Supply chain flexibility plays a pivotal role in enabling organizations to adapt their marketing strategies to evolving consumer preferences in dynamic market environments. This qualitative study explores how supply chain flexibility dimensions—responsiveness, agility, resilience, and sustainability—impact marketing strategy adaptation. Through semi-structured interviews and document analysis, insights were gathered from industry practitioners across diverse sectors. Key findings highlight that responsive supply chains facilitate quick adjustments in production, distribution, and sourcing to meet changing consumer demands. Agility enables rapid reconfiguration of operations to capitalize on market opportunities and respond to disruptions effectively. Resilient supply chains mitigate risks and maintain continuity during crises, safeguarding customer satisfaction and brand reputation. Integrating sustainability practices not only meets regulatory standards but also aligns with consumer preferences for eco-friendly products, enhancing corporate social responsibility. Technological advancements such as AI, IoT, blockchain, and cloud computing enhance supply chain visibility, optimize decision-making, and support real-time responsiveness. Despite benefits, challenges like legacy systems, organizational silos, resistance to change, and resource constraints hinder effective implementation. Overcoming these barriers requires strategic leadership, cross-functional collaboration, and continuous investment in technology and talent. Embracing supply chain flexibility empowers organizations to navigate complexities, drive innovation, and sustain competitive advantage. By aligning supply chain capabilities with marketing strategies, companies can enhance market responsiveness, customer satisfaction, and long-term growth in today's dynamic business landscape

In today's volatile and competitive business landscape, achieving synergy between supply chain agility and marketing flexibility is imperative for organizations striving to enhance resilience and maintain competitiveness. This qualitative study explores the integration of these strategic elements and their impact on organizational performance in turbulent markets. Semi-structured interviews were conducted with key stakeholders from diverse industries to capture insights into adaptation strategies, challenges, and outcomes associated with aligning supply chain agility and marketing flexibility. Findings reveal that supply chain agility, characterized by rapid response capabilities to disruptions and fluctuating demands, is essential for optimizing operational efficiency and maintaining continuity in uncertain environments. Meanwhile, marketing flexibility enables organizations to adjust strategies swiftly based on real-time market insights, enhancing customer engagement and market responsiveness. The study identifies organizational silos, resistance to change, and technological limitations as primary barriers to achieving seamless integration. Benefits of synergy include improved customer satisfaction, accelerated time-to-market for new products, and enhanced competitive advantage through personalized marketing strategies and optimized resource allocation. Moreover, organizations that effectively integrate supply chain agility with marketing flexibility reported greater innovation, employee satisfaction, and sustainable growth.

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is fundamental. This paper presents a decision-making structure for large consumers having flexibility to manage electricity or natural gas consume to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural-gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural-gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of U$ 3.98 million over a six-month analysis period.

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

This research aims to explore the complex interplay between supply chain resilience (SCR), digital supply chain (DSC), and sustainability, focusing on the moderating influence of supply chain dynamism. The goal is to understand how these elements interact within the framework of contemporary supply chain management and how they collectively contribute to enhancing sustainability outcomes. The sample size is 300 CEOs and managers. The study approach integrates quantitative research methods. Structural equation modeling (SEM) is utilized to quantitatively analyze the direct and indirect effects of SCR and DSC on sustainability. The numerous surveys we conduct among supply chain ecosystem stakeholders provide a rich picture of practical implications and contextual nuances. In sum, our early findings generally support a positive relationship between SCR and sustainability in and of itself decrying the need for more resilient supply networks for sustainability. We further find the beneficial impact of digital technologies in promoting sustainability via enhancing environmental control and controlling for efficiency in supply chains. We also offer evidence to show that supply chain dynamism compounds the positive logic between SCR and DSC and sustainability. As a final word, it must be noted that our work speaks to the burgeoning Supply Chain Dynamism as moderator literature by examining the direct and contingent effects of SCR and DSC on not only performance but sustainability. By shedding light on the moderating role of dynamism, the study provides fresh insights into the multifaceted nature of supply chain management and sustainability practices. The study's findings enhance theoretical understanding by elucidating the synergistic effects of SCR, DSC, and sustainability in dynamic supply chain settings. It augments existing theoretical frameworks by integrating concepts of resilience, digitalization, and sustainability into a comprehensive model. Practical and economical, the research offers actionable guidance for organizations aiming to improve sustainability performance through resilient and digitally advanced supply chains. By acknowledging the role of supply chain dynamism, managers can tailor strategies to manage disruptions effectively and leverage digital innovations. Economically, adopting sustainable practices can result in cost savings and competitive advantages. The research emphasizes the importance of aligning supply chain strategies with sustainability goals to drive long-term value and societal impact.

The focus of this paper is on supply chain strategy formulation. A conceptual theory approach is used for investigating and identifying the relationship between multiple elements, dimensions, forces and factors that influence and affect the supply chain strategy formulation in Greenfield context, specific to the slate mining industry. The research study involved secondary data review and series of 20 qualitative interviews, followed by 2 group discussions, one with mining and transportation experts external to the supply chain and one group discussion with supply chain internal experts. Through critical analysis, a number of problems emerge and the process of addressing these problems, results in a new framework for evaluating the relationship between business and supply chain strategy, specific to Greenfield project and integration design context.

This paper proposes the impact of plug-in electric vehicles integrated into power distribution system based on voltage dependent control. The plug-in electric vehicles was modeled as the static load model in power distribution systems under balanced load condition. The power flow analysis is determined by using the basic parameters of the electrical network. The main point of this study are compare with voltage magnitude profiles, load voltage deviation, and total power losses of the electrical power system. There are investigating the affected from constant power load, constant current load, constant impedance load and plug-in electric vehicles load, respectively. The IEEE 33 bus test system is used to test the proposed method by assigning each load type to a balanced load in steady state and applied the solving methodology based on the bus injection to branch injection matric, branch current to bus voltage matrix, and current injection matrix to solve the power flow problem. The simulation results showed that the plug-in electric vehicles load had the lowest impact compared to other loads. The lowest plug-in values for the electric vehicle loads were 0.062, 119.67 kW and 79.31 kVar for the load voltage deviation, total active power loss and total reactive power loss, respectively. Therefore, this study can be verified that the plug-in electric vehicles load were affected to the lowest of the electrical power system in condition to same sizing and position. So that, in condition to the plug-in electric vehicles load added into the electrical power system with the conventional load type or complex load type could be considered that the affected from the plug-in electric vehicles load in next study.

Green supply chain management (GSCM) is fundamentally growing as a key component of sustainability, owing to its ability to improve supply chain competitiveness and performance within firms. The body of literature on GSCM within the manufacturing sector in developing countries such as South Africa is still limited. The study examined the relationships between GSCM practices, dynamic capabilities, and supply chain performance in the South African manufacturing sector. The study was quantitative, involving a sample of 402 supply chain professionals drawn from the manufacturing sector across the four provinces of the country. Hypotheses were tested using structural equation modelling based on the SMART PLS technique. Eco-design Green distribution and green training impacted positively on dynamic capabilities, the latter of which in turn is linked to improved agility, reliability, and reduced supply chain costs. The study validates the importance of GSCM as a driver of dynamic capabilities and performance in manufacturing environments within developing countries such as South Africa.

Background: The prevalence of human T-lymphotropic virus type-1 (HTLV-1) infection is higher in women, and sexual intercourse has been described as an important route of male-to-female transmission. The present study aimed to quantify HTLV-1 proviral load (PVL) in vaginal fluid, and to investigate correlations with PVL in peripheral blood mononuclear cells (PBMC). Methods: HTLV-1-infected women were consecutively recruited at a multidisciplinary center for HTLV patients in Salvador-Brazil. All women underwent gynecological examinations to obtain cervicovaginal fluid and venipuncture for blood collection. PVL, as measured by real-time quantitative polymerase chain reaction (RT-qPCR), was expressed as the number of copies of HTLV-1/106 cells in blood and vaginal fluid samples. Light microscopy was used to assess cervicovaginal cytopathology and vaginal microbiota. Results: In the 56 included women (43 asymptomatic carriers and 13 diagnosed with HTLV-associated myelopathy/tropical spastic paraparesis-HAM/TSP), mean age was 35.9 (SD±7.2) years. PVL was higher in PBMCs [median: 23,264 copies/106 cells (IQR: 7,420 - 64,371)] than in vaginal fluid [451.9 copies/106 cells (IQR: 0 - 2,672)] (p<0.0001). PVL in PBMCs was observed to correlate directly with PVL in vaginal fluid (R=0.32, p=0.01). PVL was undetectable in vaginal fluid samples from 20 women (35.7%), most of whom were asymptomatic (19/20). Cytopathologic analyses revealed no differences between women with detectable or undetectable PVL. Conclusion: HTLV-1 proviral load is detectable in vaginal fluid and correlates directly with proviral load in peripheral blood. This finding suggests that sexual transmission of HTLV-1 from female to male may occur and vertical transmission, particularly in the context of vaginal delivery.

Today’s export-based manufacturers face the need to implement green supply chains more efficiently in order to overcome environmental barriers in increasingly competitive export markets and to improve export performance. This study examines the structural relationship between environmental collaboration, green innovation capacity, and performance based on the findings of previous studies in order to identify the factors affecting the green supply chain management (GSCM) performance of Korean export-based manufacturers. The study finds that environmental collaboration in the green supply chain environment is an important driver of green innovation capacity for export-based Korean manufacturers. It also finds that green innovation capacity has a positive effect on export performance through environmental performance. This study establishes a theoretical basis for the systematic study of the structural mechanisms of green supply chains and suggests strategic directions for export-based manufacturing firms’ successful GSCM implementation.

There are two basic methods for radial external load distribution calculation on rolling elements in a rolling element bearing: the discrete method and the integral method. Solving the discrete equilibrium quasi-static equation using the Newton-Raphson scheme, more accurate results are derived than those based on the integral method, with small theoretical and computational efforts. The Sjövall’s radial integral factors, as well as some approximations proposed in the literature, for line- and point-contacts, are revisited. Numerical approximations for the Sjövall's radial integrals are proposed. The approximations’ errors with respect to the Sjövall’s radial integral’s numerical integration are shown. The numerical results of the displacement between inner and outer rigid rings, maximum hertzian contact deflection and the most loaded element load are shown for some ball and roller bearings, for radial external load ranging from 0 to 10,000 N, and for positive, negative and zero radial clearance, solving the discrete equilibrium quasi-static equation using the Newton-Raphson method. A total of 16 figures is shown in this work, many of which are made up of sub-figures. A total of 58 figures and sub-figures is shown.

