Keywords: Linear differential arrays; beam steering; frequency-invariant beamforming.

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

Mineral nutrition is essential for plant growth and interaction of plants with biotic and abiotic stresses. Mango twig-tip dieback (MTTD) is a new type of mango decline, but its impact on the trees’ mineral nutrition is unknown. This study was conducted to determine the effect of MTTD infection on the nutrient status, balance, and resorption efficiency (RE) of mango. Leaf nutrient concentrations and deviation from the optimum percentage (DOP) indices of ‘Kensington Pride’ (KP) mango trees with low (LD) and high (HD) levels of MTTD infections were analyzed to compare the foliar nutrition status and nutrient balance between the LD and HD trees. Moreover, the nutrient resorption efficiency of MTTD-infected dried leaves (RE_D) was compared with the resorption efficiency of healthy (RE_S) leaves of KP mangoes. The concentrations of the total Ca, Mg, Cu, Fe, Mn, and Zn were lower in the HD trees than in the LD trees. But the total K content was higher in the HD trees, and its DOP index was sufficient, while the total K concentration was low and deficient level in LD trees. Moreover, DOP indices for total Ca, Mn, and Zn were less deficient in LD trees than in HD trees, and overall nutrient imbalances were exacerbated in HD trees. RE_D was significantly lower than RE_S for the total N, P, S, Cu, Fe, and Zn but significantly higher than RE_S for K. The study underscores the significant influence of MTTD on the mineral nutrition of KP mangoes, revealing distinct nutrient variations between trees with low and high MTTD infection levels. These findings have important implications for mango crop management, emphasizing the need for targeted nutrient interventions to address imbalances induced by MTTD and enhance the overall health and resistance of mango trees against MTTD infection.

The extensive use of polypropylene (PP) in various industries has heightened interest in devel-oping efficient methods for recycling and optimising its mixtures. This study focuses on formu-lating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The in-vestigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, at-taining R² values over 0.99. Symbolic regression further enhanced these models, offering signif-icant improvements. To mitigate overfitting, empirical noise and variable swapping were in-troduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the de-velopment of recycling technologies and sustainable practices in the polymer industry by opti-mising processing and enhancing the use of recycled materials.

The ASEAN Community-based Tourism (CBT) Standard has been adopted by 10 ASEAN member states in 2016 in order to collectively promote ASEAN as a competitive community-based tourism destination. Among the ASEAN member states, Lao PDR (Laos), has a unique CBT development experience through collaboration with various international development agencies. Such history has brought about issues when ASEAN CBT Standard was implemented at to provincial and district levels in Laos, including concerns of related communities on the Standard being not easily applicable considering the state of tourism development. While the application of the regional standard is a significant issue to the tourism industries in the Member States, however, there has been a lack of studies regarding the dynamics of implementation of regional tourism standards at the national level. In order to fill the research gap, this study utilized in-depth interviews with seven executives with significant experiences from the government, international development agency, international organization, and the private sector. Interviewees projected a diversity of perspectives on enabling factors and challenges in implementing ASEAN Community-based Tourism Standard at the national level focusing on Laos, which showed the complexity of CBT dynamics. The results also revealed how regional standards can be further recognized and promoted to the key stakeholders of Laos, better accepted and utilized by end users, and possibly better customized according to the local circumstances. Findings of this study evidence that collaboration among key stakeholders and the expected role of these stakeholders are critical when implementing ASEAN CBT Standard.

Asphaltene-resin-paraffin deposition (ARPD) is a complicated and prevalent issue in oil and gas industry, impacting the efficiency, integrity of petroleum extraction, production, transportation and processing systems. Considering all witnessed ARPD problems in Azerbaijan oil fields, this paper proposed a chemical method and optimized the type and the concentration of chemical inhibitors. And then the effect of selected chemical reagent on inhibiting the ARPD amount and thus en-hancing oil recovery was detected by reservoir simulation during both waterflooding and CO2 flooding production. Three new chemical compounds (namely, Chemical-A, Chemical-B and Chemical-C) were examined in laboratory conditions and their impact on rheological properties of high-paraffin oilfield samples of Azerbaijan (X, Y and Z) were investigated. Experimental results show that Chemical-C with concentration of 600 g/t has the best efficiency for alleviating the problems. After adding Chemical-C to the crude oil, the freezing point of oil is decreased from 12 °C to (-4) °C, ARPD amount is declined from 0.185 to 0.016 g and oil effective viscosity is reduced from 16.2 mPa·s to 3.1 mPa·s. It is determined that for water and CO2 flooding, higher injection pressure resulted in reduced asphaltene precipitation. Adding selected ARPD inhibitor, the oil recovery for waterflooding can increase from 52% to 62%, while it can rise from 55% to 68% for CO2 flooding.

Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos namely, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.

The research presented in this article is based on a strategic approach to a rural area in the mountainous region of Romania and is underwritten by specialists in the economy of mountainous regions in order to elaborate relevant solutions to complex problems relating to sustainable development. In essence, the research underlines and promotes the particularities of performing research for the identification and valuation of endogenous resources, that is, creating relevant strategic options for the durable development of the mountainous region, centered on the renewal of economic activities related to the existent endogenous resources, by using methods, work techniques, and instruments of strategic analysis. These factors are subordinate to the process of harmonization to the requirements of community development and environmental protection according to the new strategy for the development of the European Union 2020 (European Green Pact). This pact also emphasizes the necessity of involving the different factor categories of local accountability, promotion, activation, and the development of specific economic activities of different local contexts of sustainability. The research methodology used involves a case study whose complexity necessitated the use of quality and quantity research methods in order to underline specific elements of the rural area in the Romanian mountainous region. Specifically, we used a strategic diagnosis of the Romanian mountainous region using the PESTEL and SWOT models. In order to have relevant strategic options available, the continuity of research was achieved through the use of SOR analysis, creating a problem tree and an objectives tree.

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal-injection-based topology identification algorithm is favored for its speed and efficiency. This study proposes a new signal-injection-based topology identification algorithm aimed at solving the issues of reduced completeness and correctness of identification caused by missing feature signal records (FSRs). By analyzing the correlations between FSRs of different positions, the algorithm employs vertical and horizontal completion methods to effectively address missing data, combined with inclusion detection algorithms, to identify the topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the algorithm not only significantly improves the completeness and correctness of topology identification for LVDNs but can also increase the number of devices in operation by evaluating the status of topological equipment, further enhancing the topology identification performance.

As the demand for long-distance flights continues to increase, supersonic transport (SST) has once again become a hot research topic. However, due to its uniqueness, there was few corresponding evaluation methods to compare the supersonic civil aircraft with common subsonic aircraft. This paper proposed an evaluation method for the competitiveness of supersonic civil aircraft based on the Analytic Hierarchy Process (AHP). Key indicators were selected to form a competitiveness evaluation model from the perspective of four criterion factors of economy, comfort, adaptability, and environment. The evaluation method proposed in this paper could reflect characteristics of supersonic civil aircraft and other subsonic aircraft, and effectively compare their competitiveness. This work can be used to evaluate the social acceptance of supersonic civil aircraft and provide reliable decision-making support for airlines' design work.

Background: The study was conducted to investigate if the mRNA vaccine technology could be adapted for the ectothermic vertebrate Atlantic salmon (Salmo salar). The lipid nanoparticle (LNP) technology has been developed and optimized for mRNA vaccines in mammals, stabilizing mRNA and facilitating delivery into cells. However, its utility at the temperature and specific biological environment in ectotherms remains unclear. In addition, it is unknown if modified mRNA containing non-canonical nucleotides can correctly translate in salmonid cells. Methods: We used an mRNA transcript coding for enhanced green fluorescence protein, flanked by the untranslated regions of the hemagglutinin-esterase gene of infectious salmon anemia virus, and a 120-base-long poly(A) tail. The mRNA was generated by in vitro transcription where uridine residues were replaced with N1-methyl-pseudouridines, and then encapsulated in LNPs. Results: When transfected into the salmonid cell line CHH-1, the mRNA-LNP construct induced expression of EGFP. Furthermore, when mRNA-LNPs were injected intramuscularly into salmon, in vivo protein expression was demonstrated by immunohistochemistry. EGFP was observed in cells infiltrating the space between the muscle cells in a focal inflammatory response. Conclusion: The results indicate that the N1-methyl-pseudouridine modified mRNA encapsulated in LNPs can be used to express antigens of interest in salmonid fish.

Volatile organic compounds (VOCs) released from plants have been correlated with disease-status. Analysis of VOCs using GC-MS is time-consuming, laboratory-based, and requires specialist training. Electronic nose devices (E-nose) provide a portable alternative. Three different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana. The devices were initially trained on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated by principal component and linear discriminant analysis, cross-validation and chemometric data analysis. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3 / MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-nose devices are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS.

The multi-stage, multi-level assembly job shop scheduling problem (MsMlAJSP) is commonly encountered in the manufacturing of complex customized products. Ensuring production efficiency while effectively improving energy utilization is a key focus in the industry. For the energy-efficient MsMlAJSP (EEMsMlAJSP), an improved imperialist competitive algorithm based on Q-learning (IICA-QL) is proposed to minimize the maximum completion time and total energy consumption. In IICA-QL, a decoding strategy with energy-efficient triggers based on problem characteristics is designed to ensure solution quality while effectively enhancing search efficiency. Additionally, an assimilation operation with operator parameter self-adaptation based on Q-learning is devised to overcome the challenge of balancing exploration and exploitation with fixed parameters, thus the convergence and diversity of the algorithmic search is enhanced. Finally, the effectiveness of the energy-efficient strategy decoding trigger mechanism and the operator parameter self-adaptation operation based on Q-learning is demonstrated through experimental results, and the effectiveness of IICA-QL for solving EEMsMlAJSP is verified by comparing with other algorithms.

The normal growth and development of skeletal muscle is essential for the health of the body. The regulation of skeletal muscle by intestinal microorganisms and their metabolites has been continuously revealed. Acetate is the predominant short-chain fatty acid synthesized by gut microbiota through the fermentation of dietary fiber, however, the underlying molecular mechanisms governing the interaction between acetate and skeletal muscle during rapid growth stage remain to be further elucidated; Here, specific pathogen-free (SPF) mice, germ-free (GF) mice, and germ-free mice supplemented with sodium acetate (GS) were used to evaluate the effects of acetate on skeletal muscle growth and development of young mice with gut microbiota deficiency; We found the concentration of serum acetate, body mass gain, succinate dehydrogenase activity and expression of myogenesis maker gene of skeletal muscle in the GS group was higher than GF group following sodium acetate supplement. Furthermore, the transcriptome analysis revealed that acetate activated biological processes regulating skeletal muscle growth and development in GF group that are inhibited due to gut microbiota deficiency. In vitro experiment, we proved that acetate up-regulated Gm16062 to promote skeletal muscle cell differentiation; Overall, our findings proved that acetate promote skeletal muscle growth and development in young mice via increase Gm16062 expression.

