In many regions where drinking water supply is intermittent and unreliable, households adapt by storing water in cisterns or rooftop tanks. Both intermittent supply and stored water can be vulnerable to contamination by microorganisms with deleterious health effects. The Guadalajara Metropolitan Area is a rapidly growing urban center with over five million residents where household storage is nearly ubiquitous. This pilot study was conducted in July 2018 to examine the microbiological quality of drinking water in Guadalajara. Samples were tested for free available chlorine residual, total coliform bacteria, and E. coli. A survey on access to water and public perspectives was also conducted. Water exiting rooftop tanks exceeded regulatory limits for total coliform levels in half of the homes studied. Piped water arriving at two homes had total coliform levels that far exceeded regulatory limits. No E. coli were detected in any of the samples. Only 35% of homes had a chlorine residual between the recommended 0.2 and 1.5 mg/L. Many homes reported unpleasant odors and colors. Only 7% of residents drank the piped water. Future studies are needed, especially during April and May when many homes reported a higher disruption to water service.

Assessing the economic supply of biomass in a geospatial context while accounting for risk from natural disasters was studied. Risk levels were estimated from a component of factors which included: population density, road density, federal ownership, U.S. Environmental Protection Agency ecoregions, and Presidential Disaster Declarations. The Presidential Disaster Declarations included risks due to: coastal storm, drought, fire, flood, freezing, hurricane, mud land slide, severe ices, severe storms, snow, tornado, and tropical storm. Presidential Disaster Declarations included summaries based on a short-term time period from 2000-2011, and on a long-term time period from 1964-2011. Risk categories were developed as a function of the number of disaster declarations, agricultural-to-forest land ratio, average road density, and average population density. A significant contribution of the research was the allocation of spatially explicit data using GIS technology at the 5-digit zip code tabulation area. The average area for 5-digit ZCTAs in the Eastern U.S. study region was approximately 169 kilometers². Long-term risk (1964-2011) from disaster declarations had a greater impact on the economic availability of biomass supply relative to short-term declarations (2000-2011). The greatest risk to biomass supply came from population density relative to the other risk factors studies. Of the 25,044 total ZCTAs, 12,256 ZCTAs were in locations that did not include population density ≥ 150/km², road density ≥ 14 km/km², federal ownership, and US Environmental Protection Agency Level III ecoregions. Of the remaining 12,256 ZCTAs, 26.8% were considered to be moderate-to-high risk based on short-term declarations (2000-2011) and 29.4% were considered to be moderate-to-high risk based on long-term declarations (1964-2011). Lower risk locations for procuring biomass supply for both short-term and long-term declarations, across all risk factors, were in southern Georgia, South Carolina, and Texas.

The qualitative research on the influence of supply chain risk management (SCRM) on marketing strategies during economic uncertainty explores the complex interplay between these two critical areas. Conducted through in-depth interviews with industry professionals across various sectors, the study examines how organizations manage supply chain risks and adapt their marketing strategies in response to challenges such as natural disasters, geopolitical tensions, economic downturns, and pandemics. The findings reveal that these risks significantly impact marketing efforts by causing supply shortages, delays, increased costs, and demand fluctuations, necessitating rapid adjustments in promotional activities, pricing strategies, and communication with customers. Key strategies for integrating SCRM with marketing include leveraging technology for real-time supply chain monitoring, building strong supplier relationships, diversifying supply sources, and enhancing communication and collaboration between supply chain and marketing teams. These practices enable organizations to enhance their resilience and responsiveness, ensuring that marketing strategies are aligned with the evolving realities of the supply chain. The study also identifies challenges such as organizational silos, lack of cross-functional collaboration, resistance to change, and limited technological capabilities, which can hinder effective integration. Addressing these barriers through fostering collaboration, investing in technology, and promoting change management is essential for achieving successful integration. The outcomes of this integration include improved supply chain resilience, enhanced customer satisfaction, increased marketing agility, and a stronger competitive position. The research underscores the importance of a holistic and adaptive approach to integrating SCRM with marketing strategies, providing valuable insights for organizations seeking to navigate economic uncertainty and sustain their growth in a rapidly changing environment.

This qualitative study explores the relationship between supply chain responsiveness (SCR) and customer loyalty within the e-commerce sector. E-commerce has revolutionized global commerce, driven by technological advancements and shifting consumer behaviors. Central to e-commerce success is the ability to meet customer expectations swiftly and reliably, necessitating agile supply chain management (SCM) practices. SCR in e-commerce encompasses the capacity to adapt to dynamic market demands, minimize disruptions, and enhance customer satisfaction through efficient logistics, inventory management, and last-mile delivery. Through in-depth interviews with customers, e-commerce platform managers, and supply chain practitioners, this study investigates key themes, challenges, strategies, and impacts associated with SCR and customer loyalty. Findings reveal that customers prioritize fast order fulfillment, reliable service quality, personalized experiences, and trustworthy interactions, all of which significantly influence their loyalty and advocacy behaviors. Challenges identified include inventory management complexities, logistical hurdles in last-mile delivery, and the need for effective supplier communication. Strategic approaches to enhance SCR include leveraging data analytics for demand forecasting, adopting agile SCM practices, integrating customer feedback into service improvements, and fostering collaborative relationships with suppliers. These strategies not only optimize operational efficiency but also cultivate enduring customer relationships based on satisfaction, trust, and loyalty. The study concludes that improved SCR practices lead to increased customer satisfaction, repeat purchases, and positive word-of-mouth, thereby strengthening competitive advantage and sustainability in the e-commerce marketplace.

Water supply is a mandatory service for Tanzanians from respective legal public water utilities, and their sustainability reflects implementations of best management strategies at a local level. The objective of this study was to assess current approaches used in water quality and quantity management in Tanzania. This was achieved through secondary water data tends, on-site water quality assessments, visits of respective water supply and sanitation authorities, and assessment of their performances. It was observed that water supplied in rural-based authorities was quite different from that supplied in an urban setting as far as quality and quantity is concerned, urban-based supplies being of assistance to users over rural ones. A new strategy on water management is presented for sustainable water supply in Tanzania; it is based on controlling groundwater abstractions and preference of surface water in public water supplies. Rural water supply management must learn several practices realized at urban supplies for the betterment of respective majority water users.

The renewable energy sector plays a pivotal role in addressing climate change and achieving sustainable development goals. However, the sector faces challenges related to price inflation and supply chain disruptions, which can impede its growth and resilience. This qualitative research explores supply chain resilience strategies adopted by companies in the renewable energy sector to mitigate the impact of price inflation and disruptions. Through semi-structured interviews with key industry stakeholders, the study identifies several key themes. Supplier diversification emerges as a crucial strategy for mitigating risks associated with geopolitical tensions and trade restrictions. Inventory management plays a pivotal role in ensuring continuity in operations amidst supply chain disruptions, with a focus on optimizing inventory levels of critical components. The adoption of digital technologies offers significant potential for enhancing supply chain visibility, transparency, and responsiveness, although integration challenges remain. Collaboration and partnerships within the industry are essential for sharing resources, knowledge, and best practices, fostering innovation and collective problem-solving. Sustainable practices are integral to supply chain resilience, aligning with broader industry trends towards environmental stewardship and social responsibility. Overall, the findings highlight the multifaceted nature of supply chain resilience in the renewable energy sector and the interconnectedness of various strategies and practices. Moving forward, companies must adopt a proactive and adaptive approach to supply chain management, integrating these strategies and practices into their operations to navigate uncertainties and drive sustainable growth. This study contributes to the understanding of supply chain resilience in the renewable energy sector and provides valuable insights for industry stakeholders, policymakers, and researchers.

In modern times, sustainability in supply chain management is one of the keys to boosting the performance of the supply chain, and it enables the accomplishment of SSCM. It has been found in recent years that SSCM with assist of industry 4.0 applications, can benefit splendid productivity and efficiency in the supply chain. The numerous industry 4.0 applications that are involved are Artificial intelligence (AI), Machine Learning (ML), Internet of Things (IoT) Big Data Analytics (BDA), etc. Industry 4.0 with SSCM has gained a lot of interest from various authors and scholars across the world. Healthcare is one of the most critical sectors due to its importance in handling public health. More recently, the COVID-19 pandemic and other viruses (SARS and Ebola) have highlighted supply chain vulnerabilities that are further mitigated by their lean and globalized structures. Covid-19 underlined the need for supply chain visibility problems within the healthcare sector. When the need for medical supplies became urgent and global distribution was disrupted, companies faced the consequences of needing the technology, systems, and plans in place to correctly predict the onslaught. One report shows that about 94 percent of Fortune 1000 companies reported supply chain disruptions due to the COVID-19 global outbreak. Existing studies have discussed supply chain issues and the importance of AI in addressing these challenges. AI, like Industry 4.0 in the healthcare supply chain, is proving to be a game-changer. This paper aims to review and present the various aspects of SSCM and industry 4.0 applications to get a comprehensive literature review, which contains the analysis of multiple articles published from 2010-2020. In addition, the various challenges SSCM and industry 4.0 applications face are identified and discussed. Also, the impact of Industry 4.0 and sustainable practices brings together the healthcare supply chain. This study will help in getting a good understanding of the different industry 4.0 technologies that are important for setting up SSCM.

The Internet of Things (IoT) signifies a revolutionary technological advancement, enhancing various applications through device interconnectivity while introducing significant challenges due to these devices' limited hardware and communication capabilities. To navigate these complexities, the Internet Engineering Task Force (IETF) has tailored the Routing Protocol for Low-Power and Lossy Networks (RPL) to meet the unique demands of IoT environments. However, RPL struggles with traffic congestion and load distribution issues, negatively impacting network performance and reliability. This paper presents a novel enhancement to RPL by integrating learning automata designed to optimize network traffic distribution. This enhanced protocol, the Learning Automata-based Load-Aware RPL (LALARPL), dynamically adjusts routing decisions based on real-time network conditions, achieving more effective load balancing and significantly reducing network congestion. Extensive simulations reveal that this approach outperforms existing methodologies, leading to notable improvements in packet delivery rates, end-to-end delay, and energy efficiency. The findings highlight the potential of our approach to enhance IoT network operations and extend the lifespan of network components. The effectiveness of learning automata in refining routing processes within RPL offers valuable insights that may drive future advancements in IoT networking, aiming for more robust, efficient, and sustainable network architectures.

With the global objectives of achieving a ‘carbon peak’ and ‘carbon neutrality’, along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from January 1st to December 30th in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.

Vehicular ad hoc networks (VANET) are a subset of mobile ad hoc networks used to communicate between vehicles and infrastructure. During vehicular congestion communication, nodes compete to acquire channels, causing the channels to become congested. The congestion on the vehicle network results in increased delay and packet loss, resulting in reduced VANET performance. To address this problem, we developed a load-aware and priority adaptive traffic congestion control method in vehicular ad hoc networks (VANETs). The proposed scheme identifies less-congested road segments based on the network's load and reduces traffic congestion by suggesting other routes between nearby roadside units (RSUs). This research aims to improve the efficiency of the vehicular environment by utilizing the movement of vehicles with (RSUs) and sharing the traffic load between them. Simulation results demonstrate the effectiveness of the proposed protocol in reducing congestion and enhancing the overall performance of VANETs. To validate the proposed algorithm, we have implemented and tested the proposed algorithm using Network Simulator 3 (NS3) for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication scenarios and computed the performance of the algorithm on different parameters of the network. The simulation result of the proposed load aware and priority adaptive traffic congestion control method in VANET improved the packet delivery ratio, packet lost ratio, and end-to-end delay by 96%, 4.1%, and 1102 milliseconds, when compared to TDCCA value of 92%, 5.7%, and 1154 milliseconds, respectively

(1) Background: Hepatitis B virus (HBV) viral load (VL) is useful in determining treatment eligibility, however, due to high costs associated with the test, hepatitis B e antigen (HBeAg) is recommended as a proxy for HBV VL in resource-limited settings. (2) Methods: We used 299 archived plasma samples with hepatitis B surface antigen (HBsAg), HBeAg, and HBV VL (cut-off of 20,000 IU/mL and 200,000 IU/mL) results from people living with human immunodeficiency virus (PLWH). HBeAg and HBV VL results were analyzed to evaluate the use of HBeAg to determine CHB treatment eligibility and qualification of HBV prevention of mother-to-child transmission (PMTCT) prophylaxis for the first time in Botswana. (3) Results: Approximately 70.8% and 87.5% of participants with HBeAg-positive serology had HBV viral loads ≥20,000 IU/mL and 200,000 IU/mL respectively. Using logistic regression, the odds of having a qualifying HBV VL for CHB treatment and PMTCT prophylaxis were respectively 55.5 and 110.4 times higher in participants with positive HBeAg serology indicating a statistically significant association between HBeAg status and HBV VL. (4) Conclusions: HBeAg detection may not always correlate with high HBV VL, warranting further research focusing on this association, and other biomarkers that correlate better with HBV VL.

