The national study analyzes sea level rise (SLR) impacts based on 36 different SLR and storm surge scenarios across 5.7 million geographic locations and 3 time periods. Taking an approach based on engineering design guidelines and current cost estimates, the study details projected cost impacts for states, counties, and cities. These impacts are presented from multiple perspectives including total cost, cost per-capita, and cost per-square mile. The purpose of the study is to identify specific locations where infrastructure is vulnerable to rising sea levels. The study finds that Sea Level Rise (SLR) and minimal storm surge is a $400 billion threat to the United States by 2040 that includes a need for at least 50,000 miles of protective barriers. The research is limited in its scope to protecting coastal infrastructure with sea walls. Additional methods exist and may be appropriate in individual situations. The study is original in that it is a national effort to identify infrastructure that is vulnerable as well as the cost associated with protecting this infrastructure.

Elastic properties of materials and their changes with temperature are important for their applications in engineering. A study on the influence of phase composition of AISI 4130 alloy on Young's modulus (Ed), shear modulus (Gd), and damping (Q-1) was carried out by Impulse Excitation Technique (IET). The material characterization was carried out using confocal microscopy, XRD, MET, HV, and dilatometry. A stable structure, composed of ferrite (BCC) and pearlite (α-Fe+Fe3C), was obtained by annealing. Metastable structure of martensite (BCT) was obtained by quenching. The Ed, Gd, and Q-1 were measured, varying the temperature from RT to 900 °C. The values of Ed and Gd, at RT, were determined as 201.5 and 79.2 GPa (annealed), and 190.13 and 76.5 GPa (quenched), respectively. In the annealed steel, the values Ed and Gd decrease linearly on heating up to 650 °C, with the thermal expansion. In the quenched steel, weak changes in the dilatometric curve, Ed, Gd, and Q-1, in the range of 350-450 °C, indicated decompositions of the martensitic phase. Sharp decrease in the moduli and high peak of Q-1, was observed for both samples around 650-900 °C, revealing low lattice elastic stability of the phases during transformations α(BCC)+Fe3C↔γ(FCC).

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

In this paper, we demonstrate a method of measuring the flexural elastic modulus of ceramics at an intermediate (~millimeter) scale, at high temperatures. We used a picosecond laser to precisely cut micro-beams from the location of interest in a bulk ceramic. They had a cross-section of approximately 100 μm × 300 μm, and a length of ~1 cm. They were then tested in a thermal-mechanical analyzer at room temperature, 500°C, 800°C, and 1100°C using the four-point flexural testing method. We compared the elastic moduli of high-purity Al2O3 and AlN measured by our method with the reported values in the literature and found that the difference was less than 5% for both materials. This paper provides a new and accurate method of characterizing the high-temperature elastic modulus of miniature samples extracted from representative/selected areas of bulk materials.

Rice yield underperformance coupled with production instability in Odisha state of India is primarily due to low irrigation potential, the spatiotemporal disparity in rainfall pattern and relentless confrontation with biotic and abiotic stresses. System of Rice Intensification (SRI) was introduced in the state with the promises of higher production horizon. However, the present scenario of patchy adoption pattern has necessitated comprehensive study on dynamics and determinants of adoption of SRI. We found that, 57 percent farmers continued SRI, 28 percent discontinued and rest were practicing conventional rice farming. The focused group discussion among the farmers helped us to triangulate the determinants of SRI adoption.The increased SRI area allocation was observed at the expense of reduced number of adopting farmers. Economic scarcity of skilled labour, difficulties in transplantation and mechanical weeding, low irrigation potential and poor on-farm water management were major constraints in SRI practice. Farmers’ compliance in following different resilient SRI components that also varied spatiotemporally has resulted in realized incremental yield. SRI was proved to be resilient to severe drought during 2015-16 and yielded 27 percentage higher than conventional one with considerable decline in hazardous chemicals usage. Farmers’ decision on SRI adoption/discontinuation was analysed using probit regression analysis. The results indicated that, active social involvements of NGOs, on-farm training and demonstrations, and realized incremental rice income influenced SRI adoption. On the other hand, SRI area expansion hindered mainly because of infeasible land topography, area saturation and lack of farmers’ interest. The sustainability index was developed to analyse SRI’s performance to other conventional methods at farm level. Comparatively, SRI adopters benefitted in terms of incremental net return by enhancing ecological balance and sustained productivity of soil, use of eco-friendly and own farm inputs. Nonetheless, higher women labour displacement observed under SRI can be curtailed by providing training to young farm women.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in Edge Computing (EC), Deep Learning (DL), and Deep Transfer Learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructure. When combined with medically oriented IoT setups, DL-architectures are suitable for integrating into pandemic-compliant medical infrastructure. The development of intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 5G wireless communication has enabled ultra-wide broadband wireless communication, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such set-ups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents the design of a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge- and cloud-computing frameworks, and a suite of DL-tools. The framework uses a composite attention-driven framework incorporating various DL-pre-trained models (DPTM) for protocol adherence and contact tracing. When connected to public networks, it can detect specific cyber-attacks. The results confirm the effectiveness of the proposed methodologies.

This paper is the continuation of a previous study, which highlighted some inconsistencies in the RILEM guidelines for the interpretation of the diagonal compression test. Although improved compared to the ASTM guidelines, in fact, the RILEM guidelines underestimate the state of hydrostatic stress induced by the diagonal compression test at the center of the wallette. The new interpretation of the diagonal compression test proposed in this article shows that the RILEM guidelines actually underestimate both the hydrostatic and the deviatoric stress states at the center of the wallette. The new formulation complies with the linear elastic theory and allows us to use the the diagonal compression test to identify the three elastic coefficients of masonry. In particular, it allows the identification of the Poisson ratio, which instead takes on a conventional value in the RILEM and ASTM guidelines. The difference of one order of magnitude between the conventional and proposed Poisson’s ratio is in agreement with the experimental results on another brittle material, namely concrete. Finally, the new proposal fills the gap between the results provided by the two tests usually performed to identify the shear behavior of masonry: the diagonal compression test and the shear-compression test.

This cross-sectional survey assessed the resilient coping levels and their relationship with the socio-demographic characteristics of nursing professionals in Saudi Arabia. Adult (≥18 years) registered nurses and practicing for ≥1 year were included. Resilient coping levels (as assessed via a 4-item Brief Resilience Coping Scale; BRCS) and socio-demographic and other characteristics data were collected. Descriptive analysis and ordinal logistic regressions were used to analyze the data. Further, the psychometric properties of the BRCS were reported. A total of 216 nursing professionals were included. The mean BRCS score was 14.6 (standard deviation=3.6), with most nursing professionals (62.5%) reporting medium to high resilience coping levels. The ordinal logistic regression model demonstrated that increasing age (p=0.002), best overall health (p=0.001), and nurses posted in the outpatient department (p=0.049) and intensive care unit (p=0.032) had significantly high resilient coping levels. The internal consistency of the BRCS was good (Cronbach’s alpha=0.80). In summary, nursing professionals in Saudi Arabia showed medium resilient coping levels. Moreover, this study suggests that BRCS is a psychometrically reliable and adequate tool for assessing resilience coping levels and provides valuable insights into the relationship between resilient coping levels and socio-demographic characteristics of the nursing professionals in Saudi Arabia.

During routine bridge maintenance, edge detection allows the partial condition of the bridge to be viewed. However, many edge detection methods often have unsatisfactory performances when dealing with images with complex backgrounds. Moreover, the processing often involves the manual selection of thresholds, which can result in repeated testing and comparisons. To address these problems in this paper, the wavelet transform modulus maxima method is used to detect the target image, and then the threshold value of the image can be determined automatically according to the OTSU method to remove the pseudo-edges. Thus, the real image edges can be detected. The results show that the information entropy and SSIM of the detection results are the highest when compared with the commonly used Canny and Laplace algorithms, which means that the detection quality is optimal. To more fully illustrate the advantages of the algorithms, images with more complex backgrounds were detected and the processing results of the algorithms in this paper are still optimal. In addition, the automatic selection of thresholds saves the operator’s effort and improves the detection efficiency. Thanks to the combined use of the above two methods, detection quality and efficiency are significantly improved, which has a good application in engineering practice.

A special vibration damper is proposed for material property identification through a scratching process on a vibrating object. This paper presents the design and working principle of a scratching damper based on a scratch test device. A non-contact electromagnetic shaker is used to generate the shaking force for the vibration of the tested object. The required pressure on the scratched material during the scratching process is generated by a compression spring and adjusted by changing the amount of compression of the spring. The proposed damper is designed and tested on extruded aluminum samples for the determination of its Young’s modulus and hardness and validated by using the standard test instruments. Observations of physical dimensions of the scratching tracks are determined by microscope and were utilized to compute the scratching energy factor. Load curves are obtained at different pressure of the scratching process. The hysteresis loop energy during the scratching process of the tested object is measured and used for the determination of material properties of the object. Furthermore, energy conservation law, scratch energy release rate and loop energy release rate are used to determine the Young’s modulus and hardness of the sample. The measurement accuracy of the modulus and hardness of the tested sample is evaluated. The proposed method of material testing has several advantages over the traditional methods such as low cost and high repeatability, and may be used as an alternative to the standard modulus and hardness testers.

This article introduces simulations of theoretical material with controlled properties for the evaluation of the effect of key parameters, as volumetric fractions, elastic properties of each phase and transition zone on the effective dynamic elastic modulus (Ed). The accuracy level of classical homogenization models was checked regarding the prediction of Ed. Numerical simulations were performed with finite element method (FEM) for evaluations of the natural frequencies and their correlation with Ed, through frequency equations. An acoustic test validated the numerical results and obtained the elastic modulus of concretes and mortars at 0.3, 0.5 and 0.7 water-cement ratios (w/c). Hirsch calibrated according to the numerical simulation (x = 0.27) exhibited a realistic behavior for concretes of w/c = 0.3 and 0.5, with 5% error. Nevertheless, for w/c = 0.7, Ed approached Reuss model, similarly to theoretical triphasic materials. Hashin-Shtrikman bounds is not perfectly applied to theoretical biphasic materials under dynamic situations.

The proliferation of the Internet of Things (IoT) has revolutionized traditional services, giving rise to emerging smart infrastructures by connecting the physical and digital worlds. Sensory data is essential in IoT-based systems for providing context-aware and location-based services. Hence, the accurate localization of IoT devices is paramount. Anchor misplacement can significantly affect location information and coverage services in IoT. We study the effect of anchor misplacement in typical IoT settings where sensors are randomly deployed, can be mobile and may belong to multiple providers. We identify sensing coverage holes formed by anchor misplacement and analyze their presence and impact. To mitigate the impact of anchor misplacement on network reliability, we propose a framework to identify the affected sensor nodes and then identify and remove misplaced anchor nodes. The validity of our approach is verified, and its effectiveness is demonstrated by several experiments with different network topologies and parameters. Our results are promising and can be utilized in multiple coverage applications, such as smart agriculture systems and habitat monitoring, regardless of the sensors or deployment types. It also sheds light on best practices and methods for a reliable design of IoT-based systems.