In this study, we analyzed the microbial community of traditional fermented foods in Jeju Island to identify the distribution of useful microorganisms and confirm their anti-inflammatory and anti-melanogenic effects to determine their potential use as cosmetic ingredients. Firstly, we examined the microbial communities of Omphalius rusticus Jeotgal (OR), Spratelloides gracilis Jeotgal (SG), Chromis notata Jeotgal (CN), Turbo cornutus Jeotgal (TC), Trichiurus lepturus intestine Jeotgal (TL), Branchiostegus japonicus Sweet Rice Punch (BJ), Salted Anchovy Sauce (SA), Jeju Soy Sauce (JSS), and Jeju Soybean Paste (JSP). We found that Latilactobacillus sakei (87.2%), Tetragenococcus halophilus (37.7%), T. halophilus (96.8%), Bacillus subtilis (23.4%), T. halophilus (71.3%), L. sakei (53.7%), Lentibacillus sp. (42.9%), Enterococcus durans (14.6%), and E. durans (32.8%) were the dominant species. Secondly, to study the nine Jeju fermented foods’ anti-inflammatory and anti-melanogenic effects, we employed RAW 264.7 and B16F10 cells, classic cell models for inflammation and melanogenesis studies. Ethyl acetate extracts of the nine Jeju fermented foods all inhibited nitric oxide (NO) and melanin production in a concentration-dependent manner. Thirdly, to test the applicability of the nine Jeju fermented foods to human skin, we used the MTT assay to assess their cytotoxic effects on human keratinocytes (HaCaT cells). Finally, the topical applicability of the nine Jeju fermented foods was tested through primary skin irritation, and it was found that they did not cause any adverse effects. Therefore, extracts from the nine Jeju fermented foods have potential applications as ingredients in anti-inflammatory and anti-melanogenic products and can be used in the cosmetic industry.

The Shimensi super-large tungsten polymetallic deposit is located in Late Jurassic- Early Cretaceous Porphyry-Skarn tungsten ore belt in the south Yangtze metallogenic belt. There are 3 types of mineralization: veinlet-disseminated type, thick quartze vein type and hydrothermal crypto-explosive breccia type. Based on geological studies, this paper presents new petrographic, microthermometric, laser Raman spectroscopic and hydrogen and oxygen isotope research of the fluid inclusions from the deposit. The results show that there are five different types of fluid inclusions: liquid-rich inclusions, vapor-rich inclusions, pure liquid inclusions, pure vapor inclusions, and fluid inclusions containing a solid crystal. The homogenization temperatures of the fluid inclusion range from 140℃-270℃, the salinities are 3%-5% NaCleq and the densities of ore-forming fluid range from 0.64 g/cm3 to 0.99 g/cm3. For the analyses of Laser Raman spectroscopic, the ore-forming fluids can be approximated by a Ca2+-Na+-SO42--Cl- fluid system with small amount of CO2, CH4 and N2. Otherwise, the datas of the pressure, pH and Eh show a fluid metallogenic environment of low pressure, weak acid and weak reduction. The values of the homogenization temperature in 3 types of orebodies suggest that the mineralization is characterized by the decrease of the temperature under the condition of the fluid immiscibility. The H-O isotope values are interpreted to indicate that the ore-forming fluids are mainly composed of magmatic water, and also the meteoric water was added with the process of the magma rising.

Photovoltaic (PV) technologies are developing rapidly as a result of their ability to reduce energy consumption from conventional sources as well as the growth of global energy expenditures. Nevertheless, several impact categories during the PV panel life cycle occur and some principal sectors are affected. These impact categories are assessed by applying life cycle analysis (LCA) techniques through various software programs, databases and methods. Such analyses of energy technologies are crucial, as they can reveal the occurrence of hazardous emissions at distinct life cycle stages. In the present paper, a PV panel impact assessment through life cycle analysis is carried out. More precisely, a combination of PV technologies—monocrystalline, multicrystalline, copper indium gallium selenide (CIGS), amorphous, and ribbon—is studied. The impact categories of ozone depletion, human toxicity, particulate matter, ionising radiation, photochemical ozone formation, acidification, eutrophication, and ecotoxicity, as well as the sectors of human health, ecosystem quality, and environment, are assessed using the openLCA software, the ecoinvent database, the eco-indicator 99, IMPACT 2002+, ReCiPe, and TRACI methods.

Silicon has a theoretical specific capacity of 4200 mAh g−1, which is considered to have significant potential for anode materials in lithium-ion batteries (LIBs). However, the volume change that occurs in silicon during charging and discharging affects the development of commercial applications. In this work, a yolk-shell structured Si@void@C anode material has been developed to address this problem. The silicon nanoparticle yolk material is obtained by recycling kerf-loss (KL) Si waste from the photovoltaic industry, the carbon shell is prepared via a hydrothermal process with glucose, and the sacrificial interlayer is Al2O3. The synthesized material is used to fabricate anodes that show a high reversible capacity of 836 mAh g−1 at the current density of 0.1 A g−1 after 100 cycles, with a Coulomb efficiency of 71.4%. This work demonstrates a relatively low-cost approach to turn KL Si waste into value-added materials for LIBs.

In this work, the properties of a composition based on synthetic rubber ethylene-propylene ter-polymer and polymers with functional groups of the mixture are studied. Low efficiency of some compositions based on ethylene and propylene copolymer limits their use in the production of tires and rubber products. Therefore, we conducted a study to improve the disadvantages of SREPT-60 using butyl rubber + choro polyisoprene + butadiene-nitrile + butadiene styrene rubber and adding a plasticizer to the composition = α, methacrylate-(bis-diethylene glycol phthalate) -(btegf-9) The data obtained showed that a number of its indicators of rubber based on them were improved. Tear resistance, resistance to repeated stretching increased, adhesion of the composition to metal, chemical resistance increased, the degree of swelling in benzene and gasoline-benzene mixture decreased. Compositions based on the mixture SREPT-60 using butyl rubber + choro polyisoprene + butadiene-nitrile + butadiene styrene rubber and adding a plasticizer to the composition = α, methacrylate-(bis-diethylene glycol phthalate) -(btegf-9) +Dioctyl phthalate: (70:20:10) have high resistance to swelling in an aggressive environment (Benzene, gasoline, alcohol, acid, oil, etc.) mixture.

The stinging nettle caterpillar, Darna pallivitta (Lepidoptera: Limacodidae) is a serious invasive pest of agricultural products and a health hazard on the Hawaiian Islands first discovered in 2001. Nursery workers and homeowners have been stung by the caterpillars while handling their plants, especially rhapis palms (Rhapis sp.). Throughout its invaded range, it causes widespread damage including the many cultivated and native palm species grown in Hawaii. Of medical importance are the stinging spines of the larva, which cause dermatitis on contact with the skin. Horticulture and nursery products impacted by the limacodid pest are estimated at $84.3 million (2018 value). An eradication attempt with pesticides and lure traps was made but proved unsuccessful and the moth population continued to spread to major Hawaiian Islands (Hawaii, Kauai, Maui, Oahu). The introduction of specific biological control agents from the native region was thought to be the long-term solution for this invasive pest. Initial exploration in Indonesia and Thailand resulted in the introduction of a pupal ectoparasitoid, Nesolynx sp. (Hymenoptera: Eulophidae: Tetrastichinae), that was not specific. The oriental wasp, Aroplectrus dimerus Lin (Hymenoptera: Eulophidae: Eulophinae), idiobiont gregarious ectoparasitoid of the stinging nettle caterpillar, was introduced from Taiwan in 2004 for host specificity studies and biocontrol in Hawaii. Host range testing showed the parasitoid attacked only limacodid species and it was approved for field release in 2010. The parasitoid identity, host specificity under containment facility conditions, reproductive performance, colonization on the major infested sites were assessed. A total of 13379 parasitoids were colonized on162 release sites on four Hawaiian Islands. Evaluations were conducted using field surveys of larvae, pupal counts, and male lure traps. Field parasitism was thoroughly investigated on Oahu Island averaging 18.9 ± 5.6 % of 3923 collected larvae during 2010 – 2023. The numbers of male moths caught/trap/month were significantly reduced on Oahu Island (P

Energy resources are important sources of social and economic development, human survival and security. However, energy resources are objective and scarce, especially the fossil energy, such as coal, oil, natural gas, etc. Human survival environment is increasingly damaged, and the condition of energy is also gradually becoming deficient, so it is necessary to develop new energy resources to meet the increasing demand of material civilization. Solar energy can meet the needs of human development in the future as a kind of clean resource and it has been concerned by all countries in the world. We analyzed the effectiveness of subsidies to the PV industry of both the price and R&D by the spatial econometric model. In some region, the production capacity of photovoltaic industry has shown a significant spatial agglomeration situation in China since 2012. At the same time, benchmarking price and R&D subsidies played a positive incentive to the photovoltaic industry production capacity. It is a certain degree of inverse correlation between the scale of policy subsidies and the resource endowment of solar resources. Therefore, the countermeasures should be considered from the aspects of institutions, industry, technology and public financial policies.

This study focuses on the design, installation, and performance evaluation of a photovoltaic farm situated in Tulcea County, Romania, connected to a 20 kV distribution network. The farm, with a peak capacity of approximately 2800 kWp, benefits from Romania’s favorable climate conditions and supportive renewable energy policies. The analysis encompasses both the photovoltaic panel array and the electrical energy evacuation system. The substation at the park is equipped with technology that adheres to European standards and incorporates advanced features typical of modern electricity distribution networks. The assessment includes an evaluation of the park’s operational efficiency, with detailed calculations of the performance ratio and various power losses attributed to factors like temperature fluctuations, fouling, inverter efficiency, grid-related challenges, and availability issues. The farm contributes around 3620 MWh of electricity to the grid annually, demonstrating the effectiveness of photovoltaic installations in promoting sustainable energy solutions within regional energy networks.