High-density olive groves, despite their interesting production potential, have several limitations, also due to the high fruit load and the irrigation requirements.The study aimed to evaluate the effects of fruit load and deficit irrigation on oil yield and fruit quality, and on olive chemical composition in a high-density olive grove (cv Sikitita). Our main hypothesis was that the primary metabolism, as influenced by crop load and stress, could modify the accumulation of different phenolic classes.Different fruit loads were generated through flower thinning (66%, 50%, 33%, 0%) and two deficit irrigation treatments (-60%, -75%) were compared to the well-watered control (920 m3/ha).Thinning treatments had a limited effect on oil yield, on the other hand, deficit irrigation caused considerably less oil accumulation in the fruit on all sampling dates.Thinning 66% and deficit irrigation 75% were considered with the control for untargeted metabolomic analysis, considering three sampling dates. 233 different phenolic compounds were annotated. Multivariate HCA results indicated the impact of harvest time on the phenolic profile of olive fruits, yielding two separate clusters grouping t1 and t2 together and apart from t3. Regarding agronomic techniques, they played a differential role in the phenolic profile (supervised OPLS – DA). Fruit load mostly affected flavonoid glycosides. In contrast, the phenolic response to deficit irrigation is more heterogeneous, with phenolic acids (35%), flavonoids (25%), LMW, and other phenols (25%).

The chicken poultry business is becoming a crucial sector for our nation's sustainable food supply. To increase the health of chickens in coops and maybe increase the output of chicken meat and eggs, several innovations and inventions have been developed. Therefore, the creation of an automated chicken feeding device might be highly beneficial for the expansion of the poultry sector. This equipment may take the role of a worker in the chicken feeding process, solving the industry's labor shortage and introducing a semi-automatic procedure. The main processor of the automated chicken feeding system is a Raspberry Pi 3 Model B+. The machine consists of five main components: a Raspberry Pi with an amplifier that makes noise to draw the attention of free-range chickens, a Raspberry Pi with a pi camera that counts and detects the chickens, a Raspberry Pi with a servomotor that moves the food from storage to a container, a Raspberry Pi with a load cell that controls the number of feeds to avoid waste on foods, and a Raspberry Pi with a temperature sensor that monitors the food's freshness. This enhances the coop's atmosphere while lowering labor costs, conserving food, and ensuring that the chickens are fed on time. It also regulates the temperature sensor to ensure that the chicken food is fresh and prevents food from becoming polluted by insects and chicken excrement.

The ultimate aim of dynamic load balancing in cloud computing systems is to maximise the efficiency with which resources are utilised and workloads are distributed. Given that load balancing is a multi-objective process and that response time is a priority, the Harris hawk optimisation (HHO) algorithm was developed as a unique solution for dynamic load balancing. Based on burden distribution and resource utilisation, the HHO algorithm is responsible for dynamically assigning workloads to virtual machines (VMs). Through iterative interactions and position updates, the hawks investigate the solution space, determine the optimal method for dividing the work, and adapt to the ever-changing conditions of the workload. The HHO algorithm has been demonstrated to be effective and efficient in the management of dynamic load balancing via a series of experimental evaluations and comparisons with other load-balancing approaches. These discoveries have led to quicker response times and more efficient resource utilisation. Utilising the collaborative search behaviour of hawks, this technique provides a solution that is both practicable and effective for addressing load balancing concerns in dynamic scenarios.

This work discusses the relationship between foreign direct investment (FDI) and pollution in Spain using an autoregressive distributed lag regression model (ARDL) from 1961 to 2018. The authors apply the theoretical context of pollution haven/halo and conclude that FDI in Spain does not contribute to the transfer of green technologies for cleaner production, despite being included within the European environmental regulatory framework. The authors also verify the adequacy of the environmental Kuznets curve (EKC) and the Load Capacity Curve hypothesis (LCC) in the Spanish environmental context and find that income per capita has a positive effect on environmental sustainability and quality. In this sense, the further development of an efficient renewable energy infrastructure could efficiently drive to a better standards all pollution indexes in Spain, including nuclear energy production, contributing the emissions neutrality aimed by the EU by 2050.

Clinching is a manufacturing method of mechanical joining two or more materials without the use of heat or additional components. This process relies on high plastic deformation to create a secure bond. Clinching technology is widely used for joining various grades and thickness of materials. Especially in the automotive industry, clinching is an alternative to spot resistance welding. However, the load-bearing capacity of clinched joints is comparatively lower when compared to resistance spot welded joints. This research aimed to enhance the load-carrying capacity of clinched joints. Experimental tests were conducted on clinched joints with locally modified microstructures to optimize their placement within the joint. The microstructure distribution in the clinched joint region was analysed using light and scanning electron microscopy, as well as microhardness measurements. Two material grades, micro-alloyed steels HX420LAD+Z and dual-phase ferritic-martensitic steels HCT600X+Z, were tested. Each grade underwent five groups of ten samples, subject to identical experimental conditions of local heating by resistance spot welding (RSW) and clinching. The application of RSW to the clinched joint area resulted in increase of load-bearing capacity for both joined types of materials.

Hydrostatic bearings for liquid rocket engine turbopumps provide distinctive advantages including high load capacity even with low viscosity cryogenic fluid and extending a life span by minimizing friction and wear between rotor and bearing surfaces. Application of hydrostatic bearings into turbopumps demands reliable test data base with well-quantified operating parameters and experimentally validated accurate performance predictive tools. The present paper shows the comprehensive experimental data and validation of predicted static load characteristics of hydrostatic journal bearings lubricated with air, water, and liquid nitrogen. Extensive experiments for static load characteristics of hydrostatic bearings are conducted using a turbopump rotor-bearing system simulator while increasing supply pressure (Ps) into the test bearings. The test results demonstrate notable effects of the test fluids and their temperatures, as well as Ps, on the bearing performance. In general, the measured bearing flow rate, rotor displacement, and stiffness of the test bearings steadily increase with Ps. The static load bearing characteristics predictions considering flow turbulence and compressibility matched well with the experimental results. The work with an independent test data and engineering computational programs will further the implementation of hydrostatic bearings in high performance turbopump shaft systems with improved efficiency and enhanced reusability of liquid rocket engine sub-systems.

Regarding the high efficiency of metaheuristic techniques in energy performance analysis, this paper scrutinizes and compares five novel optimizers, namely biogeography-based optimization (BBO), invasive weed optimization (IWO), social spider algorithm (SOSA), shuffled frog leaping algorithm (SFLA), and harmony search algorithm (HSA) for the early prediction of cooling load in residential buildings. The algorithms are coupled with a multi-layer perceptron (MLP) to adjust the neural parameters that connect the CL with the influential factors. The complexity of the models is optimized by means of a trial-and-error effort, and it was shown that the BBO and IWO need more crowded spaces for fulfilling the optimization. The results revealed that the internal parameters (i.e., biases and weights) suggested by the BBO generate the most reliable MLP for both analyzing and generalizing the CL pattern (with nearly 93 and 92% correlations, respectively). Followed by this, the IWO emerged as the second powerful optimizer with mean absolute errors of 1.8632 and 1.9110 in the training and testing phases. Therefore, the BBO-MLP and IWO-MLP can be reliably used for accurate analysis of the CL in future projects.

Heavy load carrying of water, firewood, and sand/stones is a ubiquitous activity for women living in developing countries. Although the intra-abdominal pressure associated with heavy load carrying is hypothesized to increase the risk of pelvic organ prolapse (POP) among women, relevant epidemiologic data are lacking. We conducted a comparative study involving two exploratory cross-sectional studies among convenience samples of women carrying heavy loads, with different characteristics: (1) as part of their activities for daily living, in Shinyanga region, Tanzania; and (2) working as sand miners in Pokhara, Nepal. Women were categorized has having “low” or “high” load-carrying exposures based on the measured weights of the loads being carried at the time of the survey, as well as on self-reported duration and frequency of load carrying. A summary score for lower abdominal discomfort suggestive of POP was generated using questions from the Pelvic Organ Prolapse Distress Inventory (POPDI-6). Women with higher load carrying exposures had on average higher discomfort scores in both Tanzania (adjusted prevalence difference (PDa)=3.7; 95% CI: -3.8-11.3; p=0.33) and Nepal (PDa=9.3; 95% CI: -4.9-23.6; p=0.18). We identified trends suggestive of an association between increasing heavy load carrying exposures and symptoms of lower abdominal discomfort. Our findings underscore the need for larger epidemiologic studies of the potential adverse reproductive health effects of heavy load carrying activities on women in developing countries.

Reinforced concrete (RC) columns are often placed under confinement to increase their strength and ductility. Carbon fiber reinforced polymer (CFRP) materials have recently been recognized as favorable confinement systems. At present, a number of national standards and codes dedicated to the design of concrete components strengthened with CFRP in general and CFRP confinement in particular are available. These sets of rules provide design equations for confined reinforced concrete columns with circular and rectangular cross sections. Most of the standards and codes exhibit significant differences, including the used predictive models limitations, observed effects, and covered loading conditions. In this paper, five international standards and design guidelines are introduced and discussed. The purpose is to present a constructive and critical assessment of the state-of-the-art design methodologies available for CFRP confined RC columns and to discuss effects not previously considered properly. Therefore, some recent research efforts and findings from the Leipzig University of Applied Sciences are also introduced. The obtained data is used for a comparative study of the guideline predictive equations. Furthermore, it is shown that some new findings concerning the rupture strength and the maximum strength plus accompanying axial strain of a CFRP confined column are suitable to improve the current guidelines.

Concrete-filled steel columns have been extensively used in the world due to having all suitable characteristics of concrete and steel, more ductility, increasing concrete confinement using steel wall, large energy-absorption capacity and appropriate fire behavior. In present paper, concrete-filled steel square columns have been simulated under the influence of blast load using ABAQUS software. These responses will be compared for scaled distances based on the distance to source and weight of explosive material. As result, it can be seen that although concrete deformation has been restricted using steel tube, but inner layer of concrete has been seriously damaged and column displacement will be decreased by increasing scaled distance. We also concluded that concrete-filled steel columns have high ductility and blast resistance.

The implementation of waste management strategies informed by supply chain management methodologies may present a viable solution for the effective handling of waste, as exemplified by the waste management challenges observed in Indonesia. The waste management characteristics exhibited by each nation are inherently diverse, as they are influenced by various factors including supply chain management frameworks, the methodologies and technologies employed in waste processing, the cultural practices and behaviours of the populace regarding waste disposal, as well as the unique geographic conditions pertinent to each country. This study seeks to formulate a waste management supply chain framework that incorporates moderating factors influencing the relationship between sustainable macro ergonomic processes and waste management practices. The research methodology employed was purposive convenience sampling, encompassing a total of 224 participants engaged in waste management within the Semarang region of Central Java, Indonesia. The empirical results indicate that H2—TT exerts a statistically significant influence on RPPS (0.02 < 0.05), H3—TP demonstrates a significant impact on RPPS (0.001 < 0.005), H4—MGE displays a significant effect on RPPS (0.00 < 0.05), H5—seasonal floods (BM) reveal a significant effect on RPPS (0.001 > 0.05), and H6—cultural society (CC) manifests no significant impact on RPPS (0.755 > 0.05). The innovative aspect of this study lies in the finding that cultural society has not exerted an influence on waste management, whereas other macro ergonomic factors have shown a significant effect on this domain.