The growth in the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to diverse cybersecurity attacks and threats that lead to negative consequences. Cyberattacks can harm not just the IoMT devices being used but also human life. Currently, several security solutions are proposed to enhance the security of the IoMT, which uses machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. In addition, blockchain technology enables a decentralized approach to the healthcare system and eliminates some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects anomalies in data measured by medical sensors to find fraudulent data. Therefore, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. In addition, a blockchain network is used in the fog layer to ensure that the data is not altered, boosting the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% on the F1-score accuracy metric.

Calcareous sand deposits are widely distributed along the shoreline in ropical and subtropical regions. Microbially induced calcite precipitation treatment (MICP) is a relatively new method to improve the stiffness and strength of the soil. Little is known about the small-strain shear modulus and damping ratio of MICP-treated calcareous sand, which are two crucial parameters for the prediction of the dynamic behavior of soil. A series of resonant column tests are performed to investigate the dynamic performance of MICP-treated calcareous sand, with special attention paid to the influence of treatment duration and confining stress on the stiffness and damping characteristics. The relationship between the initial dynamic shear modulus and unconfined compressive strength is analyzed. Additionally, the empirical equations of the reference shear strain between treatment duration and confining stress are given. The G/G0 of MICP-cemented calcareous sand presents a higher strain sensitivity than that of untreated sand, and its attenuation pattern can be described by Hardin-Drnevich model. The σc has an apparent effect on the degradation characteristics of the dynamic shear modulus of MICP-treated calcareous sand with a low cementation level, however, its effect decreases with the increasing treatment duration. The relationship between the reference shear strain and the treatment duration and confining stress can be described by a power and a linear formula, respectively.

Foam concrete is a construction material with controllable low strength and untraditional physical properties. Its highly crushable nature leaves it a niche as an engineered energy-absorbing material in many value-added applications; however, fundamental understanding of the material properties is crucial. As foam concrete is highly cellularized and ductile, conventional concrete testing methods such as compression test are insufficient to characterize the key material attributes of foam concrete, especially when the foam density is low. The resonant frequency test (ASTM C215) is specified for evaluating dynamic Young’s modulus of normal concrete. Inspired by the non-destructive feature of this test, we investigate the possibility of using the resonant frequency test to continuously monitor the modulus build-up of foam concrete with age. For the representativeness of the samples, three variables are considered in material design—bulk density ranging from 0.4 to 1.2 g/cm3, water to cementitious materials ratio of 0.42 and 0.47, and fly ash replacement of 10 and 30% by weight of cement. After examining the different vibration modes, the fundamental transverse frequency is determined most suitable for interpreting the foam modulus. The experimental results demonstrate good accuracy of using this approach for the measurement of different samples. It is also confirmed that, for a given foam concrete, the foam modulus can be predicted by knowing the foam density and solid modulus of its base cement paste, which provides an important insight for the further studies and real applications of foam concrete.

Sensor enabled big data and Artificial Intelligence platforms has the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand while maximizing the efficiency of production systems on the other. To stimulate a sustainable, digital, and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and develop new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI) based data acquisition and processing platform, the unmet current needs can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. There is a need for a multimodal, digitized, automated biosensor and health monitoring platform containing AI algorithms for optimized stress and disease prediction in farm animals. An experimental development of non-invasive wearable and wireless physiological sensor networks that can be deployed in agricultural environments would allow for real-time extraction and integration of physiological data and display on decision support dashboards for livestock workers. By testing the effectiveness of the system in generating meaningful longitudinal data for further research into physiological and behavioral characteristics of farm animals, novel insights about animal welfare can be developed. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to the significant innovation potential. Continuous monitoring of animal health and physiological functioning promotes more environmentally and socially acceptable developmental pathways. Once demonstrated, the portable animal health monitoring platform will close a critical gap in digitized livestock farming and position the agricultural industry as a frontrunner in the sector of farm animal monitoring and measurement systems. The multimodal wearable sensor-driven AI approach is expected to significantly improve the effectiveness of livestock decision support systems and the selection of resilient animals for the next generation and provide predictive data for end-users in livestock farming.

Information and Communication Technology (ICT) is at the heart of the Smart City approach, which constitutes the next level of cities’ and communities’ development across the globe. Thereby, ICT serves as the gluing component enabling different domains to interact with each other and facilitating the management and processing of vast amounts of data and information towards intelligently steering the cities infrastructure and processes, engaging the citizens and facilitating new services and applications on various aspects of urban life - e.g. supply chains, mobility, transportation, energy, citizens’ participation, public safety, interactions between citizens and the public administration, water management, parking and many other use cases and domains. Hence, given the fundamental role of ICT in cities in the near future, it is of paramount importance to lay the ground for a sustainable and reliable ICT infrastructure, which can enable a city/community to respond in a resilient way to upcoming challenges whilst increasing the quality of life for its citizens. This paper constitutes a continuation of a series of research documents and standardization activities, which relate to the concept of Open Urban Platforms (OUP) and the way they serve as a blueprint for each city/community towards the establishment of an ICT backbone. Thereby, the current paper emphasizes on the aspects of sustainability and resilient ICT, whilst reporting on our latest activities and related developments in the research area.

This research is completed in development of researches devoted to relations between elastic modulus (MoE) and thermal expansivity (CTe) of different materials. This study experimentally confirmed the relation of MoE and CTe for thermosetting polymers and FRPs both under normal and heating temperatures. As the thermosetting polymers, the epoxy compositions were used and glass fiber and mineral additives for FRP. The experiment was based on dilatometric and elastic deformations’ testing. Also, the relaxation model of MoE/CTe was proposed, that is based on physical properties of polymers’ supramolecular structures. The comparative analyzing of MoE/CTes’ different models is done. The most accurate models are determined, and the relaxation model has demonstrated the high accuracy.

Knowing the mechanical properties of fibrous composite materials used in aeronautical industry from the design phase is a primary objective for designers. There are many methods for determining them, but they are laborious and require a long calculation time. And some of the methods are very approximate, giving only upper and lower bounds for these values. Experimental measurements also consume time and others resources. A method for sufficiently accurate and quickly obtained estimation of the engineering constants of the homogenized material is presented in the work. FEM is used to determine the natural frequencies of a standard bar, for which there are sufficiently precise classical methods for expressing these constants according to the properties of the homogenized material. In this way, these sought constants can be determined. In the work, Young's modulus will be determined for such a material, using the relationships that provide the proper pulsations for the longitudinal vibrations. Within the adopted model, transverse and torsional vibrations will be eliminated by blocking the nodes on the surface of the bars. In this way, more longitudinal self-pulsations can be obtained, so the precision in calculating the Young's modulus will increase.

When developing bone implants Young's modulus is one of the primary characteristics of the material that should be considered. The study focuses on the possibility of regulating the modulus of the alloy Ti-50.8 at % Ni by varying its initial structure as well as aging-induced microstructure in a wide range. Microstructure observations were performed using TEM and SEM observations. The calorimetric studies of martensitic transformations were carried out using "Mettler Toledo 822e". Tensile tests were performed in a test temperature range of ‒196≤T≤+100°C using an «IN-STRON 5966». The grain/subgrain size of B2-austenite strongly affects the modulus magnitude. This effect is ambiguous for a material with a grain size of 0.13–3 µm and depends on the test temperature. The effectiveness of aging on the modulus reduction depends on the initial structure: it is most pronounced in an alloy with a relatively coarse grain size of 9 µm and brings a decrease by 3.8 times at a temperature of 37°C. Bone-like elastic modulus (10–30 GPa) at a human body temperature of 37°C is realized in an aged fine-grained NiTi alloy. An ultrafine-grained sub-structure exhibits he same values of Young's modulus in the temperature range from -100°С to 25°С.

Artificial intelligence systems are increasingly becoming a component of security-critical applications. The protection of such systems from various types of destructive influences is thus a relevant area of research. The vast majority of previously published works are aimed at reducing vulnerability to certain types of disturbances or implementing certain resilience properties. At the same time, the authors either do not consider the concept of resilience as such, or their understanding varies greatly. This work presents a formalized definition of resilience and its characteristics for artificial intelligence systems from a systemic point of view. It systematizes ideas and approaches to building resilience to various types of disturbances. Taxonomy of resilience of artificial intelligence systems to destructive disturbances is proposed. Approaches and technologies for complex protection of intelligent systems, issues of their resource efficiency and other open research issues are considered. Approaches of resilience assessment for artificial intelligence system are also analyzed and recommendations are provided for their implementation.

Self-efficacy and perfectionism play an important role in high-performance activities. This cross-sectional study analyses the relationship between these constructs and resilience in a sample of 145 music students (57.9% female) with a mean age of 27.77 years. Perfectionism was assessed using the Multidimensional Inventory of Perfectionism in Sport, resilience using the Resilience Scale, and self-efficacy using the General Self-Efficacy Scale. Compared to those with low resilience, students with high resilience score higher on self-efficacy and lower on dysfunctional perfectionism. No differences were observed in the total perfectionism score or adaptive perfectionism. Self-efficacy shows significant predictive power for resilience (β = .525, p < .001). Although functional perfectionism did not significantly predict resilience, a marginal negative relationship was found between dysfunctional perfectionism and resilience (β = - .156, p = .063). The results are discussed concerning their implications for music pedagogy and teacher intervention.

Gender-based violence is considered a serious social and public health problem. Overcoming this situation implies a process that results in the favorable biopsychosocial rehabilitation, the resilient of women. The objective of this study was to analyze the tools, resources and personal and psychosocial mechanisms used by women survivors of gender-based violence. The design was an interpretative phenomenology. It carried out with 22 women who have overcome gender-based violence. Data was collected through personal interviews and narration. The results were grouped into four themes: "Process of violence", "Social resources for coping and overcoming GBV", "Personal tools for coping and overcoming GBV", and "Feelings identified, from the abuse stage to the survival stage". Several studies concluded that overcoming abuse is influenced by the women social network, and it can be the action of these people determining their survival to gender violence. Despite the recognized usefulness of these available resources, it would be desirable to strengthen them in order to be able to drive more women toward survival, assuming a strengthening of coping and overcoming, without forgetting the importance of other support mechanisms such as their family and group therapies.

In this study, the determination of static E50 and υ parameters of rock materials was investigated using P-S wave velocities and Shore hardness (SH), non-destructive measurement methods. We used multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN) models to estimate and determine these parameters. When the models defined by MLR, MNLR, and ANN were compared to the R2 values, ANN models that estimate E50 and υ parameters of rock materials using non-destructive methods (Vp, Vs, Vp/Vs, d, and SH) could be determined with higher accuracy than MLR and MNLR models. The study’s originality is based on the fact that ores such as galena, chromite, and barite have been studied for the first time on rock mechanics, and in this respect, it offers an innovative perspective. In addition, the use of all non-destructive measurement methods, Vp, Vs, and Shore hardness tests, also increases the originality of the study.

This paper presents the simplified and quick way to obtain an experimental measure of the modulus of elasticity(E) of a piece of metal rod using the Smartphone microphone and through the experimental configuration in mode of unsupport beam Euler-Bernoulli(E-B). To understand it, it is required to have knowledge about flexural vibration of E-B beams and to know the analysis of the spectogram of the audio signal. The methodology consists in measuring the flexural resonance frequency through an app installed on a Smartphone and using the E formula deduced from the E-B theoretical framework. The results of the experimental measurements have a discrepancy of less than 5% with respect to the factory value, for a carbon steel rod with circular cross section using the impact excitation technique. It describes the precision and accuracy of the experimental measurement. It is useful, because it helps to classify the materials in the Faculty of Engineering and it helps to make a more efficient quality control.