The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

Antioxidant, anti-inflammatory and chemo-preventive features have been reported for the carotenoid diatoxanthin. Diatoxanthin is only produced by a few groups of microalgae where it functions in photoprotection. Its large-scale production in microalgae is currently not feasible. In fact, rapid conversion into the inactive pigment diadinoxanthin is triggered when the cells are removed from the high-intensity light source, which will be the case during large-scale harvesting of microalgae biomass. Zeaxanthin epoxidase (ZEP) 2 and/or ZEP3 have been suggested to be responsible for the back-conversion of high-light accumulated diatoxanthin to diadinoxanthin in low light in diatoms. Using the CRISPR/Cas9 gene editing technology we knocked out the ZEP2 and ZEP3 genes in the already commercially used marine diatom Phaeodactylum tricornutum to investigate their role in the diadinoxanthin-diatoxanthin cycle, and to determine if one of the mutant strains could function as a diatoxanthin production line. Light shift experiments proved that ZEP3 encodes the enzyme converting diatoxanthin to diadinoxanthin in low light. Loss of ZEP3 caused the high-light accumulated diatoxanthin to be stable for several hours after the cultures had been returned to low light, suggesting that zep3 mutant strains could be suitable as commercial production lines of diatoxanthin.

Solar photovoltaic (PV) generation suffers from multiple serious issues, for example shading effects directly affect the performance of installed PV arrays. By studying these issues, it can play a key role in improving the limitations and performance of PV panels. The study of PV arrays under partial shading conditions is mentioned in many papers as a part of other studies and as an independent phenomenon. Therefore, it is essential to improve these studies and update them with new research and study this problem in detail to know how to limit the effects of partial shading irradiance conditions on PV arrays performances, including economical aspects. This paper proposes to study the effects of different partial shading irradiance conditions for a PV array in various configurations. The analysis is proved in detail through the simulation of the proposed PV array in the Matlab/Simulink environment. The obtained results show the performances of interconnected PV arrays under different partial shading irradiance and temperature conditions, including the solar array I-V and P-V characteristics. In addition, different PV array configurations with and without bypass and blocking protection diodes improve and minimize the effect of partial shading conditions and avoid the reductions in output power.

Integrated photovoltaic-fuel cell (IPVFC) systems have the potential to contribute to sustainable energy production for grid and off-grid applications. While there are studies focusing on fundamental science and designs for IPVFC systems, there are scarcely studies that have focused on the manufacturability of IPVFC systems, which is certainly the pathway for commercialisation of the systems. This study explores manufacturing strategies that can be considered for exploiting a family of eleven IPVFC systems. A survey and systems thinking were used to investigate the potential modularisation and supply chain management to achieve optimal lean and agile manufacturing strategy. Results show that the Photovoltaic-Thermal-Separate Converter/Inverter-Battery System received 25% of the responses. The optimal manufacturing strategy depends on the overall business strategy of the firm. Although 17% preference for System 1 is significant compared to four members of the family of IPVFC systems (Systems 2, 6, 8 and 9) that received only 2% of the responses, it indicates that there is a likelihood that the demand for System 1 will be among the top 42% of the total demand. Overall, this study provides new insights on how the family of IPVFC systems can contribute to realising greater access to clean energy, by extension contributing to the net-zero efforts using solar energy and solar hydrogen.

Halide perovskite materials gain enormous attention for their semiconducting properties, higher power conversion efficiency and potential applications in a wide range field of studies along with their two key limitations: stability and toxicity. In spite of great progress made on the halide perovskites and many promising research developments, overcoming these limitations is not realized yet. Therefore, coordination engineering of new framework for obtaining alternative new halide perovskite materials and fundamental understanding of the coordination chemistry and electronic interactions forming the structure of these newly engineered halide perovskite materials might be one way in order to overcome the issues related to both stability and toxicity. In this review, the current development of halide perovskite families, both lead halide perovskites and lead free halide perovskites followed by coordination engineering of the new frameworks to engineer new halide perovskite materials will be reviewed comprehensively. Moreover, all the concerns of the fundamental ideas of coordination chemistry and electronic interactions that are keys in forming the halide perovskite structures are discussed in detail and form the main aim of this review. Interestingly, we also discuss recent potential energy applications beyond photovoltaic and this review has completed with an essential and open question: ‘what could happen in the future of halide perovskites?’ in order to excite commercial enterprises and research institutions again as well as to get motivating new predictions on the future continuity of this field.

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of carbon peak and carbon neutrality, green building evaluation system (GBRS) has become a research hotspot in the field of green building. This paper systematically summarizes the current research progress based on GBRS, focuses on the current weight and indicators setting, analyzes it with Cite Space software, and puts forward its future development direction. The results show that the weight setting method of green building evaluation system is determined by multidimensional and multi-method. From the perspective of environment, society and economy, the scope of indicators setting is expanding. The green building evaluation system will play a guiding role in green building design, and the evaluation process will continue to be optimized and developed in the direction of intelligence and automation.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an on-line vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density；it also conducts experiments with an on-line vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant temperature stirring system, a walk-in constant temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device's evaluation is U= 0.08kg/m3, k=2.The experimental research on the on-line vibrating tube liquid densitometer can be carried out within the range of (650~1400) kg/m3, (10~70)℃, (0~50)bar the metrological performance of the vibrating tube densitometer can be researched by utilizing the straight tube type and the elbow type on-line vibrating tube densitometer. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system's vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device's conformity. The study verified the device's scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research.

Since the discovery of the phenomenon of surface-enhanced Raman scattering (SERS), it has gradually become an important tool for the analysis of material compositions and structures. The applications of SERS have been expanded from the fields of environmental and materials science to biomedicine, due to the extremely high sensitivity and non-destructiveness of SERS-based analytical technology that even allows single molecule detection. This article provides a comprehensive overview of Surface-Enhanced Raman Scattering (SERS) phenomenon. The content is divided into several main sections: basic principles and the significance of Raman spectroscopy; historical advancements and technological progress in SERS, as well as various practical applications across different fields. We also discussed how electromagnetic fields contribute to the SERS effect, the role of chemical interactions in enhancing Raman signals, modeling and computational approaches to understand and predict SERS effects.

This study investigates the impact of circular economy (CE) strategies on organizational resilience (OR) within the metal industry, focusing on a case study of a forging department in a large man-ufacturing company. The idea of the paper is to link the macroeconomic topic of circular economy (CE) with microeconomic and practical activities in the organization, exploring how CE principles can improve resource efficiency, sustainability, and organizational resilience. A systematic literature review and case study methodology were used to assess the relationship between CE and OR. The findings reveal that companies with higher circularity demonstrate better resilience, both in normal operations and during crises. Specifically, in the case study, the closure of the forging department, following a detailed analysis using the Inputs-Mediators-Outputs (IMO) model, emerged as the most sustainable option, yielding financial, environmental, and operational benefits. This decision aligned with the company’s long-term goals of energy efficiency and carbon neutrality. The research highlights the significance of integrating CE strategies with business processes to not only achieve sustainability but also to build a more adaptable and resilient organization. Future research should explore broader applications of this framework across different industries to strengthen the links between circular economy practices and organizational resilience.

Self-sustained learning (SSL) is considered a crucial factor in promoting students’ long-term learning and development in a sustainable society. To deepen the understanding of SSL, especially in the context of learning English as a second language, the current study examined the influence of intercultural communicative skills, language mindset, and positive L2 self on SSL in English learning among 1,238 college students in China. The results showed that students not only had a strong language mindset, positive L2 self, and intercultural communicative skills but also scored higher in long-term self-sustained English learning. Findings from the Structural Equation Modeling indicated that both language mindset and intercultural communicative skills positively predicted positive L2 self and self-sustained English learning. Furthermore, language mindset and intercultural communicative skills had an indirect impact on self-sustained English learning through positive L2 self. These findings suggest that to promote English learners’ SSL, instructors should help facilitate students’ intercultural communicative skills and foster a growth language mindset, as well as positive language learning beliefs.

In this study, the microbial community during the barley yeast fermentation of stevia leaves and its correlation with antioxidant, antiobesity, and anti-inflammatory properties, as well as metabolites, were investigated using UPLC-Q-TOF-MS. Stevia was fermented using commercial TSB media (TSB 1, TSB 2, and TSB 3) and sucrose (Sucrose 1, Sucrose 3, and Sucrose 3) for 1, 3, and 5 days, respectively. Stevia ferments showed higher DPPH and ABTS radical scavenging capacity compared to samples incubated with sucrose for 5 days, and all six ferments inhibited nitric oxide production in a concentration-dependent manner in LPS-induced mouse macrophages. Furthermore, UPLC-QTOF-MS analysis identified 23 related substances, including 10 terpenoids, including rubusoside, steviolbioside, and rebadioside derivatives; dulcoside A; and phlomisoside II, which are indicators of stevia, as well as five flavonoids, four phenolic acids, and four fatty acids. We also identified the microbial community during fermentation via the next-generation sequencing of the 16S rRNA gene for bacteria and the internal transcribed spacer (ITS) gene for fungi. The results showed that TSB 1 and Sucrose 1 ferments were dominated by the pathogens Enterococcus hirae (58.93%) and Cronobacter sakazakii (80.92%), while samples fermented for more than 3 days were pathogen-free and dominated by lactic acid bacteria such as Pediococcus stilesii (73.37%). Microbial community analysis using the ITS region showed that Saccharomycopsis fibuligera, classified as a yeast rather than a mold, dominated the stevia fermentation regardless of the fermentation duration. In particular, the microbial community of the fermentation with a sugar solution was dominated by S. fibuligera by more than 99% throughout the fermentation periods of 1, 3, and 5 days. Finally, to apply the stevia ferments topically to human skin, skin irritation tests were performed on 30 volunteers. The results showed that the highly concentrated extracts (1 mg/mL) of all six stevia ferments were hypoallergenic. Taken together, these findings suggest that brewer's yeast fermented from stevia leaves with a sugar solution offers promise as a natural ingredient for use in functional foods and cosmetics.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Studies have demonstrated the critical role that safety plays in preserving favourable working conditions in the construction industry, which is necessary to accomplish goals. The aim of this research was to inform stakeholders in the construction industry in developing nations about the value of safety and possible strategies for influencing their opinions regarding safety protocols. The importance of safety to the construction sector, which is crucial to the advancement of the country, has also been emphasized. However, due to a lack of adequate safety understanding among stakeholders in the construction sector, the construction industry is characterized by a great deal of instability and hazard. To determine what factors, affect productive working conditions in the construction production, this study examined safety. A conceptual framework for safe working conditions in the construction sector was developed by considering several aspects, including the person dimension, environment factor, safety behaviour, organization features, technology, and incentives. It is now imperative to bring in these dimensions to improve the safe working conditions in the construction industry, particularly in the developing countries. By putting these safety factors into practice, the construction industry can reduce safety risks, lower the number of accidents and fatalities, cut expenses related to subpar safety performance, safeguard the reputation of construction companies, boost employee morale and satisfaction with their work, enhanced employee retention, reduced absenteeism, and enhanced sustainability goals. Additionally, the proposed conceptual framework is assured to be new and to be widely accepted in the developing nations. Based on this assumption the conceptual framework is designed.

