Prestressed concrete sleepers (or railroad ties) are principally designed in order to carry wheel loads from the rails to the ground of railway tracks. Their design takes into account static and dynamic loading conditions. In spite of the most common use of the prestressed concrete crossties in railway tracks, there have always been many demands from rail engineers to improve serviceability and functionality of concrete crossties. For example, signaling, fiber optic, equipment cables are often damaged either by ballast corners or by tamping machine. There has been a need to re-design concrete crosstie to cater cables internally so that they would not experience detrimental or harsh environments. Also, many concrete crossties need a retrofit for automatic train control device and similar signaling equipment. In contrast, the effects of holes and web openings on structural capacity of concrete crossties have not been thoroughly investigated. This paper accordingly highlights the effect of holes and web openings on the toughness and ductility of concrete crossties. The outcome of this research enables better decision making process for retrofiting prestressed concrete crossties with holes and web opening in practice.

Spacecraft recovery technology is crucial in the field of aerospace, in which the parachute plays a key role in slowing down the descent speed of the spacecraft and realizing a smooth landing. In order to construct a dynamically adjustable parachute deployment strategy, it is necessary to measure the parachute dynamic load accurately in real time. However, the existing sensor measurement scheme is difficult to meet the measurement requirements due to its large structure and complex wiring. In order to meet the current demand for real-time measurement of parachute cords dynamic load, a miniature measuring instrument is designed.According to the function and technical requirements of the miniature measuring instrument, the hardware modules of the acquisition system are selected and designed, and the integration debugging and performance optimization of the microcontroller module, A/D sampling module, signal acquisition circuit and power supply module are carried out. The software of the parachute cords tension acquisition system based on the miniature measuring instrument is developed. The elastomer is modeled by using SolidWorks and statically analyzed by using AnsysWorkbench finite element analysis software. Then the final structure of the elastomer and the pasting position of the strain gage are determined through the results analysis as well as experimental verification. The hardware module of the signal acquisition system for the miniature measuring instrument is then encapsulated. The force value of the miniature measuring instrument is calibrated and tested many times by using the microcomputer-controlled electronic universal testing machine. The experimental results show that the designed miniature measuring instrument has accurate data, strong stability and good real-time performance, which meets the demand for real-time accurate measurement of miniature measuring instrument, and can provide reliable data for parachute cords parameter validation and stepless unfolding design.

Glaucoma is one of the primary causes of blindness worldwide. Canal opening surgery, a type of minimally invasive glaucoma surgery (MIGS) applied in cases of mild to moderate glaucoma, has gained increasing popularity in recent years due to its efficacy in reducing intraocular pressure, its safety profile, the simplicity of its technique, and the reduced likelihood of compromised vision. Nevertheless, the existing body of histopathological studies remains insufficient for a comprehensive understanding of post-surgical wound healing. Consequently, debates persist among researchers regarding the mechanism through which Schlemm’s canal opening surgery reduces intraocular pressure, as well as the surgical techniques that may impact the outcomes and the factors influencing surgical success. As the history of MIGS is relatively short and lacks sufficient systemic reviews or meta-analysis evaluating the influence of individual factors, this review was conducted to illuminate disparities in the researchers’ opinions at the current stage of research.

In this study the effects of three different main preparatory tillage operations [ploughing at 0.4 m (P40) and 0.20 m (P20) depth and minimum tillage at 0.20 m depth (MT) each of them carried out at two different soil water contents (WC) [low, 58% (LH) and high, 80% (HH) of field capacity] were investigated. The results obtained in this research show high values of soil strength in term of Penetration resistance (CI) and shear strength (SS) particularly in deeper soil layers at lower water content. Fossil-fuel energy requirements both for P40 LH and P20 LH were 25 and 35% higher with respect to the HH treatments and tractor slip were very high (P40 LH = 32.4%) with respect to the P40 HH treatment (16%). Therefore soil water content had significantly influenced tractor performance during soil ploughing, particularly at 0.40 m depth while MT was not influenced at all. A significant correlation between grain yield and soil penetration resistance was found highlighting how soil strength may be good indicator of its productivity. Obtained results during these field tests allowed considering MT and P20 treatments more suitable for this type of soil in climate change scenarios.

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are goals in oilseed rape (B. napus) and camelina (C. sativa). Environmental risk assessment scenarios are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness related traits. In case of a changed oil composition an increase or decrease of polyunsaturated fatty acids (PUFA) demonstrates risks for health and survival of pollinators. In case of fitness related traits other risks were identified, i. e. an increased risk of spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions. It is shown that for a comprehensive risk assessment the technological potential of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will require risk assessment and monitoring of single traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially restrict the scale of releases. This is especially relevant for NGT Brassicaceae in Europe which is a centre of diversity of this plant family.

This article provides a literature review on tubal carcinoma to offer an updated insight into its preventative strategies, diagnosis, treatment, and oncological surveillance. In addition to the search string utilized, the authors' focus extended to key scientific studies, consensus statements, guidelines, and relevant case reports essential for the proper clinical management of the disease, providing a methodologically well-structured literature review combined with the practical expertise in the oncological field. The article also includes two emblematic clinical cases that emphasize the importance of understanding the physiopathology and the current state of the art in the anatomopathological advancements of tubal/ovarian/peritoneal carcinoma, often assimilated into a single clinical entity.

The nucleus has been studied for well over 100 years, and chromatin has been the intense focus of experiments for decades. In this review, we focus on an understudied aspect of chromatin biology, namely the chromatin fiber polymer's mechanical properties. In recent years, innovative work deploying interdisciplinary approaches including computational modeling, in vitro manipulations of purified and native chromatin have resulted in deep mechanistic insights into how the mechanics of chromatin might contribute to its function. The picture that emerges is one of a nucleus that is shaped as much by external forces pressing down upon it, as internal forces pushing outwards from the chromatin. These properties may have evolved to afford the cell a dynamic and reversible force-induced communication highway which allows rapid coordination between external cues and internal genomic function.

The Mara River basin (MRB) has a world-famous ecosystem, but the vegetation has been damaged due to economic development in recent years, and there is little known about the area that will experience severe vegetation damage in the future. Based on the vegetation vulnerability system, principal component analysis, and three CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), vegetation vulnerability was calculated in the base period (2010-2019), near-term period (2020-2059) and long-term period (2060-2099) in the MRB. The spatial cluster of vegetation vulnerability was revealed by spatial correlation analysis, and the transition of vegetation vulnerability of different periods was analyzed by stochastic matrix. The results showed that in all periods, the vulnerability showed a high-high cluster in the east, and a low-low cluster midstream and downstream. From the stochastic matrix, the area of high vulnerability increased the least under the SSP1-2.6, while it increased the most under the SSP5-8.5. The vegetation vulnerability upstream increased the most from the base period to the near-term period and long-term period in the MRB. By comparing the vegetation vulnerability under different scenarios, and pointing out the areas with the highest vulnerability increase, this study can better provide comprehensive decision-making for vegetation protection in the MRB.

The understanding of spatiotemporal variations in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman-Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (EMA) in the state of Mato Grosso, spanning the period 2007-2020. The statistical metrics used for evaluation include Bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the RCP 2.6, 4.5, and RCP 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007-2020) and projected (2020-2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas.

Actuators are essential components for intelligent machines that can fulfill certain tasks in response to environmental stimuli. In recent years, actuators that can transform from a 2D ribbon shape to a 3D helical configuration under certain external stimuli have attracted significant attention due to the potential applications of the targeted helical structures in springs, propulsion generation, and artificial muscles. Inspired by the chiral opening of Bauhinia variegate‘s seedpods and the coiling of the Towel Gourd tendril with perversions, researchers have made significant breakthroughs in synthesizing state-of-the-art actuators capable of mimicking helical transformations. In this review, we give a brief overview of the shape evolution mechanisms of these two plant structures and then review recent progress in the fabrication of biomimetic helical actuators. These structures are categorized by the stimuli-responsive materials involved, including hydrogels, liquid crystal networks/elastomers, shape memory polymers, and multiwall carbon nanotubes. By providing this survey on important recent advances along with our perspectives, we hope to solicit new inspirations and insights on the development and fabrication of smart actuators, as well as the future development of interdisciplinary research at the interface of physics, engineering, and biology.

This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.

(1) Background: The aim of the work was to characterize the climatic evolution and change based on the Multi Criteria Classification through the dynamics of bioclimatic indices in viticulture across Italy and its regional administrative boundaries, focusing on latitudes and elevations. (2) Methods: The impact of climate change on viticulture in Italy was analysed and spatialized with reference to historical data from 1991 to 2021 and to the Future Scenarios up to 2080 assumed by SSP2-4.5 and SSP5-8.5, taking into account 13 GCMs. (3) Results: The bioclimatic indices have all shown a significant trend in the historical period, with an increase in temperature and a decrease in precipitation, reflecting their effects on the entire Italian territory with respect to HI, up to 44°N for CI and up to 46°N for DI, regardless of altitude. The Future Scenarios highlighted a shift towards the warmer classes of the two temperature-based indices (HI and CI) for both SSPs, especially for altitudes up to 900 m a.s.l.. The DI-based classification based on DI remained relatively stable in Italy over time, although DI values will become increasingly negative in the near future. (4) Conclusions: The climate in Italy is warming, especially in the south and in the coastal regions. By 2080, more areas will be “Very Hot” with “Warm Nights.” Drought will also increase and have a negative impact on viticulture. The importance of higher latitudes and altitudes in mitigating the effects of climate change justifies the continuing trend towards the relocation of vineyards as a medium-term solution and as an alternative to targeted cultivation methods that must be adopted in the short-term to safeguard the suitability of an area to quality viticulture.

Species distribution models (SDMs) are usually used to predict current species’ geographic distributions and to forecast the impact of climate change, with different aims, such as conservation, and biodiversity management. SDMs use has been increasing in the last decades, however, they are vulnerable to parametrization and data quality input. Thus, inappropriate input can lead to potential unreliability in results. In this context, the most used data and methodologies in SDM, and putative deviations from the consensual best practices, were identified, by analysing recent literature (2018 to 2022). Results show that the parameters presented more consistently are the chosen algorithm (MaxEnt was used in 98% of the studies), the accuracy measures, and the time windows. Many papers fail to specify other parameters, limiting the reproducibility of the studies. Some papers also fail to provide information about the target species: only a fraction of the species' range is considered, or no justification for including specific variables in the model is provided. These options can decrease reliability in predictions under future scenarios since data provided to the model is inaccurate from the start or there is insufficient information for outputs discussion.