The use of Supplementary Cementitious Material (SCM) is widely used in production of sustainable concrete. Blended cements, incorporating SCM such as Pulverized Fly Ash (PFA) and Ground Granulated Blast Furnace Slag (GGBFS) have been widely used to reduce the cement contents and avoid adverse environmental impacts of CO2 produced during cement manufacturing. The analysis of various structural properties of concrete such as compressive strength, flexural strength and modulus of elasticity is important for its structural application. In this research, flexural strength of 100mmx100mmx500mm beams made from blended cement were tested under three curing conditions i.e. winter, summer and under water and the flexural strength was calculated using EN-12390-5 at the ages of 28 days and 56 days. For modulus of elasticity, concrete cylinders 150mmx300mm were tested as per procedure described in BS 1881-121(1983) at the age of 28 days. The compressive strength, flexural strength and modulus of elasticity for blended cement incorporating PFA and GGBFS has been increased under summer curing environment. The experimental values of Modulus of Elasticity are compared with the provision of BS 1881.

To ensure sustainability of the future power grid, the rate of expansion of distributed renewable energy resources (DRER) has introduced operational challenges. These includes managing transmission constraints with DRER power injection, dispatching DRER efficiently, managing system frequency and ensuring sufficient reactive power for voltage support. Coupled with the intensification of wildfires, power infrastructure across the United States faces challenges to minimize the impact to maintain system reliability and resiliency. This research embarks on a comprehensive evaluation, beginning with an in-depth historical analysis to delineate regions most susceptible to wildfires. Utilizing a multidimensional approach, the study assesses wildfire-induced risks to power grids, by integrating historical wildfire occurrences, real-time wildfire proximities, Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation metrics, and system parameters, by employing Principal Component Analysis (PCA) based optimal weights, leading to the formulation of a novel risk factor model. This risk factor has the potential to be the key to ensuring the resilience of a renewable-rich smart grid when faced with a severe weather event. Our model’s applicability was further verified through an empirical assessment, selecting representative networks from diverse regions, providing insights into the geographical variability of risk factors. Ultimately, this study offers stakeholders and policymakers a comprehensive tool-set, empowering decisions regarding infrastructure investments, grid reinforcements, and strategic power rerouting to ensure consistent energy delivery using risk assessment against wildfire.

The power system vulnerability leads to faults and the severity of the fault may lead to prolonged load-shedding. The power system needs to be configured in extreme failure scenarios for protecting the network from further contingencies and prolonged load-shedding. Distributed generation resources (DGs) can be useful to form intentional islands after faults to maintain the continuity of power supply to loads based on their weightage during faulty periods and to reduce overall load shedding duration. Power system is bound to collapses and secondary collapse in the formed island is possible. This research represents a novel method of impedance based path finding for intentional islanding, which adapts itself with the changes in the demands, DGs outputs or further severities during restoration period. In this adaptive islanding approach, network adjusts itself with the changes in either the load demands or renewable DGs outputs and rearranges the restoration plan by curtailing or adding some of the loads through controllable switches. Further a secondary collapse in the existing island is studied by injecting multiple faults at various positions of the network to validate the system resilience to cope with severities. A short-term load forecasting approach is used to predict changes in load demands and variations in DG outputs during the islanding scheme. During the restoration period, these variations are tracked and the islands are modified accordingly. In order to minimize the overall generation cost by using less fuel, an economical approach is used in the selection of controllable DGs. The proposed approach is formulated as a multi-objective, that incorporates several operational constraints and simulation is carried out using the modified IEEE 69-bus distribution system to assess the efficacy of the proposed model.

This qualitative study explores the multifaceted strategies organizations employ to build resilient supply chains through effective procurement risk management. Conducted over a six-month period in 2024, the research involved semi-structured interviews with 25 industry professionals from sectors including manufacturing, retail, healthcare, and technology, alongside document analysis and case studies. The study identifies four key themes: risk identification and assessment, supplier relationship management, digitalization in risk management, and strategic resilience practices. The findings reveal that organizations face diverse risks, including supplier, supply market, and external environmental risks, necessitating comprehensive risk assessment methodologies. Supplier relationship management emerges as a critical factor in mitigating risks, with practices such as regular audits, collaborative risk management, long-term partnerships, and supplier diversification being crucial. The adoption of digital technologies like predictive analytics, blockchain, advanced analytics, and artificial intelligence enhances risk management capabilities, providing data-driven insights and fostering transparency. Strategic resilience practices, including inventory buffering, flexible sourcing, redundancy, and scenario planning, are essential for maintaining supply chain continuity amidst disruptions. The study underscores the importance of an integrated and proactive approach to procurement risk management that leverages robust assessment, strong supplier relationships, digital tools, and strategic planning. These findings contribute to a deeper understanding of how organizations can effectively manage procurement risks and build resilient supply chains in a dynamic global environment. The insights offer valuable guidance for procurement professionals and organizational leaders in developing resilient and adaptable supply chain strategies to navigate the complexities of today's market.

A crucial component for developing an automated speaker or speech recognition system is voice preprocessing, which filters unwanted noise and detects the speech part. This study aimed to develop a computerized model to remove background noise and improve signal quality for future applications. This model was developed on a large longitudinal dataset containing varying real-world noise and validated on both a public dataset and a locally collected test dataset in different environments. An overall voice pre-processing pipeline is presented in this study, including denoising, segmentation, feature extraction, and ease of storage. The backbone of the denoising model is a Kalman filter, where the parameters were obtained from a grid search method; the signal-to-noise ratio (SNR) was used as the performance metric. Also, the segmentation was done to remove the pauses from the audio signal before the feature extraction. Finally, the data was stored as a template with the most common features. The SNR result suggested that the Kalman filter-based proposed method performed successfully across diverse datasets. Thus, this model provides a robust and adaptable solution for real-world scenarios and also ensures the data storing quality for future applications.

In recent decades, low-yielding seismic devices based on the use of friction dampers have emerged as an excellent solution for the development of building structures with improved reparability and resilience. Achieving an optimal design for such low-yielding seismic devices requires precise control of bolt preloading levels and predictability of the friction coefficient (CoF) between the damper interfaces. While various types of friction devices exist, capable of providing significant energy dissipation, ongoing research is focused on the development of novel friction materials that exhibit a stable hysteretic response, high CoF values, minimal differences between static and dynamic CoF, and predictable slip resistance. In this context, an experimental campaign was conducted at the STRENGTH Laboratory of the University of Salerno to evaluate the behaviour of new friction shims employing specially developed metal alloys. Specifically, the influence of the characteristics of the contact surfaces in the sliding area on the behaviour and performance of the friction device was analysed. The tests followed the loading protocol recommended by EN12159 for seismic device qualification. Monitored parameters included preloading force values and the evolution of slip resistance. The friction value was determined, along with its degradation over time. Finally, the material's performance in terms of hysteretic behaviour was assessed, providing a comparison of the tested specimens in terms of slip force degradation and energy dissipation capacity.

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity to the users in a coverage free zone. The main contributions of this work include two approaches to position the UAVs and to assign user equipment (UE) to each UAV, such that the sum-rate and the coverage probability of the network is maximized. An approach can be selected depending on the prevailing scenario. The first approach uses clustering algorithm to determine the 2D positioning of the UAV and a matching algorithm is used for UE assignment by considering the characteristics of the air-to-ground propagation channels as well as the impact of co-channel interference from ABSs. Then it uses exhaustive search on different altitudes to find the optimal altitude. In the second approach, 2D positioning and UE assignment are done similarly to the first approach. However, the sub-optimal altitude is estimated using particle swarm optimization (PSO). The first approach is suitable for a system which has computational resource constraints or lower probability of line of sight (LoS) links. In contrast, the second approach is suitable for data rate greedy systems or a higher probability of LoS links.

TiNbZrTa alloys are promising for multidisciplinary applications, such as refractory and biomedical purposes due to their high thermal stability and non-toxicity. Hardness and elastic modulus are among the key features for their adequate industrial applications. The influence of porosity and Ti/Ta ratio were investigated on TiNbZrTa alloys produced by three different processing routes, i.e., (i) blend element and posterior press and sintering (BE + P&S), (ii) mechanical alloying with press and sintering (MA + P&S), and (iii) arc melting and casting. Atomic diffusion was improved during processing, resulting in lower porosity in the following order: casting < MA + P&S < BE + P&S. Ta offered limited atomic diffusion due to its high melting point compared to other constituent elements. Thus, the total porosity of alloys increased with increasing the Ta contents, i.e., by lowering the Ti/Ta ratio. However, the Ti/Ta ratio did not considerably affect the bonding energy or the elastic modulus. Hardness was increased significantly in dense alloys compared to the porous ones. However, porosity and Ti/Ta ratio did not show a clear trend in hardness among the porous alloys.

We introduce optically clear and resilient free-form micro-optical of pure (non-photosensitized) organic-inorganic SZ2080 material made by femtosecond 3D laser lithography (3DLL). This is advantageous for rapid printing of 3D micro-/nanooptics, including their integration directly onto optical fibers. A systematic study on the fabrication peculiarities and quality of resultant structures is performed. Comparison of microlenses’ resiliency to CW and femtosecond pulsed exposure is determined. Experimental results prove that pure SZ2080 is ∼3 fold more resistant to high irradiance as compared with a standard photo-sensitized material and can sustain up to 1.91 GW/cm² intensity. 3DLL is a promising manufacturing approach for high-intensity micro-optics for emerging fields in astro-photonics and atto-second pulse generation. Additionally, pyrolysis is employed to shrink structures up to 40% by removing organic SZ2080 constituents. This opens a promising route towards downscaling photonic lattices and creation of mechanically robust glass-ceramic structures.

In this paper, we generalize the concept of I-statistical convergence, which is a recently introduced summability method, with an infinite matrix of complex numbers and a modulus function. Lacunary sequences will also be included in our definitions. The name of our new method will be A^{I}-lacunary statistical convergence with respect to a sequence of modulus functions. We also define strongly A^{I}-lacunary convergence and we give some inclusion relations between these methods.

The seismic events of 2015 in Nepal exposed the challenges associated with constructing earthquake-resistant 2-story self-built stone masonry structures in high-elevation rural regions, especially post-disaster. The Department of Urban Development and Building Construction (DUDBC) provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the challenges related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study presents a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: 1) post-disaster problem assessment, 2) need identification, 3) material selection, 4) design development, and 5) validation and implementation. Based on this framework, we proposed a novel gabions building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socioeconomic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G- 2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a development framework and a two-story gabions house that is most suitable for self-built post-disaster reconstruction and resilient to local architecture and socio-economic conditions while providing structural integrity and safety.

The focus of this study is to identify whether resilience and sustainable development can be used as an image for strategic planning of the city marketing. Resilience is about building and planning for future proof the cities. How urban challenges and crisis have the lowest impact and the maximum of bounce back and evolution. Resilience is part of the sustainable development. Thus, it is important for the decision-makers to define the mission on their strategic planning in a holistically way taking into consideration the basic assets of a city, the environment, the economy and the society and how can all of them can be combined to marketing the city and take into consideration the internal and external environment. As the past few years’ city marketing has become an important tool for the urban development. The main goal is to show how city marketing can be applied on a city that tries to be more resilient and more sustainable by using strategic urban planning to set the vision, to identify the challenges and the problematic areas and to set new goals and objectives in order to plan and build to future proof the complexity of an urban system. For answering the questions of this article we use two case studies Rotterdam (Netherlands) and Thessaloniki (Greece), using a literature review and researches conducted alongside with a benchmarking of their resilient strategies as both of the cities are members of the Resilient Cities Network. From a different perspective of resilient thinking, both of the cities have managed to use resilience as a marketing image for further sustainable development.

