Fresh Approach Research Articles

Samuel as Jack-in-the-Box: The Woman of Endor as a Hidden Polemical Story*

ABSTRACT This article takes a fresh approach to the story of the woman of Endor (1 Sam 28,3-25), arguing that the difficulties it raises serve as the key for understanding the story’s polemical intent. The affinities between Samuel and the medium and their professional success indicate that Israelite prophecy and pagan manticism are one and the same. The antithesis between them and between the woman’s empathy for Saul and Samuel’s antagonism towards Saul both evince Samuel’s inferiority to the medium. Even if only adduced in order to cast a shadow over Samuel, the necromancer’s positive depiction creates the impression that belongs to a later period during which belief in necromancy was less threatening. This impression is reinforced by linguistic and substantive considerations. The reference to the war against Amalek, and Samuel’s religious declaration that heeding God’s word is more pleasing to him than cultic ritual, disclose the author’s priestly identity.

Financial Institution's Role in Supply Chain Risk Management: Strengthening Resilience and Mitigating Financial Risks

One of the most important problems that multinational organizations are dealing with in risk-oriented supply chains is efficiency assessment and choosing the best low-risk financial institution for financial assistance. We provide an integrated approach for evaluating the unpredictable, dangerous, and fragile arrangement of the financial sector, which directly affects the supply chain network's processing stages. This model is supported by fuzzy-soft tools. The real economy is currently under pressure from both the domestic economic crisis and the decline in international commerce. President Xi Jinping has often suggested stepping up initiatives to assist the real economy's high-quality growth. In this regard, the development of the model for supply chain financing offers a fresh approach to resolving the funding issues faced by businesses. To tackle the financial issues facing the marketplace, we should use the chain of custody finance model, but we also need to thoroughly understand the risks associated with it and how to manage them. False trade orders and insufficient control over core businesses are difficulties that the present logistics finance risk management must deal with. The World Wide Web, big data, and block chain technologies are all blooming, opening new avenues for risk management in block chain finance. As a result, the goal of this essay is to examine the features of supply chain finance based on the technology of block chain, examine the risks associated with financing supply chains, and debate how to use block chain innovation for supply chain banking risk management.[4] Risk Financing: To assist organizations in transferring or reducing supply chain risks, financial institutions offer risk financing solutions including insurance & alternative risk transfer methods. The creation of novel risk financing products, evaluation of their performance, and comprehension of the effects on the resilience of supply chains can all be the subject of research. Financial institutions provide supply chain financing options like factoring, discounted invoices, and supply link finance programmed that help businesses manage their working capital, improve cash flow, and lower the risk of supplier failure to pay or insolvency. The effects of different financing choices on supplier relationships, supply chain stability, and overall risk reduction may be studied. Financial institutions collaborate with businesses to detect and classify different supply chain risks. An extensive investigation of the supply network environment, encompassing suppliers, transportation, logistics, and market dynamics, is required in this phase. Data gathering techniques used may include questionnaires, interviews, & site visits. In the above rank one is axis, and the last one is Punjab national bank. Financial Institution in Supply Chain Risk Management In the above rank one is axis, and the last one is Punjab national bank.

Research and application progress of electrochemical water softening technology in China

The issues of scaling, pipeline corrosion, and the growth of sludge, bacteria, and algae within circulating cooling water systems have been consistently limiting the enhancement of production efficiency in enterprises. The trending new electrochemical water softening technology, with its environmentally friendly, high efficiency, and low energy consumption characteristics, offers a fresh approach to addressing the stable operation of industrial circulating water systems. This paper begins by detailing the mechanisms of active scale removal, sterilization, and algae control facilitated by electrochemical water softening technology. Subsequently, it compares and analyzes the crucial core components of electrochemical devices (including the selection and preparation of anode and cathode materials, as well as the power system). Furthermore, it introduces the main types and installation modes of electrochemical equipment available in the market. Additionally, it uncovers the key factors influencing the efficiency of electrochemical descaling and validates the impact of electrochemical water softening technology on industrial circulating water systems through illustrative examples. Lastly, the paper concludes by summarizing and forecasting the future development direction of this technology.

The right time: women, medicine and maternal age in 1980s Aotearoa New Zealand

In the late twentieth century, increasing numbers of women in wealthy nations waited until they were aged in their 30s to give birth and become parents. This article examines responses...

