV at each time step instead of solely the mean PV. A closure for the total PV is beneficial as we are able to account for both the mean state and extreme events.

China’s leading development of a complete battery value chain for Electric Vehicles (EV) is restructuring the global automotive sector. The competitive advantage of China's EV battery industry lies on its firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork in an EV battery cluster in South China, this paper identifies what the Chinese EV battery industry’s core competency is and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability to supply lithium-ion batteries vehicle manufacturers (OEMs) with stable and consistent quality as well as competitive prices. This competency is acquired by firms through technological learning at workshop level while taking use of their experiences accumulated in mass production of batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge on battery manufacturing at large scale is also facilitated by the industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings illuminate the likely strategic strengths, weaknesses, and futures of the Chinese EV battery industry in global competition.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

Abstract: Efforts for carbon emission reduction have been identified as a major climate change mitigation target in the Paris Agreement. The 26th Conference of Parties to the United Nations Framework Convention on Climate Change highlighted the worldwide effort for cleaner and greener mining, urging the mineral industry to embrace greener operational emissions reduction technologies and strategies. Ghana pledged at the Paris Conference to reduce CO2 emissions by 15% and increase renewable energy share to 10% of the national energy mix. Here the fundamental question is how Ghana’s mining industry can contribute to CO2 emission reduction efforts. This paper investigates barriers to low-carbon emission policy implementation among mining companies in Ghana. The questionnaire survey was conducted among Ghana’s four large-scale gold mining companies. As the sample number of available companies is limited, we used a descriptive analysis to assess the responses. The results show that the companies expressed widely different ways to respond to the issues of cost, a government policy/regulations for carbon emission reduction, and the adoption/acquisition of low-carbon technologies. Those companies that are relatively less prepared for this endeavor emphasized the uncertainties of securing sufficient energy in case of using low-carbon technologies. They also expressed a need for more trained workers to handle low-carbon technologies. All companies agreed with a expected high cost of adopting low-carbon options. There was a communication gap with the relevant government ministry about low-carbon mining options where the government should be able to facilitate the involvement of othe low technology providers, such as solar PV installation.

This article seeks to provide an improved and more comprehensive understanding of the concept and theories on wasteland. It achieves this by focusing on the Indian context allowing us to unpack the importance of including multiple perspectives of wasteland narratives; this means including more positive narratives of the potential of wasteland to inform and improve prospects for land policies in the global south. Wasteland is commonly recognized as an underutilized category of land that may transform into a valuable resource base with proper management measures. The term waste has multiple angles that carry different notions ranging from fallow to agroforestry land in the global south and Brownfield to green space in the global north. We conduct a narra-tive review approach to qualitatively analyze the concept wastelands studied in pre-existing lit-erature from 1970 to present. The unsystematic literature review approach incorporates multiple elements of wasteland discourse, like understanding the meaning of the term on a global scale, setting out the meaning of the term waste into multiple perspectives explicitly in the Indian con-text, along with different classes and management approaches of wasteland from a national per-spective. The multiple perspectives of wasteland not only generate misconception on land resource but spawn difficulties in land use policy particularly for Indian scenario. For sustainable land use policy, reclaiming wasteland would be the best possible way for India and other countries in Global South, which requires a comprehensive methodological overview on wasteland narrative.

The present work was performed to evaluate the levels of phytochemical constituents and the antioxidant and antibacterial properties of marine red seaweed (Kappaphycus alvarezii) and seagrass (Cymodocea serrulata). Quantitative phytochemical analysis, antioxidant activity and antimicrobial activity against 5 potential pathogenic bacteria was investigated. In both cases were found presence of flavonoids, tannins, phenolic compounds, glycosides, steroids, carbohydrates and ashes. Alkaloids were only found in K. alvarezii, buy not in C. serrulata. The antimicrobial properties of both K. alvarezii and C. serrulata chloroform extracts were found to be antagonis-tically effective against the gram-positive bacterium Bacillus subtilis and the gram-negative bacterium Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio harveyi and Klebsiella pneu-moniae. GC‒MS analysis revealed the presence of 94 bioactive compounds in K. alvarezii and 104 C. serrulata, including phenol, decane, dodecane, hexadecane, vanillin, heptadecane, diphenyla-mine, benzophenone, octadecanoic acid, dotriaconate, benzene, phytol, butanoic acid, and 2-hydroxyl-ethyl ether, which played a vital role in antioxidant and antibacterial activities. Thus, in view of the results, both K. alvarezii and C. serrulata could be considered as sources of ingre-dients with appreciable nutritional and medicinal value.