The paper presents an analysis of the concept of resilience within the context of a modern period example in Croatia, the Cvjetno housing estate in Zagreb, recognized as a cultural and historical complex. While extensive literature exists on the estate's architectural and historical significance, limited research explores its resilience. This study aims to fill this gap by examining Cvjetno estate's resilience to natural hazards, financial and economic challenges, and social crises. It investigates the estate's design, historical context, and physical characteristics through archival documents, historical records, and field research. The analysis focuses on the estate's planned resilience to natural disasters, particularly flooding from the nearby river Sava and seismic risks due to its location in a seismically active area. Drawing from the literature on resilient communities, the study draws on key characteristics and principles of resilience and explores how Cvjetno estate exemplifies these traits. The research contributes to a deeper understanding of resilience in urban planning and provides insights for enhancing resilience in similar historical settlements facing contemporary challenges.

Sumudu transform of the Dixon elliptic function with non zero modulus a ≠ 0 for arbitrary powers sm^N(x,a) ; N ≥ 1 ; sm^N(x,a)cm(x,a) ; N ≥ 0 and sm^N(x,a)cm²(x,a) ; N ≥ 0 is given by product of Quasi C fractions. Next by assuming denominators of Quasi C fraction to 1 and hence applying Heliermann correspondance relating formal power series (Maclaurin series of Dixon elliptic functions) and regular C fraction, Hankel determinants are calculated and showed by taking a = 0 gives the Hankel determinants of regular C fraction. The derived results were back tracked to the Laplace transform of sm(x,a) ; cm(x,a) and sm(x,a)cm(x,a).

The present paper presents findings from the follow-up of 185 of these nurses who had been involved in the front-line care of COVID-19 patients in urban centres in Northern England, during and after the “second wave” of COVID patients in November, 2020. Measures of adjustment, stress (including PTSD), and Maslowian self-actualization were added to earlier measures of personality, adjustment, work-life stress, and career intention. Cluster analysis identified three types in the 2020-2023 cohort: I ‘Actualizing Professionals’; (N=62); II ‘Strong Professionals’ (N=56); III ‘Highly Stressed Nurses’ (N=45). These latter very stressed nurses included all nurses (N=16) identified as having PTSD symptoms.

Solid-state transformer (SST) is a promising technology for smart grids due to its flexible power control (better reliability) and high efficacy (by decreasing losses) compared with traditional transformers. The SST is designed in tandem with three stages, i.e., the input, isolation, and output stages. As three converter stages are used to design the SST to make a more reliable device in practice, it is highly prone to instabilities. Moreover, the key objective of SST design is to implement a modern power distribution system to make it more intelligent and reliable. The stability issue can be even more complicated by the presence of distributed energy resources in the distribution system. Thus, the stability of SST must be measured prior to /during the design, and to determine, a state-space model is used, which is developed in this paper. Each stage of SST is modeled with its controller, and the system's stability is measured through the controllability and observability test. Further, the conversion from state-space to transfer function model is done, and the stability of SST is shown using frequency and time-domain diagrams. With these, the different plots, namely, the Bode plot, Nyquist plot, Nichols chart, Root locus, pole-zero plot, and Eigen plot, are used to ascertain the relative and absolute stability. Finally, the SST Simulink model is tested and validated through hardwate-in-the-loop simulation, i.e., OPALRT, to show its effectiveness and applicability.

Plant diseases and pests reduce crop yields, accounting for global crop losses of 30% to 50%. In conventional agricultural production systems, these losses are typically controlled by applying chemical pesticides. However, public pressure is mounting to curtail agrochemical use. In this context, employing beneficial endophytic microorganisms is an increasingly attractive alternative to the use of conventional chemical pesticides in agriculture. A multitude of fungal endophytes are naturally present in plants, producing enzymes, small peptides and secondary metabolites due to their bioactivity, can protect hosts from pathogens, pests and abiotic stresses. The use of beneficial endophytic microorganisms in agriculture is an increasingly attractive alternative to conventional pesticides. The aim of this study was to characterize fungal endophytes isolated from apparently healthy, feral wine grapes in eastern Canada that have grown without agrochemical inputs for decades. Host plants ranged from unknown seedlings to long-lost cultivars not widely propagated since the 1800s. HPLC-MS was used to identify unique endophyte-derived chemical compounds in the host plants, while dual-culture competition assays showed a range in endophytes’ ability to suppress the mycelial growth of Botrytis, which is typically controlled in viticulture with pesticides. Twelve of the most promising fungal endophytes isolated were identified using multilocus sequencing and morphology, while DNA barcoding was employed to identify some of their host vines. These fungal endophyte isolates, which consisted of both known and putative novel strains, belonged to seven genera in six families and five orders of Ascomycota. Exploring the fungal endophytes in these specimens may yield clues to the vines’ survival and lead to the discovery of novel biocontrol agents.

Organization resilience indicates the capacity of an organization or system to return to its steady condition or exceed it after going through a disruptive event that disrupts its steady condition. Qatar's oil and gas sector has shown remarkable Resilience during the COVID-19 pandemic due to its preparedness and readiness to deal with such a disruption. As a lesson learned from the recent COVID-19 pandemic, local governmental institutions need to support national preparedness and Resilience to handle emergencies and unpredictable crises by learning from the success model of Qatar's oil and gas sector. This study presents the key Resilience Engineering Indicators (REIs) that make this sector resilient and validates the six Resilience Engineering indexes or dimensions adopted from [1], [2]& [3], which include top management commitment, speaking up culture, learning, awareness, being prepared, and flexibility. The study evaluated the performance of these REIs using a 5-point Likert Scale survey questionnaire based on the relevance to resilience dimensions. The results show 10 REIs contributing to the organization's resilience and the top four most important Resilient Dimensions (RDs). Moreover, this is the first study to evaluate and assess the organizational resilience level in Qatar's oil and gas sector. This study's results can be integrated into different organizations' strategies to improve the efforts to enhance national response to disturbances in governance.

Zimbabwe has ambitious and laudable GHG mitigation targets. Compared to a coal based future emissions reductions by 33% per capita by 2030 are targeted, by implementing a set of identified nationally determined contributions (NDCs). If historical climate conditions continue, it can do this at low or negative cost. However, anticipated conditions may not continue, and of the planned emissions reductions in the NDCs, 88% would come from the expansion of hydropower, which is driven by rainfall. If climate change causes the extreme droughts witnessed in recent years to become more frequent, embarking on Zimbabwe’s NDC future (underpinned by its official system development plan) may be expensive and further cripple the economy. Note that the economy is already being strangled by constrained power supplies due to unusually dry conditions in the Zambezi river basin. If the NDC Future is pursued, but the climate becomes drier, proactive efforts might be made to overcome the power shortages. However, this may result in a rapid ramp up of greenhouse gas emissions if the country turns to coal to reinforce its system and increase its resilience against hydropower vulnerability and the costs that would otherwise ensue. If the country were to keep its NDC investments and supplement them with more aggressive deployment of clean adaptation options, strongly positive outcomes appear possible. Specifically, this would require increased deployment of renewable energy technologies, a restructured power market, and deep increases in energy efficiency investments. In so doing, the country would not only exceed its NDC targets, but also reduce costs in a manner that is climate resilient. This would not remove the country's need for hydropower and some level of coal reliance. However, it will introduce requirements to ensure flexibility in both hydropower and coal power production. For hydropower, power stations will need to provide and be recompensed for providing ‘balancing services’, that is, storing water and producing electricity when the wind is not blowing, nor sun shining. In order to ensure continuous output from mines, it may require intelligent stockpiling combined with dynamic forecasting. This would apply not only to production in Zimbabwe, but potentially for neighboring countries. Doing so would allow predictable mining activities, but allow electricity systems to absorb low cost, low carbon hydropower at high rainfall periods. To make the NDCs resilient via clean adaptation, strong institutional restructuring is required. However, internalizing those costs and moving to advanced market structures and business cases may strain the capacity of current institutions.

Pharmaceutical companies use crisis management as a vital technique to lessen the effects of catastrophes. In this study, the author highlighted the significance of supply chain management for crisis management in the pharmaceutical industry. The study concludes by discussing the role that supply chain management plays in crisis management and offering pharmaceutical businesses advice on how to run their supply chains in an emergency. We conclude after conducting a thorough analysis of the literature review and regress research. These are actions that pharmaceutical organizations can take to strengthen their supply chain management skills and enhance their crisis readiness: Identifying possible supply chain risks through routine risk evaluations; Creating backup strategies in case the supply chain is disrupted; Establishing procedures to guarantee a smooth and efficient supply of medications in an emergency; and Collaborating with vendors and other stakeholders to increase the resilience of the supply chain. The pharmaceutical industry is critical to healthcare, providing safe and effective medicines to patients. Supply chain management is essential for maintaining the flow of medicines during crises. Proactive risk assessment, backup plans, teamwork, and efficient communication are all critical for crisis management in the pharmaceutical industry, particularly in supply chain management. In India, inadequate transportation infrastructure leads to a significant expense for pharmaceutical businesses. To mitigate negative effects, such as reduced healthcare access and insurance coverage, the study suggests implementing multi-echelon, closed-loop green supply chain models. These strategies aim to minimize total costs and ensure efficient resource management. To improve the resilience of pharmaceutical and consumable medical equipment during crises, policymakers should integrate modern technologies and consider business complexity, economic development, intense competition, and changing customer needs. The relationship between manufacturers, distributors, prescribers, and insurance organizations as purchasers should also be considered.

This article offers a theoretical framework to contextualise the concept of "Safety Culture" in the field of public safety and Disaster Risk Reduction (DRR), with the aim of supporting its understanding and measurement in our communities and finally attaining more disaster resilient societies. The work discusses the role of cultural dimensions in risk management and DRR and highlights the relevance of building knowledge and practices based on the consideration of culture and cultural variability in all phases of the risk management process. After an overview of the Safety Culture concept, including its origins, developments and applications, the text explores the transferability of this concept to the context of public safety. Then, based on the existing Safety Culture models, metrics and indicators from other sectors, the paper proposes an original definition of Safety Culture for the DRR context, shaped on a series of elements and dimensions specifically outlined for this context. Finally, the expected benefits of the application of Safety Culture concepts and techniques for further advances in DRR is discussed.

Previous literature highlights the impact of COVID-19 on family functioning. Less is known about the impact of the pandemic on families of pediatric cancer patients. In order to determine universal and unique risk and resilience factors of these families during the pandemic, a qualitative analysis was conducted on families currently receiving cancer treatment at a midwestern hospital. Results of the data analysis depict ways in which these families have been impacted by and have adapted to COVID-19. These findings suggest that families of pediatric cancer patients have unique experiences in the context of COVID-19, in addition to universal experiences outlined in previous literature.