The global movement of inputs, raw materials, products, and the marketing of products requires the use of supply chains. Green logistics refers to the set of policies and measures aimed at reducing the environmental impacts generated by logistics activities. The traditional supply chain has a certain inability to adapt to the rules agreed in Coop 15. The Green Supply Chain deals with the distribution flows of goods with a view to generating low environmental impact. The Green Supply Chain (GSC) involves aspects of reducing negative impacts throughout the logistics chain, from raw material suppliers, production to the distribution of the product to the customer. In this way, this article seeks to identify the factors that generate negative impacts in the traditional Supply Chain, thus helping to decarbonize it, and create a path for the development of the GSC and thus avoid interruptions in the distribution flow due to not meeting environmental targets. The main problem in decarbonizing the Supply Chain is its difficulty in complying with the rules agreed by the nations in COOP 15. To this end, a systematic literature review was carried out based on the available literature. Based on various articles and books, the parameters that define a Green Supply Chain and the factors that generate environmental impacts in conventional chains were identified. As a result, it was possible to verify a positive relationship between the carbon emission reduction factors that are linked to the supply chain performance factors found in the literature. Of the factors relating to the decarbonization of supply chains, some were more clearly related, such as reducing carbon emissions, reducing environmental risks and the ability to operate using new technologies. Future research could build on the limitations of this study.

Considering the fault "N-1" checksum and the power flow, the single-phase power flow model is further transformed into a three-phase power flow model, and the asymmetry of the three-phase power flow is measured by the three-phase unbalance factor. The calculation model is linearized by the second-order cone relaxation and the Big-M method. At the same time, the load response and distribution network reconstruction are used to improve the reliability of the power supply network to cope with the power failure. The relationship between power supply capability and power flow constraints, main transformer capacity and distributed power parameters is analyzed by IEEE 33-node three-phase power distribution system. The feasibility of the proposed model and the accuracy of the second-order cone relaxation are verified by numerical examples, which provides a technical reference for distribution network planning.

Understanding the shoulder-specific load in handball is important for both prevention and rehabilitation of shoulder injuries. The shoulder-specific load is largely a result of the number and speed of throws. However, it is difficult to quantify number and speed of throws in handball due to limitations in the current technology. Therefore, the purpose of this study was to develop a novel method to estimate throwing speed in handball using a low-cost accelerometer-based device. Nineteen experienced handball players each performed 25 throws of varying types while we measured the acceleration of the wrist using the accelerometer and the throwing speed using 3D motion capture. Using cross-validation, we developed four prediction models using combinations of the logarithm of the peak total acceleration, sex and throwing type as the predictor and the throwing speed as the outcome. We found that all models were well-calibrated (mean calibration of all models: 0.0 m/s, calibration slope range: 0.99-1.00) and precise (R2 = 0.71-0.85, mean absolute error = 1.32-1.82 m/s). We conclude that the developed method appear to provide practitioners and researchers with a feasible and cheap method to estimate throwing speeds in handball.

Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to decompose the whole loads in a household, which leads to low identification accuracy. In this paper, an NILM approach based on multi-feature integrated classification (MFIC) is explored, which combines some non-electrical features such as ON/OFF duration, usage frequency of appliances, and usage period to improve load differentiability. The implementation of MFIC algorithm is consistent with traditional event-based method. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. Simulation results using an open-access dataset demonstrate the effectiveness and high accuracy of MFIC algorithm, with the state-of-the-art NILM methods as benchmarks.

Aluminum is a strategic mineral resource, and China’s aluminum production and consumption is fairly large. However, its supply guarantee is uncertain because of a high dependency on external raw materials. This uncertainty may expand, so finding a way to reduce the uncertainty of aluminum resource supply is especially important. This paper applies the SFA method to analyze the aluminum flows in mainland China from 1996 to 2014, and establishes a supply structure model to measure its supply guarantee degree. The results claim that: (1) China’s aluminum production can satisfy demand and even create a surplus; (2) Domestic self-productive primary and secondary aluminum increased at an annual rate of 12% and 24%; (3) The proportion of self-productive secondary aluminum in the supply structure increased from 7.7% in 1996 to 12.8% in 2014, while that of primary aluminum decreased from 79.6% to 42.8%; (4) The total supply guarantee degree decreased from 87.3% to 55.6% in this period. These results provide a feasible way to solve this plight: the proportion of secondary aluminum in the supply structure should be enhanced, and an efficient aluminum resource recycling system needs to be established as soon as possible to ensure its sustainable supply.

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining fuel load and composition spatially and temporally, are pretty scarce. The need to have fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment makes a constant search for the improvement of the use of remote sensing techniques in determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations; (2) to estimate fuel load through Random Forest and k-Nearest Neighbor (k-NN) algorithms in comparison with re-gression analyses; and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 x 3 pixel window surrounding the reference pixel. For the multiple linear regression analyses, the R² values ranged from 0.63 to 0.78, and the models fitted by the Random Forest al-gorithm ranged from 0.52 to 0.83, while those fitted by k-NN ranged from 0.30 to 0.68. Adopting the Random Forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for Cerrado grassland relative to the multiple linear regression analyses.

The tribology behavior, tribofilm formation and structure evolution of polytetrafluoroethylene (PTFE) filled with α-Al2O3 and SiO2 nanoparticles during sliding against steel counterparts under different loads were studied. It is found that both composites exhibit good wear resistance across the pressure of 1 MPa to 10 MPa, with the α-Al2O3/PTFE composite demonstrates better performance stability compared to the SiO2/PTFE composite. The high wear resistance is attributed to the formation of tribofilms at the friction interface. For the α-Al2O3/PTFE, an island-like tribofilm is formed with a thickness ranging from 100 to 200 nm, while the tribofilm of the SiO2/PTFE composite is thinner, measuring approximately 50 to 100 nm, and manifest a striped pattern. The chemical composition, both at the surface and subsurface levels, as well as the morphology of the tribofilms were studied using FTIR spectrometry, X-Ray photoelectron spectroscopy (XPS), and FIB-TEM. It is found that the difference in thickness and microstructure of the tribofilms for the two composites is mainly due to the tribochemistry of the nanoparticles. The α-Al2O3 nanoparticle plays a "cohesion" role during the formation of the tribofilm, which facilitates the formation of a thicker, more uniform, and stronger adhered tribofilm on the metallic counterpart, making it more robust against higher shear stress.

Nutrition and calorie intake are associated with subtle changes of thyroid function tests in subjects with intact Hypothalamic-Pituitary-Thyroid axis. Iodine deficiency and extreme fluctuations in calorie intake, such as those occurring during periods of starvation or overfeeding could lead to alterations of thyroid hormones. The dietary macronutrient composition could also influence the thyroid function. Recently, Low-Glycemic Load (LGL) diets are very popular and effective in the treatment and/or prevention of several medical conditions, including diabetes, obesity, cardiovascular disease, and epilepsy. In this review, we report the available data from the literature regarding the association between LGL diets and thyroid function or dysfunction. Unfortunately, there are only a few studies in this field, with inconsistent findings.

The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

Upon inbreeding, the architecture of the inbreeding load shifts as selection purges strongly deleterious recessive mutations and drift fixes many milder ones. Most small inbred populations show limited genetic variation while crosses between such populations commonly express pronounced heterosis, confirming fixation. In contrast, purging appears to be limited in that inbred populations often retain substantial inbreeding depression. In addition we have the enigma Darwin noted: purely selfing taxa are unknown. Because both purging and fixation reduce inbreeding depression and load, another mechanism must exist to sustain these. Background selection and the associations that develop among alleles in small inbred populations will shift the architecture of the load potentially creating blocks of recessive mutations linked in repulsion. This would generate pseudo-overdominance that could sustain these “PODs” and inbreeding load. Recombination and crosses between lineages could erode PODs. Crosses between populations fixed for different mutations would generate high pseudo-overdominance, enhancing heterosis and potentially POD formation. New recessive mutations arising within PODs would reinforce overdominance. PODs should generate clear genetic signatures including genomic hotspots of heterozygosity and linkage disequilibrium containing alleles at intermediate frequency generating segregating load. Results from several simulation and empirical studies match these predictions. Further simulations and comparative genomic analyses are needed to rigorously test whether PODs exist in sufficient strength and number to generate persistent inbreeding depression and load in inbred lineages.

This paper applied case study research to design architectures for green-field supply chain integration. The integration design is based on a case study of a supply chain integration of 5 companies, operating in different, but supply chain complimenting industry sectors. The case study research is applied to design and validate the architectures in a real world scenario. The supply chain integration architectures enable the conversion of individual into integrated strategies. The architectures are categorised and the process develops into a conceptual system for identifying the correlations between individual participants’ strategic areas of interest and the integrated supply chain areas of interest. The novelty of this paper is a conceptual system for green-field supply chain integration architectures, which can be applied in real world by supply chain practitioners.

Resilience is a particularly important quality for supply chains in this turbulent environment. Resilience in supply chain is the ability to retain, resume, and recover operations after an intense destructive incident. One of the strategic solutions for managing supply chain disruptions is to establish collaboration and strategic alliances in order to achieve competitive advantage. Therefore, given the increasing publication of articles in areas of strategic alliances and supply chain resilience, it is a good opportunity to review these articles, identify gaps in the current literature, demonstrate links between the two areas, and provide suggestions for future research. For these purposes, a bibliometric analysis has been performed on literature available on the Web of Science database. The distribution of articles based on year and country, influential journals, research areas, authors, affiliations, keywords, citations, and reference co-citation analysis are discussed. Results indicate that studies about strategic alliances, meant to increase resilience, are growing in areas such as “Management”, “Operations research”, “Management science”, and “Business”. Furthermore, the sources could be categorized in five clusters; namely “From knowledge concept to value creation”, “Internal and external relationships”, “Logistics and supply chain performance”, “Intellectual capital and strategic management”, and “Critical success factors and alliances”. This article can be useful for both practitioners and academics who explore the topic of strategic alliances and resilience in supply chain and also offers managers the opportunity to overcome supply chain disruptions and negative consequences of risks by becoming familiar with the key concepts of resilience. The persistence of businesses and supply chains is guaranteed through communicating with partners and even competitors in the light of alliance according to the findings of this research. Managers can pay attention to the integration of the supply chain to improve resilience and increase collaboration between suppliers and customers. Given the research results, strategic alliances can be noted in expanding organizational entrepreneurship, and shaping strategic collaboration networks in light of strategic alliances.

This qualitative study explores the pivotal role of supply chain collaboration (SCC) in enhancing marketing effectiveness within organizations. By integrating supply chain management (SCM) practices with marketing strategies, companies can optimize operational efficiencies, anticipate consumer demands, and deliver personalized customer experiences. The study emphasizes technological integration, such as IoT, big data analytics, and blockchain, which enables real-time data sharing, predictive analytics, and enhanced visibility across the supply chain. Strategic alignment between SCM and marketing functions ensures efficient resource allocation and strategic deployment of marketing investments, fostering synergistic outcomes and maximizing market impact. Collaborative innovation within supply chains drives continuous improvement and product innovation, strengthening brand reputation and customer loyalty. Additionally, supply chain resilience, achieved through robust risk management and agile strategies, enables businesses to maintain operational stability and mitigate disruptions, safeguarding customer relationships and brand integrity. This research underscores the strategic imperative for organizations to embrace SCC as a driver of sustainable growth and competitive advantage in dynamic market environments. By leveraging SCC to integrate SCM and marketing functions, companies can navigate complexities, capitalize on market opportunities, and sustain long-term success. The findings provide valuable insights for business leaders seeking to enhance marketing effectiveness through effective SCC strategies.