Enhancing Interactive IVR Using NLP

Abstract: This paper suggests a fresh approach to IVR design by incorporating natural language processing (NLP) techniques, enhancing user experiences. Traditional IVRs often frustrate users due to their rigid menu structures. By integrating NLP, we aim to create dynamic and interactive IVR systems, leading to increased user satisfaction, streamlined interactions, and improved business performance. Our self-deployment and user testing demonstrate the system's effectiveness in providing personalized and responsive support, transforming traditional IVRs into engaging communication channels.

M1 macrophage-derived exosomes promote intervertebral disc degeneration by enhancing nucleus pulposus cell senescence through LCN2/NF-κB signaling axis

Intervertebral disc degeneration (IVDD) is the primary factor contributing to low back pain (LBP). Unlike elderly patients, many young IVDD patients usually have a history of trauma or long-term abnormal stress, which may lead to local inflammatory reaction causing by immune cells, and ultimately accelerates degeneration. Research has shown the significance of M1-type macrophages in IVDD; nevertheless, the precise mechanism and the route by which it influences the function of nucleus pulposus cell (NPC) remain unknown. Utilizing a rat acupuncture IVDD model and an NPC degeneration model induced by lipopolysaccharide (LPS), we investigated the function of M1 macrophage-derived exosomes (M1-Exos) in IVDD both in vivo and in vitro in this study. We found that M1-Exos enhanced LPS-induced NPC senescence, increased the number of SA-β-gal-positive cells, blocked the cell cycle, and promoted the activation of P21 and P53. M1-Exos derived from supernatant pretreated with the exosome inhibitor GW4869 reversed this result in vivo and in vitro. RNA-seq showed that Lipocalin2 (LCN2) was enriched in M1-Exos and targeted the NF-κB pathway. The quantity of SA-β-gal-positive cells was significantly reduced with the inhibition of LCN2, and the expression of P21 and P53 in NPCs was decreased. The same results were obtained in the acupuncture-induced IVDD model. In addition, inhibition of LCN2 promotes the expression of type II collagen (Col-2) and inhibits the expression of matrix metalloproteinase 13 (MMP13), thereby restoring the equilibrium of metabolism inside the extracellular matrix (ECM) in vitro and in vivo. In addition, the NF-κB pathway is crucial for regulating M1-Exo-mediated NPC senescence. After the addition of M1-Exos to LPS-treated NPCs, p-p65 activity was significantly activated, while si-LCN2 treatment significantly inhibited p-p65 activity. Therefore, this paper demonstrates that M1 macrophage-derived exosomes have the ability to deliver LCN2, which activates the NF-κB signaling pathway, and exacerbates IVDD by accelerating NPC senescence. This may shed new light on the mechanism of IVDD and bring a fresh approach to IVDD therapy.Graphical

Multi-Area Load Dispatch with Emission and Fuel Cost Constraints Using Deep Recurrent Lstm Under Stochastic Pv and Wind Power Integration

An interconnected power system must employ multi-area economic load dispatch (MAELD) to keep generation dispatch efficient, satisfy load demands, and stay within technological limits. The goal of MAELD's operational constraints is to reduce fuel costs and emissions from multi-area power production facilities. Consequently, optimization solutions that successfully tackle MAELD problems are highly sought after. By merging an alternative model with the Deep Recurrent Neural Network (DRNN) one, this paper suggests a fresh approach to addressing MAELD problems. This paper provides a detailed account of the various optimization methodologies used to address the issue of load dispatch when non-conventional energy sources are present. Four domains utilizing 3-, 13-, and 40-unit systems were used to test the suggested DRNN and LSTM approach. Comparing this method to various optimization algorithms as firefly, Salp Swarm, Squirrel search, Particle Swarm, and Gross Hopper, results produced from the MATLAB/Simulink environment show that it delivers superior trade-off solutions without breaching limitations.

Nano-lamellar H-ZSM-5 molecular sieves for enhanced indoor air purification: Synthesis and adsorption characterization.