Rural areas are not only faced with the problems of rural depopulation but also with the problem of water supply and water quality which is a basic necessity of life. In light of this, this paper assesses the rural water supply situation and the bacteriological quality of different water sources in the Obokun Local Government Area of Osun State, Nigeria. The safety of the water sources for human consumption and possible occurrence of the water borne diseases in the study area were determined. The study employed descriptive and analytical methods of data analysis to achieve the objectives of the inquiry. Findings showed that the study area suffered from inadequate water supply and poor locations of the sources of water: The study also revealed that the microbiological quality of the water sources used was unsafe, poor and not acceptable for human consumption. In addition, many man- days-hours were wasted in search of water. It is therefore recommended that the government should make the rural water supply and the maintenance of water quality a priority in its policy formulation.

A framework for analysis that focusses on options for structuring and funding the State Owned Enterprises (SOEs) is required in order to apply concepts and information more effectively to policy analysis and program creation. Regardless of the political or economic driving reason for SOE organisation, the requirements of the people must be met in developing countries necessitate that the strategy be viewed from this angle. The types of SOEs and the applicability of fiscal federalism are examined in this article. There are certain lessons to be learnt from the past, and a bad policy can have unintended repercussions. Strategies must be developed for the organisation of SOEs while taking into account the various starting situations.

Many laboratory power supply had been designed with higher output variable voltages but with small amount of output current, in this design, a variable 1.25 to 30Vdc laboratory power supply with capable of producing up to 10.0A has been designed using a three terminal voltage regulator with pass transistors.

Additive manufacturing (AM), commonly known as 3D printing, has emerged as a transformative technology in supply chain management (SCM). This abstract explores how AM influences various aspects of SCM, including inventory management, production processes, and logistics. By enabling on-demand production and reducing reliance on traditional manufacturing methods, AM minimizes inventory costs and enhances customization capabilities. It also facilitates localized production, which can reduce lead times and transportation costs. The integration of AM into SCM fosters greater flexibility and resilience, allowing businesses to respond more swiftly to market changes and disruptions. This study highlights both the opportunities and challenges associated with adopting AM, emphasizing the need for strategic adjustments to fully leverage its potential in modern supply chains.

This study explores the barriers and opportunities faced by women in supply chain leadership, a field traditionally dominated by men. Through in-depth interviews and thematic analysis, it uncovers the pervasive cultural and societal biases that undermine women's perceived competence and suitability for leadership roles. These biases, often rooted in deep-seated stereotypes, influence hiring and promotion decisions, creating significant obstacles for women. Structural and organizational barriers, such as the lack of formal diversity policies, flexible working arrangements, and adequate mentorship, further hinder women's career progression. The dual burden of professional and domestic responsibilities also disproportionately affects women, limiting their availability for work-related travel and extended hours, which are often required in supply chain roles. Despite these challenges, the study identifies several pathways to promote gender diversity in supply chain leadership. Organizational commitment to diversity and inclusion, flexible working policies, and comprehensive mentorship programs are crucial in supporting women's advancement. Education and professional development opportunities are essential in equipping women with the necessary skills for leadership roles. Additionally, fostering an inclusive corporate culture that values diverse perspectives and addresses discrimination is vital for creating a supportive environment. Broader societal and policy-level changes, such as government and industry initiatives promoting gender diversity, are also necessary. The integration of emerging areas like sustainability, entrepreneurship, emotional intelligence, marketing, and supplier relationship management offers new opportunities for women to contribute to the industry's evolution. By addressing these barriers and leveraging the identified opportunities, it is possible to create a more inclusive and equitable supply chain industry, benefiting both the industry and society.

Dissemination of resistant pathogens via food supply chains is a notable public health danger from farm to fork. The proposed work analysed the distribution of E. coli among various sources of animal-based food supply chain. Overall, 500 samples were heaped up from livestock, poultry sectors, environment, fisheries, and dairy. Standard microbiological procedures were used to isolate and identify E. coli. Further, isolates were confirmed by using MALDI-TOF and virulence associated genes (VAGs). The phenotypic resistance pattern of the isolates was determined by molecular identification of antibiotic resistance genes (ARGs) through PCR using specific primers. Shiga toxin producing E. coli (STEC) were subjected to PCR based O typing using specific primers against different O types. Overall results revealed that 154 (30.5%) samples were confirmed as E. coli, in which77 (50%) isolates were MDR E. coli. Among these, total 52 (67.53%) isolates showed array of VAGs in which 21 (40.38%) were confirmed as STEC based on stx1 and stx2. Furthermore, 12/52 (23.07%) isolates were detected as Non-O157 STEC co-harboring mcr-1 and blaNMD-1. Overall, O26 STEC was found to be the most prevalent among different non-O157 types. Herein, results proposed that detection of such STEC MDR E. coli causing serious public health consequences in developing countries.

The paper's objectives are to identify the main benefits, limitations, and challenges facing using Blockchain technology in the Supply Chain Management (SCM). To explore the factors that affect the adoption and application of Blockchain technology in SCM. The study uses a systematic literature review methodology to analyze and compare selected related articles published between 2016 and 2022 on the usage of Blockchain technology in the SCM. Several studies showed that there are many benefits to using Blockchain technology in SCM such as enhanced security, confidentiality, traceability, transparency, data accuracy, privacy, efficiency, accountability, and trust. At the same time, there are many limitations and challenges like scalability, interoperability, legal (regulatory) issues, high implementation and maintenance costs, standardization and information, lack of trust in technology, high energy consumption, limited awareness, integration with current systems, and privacy. Blockchain technology has the potential to revolutionize various industries, but there are still several issues that need to be addressed to fully realize its potential. Understanding the benefits and difficulties of applying Blockchain technology in different industries, including the SCM, is essential in determining its effectiveness and the different strategies needed to assess its application and implementation.

Indonesia has emerged as one of the leading producers and exporters of palm oil, as supported by data from the Central Bureau of Statistics in 2021, which indicate that Indonesian palm oil exports reached an impressive value of US$ 18.44 billion, or IDR 258 trillion, in 2020, marking an 18.43 percent growth compared to the previous year. Furthermore, the domestic market for palm oil and palm kernel oil remains substantial, despite improvements in export quantities. Various industries, including cooking oil refineries, specialty fats, shortening, margarine, bath soaps, and oleochemicals, present potential markets for palm oil (CPO) and palm kernel oil (PKO). The palm oil industry encompasses four related sub-sectors, namely growers, millers, refiners, and oleochemicals, forming an integrated value chain from upstream to downstream. The current focus on environmental, economic, and social sustainability is driving companies to adopt internal green activities and expand their efforts into sustainability supply chain management (SSCM). Implementing SSCM practices is often seen as economically beneficial, as research has demonstrated their positive impact on firm performance from a resource perspective. Within the palm oil industry, a wide range of green management applications are being utilized. This study aims to examine and highlight the existing sustainable management practices across the entire palm oil supply chain, with a particular emphasis on producing high-quality palm oil. To achieve this objective, the paper utilizes a systematic review methodology, The number of papers analyzed were 62 articles published which involves an extensive review of relevant literature to identify research trends, gaps, and issues. The study seeks to contribute to the body of knowledge by expanding the theory of organizational readiness in preparing globally competitive palm oil products. The research findings hold practical significance for practitioners and academics, providing a comprehensive roadmap for implementing green management applications at the organizational level in the palm oil industry's upstream-to-downstream chain.

Climate change is an issue that is the order of the day, and among the many consequences it has on the environment one is its influence on the amount and frequency of rainfall on our planet, since our water supply depends on them. Therefore, in order to quantify these effects, a rainfall analysis has been carried out using data collected in Bilbao over the last 150 years. Thanks to this, it has been possible to observe that, although the total annual amount seems to suffer a slight decrease, hardly quantifiable with certainty, it is true that the intra-annual patterns are more concentrated at certain times of the year, with greater imbalances between one month and another than in previous years. However, taking into account the capacity of the reservoirs that supply the Greater Bilbao area, it can be concluded that this does not pose any problem for Bilbao's supply, at least at present.

The electrification of rural areas of the planet has become one of the greatest challenges for sustainability. In fact, it would be the key to guaranteeing development for the poorest of the planet, but from which most of the raw material for the food market derives. The paradigm of centralized production is not applicable in these territories, because the distribution network would involve unjustifiable costs. For this reason, studies have multiplied to ensure the energy supply, especially electricity, of off-grid utilities, to guarantee energy autonomy while reducing the dependence on specialist assistance for the management of the system. In this work, a hybrid system (HRES) is proposed that combines the exploitation of solar energy with that of the wind through the use of static devices, in order to improve the system's availability and limit the cost of operation and maintenance.

The COVID-19 pandemic has severely affected the food supply chain, including producers, retailers, wholesalers, and customers. To minimize the impacts caused by pandemics and epidemics on food supply chains, it is fundamental to implement effective policies that ensure continuity in the provision, affordability, and distribution of basic food items. This research aims to identify the main impacts of pandemics and epidemics on food supply chains and policies that can minimize these impacts. Based on a systematic literature review (SLR), 174 documents are analysed to propose a taxonomy of impacts on four supply chain links: demand-side, supply-side, logistics and infrastructure, and management and operation. The taxonomy presents the main impacts, as well as the respective mitigation policies simultaneously. In addition, the literature review leads to the development of a comprehensive causal loop diagram (CLD) with the identification of main variables and their relationship with food supply chains. Finally, a specific research agenda is proposed by identifying main research gaps. These findings provide a structured method for evaluating policies that ensure the functioning of food supply chains, particularly in disruptions such epidemics and pandemics.

The Umbulan Water Supply Project is categorized by the Shipping Infrastructure Acceleration Committee in the list of Accelerated National Strategic Projects through Presidential Regulation Number 3 of 2016 concerning the Acceleration of the Implementation of National Strategic Projects, targeted to operate in mid-2019. This is what will be the focus of the stakeholders of the Umbulan Water Supply Project. This study was to identifying and analyzing networks among stakeholders. Method: This study used a qualitative approach with exploratory methods combined with meta-analysis identification design Identification of stakeholder mapping in the context of early detection of stakeholder involvement in the implementation of the Umbulan Water Supply Project at various levels starting from the National, Provincial (East Java), District/City (Pasuruan, Sidoarjo, Surabaya, to Gresik), Sub-District (Winongan, Gondang Wetan, and Pohtjentrek). The conclusion of this study was based on in-depth interviews and focus group discussions in describing the determination of stakeholders which were divided into two, namely primary stakeholder and secondary stakeholder, and outline the result of the indicators analysis on the stakeholder network of Umbulan Water Supply Project.

Unpaid family caregivers must consider the economic trade-off between caregiving and paid employment. Prior literature has suggested labour force participation (LFP) to decline with caregiving intensity, but no study has evaluated this relationship by accounting for the presence of both kinks and discontinuities. Here we used respondents of the China Health and Retirement Longitudinal Study baseline survey who were non-farming, of working-age (aged 45-60) and had a young grandchild and/or a parent/parent-in-law. For women and men separately, a caregiving threshold-adjusted probit model was used to assess the association between LFP and weekly unpaid caregiving hours. Instrumental variables were used to rule out the endogeneity of caregiving hours. Of the 3,718 respondents in the analysis, for men, LFP was significantly and inversely associated with caregiving that involved neither discontinuities nor kinks. For women, a kink was identified at the caregiving threshold of 8 hours per week such that before 8 hours, each caregiving hour was associated with an increase of 0.0257 in the marginal probability of LFP, but each hour thereafter was associated with a reduction of 0.0014 in the marginal probability of LFP. These results have implications for interventions that simultaneously advance policies of health, social care and labour force.

Raw material logistics reflects an important aspect of global trade. Raw materials form an essential basis for society, for daily life, and range from apples to zinc. This paper addresses the analysis and optimization of supply chains of raw materials in terms of their economic viability and their sustainability. Type representatives are chosen according to annual transported quantities. Hard coal represents bulk goods, aluminum primary raw materials with medium quantities, and rare earths primary raw materials with small quantities. Their respective supply chains are analyzed and subsequently possible strategies and methods and their application are discussed. The paper shows for the first time that the selection and application of optimization priorities (e.g. profitability or sustainability) depends on the primary raw material and its integration into global production chains.

The lean supply chain of construction engineering projects is to achieve the maximum satisfaction of the owners' needs in order to effectively achieve the goal of supply chain management. This paper explores an effective method of lean supply chain cost management for construction engineering projects with target cost management, so that each participating unit on the supply chain node can fully utilizes its core competencies to minimize internal consumption and waste, and achieve the optimal overall efficiency of the supply chain. According to the requirements of the goal planning theory of the construction project company, establish a lean supply chain cost planning system for the construction project, realize the basic model of the lean supply chain cost management of the construction project, and set the target cost from the lean project of the construction project. The technical decomposition is established by the process of cost decomposition and cost pressure transmission and sub-target cost planning.

This paper developed a new theory for supply chain architecture, and engineering design that enables integration of the business and supply chain strategies. The architecture starts with individual supply chain participants and derives insights into the complex and abstract concept of green-field integration design. The paper presented a conceptual system for depicting the interactions between business and supply chain strategy engineering. The system examines the decisions made when engineering the business strategy, with regards to the supply chain design. The system derived with a new understanding of how strategies are integrated, and what are the implications for engineering successful strategies. The study revealed that supply chain design is not considered in great detail before architecting the business strategies. Thus, companies consequentially experience supply chain problems that are likely to be detrimental to the growth potentials. The paper also derived with the findings that proactive and pre-emptive involvement of supply chain participants in the strategy engineering process, would lead to a more robust strategic design.

The effective implementation of a sustainable supply chain leadership (SSCL) strategy at each organization’s level, positively impacts on corporation’s competitive advantage and sustainable development. While fears have been acknowledged in previous literature, the benefit of designing Sustainable supply chain models for operational excellence and sustainable development is a noteworthy exploration. The study collected statistical data using an online Survey- monkey questionnaire from 46 of the top 100 JSE-listed companies with the intention to elucidate the large firms’ transition from longstanding linear approaches to sustainable production and consumption to a more circular approach. A bivariate data analysis was used to generate the results in SPSS. The results indicate a positive contribution of SSCL strategy to operational excellence and sustainable development of large corporations if only the entire supply chain system is well-coordinated and implemented. This study makes a vital contribution by proposing a new SSCL model for fast-tracking corporate sustainability and enhancing corporate contributions to sustainable growth.

The Self-Forming Dynamic Membrane BioReactor (SFD MBR) is a cost-effective alternative to conventional MBR, where the synthetic membrane is replaced by the “cake layer”, an accumulation of the biological suspension over a surface of inert low-cost support, caused by a suction force. Under optimized conditions, the cake layer reveals easy to remove and quick to form again, resulting a “dynamic membrane”. The permeate of SFD MBR has chemo-physical characteristics comparable to those of conventional MBR. In this paper, two nylon meshes with a pore-size of 20 and 50 µm respectively were tested in a bench scale SFD MBR where a periodic air mass load (AML) was supplied tangential to the filtration surface to maintain filtration effectiveness. The SFD MBR equipped with the 20 µm nylon mesh coupled to 5 min of AML every 4 hours showed the best performance both ensuring a permeate with turbidity values always below 3 NTU and revealing no critical fouling. A benchmark test, conducted in the same experimental time, with identical operating conditions, and the only difference of a suction break (relaxation) instead of the AML. This latter test produced a permeate of very good quality, but it needed frequent mesh cleanings, showing that a periodic AML coupled with the use of a 20 µm mesh can be a good strategy for long term operation of SFD MBR.

Abstract: Considering the significance of postural balance in shooting ability, and consequently soldiers’ occupational performance and survivability, it is of importance to promote the use of balance training programs to improve shooters' efficiency. This study explored how a balance training program affects shooting performance. Twenty participants were divided into two equal groups. They all performed 60 shots in a shooting simulator while standing. The participants went through this process twice, before and after a 4-week balance training program. The shoot-ing was done under four different conditions: without load on a stable surface, with load on a stable surface, without load on an unstable surface, and with load on an unstable surface. The training program had a significant impact on the experimental group, improving their balance by 76 ± 9% (p < 0.01). Additionally, in the experimental group, shooting score increased by 47% and the % center of gravity by 20% (p < 0.01). The stability of the shots, measured by holding time in the target, doubled from 2.2 to 4.5 seconds (p < 0.01). These improvements were more pronounced when participants had a load and/or were on an unstable surface. In conclusion, a balance training program can be beneficial for improving shooting performance.

With a software-defined network (SDN), the control plane is separated from the data plane, allowing the control of network flows to be easy and dynamic at the same time. While the controller, which is equipped with various control applications, is capable of analyzing a large amount of data, it may become overburdened when it is faced with a large volume of traffic. The intrusion detection system (IDS) is an example of control applications that can be used to detect intrusions. It is recommended to deploy instances of the IDS across the data plane. This will improve the processing power and detection rate and as a result reduce the load on the controller at the same time. While there is no doubt that an IDS would be beneficial for improving detection rates, installing one at each switch in the data plane would be costly. To address this issue, this paper proposes the deployment of IDS chains across the data plane. The controller directs incoming traffic via alternative paths, including the IDS chain, which increases the detection rate while avoiding overloading. In order to improve transmission efficiency, our study proposes a method of balancing the distribution of flows and assigning them to specific IDS chains. This approach minimizes the cost of flow grouping through the use of a new incoming traffic grouping technique. Our method is tested and evaluated through the use of a test bed and trace-based simulation, and has been proven to effectively reduce delays and hop counts across various traffic scenarios.

One of the most important problems of geotechnical engineering is the design of pile foundations. Piles are generally designed for vertical and horizontal static loads. However, it is important to design piles against dynamic loads in regions with high seismicity. This study verified a centrifuge experiment obtained from the literature with a finite element program. With the kinematic interaction analyses made for this purpose, the effect of parameters such as layered soil, groundwater, pile stiffness, and earthquake acceleration were investigated numerically. According to the results obtained, it was understood that the earthquake magnitude is the most important parameter in kinematic interaction analyses. In addition, in layered soil conditions, the fact that 75% of the pile length is in the weak soil creates the most unfavorable situation. It has been observed that pile stiffness and groundwater level also have certain effects on the kinematic interaction.

Load forecasting is an important component of power system and resource planning for electrical grids. The adoption of electric vehicles (EVs), behind-the-meter (BTM) solar, and heat pumps will significant change the amount and variability of loads. Electrification adoption and load forecasting in arctic regions and Alaska is limited. This paper provides the first load and electrification adoption forecast for the Alaska Railbelt transmission system, including yearly adoption rates of EVs, BTM solar, and heat pumps and hourly load data. Adoption rates are based on available historical data and compared to other regional and national trends. Two forecasts are created: 1) a moderate adoption forecast based on projections from current adoption rates and comparisons to other projections, and 2) an aggressive forecast, which provides a bookend comparison at the high adoption rate of 90% for all technologies. The results of these forecasts demonstrate a significant increase in both energy, 80% and 116% for moderate and aggressive, respectively and peak load demand, 113% and 219% for moderate and aggressive, respectively. Additionally, the results indicate a maximum hourly load change of 260% and 381% for the moderate and aggressive forecasts, respectively. These findings highlight a need for resource planning, which accounts for increases in demand and suggests that significant demand management is needed to smooth and control the load fluctuations as a result of the adoption of EVs, BTM solar, and heat pumps.

Weaving technology can convert two-dimensional structures such as ribbons into three-dimensional structures by specific connections. However, most of the 3D structures fabricated by conventional weaving methods using straight ribbons have some topological defects. In order to obtain smoother continuous 3D surface structures, Baek et al. proposed a novel weaving method using naturally curved (in-plane) ribbons to fabricated three-dimensional curved structures and using this method to weave new spherical weave structures that are closer to perfect spheres. We believe that this new spherical weave structure with smooth geometric properties must correspond to new mechanical properties. To this end, we investigated the buckling characteristics of different types of spherical weave structures by the combination of test and finite element method. The results of calculations and experiments show that the failure mode of the spherical weave structure under vertical loading can be divided into two stages: a flat contact region forms between the spherical weave structure and the rigid plate and inward dimple of ribbons. The spherical weave structures using naturally curved (in-plane) ribbon weaving have better buckling stability than those woven with straight ribbons. The vertical buckling load of spherical weave structures using naturally curved ribbon increases with the width and thickness of the ribbon. In addition, this paper combines test, theoretical and finite element analysis to propose the buckling load equation and buckling correction factor equation for the new spherical weave structure under vertical compression load.

A thermal load dispatch problem minimizes the number of objectives viz operating cost and emission of gaseous pollutants together while allocating the power demand among the committed generating units subject to physical and technological system constraints. A stochastic thermal load dispatch problem is undertaken while taking into consideration, the uncertainties, errors in data measurements and nature of load demand which is random. Owing to uncertain load demand, variance due to mismatch of power demand termed as risk, is considered as another conflicting objective to be minimized. Generally multiobjective problems generate a set of non-inferior solutions are generated and supplied to a decision maker to select the best solution from the set of non-inferior solutions. This paper proposes opposition-based greedy heuristic search (OGHS) method to generate a set of non-inferior solutions. Opposition-based learning is applied to generate initial population to select good candidates. Migration to maintain diversity in the set of feasible solutions is also based on opposition-based learning. Mutation strategy is implemented by perturbing the genes heuristically in parallel and better one solution is sought for each member. Feasible solutions are achieved heuristically by modifying the generation-schedules in such a manner that violation of operating generation limits are avoided. The OGHS method is simple to implement and provides global solutions derived from the randomness of the population generated without tuning of parameters. Decision maker exploits fuzzy membership functions to decide the final decision. Validity of the method has been demonstrated by analysing systems in different scenarios consisting of six generators and forty generators.

Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC) are constantly threatening global public health. With no end date, the pandemic still persists with the emergence of novel variants that threaten the effectiveness of diagnostic tests and vaccines. Mutations in the Spike surface protein of the virus are regularly observed in the new variants, potentializing the emergence of novel viruses with different tropism from the current ones, which may change the severity and symptoms of the disease. Growing evidence has shown that mutations are being selected in favor of variants that are more capable of evading the action of neutralizing antibodies. In this context, the most important factor guiding the evolution of SARS-CoV-2 is its interaction with the host’s immune system. Thus, as current vaccines cannot block the transmission of the virus, measures complementary to vaccination, such as the use of masks, hand hygiene, and keeping environments ventilated remain essential to delay the emergence of new variants. Importantly, in addition to the involvement of the immune system in the evolution of the virus, we highlight several chemical parameters that influence the molecular interactions between viruses and host cells during invasion and are also critical tools making novel variants more transmissible. In this review, we dissect the impacts of the Spike mutations on biological parameters such as (1) increase of Spike binding affinity to hACE2; (2) bound time for the receptor to be cleaved by the proteases; (3) how mutations associate with increase of RBD up-conformation state in the Spike ectodomain; (4) expansion of uncleaved Spike protein in the virion particles; (5) increment of Spike concentration per virion particles; and (6) evasion of the immune system. These factors play key roles in the fast spreading of SARS-CoV-2 variants of concern, including the Omicron.

Based on the two-node Euler-Bernoulli beam, the tower system is discretized by finite element method, and the cubic Hermite polynomial is taken as the shape function of the beam element, and the structural characteristic matrix of the tower system is calculated, and the wind turbine-nacelle-tower multi-degree of freedom is established Finite element numerical model. The aerodynamic load calculation formula for any nacelle attitude angle is deduced. The influence of the vibration feedback of the flexible tower on the aerodynamic load of the wind turbine is studied. The results show that when the rigidity of the tower is large, the impact of tower vibration feedback on the aeroelastic load of the wind turbine is small. For a tower system with greater flexibility, the time-varying feedback of wind-induced vibration will cause greater aeroelastic load changes, especially the overturning moment of the tower top, which will cause a greater impact on the dynamic behavior of the tower in the downwind and crosswind directions. As the flexibility of the tower system increases, the interaction between tower vibration and aerodynamic load is gradually increasing. Taking the impact of the flexible tower on the aeroelastic load of the wind turbine into account, on the one hand, helps to predict the wind more accurately. The aerodynamic load of the wind turbine improves the efficiency of wind energy utilization. On the other hand, it can more accurately analyze the dynamic behavior of the flexible structure of the wind turbine, which is extremely beneficial to the structural optimization design of the wind turbine.

: This paper presents a couple of meal monitoring systems for senile dementia patients by using electronic weight and temperature sensors. These monitoring systems enable to convey the information of the amount of meal taken by the patients in real-time via wireless communication networks onto the mobile phones of their families or nurses in charge. Thereby, the nurses can easily spot the most desperate patient to take care of while the families can have relief to see the crucial information for survival of their parents at least three times a day. Meanwhile, the senile dementia patients tend to suffer the burn of their tongues because they can hardly recognize the temperature of hot meals served and therefore avoid the burn of tongues. This phenomenon can be discarded by utilizing the meal temperature monitoring system which displays alarm to the patients when the meal temperature is above the reference. These meal monitoring systems can be easily implemented by utilizing low-cost sensor chips and Arduino UNO boards so that elder-care hospitals and nursing homes can afford to exploit them with no additional cost. Hence, we believe that the proposed monitoring systems would be a potential solution to provide a great help and relief not only for the professional nursing nurses working in elder-care hospitals and nursing homes, but also for the families of the dementia patients.

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

Dynamic visualizations have been developed to exchange information that transforms over time across a broad range of professional and academic contexts. However, these visual tools may impose substantial demands on the learner’s cognitive resources that are very limited in current knowledge. Cognitive load theory has been used to improve learning from dynamic visualizations by providing certain design techniques to manage learner cognitive load without adding any oral/written explanations. This systematic review examined a series of experimental studies assessing the roles of these design techniques in learning tactical scenes of play through dynamic visualizations. Electronic databases PubMed and Google Scholar were used to search relevant articles. Eleven studies were eventually included for the systematic review based on the eligibility criteria. The present review revealed that adapting design techniques to the level of learners’ expertise, type of depicted knowledge, and level of content complexity is a crucial part of effective learning.

The variations in socioeconomic status (SES) between different social classes of a population correspond to differences in accessibility to all resources available and able to improve global health. SES can influence global health trajectory for an individual or a community, depending if SES is low or high. Sleep is sensitive to environmental stimuli, as well as living conditions. Plenty of studies linked sleep complaints with mood disorders, allostatic load or circadian disruption; but very few or none investigated deeply what happened earlier to sleep depending of SES. While SES is now known as one of the main determinants for a good health and a good aging, its influence on sleep disorders (SD) is not well understood. SES is a concept, not directly observable but estimated using indicators like income, education, occupational status and area of living. Even if recent evidence suggested that few of SES indicators like occupational status are linked with sleep disturbances, the relation between SES and health in general with sleep as an outcome or a mediator is not well documented. This scoping review synthetized studies which investigated physiological and psychological mechanisms resulting from a low SES and linked them with sleep disturbances as consequences or as mediators. This review also explore a possible role played by sleep in the relation between socioeconomic status and health inequalities.

The aim of this paper is to at first evaluate the influence of three key parameters including weld current, weld time and electrode force on nugget diameter and tensile strength in resistance projection welding. Then, a 2-D axis-symmetric finite element model is developed to simulate the projection welding and predict the nugget diameter. Finally, the FEM results are compared to experimental data to verify the simulation model and simulated results. In the finite element model, the temperature-dependent material properties were taken into account.

Policing duties may inherently be dangerous due to stab, blunt trauma and ballistic threats. The addition of individual light armour vests (ILAVs) has been suggested as a means to protect officers. However, the addition of the extra load of the ILAV may affect officer ability to conduct occupational tasks. The purpose of this study was to determine if wearing any of 3 different ILAVs (ILAV A, ILAV B, & ILAV C) affected occupational task performance when compared to that in normal station wear. A prospective, within-subjects repeated measures design was employed, using a counterbalanced randomization in which each ILAV was worn for an entire day while officers completed a variety of occupationally relevant tasks. These tasks included a victim drag, car exit and 5 meter sprint, step down and marksmanship task. Results showed that performance in each task did not vary between any of the ILAV or normal station wear conditions. There was less variability in the marksmanship task with ILAV B, however. The results suggest that none of the ILAVs used in this study were heavy enough to significantly affect task performance in the assessed tasks when compared to wearing normal station wear.

In this study, the probabilistic exergoeconomic analysis was performed for four industrial gas turbine (GT) units comprising of two (GT16 and GT19) units of 100MW GE engine and two (GT8 and GT12) units of 25MW Hitachi engine at Transcorp Power Limited, Ughelli. These four industrial GT engine units were modelled and simulated using natural gas as fuel. The design point (DP) simulation results of the modelled GT engines were validated with the available DP thermodynamic data from original equipment manufacturer (OEM). This was done before the off-design point (ODP) simulation was carried out which represents the plant operations. The results obtained from exergy analysis at full load operation show that the turbine has the highest exergy efficiency followed by compressor and combustion having the least. For turbines these were 96.13% for GT8 unit, 98.02% for GT12 unit, 96.26% for GT16 unit, and 96.30% for GT19 unit. Moreover, the combustion chamber has the highest exergy destruction efficiency of 55.16% GT8 unit, 56.58% GT12 unit, 43.90% GT16 unit, and 43.30% GT19 unit respectively. The exergy analysis results obtained from the four units show that the combustion chamber (CC) is the most significant exergy destruction with lowest exergy efficiency and highest exergy destruction efficiency of plant components. The exergoeconomic analysis results from four units showed combustion chamber energy destruction cost of 531.08 $/h GT8 unit, 584.53 $/h GT12 unit, 2351.81$/h GT16, and 2315.93$/h GT19 unit. The probabilistic results analysis based on the input parameters distributions evaluated and discussed.

The textile industry plays a vital role in the global economy and faces increasing pressures related to sustainability and efficiency. Given the industry's significant environmental impact and the need for improved operational strategies, our review addresses the critical intersection of decision-making methodologies and sustainable practices. Specifically, This review aims to explore the applications of Multi-Criteria Decision-Making (MCDM) techniques within the textile supply chain to enhance operational performance and decision-making processes. Utilizing a systematic literature review method, we analyzed articles from Scopus, Web of Science, and Google Scholar, focusing on those that address MCDM applications in the textile supply chain. The findings reveal a strong emphasis on supplier selection and evaluation as determinants of competitive advantage, with significant trends toward integrating fuzzy set theory and advanced MCDM methodologies to address uncertainties. Additionally, Sustainability emerges as a core theme, emphasizing the necessity for firms to incorporate green practices into decision frameworks. The insights suggest that by adopting innovative analytical techniques, organizations can improve supplier relationships and operational efficiency.

Traceability has been applied in the fisheries, for example in tuna, as a means of verification and validation of the quality of fish meat and its origin. Traceability as a platform in the fisheries remains elusive in many parts of the country due to slow adoption. This study reviewed the existing literature in relation to, “agri-fishery traceability system,” “fishery traceability,” and “food safety” from year 2000-2023. It aimed to elucidate the common drivers and barriers to the adoption of fisheries traceability system. The authors used PRISMA to analyse articles obtained from SCOPUS and WOS (Web of Science), DOAJ (Directory of Open Access Journal), Academia, and PubMed central databases. The results showed that 125 articles were included after the inclusion and exclusion process. There were nine articles included in the year 2000 to 2005, followed by 19 in 2006 to 2010, and 26 from the year 2011 to 2015. A total of 40 articles were recorded from the year 2016 to 2020 and 31 from the year 2021 to 2023. In terms of text data from the authors, “fish”, “supply chain”, “traceability system”, and “food safety” have the highest total link strength. Moreover, food safety, market compliance, consumer protection, product origin, quality, and seafood fraud are driving factors in the implementation of traceability system. Costs, inadequate practices, lack of support from the government, infrastructure, information, and laws and regulations are foreseen barriers to the adoption of a traceability system. Leading countries in fisheries traceability studies include, China, USA, Italy, Indonesia, UK, and Taiwan where the first and corresponding authors originated. While this review endeavored to find fish traceability studies in the Philippines, the lack of comprehensive and relevant literature published in this topic shifted our focus to finding barriers and drivers to adopting a traceability system. We recommend that government agencies that oversee various commodities, should link up with academics and non-government organizations in the implementation and monitoring of their traceability systems.

⦁ Introduction: The challenging context, resulting from a post-pandemic scenario, wars, and various natural disasters, does not allow for the ideal assurance of efficiency in the Agri-food supply chain. ⦁ Research significance Currently, Agri-food supply chains and Reverse logistics show critical characteristics such as centralization, limited flexibility, distrust, bureaucratization, Border and Documentary Compliance and there are problems about efficiency, security, and safety Agri-Food. using the Supply Chain Operations Reference (SCOR) framework, with the purpose of achieving Sustainable Development Goals, promoting transparency, traceability, and non-linearization of the agri-food supply chain. ⦁ Methodology The methodology is combined, the first part uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) identifying 72 articles with the principal contribution in the literature. The second part combines the application of Supply Chain Operations References (SCOR) and the Blockchain Technology aiming to decentralize the linear Agri-food Supply Chain and Reverse logistics. ⦁ Results/findings The results revealed a transformation of the traditional Agri-food Supply Chain, transitioning from linear management to a circular one. Additionally, after application it is possible to know a new way to manage the Reverse Logistic with more decentralization, flexibility, traceability, and reducing the unnecessary activities, which include Border and Documentary Compliance. The originality of this research is the novel combination of SCOR Model and Blockchain showing a priority about the key activities that add value and come close to the Sustainable practices. ⦁ Conclusion / Implications Based on the results is possible to conclude that the synergy between Blockchain and SCOR Model enables a new way to manage the Agri-food supply chain, improving efficiency and reducing activities without value. Also, it is possible to ensure that the practices of Reverse Logistics are improved based on terms of cost and time, and the reduction of the exposition on the Defective Product Risk. ⦁ Social Implications This paper could enhance confidence in all operations of the Agri-food supply chain as there are numerous issues regarding the security and safety of food. Additionally, it will aid in diminishing unnecessary activities and inefficient resource consumption practices, thereby accomplishing objectives 2 "zero hunger", 9 "Industry, Innovation, and Infrastructure", and 12 "Responsible consumption and production", along with their respective indicators. ⦁ Practical implications Regarding the practical implications, this research could contribute to analyses of the application of technology Blockchain in different sectors and identify the possible limitations when the main objective is to apply it on a big scale.

This qualitative research explores the adoption of blockchain technology in supply chain management, aiming to understand the drivers, barriers, and implications associated with its integration. Through in-depth interviews and thematic analysis, insights are gathered from key stakeholders in the supply chain ecosystem. The findings reveal a complex landscape, with stakeholders recognizing blockchain's potential to enhance transparency, efficiency, and trust while acknowledging challenges related to technical complexities, regulatory uncertainties, and organizational barriers. Despite these challenges, participants express optimism about blockchain's transformative potential, citing opportunities for improved traceability, supply chain resilience, and sustainability. Collaboration, governance, and ecosystem development emerge as critical factors for driving blockchain adoption within supply chains, alongside the need for continuous education and capacity-building initiatives. Overall, the study underscores the importance of addressing challenges and fostering a culture of innovation and collaboration to unlock the full potential of blockchain-enabled supply chains.

This research investigates the challenges and opportunities associated with supply chain transparency and traceability in the renewable energy sector. Using a qualitative methodology, the study involved semi-structured interviews with key stakeholders, including industry experts, supply chain managers, regulatory officials, and NGO representatives. The findings reveal that the complexity and global span of renewable energy supply chains pose significant hurdles for ensuring transparency and traceability. Variability in the maturity of traceability systems across companies further complicates the landscape, with some companies benefiting from advanced systems while others struggle with cost and technical barriers. Technological advancements, especially blockchain, offer promising solutions but require standardization and scalability to be effective. Regulatory frameworks play a crucial role, yet inconsistencies across jurisdictions create compliance challenges, underscoring the need for harmonized standards. Collaboration and partnerships are essential, providing platforms for sharing best practices and developing common standards. Enhanced transparency can lead to operational efficiencies, improved consumer trust, and stronger market positioning. Integrating sustainability into supply chain practices is fundamental, aligning with the increasing emphasis on ESG criteria among investors and stakeholders. Education and capacity-building, particularly for suppliers in developing countries, are vital for promoting transparency. Leadership and corporate culture are critical in driving these efforts, with strong commitment from the top necessary to implement effective practices. This study contributes valuable insights for stakeholders in the renewable energy sector, offering a foundation for future research and policy development aimed at advancing sustainable and ethical supply chain practices. The findings highlight the need for a holistic approach that integrates transparency and traceability into broader sustainability and business strategies.

Purpose: This study aims to investigate the role of blockchain technology in enhancing supply chain transparency in Italy.Design/Methodology/Approach: A quantitative research approach was employed, with data collected through a structured questionnaire distributed to 240 companies across various industries in Italy. The questionnaire focused on assessing blockchain adoption rates, interoperability standards, stakeholder collaboration, regulatory support, cost considerations, and perceived impacts on supply chain transparency.Findings: The findings reveal high levels of blockchain adoption among surveyed companies, with strong positive correlations observed between blockchain adoption and interoperability standards, stakeholder collaboration, and regulatory support. However, challenges such as cost considerations and potential trade-offs between interoperability and transparency were also identified. Overall, blockchain technology shows promise in improving supply chain transparency in Italy.Research Limitations/Implications: This study is limited by its focus on a specific geographical context (Italy) and the use of self-reported data. Future research could explore the dynamics of blockchain adoption in other regions and employ objective measures to assess supply chain transparency.Practical Implications: The findings offer practical insights for businesses and policymakers aiming to leverage blockchain technology to enhance supply chain transparency and efficiency.Social Implications: Blockchain adoption has the potential to foster greater trust and accountability within supply chains, benefiting various stakeholders including consumers, producers, and regulators.Originality/Value: This study contributes to the growing body of literature on blockchain technology in supply chain management, particularly within the Italian context, by providing empirical evidence on its adoption and impact on transparency.Limitations: Despite the robust methodology employed, limitations include the reliance on self-reported data and the exclusion of qualitative insights.

Ethiopian Airlines is a leading airline in the world. Its supply chain management is one of the most critical aspects in achieving its sustainability objectives. The present study aims to assess the sustainability practices and strategies of Ethiopian Airways in its supply chain, identifying areas for improvement and recommending strategies for enhancing sustainability. The study concentrates on pertinent sustainability concerns within the aviation sector while examining antecedent research on the sustainability of Ethiopian Airlines’ supply chain management strategies. The findings of the study reveal that there are several areas in need of improvement, including the implementation of waste reduction measures, the establishment of a recycling program, and the reduction of single-use plastic usage. It is suggested that promoting responsible tourism embodying sustainable practices is imperative, particularly within the airline context.

In arid and semi-arid regions like Tunisia, irrigation water is typically saline, posing a soil and crop salinization risk and yield reduction. This research aims to study the combined effects of soil matric and osmotic potential stresses on tomato root water uptake. Plants were grown in pot and field experiments in loamy-clay soils and were irrigated with three different irrigation water qualities: 0, 3.5, and 7 dS/m. The Hydrus-1D model was used to simulate the combined dynamics of subsurface soil water and salts. Successful calibration and validation of the model against measured water and salt profiles enabled the examination of the combined effects of osmotic and matric potential stresses on root water uptake. Relative yields, indirectly estimated from actual and potential transpiration, indicated that the multiplicative stress response model effectively simulated measured yields and the impact of saline water irrigation on crop yields. The gradual salinization of the root zone further evidenced this effect. The scenario considering a temperature increase of 2°C had no significant impact on crop yields in the pot and field experiments.

The article presents a model for optimizing biomass supply chains using the linear programming framework integrated with a Geographic Information System. Based on the distance from a given type of biomass resource, its calorific value, price, and transportation costs, the model identifies the optimal source of biomass, allowing it to cover the demand for the required total energy value with the lowest possible costs. The case study includes the Połaniec power plant in southeastern Poland and potential sources of forest biomass and agricultural straw within 100 km of the plant. Unit costs of biomass varied depending on biomass availability and energy demands. For energy demand on the level of 1 PJ yearly, the lowest unit costs of biomass (4.08 €/MJ) were when all kinds of biomass were available, and the highest (5.47 €/MJ) was when only stacked wood was available. As energy demand increased, unit costs increased, and the ability to meet this demand with just one type of biomass decreased. The energy biomass sector can utilize the model to benefit both biomass producers and their final buyers.

Technological advances in the last decades have led to the realization of the concept of a smart environment. A significant part of this development is decentralized ledger technology and its variant, a blockchain. The blockchain database is immutable, open to all stakeholders, secure by architecture and robust. Applying a blockchain in water supply sanitary control creates opportunities for optimization, higher quality of service, cost reduction, better sanitary standards and public control. Physical, chemical, and biological water supply contamination is a great source of public health hazards. Implementation of a blockchain for water supply IoT, from the source point to the consumption point, enables effective response to changing environments, possible cross-contamination, stormwater management or disaster and emergency action. The chapter encompasses all fundamental elements and principles of water collection, distribution and consumption, with a focus on the health hazards and sanitary requirements for potable water. The chapter listed the main contaminants, methods of their registration and elimination, and requirements for drinking water in accordance with WHO, EU Drinking Water Directive and EPA standards. Blockchain technology solutions are described for smart water supply, including smart supply management, smart contracts, tokenization, smart compliance systems, and, most importantly, effective utilization of distributed ledger technologies for sanitary monitoring of water sources, water treatment, and water distribution systems.

Currently, many manufacturing industries use machine learning, artificial intelligence, robotics, and blockchain technologies as process-streamlining instruments of industry 4.0. Before implementing industry 4.0 tools in any process of a manufacturing industry's supply chain, it is essential to evaluate their adaptability. In this paper, the adaptability of industry 4.0 procurement tools to an oil and gas-related manufacturing company's procurement process is evaluated to determine the company's level of maturity. The active participation of participants is evaluated to determine the level of maturity of the main procurement process areas. The results identified the areas of the procurement process that can be improved by implementing the suggested industry 4.0 tools and techniques.

In the transition towards sustainable energy sources, hydrogen has emerged as the predominant alternative after fossil fuels. This comprehensive literature review delves into the evolving landscape of hydrogen launching strategies within the energy market. Numerous countries have strategically initiated efforts to introduce products based on hydrogen, underscoring the global acknowledgment of the potential inherent in hydrogen. Despite witnessing significant growth in the adoption of hydrogen energy worldwide, impediments to its widespread commercialization persist. This review identifies key barriers, including cost structures, technical complexities, and marketing challenges, hindering the establishment of a robust hydrogen energy market. The article critically examines the actions already undertaken to harness hydrogen energy for commercial purposes, leveraging scientific insights and statistical data. By carefully examining the current state of hydrogen energy initiatives, the review highlights gaps in existing strategies that require thoughtful consideration. Insights derived from scientific and statistical analyses inform the identification of barriers and contribute to a nuanced understanding of the challenges faced in the journey towards hydrogen energy commercialization. Ultimately, the article presents a strategic proposal outlining marketing actions geared towards expediting the commercialization and widespread adoption of hydrogen energy. Grounded in a thorough review of existing literature and supported by scientific and statistical evidence, this comprehensive analysis offers valuable insights for stakeholders seeking to navigate the intricate landscape of hydrogen energy in the global energy market.

Internal combustion engine (ICE) vehicles contribute significantly to the global warming through their immense quantity of CO2 emissions and are on the way to be replaced by zero emission alternative solutions. Several replacement techniques with scientifically sound performances are proposed for the propulsion equipment including the related supply chains from renewable sources, but they often cannot fulfill the huge demand quantitatively. The paper discusses the quantitative aspects of the real demand with the example of the actual automotive park in Switzerland and analyses different alternatives like conventional EV’s, fuel-cell vehicles or energetic chains based on sustainable fuels like e-methanol or synthetic solar fuels. The analysis comprises energy efficiency considerations.

Background: The proportion of dietary protein may play a key role in the development of obesity in children and adolescents. Methods: Cross-sectional data of 4,336 children and adolescents from the National Health and Nutrition Survey (NHANES) during 2011 to March 2020 were analyzed. Multivariate logistic regression analysis was used to estimate odds ratios (OR) and 95% confidence intervals, and restriction cubic spline was uesd to explore nonlinear relationships of dietary protein energy supply ratio and obesity in children and adolescents. Results:In the adjusted logistic regression model, for every 1% increase in dietary protein energy ratio in children and adolescents, the increased risk of obesity was 4% (OR=1.04,95%CI:1.01-1.08). The restrictive cubic spline showed a nonlinear relationship between dietary protein energy ratio and obesity in children aged 6-11 years (P <0.05). The dietary protein energy supply ratio was included in the logistic regression model after converting to quartiles into categorical variables. The adjusted OR of children aged 6-11 years was 2.08 (95%CI:1.34, 3.23,P <0.01) for the highest quartile of dietary protein energy supply ratio compared with the lowest quartile. Conclusion: In American children, dietary protein energy supply ratios was positively correlated with obesity, independent of individual characteristics and energy.

Brain vascular health appears to be of critical importance for protection against the development of amyotrophic lateral sclerosis (ALS) as well as the slowing of disease progression. ALS patients often demonstrate cardiovascular risk factors and commonly suffer from cerebrovascular disease, with evidence of pathological alterations in the small cerebral vessels. Impaired vascular brain health has detrimental effects on motor neurons as lowered vascular endothelial growth factor levels compromise endothelial cell formation, promoting blood-brain barrier leakage and inflammation. Increased turnover of neurovascular unit cells precedes their senescence, which, together with pericyte alterations, further fosters the failure of toxic metabolite removal. We here provide a comprehensive overview of the pathogenesis of impaired brain vascular health in ALS and how novel magnetic resonance imaging techniques can aid its detection. In particular, we discuss vascular patterns of blood supply to the motor cortex with the number of branches from the anterior and middle cerebral arteries acting as a novel marker of resistance and resilience against downstream effects of vascular risk and events in ALS. We outline how certain interventions adapted to patient needs and capabilities have the potential to mechanistically target the brain microvasculature towards favorable motor cortex blood supply patterns. Through this strategy, we aim to guide novel approaches to ALS management and a better understanding of ALS pathophysiology.

Globally, inequalities in educational provision are prevalent between genders, various geographical regions, and among different socio-economic backgrounds. Consequently, this study set-out to assess the level of disparity among the Federal Regional States of Ethiopia using Gini-coefficient and Lorenz curve from the statistical data of MoE. Moreover, data were collected from 656 respondents found in the sample regions. The result of the Gini-coefficient indicated that disparity in educational provision has been reduced over the past couple of decades both at primary (0.145 to 0.032) and secondary levels (0.277 to 0.126). Emerging regions are by far lagging behind the central and established regions. The sources of variation were mainly the demand-side variables, especially the economic and contextual related issues like drought and conflicts. Therefore, educational policies designed at the central level are advised to consider the strategies to bridge the existing inequalities through equitable provision of the education system to its citizen.

This study emphasizes the necessity of introducing a blockchain-based joint logistics system to strengthen the competency of medical supply chain management (SCM) and tries to develop a healthcare supply chain management (HSCM) competency measurement item through an analytic hierarchy process. The variables needed for using blockchain-based joint logistics are the performance expectations, effort expectations, promotion conditions, and social impact of the UTAUT model, and the HSCM competency results in increased reliability and transparency, enhanced SCM, and enhanced scalability. Word cloud results, analyzing the most important considerations to realize work efficiency among medical industry-related agencies, mentioned numerous words, including sudden situations, delivery, technology trust, information sharing, effectiveness, urgency, etc. This might imply the need to establish a system that can respond immediately to emergency situations during holidays. It could also suggest the importance of real-time information sharing to increase the efficiency of inventory management. Therefore, there is a need of a business model that can increase the visibility of real-time medical SCM through big data analysis. By analyzing the importance of securing reliability based on the blockchain technology in the establishment of a supply chain network for HSCM competency, we reveal that joint logistics can be achieved and synergistic effects can be created by implementing the integrated database to secure HSCM competency. Strengthening partnerships, such as joint logistics, will eventually lead to HSCM competency. In particular, HSCM should seek ways to upgrade its competitive capabilities through big data analysis based on the establishment of a joint logistics system.

Food additives are used for a variety of technological or processing reasons, including to add or restore colour in a food. In European Union (EU) the safety of food additives was in history assessed by the Scientific Committee on Food, while the role of risk accessor is not in hands of the European Food Safety Authority (EFSA). Only additives for which the proposed uses is considered safe are on the EU list of authorized additives. Very recently – in May 2020, a scientific opinion was published by the EFSA, concluding that TiO2 can no longer be considered as a safe food additive, and the European Commission is expected to remove it from list of authorized food additives in near future. Our aim was to investigate the trends in the use of TiO2 in the food supply. A case study was conducted in Slovenia, using two nationally representative cross-sectional datasets of branded foods. Original sample contained 49,919 pre-packed food items, while analyses was done on N=12.644 foods (6.012 and 6.632 in 2017 and 2020, respectively) from 15 food subcategories, where TiO2 was found as food additive. Overall, we observed significantly decrease in use of TiO2 (3.6% vs. 1.8%; p<0.01) in the course of these three years. The most TiO2-containing foods were in the Chewing gum category (36.3%) in 2017, and Chocolate and sweets (45.9%) in 2020. Meanwhile in 2017 the largest within-category share of TiO2-containing foods was Chewing gum category, namely 70.3%, and those products presented over 85% of the market-share. In 2020 only 24.6% chewing gums contained TiO2, and those were accounting only 3% of the market share. In con-clusion, we showed overall decrease of TiO2 use and considerable improvements in certain food categories (particularly in chewing gums) despite the fact, that this additive has not yet been of-ficially removed from the list of authorized additives. Specific food categories (i.e. Chocolate and sweets) were identified, where product-reformulation is needed, and where official controls by authorities will be most relevant.

Additive manufacturing (AM) processes, including stereolithography (SL), can fabricate complex ceramic parts layer by layer using computer-aided design (CAD) models. A ceramic slurry with high solid loading is usually used in SL to fabricate the desired shape, which is further sintered to produce the final part. The traditional SL system utilizes a tank filled with printable material, known as a vat, which for ceramic slurry contributes several limitations and operational difficulties, and further renders it non-recyclable mainly due to its high viscosity and the fragility of the green state. In this study, we utilized a continuous film supply (CFS) printer integrated with a tape casting system using in-house-designed ceramic slurry to print standard prototype specimens. Various printing parameters, including viscosity, layer thickness control, and slurry recycling efficiency, were studied. In addition, post-processing optimizations of the prototype, characterizations, and the microhardness of sintered samples were studied to determine their properties and compare them with traditional methods. The effectiveness of slurry reusability was demonstrated by printing with original and recycled slurry to produce consistent densification of final parts. Post-processing was optimized to achieve a relative sinter density of 99.02% and microhardness of 12.59 GPa. This method provides new opportunities to realize dense complex ceramic features with final properties comparable to those produced by subtractive machining and traditional SL. Furthermore, slurry recycling helps to reduce the overall cost and material consumption.

With the development of big data analysis, blockchain and other technologies, the supply chain of enterprises is transforming to lean and intelligent. As an important link in the enterprise supply chain, the intelligent transformation of procurement plays an important role in the improvement of the supply chain efficiency, therefore, the construction of a common method supporting the intelligent upgrade of the enterprise procurement business has become a key concern for enterprise managers. Based on the balanced scorecard theory and the supply chain maturity model, this study combines the actual situation of procurement management in Chinese energy enterprises and constructs a procurement benchmarking system that balances the development direction of the industry and the actual needs of enterprises. Meanwhile, based on the grounded theory, three major themes of the intelligent procurement system (digital business module, procurement synergy mechanism and procurement ecosystem) are extracted to provide a methodological reference for the construction of intelligent procurement systems of energy enterprises. The study concludes with a case study of China National Energy Group Materials Company to demonstrate the application of the intelligent procurement system built in this paper, with a view to providing methodological reference for the intelligent procurement management in energy enterprises.

Biofuels and biochemicals play significant roles in the transition towards a fossil-free society. However, large-scale biorefineries are not yet cost-competitive with their fossil-fuel counterparts, and it is important to identify biorefinery concepts with high economic performance. For evaluating early-stage biorefinery concepts, one needs to consider not only the technical performance and process costs but also the economic performance of the full supply chain and the impacts on feedstock and product markets. This article presents and demonstrates a conceptual interdisciplinary framework that can constitute the basis for evaluations of the full supply-chain performance of biorefinery concepts. This framework considers the competition for biomass across sectors, assumes exogenous end-use product demand, and incorporates various geographical and technical constraints. The framework is demonstrated empirically through a case study of a sawmill-integrated biorefinery producing liquefied biomethane from forestry and forest industry residues. The case study results illustrate that acknowledging biomass market effects in the supply chain evaluation implies changes in both biomass prices and the allocation of biomass across sectors. The proposed framework should facilitate the identification of biorefinery concepts with a high economic performance which are robust to feedstock price changes caused by the increase in biomass demand.

At present time world is facing from the coronavirus disease known as Covid-19. The first case of the coronavirus was reported in the December, 2019 in the Wuhan city of China which is known as the major transportation hub of China. After the spread of Covid-19 many countries have shut down their sea ports and airports. They have banned the import and export activities. Also, China is the major distributor of the raw materials which affect the manufacturing activities across the globe due to lockdowns. India is the developing country due to the Covid-19 spread the cases reported in the India government has lockdown the country for 41 days which affected the manufacturing activities and majorly it affects the supply chains and economy of the country. In the present paper we have discussed the effect of Covid-19 on Indian economy and on supply chains in India. There are total of 18 critical barriers are found out which affected the supply chains in the India. It is expected that this study will helpful the researchers to develop the conceptual models to overcome from this issue.

This study was conducted generally by aiming assessment of the hydraulic performance of water distribution systems of Addis Ababa Science and Technology University (AASTU). In line with the main objective, this study addressed, (1) pinpointing problems of existing water supply versus demand deficit (2) evaluating the hydraulic performance of water distribution system using water GEMS and (3) recommended alternative methods for improving water demand scenarios. The University’s water supply distribution network layout was a looped system and the flow of water derived by both gravity and pressurized system. The gravity flow served for the academic and administrative staffs whereas the pressurized system of the network fed the students dormitories, cafeteria’s etc. The study revealed the existence of unmet minimum pressure requirement around the student dormitories which accounts 25.64% below the country’s building code standard during the peak hour consumption. The result of the water demand projection showed an increment of 2.5 liter per capita demand (LPCD) in every five years. Hence, first, the university’s water demand was projected and then hydraulic parameters such as; pressure, head loss and velocity were modeled for both the existing and the improved water supply distribution. The finding of the study was recommended to the university’s water supply project and institutional development offices for its future modification and rehabilitation works.

Abstract: Many countries around the world suffer from the lack of a sea port directly linked to the rest of the world. Such countries are called "landlocked countries". This leads to Weak competitiveness of their products in the global market, as well as to the high cost of the imports. Africa has the largest share of these countries, with 16 of the 43 landlocked countries around the world. The aim of this paper is to propose a general framework for criteria that can be used to choose between ports in transit countries that can be used for import or export. These criteria are related to the assessment of the sea ports in terms of infrastructure and tariffs. It is also related to transport infrastructure from the transit country to the landlocked country and the level of safety. The study identified nine criteria that could be used to compare between ports in transit countries. Using Full Consistency Method (FUCOM) to evaluate those criteria showed that the number of navigation lines is the most important criteria followed by the port service level.

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.

Improving the efficiency of the supply process in prefabricated components is challenging and requires accounting for a variety of risks involved in the management of the suppliers. The purpose of this study is to present a method to account for the systematic trade-offs between several supplier alternatives. A novel framework is presented for the whole assessment of supplier alternatives by taking advantage of the information extracted from customized building information modeling (BIM) and a database required for assessment of impacts. A data library related to assessment criteria for supply alternatives is built to facilitate the storage and sharing of information. Analytic hierarchy process (AHP) is used to select the optimal supplier that is able to provide the most satisfaction for the determined criteria. The proposed framework was also illustrated by the implementation in a mega project. The study implication is that BIM-enabled supplier selection can indeed lead to more benefits and higher values for all stakeholders.

The Smart Park (well-known as Taman Pintar) as a major educational tourism destination in Jogjakarta offers a variety of tourism attractions that are very interesting for tourists. The main purpose of tourists visiting Smart Park is to get an educational tourism experience. A subjective experience raises a specific challenge for Smart Park toward the status of competitive destination. The purpose of this study is to analyze the aspects of educational tourism experience that are affected by tourism demand and supply. Data were collected by survey technique to 150 respondents and analyzed using path analysis. The results of analysis show that tourism demand and supply contributed to the variation of tourism activities by 45.1%, while the remaining was explained by other variables, such as National Budget, Local Budget, ticket sale, and cooperation with some stakeholders. Tourism supply had a higher effect than tourism demand. Tourism demand did not partially affect tourism experience. However, the results of the path analysis indicate that tourism supply had direct and indirect effects on tourism experience through the variation of tourism activities with indirect effect being dominant. In the management of Smart Park, there is still a gap between tourism demand and supply, so that the environment of tourism experience has not been created maximally.

Monitoring training load, recovery, and wellness in maturation ages is even more relevant than in adulthood, especially during congested schedules. The objective of this study was to quantify and describe the differences in external and internal load, recovery, and perceived wellness in elite under-16 female players during a period of three competitions in seven days, based on their participation during matches. An observational design was used to quantify external load (total distance, high-speed distance, and PlayerLoad), internal load (differential sRPE), as well as recovery and wellness of 18 international under-16 players during a development tournament. The external load pattern of the starting players was undulating, with matches showing the highest internal load values compared to training sessions and non-starters. Non-starters players (whether training or matches) showed similar external load and differential sRPE values throughout the tournament (p&gt;0.05). Starting players experienced higher internal load than non-starters during matches (p&lt;0.037), without differences between training sessions (p&gt;0.05). No changes were seen in wellness or recovery (all p-values &gt;0.05), except for non-starters in session 3 compared to session 7 (p=0.024). Starting players had more weekly total minutes (p=0.018), differential sRPE (breath: p=0.002; leg: p=0.002; cognitive: p=0.003), total distance (p=0.003), and PlayerLoad (p=0.008) than non-starters. Our results indicate differing load patterns between starters and non-starters, which do not affect wellness and recovery differently. Starters accumulate more weekly total load. Coaches can use these findings to guide decisions and load compensations in high competition contexts for young female soccer players.

In a motor-controlled hydraulic cylinder (MCC), the two chambers of the cylinder are directly connected to single or multiple fixed-displacement hydraulic pumps driven by electric servo motors. In contrast to a valve-controlled cylinder, an MCC does not have valve throttling, resulting in an improved system energy efficiency. Among various MCC topologies, the two-motor-two-pump (2M2P) MCC distinguishes itself through several notable advantages, including precise cylinder pressure control and eliminating mode switch oscillations. Nevertheless, there are remaining challenges in fully realizing its operations across four quadrants and establishing an effective load-holding function within these operations. This study bridges this gap by implementing a 2M2P MCC prototype on a laboratory knuckle boom crane, enabling operation across all four quadrants. Experimental results indicate that position tracking errors remain within ±2.5 mm across three cases. Furthermore, smooth intersection of cylinder bore side and rod side pressures is observed during transitions between quadrants. In conclusion, the proposed 2M2P MCC demonstrates seamless operation throughout all quadrants, with the load-holding function smoothly activating and deactivating in all four quadrants.

Background: Previous neuroimaging studies have identified brain regions related to respiratory motor control and perception. However, little is known about the resting-state functional connectivity (FC) associated with respiratory impairment. We aimed to determine the FC involved in mild respiratory impairment without changing the saturation of percutaneous oxygen. Methods: We obtained resting-state functional magnetic resonance imaging data from 36 healthy volunteers during normal respiration and mild respiratory impairment induced by resistive load (effort breathing). ROI-to-ROI and seed-to-voxel analyses were performed using Statistical Parametric Mapping 12 and the CONN toolbox. Results: Compared to normal respiration, effort breathing activated FCs within and between the sensory perceptive area of respiration (the postcentral gyrus, anterior insular cortex (AInsula), and anterior cingulate cortex) and visual cortex (the visual occipital, occipital pole (OP), and occipital fusiform gyrus). Graph theoretical analysis showed strong centrality in the visual cortex. A significant positive correlation was observed between the dyspnoea score (modified Borg scale) and FC between the left AInsula and right OP. Conclusions: These results suggested that the FCs within the respiratory sensory area via the network hub may be neural mechanisms underlying effort breathing and modified Borg scale scores.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

Commercial buildings situated in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This research paper aims to address this gap by investigating the application of innovative deep learning models in day-ahead hourly cooling load prediction for commercial buildings situated in tropical climates. A range of deep learning techniques, including Deep Neural Network, Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. An extensive comparative analysis is conducted to identify the most effective hybrid model. Sequence-to-sequence model provided better performance compared to the other single and hybrid models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. Further, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies.

Against the backdrop of the current carbon peaking and carbon neutrality policies, higher requirements have been put forward for the upgrading and construction of smart grids. Non-intrusive load monitoring (NILM) technology is a key technology for advanced measurement systems at the end of the power grid. It obtains detailed power information of the load, without the need for traditional hardware deployment. The key step to solve this problem is load decomposition and identification. This paper first utilized Long Short Term Memory-Denoising Autoencoder(LSTM-DAE) to decompose the mixed current signal on the household busbar and obtain the current signals of multiple independent loads that constituted the mixed current. Then, the obtained independent current signals were combined with voltage signals to generate multi-cycle colored Voltage-Current(VI) trajectories, which were color-coded according to the background. These colored VI trajectories with background colors formed a feature library. When the CNN network was used for load recognition, considering the influence of hyperparameters on recognition results, the BOA algorithm was used for optimization, and the optimized CNN network was employed for VI trajectory recognition. Finally, the proposed method was validated using the PLAID dataset. Experimental results showed that the proposed method exhibited better performance in load decomposition and identification.

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend to introduce more technology into the classroom. One such change would be the use of smart synchronous hybrid learning environments (SSHLE), which are settings with both in-person and online students concurrently, and in which technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater orchestration load on participants and a reduction in teacher agency. In this context, the aim of this paper is to analyse the orchestration load and teacher agency in different SSHLEs. The NASA-TLX model was used to measure the orchestration load in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicate that the orchestration load of the teacher tends to be high (between 60 and 70 points out of 100 of the NASA-TLX workload), especially when they lack experience in synchronous hybrid learning environments, and the orchestration load of the students tends to have average values (between 50 and 60) in the SSHLEs analysed. Meanwhile, the teacher agency does not appear to be altered but shows potential for improvement.

This paper presents a comparison of two methods to represent variable blade inertia in two codes for aero-servo-elastic simulations of wind turbines: the nonlinear aeroelastic multi-body model HAWC2 and the nonlinear geometrically exact beam model BeamDyn for OpenFAST. The main goal is to enable these tools to simulate the dynamic behavior of a wind turbine with variable blade inertia. However, in order to simulate the structural response of wind turbines with variable blade inertias, the source code of load simulation tools need to be modified. This is due to the fact that, currently, state-of-the-art load simulation tools for wind turbines are unable to simulate variable rotor blade inertias. The validity of the modified codes is proven based on a simple beam model. The validation shows very good agreement between the modified codes of HAWC2, BeamDyn and an analytical calculation. The add-on of variable blade inertias is applied to reduce the mechanical loads of a 5 megawatt reference wind turbine with an integrated hydraulic-pneumatic flywheel in its rotor blades.

In the course of SARS-CoV-2 infection, the 3CL or nsp5 protease plays a pivotal role as the most important viral protease required for the maturation of viral proteins during host infection. Herein, we simulated for 500 ns 3CLproWT, 3CLproH41A, , 3CLproBeta, and 3CLproOmicron, in complex with the substrates nsp 4|5 and nsp 5|6. Our results show that mutations in the 3CLpro present in the SARS-CoV-2 variant of concerns (VOCs) did not lead to significant conformational changes or changes in substrate binding affinities. However, significantly high cleavage rates for the boundary between nsp4 and nsp5 were obtained for 3CLproBeta and 3CLproOmicron and may play a key role in the viral replication and virus fitness gain. Our molecular dynamics data suggest that the cleavage rate of nsp4|5 may be related to the increased amount of viral load observed for these VOCs, releasing more nsp4 than other non-structural proteins. Based on our hydrogen bonding analyses, we also discovered that Gly143 and Glu166 are key residues in substrate recognition, suggesting that these residues may be incorporated as pharmacophoric centers for Beta and Omicron variants in drug design. Our results suggest that Gly143 and Glu166 are essential residues to interact with Gln6 of the different substrates and, therefore, are potential broad-spectrum pharmacophoric centers of SARS-CoV-2 3CLpro.

Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of the heterogeneity of programmed cell death. The purpose of this work is to investigate and develop a cuproptosis-associated lncRNA-based LUAD prediction marker. We firstly performed bioinformatic analysis of the Cuprotosis database and The Cancer Genome Atlas (TCGA) database to obtain 19 cuprotosis-related gene datasets and transcriptional data for LUAD. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were utilized to construct cuproptosis-associated lncRNA modes. LUAD patients were thus classified into high-risk and low-risk categories based on prognostic risk values, with a median of It acted as a boundary. Risk models were evaluated and validated using Kaplan-Meier analysis, principal component analysis (PCA), gene set enrichment analysis (GSEA) and nomograms. Utilizing the TCGA-LUAD dataset, we identified seven predicted cuproptosis-associated lncRNAs in tumor microenvironment to create the risk model. 95.54% (214/224) of high-risk category tumor samples included cuproptosis-associated gene alterations, compared to 85.65% (203/237) of low-risk category tumor samples, with TP53 accounting for the bulk of occurrences. According to these findings, risk value was superior to other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival (OS). The predictive validity of the cuproptosis-associated lncRNA-based risk model for LUAD is high, and this may have implications for how lung cancer patients are treated individually.

Many combinatorial optimization problems are hard to solve within the polynomial computational time or NP-hard problems. Therefore, developing new optimization techniques or improving existing ones still grab attention. This paper presents an improved variant of the Ant Colony Optimization meta-heuristic called Enhanced Hyper Cube Framework ACO (EHCFACO). This variant has an enhanced exploitation feature that works through two added local search movements of insertion and bit flip. In order to examine the performance of the improved meta-heuristic, a well-known structural optimization problem of laminate Stacking Sequence Design (SSD) for maximizing critical buckling load has been used. Furthermore, five different ACO variants were concisely presented and implemented to solve the same optimization problem. The performance assessment results reveal that EHCFACO outperforms the other ACO variants and produces a cost-effective solution with considerable quality.

Sediment plumes, released to the Bering Sea from the delta front of the Yukon River, Alaska, are initiated mainly by glacier-melt sediment runoffs in the glacierized regions of the Yukon River drainage basin. The surface sediment plumes are extended around the fan-shaped Yukon River delta, which is followed by the northwestward dispersion. During continuous measure-ments of the Yukon River discharge and sediment load, behaviors of the sediment plumes were explored by shipboard observations in the Bering Sea offshore from the Yukon delta. At the high river sediment load of ca. 3000 kg/s, the plume partially plunged into the sea bottom layer. The plunging probably originated in the nepheloid-layer formation from the flocculation of river-suspended sediment, of which more than 90 %wt. is silt and clay (grain size d < 0.063 mm). In order to numerically obtain the area of the surface sediment plumes, a satellite image analy-sis was performed by using three near-infrared bands in MODIS/Aqua or MODIS/Terra. The plume area was significantly correlated (R2=0.735, p<0.01) to the sediment load averaged for the two days with time lags of 20 days and 21 days to the date of a certain satellite image. Hence, the dispersion of plume-suspended sediment appears to be controlled by the sediment runoff events in the Yukon River rather than the northward “Alaskan Coastal Water”.

The tiered laboratory framework for HIV viral load monitoring accommodates a range of HIV viral load testing platforms, with quality assessment critical to ensure quality patient testing. HIV plasma viral load testing is challenged by the instability of viral RNA. An approach using an RNA stabilizing buffer is described for the Xpert® HIV-1 Viral Load (Cepheid) assay and was tested in remote laboratories in South Africa. EDTA-plasma panels with known HIV viral titres were prepared in PrimeStore molecular transport medium for per-module verification and per-instrument external quality assessment. The panels were transported at ambient temperatures to 13 testing laboratories during 2017 and 2018, tested according to standard procedures and uploaded to a web portal for analysis. A total of 275 quality assessment specimens (57 verification panels and two EQA cycles) were tested. All participating laboratories met study verification criteria (n=171 specimens) with an overall concordance correlation coefficient (ρ_c) of 0.997 (95% confidence interval [CI]: 0.996 to 0.998) and a mean bias of -0.019 log cp/mL (95% CI: -0.044 to 0.063). The overall EQA ρ_c (n=104 specimens) was 0.999 (95% CI: 0.998 to 0.999), with a mean bias of 0.03 log cp/mL (95% CI: 0.02 to 0.05). These panels are suitable for use in quality monitoring of Xpert® HIV-1 VL and are applicable to laboratories in remote settings.

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

An adaptive control technique has been implemented for DSTATCOM for power quality enhancement in three phase four wires(3P4W) distribution system. The reference source currents are extracted from active components of load current for each phase by using this technique. It has also been used for load balancing, harmonic suppression, power factor improvement and reactive power compensation in distribution system. The adaptive control technique has been tested at varying load in steady state and dynamic conditions. This technique is developed using MATLAB/Simulink and source side harmonics are mitigated under different load conditions which are acceptable in accordance with IEEE standard.

This study examined the effects of incorporating 8 weeks of biweekly upper limb loaded plyometric training (elastic band) into the in-season regimen of handball players. Trial participants were assigned between control (n = 15, age: 18.1±0.5 years, body mass: 73.7±13.9 kg) and experimental (n = 14, age: 17.7±0.3 years, body mass: 76.8±10.7 kg) groups. Measures obtained pre- and post- included a cycle ergometer force-velocity test, ball throwing velocity in three types throw, 1-RM bench press and pull-over, and anthropometric estimates of upper limb muscle volumes. Gains in the experimental group relative to controls included absolute muscle power (W) (Δ23.3%; t-test p<0.01; d=0.083), relative muscle power (W.kg-1) (Δ22.3%; t-test p<0.01; d=0.091), and all 3 types of ball throw (Δ18.6%, t-test p<0.01, d=0.097 on jumping shot; Δ18.6%, t-test p<0.01; d=0.101 on 3-step running throw; and Δ19.1%, t-test p<0.01, d=0.072 on standing throw). Furthermore, a significant improvement by time interactions was observed in both groups on 1-RM bench press and pull-over performance. However, upper limb muscle volumes remained unchanged in both groups. We conclude that adding biweekly elastic band plyometric training to standard training improves measures important to game performance. Accordingly, such exercises can usefully be adopted as a part of handball training.

The growing number of the accessed energy-efficient frequency conversion air conditioners is likely to generate a large number of harmonics on the power grid. The following shortage in the reactive power of peak load may trigger voltage collapse. Hence, this conflicts with people’s expectations for a cozy environment. Concerning the problems mentioned above, an active management scheme is put forward to balance the electricity use and the normal operation of air conditioning systems. To be specific, schemes to suppress the low voltage ride through (LVRT) and harmonic are designed firstly. Then to deaden the adverse effects caused by nonlinear group load running on the grid, and to prevent the unexpected accidents engendered from grid malfunction, the dynamic sensing information obtained by an online monitor is analyzed, which can be seen as an actively supervise mechanism. The combined application of active and passive filtering technology is studied as well. Thirdly, the new energy storage is accessed reliably to cope with peak-cutting or grid breaking emergencies, and the fuzzy control algorithm is researched. Finally, system feasibility is verified by functional modules co-operation simulation, and active management target is achieved under scientific and reasonable state-of-charge (SOC) management strategy.