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day, due to official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17\% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method here called tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (Hierarchical) available in the language portfolio in which the project has been executed (Julia language). As a result, both methodologies achieve similar results, but the one presented in this article shows a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M.

Variations in the implant thread shape and occlusal load behavior may result in significant changes in the biological and mechanical properties of dental implants and surrounding bone tissue. Most previous studies consider a single implant thread design, an isotropic bone structure, and a static occlusal load. However, the effects of different thread designs, bone material properties, and loading conditions are important concerns in clinical practice. Accordingly, the present study performs Finite Element Analysis (FEA) simulations to investigate the static, quasi-static and dynamic response of the implant and implanted bone material under various thread designs and occlusal loading directions (buccal-lingual, mesiodistal and apical). The simulations focus specifically on the von Mises stress, displacement, shear stress, compressive stress and tensile stress within the implant and the surrounding bone. The results show that the thread design and occlusal loading rate have a significant effect on the stress distribution and deformation of the implant and bone structure during clinical applications. Overall, the results provide a useful insight into the design of enhanced dental implants for an improved load transfer efficiency and success rate.

The Covid-19 pandemic strained global supply chains, and companies faced numerous produc-tion, logistics, or material management problems. It is claimed that the digitalization of supply chains can have the potential to make supply chains more resilient to interruptions and disrup-tions. This article focuses on describing the experiences of three large Austrian family-owned business during the supply chain disruptions caused by the pandemic and the Ukraine war. This assessment is done from the perspective of the supply chain leaders in the organization. We provide insights on what worked and what did not for these firms and what we can take away from their experiences.

One of the most remarkable features in the 20th century was the digitalization of technical progress, which changed the output of companies worldwide and became a defining feature of the century. The growth of information technology systems and the implementation of new technical advances, which enhance the integrity, agility and long-term organizational performance of the supply chain, can distinguish a digital supply chain from other supply chains. For example, the Internet of Things (IoT)-enabled information exchange and Big Data analysis might be used to regulate the mismatch between supply and demand. In order to assess contemporary ideas and concepts in the field of data analysis in the context of supply chain management, this literary investigation has been decided. The research was conducted in the form of a comprehensive literature review. In the SLR investigation, a total of 71 papers from leading journals were used. SLR has found that data analytics integrate into supply chain management can have long-term benefits on supply chain management from the input side, i.e., improved strategic development, management and other areas.

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. Circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e. able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital Twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of Cognitive Digital Twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. Emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.

(1) Background: Presentation of the method of increasing the competitiveness of the mining industry through the demonstration of the possibility and potential in closing the loop of supply chains through waste management; (2) Methods: The source literature analysis and an arithmetic analysis of the data of statistical materials and data obtained from business in static and dynamic perspectives; (3) Results: According to the latest data available, in Poland, mining wastes account for over a half of industrial wastes, while the annual growth of mining wastes is on the level of 4%. The recorded recovery of mining wastes is on the level of ca. 19%. The exemplary company that deals with the recovery of carbon wastes recovers ca. 80% of material, which is handed over for utilization (4) Conclusions: It has been found that the structure of the level of mining wastes and the level of their recovery rates demonstrate a large potential of closing the loop of chains in this industry and, at the same time, a potential to extend the chains through directing the material from recovery to various sectors. This constitutes an example of the direction of activities that are in line with the EU strategies for the economies of other states which possess this type wastes.

The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

Variable cross-section arch is widely used in practical engineering because of its beautiful arc and excellent mechanical properties. However, there is still no systematic and comprehensive study on the out-plane buckling of variable cross-section arch. In view of this, this paper is concentrated on an elastic analytical research of out-plane buckling of arches with variable cross-section under a uniformly distributed radial local load. The pre-buckling and out-plane buckling behaviour of a variable cross-sectional arch under an external load is quite different from that of an arch with uniform cross-section. The Castigliano's second theorem is used to establish pre-buckling force method equilibrium equations for variable cross-sectional arches under a uniformly distributed radial local load, and corresponding analytical solutions of normal stress, axial compression and the bending moment are obtained. Based on the energy method and the Ritz method, analytical solutions of the critical load for the elastic out-plane buckling of arches with variable cross-section are derived. Comparisons with ANSYS results indicated that the analytical solutions are able to accurately predict the pre-buckling internal forces and critical out-plane buckling load of variable cross-section arches subjected to a uniformly distributed radial local load. It is found that the internal forces and the out-plane buckling load of an arch are significantly affected by the variation of cross-sectional height. With the ratio of cross-sectional height of the top to the end increase, the bending moment decreases and the axial force and critical out-plane buckling load increase. Analytical solutions of pre-buckling internal force and critical out-plane buckling load problems for arches with variable cross-section have a wider significance, since they can provide an effective explicit analytic method for the optimal design of arch structure.

The variation of mutual inductance and load parameters will affect the transmission power and efficiency of the inductive power transfer (IPT) system. The identification of mutual inductance and load parameters is an essential part of establishing a stable and reliable IPT system. This paper presents a joint identification method of load and mutual inductance for LCC-S IPT system, which does not require the establishment of primary and secondary communication and related control. Firstly, the resistance-inductance characteristics of the equivalent load of the rectifier are analyzed by simulation, and then the rectifier and system load are equivalent to the circuit model of resistance and inductance in series. Secondly, the characteristics of the reflected impedance are analyzed, and the functional relationship between the transmitter impedance and the rectifier impedance is established by using the ratio of the real part to the imaginary part of the reflected impedance, which realizes the decoupling of the load and the mutual inductance. Thirdly, the functional relationship between the equivalent impedance of the rectifier and the load resistance of the system is obtained by data fitting. Then, the equations of the above two functional relationships are combined. By measuring the voltage of the parallel compensation capacitor at the transmitting side, the current of the transmitting coil and the phase difference between the two, the battery load can be solved first, and then the mutual inductance can be calculated, so that the high-precision identification of the load and mutual inductance can be realized. Finally, an experimental platform of LCC-S IPT system is built for experimental verification. The experimental results show that the maximum identification errors of mutual inductance and load are 5.20 % and 5.53 %, respectively, which proves that the proposed identification method can achieve high precision identification.

This study aims to improve the shaft with hexagon joints to be a type not requiring welding or bolts in the static load test . In order to evaluate the bearing capacity of helical piles, two sites were selected to conduct pile installation for the field test and the pile load test. For the pile load test, the static pile load test and the dynamic pile load test were carried out, and torque was measured during pile installation for the field test to compare and analyze expected bearing capacity and thus assess the feasibility of the method for estimating the bearing capacity. The field pile load test revealed the bearing capacity of the gravity grout pile was the same or greater than 600kN in the static pile load test in accordance with AC 358 Code. The non-grout pile showed the bearing capacity the same or smaller than 600kN, suggesting gravity grouting is required. Moreover, the field pile load test was used to establish the bearing capacity equation considering the torque in pile installation, and a small number of samples were used to establish the equation which can be used as a basic data.

Nipa palm is a plant of great value to communities due to its social, environmental, and economic benefits. Effective management of the supply chain for products derived from nipa palm resources is essential to provide communities with suitable channels for selling each type of product. This study aims to map the supply chain structure of six different nipa palm products. Data were collected through semi-structured interviews with 100 households engaged in nipa palm cultivation. The analysis focused on the characteristics of the products, existing challenges, and the current supply chain dynamics. The findings revealed that among the six products, palm sugar had the highest production volume. The most significant challenges identified were related to labor shortages and limited marketing channels. To achieve sustainable supply chain management, it is crucial to maintain a balance between supply and demand. The study recommends exploring appropriate marketing channels for each product, with particular emphasis on online platforms that allow communities to sell directly to consumers.

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.

In a context of increasingly necessary energy transition, precise modelling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSO) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modelling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow the estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e. data with sampling rates lower than the AC base frequency. We first review the many degrees of freedom of these approaches, what has already been done in literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported MAE and F$_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10\,s, a large field of view, the usage of GAN losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

The sudden onset of natural hazards such as a flash flood events can be devastating to a country or region, often affecting numerous communities, and humanitarian supply chains play a major role in enabling the timely recovery of economic and social activities. However, the uncertainty and suddenness of natural disasters such as flash floods, as well as the potential for aftershocks, require humanitarian supply chains to be resilient during the relief process. This research first adopts the Delphi and literature review methods to identify the key indicators in the humanitarian supply chain to form an evaluation index system. Then, taking the 2021 mega-flash flood event which affected four cities in the mountainous region under the jurisdiction of the Zhengzhou municipal government, the ANP method is used to calculate the weights of each indicator and combines the weighted results with the Multi-styling and stereotyping method under Pythagorean fuzzy (PFs-VIKOR) to make an evaluation of the resilience of the humanitarian supply chain in the relief process. The findings suggest that policy makers and decision makers should pay close attention to the coordination of the parties involved in the humanitarian supply chain and improve the level of resilience of the entire supply chain by enhancing the resource scheduling capacity and responsiveness. At the same time, the VIKOR evaluation of the 4 cities highlighted the humanitarian supply chain in Dengfeng City to be most effective in this event due to the close cooperation and positive response of all parties involved in the humanitarian supply chain. The findings of this research provide some useful suggestions and guidance to the various practitioners involved in the humanitarian supply chain. Furthermore, the evaluation of the performance of the four cities in this mega-flash flood event provides some helpful indication of the importance of the various emergency measures which can help to inform policy recommendations for the Zhengzhou municipal government.

‘‘Sustainable Supply Chain Management’’ (SSCM) has become a hot topic on which eminent researchers are working on every day. However, it is clear that we still observe in this field of research, an artistic vagueness on the fundamental differences between Sustainable Supply Chain Management and Green Supply Chain Management (GSCM) Moreover, it is regrettable to note that previous researchers haven't yet thought about having a specific theory which can be applied to SSCM, the use of macro theories still being observed. In addition, we observe a lack of research in the oil and gas industry especially in Developing Countries when we know that this industry is the most lucrative and polluting. Therefore, this paper’s purpose consists to make a Systematic Literature Review (SLR) of 38 papers published from 2005 to 2023 especially in oil and gas industry and in Developing Countries, in order to make a clear distinction between SSCM and GSCM while highlighting the practices related to them and at the same time, generating a united theory which will bring together the triple bottom line of the SSCM. As findings, the authors built a Conceptual Framework that applies the triple bottom line of the SSCM in oil and gas industry by demonstrating that GSCM which is more related to environmental issues and is a part of the SSCM. Therefore, in order to be more efficient and to have a great impact on their performances, Oil and gas companies should apply SSCM practices rather than GSCM practices which are very different from what is applied in Developed Countries because of certain barriers and pressures.