This paper proposed a damping method for enhancing oscillatory stability performance of power systems with high penetration of renewable energy by a resilient wide-area multi-mode controller. The resilient wide-area multi-mode controller is used as an additional controller in a renewable energy system with a battery energy storage to enhance the damping of the critically weak modes. The weak modes are likely to be triggered in the event of line outages or any other disturbances, and the system may become unstable in the absence of proper corrective and preventive control. A firefly algorithm has been employed to design such a controller. Eigenvalue analysis and time-domain simulation are used to analyze the performance of the proposed controller in a realistic representative power system. From the simulation results, it is evident that the oscillatory stability performance of the renewable rich power system can be enhanced with the proposed control to keep the damping on critical modes to the industrial standards. Furthermore, renewable energy penetration can be increased significantly in the realistic representative system by introducing the proposed controller without disturbing the oscillatory stability margin.

Analysis of Fatigue Strain, Fatigue Modulus and Fatigue Damage for the modeling of concrete plays a vital role in the evolution material behaviour which is heterogeneous and anisotropic in nature. In this paper, the Level-S nonlinear fatigue strain curve, fatigue modulus curve, residual strain curve of concrete in compression, tension, flexure and torsional fatigue loading were proposed using strain life approach. The parameters such as physical meaning, the ranges, and the impact on the shape of the curve were discussed. Then, the evolution model of fatigue modulus was established based on the fatigue strain evolution model, fatigue modulus evaluation model, residual strain evaluation model, secondary strain evaluation model. The hypothesis of fatigue modulus is inversely related with the fatigue strain amplitude. The fatigue evolution of concrete damages the bond between material grains, changed the orientation of structure of molecules and affects the elastic properties resulting in the reduction of material stiffness and modulus by utilizing strain life analysis, regarding stiffness degradation and inelastic deformation by formation of microcracking, macro cracking, cracking which is heterogeneous and anisotropic in nature . This paper presents the Fatigue Strain Life Model and analyses of fatigue strain, fatigue modulus and damage parameters of concrete which is capable of predicting stiffness degradation, inelastic deformation, strength reduction under fatigue loading. Hence, the obtainable results were compared with experimental results for the validation of the proposed model.

Green investors have expressed concerns about the environment and sustainability due to the high energy consumption involved in cryptocurrency mining and transactions. This article investigates the safe haven characteristics of clean energy stock indexes in relation to 3 cryptocurrencies, taking into account their respective levels of "dirty" energy consumption from May 16, 2018, to May 15, 2023. By virtue of analyzing a tumultuous era in the global economy, the level of integration between clean energy stock indexes and cryptocurrencies will be inferred by using Gregory and Hansen's methodology. Furthermore, to assess the presence of a volatility spillover effect between clean energy stock indexes and “dirty-classified” cryptocurrencies, the ?-test of heteroscedasticity of two samples from Forbes and Rigobon will be employed. The empirical findings show that clean energy stock indexes may offer a viable safe haven for dirty energy cryptocurrencies. However, the precise associations differ depending on the cryptocurrency under examination. The implications of the study's results are significant for investment strategies, this knowledge can inform decision-making procedures and facilitate the adoption of sustainable investment practices. Investors and policymakers can gain a deeper understanding of the interplay between investments in renewable energy and the cryptocurrency market.

This paper aims to provoke fundamental thinking and action around the value and importance of socially-equitable development to the economic advancement, resilience, and prosperity of communities, as we contend with the 21^st Century grand challenge of the changing climate and disasters. As local communities and the global community have experienced an increased frequency, intensity and duration of natural and man-made disasters over the past several decades, opportunities have also grown to identify and reap the benefits of socially-equitable economic development. Reflecting on the COVID-19 pandemic, we discuss the critical importance of socially-equitable economic development to the resilience and sustainability of communities and the infrastructure that supports them. To this end, we: (1) examine what constitutes socially-equitable economic development at different spatial scales of community; (2) explore whether socially-equitable development can occur at different scales of community; (3) explicate the importance of formally considering the inputs, processes, outputs and outcomes for socially-equitable development; (4) explain why the pursuit of equal distribution of the benefits and burdens of development is a necessary but not sufficient endeavor for socially-equitable economic development; (5) analyze the relationships between socially-equitable development, and resilient and sustainable infrastructure and communities; (6) explain why socially-equitable development should be a key component of infrastructure and community resilience strategies in the 21^st Century; and, (7) explain why socially-equitable development can ultimately be viewed as a long-term strategy for prosperity.

Achieving and sustaining stability for economic growth remain the greatest and most immediate development challenge for Ethiopia. For natural resource-based economies especially maintaining stability and growth depends fundamentally upon climate change adaptation and mitigation. The close links between climate and Ethiopia’s economy are reflected by the strong relationship between GDP growth rate and rainfall variability. A study by the World Bank projects that unless steps to build resilience are effective, climate change will reduce Ethiopia’s GDP growth by between 0.5 and 2.5% each year. Along with the challenges posed by climate change, a number of development opportunities are emerging in response to climate change which includes access to international climate finance. The international response to climate change in the form of external development finance plays a key role to support developing countries in their transition to a low-carbon, climate-resilient and sustainable development pathway. Therefore, this study was conducted to assess the flow and the overall contribution of climate finance to sustainable development in Ethiopia. Specifically, focused on outlining how climate finance is currently reconciled in the existing Ethiopian climate change governance and its contribution to sustainable development. In order to achieve these objectives, data were collected from different sources. The Rio Marker methodology applied to review climate financial flow over the 5 year period. The result reveals that, climate change is central to development agendas despite its recent emergence in the mainstream, with various initiatives under way to combat or reduce its impacts in Ethiopia. In addition, the amount of climate finance from the developed countries to Ethiopia shows some fluctuation for the past five years. In general, the overall flow of climate finance mostly targeted climate adaptation actions which spur and enable the transition towards climate-resilient growth and sustainable development.

In this paper, an upper bound of the longitudinal elastic modulus of unidirectional fibrous composites is proposed according to strength of materials approach, on the premise that the fiber is much stiffer than the matrix. In the mathematical derivations, the concept of boundary interphase between fiber and matrix was also taken into account and the main objective of this work is the attainment of an upper bound for the interphase stiffness with respect to fiber concentration by volume. The novel element here is that the authors have not taken into consideration any specific variation law to approximate the interphase modulus. The theoretical results arising from the proposed formula were compared with those obtained from some reliable theoretical models as well as with experimental data found in the literature, and a satisfactory agreement was observed.

This study investigates the relationship between clean and dirty energy markets, specifically focusing on clean energy stock indexes and their potential as hedging assets and safe havens during periods of global economic uncertainty. The research analyzes five clean energy indexes and five dirty energy indexes from May 2018 to May 2023, considering events like the global pandemic and the Russian invasion of Ukraine. The main objective is to examine the causal relationship among different stock indexes pertaining to dirty and clean energy by using the Granger causality test (VAR Granger Causality/Block Exogeneity Wald Test) to determine whether clean energy indexes can predict future prices of dirty energy indexes. However, the findings reveal that clean and dirty energy indexes do not exhibit hedging characteristics or serve as safe havens during times of economic uncertainty, rejecting the research question. These results have important implications for investment strategies, as assets lacking safe haven characteristics may not preserve portfolio efficiency in uncertain times. The study's insights provide valuable guidance for investors, poli-cymakers, and participants in energy financial markets. It highlights the need to adapt investment approaches and seek alternative options to navigate uncertain economic conditions effectively.

Groundwater is the most extracted raw material in the world with global annual withdrawal rates of 800–1500 km3/year. In East and Southern Africa, 70 % of the population are reliant on shallow groundwater as their primary drinking water source. With increased population growth, intensification of agriculture and industrialization, conflicting demands on groundwater present a challenge to achieve the Sustainable Development Goals (6,3,11,12,15). Between 2015 and 2018, the Horn of Africa was affected by a series of climatic induced events, namely El Nino, La Nina and the Indian Ocean Diopole. These events modified the variability of rainfall patterns and resulted in long periods of low rainfall, low recharge and high evapotranspiration. As a result, shallow aquifers in alluvial deposits of Somali region have low yields and produce brackish and saline water. That situation prompted humanitarian water professionals to finance the transportation of water from selected locations with high groundwater potential through water trucks to areas facing groundwater depletion and drought. To address this challenge, UNICEF explored alternative, sustainable deeper groundwater sources that could be extracted using solar water pumping technology for multi water use. This paper describes a three-phase methodology of deep groundwater development of wells in the Ogaden Jesoma sandstone aquifers of the Somali region of the Horn of Africa to a depth of 600 meters below ground level. The results concluded that the deep sandstone aquifer of Jesoma can provide fresh water with yields of 15 l/s to the local population of Somali region. to the study provided insights into deep groundwater identification and development as well as adaptive deep boreholes drilling as a source for climate resilient water supplies.

Metal nanoframes have gained tremendous attention in the domain of modern research and development due to their distinctive 3D spatial structure, efficient physiochemical properties, and comparatively good activity. Different strategies have been implicated by the researchers to design nano frame of varying chemical nature and shapes. Most of the synthetic protocols being adopted to design a nano framee, mainly consist of two main steps, nucleation and growth of solid particles and secondly excavation of the interiors. In this context, many synthetic methods are overviewed. To show their unprecedented performance or activity, a few applications in catalytic, biomedical, theranostic, SERS, sensing of different materials, reduction of CO2, etc are also discussed.

This paper seeks to explore some of the issues to be welcomed but also warned against in general but also specified from an illustrative sample of ‘smart’ projects that caused outcomes that were neither ‘smart’ nor ‘resilient’. These give pointers to a ‘thirdspace’ as a descriptor of the most advanced application of sustainability thinking regarding relational spatial development planning. This contribution examines difficulties in delivering both ‘smart’ and ‘resilient’ responses of interest or value to citizens confronted with recurring crises that derive directly or with implications for sustainability issues. Identified are constraints and issues acting as obstacles to governance, management ‘layering’ and ‘learning’ causing failures of many weak or never-delivered projects. Critiques of static, or worse ‘magical’ thinking are increasingly invoked in consequence. In the analysis of ‘live cases’ all these problems showed up ‘in real life’. The author was pleased that his mentor, the late UCLA planning theorist Ed Soja’s application of the idea of ‘Thirdspace’ received prescient vindication from the results of this adjudication.

Pollinators are being threatened globally by urbanisation and agricultural intensification, driv-en by a growing human population. Understanding these impacts on landscapes and pollinators is critical to ensuring a robust pollination system. Remote sensing data on land use attributes have previously linked honeybee nutrition to land use in the Western Honeybee (Apis mellifera L.). Here, we instead focus on the less commonly studied Apis cerana japonica – the Japanese Honeybee. Our study presents preliminary data comparing forage (honey and pollen) with land use across a rural-urban gradient from 22 sites in Kyushu, southern Japan. Honey samples were collected from hives between June 2018 and August 2019. Pollen were collected and biotyped from hives in urban and rural locations (n = 4). Previous studies of honey show substantial vari-ation in monosaccharide content. Our analysis of A. cerana japonica honey found very little varia-tion in glucose and fructose (which accounted for 97% of monosaccharides), despite substantial differences in surrounding forage composition. As expected, we observed temporal variation in pollen foraged by A. cerana japonica, likely dependent on flowering phenology. These prelimi-nary results suggest that the forage and nutrition of A. cerana japonica may not be negatively af-fected by urban land use. This highlights the need for further comparative studies between A. cerana japonica and A. mellifera as it could suggest a resilience in pollinators foraging in their na-tive range.