This research paper examines a cosmological model in flat space-time via $f(R,G)$ gravity where $R$ and $G$ are respectively the Ricci scalar and Gauss-Bonnet invariant. Our model assumes that $f(R,G)$ is an exponential function of $G$ combined with a linear combination of $R$. We scrutinize the observational limitations under a power law cosmology that relies on two parameters - $H_0$, the Hubble constant, and $q$, the deceleration parameter, utilizing the 57-point $H(z)$ data, 8-point BAO data, 1048-point Pantheon data, joint data of $H(z)$ + Pantheon, and joint data of $H(z)$ + BAO + Pantheon. The outcomes for $H_0$ and $q$ are realistic within observational ranges. We have also addressed Energy Conditions, $Om(z)$ analysis and cosmographical parameters like Jerk, Lerk and Snap. Our estimate of $H_0$ is remarkably consistent with various recent Planck Collaboration studies that utilize the $\Lambda$CDM model. According to our study, the power law cosmology within the context of $f(R,G)$ gravity provides the most comprehensive explanation for the important aspects of cosmic evolution.

This study analyzes future hydrological droughts in LCKW using the SWAT model and data from 2010-2021, evaluating the Streamflow Drought Index (SDI) for 5, 10, 25, and 50-year return periods (RP) for 2029 and 2039 under RCP4.5 and RCP8.5 scenarios. The model shows high accuracy (R² = 0.82, NSE = 0.78). Under RCP4.5, streamflow may increase by 34.74% and 18.74%, while RCP8.5 predicts significant decreases of 37.06% and 55.84%. Historical data reveal frequent short-term droughts during SDI-3, notably in 2014–2016 and 2020–2021, with SDI-6 indicating less frequent but severe droughts, especially in 2015 and 2020. Future projections under RCP8.5 suggest wors-ening drought conditions in 2029 and 2039, with critical drought periods from January to April and November. Wavelet analysis indicates a high risk of severe hydrological drought in LCKW due to low annual recurrence rates, with the highest drought severity over a 50-year RP. The study high-lights an increasing likelihood of frequent and severe droughts, particularly under high emission scenarios such as RCP8.5, which could result in significant water shortages and ecosystem disrup-tions. It emphasizes the significance of adaptive water resource management strategies in mitigating negative impacts on water security and ecosystem health.

Despite the expansion of financial institutions and proliferation of mobile financial services, reaching the unbanked and bringing them under formal financial services has become a policy concern in many developing countries. Due to the lack of financial accounts, unbanked people prefer informal, risky, and inconvenient mechanisms for receiving, sending and transferring money. Previous studies rely much on common intervention like no account maintenance and opening fees, easy documentation processes and money subsidies for opening financial accounts. This study aims to examine the impact of the motivational workshop on opening savings accounts through causality among the unbanked people in a setting where the respondents are unbanked despite having all the requirements and many institutional offers for opening savings accounts. We encouraged the unbanked people through a one-hour-long motivational workshop to open saving account. Based on our cross-sectional data and randomized controlled trial experiment among 505 unbanked Village Defense Party (VDP) members at Dhubil Union under Sirajganj in Bangladesh, we have the evidence that motivational workshop positively impacts opening ac-counts by 32.33 percent. However, the account opening rate differs in terms of respondent’s pref-erence for financial institutions. Our study also finds that unbanked people have the highest pref-erence for mobile financial services for opening accounts resulting in 15.33 percent. The result of the study has some policy implications for adopting effective strategies of financial access in many developing countries.

Machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available because of its ability to find complex patterns in high dimensional and heterogeneous data. While the complexity of ML models is what makes them powerful, it also makes them difficult to interpret. Fortunately, recent efforts to develop approaches that make the inner workings of ML models understandable to humans have improved our ability to make novel biological insights using ML. Here we discuss the importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied. Finally, we identify challenges and promising future directions for interpretable ML in genetics and genomics.

Puerto Rico's geographic and climatic conditions render it highly susceptible to natural disasters, particularly hurricanes. The devastation caused by Hurricane Maria in 2017 highlighted the fragility of the island's centralized energy infrastructure, which led to prolonged power outages and severe disruptions in essential services. This review explores the potential of biomass energy to enhance resilience in post-disaster scenarios, focusing on Puerto Rico as a case study. Biomass energy, derived from organic materials like agricultural residues, woody biomass, and municipal solid waste, offers a renewable and locally sourced power solution that can be rapidly deployed. Key biomass conversion technologies, including direct combustion, gasification, anaerobic digestion, and pyrolysis, are examined for their applicability and benefits in emergency energy provision. The implementation of biomass-powered microgrids in Puerto Rico, such as those by Casa Pueblo and Arensis, demonstrates the feasibility and effectiveness of decentralized energy systems in disaster recovery. These microgrids provide reliable power to critical infrastructure, reduce dependence on fossil fuels, and support waste management efforts. The review also discusses ongoing research and development in advanced biomass technologies, such as second-generation biofuels, algae-based biomass, and biomass-to-hydrogen conversion, which can further enhance energy efficiency and sustainability. Additionally, the economic, environmental, and social impacts of biomass energy are evaluated, highlighting its role in job creation, waste reduction, emission control, and community resilience. Policy recommendations include streamlining permitting processes, enhancing financial incentives, developing feedstock supply chains, and incorporating biomass into disaster recovery plans. The findings suggest that integrating biomass energy into Puerto Rico’s energy strategy can significantly improve its capacity to respond to and recover from natural disasters, promoting long-term sustainability and resilience.

Since the beginning of the 21th century, infrared Nd:YVO4 pulsed lasers have been widely applied especially in some actual industrial processes. In the working process of a laser-aided etching device, the “match-head” effect must be effectively controlled by suppressing the first giant pulse for a solid-state Q-switched laser. In the process of optimizing the infrared Nd:YVO4 pulsed laser by adjusting the slope parameters of the radio frequency (RF) modulation to suppress the first giant pulse, it has been come across that some abnormal horizontally polarized emission with a very short time appears before the formal vertically polarized emission when the gate signal is artificially started. Actually, abnormal horizontally polarized emission will bring some unexpected machining traces during the production process and even greater dangers. The experimental results show that with the increase of the slope duration of a RF signal, the existence time of abnormal output horizontal polarized light will be shortened, and the horizontal giant pulse and vertical giant pulse are well suppressed. When the slope duration is greater than 0.18 ms both horizontal and vertical giant pulses will disappear. The horizontal polarized light can be thoroughly suppressed when the slope duration is greater than 13.7 ms. Compared with the method of adding polarizer to eliminate abnormal output horizontal polarized light, this method does not add elements in the laser, ensuring that the laser volume is relatively small, and does not affect the quality of the normal output laser. The research conclusion is thought to be of great practical significance especially for processing transparent materials.

The Circular Bioeconomy (CBE) combines the concepts of bioeconomy and circular economy. As an alternative concept to the current fossil-based, linear economy, it de-scribes an economy based on the efficient valorization of biomass. It is regional in nature and aims to improve sustainability. An analysis of the transition process, by identifying its success criteria and assessing its impacts through the modelling of technology-specific scenarios is necessary to ensure that CBE concepts are sustainable. However, a compre-hensive consideration of regional influences on both is lacking. Based on extensive literature research and an expert survey, we (i) present a compre-hensive catalog of CBE success criteria and discuss their region-specific character and (ii) develop a methodology based on evaluation matrices that enable to match CBE technolo-gies with regions. The matrices support the evaluation of technological and regional characteristics influencing the successful CBE implementation. The results show that the success criteria "biomass resources", "technological", and "social" are perceived as highly important, and that most of the success criteria are both region- and technology-specific, highlighting the relevance of developing matrices to match them. We describe such matrices indicatively for the two broadest and most im-portant success criteria clusters “social acceptance” and “biomass supply chain”. With this, we substantiate the regional nature of CBE and raise the awareness on the importance of considering regional conditions in CBE transition processes. Furthermore, we provide practical guidance on how regional conditions can be reflected in the selection of technologies, e.g. in regional CBE technology scenarios.

The valorization of aquaculture/fisheries processing by-products, as well as unavoidable/unwanted catches and discards in Greece is currently an underutilized activity despite the fact that there are several best practices in Northern Europe and overseas. One of the main challenges is to determine whether the available quantities for processing are sufficient to warrant valorization of discards and fish side streams. This is the first attempt to systematically record and analyze the available quantities of fish by-products and discards in Greece in a spatial and temporal way in an effort to create a national exploitation Master Plan for the valorization of this unavoidable and unwanted biomass. A thorough survey conducted within the VIOAXIOPIO project unveiled a substantial biomass of around 19,000 tonnes annually that could be harnessed for valorization. Furthermore, the production of various High Added Value Biomolecules (HAVB) was investigated, and experimental trials were conducted to assess the potential yields and the collected data were used to formulate four valorization scenarios.

Wearable devices have a significant impact on society and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using IoT can be a crucial tool for emergency management, reducing risk in rescue operations and ensuring the safety of workers.The massive amounts of data, high network traffic, and computational demands of a typical IoT monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities.This article presents a cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable IoT device. It enables the management of ECG, breathing patterns, and other vital signs during work sessions, and sends real-time alarm events to a personnel management center. The platform was extensively used and tested in a large-scale monitoring application for tracking the activities of emergency room and ambulance workers, and the results of this testing are presented.

The issue of when and how to return to business following COVID-19 lockdown is occupying the minds of policymakers, C-Suite executives and managers the world over. We are concerned by the extent to which it appears that these decisions are being taken on a wing and a prayer, while being pitched to the public as though they shouldn’t be questioned. In this paper, we compare the likely impact of COVID-19 infections from travellers coming from the main countries that visit Greece, to the revenues they generate for the Greek economy. We find that arrivals from some countries should be excluded but aren't, while arrivals from other countries that are excluded perhaps shouldn't be. We show that a rational choice around limitations on the reopening of tourist markets depends on the demand for travel to Greece. We conclude that the current policy is largely economically rational, with some exceptions, but also speculate that Greece may not be ready to handle the resulting infection load.

In situations of uncertainty, scenarios serve as input for evidence-based decision making. However, past experience shows that not all scenarios are treated equally, and we hypothise that only those based ion a world view shared by decision makers are perceived as credible and receive full attention. While intuitively plausible, this hypothesis has not been analysed by quantitative correlation analyses, so instead of drawing on quantitative data the paper analyses the archetypical scenarios developed in the ALARM project to substantiate the plausibility by a comparative analysis of world views, value systems and policy orientations. Shock scenarios are identified as a means to explore the possibility space of future developments beyond the linear developments models and most scenario storylines suggest. The analysis shows that the typical scenarios are based on mutually exclusive assumptions. In conclusion, a comparison of storyline and empirical data can reveal misperceptions, policy failures and the need to rethink world views as a necessary step to open up to new challenges. Deeply held beliefs will make this a transition unlikely to happen without severs crises, if not dedicated efforts to reveal the role of world views for scenarios and policies are undertaken.

Tomato leaf curl disease (TLCD) is the most common viral disease in the tomato plant. It is caused by begomoviruses, which are viruses that cause plant development to be slowed. Many of the traditional disease management methods are still in use. They are, however, ineffective and out of date. Modern biotechnology is being used to detect illness in tomato plants as early as possible, thus reducing damage to the plants. Through genetic engineering, the spread of viruses may be controlled or prevented entirely. Here reviewed many methods for decreasing or eliminating the viral influence on crop growth through biotechnology and genomics. We also investigated the possibility of genetic engineering to reduce or remove the virus TLCD impact on tomato crop development.