The world is facing an unprecedented socio-economic crisis caused by the novel coronavirus infection (COVID-19). It is also spreading through the import and export food supply chains. The Chinese authorities have discovered the COVID-19 virus in various imported frozen meat packages. Traceability plays a vital role in food quality and food safety. The Internet of Things (IoT) provides solutions to keep an eye on environmental conditions, product quality, and product traceability. These solutions are traditionally based on the centralized architecture, which does not guarantee tamper-proof data sharing. The blockchain is an emerging technology that provides tamper-proof data sharing in real-time. This article presents Hyperledger Fabric-based blockchain use case and a quick reference guide to develop the blockchain network for tracking and tracing the supply chain to minimize the risk of COVID-19 in the frozen meat supply chain.

Load balancing to alleviate network congestion remains a critical challenge in Vehicular Ad-Hoc Networks (VANETs). During route and response scheduling, RSUs risk being overloaded beyond their calculated capacity. Despite recent advancements like RSU-based load transfer, NP-Hard hierarchical geography routing, RSU-based MAC schemes, simplified clustering, and network activity control, a significant gap persists in employing a load-balancing server for effective traffic management. We propose a server-based network congestion handling mechanism (SBNC) in VANETs to bridge this gap. Our approach clusters RSUs within specified ranges and incorporates dedicated load balancing and network scheduler RSUs to manage route selection and request-response scheduling, thereby balancing RSU loads. We introduce three key algorithms: optimal placement of dedicated RSUs, a scheduling policy for packets/data/requests/responses, and a congestion control algorithm for load balancing. Using the VanetMobiSim library of Network Simulator-2 (NS-2), we evaluate our approach based on residual energy consumption, end-to-end delay, packet delivery ratio (PDR), and control packet overhead. Results indicate substantial improvements in load balancing through our proposed server-based approach.

As the most important component of soft robot, the pneumatic actuator can drive robot movement. However, the effect of the actuator pneumatic network geometry on the actuator load capacity is insufficient. In this paper, the finite element analysis method is applied to calculate the load force and stability torque of the pneumatic actuator under a wide range of length and height dimensions of the pneumatic network. The effect of the network size on the structure rigidity, load capacity and deformation of the actuator is analyzed. Based on the calculation results, the effect of network structure size on the deformation and load capacity is analyzed, and the optimal geometric results with the maximum load capacity are obtained. This study provides theoretical guidance for the structural design and optimization of the pneumatic actuator.

This work presents the results of three connection types in double-shear with dowel fasteners, using the simplified equations from the Eurocode 5. All design parameters were established and compared using three different wood strength and density properties, which constitute the members connections. Eighty-one connections were obtained, allowing to conclude about the number of fasteners needed to the applied tensile load. An increase in the number of dowels was obtained with the increased applied tensile load, lower dowel diameter, lower wood density, and lower strength material in all connection types in the study. The design characteristic load-carrying capacity per shear plane and fastener also decrease with the previously considered parameters.

This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classifica-tion in Induction Motors (IMs), utilizing the Machinery Fault Prevention Technology (MFPT) Bearing Dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customization. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset's unique characteristics. Phase 1 involves the exploration of load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a wide neural network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in en-hancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

This study aims to summarize the literature on sustainable global supply chain management so that researchers and practitioners can see the trends in the area in a single place. Systematic literature review (SLR) or Content analysis is used as a methodology of this literature review, studies that are published within the time frame of 2010 to 2020 are included in this literature. Dimensions to analyze each article includes; year of publications, methodology of research, data collection type, unit of analysis, industry, a country in which data is being collected, respondents’ types, the theory that is used in the study, dimensions of the sustainability and finally the purpose of study either it is to test the already existing theory or building up a new theory. I found that sustainable global supply chain management is an emerging field and there is potential in the area for researchers to explore the area.

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

The present study is to address performance of the dairy supply chain and emphasizes the significance of reliability of the chain as a fundamental and critical attribute for fulfilling the safety requirements. In order to evaluate the performance of the dairy supply chain, the present study utilizes the reliability attribute, which is an essential and informative system performance indicator. The study considers not only the inputs and outputs but also internal activities and relationships between customers, manufacturers, and suppliers. To minimize calculations and resolve uncertainties and vagueness that may be experienced in the performance evaluation process, this study proposes the use of the Fuzzy Inference System (FIS) approach and evaluates the reliability at each level of the supply chain: supply, manufacturing, and distribution. Combining the reliability of different levels, the study finally calculates the reliability of the entire supply chain. The proposed methodology offers a practical approach for evaluating the reliability performance of a dairy supply chain and can be easily extended to other industries as well. To verify the proposed methodology, a real case in the Iranian dairy industry is chosen to be studied. Results indicate that the proposed methodology can effectively identify bottlenecks or weaknesses in the dairy supply chain and help develop effective strategies to address them. Analyzing factors influencing supply chain reliability can optimize operations, reduce costs, and improve the overall quality of dairy product quality delivered to consumers.

This qualitative research explores consumer perceptions of supply chain responsiveness and its impact on brand loyalty within the apparel industry. The study investigates how consumers perceive supply chain practices such as reliability, agility, and sustainability, and examines their influence on brand loyalty decisions. Thirty participants, representing diverse demographics and purchasing behaviors, were interviewed to gather insights into their awareness, expectations, and experiences related to supply chain responsiveness. Findings reveal that consumers prioritize supply chain reliability, expecting brands to consistently deliver high-quality products, accurate sizing, and timely responses to market trends. Sustainability practices also emerged as a significant factor, with consumers favoring brands that demonstrate ethical sourcing and environmental responsibility. Technology plays a crucial role in enhancing supply chain responsiveness, as consumers value real-time updates, personalized experiences, and seamless transactions. Moreover, the study underscores the impact of supply chain disruptions on brand trust and loyalty, highlighting the importance of resilience and contingency planning in supply chain management. Brands that effectively navigate disruptions through agile strategies can enhance consumer trust and loyalty, even in challenging circumstances. Overall, this research contributes to understanding the intricate relationship between supply chain dynamics, consumer perceptions, and brand loyalty in the apparel industry. It offers practical implications for apparel brands seeking to enhance consumer satisfaction, differentiate themselves in a competitive market, and foster sustainable growth through strategic supply chain management.

The concept of supply chain leadership strategy has garnered increasing interest from policymakers and experts, because it is among the most dependable practices to sustainable innovation and competitiveness for large firms across developing economies. Effective implementation of supply chain leadership strategic actions at each level in the company can create sustainable innovative and competitiveness. The study collected statistical data using an online Survey- Monkey questionnaire from 46 of top 100 JSE-listed companies with the intention to discover supply chain leadership strategic actions that drive sustainable innovation and competitiveness in the company. The findings expose a significant empirical insight for companies to understand and leverage supply chain leadership strategic actions to influence sustainable innovative position in a developing economy. These results suggest that firms operating within emerging markets should embrace and acknowledge the importance of sustainable innovation in business practices in the provision of goods and services. This study therefore delivers major contributions to the literature to assist in planning sustainable innovation and supply chain practices in a developing economy to survive in a competitive global environment. The paper also offers a vigorous awareness and knowledge of the common characteristics and strategies for supply chain management that needs to be prioritized by the JSE-listed companies.

Grain supply is the lifeblood of a country, and the stability of the supply chain is a crucial prerequisite for ensuring national grain security. This paper draws on the definition of resilience in physics and economics. It takes supply chain fracture resilience, impact resilience, and synergy resilience as the secondary indicators. It constructs a comprehensive evaluation indicator system of the grain supply chain resilience, measures the resilience indicator of China's grain supply chain from 1996 to 2021, and analyzes the role of supply, cost, exchange rate, and other external shocks in influencing the resilience of China's grain supply chain on this basis. The study found that the overall level of China's total grain supply chain resilience has been growing year by year and can be divided into three stages: low-level stabilization stage, continuous growth stage, and high-level stabilization stage. Grain supply chain fracture resilience has been growing steadily, grain supply chain impact resilience fluctuation is more obvious, and grain supply chain synergy resilience has been changing more gently. In the inquiry of the impact of external shocks on the resilience of China's grain supply chain, it was found that world grain exports and the RMB exchange rate have a significant positive impact on China's grain supply chain resilience level, while the international oil price has a significant negative impact. Based on this, the paper puts forward suggestions for ensuring stable production and supply in the grain market, improving the structure of foreign trade in grain, and actively coping with international commodity price shocks.

There are two purposes for this article. The first purpose is to perform a reiew of previous research on global supply chain management (GSCM) principles that can adapt and survive adversity, such as the COVID-19 pandemic. The second purpose is to put forth a conceptual framework for the GSCM that is sustainable in the event of future turbulence similar to that experienced during the COVID-19 pandemic. With regard to the issues raised, the article posits the following questions. Is there, perhaps, a sustainable post-COVID-19 pandemic GSCM? Can the COVID-19 pandemic-related international logistics system (GSC) turbulences be predicted with certainty using these sustainable post-COVID-19 pandemic GSCM concepts? Putting it all together, the paper concludes that, while debatable, sustainable GSCM is feasible and can be accomplished utilizing JIT, supply chain mitigation, and supply chain visibility, as demonstrated by the manufacturing firms referenced in the article.

The degree of collaboration among supply chain partners and the structure of the network are important determinants of the level of satisfaction customers derive from the products or services. However, the effects of these dimensions on customer satisfaction at the downstream section of the supply chain remain under-researched in Nigeria. This study precisely examined the effects of collaboration and supply chain design on customers’ satisfaction at the downstream end of the chain using Ekiti State as study area. The study employed descriptive survey design with the use of structured Likert scale questionnaire administered to 381 retailers of noodles in Ekiti State. The research hypotheses were analysed using simple linear regression as statistical technique with the aid of SPSS version 22.0. At the end of the study, it was observed that both collaboration and supply chain design were significant predictors of customers’ satisfaction of instant noodles in Ekiti State. However, collaboration among supply chain partners emerged as stronger determinant of customers’ satisfaction than supply chain design. The study concludes that these two practices of supply chain management are highly important criteria any manufacturing firm especially in the noodles industry must pay close attention to in order to satisfy her consumers.

Due to related uncertainties brought by changes in energy consumption and the integration of rooftop photovoltaic systems, the accuracy and availability of load profiling data are difficult to achieve. However, project managers and planners use a precise load profile as a crucial tool when determining whether the feeder must be updated or de-loaded. The paper aims to create a non-intrusive monitoring system that predicts the synthetic load profiles behavior of energy consumption using a Light Gradient Boosted and Support Vector Machine (SVM) machine learning technique. The most effective ML algorithm is chosen, and it has the potential to be assessed and verified using validation curves, residuals model generation, and prediction error metrics based on the following key statistical indicators: Mean Absolute Error, Mean Square Error, Root Mean Square Error, R-Square, Root Mean Squared Logarithmic Error, and Mean Absolute Percentage Error. The most effective ML has the potential to be assessed and verified using validation curves, residual model generation, and prediction error metrics based on the following key statistical indicators. The result shows that the estimation of the Irms avg -based on LightGBM is more accurate than the SVM because of its quick, economical, and difficult to overfit, particularly with high-dimensional data, speed, and efficiency of the MAE (3.0698), MSE (15.2757), RMSE (3.9020), RMSLE (0.1433), and MAPE (0.0049) respectively. Additionally, the machine learning model shows that, when compared to the SVM, the LightGBM model had the highest accurate prediction, with R-Square values of 89.8%, 90.3%, and 88.5% for Irms_A_avg, Irms_B_avg, and Irms_C_avg, respectively for Brakfontein Substation supply region, and best represents a diverse customer base.