Photovoltaic (PV) systems, which are renewable energy sources, are increasingly being utilized in distributed generation. To maintain a stable energy balance, energy storage systems such as batteries (BAT) play a vital role. Electric vehicles (EVs) offer a promising solution with rechargeable battery systems for operation. The BATs must maintain their state of charge within design boundaries, despite intermittent PV and load power fluctuations, and advanced power control and management techniques are essential for their effective operation. The article evaluates an intelligent controller that uses a Sliding Mode Control adaptive deep learning algorithm Convolutional Neural Networks (SMC-CNN) and a unique Strategy for managing supervisory power. (SPMS) for photovoltaic systems with battery energy storage for better regulation of DC bus voltage. SMC-CNN offers exceptional resilience and seamless functionality, minimizing fluctuations in DC bus voltage in the control strategy design requirements. The primary goals are maintaining a consistent power supply and ensuring continuous service by preventing system components from exceeding capacity. This study aims to enhance the control of DC bus voltage in the PV and battery system, with the primary significance focusing on the following aspects. The advanced SPMS has been created, incorporating control system constraints to enhance SOC balancing speed and reduce fluctuations in DC bus voltage. Power flow management involves optimizing energy flow between the PV system, battery system, and load while minimizing battery capacity requirements. The proposed SMC-CNN and SPMS are demonstrated through real-time simulation using Matlab/Simulink, as demonstrated in comprehensive case studies.

Embracing Fourth Industrial Revolution(4IR) techniques is a need for a long-term sustainability and to do automation in the processes of Industrial and education sector. Rapid advancement in the information technology and other engineering technologies demand more proactive steps in the education institutes. In the post pandemic world, there is an increasing demand of changes in teaching and learning methodologies with the support of modern digital technologies. So, all of the teaching institutes need to have a significant step change in the areas of learning and teaching. The impact of 4 IR is more on the education sectors compared to previous generation’s industrial revolution. Keeping track of key technologies can be quite a challenge as they keep evolving at a very fast pace. Fourth Industrial revolution is the current technological digital revolution in which major focus is on cyber physical and biological systems such as Artificial Intelligence, Robotics, Internet on Things, Virtual Reality etc., The educators and institutions should establish the pedagogies and teaching with the aid of Industry 4.0 techniques to evolve education 4.0. So, it is very important to understand the challenges and competencies required about the implementation of 4IR by the faculty members and students who are currently studying in Higher Education Institutes (HEIs). This paper aim is to do a systematic review about industry 4.0 techniques and its application in relate to the online learning and teaching, change in pedagogies, enhancement of data management in HEIs, digital and virtual laboratories etc.

Land use and cover change (LUCC) is an important method to investigate the causes of global environment change. We utilized the emergy ecological footprint (EEF) model to construct a land-use change model to be used as a systematic measuring tool for monitoring sustainable development trends. In particular, we estimated the eco-security of the Cing-jing region as a case study so that responsible agencies can use it to maintain a balance between ecological preservation and tourism development. The results indicated the following: First, the ecological environment of the Cing-jing region satisfied the safety standard in 2008–2014; however, the related indices increased considerably. Second, the grey model predicted a decrease in 2015–2024 ecological carrying capacities of Cing-jing and a large increase in capita EFs, resulting in a larger ecological deficit and higher EFI. The eco-security from 2015–2024 was higher compared to 7 years ago and is predicted to reach the Grade 2 intermediate level in 2022; thus the Cing-jing region is gradually becoming ecologically unsustainable. Strengths of our study included the use of EEF theory in a quantitative analysis of slope lands for the effective evaluation of ecological security. Finally, we expanded our research to include ecological security issues.

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the missense mutations found in the 2,535 proteins involved in Mendelian rare diseases (MRD), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRD show different mutation site distributions at different three-dimensional protein depths. We then predicted the depth-dependent effects of mutations for the 20,248 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

Matters relating to preservation of environmental resources and climate change, social inclusion and diversity, and open and sound governance have rapidly gained global attention. However, little is known about the construction industry in meeting the environmental, social and governance (ESG) criteria towards sustainable development. In this study, the purpose is to identify the key ESG indicators for construction development. A systematic literature review was undertaken using data from Scopus, PubMed, Google Scholar, JSTOR, ProQuest, and Web of Science. The results show some of the key ESG indicators as climate resilience and green construction activities (at the project level), and active stakeholder engagement and effective project governance (at the firm level) and meeting environmental and social regulation requirements (industry/national level). The limitation of the article is in the sample size of twenty-six journal articles with future expectation of increment as ESG gain more acceptance in the construction industry. This study will be significant for the design of practice framework and policies on ESG in the construction industry. Comprehensively, this study presents relevant ESG indicators for construction management and presents essential information for future research.

Redox mediators comprising I–, Co3+, and Ti3C2Tx MXene were applied to dye-sensitized solar cells (DSCs). In the as-prepared DSCs (I-DSCs), wherein hole conduction occurred via the redox reaction of I−/I3− ions, the power conversion efficiency (PCE) was not altered by the addition of Ti3C2Tx MXene. The I-DSCs were exposed to light to produce Co2+/Co3+-based cells (Co-DSCs), wherein the holes were transferred via the redox reaction of Co2+/Co3+ ions. A PCE of 9.01% was achieved in a Co-DSC with Ti3C2Tx MXene (Ti3C2Tx-Co-DSC), which indicated an improvement from the PCE of a bare Co-DSC without Ti3C2Tx MXene (7.27%). It was also found that the presence of Ti3C2Tx MXene in the redox mediator increased the hole collection, dye regeneration, and electron injection efficiencies of the Ti3C2Tx-Co-DSC, leading to an improvement in both the short-circuit current and the PCE when compared with those of the bare Co-DSC without MXene.

Automobile industry is recognized as one of the most influential sectors shaping the global economies, societies, and individual lifestyles. Therefore, fierce competitions among different companies are continuously undergoing, and innovations deserve special attention to improve the competitiveness. In the past several years, additive manufacturing (AM) is emerging as an innovative technology in the applications of automobile industry with significant advantages over traditional techniques. As a result, increasing attention has been attracted to combining AM technology with the development of automobile industry. Currently, many automobile players are optimizing their industrial layout by incorporating with innovative AM techniques, and meanwhile, many research progresses have been achieved to meet the market demand. This article aims at presenting a timely review to conclude the recent advances in the application of AM techniques in automobile industry, focusing on the available AM techniques, printable materials, and the industry applications. The future research opportunities and challenges are also outlooked. Hopefully, this work can be useful to the related researchers, as well as the game players in the industry of this field.

This study examines the environmental challenges faced by Malaysian industries in adhering to industrial effluent regulations with the research question, what are the challenges in complying with environmental legislations from industries? It investigates the primary factors contributing to non-compliance and the perspectives, knowledge, and needs of Malaysian industries regarding environmental compliance. This study recommends the best strategy for promoting self-compliance. Thirteen stakeholders from various industries with environmental management experience were interviewed. Over the past seven years, these industries have shown low com-pliance with the Industrial Effluent Regulation 2009, prompting an exploration of their adaptability to regulations. Challenges identified include cost, employee attitudes, technology, management support, public complaints, and jurisdictional issues. Responses varied from divergent to unified viewpoints, highlighting the complexity of compliance issues. Respondents suggested enhancing compliance through behavioral changes, including incentives, enforcement, awareness, guidance, industry ratings, management commitment, and public disclosure of emissions. Suggestions for improvement and innovative solutions emerged based on participants' experiences, emphasizing the importance of continuous improvement. The study emphasizes the government's crucial role in ensuring regulation adherence but underscores the need for analyzing barriers and exploring alternative approaches. Understanding industry perspectives and strategies is essential for progress. Compliance, however, requires an analysis of the current challenges and adopting adaptive measures. It is important to understand industries' perspectives and strategies to move forward.

In an attempt to reuse fibrous textile waste and, at the same time, to address dye pollution in textile wastewater, waste cotton-based yarn was utilized as both a cheap and sustainable adsorbent and a precursor for carbon adsorbent production. Cotton and cotton/polyester yarn samples underwent thermal modification through carbonization followed by chemical activation with KOH. Various techniques including nitrogen adsorption-desorption isotherms, scanning electron microscopy, Fourier transform infrared spectroscopy, and surface charge determination were employed to analyze the morphological and surface properties of the cotton-based adsorbents. Adsorption properties were evaluated by testing the removal of selected cationic and anionic dyes from water. The impact of temperature, contact time, initial concentration, and pH of the adsorbate solution on adsorption was investigated, with experimental data analyzed using theoretical models. While carbonization alone did not significantly enhance adsorption properties, activated samples exhibited high efficacy in removing both cationic and anionic dyes from water. Despite the negative influence of the polyester component in the carbon precursor on the efficiency of activated samples in removing methyl orange, results indicated that activated cotton and cotton/polyester yarn could be used to prepare highly efficient adsorbents for the rapid removal of methylene blue from real wastewater samples.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

The development of biofuels and biodiesel industry specifically, has resulted in the generation of large amounts of glycerol as a surplus. The conversion of glycerol, into chemicals with indus-trial interest, through anaerobic fermentation using E. coli as a biocatalyst is an attractive alter-native to improve economic performance in the biodiesel industry, for example through the bi-otransformation of residual glycerol into ethanol. For 2019, the size of the global ethanol market was valued at $ 89.1 billion US dollar. Biodiesel can be produced from several materials like rapeseed, palm, soya, corn, palm kernel, frying oil or olive-pomace oil (OPO). The last, can be extracted from alperujo, byproduct of olive oil process. However, olive-pomace oil converting into biodiesel is not enough established to achieve a global olive-oil/biodiesel derived process with minimized waste generation. Traditionally biodiesel production process produces an equivalent of 10% by mass of glycerol as a waste or by-product. This work explores the conver-sion of residual glycerol into chemicals like ethanol and hydrogen with a focus on projecting raw material potential availability from OPO, contributing to the promotion of the use of biofu-els and biodegradable compounds.