Although promoting healthy eating is a policy objective, the controllability of dietary habits remains uncertain. The personal dietary patterns reflect many factors, of which some are relatively controllable for individual, and others are not. In this article, assuming that some sort of information about controllability of dietary habits is contained in the observed pattern of food consumptions, we focused on dietary pattern on its own. We introduced a statistical descriptive model for data on food frequency questionnaire, estimated strength of pairwise linkage between foodstuffs, and grouped foodstuffs by applying community detection to the networks of the estimated inter-food linkages. Those linkages represent co-movement of pairs of foods consumption. Furthermore, we demonstrated an analysis on the relationship between health of mind and dietary habits considering the aspect of controllability of dietary habits. Using an observational study in Japan, we obtained the following results: 115 foodstuffs were divided into 3 groups for both of genders, but the compositions were different by gender; in the analysis of mental health, some stress response items were associated with the dependence on some of those food groupings. As the grouping of foodstuffs based on our estimation depicted the internal structure of dietary habit that a healthy eating policy should regard as constraint, it follows that we should design the policy along the line with that grouping.

Older adults with obesity have an increased risk for cognitive decline and subsequent Alzheimer’s disease. Previous research has focused on the impact of individual nutrients and overall dietary patterns to improve cognitive function. The Brain and Body Health (BB-Health) study aims to demonstrate the feasibility of a multicomponent nutritional supplement (MCNS) and weight loss (WL) intervention for individuals with overweight and obesity who are not consuming a healthy diet. BB-Health is a parallel-group randomized placebo-controlled 2x2 factorial pilot trial comparing the MCNS, WL, and MCNS+WL with a Control group. Eligible participants are 55 to 85 years of age with a body mass index 27.0 to 39.9 kg/m2. Major exclusion criteria include type 2 diabetes, moderate or severe cognitive impairment, and self-reported healthy dietary pattern. The BB-Health feasibility trial is registered as NCT05542199 at clinicaltrials.gov. The BB-Health objectives are to report: 1) recruitment and enrollment, including pace and descriptive statistics of the study sample; 2) adherence to the administered interventions; 3) successful data collection and performance of outcome assessments, including evaluation of change in key outcomes over time that are powered for this pilot study; and 4) summary statistics on outcomes for use in planning a subsequent, larger trial. The outcomes of interest are age-sensitive cognitive assessments, cerebral hemodynamics, and related health parameters. All measurements are collected at baseline, 3-, 6-, and 12-month visits. If the BB-Health protocol demonstrates feasibility, the novel MCNS with and without a parallel WL intervention has potential to be administered in a confirmatory randomized trial to evaluate ability to prevent age-related cognitive decline and improve brain health. The BB-Health Feasibility Study could significantly contribute to our understanding of the influence of diet and weight management on cognitive health

As the leader of the largest economy, President of the United States has substantive influence on addressing the global climate change problem. However, presidential election is often dominated by issues other than energy problems. This paper focuses on the on-going 2016 presidential election, examining the energy plans proposed by the leading Democrat and Republican candidates. Our data from the Iowa caucus survey in January 2016 suggests that voters are more concerned about terrorism and economic issues than environmental relative issues. We then compare the Democratic and Republican candidate’s view of American’s energy future, and evaluate their proposed renewable energy targets. We find that the view on renewable energy is polarized between Democratic and Republican candidates, while candidates from both parties agree on the need for energy efficiency. Results from our ordinal least squares regression models suggest that Democratic candidates have moderate to ambitious goals for developing solar and other renewable energy. The Republican candidates favor fossil fuel and they neglect to provide any plan for renewable energy. In addition, this trend of polarization has grown more significant when compared with the past three presidential elections. Our observation suggests that energy issues need to be discussed more to draw broader attention to salient issues of diversifying and decarbonizing the nation’s energy system.