This paper explores in depth the mechanism of enterprise digital capability effects in supply chain digital development by reconstructing the supply chain cooperation capabilities (digital diffusion capability and digital collaborative capability). We use the questionnaire data covering 272 Chi-nese manufacturing enterprises and apply a structural equation model to test the hypothesis. The results show that enterprise digital capability does not directly have a significant impact on sup-ply chain digitalization, and the supply chain cooperation capabilities play mediation role be-tween enterprise digital capability and supply chain digitalization. The results are robust due to we thoroughly consider the action boundary of enterprise capabilities, and verify the systematic requirements of supply chain digitalization and the high-order of enterprise digital capability. These provide a theoretical basis for enterprises to promote the digital transformation of supply chain through digital technology.

This qualitative research investigates how supply chain innovations drive marketing differentiation in consumer goods companies. In a competitive global marketplace, firms are increasingly leveraging advanced supply chain management (SCM) practices to enhance operational efficiency and create distinctive market positions. The study explores five key supply chain innovations—digitalization, sustainability practices, predictive analytics, agile supply chain models, and collaborative partnerships—and their impact on marketing differentiation strategies. Data were collected through semi-structured interviews with 20 executives and managers from leading consumer goods companies, analyzing themes related to innovation adoption, challenges, and outcomes. Findings indicate that digital technologies such as IoT, AI, and blockchain are pivotal in improving supply chain visibility, optimizing inventory management, and enabling real-time decision-making, thereby supporting personalized customer experiences and agile responses to market dynamics. Sustainability practices, including sustainable sourcing and green logistics, emerge as critical drivers of brand reputation and consumer trust, aligning with growing consumer preferences for eco-friendly products. Predictive analytics facilitate better demand forecasting and pricing strategies, while agile supply chain models enhance flexibility and responsiveness in delivering products faster to market. Despite benefits, challenges include integrating innovations with legacy systems, managing resistance to change, and addressing data security concerns. Strategies for overcoming these barriers include leadership commitment, cross-functional collaboration, talent development, and strategic partnerships. By embracing these strategies and innovations, consumer goods companies can strengthen their competitive positioning, enhance customer satisfaction, and achieve sustainable growth in a rapidly evolving marketplace.

This qualitative investigation delves into the intricate interplay of cultural factors within supply chain integration, aiming to elucidate how diverse cultural dimensions shape communication dynamics, decision-making processes, and collaborative efforts among global supply chain practitioners. Employing a phenomenological approach, the study explores the lived experiences and subjective perceptions of supply chain managers and professionals, providing nuanced insights into the profound impact of national culture, organizational culture, digital technologies, and sustainability considerations on supply chain operations. Key findings highlight the significant influence of national culture, as conceptualized by Hofstede's cultural dimensions, on supply chain interactions. These dimensions—such as power distance, individualism versus collectivism, uncertainty avoidance, and masculinity versus femininity—play crucial roles in shaping how individuals perceive authority, manage risks, negotiate agreements, and engage in collaborative practices across diverse cultural contexts. Cultural norms and values deeply influence both intra-organizational dynamics and inter-organizational relationships within global supply chains. Organizational culture emerges as a pivotal determinant of supply chain integration success. Cultures characterized by transparency, trust, inclusivity, and a commitment to continuous improvement create environments conducive to effective cross-border collaboration. Conversely, organizations with conflicting or ambiguous cultural norms face challenges in aligning goals, fostering mutual understanding, and maintaining open communication channels with international partners. The study underscores the transformative role of digital technologies in enhancing supply chain management practices. Technologies such as blockchain, artificial intelligence, and the Internet of Things improve supply chain visibility, optimize operations, and enhance decision-making processes, strengthening resilience against disruptions. The adoption and integration of these technologies depend on organizational readiness, technological capabilities, and cultural acceptance within supply chain networks. Cultural factors influence the adoption pace, implementation strategies, and utilization patterns of digital technologies, emphasizing the need for strategic alignment between technological investments and cultural norms to maximize their potential benefits. Furthermore, sustainability considerations are critical in shaping cultural values and priorities within supply chain networks. Organizations increasingly recognize the importance of environmental sustainability, ethical sourcing practices, and corporate social responsibility in shaping their reputations, stakeholder relationships, and long-term business goals. Cultural factors significantly influence organizations' approaches to sustainability, guiding their strategic decisions and operational practices.

This qualitative research study delves into the intricate dynamics of how organizations adapt to supply chain disruptions during crises, offering profound insights into the strategies, practices, and challenges encountered in enhancing supply chain agility. Through in-depth interviews with supply chain managers across diverse industries, key themes such as proactive risk management, technological integration, collaboration with supply chain partners, and adaptive leadership have emerged as crucial determinants of agility and resilience. Additionally, the study highlights the significance of organizational culture, effective communication, and external factors such as regulatory requirements and market dynamics in shaping supply chain agility. By embracing a holistic approach that integrates these multifaceted factors, organizations can bolster their capacity to anticipate, detect, and respond to disruptions, thereby maintaining or enhancing overall performance even in the face of uncertainty and complexity. The insights gleaned from this research are poised to inform theory development, guide managerial practice, and influence policy-making in the realm of supply chain management, equipping organizations with the knowledge and tools needed to navigate future disruptions successfully.

Various methods of water purification, the aim of which is to obtain such a purity class that makes it suitable for consumption are presented in the article. It is a review of solutions, ranging from methods known and used for over 100 years, through research and experiments underway, to those that are only a concept. Some of the solutions are so effective that they should also be combined with the possibility of safe storage of purified water. Flexible tanks are used for this, which significantly improve logistics and provide a supply of water in all places where it is needed.

The increasing demand for higher power in rocket launchers is driving the development of higher power nozzles, achieved essentially by increasing the expansion ratio. However, this can lead to flow separation and resulting unsteady, asymmetrical forces, called side loads, which can limit the life of both the nozzle itself and other engine components. If this problem can be overcome, engine performance can be significantly increased. Therefore, various separation control methods have been proposed, but none have been fully implemented to date due to the uncertainties associated with modeling and predicting the flow phenomena. A numerical study of high area ratio rocket engine is presented to analyze the aeroelastic performance of the structure under the condition of flow separation. The finite volume method, the 2nd order upwind scheme and the extended wall function are used to analyze the flow inside the rocket nozzle and the transition of the Free Shock Separation (FSS) to Restricted Shock Separation (RSS) separation pattern ) to be discussed in detail. Since the location of the flow separation point depends heavily on the flow.

Feline coronavirus (FeCoV) causes enteric disease in cat worldwide. In general, FeCoV infection is characterized by self- limiting gastroenteritis but not understood changes can give rise to mutants that lead to the development of feline infectious peritonitis (FIP). In cats, FeCoV replicates in the intestines and spreads by fecal-oral transmission and persists in multi-cats households by infec-tion and re-infection by the same or another viral strain. Although current evidence shows that probiotics have promise for enhancing health benefits protecting from viral infection, today there are no studies regarding the use of probiotics in reducing fecal emission of feline coronavirus in naturally infected cats. The aim of the present study was to evaluate the effect of specific mul-ti-strain probiotics on FeCoV viral load in 25 naturally-infected cats. The study population con-sisted of purebred Birmanian cats, of which 14 females and 11 males, aged from1 to 4 years, and positive for FeCov by Real-time PCR on fecal sample. Two microcaps of probiotic formulation containing billion bacteria were administered to all cat once a day for 60 consecutive days. After probiotic administration, clinical conditions and FeCov viral load was assessed by Real-time PCR on fecal samples. All animals did not present alteration in the clinical condition at T1 (after probiotic administration); seventeen out of 25 cats tested negative at the end of the study, while the remaining 8 cats tested positive at the end of the study. The results of the present study suggest that high concentrations of live probiotics supplementation are well-tolerated and may poten-tially reduce the fecal FeCoV viral load.

VHF Data Exchange System (VDES) is considered as an important component of the future maritime communication system by the International Maritime Organization (IMO). On the basis of existing Automatic Identification System (AIS), VDES adds the other two higher capacity subsystems: Application Specific Message (ASM) and VHF Data Exchange (VDE). The Random Access Channel (RACH) of VDE was first introduced in the International Telecommunication Union (ITU) Recommendation M.2092-0. As the slot planning of RACH in VDE is interval, which is significantly different from the continuous slot map for access algorithms in AIS, the existing slot access algorithms cannot meet the requirements of VDE. The simulation results show that the VDE slot map can reduce the normalized throughput of the existing algorithm by 39%. A novel random access algorithm called Adaptive Traffic Load Contention Resolution Diversity Slotted ALOHA (ATL-CRDSA) is proposed in this paper. The algorithm combines the load control strategy and contention resolution scheme to overcome the challenges of the new RACH of VDE. Simulation results show that ATL-CRDSA has remarkable improvement on RACH, and makes RACH very efficient and provides low latency of the packets. The insights gained from this study may be of assistance to the Media Access Control (MAC) layer design for upcoming versions of VDES standard.

We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the PJMISO for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five-minute forecasts for 24 hours.

To satisfy the interests of multiple agents and those of comprehensive indicators such as peak-to-valley differences and load fluctuations occurring on the network side, this paper presents a flexible load demand-side response optimization method that considers the benefits of peak-to-valley smoothing. First, load aggregation modelling of air conditioning and electric vehicles was conducted, and the complementarity of the power consumption behaviour of different types of flexible loads was used to improve the responsiveness of the load aggregator. Second, considering demand-side responses and taking into account the interests of both supply and demand, the load fluctuation and peak-to-valley difference on the network side are reduced, and a flexible load double-layer optimization model incorporating the peak-to-valley smoothing benefit is established. Finally, the effectiveness of the proposed optimization model is verified by using the KKT condition and the big M method to evaluate this two-layer optimization problem as a single-layer optimization problem. Comparative examples demonstrate that the proposed two-layer optimization method can improve the revenue of the distribution network operators and the load aggregators by using the complementary nature of the load response characteristics of electric vehicles and air conditioners. Moreover, the proposed method can effectively reduce the load peak-to-valley difference and load fluctuation of the distribution network by introducing the peak-to-valley smoothing benefit model.