The experimental results of forces and efforts derived from the opening of incisions in the orbital cavity in a pig’s head are presented in this article. The different areas of the incision openings are related to the needs at the incision procedure for a dacryocystorhinostomy. In terms of the experimental procedure, an origin and a plane are defined so as to allow the location of the opening of the incision. The incisions are retracted along an axis of said origin. This procedure has been based on the mathematical model developed for this work, which consists of a procedure for determining the behavior of an incision when a force is applied to retract the skin. The experimental data obtained, suggests the existence of an almost linear relationship between the increment of resistance in relation to the time obtained for each opening, the same of which is deemed to be consistent with the behavior of an elastic material.

Background: Learning arrangements in health care profession education are increasingly taking place in digital environments. VR in nursing education, as one of the digital elements, is the subject of controversial debate. On the one hand it supports the authenticity of case studies by adding realistic perspectives and information. On the other hand, the costs of developing and maintaining software and hardware hinder long-term implementation. Methods: In this paper, we describe our findings and insights from two different research projects focused on the incorporation of digital tools, particularly virtual reality (VR), into nursing education. Based on the German context, our aim is to promote the adoption of innovative digital methods in nursing education and to offer invaluable experiences from the field. Results: Starting with a brief recapitulation of the projects, we then elucidate the pedagogical strategies for embedding VR-driven scenarios in nursing education. Based on our experiences during the projects we identify various positive aspects, such as change of perspective and simulating acute situations. Key findings: Although potential drawbacks remain, we advocate long-term implementation and specific use of VR at the interface between theory and practice. Nevertheless it is crucial to establish regular evaluation observing the value of digitalisation, especially VR; for nursing education.

Background: Infantile tibia vara (ITV) is a rare proximal tibia deformity in infancy, leading to progressive knee varus. High tibial osteotomy is commonly practiced but has high recurrence rates. This study analyzed factors affecting treatment failure and recurrence in children undergoing opening-wedge high tibial osteotomy (OWHTO) for ITV. Methods: We retrospectively studied children with ITV who had OWHTO with press-fit cancellous bone allograft between 2000 and 2020, with ≥2-year follow-up. Outcomes included recurrence (knee varus with tibiofemoral angle >10°), complications, and reintervention. Results: We analyzed 39 knees in 29 patients (mean age: 4.8 ± 1.9 years; median follow-up: 7.4 years). Recurrence occurred in 22 cases (56%). Age at surgery significantly influenced recurrence, with rates of 16% before age 5 versus 95% later (hazard ratio: 12.0, p = 0.001). Langenskiöld stage also affected recurrence (β-coefficient: 2.7, 95% C.I. 1.0–4.5, p = 0.002; pseudo-R-squared: 0.50, p = 0.001), with recurrence in all stage III IV or higher cases. Conclusions: Early diagnosis and treatment before age 45, ideally with Langenskiöld stage III or lower, are crucial for stable correction with OWHTO alone. Late, high-grade ITV may require combined, acute or gradual, and/or staged correction. Further evidence is needed for optimal management.

The article contains the results of experimental studies for a multi-opening orifice with substitute constriction factor of β = 0.5 (m = 0.25), mounted in a DN50 hydraulic measuring flume. Flow measurements were taken for a progressing turbulent flow within Reynolds numbers (Re = 4700...19500). Based on experimental data, flow characteristics as well as discharge coefficient C characteristics were determined. Relative expanded uncertainty of determining a discharge coefficient C was estimated within the changes of volume flow qv from 0.35 dm3/s to 0.68 dm3/s. The value does not exceed 1.25% within the changes of Reynolds numbers 9800 ≤ Re ≤ 19500.

In C. elegans, gap junctions couple cells of the somatic gonad with the germline to support germ cell proliferation and gametogenesis. We previously characterized a strong loss-of-function mutation (T239I) affecting the second extracellular loop (EL2) of the somatic INX-8 hemichannel subunit. These mutant hemichannels form non-functional gap junctions with germline-expressed innexins. Here we describe the characterization of mutations that restore germ cell proliferation in the T239I EL2 mutant background. We recovered seven intragenic mutations located in diverse domains of INX-8 but not the EL domains. These second-site mutations compensate for the original channel defect to varying degrees, from nearly complete wild-type rescue, to partial rescue of germline proliferation. One suppressor mutation (E350K) supports the innexin cryo-EM structural model that the channel pore opening is surrounded by a cytoplasmic dome. Two suppressor mutations (S9L and I36N) may form leaky hemichannels that support germline proliferation but cause the demise of somatic sheath cells. Phenotypic analyses of three other suppressors reveal an equivalency in the rescue of germline proliferation and comparable delays in gametogenesis but a graded rescue of fertility. These latter mutations may be useful to probe interactions with the biochemical pathways that produce the molecules transiting through soma-germline gap junctions.

This work examines drought and wet events based on Standardized Precipitation-Evapotranspiration Index (SPEI) over Kenya from 1981 to 2016. Spatiotemporal analysis of dry and wet events is conducted for 3 and 12-month SPEI. The drought incidences were observed during the period 1984, 1987, 2000, 2006, 2009, 2015, and 2016 for SPEI-3 whilst the SPEI-12 demonstrated the manifestation of drought during the year 2000 and 2006. SPEI clearly shows that the wettest period, 1997 and 1998 that coincide with the El Nino event in both time steps. SPEI -3 shows a reduction in moderate drought events while severe and extreme cases were on increase towards the end of the twentieth century. Conversely, SPEI-12 depicts an overall increase in severe drought occurrence over the study location with observed intensity of -1.54 and cumulative frequency of 64 months during the study period. The trend of wet events is upwards in the western and central highlands while the rest of the regions show increase in dry events during the study period. Moreover, moderate dry/wet events predominate whilst extreme events occur least frequent across all grid cells. It is apparent that the study area experiences mild extreme dry events in both categories although moderately severe dry events dominate most parts of the study area. High intensity and frequency of drought is noted in SPEI-3 while least occurrences of extreme events are recorded in SPEI-12. Although drought event prevails across the study area, there is evidence of extreme flood conditions over the recent decades. These findings form a good basis for next step of research that will look at projection of droughts over the study area based on regional climate models.

The population’s mobility in urban areas is a necessary variable in the modeling of risk scenarios caused by atmospheric contamination. The inclusion of this concept makes static models more dynamic while considering people within a city to be an entity with complex mobility processes. We propose a conceptual and methodological tool to make the representation of the social, economic and territorial components, as well as the patterns in the population´s mobility to delimitate risk areas for human health by exposure of contaminants. In the volatile organic compounds (VOC), benzene, ethylbenzene, toluene and xylene (BTEX) are amongst the most dominant substances in fugitive vapor emissions in gas stations (GS). In urban areas, the exposure to BTEX by residential proximity and proximity to other facilities, which cause intra-urban agglomeration, can impact and affect human health. This model seeks to facilitate the focalization, identification and prioritization of risk areas by BTEX environmental contamination. This article goes beyond de conceptual framework. It suggests methodological and instrumental aspects to be applied in other cities. The government agencies must consider these results when establishing rules, permissions and procedures to reduce environmental pollution for managing the risk in a complex urban environment.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into missing data problem formulation, assumption specification, and method development. In this paper, we highlight the gap particularly for observational data from healthcare facilities. We address this gap by identifying ten fundamental missingness scenarios arising during data measurement, recording, and pre-processing in observational health data, influenced by physicians, patients, healthcare facilities, and data scientists. We analyze the effect of scenarios on estimand formulation, missing data identification, estimation, and sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis (complete-case analysis, Missforest imputation, and inverse probability weighting estimation) for two estimands (variable mean estimation and classification accuracy). We advocate for our analysis approach as a reference for the analysis of observational health data. Furthermore, we posit that the proposed analysis framework is applicable to other medical domains, including medical wearable data analysis.

This paper presents an efficient topology optimization approach considering fail-safe requirements in the framework of the solid isotropic material with penalization method. Existing methods either are computationally extremely expensive or do not consider fail-safe requirements in the optimization. The current approach not only obtains the fail-safe optimization structure, but also significantly improves the computational efficiency of fail-safe topology optimization. In this method, the worst-case compliance is considered to minimize as the optimization objective, using the Kreisselmeier–Steinhauser function to approximate the non-differentiable max-operator. The sensitivity is derived by the adjoint method, and a general fail-safe optimality criteria method is developed to update design variables. The damage scenarios greatly reduces in the process of failsafe strategy optimization by employing the density-based filter method. Finally, the effectiveness and computational efficiency of this method for fail-safe optimization is verified by several numerical examples.

In the last two centuries, land use change (LUC) has been the most important direct changes driver for terrestrial ecosystems. To contrast the consequent ecosystems degradation, forward-looking spatial policies and target landscape and land-use planning processes, promoting a sustainable land use change, are needed. The present paper proposes a framework of action including different landscape planning and ecological approaches: from the spatial modelling to recognize the LUC and build different scenarios, to the ecosystem services (ESs) assessment to evaluate the possible environmental impacts. Three different scenarios were built: Trend, No-Tillage and Energy crops. The Sediment Delivery Ratio and Carbon Storage and Sequestration ESs were assessed and compared for each scenario. The aim of the paper is to support decision-makers and local communities into the landscape planning process. Results show that a regional development in line with past trend could lead to further land degradation. Instead, the two scenarios proposed in compliance with EU policies, could bring benefits only if related to moderate LUCs and respecting the naturally grass-vegetated land. From the local to global scale, a guided and shared LUC management allows implementing sustainable development, basing on a deep knowledge of physical-environmental but also social and economic issues.

In this review, we explore a broad-based view of technologies for supporting human activities on the Moon. Primarily, we assess the state of life support systems technology beginning with physicochemical processes, waste processing, bioregenerative methods, food production systems and the robotics and advanced biological technologies that support the latter. We observe that the Moon possesses in-situ resources but that these resources are of limited value in CELSS – indeed, CELSS technology is most mature in recycling water and oxygen, the two resources that are abundant on the Moon. This places a premium on developing CELSS that recycles other elements that are rarified on the Moon including C and N in particular but also other elements such as P, S and K which might be challenging to extract from local resources. Although we focus on closed loop ecological life support systems, we also consider related technologies that involve the application of biological organisms to bioregenerative medical technologies and bioregenerative approaches to industrial activity on the Moon as potential future developments.