The Ranau Earthquake that struck on 5, June 2015 and follow by February 2018 and April 2021, were a new disaster in Sabah and caused many Sabahan to panic. The unpredicted disaster also caused a serious impact on all aspects of life in Sabah. The earthquake has caused severe damage to eight primary schools in the vicinity of the epicenter; although no casualties were reported. However, the disaster has passing deep psychological effects among students. In this study, we examine how the primary school teachers enabled the student to be resilient during and after the disaster. Based on the interviews of 16 primary school students it was revealed that most of the teachers used WhatsApp to support resilience during and after the earthquake. Interviews with 16 primary school teachers revealed there were two main reasons for them to communicate with students namely, delivering emotional aid and monitoring their stress. Based on student interviews, five content categories of emotional support were identified: caring, reassuring, emotion sharing, belonging, and distracting. The main contribution of this study is social media can be used as a spontaneously and proactive tool to support student's resilience during and after the earthquake trauma.

Rice wheat cropping system prevailed in more than fifty percent areas of rice belt of central and upper Punjab. In this area yield of wheat is curtailed every year due to late harvesting of rice crop. In addition to this, lack of machinery for direct sowing of wheat into combine harvested rice is a major limitation for timely wheat sowing due to presence of heavy straw load dropped by the combine harvester. A field experiment was planned and executed during two consecutive years 2017-18 and 2018-19 at Agronomic Research Station Farooqabad to evaluate the various stubble management techniques for the sowing of wheat in a rice-wheat cropping system. The Experiment consist of four treatments Happy seeder (Zero tillage), conventional sowing without burning of rice stubble, Rotavator two times, and Conventional sowing with the burning of rice stubble. The trail was arranged as a Randomized Complete Block Design with four replications having a net plot size of 8 m x 20 m. The Treatments have significant effects on Plant height, Number of productive tillers, 1000- grain weight, and grain yield. Maximum plant height (94.74 and 94.25 cm), numbers of productive tillers/m2 (324.00 and 322.25), 1000-grain weight (36.35 and 36.20 g), grain yield (3780 and 3613 kg/ha), and benefit-cost ratio (2.54 and 2.43) were recorded from the treatment happy seeder (Zero tillage) during both years respectively. It is concluded that the sowing of wheat with a happy seeder (Zero tillage) is an appropriate technique in the rice-wheat cropping system by protecting the environment and soil health from the burning of rice stubbles.

The COVID-19 pandemic has put lifestyles in question, changed daily routines and limited citizen freedoms that seemed inalienable before. A human activity that was greatly affected since the beginning of the health crisis is mobility. Focusing on mobility, we aim to discuss the transformational impact that the pandemic brought on this specific urban domain, especially with regards to the promotion of the smart growth agenda and the acceleration towards the smart city paradigm. We collect 60 initial policy responses related to urban mobility from 86 cities around the world and analyse them based on the challenge they aim to address, the exact principles of smart growth and sustainable mobility that they encapsulate and the level of ICT penetration. Our findings suggest that emerging strategies, although mainly temporary, are transformational, in line with the principles of smart growth. As a result the pandemic becomes an opportunity for shifting towards more sustainable urban planning and mobility practices. However, most policy responses adopted during the first months of the pandemic fail to leverage advancements made in the field of smart cities, and to adopt off-the-shelf solutions such as in monitoring, alerting and operations management.

There are strong indications that the built environment intertwines consistently with the advent of Covid-19 and the need for post-disaster recovery. The advent of the Covid-19 pandemic has exerted adverse effects on both human and global developments; while the efforts to combat this menace call for an integrated human social capital index. This research seeks to adequately enhance the comprehension of how the built environment can be enhanced through resilience in the backdrop of the Covid-19 pandemic. The study's goal is to investigate the impact of a resilient built environment on increasing resilience in the aftermath of the Covid-19 pandemic in Nigeria. Overall, the quantitative study test the impact of four built environments resilience indices (Built Environment capitals; Disaster management indices, Awareness of the Covid-19 Pandemic, and Built Environment adaptive strategies) on Human social capital and Covid-19 pandemic Indices. This study reveals the role of human social capital in achieving a resilient built environment in the wake of Covid 19 pandemic in Nigeria. Also, built environment capitals, disaster management indices, and awareness on the Covid-19 have indirect effects on Covid-19 pandemic indices through the human social capital. The implication of the study is useful for Post-Covid 19 recovery; which is important for future planning of the built environment in Nigeria.

Rapid urbanization and climate change effects may cause dramatic change of ecosystem functions in cities, thereby inevitably affecting the growth performance of ancient trees. Few studies have explored species diversity and spatial differentiation in Benin urban area. Here, we took Cotonou city to do that. The objective of the study conducted in the city of Cotonou, was to determine the predominant level of plant diversity in the city's land-use units. The urban green frame was subdivided into six land use units, namely establishments, residences, green spaces, commercial areas, administrative areas, and roads. The forest inventories were carried out in 149 plots with surfaces evaluated at 2500 m2 each. A total of 62 tree species in 55 genera and 27 families were recorded. The results show that the flora of the city of Cotonou is characterized by a strong preponderance of exotic species. The most abundant species with high ecological importance (IVI) in the different type of land use of the city are Terminalia catappa (IVI=121.47%), Terminalia mentally (IVI=90.50%), Mangifera indica (IVI=64.06%) and Khaya senegalensis (IVI=151.16%). This study shows that a directed development of this urban vegetation could improve the resilience of urban life to climate hazards through the provision of urban ecosystem services, potential ecological infrastructure foundations and urban nature-based solutions.

There is consensus that climate variability and change is impacting food security in Eastern Africa, and that conventional extension approaches, based on top-down model of information dissemination and technology transfer, are too inadequate to help smallholder farmers tackle increasingly complex agro-climatic adversities. Innovative service delivery options exist but are mostly operated in silos with little effort to explore and blend them. There are efforts to develop a blended Climate-Resilient Farmers Field School methodology to address the gaps, with objective to improve participants’ knowledge, skills and attitude to apply the blended approach and to sensitize actors on what needs to be advocated at the policy level. Some 661 local trainers/facilitators (ToT/ToFs), 32% of them women and 54% youth, were trained across Kenya, Tanzania, and Uganda, with additional 76 Master Trainers (MToTs) trained to backstop the ToT/ToFs. Through the implementation, the process reached 36 agribusinesses covering some 237,250 smallholder farmers trained across Kenya, Tanzania, and Uganda on CSA technologies, practices, and innovations by the end of 2020. The blended approach offers lessons to transform extension to help farmers improve food security and resilience. Preliminary findings indicate that the process is rapidly shaping individual adaptive behavior and group adaptive thinking. Lessons also show a strong need for agronomists to work more closely with agro-meteorologists to ensure that farmers are properly guided to participate appropriately in the co-generation and application of climate information and agro-weather advisories, which they can interpret easily and utilize for their agricultural production purposes. Experience from this initiative can be leveraged to develop scalable participatory extension and training models

China is facing the development requirements of the dual carbon goal and the construction needs of resilient cities. Currently, high-strength composite structural systems are a feasible solution, but their research and application in the construction field are insufficient, especially for the combination forms of concrete above C100 and steel above Q550. In response to this issue, this paper proposes a new high-performance structural system, namely the composite frame - high-strength steel plate wall core tube resilient structure system, which includes high-strength steel plate (Q550) - ultra-high strength concrete (C100) shear walls, replaceable energy dissipation coupling beams, and composite frames. Taking a 200 meter building as an example, this paper designs and establishes calculation models for a conventional reinforced concrete frame-core tube structure and a high-performance structure based on PKPM software, and establishes elastoplastic analysis models for both based on SAUSAGE software, and then conducts dynamic elastoplastic time history analysis and seismic resilience assessment of structures under design basis earthquakes (DBEs), maximum considered earthquake (MCEs) and extremely rare earthquakes (EREs). Research has shown that compared to conventional structures, new high-performance structures can effectively reduce the size of shear walls, reduce the self-weight of the structure, and enhance the space for building use; The story drift ratio has higher redundancy than specification limits, lower plastic damage and overall stiffness degradation of the structure, and better seismic performance; The seismic resilience has significantly improved, especially in the case of casualties, which can better ensure the safety of people's lives and property.

Communication infrastructures may be partially or completely destroyed due to the natural disasters, especially the disruption of a few critical network devices, leading to the collapse of communication services.To address this issue, we constructed a layered, multi-domain data transmission architecture based on a UAV-assisted resilient network. Initially, the UAV controllers perceived the network status and learning the temporal- spatial characteristics of air-to-ground networks links. Subsequently, we developed a multi-domain routing algorithm based on federated reinforcement distillation, called FedRDR, enhancing the generalization capabilities of routing decision model through the augmentation of training data samples.We conducted simulation experiments, and the results indicate that the FedRDR algorithm has an average communication data volume of approximately 45.3KB between each domain controller and server. Compared to parameter transmission through the federated reinforcement learning algorithm, FedRDR can reduce the amount of transmitted parameters by approximately 29\%. Therefore, the FedRDR routing algorithm promotes knowledge transfer, accelerates the training process of intra-domain intelligent agents, and decreases the communication data volume between domain controllers and servers.

Energy poverty, defined as a lack of access to reliable electricity and reliance on traditional biomass resources for cooking, affects over a billion people daily. The World Health Organization estimates that household air pollution from inefficient stoves causes more premature deaths than malaria, tuberculosis, and HIV/AIDS). Increasing demand for energy has led to dramatic increases in carbon emissions. The need for reliable electricity and limiting carbon emissions drives research on Resil-ient Hybrid Energy Systems (RHES) that provide low-carbon energy through combined wind, so-lar, and biomass energy with traditional fossil energy, increasing production efficiency and relia-bility, and reducing generating costs and carbon emissions. Microgrids have been shown as an ef-ficient means of implementing RHES, with some focused mainly on reducing the environmental impact of electric power generation. The technical challenges of designing, implementing and ap-plying microgrids involve conducting a cradle-to-grave life cycle assessment (LCA) to evaluate these systems' environmental and economic performance under diverse operating conditions to evaluate resiliency. A sample RHES has been developed and used to demonstrate implementation in rural applications. This system can provide reliable electricity for heating, cooling, lighting, and pumping clean water. This paper's primary focus is the challenges of using resilient energy sys-tems in the Middle East.

Solid waste management is challenging in many countries, especially developing economies. The impacts of the pandemic continue to be felt and have indicated secondary impacts on waste management. This work draws on data on household solid waste (HSW), healthcare waste (HCW), and recyclables collected in the Rio de Janeiro municipality, analyzed from January 2018 to December 2022. As expected, the collection of HCW in Rio de Janeiro municipality experienced a noticeable increase in 2020 and 2021, with an average of 46.9 tons and 54.3 tons collected, respectively. Notably, 2021 saw a significant 30% increase compared to 2019 (p-value<0.001). No significant difference was observed in recyclables collected from 2018 to 2021. The average recyclable amount was 3,638 and 3,747 tons per month in 2020 and 2021. In contrast, the monthly amount was equal to 4,819.5 tons in 2022, representing an increase higher than 32% compared to previous years (p-value<0.05). Our findings can be used to adapt and improve waste systems to cope with similar emergency scenarios in the future, serving as a template for municipalities with similar challenges.