Urbanization degrades natural habitats and creates new urban ecosystems, like domestic gardens. The plant composition of these gardens varies with socio-economic factors and urban planning levels. However, the diversity and impact of introduced species are often poorly assessed, causing potential ecological imbalances, particularly in Lubumbashi (DR Congo). The objective was to analyze the spatial structure, plant diversity, propagation strategies, and ecological functions of domestic gardens. Three distinct neighborhoods were selected: planned, unplanned and residential neighborhood. 20 avenues (with 5 plots per avenue) were chosen to represent the diversity within each neighborhood, and stratified random sampling of plots was conducted to analyze gardening practices. Gardens were classified into types, and their vegetation was evaluated based on species origin and ecological impact. The analysis of domestic gardens in Lubumbashi reveals significant variations across different neighborhood types. Residential neighborhoods exhibit larger average garden sizes (315.1 m²), higher species richness (22 species), and larger plot sizes (1032 m²) compared to unplanned and planned neighborhoods, where garden areas and species richness are notably lower. Rectangular gardens dominate in unplanned areas, while planned neighborhoods feature more intentional landscaping elements, such as flowerbeds and hedges. The use of gardens for food production is prominent in planned areas (40.7%), whereas residential neighborhoods prioritize ornamentation (51.4%). The study identifies 232 species across 68 families, with a predominance of exotic species (80%) in all neighborhoods, particularly in unplanned areas (82.25%). The data reveals that Mangifera indica and Persea americana are abundant in all neighborhoods, illustrating their adaptability to different urban contexts. Herbaceous species are most common, followed by woody plants, with vines being sparse. Species dispersal is primarily driven by human activities (anthropochory), accounting for over 85% in all neighborhoods. These findings highlight the strong human influence on the composition and structure of domestic gardens in Lubumbashi, emphasizing the dominance of exotic species and the importance of anthropogenic factors in shaping urban green spaces. Urban policies should incorporate strategies to minimize the negative impacts of exotic species on native flora.

The research and development of non-viral gene therapy has been extensive over the past decade and has received a big push thanks to the successful approval of non-viral gene therapy products in recent times. Despite these developments, gene therapy applications in cancer have been limited. One of the main causes of this has been the imbalance in development of delivery vectors as compared to nucleic acid payloads. This paper reviews non-viral vectors that can be used to deliver nucleic acids for cancer treatment. It discusses various types of vectors and highlights their current applications. Additionally, it also discusses perspective on regulatory landscape to facilitate commercial translation of gene therapy.

Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most oftenly by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad-hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and non-linear local contrast enhancement and colour image daltonisation illustrate the behaviour of the method.

In vertical coordinates, (x,y,z), the gradient Richardson number Ri=N^2⁄S^2 is the square of the ratio between buoyancy frequency N and vertical-shear amplitude S, where N^2=-(g⁄ρ) ∂ρ⁄∂z and S^2=(∂u⁄∂z)^2+ (∂v⁄∂z)^2, with ρ potential density, (u,v) the horizontal velocity components and g gravity acceleration, so that a decrease in stratification brings a decrease in dynamic stability and vice versa. In the isopycnic coordinate system, (x,y,ρ), however, Ri=M^2⁄〖S_ρ〗^2 , is the ratio between 〖M^2≡N〗^(-2) and the squared diapycnal shear 〖S_ρ〗^2=(ρ⁄g)^2 [(∂u⁄∂ρ)^2+ (∂v⁄∂ρ)^2 ], so a decrease in stratification leads to an increase in dynamic stability and vice versa. The apparently different role of stratification arises because S and S_ρ are related through the stratification itself, S_ρ=S⁄N^2 or, equivalently, τ=〖t_o〗^2⁄t_d , which is interpreted as a natural oscillation time τ≡S_ρ that equals the buoyancy or oscillation time t_o=N^(-1) normalized by the ratio t_o⁄t_d , where the deformation time is t_d=S^(-1). Here we follow simple arguments and use field data from three different situations (island shelf-break, Gulf Stream and Mediterranean outflow) to endorse the usefulness of the isopycnal approach. In particular, we define the reduced squared vertical σ^2= S^2-N^2 and reduced squared diapycnal shear 〖σ_ρ〗^2= 〖S_ρ〗^2-M^2, which are positive for unstable conditions and negative for stable conditions. It turns out that Ri and σ^2 remain highly variable for all stratification conditions; in contrast, the mean 〖σ_ρ〗^2 values display a univocal tendency, its magnitude decreasing with stratification. We propose 〖σ_ρ〗^2 and 〖S_ρ〗^2 to be good indexes for the occurrence of effective mixing under high stratification conditions.

The samples from lunar farside have great significance for the study of the Moon, and even the solar system. Chang’e-6 landed successfully on the southern mare of the Apollo basin and returned ~2 kg samples from lunar farside. To provide a better understanding for the background of the returned samples, we conducted detailed crater size-frequency distribution (CSFD) measurements in the Chang’e-6 landing region, the southern mare of the Apollo basin. The southern mare is divided into western mare (W region) and eastern mare (E region), and then subdivided into five sub-units (W1, W2, W3, W4, W5) and three units (E1, E2, E3), respectively, according to the elevation, Ti and Fe abundances. Within the W2 and W5 region, more detailed sub-units were divided. The results show that the southern mare generally have two epochs, one of the Imbrian period and the other of the Eratothenian period. The basalt eruption lasted for ~1.7 Ga, from 3.28 Ga of the eastern mare to 1.54 Ga of the western mare. W region is younger than E region, while the three units of E region has an age of ~3.2 Ga. The ages of the western mare basalts range from 2.98 Ga to 1.54 Ga, lasting for 1.4 Ga. It’s worth noting that the age of the basalt at the Chang’e-6 sampling site is ~1.68 Ga, indicating the samples returned may include components with this very young age.

In this study, we explore the impact of quantum effects on synaptic activity and their implications for the deterministic model of neural processes. By simulating synaptic events in a large-scale neural network, we demonstrate how small probabilistic quantum events can accumulate and introduce variability in neural behavior. Our model incorporates Dirac delta functions to simulate sudden changes in synaptic probabilities, creating a non-linear, seesaw effect in the cumulative synaptic activity curve. The results reveal that even minimal quantum effects, when scaled to the vast number of synapses in the human brain, can lead to significant dissociations in neural activity. These findings challenge the traditional deterministic view, suggesting that neural processes—and by extension, decision-making and movements—may inherently incorporate elements of randomness. This probabilistic perspective aligns with theories of free will and underscores the need for further empirical research to validate the role of quantum mechanics in neuroscience. Our study provides a theoretical framework for understanding the complex interplay between quantum effects and neural dynamics, opening new avenues for exploring the nature of consciousness and the fundamental principles governing reality.

Relationships of the three bryophyte lineages (liverworts, mosses, and hornworts) to vascular plants is a pivotal question in the study of origin and evolution of land plants. In recent decades, this question has been subject to intense phylogenetic analyses using morphological characters, multigene DNA sequences, and genome structural characters. A tentative consensus reached ten years ago suggested that bryophytes are a paraphyletic group, with liverworts being sister to all other land plants and hornworts being sister to vascular plants. However, several more recent nuclear phylogenomic studies have concluded that bryophytes represent a monophyletic group that is sister to vascular plants. A discussion is presented here on strengths and weaknesses of different types of characters (morphological traits, nucleotide sequences, and genome structural arrangements), and their suitability for resolving deep phylogenetic relationships. Moreover, several criteria for credible phylogenetic reconstruction are proposed. Strong statistical support for reconstructed relationships should be derived from high quality, independent characters selected for suitability to the particular question being addressed. The relationships inferred in a study should be congruent with those from as many other lines of phylogenetic evidence as possible. Any incongruities should be explicable by well-understood biological mechanisms. It is concluded that the relationships of the three bryophyte lineages to vascular plants should currently be viewed as unresolved. This is a difficult phylogenetic problem; the land plants underwent a rapid radiation a long time ago. Yet, further exploration of analytical methods and careful choice of characters should lead to eventual elucidation of diversification patterns among early land plants.