Background: The practices of various health-care professionals have been improvised to accommodate the on-going covid-19 pandemic situation. Different guidelines have been set in place to ease the process of re-opening of non-elective healthcare services like out-patient physiotherapy clinics. Although the measures taken should be guided by evidence based information, major consensus amongst practicing therapists needs to guide the India physiotherapy clinics. Objective: To identify and present the opinions of different physiotherapists about the various strategies for re-opening the out-patient physiotherapy clinics. Methods: An online cross-sectional survey was conducted. Over 169 participants were selected to participate in the survey according to the pre-decided inclusion and exclusion criteria. The data was collected and saved via google forms. Result and conclusion: A majority of respondents had a consensus over different strategies for re-opening the physiotherapy OPDs. These were regarding different measures to be adapted including modifications in the clinic infrastructure and the practice pattern. This would help in smoothly re-instating the physiotherapy services post the covid-19 lockdown.

Opening gated-communities (GCs) has been widely discussed for urban inclusion and revitalization. With the policies of opening GCs being promoted in China, quantitative and comprehensive evaluation of the potential benefits is heavily needed. Taking Shanghai as an example, this study quantifies and analyzes the accessibility benefits and risks of opening GCs with factors including GC types, opening levels, travel modes, and travel destinations considered. We found that (1) opening GCs can bring 50m+ accessibility gains to 17% and 52% of the residents in Moderately Opening (MO) and Completely Opening (CO) scenarios, respectively. (2) Cycling benefits more than walking in all cases and scenarios. (3) For different GCs, conventional GCs have fewer benefits in MO but more in CO than the newly-established one. For different facilities, trips to bus stations demonstrate the largest accessibility gains. (4) The accessibility benefit of a residential building is highly determined by its closeness to the gates and relative location in the block. (5) Only 1% and 5-7% of external trips may penetrate the opened communities in MO and CO scenarios, respectively, which are far less than both expectation and the benefits. These findings precipitate at least two policy implications in China.

Ambrosia trifida is a kind of malignant invasive plant, which has very high reproductive and en-vironmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants, destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of Ambrosia trifida with climate change before implementing scientific control measures. Based on 106 Ambrosia trifida distribution data and 14 environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained by using the MaxEnt model optimized by Kuenm package, the potential suit-able areas of Ambrosia trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050,2070) were predicted. The changes of Ambrosia trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of Ambrosia trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of Ambrosia trifida.

We analyze the Covid-19 mortality scenario in India and compare it with those in other large-population regions such as Asia-excluding-China, Africa, European Union, South America, and USA. We compare existing fatality data and offer an interpretation for low fatality based on immunity due to endemic malaria and TB. We identify the hot climate in the past summer as a possible cause for low death count in southern-hemisphere countries without endemic malaria and TB. We also make India-specific observations for easing the lockdown and estimations for the time required to attain herd immunity. Whatever optimism we present should be viewed as a guarded optimism. There should not be room for complacency.

Sea level rise due to global warming is a continuing and, disappointingly, accelerating process, which has already affected and will further impact coastal lowlands and the social and economic activities in these areas. Delos Island, situated in the middle of the Cyclades in the Aegean Sea, was considered the most sacred of all islands in ancient Greek culture and a trading hub for the entire eastern Mediterranean. Uninhabited since the 7th century AD, and consistently the focus of research and touristic attention, the island is designated as an archaeological site and inscribed on the Unesco World Heritage List. Previous studies on the relative sea level (rsl) changes suggest a steadily rsl rise during the last 6,300 years, starting from a sea level at -4.80±0.20m in the Late Neolithic. The seafront of the ancient city of Delos is subject to the effects of the rsl rise, which have caused significant coastline retreat and exposure to the northerly winds and waves, whereas parts of the coastal lowland where the remains of the ancient city lie are inundated, forming extended wetlands. The future impacts of rsl rise on the seafront of ancient Delos are illustrated on very high-resolution digital surface models, evaluating both the flooding risk under different climatic projections, as provided by the IPCC AR6 report, and the ongoing land subsidence, as recorded by GNSS data. An rsl rise ranging from 87cm (SSP1-2.6 scenario) to 148cm (SSP5-8.5 scenario) is anticipated by 2150, requiring both resilience strategies and adaptation solutions as well as mitigation policies to cope with the effects of climate change.

The paradigm shift has revealed a need among industrial designers to have entrepreneurial capabilities. Being the most effective modality to exercise discipline today. The implementation of this approach requires reviewing the discursive scaffolding that the design has been using to set immovable high standards of complexity and quality, reducing the field of professional action and limiting the intervention alternatives. This article considers the dynamics and trends in people's lifestyles, explores possible strategies and specific content for the entrepreneurial training required by design.

Portugal, in line with the European Union, is aiming for carbon neutrality by 2050 (Net 1 Zero), which implies a transition to sustainable energy sources. Climate change is all too evident, extreme weather periods are occurring in a cyclical manner, with greater brevity to such an extent that the grid operator must deal with production scenarios where it can no longer rely on hydroelectric production given the recurring drought situation. This situation increases dependence on thermal production using natural gas and imports. This has significant economic implications. Portugal has exploited its onshore wind potential, reaching an installed capacity of 5.671 MW by 2022. However, the expansion of onshore wind energy is limited to reinforcing existing infrastructure. To overcome these challenges, it is necessary to expand the exploitation of offshore wind potential that is already underway. This article proposes the location of offshore wind production platforms along the Portuguese coast. This allows for an analysis of offshore production and its optimization according to the minimum cost per MWh in the face of extreme scenarios, i.e., in periods of extreme drought where hydroelectric production capacity is practically non-existent. The model is fed by using market price indications and the amount of energy needed for the following day. Using forecast data, the model adapts offshore wind production for the following day according to the minimization of the average market price. The results of the simulations allow us to conclude that despite the high cost of offshore technology (in deep waters), in extreme climate scenarios, it enables cost reduction and a clear decrease in imports.

In this paper we investigate how the Reionization process is affected by early galaxy formation in different cosmological scenarios. We use a semi-analytic model with suppressed initial power spectra to obtain the UV Luminosity Function in thermal Warm Dark Matter and sterile neutrino cosmologies. We retrace the ionization history of intergalactic medium with hot stellar emission only, exploiting fixed and variable photons escape fraction models ( fesc). For each cosmology, we find an upper limit to fixed fesc, which guarantees the completion of the process at z <6.7. The analysis is tested with two limit hypothesis on high-z ionized hydrogen volume fraction, comparing our predictions with observational results.

Coastal zones experience increasing climatic and human pressures, which lead to growing risks and tipping points (TPs) under future scenarios and natural resource scarcity. To avoid crossing TPs with irreversible coastal losses, this paper proposes the development of adaptation pathways based on advanced coastal oceanography and engineering knowledge that enables an objective assessment of evolving coastal risks. These pathways feature sequential interventions, combining conventional engineering works with nature-based solutions (NbS), steered by simulations and observations as a function of available coastal resources (space and sediment) and risks for infrastructure and socioeconomic assets. Such an adaptation has been developed for urban and peri-urban Mediterranean beaches, considering conventional coastal engineering together with NbS. Both types of interventions are assessed in terms of key physical variables, which serve to evaluate performance and estimate TPs. This analysis supports the new coastal protection and management plan promoted by the local government and the coastal adaptation plan of the central government. The approach and results illustrate the potential of adaptation pathways for beach sustainability, enhancing the compatibility between short/long-term coastal protection objectives under present/future climate and management scenarios. The adaptation pathways development underpins an increasing stakeholder cooperation to achieve shared decisions for coastal sustainability.

Factory safety inspections are crucial for ensuring safe production. However, manual inspections present issues such as low efficiency and high workload. Inspection robots provide an efficient and reliable solution for completing patrol tasks. The development of robot localization and path planning technologies provides guarantees for factory inspection robots to autonomously complete inspection tasks in complex environments. This paper studies mapping and localization, as well as path planning methods for robots in order to meet the application requirements of factory inspections. Two SLAM application systems based on multiple-line laser radar and vision are designed for different scenarios in consideration of the limitations of cameras and laser sensors in terms of their own characteristics and applicability in different environments. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that integrates the A-star algorithm and time elasticity band algorithm is proposed. This algorithm effectively solves the problem of path planning in complex environments that is prone to falling into local optimal solutions, thereby improving the inspection efficiency of robots. Experimental tests show that the designed SLAM and path planning methods can meet the needs of robot inspection in complex scenes and have good reliability and stability. The code used in this article is open source and can be accessed at https://github.com/Mxiii99/RSPP_CS.git.

In response to the problem of poor detection ability of object detection models for small-scale targets in intelligent transportation scenarios, a fusion method is proposed to enhance the features of small-scale targets, starting from feature utilization and fusion methods. The algorithm is based on the YOLOv4 tiny framework and enhances the utilization of shallow and mid level features on the basis of FPN, improving the detection accuracy of small and medium-sized targets; In view of the problem that the background of the intelligent traffic scene image is cluttered and there is more redundant information, the CBAM attention module is used to improve the attention of the model to the traffic target; To address the problem of data imbalance and prior bounding box adaptation in custom traffic datasets that expand traffic images in COCO and VOC, we propose a Copy Paste method with improved generation method and a K-means algorithm with improved distance measurement to enhance the model's detection ability for corresponding categories. Comparative experiments were conducted on a customized 260 thousand traffic dataset containing public traffic images, and the results showed that compared to YOLOv4 tiny, the proposed algorithm improved mAP by 4.9% while still ensuring the real-time performance of the model.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

This paper presents a scenario evolution model for maritime accidents using Bayesian networks (BN) to predict the most likely causes of specific types of maritime incidents. The BN nodes encompass accident type, life loss contingency, accident severity, the quarter and time period of the accident, and the type and gross tonnage of the ships involved. We analyzed 5,660 global maritime accidents from 2005 to 2020. Using Netica software, we constructed a Tree Augmented Network (TAN) model, accounting for interdependencies among risk influencing factors. We validated the results through sensitivity analysis and historical accident records. Forward causal inference and reverse diagnostic inference were then performed on each node variable to investigate the accident development trend and evolution process under predetermined conditions. The findings indicate that the model can effectively predict the likelihood of various accident scenarios under specific conditions, as well as the extrapolation of accident consequences. Forward causal reasoning reveals that general cargo ships with a gross tonnage of 1-18,500 t are most likely to experience collision, grounding, and stranding accidents in the first quarter. Reverse diagnostic reasoning indicates that during early morning hours, container ships, general cargo ships, and chemical ships with a tonnage of 1-18,500 t are less likely to involve life loss in the event of collision accidents.

Nowadays, hydropower is the principal renewable; however, climate change increases extreme events such as floods, droughts, erosion, and sedimentation of rivers that produce uncertainty in hydroelectric generation. Thus, this document aims to analyze the climate change projections in the hydropower systems of Ecuador based on data from 14 projects studying the scenarios ac-cording to the Shared Socioeconomic Pathways from the Intergovernmental Panel on Climate Change. The study period starts from 2010 to 2020 with historical data, collects the tendency, defines a database year, and then projects the scenarios to 2050. The quantitative methodology uses a statistic on Ecuador's hydropower obtained inflow time series to calculate the deviation over the last years and develop a model to simulate future generation. The results show that the hydropower in Ecuador is expected to decrease considerably through 2050 due to meteorological changes. In this calculation of the Shared Socioeconomic Pathways, the selected scenarios show a reduction in SSP5 of 11.5%, SP2 of 16.2%, and SSP4 of 18.2% to 2050, concluding that the oppor-tunities for hydroelectric production facing climate change are variable, but the challenges are broad. In Ecuador, the projections of hydropower plants represent a sensitive issue of their re-ductions, especially knowing that the country had an energy grid in 2020 that depended on 87% of hydroelectric production.