(1)Background: To perform a simulation-based analysis of 2019 COVID-19 outbreak and how would the electronic commerce indursty help China’s economy resilient. (2)Methods: As the epidemic continues, it is possible to use Computable General Equilibrium model to simulate the economic consequences and analyse the role of electronic commerce industy in COVID-19 outbreak. (3)Results: Estimates and models produced at the time of the outbreak suggested that COVID-19 could have a catastrophic effect on China’s economy. National statistics were examined for anomalies that corresponded to the timing of COVID-19 outbreak and, where possible, the size of any gain or loss found estimated. Our analysis suggests that the electronic commerce industry could help China’s economy to recover by stimulating consumption, improving technological level and expanding investment. (4)Conclusions: This exercise holds important lessons for estimating the electronic commerce’s role of similar events – such as pandemic influenza – and measures to recover the economy. We suggest that electronic commerce industry should be paid more attention in economic development, especially in epidemic time. The implications of our findings are discussed in the light of a prospective epidemic.

An increasing number of studies have shown that safety culture factors have a substantial influence on safety performance in a variety of industrial sectors. These factors' impact on safety performance is unclear, especially at the public service and statutory authorities. On the other hand, the understanding of indicators for safety performance in every working sector in Malaysia is on the continuing progress. Hence, this study's contribution is to explore the influence of safety culture factors (i.e., management commitment and supervision in safety, safety system) and safety competence on safety performance in government paramedic training institutes. IPMA (importance-performance map analysis) is a technique used in Smart PLS to determine the significance and performance of each of these factors. The study was conducted via an online survey and involved 258 safety and health committee members in Ministry of Health paramedic training institute. As a matter of relevance, the IPMA's empirical data study revealed that management commitment and supervision in safety were the predominant factors in determining safety performance. Meanwhile, for performance, the findings showed that worker involvement, safety system, and safety competence perform well in determining the safety performance.

Attack on health has become a significant concern for non-belligerents of war, including healthcare personnel and facilities as witnessed in the ongoing Sudan conflict. About 1.5 billion people living in fragile and conflict-affected settings (FCAS) have a heightened need for essential health services. Conflicts often lead to the disruption of building blocks of health systems, lack of access to health facilities, failure of essential medical supply chains, the collapse of political, social and economic systems, migration of health care workers, and upsurges in illness. While health indicators often decline in conflict, health can also bring peace and harmony among communities. Investment in health systems and health diplomacy is a neutral starting point for bringing peace and mitigating conflicts. The international commitment towards Sustainable Development Goals (SDGs) provides the impetus to emphasize the relationship between health and peace with the amalgamation of SDG 3, SDG 16, and SDG 17. The inspection of how health should be used as a ‘tool for peace’ and not as leverage during the war must be reiterated by international institutions.

The burden of COVID-19 pandemic on health systems and the physical and mental health of healthcare workers (HCWs) has been substantial. This cross-sectional study aims to assess the effects of Covid-19 on the psychological wellbeing of mental health workers who provide care to a vulnerable patient population that have been particularly affected during this crisis. A total of 387 HCWs from across a large urban mental health service completed a self-administered questionnaire consisting of socio-demographic, lifestyle and work-based information and validated psychometric scales. Depression and anxiety were measured using the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale (GAD-7) respectively, sleep problems with the Athens Insomnia Scale (AIS), burnout with the Maslach Burnout Inventory (MBI) and resilience with the Resilience Scale-14 (RS-14). Multivariable logistic regression analysis was performed to determine potential mediating factors. Prevalence of burnout was notable, with 52% recording moderate/severe in Emotional Exhaustion, 19.5% moderate/severe in Depersonalisation and 55.5% low/moderate Personal Accomplishment. Over half of all respondents (52%) experienced sleep problems; the presence of depressive symptoms was a significant predictor of insomnia. An increase in potentially harmful lifestyle changes, such as smoking, alcohol consumption and over-eating was also observed. However, high Resilience was reported by 70% of the sample and the importance of this is highlighted. Female gender was associated with increased levels of depression and emotional exhaustion while those with a history of mental health conditions were most at risk of affective symptoms, insomnia and burnout. Overall, our study revealed considerable levels of psychological distress and maladaptive coping strategies but also resilience and satisfaction with organizational support provided. Findings can inform tailored interventions in order to mitigate vulnerability and prevent long-term psychological sequelae.

Understanding and measuring rock characteristics is crucial for scientific and practical applications. This study investigates stone quarries in Morelia, Michoacán, Mexico, evaluating ten quarries (both volcanic and crushed) using diverse analytical methods. Physical properties such as density were measured alongside mechanical tests like uniaxial compressive strength and ultrasonic pulse velocity. Results demonstrate that crushed materials exhibit superior physical and mechanical properties compared to volcanic stones. Advanced techniques including high-magnification scanning electron microscopy and X-ray fluorescence provided insights into surface morphology and elemental composition. Additionally, a novel finite element model was constructed, leveraging dynamic modulus of elasticity (Ed) derived from ultrasonic pulse velocity and maximum load data from uniaxial compressive strength tests. This model facilitated the classification of quarries based on their engineering characteristics. The findings underscore the model’s potential applicability to quarry assessments globally, offering valuable insights into optimizing stone resource utilization and enhancing structural integrity in construction projects.

The impact of climate change on water resources is a global issue with significant regional implications. The Ghba subbasin has experienced a higher socioeconomic change. In this paper, we assess the potential impact of climate change and human activity on water supply and demand in the Ghba subbasin of Ethiopia. We then evaluate the effectiveness of regional adaptation strategies for water supply management under these conditions. To do this, we use the statistical downscaling model (SDSM) and the water assessment and planning tool (WEAP) to simulate and analyse future water conditions. After running the model through a calibration and validation process, minimum (Tmin) and maximum (Tmax) temperatures, water demand and unmet water demand were projected for 2100 horizon, using different climate change scenarios. The results showed that the model's performance, calibration, and validation were all satisfactory. The analysis showed that, under A2 and B2 emission scenarios of the Intergovernmental Panel on Climate Change (IPCC), the mean precipitation will increase by 31% and 26%, respectively. The projected mean Tmax and Tmin will be warmer than the baseline period, with Tmax increasing by 1.5°C (A2) and 1.3°C (B2), and Tmin by 1.7°C (A2) and 1.5°C (B2) by 2100. The results also illustrate that in all scenarios, water consumption would rise and that this need will not be satisfied, and the pressure on water resources will intensify, resulting in a water shortage. The findings show that future water demand under the influence of climate change is projected to exceed 210 million cubic meters (MCM) by 2100. The findings show that while the evaluations of the recommended adaption techniques are effective, they are not enough to guarantee the Ghba subbasin's water sustainability.

forging a resilient path towards a low carbon future, this research delves deep into the intricate dynamics of the petroleum and petrochemical industries. By uncovering the challenges posed by transitioning to a low carbon economy, this study emphasizes their transformative potential as catalysts for growth. Through an in-depth analysis of the oil and natural gas sector, it not only sheds light on the carbon footprint of Nigeria's petroleum and petrochemical industries but also identifies the opportunities for sustainable transformation within these sectors. Ultimately, this research aims to pave the way for a greener future by unraveling the complexities and intricacies of building a low carbon economy."By incorporating the unique attributes of the petroleum and petrochemical industries and considering the evolving domestic and global context, this research investigates the challenges and prospects associated with the shift towards a low-carbon economy. It delves deep into the complexities of the sector, taking into consideration the distinct characteristics of petroleum and petrochemical production. Through a comprehensive analysis, this study aims to uncover the potential barriers and opportunities that arise from the pursuit of a sustainable and low-carbon future within the petroleum and petrochemical industries. In addition, this research thoroughly examines the current global and local conditions, shedding light on the opportunities for growth and transformation within the petroleum and petrochemical sectors. By presenting insightful perspectives and recommendations, the study aims to provide valuable guidance for fostering the development of a low-carbon economy within these industries. Ultimately, the research seeks to drive positive change by offering actionable insights and strategies to propel the transition towards a sustainable future.

In response to growing environmental concerns and the need for sustainable resources, researchers worldwide are examining how to develop materials that are environmentally friendly and that can be effectively recycled as they reach the end of their useful life. Particularly interesting are polyurethanes (PUs), a broad class of synthetic polymers utilized in several applications. However, because of the high energy consumption and unwanted chemical side reactions, their chemical recycling is challenging. A possible method for recovering PU is biological recycling, which breaks down complex compounds using microbes and enzymes. The biodegradation susceptibility of novel PUs using tree bark extractives as building blocks is examined in this work. The PUs were synthesized with partial or complete substitution of conventional fossil-based polyol (PEG 400) with hydrophilic extractives from black alder bark. Biodegradation experiments in sewage water and compost-enriched soil were conducted over 60 days. Results indicate increased biodegradation in PUs containing bark-based polyols, showing weight losses of up to 15.7% and 15.6% in sewage water and soil, respectively, compared to 9.6% and 12.4% for fossil-based PU. Analytical pyrolysis and FTIR analysis revealed more significant structural changes in extractives-containing PU materials after biodegradation compared to fossil-based PU, indicating the cleavage of covalent bonds mainly in carbohydrate and aromatic constituents of biopolyol. But the urethane bonds revealed the highest stability towards biodegradation compared to others linkages in both fossil-based and bio-based PU. The study demonstrates the potential of using bark-derived polyols to design PU materials suitable for biological recycling, contributing to more environmentally friendly waste management strategies. Further investigations are needed to optimize the conditions for the biological conversion of PU materials synthesized with bark-based polyols.

Researchers have reported various impacts of the COVID-19 outbreak on small-scale fishers, such as stagnating market demands, reduction of market price and income, etc. While literature have heeded to these impacts in a relatively short time frame, scant evidence exists on the changing impacts over time and on the detailed processes of how fishers have been coping with the challenges in a longer time period. Furthermore, few studies have comprehensively analysed the impacts and strategies from multiple perspectives. This study aims to explore the perceived impacts of the COVID-19 outbreak on small-scale fishing communities and to highlight the coping strategies adopted by fishers over a year since the initial outbreak, through a case study in Trang Province, Thailand. Analysis of both qualitative and quantitative data obtained through semi-structured interviews indicated that fishers wisely utilised natural, financial and social capitals at the early stages of the outbreak, while human capitals were essential for recovering from the impacts in the later stages. Our findings suggest that the adaptive capacity to flexibly change livelihood strategies are crucial, while alternative income source may not necessarily help small-scale fishers under stagnating global economy.

As a rapidly growing coastal megacity, Shanghai is continuously threatened with land subsidence issues since 1920s. Land subsidence was controlled in 1960s, however in 1990s, unconscious and dangerous urban underground space (UUS) exploration and tunneling development are causing further land subsidence. It is imperative to study previous relations towards future adaptive and resilient scenario modelling and planning. There are multiple cause-effect factors determined in the urban built environment of Shanghai megacity. This paper presents the current evidence based on the relations of the multifactor of the spectrum. Methods consist of understanding the cause-effect relations and spatiotemporal from the crucial period of 1960-2020. Data are collected secondarily from multiple open sourced databases. The results determine Shanghai are highly influenced by the UUS development induced-subsidence, tunneling leakage and weak spatial modelling. Spatiotemporal pattern has shown a mixed positive-negative impact: population, land subsidence is growing in parallel distribution (positive) with tunneling leakage, construction of tunneling, metro system, UUS development, building price, reconstruction area, GDP growth, land price, arable land decrease and further tunnel settlement in Urban City Centre, Pudong New Area, Minhang, Baoshan and Songjiang districts. These results are useful for further adaptive and resilient scenario modelling and spatial planning.