The sustainability of cities often changes as rapid urban developments and population growth affect the carrying capacity. Understanding the dynamics of carrying capacity then becomes crucial for tourist-oriented cities. This study focused on Baguio City, the "Summer Capital of the Philippines," attracting large crowds on holidays and actively promoting various events on non-holidays, thereby having temporal context variations classified as regular days, days with events, and holidays. Using the enhanced urban carrying capacity load number (EUCCLN) model, load number (LN) changes for different temporal contexts are calculated based on spatial indicators grouped into air, traffic, economy, and waste. The city experiences the worst pressures on air quality during holidays with $99.23\%$ districts under very high to critical levels followed by regular and days with events. However, the total LN is balanced by favorable economic conditions with only $9.23\%$ at critical. EUCCLN effectively pinpoints areas experiencing critical states at any time, advocating targeted government monitoring. We demonstrate that Night Time Light, for places with no economic data available, can be used as a proxy to highlight areas requiring improved urban vibrancy. This work suggests that EUCCLN monitoring urban pressures can easily be expanded to nowcasting with real-time data.

From ancient times to the present, mankind has struggled with infectious diseases that pose a serious threat to people's lives, COVID-19 pandemic has given a huge impact to the human society, which has triggered a lot of thinking. In the future, humans may encounter various unexpected disasters or awful events, therefore, should learn from COVID-19 pandemic incident that need to pay special attention to the emergency supply of manpower and materials, whether a major disaster or not. That is emergency medical management is very important in the event of a sudden outbreak. What’s more, the preparation and scheduling of emergency medical supplies well safeguards people’s lives, health, and safety. However, owing to the sudden onset of an epidemic and given the population trends, the scale of infections and the demand for supplies in the epidemic area are uncertain. The main influencing factors and mechanisms of emergency material demand under the development trend of emergencies can be studied, and materials can be allocated efficiently and effectively, so as to minimize damage. In this study, it improves upon the dynamics model of infectious diseases firstly, by establishing a demand forecasting model for a certain area, taking the Hubei province as an example. Furthermore, this dynamics model is used to predict demand for emergency supplies, such as masks, respirators, and food in the area where the disaster will occur. Experimental results demonstrate the effectiveness and reliability of this prediction model, which supports for the development of emergency material management systems, ulterior, this study provides a framework for the distribution of emergency supplies and a guideline for relief efforts.

Umbria is an Italian region with a highly fragmented water supply, with many small water distribution systems fed by local sources. The chlorination in these small water systems faces many difficulties related to the high demand variation, low economic relevance, and access to infrastructures. As a result, there are many noncompliances with the quality of the supplied water, which causes a decrease in the performance indicators and tariff. This research investigates the possibility of using a low-cost chlorination system to consider different environmental conditions (e.g., temperature variations of water and environment, pH, ...) and discontinuous consumption. A device for this specific use is developed and tested by laboratory tests and is ready for use in the field.

The current trend in supply chain development requires the application of new knowledge to meet the challenges posed by new technologies. One such technology is blockchain, which facilitates supply chain solutions through the use of innovative data transfer, storage and verification systems. However, the use of blockchain can be challenging for certain stakeholders, such as small carriers, who may lack the necessary technical expertise or access to the technology. In this paper, we explore the potential for engaging small carriers that provide services within the blockchain supply chain but face technological barriers. We identify the barriers and opportunities for these carriers to participate, focusing on a case study of a small carrier that transports temperature-sensitive cargo. In addition, we introduce in-vehicle temperature measurement for use in cold chains and propose an ecosystem that can benefit different stakeholder groups.

Sustainable supply chain management (SSCM) represents a critical strategy for the electronics industry to balance economic objectives with environmental and social responsibility across complex global production networks. This multiple case study analysis investigates SSCM integration within leading Korean electronics manufacturers Samsung, LG, and Hyundai. In-depth qualitative data, focus groups, and sustainability report analysis reveals the policies, practices, results and implementation barriers related to incorporating sustainability criteria across procurement, manufacturing, distribution and recycling functions. Findings show sustainability considerations increasingly driven by cost, risk, reputational and regulatory factors, with vertical coordination initiatives helping transfer knowledge to assist suppliers on compliance and emissions reductions. Yet substantial obstacles persist around monitoring, coordination, transparency and technical capabilities, especially amongst lower tiers. The paper discusses how common reporting standards, ethical sourcing certification and partnerships can accelerate electronics supply chain sustainability. By mapping integration achievements and gaps, this study informs managerial best practices and policy reforms toward promoting socially and ecologically responsible models for Korea's vital export industry as it navigates rising stakeholder pressures.

In order to achieve the target, several activities are being undertaken, including the development of multipurpose reservoir planning for the water supply and sanitation infrastructure, irrigation, and hydropower development for the H project. To undertake this work, the main data given was rainfall data, flow data, catchment area, base population for water supply, and so on. This report presents a specifically preliminary study and design of water supply, irrigation, and hydropower development for the project, which is carried out in accordance with the scope of work given by our professor. The preliminary study and design are therefore to investigate scenarios and alternatives and prepare a document of water supply, irrigation, and hydropower infrastructures for the provision of the assignment. This report, as required in the assignment, contains project background, water sources assessment, selection of suitable hydropower potential, and irrigation sizing, design criteria, descriptions of the proposed scheme, preliminary design of water supply components. The objective of the project is to recommend and design a cost-effective water supply system, irrigation, and hydropower development to supply reliable, sufficient, and safe water from a defined water source to the H project. The specific objectives of the investigation works are: • To assess potential water source alternatives for water supply • Selecting appropriate size for the reservoir which could satisfy all water demand • Assess and select suitable irrigation development and hydropower potential. • Preliminary study and design of all components of the reservoir and appurtenant structures The scope of the project generally focuses on preliminary study and design of water supply, irrigation and hydropower development components from water sources potential and the water balance output.

Life Cycle Assessment (LCA) is a widely recognized tool used to evaluate environmental impacts of a product or process, based on the environmental inventory database and bills of material. Data quality is one of the most significant factors affecting the analysis results. However, currently most datasets in inventory databases are generic i.e., they may represent material and energy flow of a process at market average, instead of a specific process used by a manufacturer. As a result, stockholders are unable to track their supply chain to find out the actual environmental impact from each supplier and to compare the environmental performance of alternative options. In this paper, we developed a new framework i.e., blockchain based LCA (BC-LCA), where block-chain technology is adapted to secure and transmit inventory data from upstream suppliers to downstream manufacturers. With BC-LCA, more specific data can be acquired along the supply chain in a real-time manner. Moreover, the availability, accuracy, privacy, and automatic update of inventory data can be improved. A case study is provided based on an industrial supply chain, to demonstrate the utilization of BC-LCA.

The power generation from hydroelectric plants has increased worldwide, contributing to the participation of the renewable sources to the energy matrix. In the semiarid Brazilian Northeast (NEB), thousands of small dams have been built over time as a solution for water supply. Although incompatible with hydropower generation due to the conflict with human water supply in the region, small reservoirs accumulate water and hydraulic energy at high altitudes. In this work, simulations were performed to assess how the arrangement of various sized reservoirs impact the power demand for water distribution in the Banabuiú River Basin – BRB (19,800 km²), Brazil. The power required to pump water from all 1,405 reservoirs to the districts is 6.5 GWh/year, whereas in the scenario with the 12 larger strategic reservoirs only, the power demand reaches 45.3 GWh/year. Although representing roughly 60% of the water availability of the BRB and being able to supply all the districts, the Arrojado Lisboa reservoir alone would demand 195 GWh/year to supply water to the entire basin, i.e. 30 times the power required in the real reservoir arrangement. By storing water at high altitudes and distributing it spatially, the small reservoirs increase the energy efficiency on the water distribution system.

Risk modelling along with multi-objective optimization problems have been at theepicenter of attention for supply chain managers. In this paper, we introduce a datasetfor risk modelling in sophisticated supply chain networks based on formal mathematical models. We have discussed the methodology and simulation tools used to synthesize the dataset. Additionally, the underlying mathematical models are discussed in granular details along with providing directions to conducting statistical analyses or neural machine learning models. The simulation is performed using MATLAB ™Simulink and the models are illustrated as well.

This article makes a general analysis of the supply chain of photovoltaic systems in Colombia, taking as a starting point a conceptual base and the current situation of the country in this sector. This article especially will contemplate the non-interconnected areas (ZNI) to the electricity grid in the national, due to its complexity in the logistics issues. Likewise, the work performs a graphical representation of the stages and processes of the supply chain using a powerful-modeled tool such as the Petri Nets (PNs). Finally, it identifies some of the flaws in the operation and the joint of all the links of the logistic part and from there it is based the importance of the strategy of orientation to the supply chain (SCO) as a possible alternative to improve and to enhance the logistic processes of this type of systems.

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Nevertheless, a practical application of this concept in the residential sector should address the underlying concerns raised by the technical specifications of case studies. From one side, such an operation must handle common matters related to the essence of any NILM system. Although this aspect has been thoroughly investigated by basic research, it is limited to the properties of public datasets. On the other side, it must deal with specific concerns corresponding to uncommon situations. These circumstances impose further restrictions on existent NILM schemes, however, they have been overlooked due to the lack of pertinent databases to scrutinize. Accordingly, this paper presents applied research on a potential solution to NILM for Quebec residences. It carries out a relevant investigation into the multi-faceted nature of this problem in order to reveal barriers to feasible implementations in the context of Quebec. This work commences with a concise discussion about the NILM idea to highlight its essential requirements for a fruitful practice. Afterward, it provides a comparative statistical analysis to represent the specificity and potential challenges of the case study in accordance with NILM necessities. For this purpose, the examination exploits data from real-world measurement systems in the same and European regions. Subsequently, this study focuses on a load identification exercise by proposing a combinatory approach that utilizes the promise of sub-meter smart technologies to integrate the intrusive aspect of load monitoring with the non-intrusive one. The former is aimed at extracting overall heating demand from the aggregate one by a supervised procedure based on Deep Learning (DL) models while the latter is designed for disaggregating the residual load through an unsupervised process on the basis of clustering techniques. The results demonstrate that geographically-dependent cases create electricity consumption scenarios under which existing NILM methods can be questioned. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly, in Quebec houses.

Waterbomb structures are origami-inspired deformable structural components used in new types of robots. They have a unique radially deployable ability that enables robots to better adapt to their environment. In this paper, we propose a series of new waterbomb structures with square, rectangle, and parallelogram base units. Through quasi-static axial and radial compression experiments and numerical simulations, we prove that the parallelogram waterbomb structure has a twist displacement mode along the axial direction. Compared with the square waterbomb structure, the proposed optimal design of the parallelogram waterbomb structure reduces the critical axial buckling load-to-weight ratio by 55.4% and increases the radial stiffness-to-weight ratio by 67.6%. The significant increase in the radial stiffness-to-weight ratio of the waterbomb structure and decrease in the critical axial buckling load-to-weight ratio make the proposed origami pattern attractive for practical robotics applications.

This study examines the balancing between urban transportation security and supply chain efficiency amid global disruptions like the COVID-19 pandemic, attacks on maritime lanes, and geopolitical conflicts. The authors combine bibliometric network analysis and natural language processing (NLP) to review scholarly literature from 2017 to 2023. This approach identifies and visualizes evolving thematic trends. Findings reveal a shift towards integrating advanced technologies such as blockchain, AI, and IoT to enhance security without compromising efficiency. This study highlights the significance of integrating urban planning principles and sustainable practices to enhance transportation security and supply chain efficiency within urban environments. Actionable insights for policymakers and supply chain managers emphasize the adoption of integrated, technology-driven security solutions to foster resilient and sustainable supply chains. This research contributes to academic discourse by refining the understanding of evolving priorities in transportation security and supply chain management, offering both theoretical and practical implications for secure and efficient global supply chains.

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

The primary aim of this study was to investigate the variations in cognitive load as self-efficacy gradually increases and examine the potential within-mediating effect of state anxiety between self-efficacy and cognitive load. The experiment manipulated the self-efficacy of 49 adult participants through the false feedback paradigm, employing a within-subject experimental design and using facial expression recognition as the experimental task. The data were analyzed using Hierarchical Linear Modeling. The results revealed that self-efficacy negatively predicted state anxiety and cognitive load, while state anxiety positively predicted cognitive load. Moreover, state anxiety played a complete mediating role at multiple levels in this process.

(1) Background: The purpose of this study was to assess the external load demands in futsal, con-sidering both home and away matches and their outcomes, in order to plan microcycles throughout the season based on the external load of each match. (2) Methods: The external load of 10 players from a First Division team in the Spanish Futsal League was recorded. The total dura-tion of player involvement with the clock stopped was 18.15 ±7.04 minutes. The players' external load was monitored using OLIVER devices. To analyse the influence of the match outcome and location on the external load, a univariate general linear model (GLM) analysis was conducted. (3) Results: There are no differences between the variables, except for accelerations of 2 to 3 m/s² (m) per minute and the number of accelerations of 2 to 3 m/s² per minute. Despite not showing significant differences, there is a trend indicating a higher demand for external loads in away matches as well as in matches won. (4) Conclusions: These findings are important for planning microcycles and providing the appropriate dosage to each player to achieve optimal performance in matches.

Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces a Perfect Load Forecasting Strategy (PLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed PLFS consists of two sequential phases, which are; Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selec-tion is done using Advanced Leopard Seal Optimization (ALSO) as a new natural inspired opti-mization technique, Citation: To be added by editorial staff during production. Academic Editor: Firstname Last-name Received: date Revised: date Accepted: date Published: date Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). While outlier rejection is accomplished through Interquartile Range (IQR) as a measure of statis-tical dispersion. On the other hand, actual load forecasting takes place in LFP using a new pre-dictor called; Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed PLFS has been tested through excessive experiments. Results have shown that PLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.

The objective was to compare the failure loads of glass ceramic, and polymer crowns against yttria-partially stabilized zirconia (Y-PSZ) crowns with varying yttria concentrations. Monolithic crowns of zirconia (Cercon XT, Katana UTML, BruxZir Anterior), glass ceramic (Celtra press, IPS e.max press, Lisi press), and polymeric materials (Trilor, Juvora, Pekkton) were fabricated and cemented to epoxy abutments. The total number of specimens was 135 for crowns and 135 for discs (15 specimens per material type and design). A universal testing machine was used to perform compressive loading of all crowns to failure with a steel hemisphere along the longitudinal axis of the abutments. Energy dispersive spectroscopy (EDS) was used to identify the concentration (mol% yttria-partially stabilized zirconia) for each zirconia brand. The data were analyzed using a generalized linear model and regression analysis. The results revealed significant differences (P < .05) in mean failure loads for different crown materials: Trilor (6811 ± 960 N) > Juvora (5215 ± 151 N) > Cercon (4260 ± 520 N) = BruxZir (4186 ± 269 N) = e.max (3981 ± 384 N) > Katana (3195 ± 350 N) = Lisi (3173 ± 234 N) = Pekkton (3105 ± 398 N) > Celtra (2696 ± 393 N). EDS showed that the zirconia materials contained yttria at different concentrations (BruxZir = 5Y-PSZ, Cercon = 4Y-PSZ, Katana = 3Y-PSZ). The yttria concentration had a significant effect on the failure load of the Katana (3Y-PSZ) crowns, which revealed lower failure loads than the Cercon (4Y-PSZ) and BruxZir (5Y-PSZ) crowns. New zirconia materials are provided with different yttria concentrations (3Y-, 4Y-, and 5Y-PSZ). Since a higher yttria concentration increases the translucency, clinicians should be aware that 4-5 wt% yttria-partially stabilized zirconia materials provide comparable or higher mechanical properties than e. max glass ceramic for dental application of crowns.

In view of the floating rectangular aerostatic guideway problem of low load capacity and stiffness, this paper puts forward a kind of closed rectangular aerostatic guideway and its optimized design. Firstly, the analytic calculation formula of a closed rectangular aerostatic guideway was deduced. The use of MATLAB software for the numerical simulation and the parameter influence analyses was carried out in terms of the gas supply pressure on load capacity and stiffness. A simulation model of the rectangular air bearing was also built using the Fluent software, and the model was used for simulation calculations. Secondly, the static performance of the rectangular air bearing was analyzed by analyzing the distribution position and number of orifices on its load capacity and stiffness. On this basis, the response surface optimization design method is used to analyze the variation law of the load capacity and stiffness of the air bearing under different restrictor structural parameters. Finally, the optimal design parameters for the rectangular air bearing are obtained. The simulation results show that the optimized rectangular air bearing designed in this paper has a good load capacity and stiffness of 644.58N and 63.405N N∙μm^(-1).

The purpose of this study was to compare Electromyographic (EMG) with Mechanomyo- 1 graphic (MMG) recordings during isometric conditions, and during a simulated load-lifting task. 2 Twenty-two males (age: 25.5 ± 5.3 years) first performed maximal voluntary contractions (MVC) and 3 submaximal isometric contractions of upper limb muscles at 25%, 50% and 75% MVC. Participants 4 then executed repetitions of a functional activity simulating a load-lifting task above shoulder level, 5 at 25%, 50% and 75% of their maximum activity (based on MVC). The low frequency part of the 6 accelerometer signal (< 5 Hz) was used to segment the 6 phases of the motion. EMG and MMG were 7 both recorded during the entire experimental procedure. Root mean square (RMS) and mean power 8 frequency (MPF) were selected as signal extraction features. During isometric contractions, EMG 9 and MMG exhibited similar repeatability scores. They also shared similar RMS vs force relationship, 10 with RMS increasing to 75% MVC and plateaued to 100%. MPF decreased with increasing force to 11 75% MVC. In dynamic condition, RMSMMG exhibited higher sensitivity to changes in load than 12 RMSEMG. These results confirm the feasibility of MMG measurements to be used during functional 13 activities outside the laboratory. It opens new perspectives for future applications in sports science, 14 ergonomics and human-machine interfaces conception.

This paper has designed a four-channel wide dynamic microwave rectifier circuit for the shortcomings of uneven output voltage of each branch of the multi-channel rectification technology. A voltage-doubling rectifier circuit with identical structure was used in each branch. The four rectifier branches shared the same load at the output terminal, allowing the rectifier circuit to have an RF-DC conversion efficiency greater than 50% over the input power range from 12mW to 274mW, widened by 274% compared to a single channel. Not only the advantages of multi-channel technology with wide dynamics were retained, but also the output voltage was highly consistent and could be directly output in parallel without complex power electronics conversion.

White Spot Syndrome Virus (WSSV) has emerged as one of the most prevalent and lethal viruses globally, and infects both shrimps and crabs in the aquatic environment. This study aimed to investigate the occurrence of WSSV in different ghers of Bangladesh and the virulence of the circulating phylotypes. We collected 360 shrimp (Penaeus monodon) and 120 crab (Scylla sp.) samples from the South-East (Cox’s Bazar) and South-West (Satkhira) coastal regions of Bangladesh. The VP28 gene-specific PCR assays and sequencing revealed statistically significant (p < 0.05, Kruskal Wallis test) differences in the prevalence of WSSV in shrimps and crabs between the study areas (Cox’s Bazar and Satkhira), and over the study periods (2017-2019). The mean Log load of WSSV varied from 8.40 (Cox’s Bazar) to 10.48 (Satkhira) per gram of tissue. The mean values for salinity, dissolved oxygen, temperature and pH were 14.71±0.76 ppt, 3.7±0.1 ppm, 34.11±0.38˚C and 8.23±0.38, respectively in the WSSV-positive ghers. The VP28 gene-based phylogenetic analysis showed an amino-acid substitution (E→G) at 167th position in the isolates from Cox’s Bazar (referred to as phylotype BD2) compared to the globally circulating one (BD1). Shrimp PL artificially challenged with BD1 and BD2 phylotypes with filtrates of tissue containing 0.423 X 109 copies of WSSV per mL resulted a median LT50 value of 73 hrs and 75 hrs, respectively. The in-vivo trial showed higher mean Log WSSV copies (6.47±2.07 per mg tissue) in BD1 challenged shrimp PL compared to BD2 (4.75±0.35 per mg tissue). Crabs infected with BD1 and BD2 showed 100% mortality within 48 hrs and 62 hrs of challenge, respectively with mean Log WSSV copies of 12.06±0.48 and 9.95±0.37 per gram tissue, respectively. Moreover, shrimp antimicrobial peptides (AMPs) penaeidin and lysozyme expression was lower in BD1 challenged group compared to BD2 challenged shrimps. These results collectively demonstrated that relative virulence properties of WSSV based on mortality rate, viral load and expression of host immune genes in artificially infected shrimp PL could be affected by single aa substitution in VP28.

Sometimes a pavement deflects only because of seasonal volume change of expansive soils in the pavement subgrade. Engineering practitioners expect an implementable and straightforward analysis method for a geosynthetic-reinforced pavement subjected to the swelling/shrinkage issue of expansive clayey subgrade, in an effort to find the bending moment, shear force and tension force distributions through the reinforced pavement, which are induced from the volumetric changes of subgrade soils. The virtual load method (VLM) was proposed in the past following the Timoshenko beam theory to analyze geosynthetic-reinforced pavement on expansive soils. In the VLM, the unknown virtual distributed load was obtained in the way by applying the inverse theory for the identification of material parameters of the pavement-foundation system. It was seen that the selection of the number of material parameters to obtain virtual load significantly affects the accuracy of the structural properties of the pavement and tensile properties of the geosynthetics if the linear least square method is used. In this paper, a unique numerical scheme was proposed in the hopes of solving the issue. After a forward problem was solved numerically, the Timoshenko beam deflection was taken as a start-up for the inverse problem to back analyze the load applied to the pavement. Solutions from forward/backward examples have explicitly shown the accuracy achieved related to the bending moment in the pavement and tension in the geosynthetic reinforcements. The proposed methodology can be applied for an in-depth understanding of the geosynthetic function for the mitigation of longitudinal cracks on pavements caused by heave/shrinkage of expansive soils.

A theoretical nonlocal thermoelastic model for studying the effects of the thermal conductivity variability on a rotating nanobeam has been described in the present article. The theory of thermal stress is employed using the Euler–Bernoulli beam model and generalized heat conduction with phase lags. It is believed that the thermal conductivity of the current model varies linearly according to temperature. Due to variable harmonic heat, the considered nanobeam excited and was subjected to a time-varying exponential decay load. Using the Laplace transform process, the analytical solutions for displacement, deflection, thermodynamic temperature and bending moment of rotating nanobeams are provided in final forms and a numerical example has been taken to address the problem. A comparison of the stated results was displayed and additionally, the influences of non-local parameters and varying load were analyzed and examined. We also investigate how the linear changes in the temperature of physical properties can influence both the static and dynamic responses to the rotating nanobeam.

Background: The question of whether children are less likely to pass on SARS-CoV-2 is important for planning society’s response to the pandemic. A document available on the Charité hospital website has not been formally published but is frequently referred to as supporting the notion that viral loads are similar in children and adults. Methods: The summary data from this document was extracted and used to produce summary statistics for three age groups: younger children (ages up to 11), older children (ages 12 to 19) and adults (ages 20 and above). Viral loads between the two children’s groups and the adult group were compared using Welch’s t test, which only requires group means and standard deviations and which is robust against moderate departures from normality. Findings: Viral loads were significantly lower than in adults for both younger children (p=0.04) and older children (p=0.04). Overall, viral loads were lower in children than adults (p=0.005). Interpretation: For both younger and older children, on average those with detectable SARS-CoV-2 have significantly lower viral loads than adults. Funding: No funding was received for this analysis.

Background: World Health Organization (WHO) declared that COVID-19 as a pandemic on March 11, 2020. There is sudden need of statistical modeling due to onset of COVID-19 pandemic across the world. But health planning and policy requirements the estimates of disease problem form clinical data. Objective: To predict recovery rate, cases load rate on the basis of cumulative confirmed Novel Corona virus (NCV) cases, recovered cases and deaths form COVID-19 in India. Methods: The reported COVID-19 cases in the country were obtained from website (https://datahub.io/core/covid-19#resource-covid-19_zip/). The cumulative NCV confirmed cases; recovery cases and deaths were used for estimating recovery rate, cases load rate and death rate till date 24 April 2020. Results: A total of 24530 NCV confirmed cases were reported nationwide in India on 24 April 2020. It is found that the recovery rate increased 22% and case load rate decreased 74%. Death rate is found to be very low 3%. The difference of cases load rate and recovery rate (delta) coincide at 50 % then NCV cases expected would be declined. Conclusion: The epidemic in the country was mainly caused by the importation of India. Lockdown as restricting the migration of population and decided to quarantine of population may greatly reduce the risk of continued spread of the epidemic in India. This study predicts that by 20 May 2020, the cases load rate lesser than recovery rate there after COVID-19 patients would be started to reducing.

The aim of this study was to clarify effects of 3-week work-matched high-intensity intermittent cycling training (HIICT) with different cadences on VO2max in university athletes. Eighteen university athletes performed HIICT with either 60 rpm (n = 9) or 120 rpm (n = 9). HIICT consisted of eight sets of 20-s exercise with a 10-s passive rest between each sets. The initial training intensity was set at 135% of VO2 max and was decreased by 5% every two sets. Athletes in both groups performed 9 sessions of HIICT during 3-week. The total work-load and achievement rate of the work load calculated before experiments in each group were used for analysis. VO2max was measured pre and post-training. After 3-week of training, no significant differences in the total work-load and achievement rate of the work load were found between the two groups. VO2max similarly increased in both groups from pre to post training (p = 0.016), with no significant differences between the groups (p = 0.680). These results suggest that cadence during HIICT is not training variable affecting effect of VO2max.

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

This paper proposes a novel blockchain-based system designed to revolutionize transparency, traceability, and reliability within the organic food supply chain. Addressing persistent challenges such as certification fraud, opaque supply chains, and the high costs associated with organic food production, stakeholders including farmers, processors, distributors, retailers, and consumers stand to benefit from the system’s innovative approach. By leveraging blockchain technology and a robust system architecture, the proposed system offers enhanced transparency, improved traceability, and streamlined certification processes. Through detailed exploration of key components such as user interfaces, smart contracts, consensus mechanisms, IoT integration, and governance frameworks, the paper illustrates the practical application of blockchain technology in improving transparency and traceability throughout the food supply chain. A case study focusing on the organic Tofu supply chain further demonstrates the system’s potential to enhance sustainability, efficiency, and integrity in organic food production and distribution networks. The paper concludes by discussing potential implementation challenges and suggesting areas for future research and development to advance the impact of blockchain technology on the food supply chain.

The COVID-19-induced strain on global supply chains led to significant market imbalances and unprecedented inflation, particularly affecting urban economies. Containment policies and stimulus packages resulted in unpredictable demand shifts, challenging urban supply chain planning and resource distribution. These disruptions underscored the need for robust risk management models, especially in cities where economic activity and population density exacerbate supply chain vulnerabilities. This study develops a comprehensive risk model tailored for G-7 urban economies, analyzing the causal and cointegration relationships between key economic indicators. Using Granger causality tests and a factor-augmented vector autoregression (FAVAR) approach, the study examines complex time series and high-dimensional variables, focusing on urban-specific indicators such as the Composite Leading Indicator (CLI) and Business Confidence Indicator (BCI). Our results indicate strong causal relationships among these indicators, validating CLI as a reliable early predictor of urban economic trends. The findings confirm the viability of this urban supply chain risk management model, offering potential pathways for strengthening urban resilience and economic sustainability in the face of future disruptions. This approach positions the study within the context of urban science, emphasizing the impacts on cities and how urban economies can benefit from the developed risk model.

In this paper, we explore the theoretical foundation of Graph Neural Network (GNN) model and apply it to various traditional supply chain logistics problem. In particular, we study the Route Optimization Problem, Demand Forecasting Problem, Risk Assessment and Anomaly Detection, Supplier Section and Procurement Optimization, Inventory Optimization Problem, and Green Supply Chain Problem. The study helps us the expand the use the GNN and gives daily supply chain management a boost.

An integration between the principles in supply change management in sport tourism and sustainability balanced scorecard leads to development of guidance and assessment criteria for a city to be a sustainable sport tourist destination. This paper aims to present a sport tourism sustainability management model (STSM), consisting of 5 perspectives, namely financial perspective, customer perspective, internal process perspective, learning and growth perspective and sustainability perspective. By using Delphi technique to obtain a consensus from experts, university lecturers, independent organization/association and business entities in relation to sport tourism management, the researcher defines elements to assess complex perspective for sustainable development of sport tourism. The result shows that there are 5 perspectives and 18 elements in relation to sustainable development of sport tourism. All perspectives and elements have high consensus as measured by Kendall’s Coefficient of Concordance (W) of .488. To validate the model, the researcher examines correlation among the five perspectives with structural equation model and finds that the absolute fit is satisfactory, with the value of CMIN/DF at 1.830, RMSEA at .046, GFI at .951, AGFI at .919 and RMR at .038. In addition, the incremental fit also demonstrates positive result with the value of NFI at .962, CFI at .982, TLI at .973 and IFI at .982. As the model aligns and explains empirical data, it can support decision making for management team, effective and efficient strategy drafting for sustainable development in sport tourism and improving livelihoods of residents.

The level of technological development of any civilization can be gaged in large part by the amount of energy they produce for their use, but also encompasses that civilization’s stewardship of their home world. Following the Kardashev definition, a Type I civilization is able to store and use all the energy available on its planet. In this study, we develop a model based on Carl Sagan’s K formula and use this model to analyze the consumption and energy supply of the three most important energy sources: fossil fuels (e.g., coal, oil, natural gas, crude, NGL and feedstocks), nuclear energy and renewable energy. We also consider environmental limitations suggested by United Nations Framework Convention on Climate Change, the International Energy Agency, and those specific to our calculations to predict when humanity will reach the level of a Kardashev scale Type I civilization.

In recent years, Blockchain has been favorably adopted in the Supply Chain industry as it provides guaranteed transparency and traceability. The flexibility of Blockchain allows different applications to enable to exchange information; a significant middleweight layer is responsible for information transfer in the Agricultural Sector. The products that are manufactured at a global level in the agriculture industry are improved in safety, validation of some criteria, and quality. In the agriculture industry, the increasing number of complications associated with food safety and impurity risks needs high-level effective traceability solutions that act as necessary quality management tools to make sure satisfactory safety of crops. The agricultural supply chain today has complex ecosystems, consisting of several stockholders to authenticate criteria which are important like crop development stages, monitoring and validation, and compliance with the quality standard. In this proposed research, a systematic literature review will be done that includes smart contracts, Blockchain, and business transactions exclusively for crop production traceability across the agricultural and food supply chain. By using Blockchain in the agriculture sector, productivity, consistency, safety, reliability, and advanced security are increased. All transactions are kept and recorded on the immutable record of Blockchain associates with a decentralized system. Thus, it provides more traceability and clarity in the agriculture system in asafe, trustworthy, and effective way. A systematic literature review is thus enforced to classify the papers which are selected by the following classification: crop traceability, contribution type, research type, and their approach. For this systematic review, the papers which extracted are classified according to defined criteria. The purpose of this study is to fill the gap by collecting and analyzing studies available within the literature aiming to firstly, gain complete insight on the integration of Blockchain in the agriculture sector. Secondly, provide a summary of the present state of research on this area and identify gaps in existing studies. To achieve this aim an SLR was conducted. The findings of this SLR are discussed and researchers were provided with suggestions on possible directions for future research.

Rice is the grain feeds more than half of the world population. It is an important staple food provides twenty percent of the calories consumed worldwide. Food safety affects the health of consumers. Establishing an effective traceability system is one of the primary measures to protect rice quality and food safety. Blockchain-based traceability system has the characteristics of data tamper-proofing, decentralization, and co-governance. This paper proposed a rice traceability scheme based on blockchain technology that selects the critical nodes in the rice industry chain to construct a blockchain network to achieve transparent data sharing. It is meaningful for both enterprises and consumers. It can let enterprises get the real quality information of rice and related raw materials. Consumers can get the true information of rice and rice products to buy safe and healthy food. The scheme is proved feasible by simulating the traceability of the rice supply chain.

The present article proposes a complete framework for Supply Chain Strategy (SCS) analysis that is adapted to the specific characteristics of the agrifood chain, thereby facilitating management of the former. As a specific case of analysis, the horticultural supply chain, originating in Spain and ending with the European consumer, is analyzed, taking as a reference the marketing companies at origin (mainly social economy companies, i.e.; cooperatives). In addition, a survey of marketing companies is conducted to possibly determine which explicit cooperative growth strategies may include horizontal and vertical collaboration relationships with other members of the chain. The aim is to analyze with whom the cooperative collaborates within the supply chain and the key points of such collaboration. A model analyzing the influence of collaboration on company performance is also considered. The results reveal that, in recent years, aspects related to quality and health have been surpassed and replaced by the concept of sustainability within a framework of collaboration with customers. In any case, it becomes evident that there is a need to expand collaboration within the chain by incorporating the supplier of the supplier, with the aim of making the chain more profitable.