With the increasing interest in CM as a potential source of nutrients and bioactive compounds, there arises a necessity to delve into methods for its processing and drying. Over recent decades, a significant amount of research has been conducted on the chemical composition of CM and its potential health benefits. Consequently, this study is directed towards examining CM drying technology, including an analysis of the influence of different drying methods on its chemical composition and nutri-tional properties. The objective of this review is to systematically summarize the available literature on CM drying processes and their impact on its chemical composition. Special attention is devoted to comparing various drying methods such as freeze-drying, spray drying, and freeze-drying, as well as investigating factors affecting the quality of dried CM. To achieve this goal, we scrutinize a series of studies published between 2003 and 2022 to identify optimal drying methods that promote the maximum preservation of CM's nutritional components and bioactive compounds. Additionally, we explore possibilities for reducing drying time to enhance production efficiency. This research is grounded in the analysis of data from diverse scientific research databases, including Scopus and Google Scholar, encompassing a systematic review of scientific literature and a comparative analysis of the obtained results.

With the global increase of photovoltaic (PV) modules deployment in recent years, the need to explore and realize their reported failure mechanisms has become crucial. Despite PV modules being considered reliable devices, failures and extreme degradations often occur. Some degradations and failures can be minor and cause no critical harm if within the expected range. Others, may start mildly and then deteriorate faster to become catastrophic, especially in harsh environments. This paper conducts a state-of-the-art literature review to scan PV failures, types, and their root cause based on PV’s constructed components (from protective glass to junction-box). It outlines the hazardous consequences beyond PV module failures, describing what harm they can bring to the PV system. As we delve into the literature, it becomes clear that every component is vulnerable to certain types of failures; some can deteriorate within themselves, and others infect further PV components resulting in emerging more severe failures. In the end, the review briefly summarises PV failure detection techniques, emphasising electrical characterization techniques, and disclosing the need to engage more electrical parameters. Most importantly, this review can prepare the stage for the PV research community to identify the most prevalent degradation processes. This, in turn, encourages researchers to investigate them throughout modelling and experimental studies to forecast them at the early onset in order to protect the PV systems from hazardous malfunctions.

Biosolids from stabilized sludge presents a high fertilization potential due to its rich content of nutrients and organic matter. Three formulas of organo-mineral fertilizers based on biosolids were manufactured having at least 2 % N, 2 % P2O5, and 2 % K2O and characterized by classical and advanced methods. The content of N and P in the final formula was measured using specific reactants combined with VIS-nearIR spectroscopy. Additionally, all three formulas were characterized by advanced 1H NMR relaxometry, double-quantum and FT-IR spectroscopy. Structural characterization was performed using SEM, EDX and X-ray diffraction. Four dynamical components were identified in the T2-distribution showing that the rigid component is into a percentage larger than 90 %. In order to evaluate the release properties of organo-mineral fertilizers dynamic measurements of electric conductivity and pH were performed, by placing 0.25 g of formulas (V1, V2 and V3) in 200 ml of distillate water. It was found that V3 present the smallest release velocity, but release the largest amount of fertilizers. Two release mechanisms were also observed.

The effects of rare earth Ce on the microstructure and properties of cast pure copper were investigated through thermodynamic calculations, XRD analysis, mechanical testing, metallographic microscopy, and scanning electron microscopy (SEM). The experimental results demonstrate that the reaction between rare earth Ce and oxygen as well as sulfur in copper exhibits a significantly negative Gibbs free energy value, indicating a strong thermodynamic driving force for deoxidation and desulfurization reactions. Ce is capable of removing trace amounts of O and S from copper. Moreover, the maximum solid solubility of Ce in Cu falls within the range of 0.009% to 0.01%. Furthermore, Ce can refine columnar grains while enlarging equiaxed grains in as-cast copper.Upon addition of rare earth Ce, the tensile strength increased by 8.45%, elongation increased by 12.1%, and microhardness rose from 73.5HV to 81.2HV-an increase of 10.5%. Overall, rare earth Ce has been found to enhance both the microstructure and mechanical properties of cast pure copper.

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.

The fast fashion industry has a significant impact on the environment at every stage of the product life cycle. There is a lack of research in the literature on the impact of fast fashion on the environment through logistics and transport. In this area, the greatest environmental impact is caused by air emissions, which is very important from the point of view of climate change. Therefore, this article deals with the analysis of the carbon footprint resulting from the fast fashion logistics process. The identification and assessment of fast fashion companies' sustainability efforts in the area of transport-related emissions and sustainable transformation strategies was conducted using a case study method for selected brands (H&M Group; Inditex, Shein). The study uses secondary data available in non-financial reports from 2023. After analyzing the data and content in non-financial reports on transport-related emissions, sev-eral important conclusions emerge. As a dominant practice, companies emphasize their reliance on external frameworks and standards, in particular the Global Reporting Initiative and the Task Force on Climate-related Financial Disclosures. Companies prioritize reducing emissions from significant factors, such as production processes. The data disclosed in the reports (scope 1, 2, 3) are not clearly formulated, but they can be used to calculate the level of CO2 emission reduction/increase. The ar-ticle contains recommendations for companies to improve their reporting by including additional indicators expressing the efficiency of transport-related emissions.

EIMIs face the challenge of adapting to the Paris Agreement and the European Green Deal to achieve carbon neutrality. This change is being driven by consumer interest in low-carbon products, regulatory requirements from climate agreements and investors prioritizing sustainability. This pressure is pushing companies to go beyond short-term financial gains and integrate sustainability into their business. The growing awareness of the environmental impact of business models (BM) has fueled the development of Sustainable Business Models (SBMs). This study aims to link the discussion on the role of SBMs in the drivers of decarbonization, to improve the understanding of how the drivers can be integrated and enable new BMs for EIMIs. Based on a structured literature review, semi-structured interviews were conducted in the EIMIs. The study identifies BM-components that are influenced by these drivers and require sustainable business model innovation (SBMI). The results show that decarbonization drivers have a significant impact on all BM components. Experts emphasize the importance of collaborative approaches and cooperation throughout the value chain. This research elaborates on the importance of systematic analysis to understand how companies can effectively manage decarbonization drivers and suggests in-depth exploration within BM-research to uncover potential SBM design options.

Background: Across the world, antibiotic resistance is a grave problem. To address this issue, it is necessary to investigate new antibiotic sources. Aim of the study: Egypt's diverse soil habitats were used to produce bacterial Myxopyronin B, which was then tested for antimicrobial activity in preclinical animal studies and randomized human clinical trials stages 1/2. Type of the study: Screening experimental study. Methodology: Egypt's various soil conditions were examined for the development of bacterial isolates that produced the antibiotic compound Myxopyronin B. Reversed phase HPLC was used to purify Myxopyronin B. To determine the test antibiotic's minimum inhibitory concentration( MIC) and invitro antibacterial activity, the Broth microdilution method and the Paper disc diffusion assay were utilized. Moreover, in vivo antibacterial spectrum, adverse medication responses, and pharmacokinetics were found in preclinical animal testing stages and phases 1/2 of randomized clinical studies involving volunteer humans. Results: Myxopyronin B was generated from the culture supernatant of Myxococcus SDU36, the main soil bacterial isolate cultured on a Casein yeast peptone plate. With MICs less than 100 mcg/ml, the test antibiotic prevented the growth of several Gram +ve bacteria, whereas, at MICs higher than 100 mcg/ml, it inhibited the development of a small number of Gram -ve bacteria, including Escherichia coli. However, eukaryotic cells—such as those found in fungi and humans—were unaffected. The test antibiotic was seen to inhibit prokaryotic DNA-dependent RNA polymerase( RNLP), indicating its bactericidal activity. Mean Cmax was 9-10 mcg/ ml at mean Tmax 1 hour when 600 mg dose was orally administered in randomized human clinical trials phases 1/2, and T1/2 reached 2.25 hours following first-order kinetics of elimination. Duration of its action was nearly 8 hours after oral administration. Rare toxicity was detected during preclinical and randomized human clinical trials phases 1/2 in the form of mild diarrhea and GI upset in less than 7 % of experimental candidates. It exhibited approximately 90% plasma protein binding, especially with albumin which resulted in prolonged therapeutic action. It showed a concentration dependant antibiotic killing effect. With the concentration-dependent killing pattern, the higher the drug concentration relative to the pathogen minimum inhibitory concentration( MIC), the greater the rate and extent of antimicrobial activity. Conclusion: The present study was promising due to the production of the bactericidal antibiotic Myxopyronin B from Myxococcus sp. SDU36 isolated from different soil environments in Egypt.

Chymotrypsin from the digestive organs of bigfin reef squid, a major commercial squid species in Thailand, was purified to 41.27-fold with 5.74% yield by a sequential purification process including ammonium sulfate precipitation, size exclusion and ion exchange chromatography. According to SDS-PAGE, the molecular weight of purified chymotrypsin was 43 kDa. Native-PAGE analysis revealed a single band for this purified enzyme. Optimum pH and temperature for chymotrypsin activity of the purified enzyme were pH of 7.0 with a temperatures of 55°C. The purified chymotrypsin remained stable throughout a wide range of pH levels (6–11) and at relatively high temperature (50°C). It was significantly inhibited by PMSF and TPCK. The values of the kinetic constants Km and Kcat were found to be 1.33 mM and 31.46 s‒1, respectively. The purified chymotrypsin has the N-terminal amino acid IVGGQEATPGEWPWQAALQV. This study provided new information about the biochemical properties of pure chymotrypsin from bigfin reef squid, which is useful in the future investigation, aquaculture and application of bigfin reef squid.

In order to help the transportation industry achieve the goal of carbon peak and carbon neutrality, this study is based on the selection of eight variables, such as population size, per ca pita GDP, personal vehicle ownership, passenger and freight turnover, and green space coverage, as the factors influencing the carbon emissions of the transportation industry in Jiangsu Province, and the prediction and analysis of the transportation carbon emission trends from 2000 to 2021, and the prediction and analysis of the carbon emissions of the transportation industry and the time of the peak of the carbon emissions in Jiangsu Province. The trend of transportation carbon emissions from 2000 to 2021 was predicted and analyzed, and the carbon emissions and peak time of carbon emissions in the transportation industry in Jiangsu Province were predicted and analyzed. Comparing the predicted results with other models to verify the accuracy of the results, it was found that the WOA-SVM model had the smallest error among several models. On this basis, targeted measures are proposed to accelerate the process of carbon peak and ensure the smooth achievement of carbon neutrality goals in Jiangsu Province. The results indicate that under the current policy measures, the peak carbon emissions in Jiangsu Province will occur in 2038, with a peak of 48.72 million tons. Jiangsu Province should actively adopt energy-saving and emission-reduction measures, build a green and low-carbon transportation development model, and achieve the carbon peak target ahead of schedule.