The rapid growth of cities has been accompanied by problems with urban air quality, making air pollution challenging to manage. In this situation, people focus on indoor building materials to improve air quality. In this paper, a novel bola-type surfactant was synthesized and used as a template, using ethyl orthosilicate and sodium meta-aluminate as the silicon and aluminum source, in the ratio of n(NaOH): n(NaAIO2): n(SiO2): n(SDA): n(H2O) as 30:2.5:120:5:4800. Hydrothermal preparation of ZSM-5 molecular sieves with a nanosheet structure (H-ZSM-5) was accomplished. The manufactured lamellar ZSM-5 molecular sieves were examined using X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and adsorption and desorption techniques. Traditional microporous ZSM-5 had a considerably lower static adsorption of formaldehyde molecules. The findings demonstrated that the nano-lamellar H-ZSM-5 molecular sieve can purify and eliminate larger molecular VOCs inside because it has the ability to adsorb larger molecular diameter VOCs. Additionally, the effectiveness of the adsorption was assessed using toluene vapour molecules with higher molecular diameters. The findings demonstrate that the nanosheet H-ZSM-5 molecular sieve can remove bigger molecule VOCs from indoor air and can be utilised to purify indoor spaces. This study offers a fresh approach to indoor environmental cleanup by demonstrating the capability of nano-lamellar H-ZSM-5 molecular sieves for molecular adsorption.

Efficient Hydrogen Production over Molybdenum Tungsten Bimetallic Oxide NF/PMonW12-n Catalyst on Nickel Foam.

Developing inexpensive, efficient, and stable catalysts is crucial for reducing the cost of electrolytic hydrogen production. Recently, polyoxometalates (POMs) have gained attention and widespread use due to their excellent electrocatalytic properties. This study designed and synthesized three composite materials, NF/PMonW12-n, by using phosphomolybdic-tungstic heteropolyacids as precursors to grow in situ on nickel foam via the hydrothermal process and subsequent calcination. Then, their catalytic performances are systematically investigated. This work demonstrates that the NF/PMonW12-n catalysts generate more low valent oxides under the synergistic effect of Mo and W, further enhancing activity for hydrogen evolution reaction (HER). Among these electrocatalysts, NF/PMo6W6 exhibits the perfect HER performance, η10 is only 74 mV. It also shows great stability during long-term electrolysis. The current study introduces a fresh approach for producing electrocatalysts that are both cost-effective and highly efficient.

Harvesting Energy Via Water Movement and Surface Ionics in Microfibrous Ceramic Wools

Due to the push for carbon neutrality in various human activities, the development of methods for producing electricity without relying on chemical reaction processes or heat sources has become highly significant. Also, the challenge lies in achieving microwatt‐scale outputs due to the inherent conductivity of the materials and diverting electric currents. To address this challenge, our research has concentrated on utilizing nonconductive mediums for water‐based low‐cost microfibrous ceramic wools in conjunction with a NaCl aqueous solution for power generation. The main source of electricity originates from the directed movement of water molecules and surface ions through densely packed microfibrous ceramic wools due to the effect of dynamic electric double layer. This occurrence bears resemblance to the natural water transpiration in plants, thereby presenting a fresh and straightforward approach for producing electricity in an ecofriendly manner. The generator module demonstrated in this study, measuring 12 × 6 cm2, exhibited a noteworthy open‐circuit voltage of 0.35 V, coupled with a short‐circuit current of 0.51 mA. Such low‐cost ceramic wools are suitable for ubiquitous, permanent energy sources and hold potential for use as self‐powered sensors and systems, eliminating the requirement for external energy sources such as sunlight or heat.

Analysis of nonlinear problems using the Dual Mesh Control Domain Method with arbitrary meshes

The Dual Mesh Control Domain Method (DMCDM), proposed and developed by the second author, is a recent advancement in the field of numerical methods. This method represents a blend of the Finite Element Method (FEM) and the Finite Volume Method (FVM), each having its primary domains of popularity. The DMCDM incorporates the concepts of interpolation, duality, and numerical integration from the former and the idea of satisfying the integral (global) form of the differential (or balance) equations from the latter. In this paper, we present a fresh and systematic approach to solving two-dimensional nonlinear problems, including advection–diffusion problems, with arbitrary primal meshes. We develop an approach where element-level equations are derived and assembled in a manner similar to that of the FEM, as opposed to using nodal equations, as has been the convention in all of the previous works involving the DMCDM. Additionally, we extend the previous works to include higher-order interpolation, specifically quadratic interpolation. In short, we provide formulations for four-node bilinear and nine-node biquadratic elements of quadrilateral shape, enabling the application of the DMCDM to arbitrary primal and dual meshes. Finally, we explore the Newton iteration method for linearizing the nonlinear system of equations that arises from the DMCDM discretization. We present numerical results and comparisons against available exact and numerical solutions to verify the accuracy and robustness of the DMCDM.

Small-sample damage detection of bleacher structure based on GAN and MSS-CNN models

The damage scales and forms of bleacher structure are diverse, and the training by using neural network models may be inadequate when the data sample is limited, resulting in challenges such as overfitting or the inability to generalize new damage scenarios. In order to address the issue of damage detection in bleacher structures with small samples, this paper proposes a multi-scale stride convolutional neural network (MSS-CNN) model. It is trained as a generator and discriminator within a generative adversarial network (GAN) framework. By utilizing GAN to generate data and integrating real data with generated data, the mixed data is input into the MSS-CNN model for training, ultimately yielding damage detection results. In order to validate the effectiveness of this approach, a series of experimental studies are conducted by using a bleacher simulator at Qatar University as the research subject. Furthermore, the model is compared with ResNet, multi-layer perceptron, and support vector machine under identical experimental conditions by comparing real and mixed data. The experimental results consistently demonstrate the superior performance of the MSS-CNN model across multiple experiments. This paper presents a fresh research approach and perspective for addressing the challenge of small-sample damage detection in bleacher structures.

Improving electrochemical performances of LiNi0.5Mn1.5O4 by the strategy of oxygen vacancy doping

Despite the dazzling potential to be utilized as a high energy density lithium-ion battery cathode, the practical application of LiNi0.5Mn1.5O4 (LNMO) is hampered by the structural distortion related to Jahn-Teller effect of Mn3+ and the capacity degradation related to Mn2+ dissolution at high voltage. Herein, LNMO cathode materials doped with different contents of oxygen vacancies are synthesized by directly adjusting the oxygen ratio in the calcination atmosphere. Experimental and theoretical calculation results show that doping an appropriate amount of oxygen vacancies can regulate the electronic structure of Mn and Ni around oxygen vacancy, adjust the relative amount of Mn3+/Mn4+, increase the degree of disorder, and improve the electronic and ionic conductivity of LNMO, thereby enhancing its electrochemical performance. Especially, the modified LNMO exhibits a high capacity of 121.2 mAh g−1 at 0.5 C, a good capacity retention of 87.6 % after 100 cycles, and an excellent rate performance when the content of oxygen vacancies is about 20.60 %, corresponding to the oxygen ratio of 50 % in the calcination atmosphere. This study emphasizes the influence of doping oxygen vacancies on LNMO performance, enhancing its electrochemical properties without the introduction of additional elements. Moreover, these findings offer a fresh approach to enhancing the electrochemical performance of advanced batteries.

Solving Fuzzy Assignment Problem of Pythagorean Fuzzy Numbers Exploiting a Software Tool

A fresh approach to computing assignment issues in the pythagorean fuzzy domain is the focus of this body of work. Pythagorean fuzzy numbers are utilised in this work for the sake of analysing working hours and working costs. For converting fuzzy sets integers into a crisp number, a new ranking process is used. Optimal solutions have to be found by using software techniques for minimization, maximization and unbalanced assignment problems. In the end, numerical examples are being solved by using the proposed technique.

Modelling a foreland fold-and-thrust petroleum system: a Permian example in the NW Sichuan Basin, Southwest China

ABSTRACT Foreland thrust belts are globally significant areas for oil and gas exploration. These belts undergo unique tectonic activity, resulting in distinct variations in the accumulation of oil and gas. Studying the genetic evolution of deep oil and gas in these thrust belts is a crucial means of revealing the patterns of deep oil and gas accumulation. This study utilized a novel approach that combines reservoir geochemistry and basin simulation to determine the migration process of oil and gas in the foreland thrust fault belt of northwestern Sichuan Province. To achieve this goal, we established a thermal evolution model of lower Palaeozoic source rocks through organic chemical analysis. The balanced profile technique was used to determine the paleotectonic evolution pattern of the critical period in this area. Additionally, the mixed flow migration algorithm was employed to simulate the evolution of the Palaeozoic petroleum system. Through basin petroleum system modelling (BPSM), it was discovered that the bitumen found in the thrust nappe belt originated from the lower Cambrian strata. Furthermore, the gas reservoir in the hidden fault belt primarily migrated from the lower Cambrian strata and was also partially mixed with thermogenic gas from Permian source rocks. After establishing the genetic models of various tectonic belts in the study area, it became clear that there are notable differences in hydrocarbon accumulation within these belts. The BPSM results revealed that the oil present in the thrust nappe belt was generated in Lower Cambrian source rocks through early Indosinian hydrocarbon expulsion and continuous charging. The gas found in the thrust nappe belt was produced through the process of oil cracking in the deep reservoir located in the hidden front belt during the late Indosinian. The depth of the thrust nappe belt has helped to preserve hydrocarbon reservoirs. The natural gas present in the hidden fault belt primarily originates from lower Cambrian source rocks, which are combined with gas generated by oil cracking in lower Permian source rocks after the Yanshanian. Therefore, hydrocarbons accumulate deeply in the footwall of the thrust fault belt. The BPSM offers a fresh approach to hydrocarbon genetic models of thrust fault zones in the western Sichuan Basin, and it is beneficial for accurately evaluating the potential for deep hydrocarbon resources in comparable geological settings.

Enhancing the Creative Arts Skill of Generalist Teachers in the Ga East Municipality, Ghana

The objective-based curriculum previously in use was replaced by a standards-based curriculum for the 2019–2020 school year, bringing a fresh approach to the fundamental study of visual arts. However, the current curriculum places greater emphasis on art history and cultural practices, presenting challenges for generalist teachers in the Ga East Municipality who lack significant training or experience in creative arts. This study aimed to explore the impact of different levels of educational leadership on enhancing the teaching skills of generalist teachers in creative arts education. Employing a convergent parallel mixed methods approach, both qualitative and quantitative data were analysed. The study collected a total of 210 questionnaire responses and conducted interviews with 3 participants. The respondents included 185 generalist teachers, 25 headteachers, and 3 municipal education officers, selected through purposive sampling. The study held importance for the practical ability of generalist teachers to navigate complex artistic concepts through adaptability and flexibility in their teaching methods. The findings demonstrated that implementing innovative intervention strategies, such as collaborative projects, professional development training, integration of online resources, and pre-service education into the curriculum, while equipping generalist teachers with enhanced skills, can effectively inspire and engage students. Additionally, it highlighted the necessity for generalist teachers to exhibit passion, enthusiasm, instructional prowess, and imaginative skills to foster creativity, critical thinking, and self-expression in students through creative arts, ultimately influencing the quality of future art education.

An overview of the changing environment of communities for the innovation of smart and sustainable products in SMEs

With a focus on servitization and sustainability, the rise of Sustainable and Smart Products (SSP) provides a fresh approach to creating innovative products. When it comes to innovation for sustainability in this setting, businesses have significant challenges as SSPs become more complex. The adoption of ecosystem-based co-innovation in lieu of conventional organization-based methods of innovation strategy is thus imperative. Through the effective sharing of resources and expertise across organizations, innovation ecosystems provide a feasible paradigm for augmenting SSP innovation. It is remarkable that despite independent examinations into both SSP and innovation ecosystems, their confluence has yet to be thoroughly examined. To explain the dynamics of ecosystem-based SSP innovation and improve innovation performance and sustainability outcomes, this study aims to close this gap by undertaking extensive research on SSP Innovation Ecosystems (SSPIE). Examining SSPIE's history, definition, and salient features, the article clarifies what makes it stand out in the rapidly changing market for smart and sustainable goods. This study contributes substantially to the rapidly developing subject of SSPIE by identifying current research gaps and providing insightful advice on addressing the difficulties involved in SSP innovation.

Of sea, rivers and symbiosis: Diversity, systematics, biogeography and evolution of the deeply diverging florideophycean order Hildenbrandiales (Rhodophyta)

The Hildenbrandiales, a typically saxicolous red algal order, is an early diverging florideophycean group with global significance in marine and freshwater ecosystems across diverse temperature zones. To comprehensively elucidate the diversity, phylogeny, biogeography, and evolution of this order, we conducted a thorough re-examination employing molecular data derived from nearly 700 specimens. Employing a species delimitation method, we identified Evolutionary Species Units (ESUs) within the Hildenbrandiales aiming to enhance our understanding of species diversity and generate the first time-calibrated tree and ancestral area reconstruction for this order. Mitochondrial cox1 and chloroplast rbcL markers were used to infer species boundaries, and subsequent phylogenetic reconstructions involved concatenated sequences of cox1, rbcL, and 18S rDNA. Time calibration of the resulting phylogenetic tree used a fossil record from a Triassic purportedly freshwater Hildenbrandia species and three secondary time points from the literature. Our species delimitation analysis revealed an astounding 97 distinct ESUs, quintupling the known diversity within this order. Our time-calibration analysis placed the origin of Hildenbrandiales (crown age) in the Ediacaran period, with freshwater species emerging as a monophyletic group during the later Permian to early Triassic. Phylogenetic reconstructions identified seven major clades, experiencing early diversification during the Silurian to Carboniferous period. Two major evolutionary events—colonization of freshwater habitats and obligate systemic symbiosis with a marine fungus—marked this order, leading to significant morphological alterations without a commensurate increase in species diversification. Despite the remarkable newly discovered diversity, the extant taxon diversity appears relatively constrained when viewed against an evolutionary timeline spanning over 800 million years. This limitation may stem from restricted geographic sampling or the prevalence of asexual reproduction. However, species richness estimation and rarefaction analyses suggest a substantially larger diversity yet to be uncovered—potentially four times greater. These findings drastically reshape our understanding of the deeply diverging florideophycean order Hildenbrandiales species diversity, and contribute valuable insights into this order's evolutionary history and ecological adaptations. Supported by phylogenetic, ecological and morphological evidence, we established the genus Riverina gen. nov. to accommodate freshwater species of Hildenbrandiales, which form a monophyletic clade in our analyses. This marks the first step toward refining the taxonomy of the Hildenbrandiales, an order demanding thorough revisions, notably with the creation of several genera to address the polyphyletic status of Hildenbrandia. However, the limited diagnostic features pose a challenge, necessitating a fresh approach to defining genera. A potential solution lies in embracing a molecular systematic perspective, which can offer precise delineations of taxonomic boundaries.

Improving Weather Forecasting Accuracy Using Machine Learning

Weather forecasting has several applications in our daily lives, ranging from agriculture to event planning. Previous weather forecasting models relied on a complex combination of mathematical instruments, which was insufficient to achieve a higher categorization rate. We offer fresh revolutionary approaches for estimating monthly rainfall using machine learning algorithms in this study. Weather forecasts are created by gathering quantitative information about the current state of the atmosphere. Machine learning algorithms may learn complicated mappings from inputs to outputs using only samples and with little effort. The dynamic nature of the atmosphere makes accurate weather prediction challenging. The fluctuation in weather conditions in previous years must be used to anticipate future weather conditions. It is extremely likely that it will match within the next two weeks of the preceding year. We proposed using linear regressions with the Random forest algorithm to forecast weather using characteristics such as temperature, humidity and wind. It will forecast weather based on prior records thus, this prediction will be accurate.

Personalized medicine and nutrition in hepatology for preventing chronic liver disease in Mexico.

Chronic liver disease is a global health issue. Patients with chronic liver disease require a fresh approach that focuses on the genetic and environmental factors that contribute to disease initiation and progression. Emerging knowledge in the fields of Genomic Medicine and Genomic Nutrition demonstrates differences between countries in terms of genetics and lifestyle risk factors such as diet, physical activity, and mental health in chronic liver disease, which serves as the foundation for the implementation of Personalized Medicine and Nutrition (PerMed-Nut) strategies. Most of the world's populations have descended from various ethnic groupings. Mexico's population has a tripartite ancestral background, consisting of Amerindian, European, and African lineages, which is common across Latin America's regional countries. The purpose of this review is to discuss the genetic and environmental components that could be incorporated into a PerMed-Nut model for metabolic-associated liver disease, viral hepatitis B and C, and hepatocellular carcinoma in Mexico. Additionally, the implementation of the PerMed-Nut approach will require updated medicine and nutrition education curricula. Training and equipping future health professionals and researchers with new clinical and investigative abilities focused on preventing liver illnesses in the field of genomic hepatology globally is a vision that clinicians and nutritionists should be concerned about.