The RMA11 water quality model, which has been previously calibrated and validated, was coupled with the RMA10 3D hydrodynamic model to evaluate sanitation scenarios in Buenaventura Bay. The bay receives direct discharge of untreated wastewater from 500,000 people through 695 outlets along the coast. Five different effluent reduction scenarios were proposed and compared based on fecal coliform concentration as an indicator. The areas where values exceed the standard for primary contact were also used to evaluate the scenarios. The model results indicate poor water quality in the bay and how that the situation will become even more severe if immediate action is not taken. The proposed reduction in discharges in stages will have temporarily more severe effects than the current situation, but these will end when the treatment plant becomes operational. However, even with the plant in operation, complete sanitation of the bay cannot be achieved, and further measures must be considered.

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems, we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we look at quantum speedup ratios of HHL Algorithm for different scenarios of linear programming. In a first scenario we look quantum speedup ratio (S(N)) as a function of phase transition and the ratio (κ) between the greatest and smallest eigenvalues of the matrix in linear equation system. As a second scenario, we investigate the changes in S(N) as a function of κ and s, which is the coefficient for defining the matrix as s-sparse.

The objective was to estimate the monetary loss of potato producers up to the year 2100 as a result of temperature and precipitation impacts, taking into account the A2 and B2 scenarios of the IPCC (Intergovernmental Panel on Climate Change). The Pooled Production Panel Model was used, whose database was prepared taking into account climatic variables (temperature and precipitation) and agricultural variables (production, harvested area, farm-gate price) for the period 1996 - 2020, which form the independent variables of the study. The estimations used 60 observations and a total of 38 estimations were run in the econometric software EViews8, of which Equation 05 of the Production Pooled Panel Model was chosen as the best. The model obtained used temperature and precipitation forecasts from Brazil's INPE (National Institute for Space Research), validated for the study area. The results indicate a concave function between potato production (t/ha), temperature and precipitation. Finally, based on the A2 climate scenario, which is the most pessimistic and using the period 2021 - 2100, a loss for potato producers of approximately 8'927,521.48 million soles was estimated.

Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots, trolls, among others. In this scenario, the potential of bots to facilitate the spread of disinformation is recognised, however, the analysis of how they do this is still in its initial stages. Taking into consideration what was previously stated, this paper aimed to model and simulate scenarios of disinformation propagation in social networks caused by bots based on the dynamics of this mechanism documented in the literature. For achieving the purpose, System dynamics was used as the main modelling technique. The results present a mathematical model, as far as disinformation by this mechanism is concerned, and the simulations carried out against the increase in the rate of activation and deactivation of bots. Thus, the preponderant role of social networks in controlling disinformation through this mechanism, and the potential of bots to affect citizens, is recognised.

Followed by the aspect of respect for rights 20, the social and environmental issue has a score of 19.9, the dialogue issue is 19. In short, the proximity plan indicates that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín environmental dialogue table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are closer together today. The actor from the Archbishopric of Huancayo referred to communication, is within the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are currently closer together. The actor of the Archbishopric of Huancayo referred to communication, is within the driving variables of power.

We report an unusual transformation where a transient formation of a nitrene moiety initiates a sequence of steps where leading to a remote oxidative C-H functionalization (R-CH3 to R-CH2OC(O)R’) and concomitant reduction of the nitrene into an amino group. No external oxi-dants or reductants are needed for this formal molecular com-proportionation. Detected and iso-lated intermediates and computational analysis suggest that the process occurs with pyrazole ring opening and recyclization.

Genotype imputation is widely used to enrich genetic datasets. The operation relies of panels of known reference haplotypes with typically whole-genome sequencing data. How to choose a reference panel has been widely studied and it is essential to have a panel that is well matched to the individuals who require imputation of missing genotypes. However, it is broadly accepted that such an imputation panel will have an enhanced performance with the inclusion of diversity; haplotypes from many different populations. We investigate this observation in this work by examining in fine detail exactly which reference haplotypes are contributing at different regions of the genome. This is achieved using a novel method of inserting synthetic genetic variation into the reference panel in order to track the performance of leading imputation algorithms. We show that while diversity may globally improve imputation accuracy, there can be occasions where incorrect genotypes are imputed following the inclusion of more diverse haplotypes in the reference panel. We however demonstrate a technique for retaining and benefitting from the diversity in the reference panel whilst avoiding the occasional adverse effects on imputation accuracy. What is more, our results elucidate more clearly the role of the diversity in a reference panel than has been shown in previous studies.

The issue of women’s participation in top management and boardroom positions has received increasing attention in the academic literature and the press. However, the pace of advancement for women managers and directors continues to be slow and uneven. The novel framework of this study organizes the factors at the individual, organizational and public policy level that affect both career persistence and the advancement of women in top management positions; namely, factors affecting 1) career persistence (staying at the organization) and 2) career advancement or mobility (getting promoted within the organization). In the study location, Chile, only 32 percent of women “persist”, or have a career without interruptions, mainly due to issues with work–family integration and organizational environments with opaque and challenging working conditions. Women who “advanced” in their professional careers represent 30 percent of high management positions in the public sector and 18 percent in the private sector. Only 3 percent of general managers in Chile are women. Women in Chile have limited access and are still not integrated into business power networks. Our findings will enlighten business leaders and public policy-makers interested in designing organizations that retain and promote talented women in top positions.

Driver’s distractions are more prone to occur with automated driving vehicles, which might turn into cancelling some of their advantages. Traffic reports, naturalistic observations and in-lab tests are carried out to determine the influence of driver’s distractions in car crashes and to develop technology to detect and prevent them from happening. One of the best strategies to cope with this problem is education. In this work we propose a simulation environment to design laboratory experiments to study driver’s distractions as well as simple games to train drivers to take over the control of the vehicle when required. The result framework enables drivers to experience driving highly automated cars in their neighborhoods or in places of their choice with different levels of events to check their ability in taking over the control when required.

We have modelled the energy consumption of prototype and real buildings under present and future climatic conditions with the EnergyPlus model to develop a better understanding of the relationship between changing climate conditions and energy demand. We have produced detailed meteorological information with 50 meters of spatial resolution through dynamical downscaling process combining regional, urban and computational fluid dynamics models which include the effects of the buildings on urban wind patterns. The city of Madrid has been chosen for our experiment. The impact on energy demand and their respective economic cost are calculated for year 2100 versus 2011 based on two IPCC climate scenarios, RCP 4.5 (stabilization of emissions) and RCP 8.5 (not reduction of emissions). Findings show that climate change will have a significant impact on the energy demand for buildings. Space heating demand will be increased by the RCP 4.5 and cooling demand will be increased for the RCP 8.5 in the analysed buildings.

A numerical approach for generating a limited number of water demand scenarios and estimating their occurrence probabilities in a Water Distribution Network (WDN) is proposed. This approach makes use of the demand scaling laws in order to consider the natural variability and spatial correlation of nodal consumptions. The scaling laws are employed to determine the statistics of nodal consumption as a function of the number of users and the main statistical features of the unitary user's demand. Besides, consumption at each node is considered to follow a Gamma probability distribution. A high number of groups of cross-correlated demands, i.e., scenarios, for the entire network were generated using Latin Hypercube Sampling (LHS) and the numerical procedure proposed by Iman and Conover. The Kantorovich distance is used to reduce the number of scenarios and estimate their corresponding probabilities, while keeping the statistical information on nodal consumptions. By hydraulic simulation, the whole number of generated demand scenarios was used to obtain a corresponding number of pressure scenarios on which the same reduction procedure was applied. The probabilities of the reduced scenarios of pressure were compared with the corresponding probabilities of demand.

In recent years, Synthetic Aperture Radar (SAR) has received attention in the Automatic Target Recognition (ATR) field due to its ability to acquire images both day and night, combined with the superior resolution offered by the latest sensors. However, despite advances, there remains a deficiency in dataset quality and quantity. This has necessitated the use of simulated data to overcome challenges associated with obtaining large quantities of high-quality images of targets and their subsequent labeling. In this study, we trained a Convolutional Neural Network (CNN) using a simulated dataset, subsequently employing it to classify real images from the MSTAR dataset. Traditional training methodologies often struggle with generalization when confronted with domain disparities between simulated and real data. To mitigate this, we exploited Margin Disparity Discrepancy (MDD), a domain adaptation technique not previously explored in SAR context. Findings from this study showcased a 13% increase in classification accuracy and improved generalization using MDD. Through Explainable AI (XAI) techniques such as t-SNE and other metrics like Kullback Leibler Divergence (KLD), we revealed a more distinct alignment of feature spaces between the two domains after domain adaptation, emphasizing MDD’s role in enhancing model accuracy and generalization.

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the Multiple Criteria Decision Making (MCDM) method TOPSIS-2N to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and / or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with a MCDM method to evaluate companies to be incubated does not exist in the literature.

This article aims to serve as a guide for the construction of supply chain simulation models designed with a lean approach, using Promodel software. To achieve this, a supply chain was designed for a fictitious company located in the City of Celaya, Guanajuato and a set of suppliers located in different cities within the same State. It was used as a google tool to define the distances between each of the companies. As a final result, a representative model of a supply chain was obtained, as well as a methodology that allows the construction of lean supply chains regardless of the number of companies that comprise it. The effect of the variability in the delivery times between suppliers was incorporated into the simulation model, as well as an equation that calculates the pollution emissions of the vehicles that integrate the network that moves the products between the companies. With this work it is possible to represent networks of supply chains of real world companies, where the variability and contamination factor is included, to facilitate the decision making regarding the number of vehicles, inventory levels, quantities to be shipped, frequency in the shipments, etc. with the purpose of contaminating as little as possible and at the same time preventing interruptions in the supply chain using the least amount of resources possible.

Beginning with navigation system design, this paper presents a comprehensive strategy for enhancing the search and rescue capabilities of agile mobile robots. Towards this, the autonomous ground vehicle (AGV) utilizes surface classification to determine and prioritize the terrain it is traversing. Our developed system design incorporates real-time terrain data with task objectives at a high level, ensuring that the robot can effectively navigate complex and ever-changing environments. This design, in conjunction with the introduction of a novel lightweight surface classification model, forms the basis of our adaptive terrain perception and decision-making systems, enabling robots such as Jackal to adapt rapidly and make the decisions necessary to complete the task. Subsequently, we exhaustively validated these systems through a series of extensive experiments in a variety of terrains, including normal and mixed terrains, demonstrating their robustness and efficacy in real-life situations.

Therapeutic antibodies targeting immune checkpoints have shown limited efficacy in clinical trials in glioblastoma (GBM) patients. Ultrasound-mediated blood-brain barrier opening (UMBO) using low-intensity pulsed ultrasound improved drug delivery to the brain. We explored the safety and the efficacy of UMBO plus immune checkpoint inhibitors in preclinical models of GBM. A Blood-brain barrier (BBB) opening was performed using a 1 MHz preclinical ultrasound system in combination with 10µl/g microbubbles. Brain penetration of immune checkpoint inhibitors was determined, and immune cell populations were evaluated using flow cytometry. The impact of repeated treatments on survival was determined. In syngeneic GL261-bearing immunocompetent mice, we showed that UMBO safely and repeatedly open the BBB. BBB opening was confirmed visually and microscopically using Evans’s blue dye and magnetic resonance imaging. UMBO plus anti-PDL-1 was associated with a significant improvement of the overall survival compared to anti-PD-L1 alone. Using mass spectroscopy, we showed that the penetration of therapeutic antibodies can be increased when delivered intravenously compared to non-sonicated brains. Furthermore, we observed an enhancement of activated microglia percentage when combined with anti-PD-L1. Here, we report that the combination of UMBO and anti-PD-L1 dramatically increases GL261-bearing mice's survival compared to their counterparts treated with anti-PD-L1 alone. Our study highlights the BBB as a limitation to overcome to increase the efficacy of anti-PD-L1 in GBM and supports clinical trials combining UMBO and in GBM patients.

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making the emitter identification in remote sensing practices more essential. Effective recognition of Radio Frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to the diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a Progressive Maximum Similarity-based Unsupervised Domain Adaptation (PMS-UDA) method for RF signal attribute recognition. Initially, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenario. Experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as cross both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and GPT modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, mitigating interference from unnecessary variations. We introduce the REB-KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts, and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model's effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.

Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on the of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite's reproductive cycle inside the mosquito. The intensity and frequency of the rainfall modulate the development of the mosquito population. In this study, the Liverpool Malaria Model (LMM) is used to simulate the spatio-temporal variation of the malaria incidence in Senegal. The simulations are based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and biased-corrected CMIP6 models, separating historical and projections for 3 Shared Socio-economic Pathways scenarios (SSP126, SSP245 and SSP585). Our results highlight a strong increase in temperatures, especially towards eastern Senegal under the SSP245 but mainly the SSP585 scenarios. The ability of the LMM model to simulate the seasonality of malaria incidence is assessed. The model reveals a period of high malaria transmission between September and November with a maximum reached in October. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and for the extreme scenario. This study is importance for the planning, prioritization, and implementation of control activities in Senegal.

Accessing energy in the world is crucial nowadays. Energy is an essential factor to achieve other SDGs including SDG7. However, the determination and evaluation of the relationship between different energy scenarios to achieve SDG 7 and other SDGs have not done yet. This paper seeks to fill this gap by investigating how energy seniors can contribute to achieving SDG 7 and other SDGs. Web of Science, ScienceDirect and Scopus databases were utilized for conduction a systematic review. A total finial 25 from 249 papers were filtered from 2015 to December 2020 via inclusion and exclusion criteria. This review involves six seniors of energy which primary linked to achieving the SDG 7 and other SDGs: modern energy 20% (n = 5/25), energy access 16% (n = 4/20), energy efficiency 8% (n= 2/16), renewable energy 28% (n= 7/14), energy services 8% (n= 2/7), and miscellaneous energy 20% (n= 5/5). This systematic review explores the opportunities, constraints and limitations, recommendations, and new directions. The results show that different energy scenarios contribute to achieving mainly (SDG7) and other SDGs. The outcomes from this systematic review provide a sense of direction for future researchers for future studies in this domain.

The Philippines is both a biodiversity hotspot and a megadiverse country. The country also has experienced one of the highest rates of deforestation in Southeast Asia and is among the first countries to introduce a massive reforestation program to address the country’s rapid biodiversity and forest loss. Drawing upon an empirical study from the Leyte island and other relevant case studies from the Philippines, in this chapter, we demonstrate that recovering secondary forests following shifting cultivation, locally known as kaingin have the high potentials for biodiversity and carbon co-benefits. Based on our empirical study, we also found that secondary forest regrowing after kaingin use can potentially be used as a cost-effective reforestation measure with multiple benefits to people and the environment in upland areas of the Philippines. We also discuss measures that are essential for such programs to be successful.

The breeding of stress-tolerant cultivated plants that would allow for a reduction in harvest losses and undesirable decrease in quality attributes requires a new quality of knowledge on molecular markers associated with relevant agronomic traits, on quantitative metabolic responses of plants on stress challenges, and on the mechanisms controlling the biosynthesis of these molecules. By combining metabolomics with genomics, transcriptomics and proteomics datasets a more comprehensive knowledge of the composition of crop plants used for food or animal feed is possible. In order to optimize crop trait developments, to enhance crop yields and quality, as well as to guarantee nutritional and health factors, that provides the possibility to create functional food or feedstuffs, the knowledge about the plants’ metabolome is crucial. Next to classical metabolomics studies, this review focusses on several metabolomics based working techniques, such as sensomics, lipidomics, hormonomics and phytometabolomics, which were used to characterize metabolome alterations during abiotic and biotic stress, to find resistant food crops with a preferred quality or at least to produce functional food crops are highlighted.

Switzerland, a wealthy country, has a cutting-edge healthcare system, yet per capita, it emits over one ton of CO2, ranking among the world's most polluting healthcare systems. To estimate the carbon footprint of the healthcare system of Geneva’s canton, we collected raw data on the activities of its stakeholders. Our analysis shows that when excluding medicines and medical devices hospitals are the main greenhouse gas emitter by far, accounting for 48% of the healthcare system’s emission, followed by nursing homes (20%), private practice (18%), analysis laboratories (7%), dispensing pharmacies (4%), the homecare institution (3%) and the ambulance services (

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-adverse situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances the network’s lifetime. Many hybrid combination algorithms are proposed for clustering and the routing protocol to improve the network lifetime in disaster scenarios. However, the performance of these protocols is widely varied based on the underlying network configuration and the optimisation parameters considered. In this research, we have used the clustering parameters that are most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. Then, we proposed a bio-inspired hybrid (BAO-PSO) algorithm where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm is compared with the benchmark protocols LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime are considered performance matrices. The simulation result showed that the proposed algorithm increases network lifetime by 10%-20% by comparison with the benchmark algorithm.

This study is a two-phase hybrid study on the behavioral activities of undergraduate students during the COVID-19 pandemic lockdown. A questionnaire survey of students during the whole school period and a semistructured interviews with 13 undergraduates of different majors were conducted. First, we found that compared with primary and secondary school students, undergraduates showed many problems with self-time management. Second, the behavior of undergraduates at home may result from multiple factors, such as past experience, professional identity and behavioral habits. When secondary school provided a relatively relaxed learning environment or secondary school teachers placed more emphasis on continuous effort, and when students had a strong sense of the social value of their undergraduate major, students worked harder and achieved more during the pandemic. Otherwise, it was easier for students to slack off and be passive during the pandemic. Altogether, this study deepens our understanding of undergraduates' self-management of free time, and the differences in the values underlying different behaviors and the formation process of various behaviors.

To validate the accuracy and reliability of onboard sensors for object detection and localization in driver assistance, as well as autonomous driving applications under realistic conditions (indoors and outdoors), a novel tracking system is presented. This tracking system is developed to determine the position and orientation of a slow-moving vehicle (e.g. car during parking maneuvers), independent of the onboard sensors, during test maneuvers within a reference environment. One requirement is a 6 degree of freedom (DoF) pose with a position uncertainty below 5 mm (3σ), an orientation uncertainty below 0.3° (3σ) at a frequency higher than 20 Hz, and a latency smaller than 500 ms. To compare the results from the reference system with the vehicle’s onboard system, a synchronization via Precision Time Protocol (PTP) and a system interoperability to Robot Operating System (ROS) is implemented. The developed system combines motion capture cameras mounted in a 360° panorama view set-up on the vehicle with robotic total stations. A point cloud of the test site serves as a digital twin of the environment, in which the movement of the vehicle is simulated. Results have shown that the fused measurements of these sensors complement each other, so that the accuracy requirements for the 6 DoF pose can be met, while allowing a flexible installation in different environments.

In this article we present a deployment of 11 nodes using the three different SUN (Smart Utility Network) modulation schemes, as defined in the IEEE 802.15.4-2015 standard. The nodes were deployed in a 110.044 m2 warehouse for 99 days, and the resulting dataset contains a total of 10.710.868 measurements with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment) and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in average RSSI (i.e., between -82.1 dBm and -101.7 dBm) and CCA (i.e., between -111.2 dBm and -119.9 dBm) values, which are caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, this values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are required for low-power wireless communications to meet the dependability requirements of industrial applications. For that purpose, we also introduce the concept of modulation diversity, which can be combined with packet repetition to meet such requirements (i.e., PDR>99%) while minimizing the energy expenditure of nodes and meeting regulatory constraints.

This study builds on our previous Systematic Literature Review (SLR) that assessed the applications and performance of zk-SNARK, zk-STARK, and Bulletproof Non-Interactive Zero-Knowledge Proof (NIZKP) protocols. To address identified research gaps, we designed and implemented a benchmark comparing these three protocols using a dynamic MiMC hash application. We evaluated performance across four general-purpose programming libraries and two programming languages. Our results show that zk-SNARK produced the smallest proofs, while zk-STARK generated the largest. In terms of proof generation and verification times, zk-STARK was the fastest, and Bulletproof the slowest. Interestingly, zk-SNARK proofs verified marginally faster than zk-STARK, contrary to other findings. These insights enhance our understanding of the functionality, security, and performance of NIZKP protocols, providing valuable guidance for selecting the most suitable protocol for specific applications.

This study reports the likely real-world effects of fleet replacement with electric vehicles (EVs) and higher efficiency EURO6 vehicles on the exhaust emissions of NOx, PM, and CO2 in the seven boroughs of the West Midlands (WM) region, UK. National fleet composition data, local EURO distributions and traffic compositions were used to project vehicle fleet compositions for different roads in each borough. A large dataset of real-world emission factors including over 90,000 remote-sensing measurements, obtained from remote sensing campaigns in five UK cities, was used to parameterise the emission profiles of the studied scenarios. Results show that adoption of the fleet electrification approach would have the highest emission reduction potential on urban roads in WM boroughs. It would result in maximum reductions ranging from 35.0-37.9%, 44.3-48.3%, 46.9-50.3% for NOx, PM, and CO2, respectively. In comparison, the EURO6 replacement fleet scenario would lead to reductions ranging from 10.0-10.4%, 4.0-4.2%, and 6.0-6.4% for NOx, PM, and CO2, respectively. The studied mitigation scenarios have higher efficacies on motorways than on rural and urban roads because of the differences in traffic fleet composition. The findings presented will help policymakers choose climate and air quality mitigation strategies.

We present a novel method for online measurement of multi-point opening distances of midpalatal suture during rapid palatal expansion (RPE) using fiber optic Fabry-Perot (F-P) sensors. The sensor consists of an optical fiber with a cut flat end face and an optical reflector, which are implanted into the palatal base structure of an expander and is capable of measuring the precise distance between two optical reflective surfaces. As a demonstration, a 3D printed skull model containing the maxilla and zygomaticomaxillary complex (ZMC) was produced and a miniscrew-assisted rapid palatal expander (MARPE) with two guide rods was used to generate the midpalatal suture expansion. The reflected spectrums of the sensors were used to dynamically extract cavity length information for full process monitoring of expansion. The dynamic opening of the midpalatal suture during the gradual activation of the expander was measured, and a displacement resolution of 2.5 μm was demonstrated. The angle of expansion was derived and the results suggested that the midpalatal suture was opened with a slightly V type expansion of 0.03 rad at the first loading and subsequently expanded in parallel. This finding might be useful for understanding the mechanical mechanisms that lead to different types of expansion. The use of a fiber-optic sensor for mounting the rapid palatal expander facilitates biomechanical studies and experimental and clinical evaluation of the effects of RPE.

Dirofilaria immitis infection is one of the most severe parasitic diseases of dogs. Prevention is achieved by the administration of drugs containing macrocyclic lactones (MLs). These products are very safe and highly effective, targeting the third and fourth larval stages (L3, L4) of the parasite. Until 2011, claims of ineffectiveness of MLs, reported as “Lack of Efficacy” (LOE), were generally attributed to owners’ non-compliance, or other reason for inadequate preventative coverage. There was solid argumentation that a resistance problem is not likely to occur because of i) the great extent of refugia, ii) the complexity of resistance development to MLs, and iii) the possible big number of genes involved in resistance selection. Nevertheless, today it is unequivocally proven that ML resistant D. immitis strains exist, at least in the Lower Mississippi region, USA. Accordingly, tools have been developed, to evaluate and confirm the susceptibility status of D. immitis strains. A simple, in-clinic, microfilariae suppression test, 14-28 days after ML administration, and a “decision tree” (algorithm), including compliance and preventatives’ purchase history, and testing gaps, may be applied for assessing any resistant nature of the parasite. On the molecular level, specific SNPs may be used as markers of ML resistance, offering a basis for validation of clinically suspected resistant strains. In Europe, no LOE/resistance claims have been reported so far, and the existing conditions (stray dogs, rich wildlife, majority of owned dogs not on preventive MLs treatment) do not favor selection pressure on the parasites. Considering the genetic basis of resistance and the epizootiological characteristics of D. immitis, ML resistance neither establishes easily nor spreads quickly, a fact confirmed by the current known dispersion of the problem, which is limited. Nevertheless, ML resistance may propagate from an initial geographical point, via animal and vector mobility, to other regions, while it can also emerge as an independent evolutionary process in a new area. For these reasons and considering the current chemoprophylaxis recommendations and increasing use of ML endectoparasiticides as a potential selection pressure, it is important to remain vigilant for timely detection of any ML LOE/resistance, in all continents where D. immitis is enzootic.

This research introduces an Intelligent Robotic Arm with Adaptive Collision Avoidance, employing real-time current fluctuation analysis for human-proximity detection. Precision and versatility in motors and actuators are given top priority in the robotic arm setup. Motor power lines are continually monitored by integrated high-precision current sensors, and a real-time data processing system separates natural fluctuations from those caused by outside influences. When a human comes into contact with the surface of the robotic arm, the system excels at detecting their closeness by identifying unique patternsin the variations of current. This sets off an adaptive collision avoidance system, which quickly flips the main power supply’s kill switch to bring the vehicle to an emergency stop. A crucial component is adaptability, as the system dynamically modifies sensitivity in response to environmental condi- tions. Comparative studies show that this approach performs better than conventional static sensing techniques. Experiments under control confirm the great precision, accuracy, and low false positive rate. The use of a failsafe technique improves system integrity by preventing false alarms and enabling recovery after an emergency

To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method is proposed. First, a scenario generation scheme considering waterlogged faults of power facilities is constructed based on the storm water management model (SWMM) and the extreme learning machine method. These scenarios will be merged to several typical scenario sets for further processing. The outage of power facilities will induce power flow transfer which may consequently lead to transmission lines’ thermal limit violation. Semi-invariant and Gram-Charlier level expansion methods are adopted to analytically depict the probability density function and cumulative probability function of each line’s power flow. The optimal solution is performed by a particle swarm algorithm to obtain proper load curtailment at each bus, and consequently the violation probability of line thermal violations can be controlled within an allowable range. The volume of load curtailment as well as their importance are considered to quantitatively access the risk of power supply security under extreme precipitation scenarios. The effectiveness of the proposed method is verified in case studies based on the IEEE 24-bus system.

As the production of battery cells experiences exponential growth and electric vehicle fleets continue to expand, an escalating number of traction batteries are nearing the conclusion of their operational life for mobility purposes, both presently and in the foreseeable future. Concurrently, the heightened interest in sustainable energy storage solutions has spurred investigations into potential second-life applications for aging traction batteries. Nonetheless, the predominant practice remains the removal of these batteries from electric vehicles, signifying the end of their life cycle, and their subsequent incorporation into recycling processes, with limited consideration for life-extending measures. This study seeks to elucidate the reasons behind the deprioritization of battery repurposing strategies. Therefore, the research team conducted two industry studies with over 20 battery experts from Europe, revealing concerns about the economic viability of repurposing batteries for stationary storage applications. A literature review of studies published since 2016 confirmed the industry's struggles to address this issue theoretically. In conclusion, a research question was formulated, and a solution approach was delineated to assess the economic prospects of aged traction batteries within the industry's landscape in the future. This solution approach encompasses pertinent market analysis, the identification of representative second-life applications, as well as the formulation of a methodology for evaluating the residual value of these batteries.

Global temperature rise due to hydrocarbon gases emission that are produced by generating the electrical power has a great attention by the researchers to reduce it till zero emission is successfully achieved. Sustainable energy source such as solar energy, wind, hydro-energy and sea wave energy are focal areas to replace the fossil fuel by clean energy. In this article, daylight is used to minimize the power consumption that required for indoor lighting using electric roller blind. Smart controller is designed to adjust the position of the roller blind stepper motor, and hence, adjust the roller blind opening, based on the preset light intensity, to achieve precise utilization of daylight inside the room. If the desired Lux is not achieved for any reason, the smart controller adjusts the LED circuit current to boost the light intensity to achieve precisely the desired Lux. Comprehensive test cases using MATLAB-Simulink is carried out to verify the performance of the proposed smart controller. Techno-economic analysis is introduced to evaluate the benefits of installing the controller. Summary and recommendation are given at the end.

This study establishes life cycle assessment model to quantitively evaluate and predict material resource consumption, fossil energy consumption and environmental emissions of plug-in hybrid electric vehicles (PHEVs) by employing the GaBi software. the envi-ronmental impact of different vehicle working conditions, power battery degradation scenarios and mileage scenarios on the operation and use stage of PHEV, BEV and HEV is distinguished. The findings indicate that under urban, highway, and aggressive driving conditions, PHEVs' life cycle material resource and fossil fuel consumption exceed that of BEVs but are less than HEVs. Battery degradation leads to increased material resource consumption, energy use, and environmental emissions for both PHEVs and BEVs, en-vironmental emission of BEVs are more sensitive than those of PHEVs to the impact of power battery degradation. Among different-mileage scenarios, PHEVs demonstrate the least sensitivity to increased mileage regarding life cycle material resource consumption, with the smallest increase. Future projections for 2025 and 2035 suggest life cycle GWP of HEV, PHEV and BEV in 2035 is 1.21×104, 1.12×104 and 1.01×104 kg CO2-eq respectively, which is decreased by 48.7%, 30.9% and 36.1% compared with those in 2025. The out-comes of this study are intended to bolster data support for the manufacturing and de-velopment of PHEV, BEV and HEV under different scenarios and offer insights into the growth and technological progression of the automotive sector.

The assessment and prediction of drought risk under future climate change and land use land cover (LULC) scenarios is critically important for drought prevention and mitigation, as it enables a clearer understanding of potential shifts in drought patterns. The primary aim of this study is to evaluate sub-seasonal and seasonal meteorological and hydrological drought hazards across the Yellow River Basin (YRB) under projected future climate conditions and LULC patterns. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent future climate for 2025-2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future LULC distributions. Meteorological and hydrological drought risks across different YRB zones are evaluated through a copula-based risk assessment approach, based on the joint probability distribution of drought duration and severity. The results indicate that sub-seasonal meteorological and hydrological droughts will likely be the primary concern moving forward. Specifically, the upper YRB (zones A, B, C) exhibits greater vulnerability to sub-seasonal meteorological drought, while the Loess Plateau (zones C, E) shows higher susceptibility to sub-seasonal hydrological drought. Moreover, zone F in the downstream region may experience increased seasonal hydrological drought risk due to projected urban expansion in the middle and lower portions of the YRB.

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To tackle this problem, this study proposes a foggy weather detection method, YOLOv5s-Fog, based on the YOLOv5s framework. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer, SwinFocus. Additionally, this research incorporates decoupled head into the model and replaces the conventional non-maximum suppression method with Soft-NMS. Experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

In this article, we present the use of the coupled land surface model and groundwater flow model SWAT-MODFLOW with the decision support tool WEAP (Water Evaluation and Planning software) to predict future surface water abstraction scenarios in a complex river basin, under conditions of climate change. The modeling framework is applied to the Dee River catchment in Wales, United Kingdom. Regarding hydrology, the coupled model improves overall water balance and low streamflow conditions, compared to a stand-alone SWAT model. The calibrated SWAT-MODFLOW is employed with high resolution climate model data from the UKCP18 project with future scenario of RCP85 from 2020 to 2040. Then, water supply results from SWAT-MODFLOW are fed into WEAP as input for the river reach in the downstream region of the river basin. This system is utilized to create various future scenarios of surface water abstraction of public water supply in the downstream region: maximum licensed withdraw, 50 % authorized abstractions, monthly time series with 1% increase of water use, and maximum water withdraw year based on historical records repeated every year with 1% increase of water use, to estimate the unmet demands and streamflow requirement. This modeling approach can be used in other river basins to manage scenarios of supply and demand.

Road freight transportation is a key function of modern societies. At the same time, road freight transportation accounts for significant emissions. To reach the UN sustainability goals, sustainable road freight transportation is key. Digitalization, including automation, digitized information, and AI provide opportunities to improve efficiency, reduce costs, and increase service levels in road freight transportation. Digitalization may also radically change the business ecosystem in the sector. In this paper, the question “How will digitalization change the road freight transportation landscape?” is addressed by developing four different future scenarios, using Sweden as a case study. For each of the four scenarios the impacts on the road freight transportation sector are investigated, and opportunities and barriers to reach a sustainable transportation system in each of the scenarios are discussed. In all scenarios an increase in vehicle kilometers travelled is predicted, and in three of the four scenarios significant increases of recycling and urban freight flows are predicted. The scenario development process highlighted how there are important uncertainties in the development of the society that will be highly important for the development of the digitized freight transportation landscape. One example is the sustainability paradigm, which was identified as a strategic uncertainty.

The megacities´ sewage creates socioeconomic dependence related to water availability in the nearby zones, especially in countries with hydric stress. The present paper studies the water balance progression of realistic scenarios from 2005 to 2050 in the Mezquital Valley, the receptor of Mexico City untreated sewage since 1886, allowing agriculture irrigation in unsustainable conditions. WEAP model calculated the water demand and supply. Validation was performed with outflows data of the Tula River and simulated three scenarios: 1st) Steady-state based on inertial growth rates, 2nd) Transient scenario concerned climate change outcomes, with minor influence in surface water and hydric stress in 2050; 3rd) Transient scenario perturbed with a planned reduction of 36% in the imported wastewater and the start-up of a massive Water Treatment Plant, allowing drip and sprinkler irrigation since 2030. In the 2005-2017 period, 59% of the agriculture depended on the flood irrigation with megacity sewage. The water balance scenarios evaluated the sectorial supply of the ground and superficial water. Drip irrigation would reduce 42% of agriculture demands, but still does not grant the downflow hydroelectric requirements, aggravated by the lack of wastewater supply since 2030. This research alerts about how present policies compromise future Valley demands.

Abstract: Currently, thermal power is the largest source of power in the world. Although the impacts of climate change on cooling water sufficiency in thermal power plants have been extensively assessed globally and regionally, their economic consequences have seldom been evaluated. In this study, the Asia-Pacific Integrated Model Computable General Equilibrium model (AIM/CGE) was used to evaluate the economic consequences of projected future cooling water insufficiency on a global basis, which was simulated using the H08 global hydrological model. This approach enabled us to investigate how the physical impacts of climate change on thermal power generation influence economic activities in regions and industrial sectors. To account for the uncertainty of climate change projections, five global climate models and two representative concentration pathways (RCPs 2.6 and 8.5) were used. The ensemble-mean results showed that the global gross domestic product (GDP) loss in 2070–2095 due to cooling water insufficiency in the thermal power sector was −0.21% (−0.12%) in RCP8.5 (RCP2.6). Among the five regions, the largest GDP loss of −0.57% (−0.27%) was observed in the Middle East and Africa. Medium-scale losses of −0.18% (−0.12%) and −0.14% (−0.12%) were found in OECD90 (the member countries of the Organization for Economic Co-operation and Development as of 1990) and Eastern Europe and the Former Soviet Union, respectively. The smallest losses of −0.05% (−0.06%) and −0.09% (−0.08%) were found in Latin America and Asia, respectively. The economic impact of cooling water insufficiency was non-negligible and should be considered as one of the threats induced by climate change.

Under the "dual carbon" target in China, virtual power plants (VPPs) play an important role in improving grid security and promoting clean and low-carbon energy transformation. VPPs can integrate and control distributed resources to participate in the energy market and make full use of distributed resources. However, the intermittency and volatility of renewable energy and the "heat-driven" working mode of CHP units create contradictions that seriously affect the peak-shaving ability of VPPs and lead to high carbon emissions. To solve these problems, this paper aggregates CHP units, wind power, photovoltaics, carbon capture, hydrogen energy storage, and electric boilers into a new type of virtual power plant. The "hydrogen energy storage-electric boiler" joint decoupling CHP working mode is used to strengthen the coupling relationship between electric-thermal-hydrogen load. At the same time, a tiered carbon trading mechanism is considered, with the net profit of the VPP as the optimization objective, balancing economic and environmental considerations. A low-carbon economic dispatch model for VPPs is established, and a genetic algorithm is used for optimization. Three different scheduling strategies are set, and simulations are conducted in three different seasonal scenarios. The results show that the net profit in the cooling season increased by 50.4%, and carbon emissions decreased by 42.3%. In the transitional season, the net profit increased by 39.2%, and carbon emissions decreased by 44.9%. In the heating season, the net profit increased by 19.4%, and carbon emissions decreased by 43.4%. Overall, the proposed dispatch strategy can improve the new energy consumption capacity and total revenue of VPPs while achieving the goal of reducing carbon emissions.

When integrating data from different sources, there are problems of synonymy, different languages, concepts of different granularity. This paper proposes a simple but effective approach to evaluate the semantic similarity of short texts, especially keywords. The method is capable of matching keywords from different sources and languages by exploiting transformers and WordNet-based methods. Key features of the approach include its unsupervised pipeline, mitigation of the lack of context in keywords, scalability for large archives, support for multiple languages and real-world scenarios adaptation capabilities. The work aims to provide a versatile tool for different cultural heritage archives without requiring complex customization. The objectives of the paper are to explore different approaches to identifying similarities in 1- or n-gram tags, to evaluate and compare different pre-trained language models, and to define integrated methods to overcome limitations. Tests to validate the approach have been conducted using the QueryLab portal, a search engine for cultural heritage archives, to evaluate the proposed pipeline.

Integrating a grid-connected battery into a Renewable Energy Community boosts Collective-Self-Consumption of photovoltaic produced energy and allow to perform Energy Arbitrage in the day-ahead and intraday electricity market. However, is it a cost-effective investment? The proposed analysis relay on Net Present Value assessment. Ageing affects are integrated and sensitivity analyses are conducted: considering community customers number, electricity mar-ket prices, battery cost, size and the decision to engage in energy arbitrage. Each scenario under-goes a 20-year hourly simulation using an ageing aware rolling-horizon 24-hours-looking-ahead scheduling, optimized with Mixed-integer linear Programming. Forecast errors, provid-ing of ancillary services, real time control and grid constrains are not taken into account. Simulations conducted on the Italian market indicates that a battery dedicated solely to collec-tive self-consumption is not economically appealing. However, integrating Energy Arbitrage, despite impact on battery lifespan, halves the payback period and enhances the attractiveness of larger battery investments. The Net Present Value is contingent upon battery size, customer number and market prices. However, if battery cost does not exceed 200 €/kWh the investment become cost-effective across all scenarios.

Fats and oils are the most common contaminants in wastewater and are usually discarded through physical processes. This paper studies its elimination through an environmentally friendly biological treatment, yielding good results on both laboratory scale and in the field. In this study a comparative evaluation of the biodegradation of fats and oils in two scenarios were developed in an activated sludge plant at laboratory scale, and a wastewater treatment plant. The full-scale values for some key parameters are compared, such as the oil concentration in the influent and effluent, mass loading and removal efficiency and biodegradation systems. Activated sludge plant at laboratory scale working on a mass load range from 0.2 to 0.8 (kg COD / day / kg MLSS) initially reaches levels of 75% biodegradation thereafter influent concentration is increased and thereby the mass load is increased in a range of working system under high load and biodegradation rates ranging from 71 to 64% are achieved. The actual system consists of a treatment plant wastewater with an aerobic digester for sludge treatment. Fats and oils are retained in a previous degreaser to biological treatment and subsequently sent to the aerobic sludge digester, constituting of thus on a single substrate, resembling an activated sludge plant with extended aeration mode, and levels of biodegradation in the range of 69 to 92%. From this work, we can say that the choice of biological treatment for fats and oils is feasible and adequate. Furthermore, the biomass presents great adaptability to the oil substrate, favored in this case for being the only source of carbon, therefore fats and oils should be removed using biological treatment, instead of the flotation procedure or at most using it as an intermediate process

This paper is an extension to previous work that introduced successful classical-control for indoor lighting system that simultaneously control LED system and roller curtain to achieve accurate light intensity for indoor lighting applications. In this paper, Multi Input Multi Output Fuzzy Logic Controller (MIMO FLC) is proposed to replace the classical-controller. Detailed method is provided to illustrate the steps to design the required MIMO FLC that has two fuzzy input, two fuzzy output and forty-five fuzzy rules with mamdani fuzzy engine. The proposed MIMO FLC is simulated in Matlab using Simulink tools. The results are illustrated, analyzed and compared with the classical-control. The response showed very close performance to the classical-controller. The maximum average error and the maximum error with respect to “Desired Lux” inside the room is -6% and -10% respectively.

This paper performs non-parametric Mann Kendall (MK) trend analysis of historical hydroclimatic data (1961-2016), an ensemble climate model validation and a computation of 16 Expert Team on Climate Change Detection and Indices (ETCCDI) temperature and rainfall extremes indices. The climate indices are evaluated using MK test and annual trend analysis for two Representative Concentration Pathways (RCP4.5 & RCP8.5) future scenarios from 2020 to 2045 over Mono River Basin (MRB) in Togo. The annual and seasonal trend analyses are assessed on historical potential evapotranspiration, mean temperature, rainfall and discharge data. Results show positive and negative trends of hydroclimatic data over MRB from1961 to 2016. Mean temperatures increase significantly in most of the stations while a negative non-significant trend is noticed for rainfall. Meanwhile, the discharge presents a significant seasonal and annual trend for three gauge stations (Corrokope, Nangbéto and Athiémé). Validation of the ensemble climate models reveals that the model under-estimates observations at Sokode, Atkakpamé and Tabligbo stations, however linear regression and spatial correlation coefficients are higher than 0.6. Moreover, the percentage of bias between climate model and observations are less than 15% at most of the stations. Finally, the computation of extreme climatic indices under RCP4.5 and RCP8.5 scenarios shows a significant annual trend of some extreme climatic indices of rainfall and temperature at selected stations between 2020 and 2045 in the MRB. Therefore, relevant governmental politics are needed to elaborate strategies and measures to cope with projected climate changes impacts in the country.

The paper aims to introduce user-friendly modelling approaches to analyze how abiotic factors influence various trophic levels within the marine ecosystem, both naturally and through human impact. It specifically investigates the connections between environmental parameters (like temperature, salinity, and nutrients) and plankton along the Romanian Black Sea coast during the warm season (May-September) over a decade. Utilizing machine learning (ML) algorithms and data collected during this period, models were developed to project the proliferation of zooplankton. During the warm season, water temperature emerged as a significant factor affecting copepods and “other groups” zooplankton densities, while no discernible impact was noted on Noctiluca scintillans blooms. Salinity fluctuations notably influenced typical phytoplankton proliferation, with phosphate concentrations primarily driving widespread blooms. Two scenarios were explored for forecasting zooplankton growth: Business as Usual, predicting modest increases in temperature, salinity, and constant nutrient levels, and the Mild scenario, anticipating more substantial temperature and salinity increases while nutrients decrease significantly by 2042. The findings highlight that under both scenarios, Noctiluca scintillans displays notably high densities, with the second scenario projecting even higher values, surpassing the first by around 70%. These densities indicate characteristics of a eutrophic ecosystem, suggesting the potential implications of altered abiotic factors on ecosystem health.