The decline in global pollinator populations has raised serious concerns about the sustainability of ecosystems and agricultural productivity. Urban environments, often characterized by habitat loss and environmental stressors, are considered challenging habitats for pollinators. However, urban green spaces, including street and ornamental trees, can play a crucial role in supporting pollinators by providing essential food resources. This study aimed to evaluate the sugar and amino acid production of eight urban tree species to determine their potential contributions to pollinator nutrition. Our findings revealed significant variability among species regarding nectar volume, sugar content, and amino acid composition, emphasizing the importance of considering multiple factors when assessing the utility of floral resources. Consequently, Tilia amurensis, Heptacodium miconioides, Aesculus turbinata, and Wisteria floribunda demonstrated superior potential in supporting urban pollinator communities. Contrastingly, Sorbus commixta, Styrax japonicus, and Cornus kousa, often favored for their aesthetic appeal, showed less potential. This study underscores the need for urban tree selection that prioritizes species that provide diverse and abundant nutritional resources for pollinators while balancing aesthetic and ecological values. Our results offer a foundation for better-informed urban biodiversity management and contribute to creating pollinator-friendly urban environments.

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

A photovoltaic-thermal (PVT) collector is a solar-based micro-cogeneration system which generates simultaneously heat and power for buildings. The novelty of this paper is to conduct energy and exergy analysis on PVT collector performance under two different European climate conditions. The performance of the PVT collector is compared to a PV panel. Finally, the PVT design is optimized in terms of thermal and electrical exergy efficiencies. The optimized PVT designs are compared to the PV panel performance as well. The main focus is to find out if the PVT is still competitive with the PV panel electrical output, after maximizing its thermal exergy efficiency. The PVT collector is modelled into Matlab/Simulink to evaluate its performance under varying weather conditions. The PV panel is modelled with the CARNOT toolbox library. The optimization is conducted using Matlab gamultiobj-function based on Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The results indicated 7.7% higher annual energy production in Strasbourg. However, the exergy analysis revealed a better quality of thermal energy in Tampere with 72.9% higher thermal exergy production. The electrical output of the PVT is higher than from the PV during the summer months. The thermal exergy- driven PVT design is still competitive compared to the PV panel electrical output.

The Akosombo Nile tilapia is the only approved local strain for aquaculture production in Ghana. In this study, an exotic red tilapia sample suspected to be cultured in a fish farm within the Lower Volta River Basin of the Lake Volta was collected for species identification. A molecular screening using DNA barcoding sequencing was performed on the samples targeting the cytochrome C oxidase subunit 1 mitochondrial gene (COX1). We confirmed the identity of the red tilapia fish to be a non-native Oreochromis sp. recently introduced into the country. The data obtained clearly demonstrates a breach of biosecurity that potentially threatens Ghana’s native tilapia genetic resources and aquaculture development.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

The paper presents the author’ original modification of the residual method in order to construct an efficient tool for assessing the condition of photovoltaic cells, in particular – panels made of amorphous cells which are characterized by significant random operation parameter changes. The solution constructed has been verified through experiments, which show the usefulness of the proposed methodology as it allows to minimize the risk of obtaining false diagnostic information in the process of assessing the condition of photovoltaic panels.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. Python, a versatile programming language, holds vast potential for Sentiment Analysis (SA). Leveraging the Requests and TextBlob libraries, we have crafted a user-friendly code that enables Industrial Engineers (IE) and managers, particularly those who are new to Python, to extract and analyze sentiment efficiently. We aim to provide IE/managers in service companies requiring SA capabilities with a simple yet effective Python solution. While machine learning resources like PyTorch/TensorFlow are commonly utilized in SA, offering pre-built algorithms and tools for training, and implementing machine learning models, we sought to exploit Python's versatility by integrating additional web-scraping libraries. Thus, by using a lexicon-based approach, we intend to deliver an informative and practical article. The code in this article is twofold; firstly, it can be easily adapted by IE/managers possessing basic Python skills; secondly, we aim to inspire junior IE/managers to develop their own customized coding solutions tailored to specific organizational needs.

The present study seeks to assess the Brand Equity in the network of new fashion designers in the Canary Islands. To this end, we have adapted the Brand Equity model to an online context and proposed a set of metrics for four dimensions: estimate, relevance, awareness, and differen-tiation. The multidimensional approach to online Brand Equity was employed to measure the online Brand Equity of 129 fashion designers from the Canary Islands. The study's findings re-vealed that the number of social media profiles significantly impacts brand awareness and dif-ferentiation. Moreover, the digital maturity level influences brand relevance and overall Brand Equity . Our study highlights the practical value of web data-driven metrics for measuring online Brand Equity , which can be utilised for diagnostic, evaluative, or predictive purposes. The study's outcomes have substantial implications for marketers in the fashion industry aimed at improving online Brand Equity

The growing interest in the use of energy storage systems to improve the performance of tramways has prompted the development of control techniques and optimal storage devices, displacement, and sizing to obtain the maximum profit and reduce the total installation cost. Recently, the rapid diffusion of renewable energy generation from photovoltaic panels has also created a large interest in coupling renewable energy and storage units. This study analyzed the integration of a photovoltaic power plant, supercapacitor energy storage system, and railway power system. Random optimization was used to verify the feasibility of this integration in a real tramway electric system operating in the city of Naples, and the benefits and total cost of this integration were evaluated.

The Mining Industry has changed significantly in recent decades with the introduction of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the Mining Industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the Mining Industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.

This paper delves into the multifaceted domain of the aerospace industry, examining its evolution, current challenges, and imperative focus on quality management and process integration. The aerospace sector, driven by technological advancements and a burgeoning global demand for air travel and freight transport, necessitates a thorough analysis of its industrial fabric and operational intricacies. This research endeavors to analyze the dynamics of the aerospace industry, pinpoint its challenges, and propose an integrated approach to enhance efficiency, quality, and sustainability. The primary goals encompass understanding the evolving industry landscape, identifying critical challenges, and offering innovative solutions by amalgamating principles of Industry 4.0 into quality management and processes within the aerospace sector. Through an in-depth exploration of various facets, this research underscores the pivotal role of efficient processes and integrated quality management in achieving sustainable growth and competitiveness in the aerospace industry. By aligning with the paradigm of Industry 4.0, organizations can optimize their operations and contribute to the industry's advancement, delivering safer and more cost-effective aerospace products. The study adopts a multifaceted approach, incorporating extensive literature review, critical analysis of industry trends, examination of quality management frameworks, and a thorough evaluation of the integration potential of Industry 4.0 technologies. The research also involves case studies and expert insights to validate the proposed approach. The investigation reveals that by leveraging Industry 4.0 technologies and embracing an integrated approach to quality management, the aerospace industry can significantly enhance operational efficiency, product quality, and overall sustainability. The seamless integration of processes and the implementation of advanced quality frameworks pave the way for a more competitive and future-ready aerospace industry, meeting the evolving demands of a globalized world.

All solar thermal systems, due to their thermal nature, can be hybridised or operated with both fossil fuel and solar energy. By enhancing availability and dispatch ability, hybridization has the potential to boost the value of concentrating solar thermal technology. Hybrid photovoltaic thermal systems (PV/T) offer a solution to improve on the limited capacity growth seen with standard solar panels. The analysis focuses on the creation of alternative structural and design so-lutions that may be used to boost lacking efficiency that was previously hampered by limited spectrum absorption of sun light. As a result, the spectrum is separated into two spectrum ranges, one that directly matches the bandgap of PV materials and the other that is absorbed for thermal absorption outputs. Most two channel systems are used to capture or reflect sunlight, with the layer specifically located right above the PV cell. Another layer of nanofluid is employed to evacuate heat from the PV cell, acting as the second channel, to specifically eliminate overheating difficulties. To address concerns that have arisen as a result of years of observation, the most recent study involves nano-particle-based fluids employed in PV/T, which uses a cooling medium to provide an alternate heat transfer option. Aside from nanofluids, there are phase change materials, channel alterations that result in a higher flow rate and hence improve overall performance.

Solar powering the increasing fleet of electrical vehicles (EV) demands more surface area than may be available for photovoltaic (PV) powered buildings. Parking lot solar canopies can provide the needed area to charge EVs, but are substantially costlier than roof- or ground-mounted PV systems. To provide a lower-cost PV parking lot canopy to supply EV charging beneath them, this study provides a full mechanical and economic analysis on three novel PV canopy systems: (1) exclusively wood, single parking spot spanning system, (2) wood and aluminum double parking spot spanning system, and (3) wood and aluminum cantilevered system for curbside parking. All systems can be scalable to any amount of EV parking spots. The complete designs and bill of materials (BOM) of the canopies are provided along with basic instructions and are released with an open source license that will enable anyone to fabricate them. The results found single-span systems have cost savings of 82%-85%, double-span systems save 43%-50%, and cantilevered systems save 31%-40%. In the first operation year, the PV canopies can provide 157% of energy needed to charge the least efficient EV currently on the market if it is driven the average driving distance in London ON, Canada.

The research that we are referring to examines the morphological, structural, and optical characteristics of kesterite (Cu1-xAgx)2ZnSnS4 (CAZTS) thin films, which are produced using a process known as thermal evaporation (TE). The study's main goal was to determine how different Ag contents affect the characteristics of CAZTS systems. X-ray diffraction (XRD) and Raman Spectroscopy: were used to confirm the crystal structure of the CAZTS thin films. Using a mathematical model of spectroscopic ellipsometry, the refractive index (n) represented the part of the complex refractive index, the extinction coefficient (k) portrayed the imaginary one, and also the energy band gap of the fabricated films were all calculated. The energy band gap of the thin films was also calculated. This is a crucial parameter for solar cell applications, as it determines the wavelength of light that the material can absorb. The energy band gap was found to decrease from 1.74 eV to 1.55 eV with increasing Ag content. The ITO/n-CdS/p-CAZTS/Mo heterojunction was well constructed, and the primary photovoltaic characteristics of the n-CdS/p-CAZTS junctions were examined for use in solar cells. Different Ag contents of the CAZTS layers were used to determine the dark and illumination (current-voltage) characteristics of the heterojunctions. The study's findings collectively point to CAZTS thin layers as potential absorber materials for solar cell applications.