Achieving high-yield potential is always the ultimate objective of any breeding program. However, various abiotic stresses like salinity, drought, cold, flood, and heat hampered rice productivity tremendously. Salinity is one of the most important abiotic stresses that adversely affect rice grain yield. The present investigation was undertaken to dissect new genetic loci, which are responsible for salt tolerance at the early seedling stage in rice. A bi-parental mapping population (F2:3) was developed from the cross between BRRI dhan28/Akundi, where BRRI dhan28 (BR28) is a salt-sensitive irrigated (boro) rice mega variety and Akundi is a highly salinity tolerant Bangladeshi origin indica rice landrace that is utilized as a donor parent. We report stable QTLs for salt tolerance from a common donor (Akundi) irrespective of two different genetic backgrounds (BRRI dhan49/Akundi and BRRI dhan28/Akundi). A robust 1k-Rice Custom Amplicon (1k-RiCA) SNP marker genotyping platform was used to genotype this bi-parental population. After eliminating markers with high segregation distortion, 886 polymorphic SNPs built a genetic linkage map covering 1526.5 cM of whole rice genome with an average SNP density of 1.72 cM for the 12 genetic linkage groups. A total of 12 QTLs for nine different salt tolerance-related traits were identified using QGene and inclusive composite interval mapping of additive and dominant QTL (ICIM-ADD) under salt stress on seven different chromosomes. All of these 12 new QTLs found unique, as no other map from the previous study has reported these QTLs in the similar chromosomal location and found them different from extensively studied Saltol, SKC1, OsSalT and salT locus. Twenty-eight significant digenic/epistatic interactions identified between chromosomal regions linked to or unlinked to QTLs. Akundi acts like a new alternate donor source of salt tolerance except for other usually known donors like Nona Bokra, Pokkali, Capsule, and Hasawi used in salt tolerance genetic analysis and breeding programs worldwide, including Bangladesh. Integration of the seven novel and stable QTLs (qSES1, qSL1, qRL1, qSUR1, qSL8, qK8, qK1) reported in this investigation will expedite the cultivar development that is highly tolerant to salt stress.

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. Up to date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM(tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry, provided measures of pasture biomass from many overlapping high-resolution images. Repeated remotely piloted aircraft system measures throughout a growing season, were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM(tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

Background: Early diagnosis of postoperative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. Prognostic models based on various data could help in the early diagnosis of postoperative complications after cardiac surgery. Objectives: The purpose of the study was to determine whether an integrated approach based on clinical data along with metabolites and biomarkers had greater predictive value than the models built on less data. Methods: The study included patients (n=62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass), who were retrospectively divided into two groups depending on the presence (n=26) or absence (n=36) of postoperative complications of all types. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers (interleukin-6, procalcitonin, NT-proBNP, high-sensitivity troponin T) and metabolites (8 aromatic and 2 dicarboxylic acids were detected using gas chromatography-mass spectrometry). Results: Patients before surgery, compared with healthy donors, differed in the concentration of metabolites (p &lt; 0.05), and had concentrations of a number of biomarkers exceeding the corresponding reference values. Multivariate PLS-DA models, predicting the presence or absence of postoperative complications, were built using clinical data (ROC-AUC=0.71), concentrations of metabolites and biomarkers (ROC-AUC=0.60), and the entire data set (ROC-AUC=0.75). For comparison we built univariate models using the EuroScore2 (ROC-AUC=0.59) and SOFA (ROC-AUC=0.76) scales; concentrations of lactate (ROC-AUC=0.71), the dynamic changes of 4-hydroxyphenyllactic acid (ROC-AUC=0.69), and the sum of three sepsis-associated metabolites (ROC-AUC=0.70). The proposed complex model using entire data set was comparable to the SOFA scale, which confirms the expediency of searching for new predictive models based on a variety of factors.

Milk serves as a crucial source of natural bioactive compounds essential for human nutrition and health. The gastrointestinal digestion of milk proteins releases various biologically active peptides that positively impact human health and enhance physiological performance. With the growing production of cheese, Greek yogurt, and other dairy products, the volume of unutilized acid whey is significantly increasing. Acid whey, containing almost all whey proteins and approximately one-quarter of casein, presents a valuable raw material for generating peptides with potential health benefits. These peptides exhibit properties such as antioxidant, antimicrobial, anti-inflammatory, anticarcinogenic, antihypertensive, antithrombotic, opioid, mineral-binding, and growth-stimulating activities, contributing to improved human immunity and the treatment of chronic diseases. Enzymatic hydrolysis of milk proteins has been developed as a method to produce these peptides, facilitating their use in food fortification. To incorporate whey into functional beverages, it undergoes fermentation by lactic acid bacteria with proteolytic activity. To specifically enrich these beverages with peptides exhibiting targeted bioactivities, it is essential to select lactic acid bacteria strains possessing the requisite protease profiles. Additionally, the sensory properties of the final product can be enhanced using starter cultures with diverse metabolic capabilities, even in the absence of added flavoring agents. Given the imperative to develop waste-free technologies and enhance the profitability of the dairy industry, there is a growing need to efficiently utilize and transform acid whey into a broad spectrum of valuable products and food additives. This approach is justified not only by the nutritional benefits of the bioactive components in whey but also by the commercial potential of safe and health-enhancing products.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

The impact of interaction SS with MJO on rainfall along Java Island to East Nusa Tenggara has been studied using daily rainfall observation data from 2140 weather observation stations, which are also equipped with GSMaP, OLR, and ERA5 data. There were 61 SS events found, 17 during active MJO and 44 in inactive MJO. Rainfall increases more in the western study area when the active MJO and more in the eastern study area when the inactive MJO is due to SS. SS reduces rainfall in the Jakarta region, regardless of the activity or inactivity of the MJO. SS causes extreme rainfall to only occur in a small part of certain areas, so it tends to significantly reduce the possibility of extreme rainfall. In the southern part of the IMC, SS predominates over MJO in supporting increased water vapor transport. Large-scale synoptic circulations like the monsoon, SS, and MJO can interact with local land-sea wind circulation to produce spatiotemporal variability in rainfall on Java Island. Rainfall mostly increases in the afternoon and decreases in the morning when SS occurs, whether there is MJO or not. Convective instability analysis indicates that SS increases precipitation, most likely by lowering CIN and raising VIMFC.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

The Simultaneous Localization And Mapping (SLAM) scanner is an easy and portable Light Detection And Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO Scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data are used: the Total Station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons are tested: cloud to cloud, cloud to mesh, mesh to mesh, and edge detection board assessment. However, the results of the assessments confirm that the accuracy of indoor SLAM scanner measurements (5 mm) is greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud to cloud provides the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it is confirmed that SLAM scanners are effective tools for data acquisition.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

The temporary immersion system bioreactor named GreenTray®[2], [3]presents advantages over the existing ones. Firstly, there is no need to use forceps to move the shoots or plantlets in or out of the recipient, neither the use of scalpel to divide them. Secondly, the basis of the shoot can remain in the GreenTray® and can sprout again in several cycles of growth. These two characteristics reduce the costs in the process of micropropagation. Besides, shoots or plantlets development is enhanced by the aeration of the recipient, thus enriching with CO2 the recipient atmosphere. The chapter describes and provides images and values of interest in (1) the micropropagation process of some commercial Prunus or Pyrus rootstocks; (2) the plantlet growth under autotrophic conditions; and (2) the inoculation of plants with pathogens that reproduce in vitro the symptoms observed in vivo, using the GreenTray® bioreactor.

The aim of this retrospective study was to compare the clinical outcomes of treating isolated surgical neck fractures of the humerus using either the intramedullary nail or the MIROS (Minimally Invasive Reduction and Osteosynthesis System®) system. The study included 42 patients who underwent surgical treatment at the Polyclinic from January 2014 to June 2020. The results indicated that both treatments were effective; however, the MIROS system exhibited a higher complication rate. Intramedullary nailing was more reliable, thus recommended as the primary treatment method. This study underscores the importance of selecting the appropriate fixation technique to minimize complications and enhance patient outcomes.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. Our results revealed that the denitrification rate (DR), dissimilatory nitrate reduction to ammonium rate (DNRAR) and nitrogen fixation rate (NFR) of rhizosphere sediments exhibited a significant increase in artificially cultivated ditch. Compared with those in natural ditches, the richness of the bacterial community and the relative abundances of Bacteroidota, Verrucomicrobiota, Firmicutes, Anaeromyxobacter and Geobacter, which are involved mainly in the nitrogen cycle, in the rhizosphere sediments of artificially cultivated ditch were significantly greater due to improvements in environmental conditions. In artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, while DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels; and the dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

Pyrops candelaria is one of the common pests of fruit trees, but the research on the pathogenic microorganisms it may carry is very limited. Therefore, it is essential to reveal the pathogenic microbes it carries and their potential hazards. This study found a new virus from the transcriptome of P. candelaria, which was first reported in P. candelaria and named as PyCaV (Pyrops candelaria associated virus). RACE and bioinformatics assay revealed that the full-length of PyCaV is 10855 bp with the polyA tail, containing a single open reading frame (ORF) encoding a polyprotein consisting of 3171 amino acid (aa). The virus has a typical Iflavirus structure, including two rhv domains, a RNA helicase domain (HEL), a 3C cysteine protease domain (Pro), and a RNA-dependent RNA polymerase domain (RdRp). Further phylogenetic analysis revealed that this virus belongs to family Iflaviridae and sequence alignements analysis suggedted PyCaV is new member in unassigned genus of family Iflaviridae. Further in-depth analysis of virus-infection-part showed that PyCaV is distributing in the whole longan lanternfly, including its head, chest, and abdomen, but more PyCaV was identified in the chest. The distribution of PyCaV in different parts of P. candelaria was further explored, which showed that more PyCaV were detected in its piercing-sucking mouthparts and chest viscera. Statistical analysis showed that the PyCaV infection was affected by time and locations.

Apis mellifera, especially weak ones, are highly vulnerable to Carpoglyphus lactis mites, which can rapidly infest and consume stored pollen, leading to weakened colonies and potentially colony collapse. This study aimed to ascertain and investigate the prevalence of this mite in honeybee colonies across 9 provinces in the Republic of Korea (ROK). A total of 615 honeybee colonies were collected, from 66 apiaries during the spring and 58 apiaries during the summer of 2023. A 1,242 bp segment of the the Cytochrome c oxidase subunit 1 (COI) gene was amplified using the polymerase chain reaction method. The detection levels of C. lactis in the honeybees were compared between winter and summer. Based on the COI sequence analysis, the nucleotide sequence similarity of C. lactis mites isolated in the ROK with those from China (NC048990.1) was found to be 99.5%, and with those from the United Kingdom (KY922482.1) was 99.3%. This study is the first report of C. lactis in Korean apiaries. The findings in this study demonstrate a significantly higher detection rate in winter, which is 4.1 times greater than that in summer (p &lt; 0.001). Furthermore, the results underscore the usefulness of molecular diagnostic techniques for detecting C. lactis mites.