A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

Background: Breast cancer and other neoplasms like acute lymphocytic leukemia pose a serious threat to public health worldwide. Treatment for malignancies including acute lymphocytic leukemia and breast cancer requires enzymes that break down L-glutamine. An amidohydrolase called L-glutaminase can be an effective chemotherapeutic tool for treating various cancers. Methods: On mineral L-glutamine agar selective medium (MGA), a few bacterial isolates were examined in the current investigation to see if they produced L-glutaminase. However, the most effective positive bacterial isolates generating L-glutaminase were identified using morphological and biochemical testing. Molecular detection using the direct Southern blotting approach was also used to determine the main positive isolate that produces L-glutaminase. L-glutaminase synthesis by bacteria was evaluated for its characteristics. Using an MTT test, the anticancer activity was evaluated. Results: exclusive bacterial isolates that used L-glutamine as their sole source of metabolic nitrogen exhibited favorable growth on MGA. PH 7.4 and a temperature of 37 0C were the ideal environmental and physiological conditions for developing positive bacterial isolates. From the soil samples taken from various soil conditions in Egypt, the morphological and biochemical analyses showed that Bacillus cereus 14579 was the main positive bacterial generating L-glutaminase isolate. At a pH of 7.4 and a temperature of 37 °C, the following activators—KCL, ZnSO4, FeSO4, K2HPO4, and MgSO4—provided the high-grade circumstances for characterizing L-glutaminase synthesis. Bacillus cereus-produced L-glutaminase has shown excellent bioavailability and effectiveness as an anticancer therapy. The yield [productivity] was 5.2 U/ml during the original manufacture from MGA, but it increased to 42.96 U/ ml by bacterial recombinant DNA manufacturing. The molecular mass of L-glutaminase was roughly 37 KDa. L-glutaminase was purified, yielding final enzyme recovery of 55 1.23%, total activity 12,990± 19.76 ( U), specific activity 384.66± 8.92( U/ mg of protein), and purification fold 2± 2.99. Furthermore, the L-glutaminase activity was raised by 19%, 23%, 15%, and 9%, respectively, by the enzymatic activators Mn2+, K+, Na+, and Fe3+. It demonstrated strong DPPH scavenging activity (IC50 = 61 μg/ mL) and anticancer activity( IC50 = 40.72, 9.7, 7.39, 20.61, 51.28, and 11.55 μg/ mL, respectively) against human liver (HepG-2), colon ( HCT-116), breast ( MCF-7), lung( A-549), lymphocytic( CCL-120), and cervical( Hela) cancer cell lines. A stronger affinity for its substrate was shown by the kinetic parameters of Km and Vmax, which were 13.2 10-5 M and 119.86 μmol/ ml/ min, respectively. Conclusion: Acute lymphocytic leukemia and breast cancer are examples of auxotrophic malignancies for which L-glutamine serves as the only metabolic source, and L-glutaminase generated by Bacillus cereus was an appropriate enzyme in the therapy of these diseases.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

Microgrids offer flexibility in power generation in a way of using multiple renewable energy sources. In the past few years, microgrids become a very active research area in terms of design and control strategies. Most of the microgrids use DC/DC converters to connect renewable energy sources to the load. In this paper, the simulation model of a DC microgrid with three different energy sources (Lithium-ion battery (LIB), photovoltaic (PV) array, and fuel cell) and external variant power load is built with MATLAB/Simulink and the simulative results show that the stability of DC microgrid can be guaranteed by the proposed maximum power point controller MPPT. The three energy sources are connected to the load through DC/DC converters, one for each. This type of topology ensures protection for each energy source as well as optimum stability at the load.

This work focuses on identifying the most effective machine learning techniques and supervised learning models to precisely estimate power output from Photovoltaic (PV) plants. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest, Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Linear Regressor (LR), Gradient Boosting, and k-Nearest Neighbors (KNN). The methodology applied starts with meticulous data preprocessing steps aimed at ensuring dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold, is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R2). Among the array of models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048% and an R2 score of 0.968. Furthermore, the best-performing model is integrated into a MATLAB application for real-time predictions, thereby enhancing usability and accessibility for a wide range of applications in renewable energy.

This work focuses on short-term forecasting of PV output power. The multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with good success. Their performances in forecasting PV power have been compared with other algorithms but not with themselves. The study aims to compare performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions. The performance of MLPNN, CNN and kNN are being compared using solar PV (hourly) energy yield data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in south Africa that may be prone to extreme weather conditions. The kNN algorithm was found to have RMSE value of 4.95% and MAE value of 2.74% at its worst performance, and RMSE value of 1.49% and MAE value of 0.85% at its best performance. It outperformed the others by a good margin and kNN could serve as a fast, easy, and accurate tool to forecast solar PV output power.

The evolution of photovoltaic cells is intrinsically linked to advancements in the materials from which they are fabricated. This review paper provides an in-depth analysis of the latest developments in silicon-based, organic, and perovskite solar cells, which are at the forefront of photovoltaic research. We scrutinize the unique characteristics, advantages, and limitations of each material class, emphasizing their contributions to efficiency, stability, and commercial viability. Silicon-based cells are explored for their enduring relevance and recent innovations in crystalline structures. Organic photovoltaic cells are examined for their flexibility and potential for low-cost production, while perovskites are highlighted for their remarkable efficiency gains and ease of fabrication. The paper also addresses the challenges of material stability, scalability, and environmental impact, offering a balanced perspective on the current state and future potential of these material technologies.

In the last two decades, the continuous ever growing demand for energy has determined a significant development in the production of photovoltaic (PV) modules. Anyway, a critical and very important issue in the module design process is the adoption of suitable encapsulant materials and technologies for cell embedding. Therefore, adopted encapsulants have a significant impact on modules efficiency, stability and reliability, and to ensure the unchanged performance of PV modules in time, the encapsulant materials must be selected properly. The selection of encapsulant materials must maintain a good balance between the encapsulant performance in time and costs, related to materials production and technologies for cells embedding. However, the encapsulants must ensure excellent isolation of active photovoltaic elements by the environment, preserving accurately the PV cells against humidity, oxygen and accidental causes that may compromise the PV modules function. This review provides an overview of different encapsulant materials, their main advantages and disadvantages in adoption for PV production, and also in relation to used encapsulant technologies for cell embedding, additives and the interaction of these materials with other PV components.

This work presents a control scheme to control the grid-connected single-phase photovoltaic (PV) system. The considered system has four 250W solar panels, a non-inverting buck-boost DC-DC converter, and DC-AC inverter with LCL filter. The control system aims to track and operate at the maximum power point (MPP) of PV panels, regulate the voltage of DC link, and supply the grid with a unity power factor. To well achieve these goals, the proposed control system consists of three parts, that are MPP tracking controller module with a fuzzy-based modified incremental conductance (INC) algorithm, a DC-link voltage regulator with a hybrid fuzzy proportional-integral (PI) controller, and a Current Controller module using the linear quadratic regulator (LQR) for grid-connected power. Based on fuzzy control and LQR, this work introduces a full control solution for grid-connected single-phase PV systems. The key novelty of this research is to analyze and prove that the newly proposed method is more successful in numerous aspects by comparing and evaluating the previous and present control methods. The designed control system settles quickly, which is critical for output stability. In addition, as compared to backstepping approach used in our past study, the LQR technique is more resistant to sudden changes and disturbances. Furthermore, backstepping method produces the larger overshoot, which has a detrimental impact on efficiency. Simulation findings under various weather conditions were compared to theoretical ones to indicate that the system can deal with variations in weather parameters.

The aim of this work is to introduce new ways to model the I-V characteristic of a PV cell or PV module using straight lines and Bézier curves. This is a complete novel approach, Bézier curves being previously used mainly for computer graphics. The I-V characteristic is divided in three sections, modeled with lines and a quadratic Bézier curve in the first case and with three cubic Bézier curves in the second case. The result proves to be accurate and relies on the fundamental points usually present in the PV cell datasheets: Voc (the open circuit voltage), Isc (the short circuit current), Vmp (the maximum power corresponding voltage) and Imp (the maximum power corresponding current) and the parasitic resistances Rsh0 (shunt resistance at Isc) and Rs0 (series resistance at Voc). The proposed algorithm completely defines all the implied control points and the error is analyzed. The temperature and irradiance influence is also analyzed. The model is also compared using the least squares fitting method. The final validation shows how to use Bézier cubic curves to accurately represent the I-V curves of an extensive range of PV cells and arrays.

The aim of this research work is to study the influence of temperature, solar irradiation and wind dust on the production of solar energy. In addition, it aims to study the assessment of water quality, in particular groundwater used for cooling and cleaning photovoltaic panels (quality analysis). it's an important source, stable and sustainable can be used entirely for cooling and cleaning PV modules from the sales dust, in order to improve their performance. To date, all research studies carried out in this context of cooling and cleaning using water have not taken account of the nature of this water. We have taken a sample of water from a well in a rural area in the Darra Tafilalet region of Morocco. This region has an arid and semi-arid climate, with very high temperatures and dust-laden winds, particularly in summer. These meteorological characteristics influence the efficiency of cells, particularly photovoltaic (PV) panels, reducing their performance. Next, the water sample was analysed in the laboratory. The results obtained show that the groundwater in this area is very sensitive to cooling and cleaning photovoltaic panels. Moreover, these results indicate that the temperature, solar irradiation and wind-blown dust have a significant influence on energy production.

With the development of human-computer interaction, the importance of emotion recognition is increasing. Emotion recognition technology provides practical benefits in various industries such as improving user experience, education, and organizational productivity. In education, emotion recognition can enable real-time understanding of students' emotional states and provide tailored feedback, and in the workplace, by monitoring employees' emotional states, it can improve work performance and job satisfaction. Therefore, multimodal-based research that combines text, speech, and video data for emotion recognition is being conducted in various industries. In this study, we propose an emotion recognition method that combines text and speech data, reflecting the characteristics of the Korean language. For text, KoELECTRA is used to perform embedding, and for speech, MFCC and pitch analysis are used to extract features. Finally, a multimodal transformer model is proposed that combines these two data types to perform emotion recognition. The multimodal transformer model processes text and speech data separately, and then learns the interaction between the two modalities through a Cross-Modal Attention mechanism. Through this, the complementary information from text and speech is effectively combined, improving emotion recognition performance. Experimental results show that the proposed model outperforms single-modality models, achieving a high accuracy of 73.13% and an F1-Score of 0.7344 in emotion classification. This study contributes to the advancement of emotion recognition technology by combining various language and modality data, and higher performance can be expected through the integration of additional modalities in the future.

In this paper, a land utilization efficiency evaluation model, which takes environmental loss into consideration, has been structured via taking advantage of the slack-based measure (SBM) model. Meanwhile, based on the panel data from 280 prefecture-level cities in China from 2003 to 2013, the paper thoroughly probed into, and discussed, the effect imposed by industry clustering and specialization on the utilization efficiency of urban land. Research results indicate several conclusions, as follows: (1) Taking environmental loss into account, the land utilization efficiency of prefecture-level cities in China is generally low. During the research period, the average value of the land utilization efficiency of prefecture-level cities in China is only 0.349, with, first, a declining trend, and then a rise. Geographically speaking, the land utilization efficiency presents a “depression in the center” phenomenon which means the land utilization efficiency of prefecture-level cities in the central China are relatively lower than in the east and west. Now, the difference among the urban land utilization efficiency in China significantly reflects the distinctions among Eastern, Western, and Central China. Moreover, the contribution degree of the difference of the land utilization efficiency among cities of central China to the aggregation difference shows an ascending momentum. Additionally, the relation between the population scale and land utilization efficiency in cities manifests as a U shape; (2) theoretically speaking, the relation between industry clustering and urban land utilization efficiency presents an inverted-U shape. However, this kind of relation is not significant in Western and Central China and medium-sized cities. Moreover, most of cities are still relatively far away from the inflection point or the critical value; and (3) the industry professional level has imposed a positive influence on urban land utilization efficiency. However, that influence is not significant in Eastern China and large cities. Consequently, strengthening the industry professional development of Western and Central China and small and medium-sized cities, facilitating diversified development of industries in Eastern China and large cities, and accelerating industrial clustering, all of these measures above will be conducive to improving urban land utilization efficiency in China.

Nowadays Air-Source Heat Pumps (ASHP) in combination with a Photovoltaic (PV) installation are a very promising option for a necessary and urgent energy transformation in European Union (EU). It is extremely important to develop solutions that will help maximize the use of energy generated from renewable energy sources (RES). Such issues include the problem of insufficient use of generated electricity in PV on-grid microinstallations in residential buildings. This paper's aim is to analyze the results of one-year-round operation of a PV array grid-connected hybrid installation with ASHP for domestic hot water (DHW) preparation in a residential building in Cracow, Poland in the context of increasing self-consumption (SC) of PV energy. Models of systems are built and simulated in Transient System Simulation Tool 18 (TRNSYS) software. Simulations were carried out for different scenarios involving different building electricity consumption profiles, PV system capacity and specified runtime management of ASHP. The novelty of this study lies in the evaluation of the impact of a certain range of conditions on the energy performance of the system, in particular on SC. The results showed that the use of ASHP, with specified runtime management, results in an increase in monthly SC values from 7 to 18%, and annual SC values up to 13%. Also determining the appropriate size of the used PV system depending on whether it is present ASHP in the installation is crucial to increase the value of the SC parameter. Overall, this study provides valuable insights into the potential benefits of PV panels and ASHP operating together, in particular on SC values.

Photovoltaic (PV) generation is potentially uncertain. Probabilistic PV generation forecasting methods have been proposed with prediction intervals (PIs). However, several studies have dealt with geographically distributed PVs in a certain area. In this study, a two-step probabilistic forecast scheme is proposed for geographically distributed PV generation forecasting. Each step of the proposed scheme adopts ensemble forecasting based on three different machine-learning methods. In this case study, the proposed scheme was compared with conventional non-multistep forecasting. The proposed scheme improved the reliability of the PIs and deterministic PV forecasting results through 30 days of continuous operation with real data in Japan.

Flexible polymer-based dye-sensitized solar cells (DSSCs) offer promising potential for lightweight, cost-effective and versatile photovoltaic applications. However, the critical challenge in their widespread applications is the weak thermal stability of the majority of polymeric substrates, which can only withstand a maximum temperature processing of 150°C. In this study, we propose a facile and low-cost strategy to develop at low temperature DSSC flexible photoanode based on a polymeric matrix. Highly porous nanocomposites fibrous mats composed of polyethylene terephthalate (PET) and titanium dioxide (TiO2) nanobars were prepared through electrospinning process using different configurations (uniaxial electrospinning, coaxial electrospinning, and electrospray-assisted electrospinning). These techniques enabled precise control of the microstructure and the positioning of TiO2 within the composite nanofibers. Therefore, the as-produced photoanodes were loaded with N719 dye and tested in DSSC prototype using iodide-triiodide electrolyte and platinum (Pt) coated counter electrode. The results show that incorporating TiO2 on the fiber surface through the electrospray-assisted electrospinning enhanced the performance of the nanofiber composite, leading to improved dye loading capacity, electron transfer efficiency and photovoltaic performance.

This research article investigates the application of artificial intelligence (AI) in human resource management (HRM) within the Australian commercial media sector, addressing a critical gap in scholarly knowledge. Despite increasing interest in AI’s potential, there is a lack of consensus among managers and HR professionals regarding its application. Through qualitative interviews with 22 industry professionals, this study examines perceptions, understanding, and the potential for leveraging AI across key HRM functions. Findings indicate significant potential for AI to enhance HRM processes, particularly in job/work design and staffing. This research contributes to a deeper understanding of AI’s role in HRM and offers insights to optimize organizational efficiency and effectiveness in the Australian commercial media sector.

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle.

The prohibitive costs of small-scale solar photovoltaic (PV) racks decreases PV adoption velocity. To overcome these costs challenges, an open hardware design method is used to develop two novel variable tilt racking designs. These are the first stilt mounted racking designs that allow manual change of tilt angle from zero to 90 degrees by varying the length of cables. The racks are designed using the calculated dead, wind and snow loads for Canada as conservative design for most of the rest of the world. Structural capacities of the wooden members are then ascertained and resisting bending moment, shear force, tensile force, and compressive force for them is calculated. A structural and truss analysis is performed to ensure that racking design with-stands the applicable forces. Moreover, implications of changing the tilt angle on the wooden members/cables used to build the system are also determined. The systems offer significant economic savings ranging from 1/3rd to 2/3rd the capital expenses of the commercially available alternatives. In addition, the racking designs are easy-to-build and require minimal manufacturing operations, which increases their accessibility. The stilt-mounted designs can be employed for agrivoltaic settings while allowing farm workers shaded ergonomic access to perform planting, weeding, and harvesting.

This article explores how digitalisation is impacting the hospitality industry and assesses the evolving role of Information Technology (IT) strategy in the digitalisation process. The research approach is qualitative and inductive, based on six in-depth interviews with senior IT professionals in the hospitality industry. Findings indicate significant differences in the role of IT strategy in guiding digitalisation in the companies studied. The depth of information provided by the interviewees supports the development and application of a model that profiles the companies regarding their degree of digitalisation and technology integration. Analysis of interview material allows the identification of key properties for successful digitalization: process agility, workforce adaptability, and technology manageability, along with a clear data culture and ensured cybersecurity. However, disparate systems and technologies, and a lack of data integrity, are key issues that leave hospitality companies with difficult choices in progressing digitalisation initiatives. The applied model and identification of key properties for successful digitalisation contribute to the development of related theory and can also be used as a reference point for senior IT professionals working in the industry.

Purpose- This study seeks to investigate the collaboration between Digitalization and traditional manufacturing industry and investigate, how digitalized management brings new chances and challenges of management. Design/methodology/approach – The author designed a content analytical study researching the role of digitalization in traditional manufacturing industries. The article presents a selective but systematical review of recent digitalization trend and digital twin to be applied for innovative management in Industry 4.0 era. Findings– Digitalization is highly correlated with traditional manufactory in Industry 4.0 era. The study brings a new outlook on the next wave of Digitalization and review of associated challenges and opportunities for management and innovation in traditional manufacturing industries. Research limitations/implications – The research focuses on two branches of traditional industries only: machine tools building and metalworking industries. Future research needs to examine other traditional industries. The research is limited to 2 high related traditional manufacturing industries, machine tools and metalworking industry. Practical implications – The study and application of digitalization should be highly recommended and launched in traditional manufacturing industries. Universities, institutes and vocational schools should update, optimize their courses systems according to prediction of future industry. Originality/value – The paper enhances understanding about the collaboration between digitalization and traditional manufacturing industry. This paper analyzes an important issue the digitalization serves traditional industries. It is unique in its broad analysis of the related terms – Digitalization, Machines 4.0, Industry 4.0 and innovative management in Industry 4.0 era.

The objective of the proposal is to find the optimal geometric shape of a photovoltaic panel that maximizes the collection of solar radiation. The optimal shape will be determined based on direct radiation, as it is the most relevant radiation for the photovoltaic process. To achieve this goal, we will use computer simulations to evaluate the performance of different geometric shapes under different conditions of solar radiation. Specifically, we will simulate the behavior of a photovoltaic panel with different shapes and orientations, and we will compare their energy yield over a given period of time. The simulations will be based on a mathematical model that takes into account the incidence angle of solar radiation on the panel, as well as other factors such as the reflection and absorption of radiation, and the temperature of the panel. The simulations will be carried out for different locations, with different solar radiation conditions, and for different periods of time, in order to obtain a comprehensive assessment of the performance of the different shapes. The results of the simulations will be analyzed to identify the optimal shape of the photovoltaic panel. This shape will be compared with the performance of flat panels, both static and with tracking systems, in order to evaluate the potential improvement in energy yield.

Of all the renewable energy sources, solar photovoltaic (PV) power is estimated to be a popular source due to several advantages such as its free availability, absence of rotating parts, integration to building such as rooftops, and less maintenance cost. The nonlinear current-voltage (I–V) characteristics and power generated from a PV array primarily depend on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, and partial shading of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is that realized that the RIO algorithm has improved convergence characteristics in terms of finding the optimal solution than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO-based MPPT.

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

Nanoparticles -- regularly patterned or randomly dispersed -- are a key ingredient for emerging technologies in photonics. Of particular interest are scattering and field enhancement effects of metal nanoparticles for energy harvesting and converting systems. An often neglected aspect in the modeling of nanoparticles are light interaction effects beyond classical electrodynamics stemming from electron dynamics in confined and accelerated systems. We give a detailed account on free electron phenomena in metal nanoparticles and discuss analytic expressions, stemming from microscopic and semi-classical theories. These can improve standard computational schemes to produce more reliable results on the optical properties of metal nanoparticles. We combine these solutions into a single framework and study their joint impact on isolated Au, Ag, and Al nanoparticles as well as dimer structures.

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

To enable net zero sustainable thermal building energy this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HP) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using an System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates.