Transformation Steps Research Articles

Digitalization of operations and supply chains: Insights from survey and case studies

PurposeThis article aims to understand where industry is in terms of digitalizing their operations, what features of this transformation are essential for practitioners, and what barriers they are facing during their journey. In addition, the authors aim to provide recommendations for organization to start their digital transformation.Design/methodology/approachThrough literature review, the authors summarize the emerging tools and technologies in operations and supply chains to inform the practitioners. Then, the authors use surveys conducted on 183 operations and supply chain professionals, and use statistical tools to examine the association between variables of the data set. The authors present real-life case studies to explain important steps of a digital transformation project.FindingsThe survey results indicate that real-time monitoring and data analytics are viewed as the most important and needed tools for organizations. High cost, lack of stakeholder buy-in and lack of successful business use cases are major barriers for companies when starting a digital transformation.Practical implicationsThe authors provide recommendations for practitioners based on the survey responses, and outline that starting small, focusing on stakeholder buy-in and implementation of software are the three key steps for a successful transformation journey.Originality/valueMain contributions of this article are to understand practitioner perspectives in digitalization and provide guidelines for organizations to follow when transforming their operations. This research closes the gap between academic research and practice by collaborating with operations and supply chain professionals.

Synthesis, design, biological and anti-oxidant assessment of some new 9H-carbazole derivatives

This research includes the synthesis of 9H-carbazole compounds (1-7) through the prepared ester by condensation 9H-Carbazole with chloroacetic acid in the presence of triethylamine and benzene as a solvent. The compound (3) was prepared from ester and ethanol in the presence of H2SO4 as catalyst by using MWI. The synthesis of amide carbazole derivatives (3,4,5) was accomplished by using microwave irradiation with aromatic amine (p-, o-bromoaniline and m-chloroaniline). The synthesis of derivatives (6,7) was performed by Suzuki coupling reaction during which derivative (5) was treated with aryl boronic acid using pd(pph3)4 ʺpalladium-tetrakis(triphenyl phosphine)ʺ as a catalyst via oxidation, transformation and reduction steps. The synthesized compounds were evaluated by FT-IR, 1H-NMR, 13C-NMR spectra. The assay of biological/antibacterial activity of synthesized compounds against two types of bacteria i.e. gram-positive (Staphy. Aureus) and gram-negative (E. coli) were performed. The results exhibited excellent activity of all synthesized compounds as compared with ciprofloxacin drug. The antioxidant activity of the synthesized compounds using DPPH test showed good findings.

IRAWNET: A Method for Transcribing Indonesian Classical Music Notes Directly from Multichannel Raw Audio

A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are that raw audio only contains an amplitude in each timestamp, and the signals of the left and right channels have different amplitude intensities and onset times. Thus, this study addressed these issues by proposing the IRawNet method with fused feature layers to merge different amplitude from multichannel raw audio. IRawNet aims to transcribe Indonesian classical music notes. It was validated with the Gamelan music dataset. The Synthetic Minority Oversampling Technique (SMOTE) overcame the class imbalance of the Gamelan music dataset. Under various experimental scenarios, the performance effects of oversampled data, hyperparameters tuning, and fused feature layers are analyzed. Furthermore, the performance of the proposed method was compared with Temporal Convolutional Network (TCN), Deep WaveNet, and the monochannel IRawNet. The results proved that proposed method almost achieves superior results in entire metric performances with 0.871 of accuracy, 0.988 of AUC, 0.927 of precision, 0.896 of recall, and 0.896 of F1 score.

ФУНКЦИОНАЛЬНЫЕ ВОЗМОЖНОСТИ СИСТЕМ РЕЛЕЙНОЙ ЗАЩИТЫ И АВТОМАТИКИ ЦИФРОВЫХ ПОДСТАНЦИЙ

A comprehensive analysis of the data on the technical accounting of the functioning of electrical relay protection complexes in the unified national electric power grid of Russia is presented, thanks to which the main types, causes and patterns of incorrect and emergency operation of the entire range of protections are established. An approach to the potential transition to servicing industrial electric power facilities based on the use of the entire set of relay protection functionality to solve the problems of automating maintenance procedures on a new hardware and methodological basis is proposed. Optimal criteria for evaluating the operability of electrical relay protection complexes and automation of digital transformer substations are formulated, which additionally take into account the influence of completeness and reliability of electrical information on the condition of protected electrical equipment affecting the quality of protection. It is shown that the organization of maintenance of electrical equipment by means of relay protection and automation according to the actual condition at an energy facility with a developed system of automated information collection and processing is a natural step of transformation in the electric power industry. The analysis of the relevance of the current technology of maintenance and repair of electrical equipment showed a significant reduction in the impact on the functional groups of measurement, information environment and receiving modules of electrical relay protection complexes and automation of digital substations.

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis.

Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.

Recent Advances in Photoinduced Oxidative Cleavage of Alkenes.

Oxidative cleavage of alkenes leading to valuable carbonyl derivatives is a fundamental transformation in synthetic chemistry. In particular, ozonolysis is the mainstream method for the oxidative cleavage of alkenes that has been widely implemented in the synthesis of natural products and pharmaceutically relevant compounds. However, due to the toxicity and explosive nature of ozone, alternative approaches employing transition metals and enzymes in the presence of oxygen and/or strong oxidants have been developed. These protocols are often conducted under harsh reaction conditions that limit the substrate scope. Photochemical approaches can provide milder and more practical alternatives for this synthetically useful transformation. In this review, we outline recent visible-light-promoted oxidative cleavage reactions that involve photocatalytic activation of oxygen via electron transfer and energy transfer. Also, an emerging field featuring visible-light-promoted oxidative cleavage under anaerobic conditions is discussed. The methods highlighted in this review represent a transformative step toward more sustainable and efficient strategies for the oxidative cleavage of alkenes.

Nanoscale constraints on the nucleation and evolution of granular zircon from reidite in impactites at the Chicxulub impact structure

Zircon with granular texture from hypervelocity impact structures can be used to estimate the thermodynamic conditions of impact processes, including pressure and temperature, and in some cases the timing of impact events via U-Pb geochronology. However, two disparate formation models have been proposed to explain the occurrence of zircon neoblasts that preserve systematic orientation relations; one involves zircon-reidite phase transformations (FRIGN zircon), whereas the other features melting and thermal dissociation of zircon in the absence of reidite. Distinguishing between these models is hampered by the lack of observational constraints on the intermediate transformation steps at nanoscale, and what processes give rise to observed systematic orientation relations among zircon neoblasts. Here we report new analyses of reidite-bearing and granular zircons from peak ring core samples of the Chicxulub impact structure using nanoscale methods. We describe lamellar and lense-like reidite habits associated with reidite twins in shocked zircon from impact melt-bearing breccia, along with the first observation of nanoscale zircon granules forming locally within preserved reidite lamellae. The crystallographic orientation of the zircon nano-granules matches the orientations predicted by the FRIGN zircon model, confirming they formed directly by solid-state reversion of reidite to zircon, and represent the earliest stages of the formation of granular zircon. Minor occurrences of baddeleyite at the interface of reidite and neoblastic zircon domains suggest that the reversion of reidite to zircon can occur together with local ZrSiO4 dissociation driven either by the loss of SiO2, which creates excess zirconia, or by local thermal dissociation of reidite. Other partially- and fully-granular zircon grains from the same impact melt-bearing breccia also preserve systematic orientation relationships among zircon neoblasts, consistent with having transformed directly from reidite. The observation that zircon neoblasts maintain systematic orientations from nanoscale to microscale in granular zircon supports the idea that neoblast orientations are encoded at the nucleation stage via solid state phase transformation. Observations in this study provide direct evidence to explain the nature of systematic high-pressure phase transitions involving zircon and have implications for unraveling the pressure-temperature history of zircon phase transitions in large impacts on Earth or other planetary bodies.

Correlation Joins over Time Series Data Streams Utilizing Complementary Dimension Reduction and Transformation

A common analysis task over a stream of time series is to find all pairs of windows whose correlation is above a given threshold. For a large number of streams, doing so naively, i.e., checking the Cartesian product, is too expensive. In essence, finding correlated pairs in a non-naive way boils down to a high-dimensional similarity join in a Euclidean space. While there are similarity join algorithms, such as Quickjoin and ε-kdB tree, they are inefficient for high-dimensional data. We propose CorrJoin, short for Correlation Join, that combines a complementary dimension reduction and transformation step with a subsequent double-filtering step. In the first step, we reduce the dimensionality of data stream windows by combining a fast but inaccurate method, Piecewise Aggregate Approximation (PAA), with an accurate and slow one, Singular Value Decomposition (SVD). Not only does SVD compensate for the weaknesses of PAA, it also transforms the data to make the first filter based on bucketing more effective. The second filter, which uses Euclidean distances, reduces the number of false positives before computing exact correlations. Our experiments reveal that in common settings, CorrJoin is an order of magnitude faster than state-of-the-art approaches (up to 20 times faster than Quickjoin).

Beyond a garden: Alignment of Sustainable Development Goals with botanic gardens

The United Nations’ (UNs) 2030 “Sustainable Development Goals are a universal call to action to protect the planet and improve the lives and prospects of everyone, everywhere.” This requires much effort of in governmental policies along with public and private sector practices. With regard to this, BGs have a crucial role in bringing this vision of the future. Of the 17 SDGs, Target 15—Life on land is directly associated with the traditional work of botanic gardens (BGs). But other four goals (2—Zero hunger, 4—Quality education, 11—Sustainable cities and communities, 13—Climate action) are also worthy of review and analyze when considering BGs. They could meet the needs of both the local and global communities and are effective in shaping the future and promoting the 2030 goals. In this context, paying attention to the UN's global and inclusive call, this paper focuses on Eden Project, United Kingdom, Ecorium of National Institute of Ecology, South Korea and Konya Tropical Botanical Garden, Turkey. Missions of the projects rely on conservation, restoration, education and community engagement. They address several global concerns, including climate and biodiversity emergency. Aim of this paper is to investigate to what extent the selected BGs can contribute to the SDGs. Results underline BGs as a valuable input in decision-making and policy action concerning environmental justice, equity, and inclusion that encourage transformative steps in achieving the SDGs.

The six stages of digital transformation and practical steps to get started in your organisation

Many of today’s most successful supply chains are ones that have executed a digitisation of information processes. Digital information unlocks game-changing experiences that we have recently grown accustomed to, such as two-day online delivery and order visibility. Digital transformation has quickly become a popular buzzword in supply chain management, a term that everyone understands as important for the future. Unfortunately, many struggle to get started with this concept, facing an immense resistance to change and a lack of leadership support. This paper compares and contrasts companies that are data-driven and those that are not, introduces the six stages of digital transformation and helps the reader to identify where their company currently is. It also provides a set of practical steps to get started in your organisation and discusses some roadblocks that you are likely to encounter. The paper will be especially impactful for those looking to drive digital change in their organisations by leading the effort to analyse, simplify and digitise legacy processes.

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet problem through a strategic amalgamation of Hybrid Feature Selection methods—Categorical Analysis, Mutual Information, and Principal Component Analysis—and a robust ensemble of machine learning techniques. We uniquely combine these feature selection tools to refine the input space, enhancing the detection capabilities of the ensemble learners. Extra Trees, as the ensemble technique of choice, exhibits exemplary performance, culminating in a near-perfect 99.99% accuracy rate in botnet classification across varied datasets. Our model not only surpasses previous benchmarks but also demonstrates exceptional adaptability to new botnet phenomena, ensuring persistent accuracy in a landscape of evolving threats. Detailed comparative analyses manifest our model's superiority, consistently achieving over 99% True Positive Rates and an unprecedented False Positive Rate close to 0.00%, thereby setting a new precedent for reliability in botnet detection. This research signifies a transformative step in cybersecurity, offering unprecedented precision and resilience against botnet infiltrations, and providing an indispensable blueprint for the development of next-generation security frameworks.

Resolving Fast Relative Kinetics in Inorganic Solid-State Synthesis.

Solid-state syntheses are generally regarded as being slow, limited by transport, and, as such, are often only stopped to check the products after many hours at high temperature. Here, using a custom-designed reactor to rapidly initiate solid-state syntheses, we are able to capture the earliest stages of a reaction using in situ X-ray scattering. For the reaction of TiO2 and Li2CO3 to form spinel lithium titanate (Li4Ti5O12)─an anode material for fast-charging applications─we capture two distinct kinetic regimes, including fast initial kinetics in the first seconds-minutes of the reaction that account for significant product formation. We use an Avrami model to compare the reaction at high temperatures (700-750 °C), which results in the rapid formation of Li4Ti5O12 within minutes, and lower temperatures (482 °C), consistent with conditions that might be chosen based on "Tamman's rule", a common heuristic. Our analysis reveals characteristic Avrami slopes (i.e., dimensionalities) for each step in the chemical transformation. We anticipate that the fast initial reaction kinetics found here are likely to be common in the synthesis of other materials used in battery electrodes, solid-state electrolytes, ion-conductive membranes, etc. where ion transport is a prerequisite for functionality.

Agrobacterial Transformation Enhancement by Improved Competent Cell Preparation and Optimized Electroporation.

The introduction of plasmids into Agrobacterium cells is one of the key steps in the Agrobacterium-mediated transformation of plants for gene editing applications. Depending on chromosomal background, some Agrobacterium strains exhibit a very low transformation efficiency, which results in a low number of colonies for subsequent screening and thus limits the potential for automated high-throughput transformation processes, especially with low copy or large plasmids. This study demonstrates improvements of transformation frequency by modifying the competent cell preparation process and optimizing electroporation parameters for two Agrobacterium strains. The competent cell preparation process was modified by prolonging bacterial growth in the log phase and optimizing the endpoint cell density for cell harvest which resulted in a significant cell yield increase and transformation frequency improvement. Optimization of electroporation by fine-tuning the parameters not only resulted in a 30-fold transformation frequency increase but also revealed a strain-dependent requirement for field strength and electric pulse length. To further improve transformation of a recalcitrant strain, different concentrations of dimethyl sulfoxide (DMSO) in recovery medium were examined. The study revealed an important role of DMSO in transformed cell recovery, with 5% DMSO resulting in the highest transformation frequency. The significant improvements in Agrobacterium transformation frequency addressed a critical bottleneck towards establishing a high throughput process.

Brønsted ionic liquids as catalysts for synthesis of 2-pyrazoline derivatives from ketazines: A combined experimental and theoretical approach

Brønsted ionic liquids (BILs) were designed and applied to catalyze intramolecular cyclization of ketazines to 2-pyrazoline derivatives. Under the optimal conditions as 90 °C, 15 mol% [PsBim][CF3SO3] dosage and 4 h, the conversion rate and selectivity of the reaction are up to 98 % and 99 % respectively. Through the substrate screening, it shows that the steric hindrance of the R group in ketazines is a big factor of cyclization activities, following the order of methyl (98 %) > ethyl (93 %) > n-propyl (87 %) > n‑butyl (81 %) > isopropyl (52 %) > isobutyl (46 %)> neobutyl (13 %). Moreover, the results of the density functional theory (DFT) calculations indicate that the cations and anions of BIL can synergistically participate in the ketazines configuration transformation step so as to significantly reduce the Gibbs free barrier energy from 30.65 to 20.12 kcal/mol, thereby promoting the cyclization reaction to synthesize 2-pyrazoline derivatives.

Scope, Myths and Misunderstandings of Medical Doctors Regarding the Advanced Nurse Practitioners: A Case Report

The revision of the Pakistan Nursing and Midwives Council Act in 2023 marked a significant turning point by granting nurses with an MS Nursing degree the autonomy to practice independently. This transformative step, however, triggered a contentious debate within the medical community, particularly among doctors. This case report delves into the intricate web of myths and misunderstandings that encircle the scope of practice of Advanced Nurse Practitioners (ANPs) in Pakistan. Through an evidence-based analysis, this report aims to unravel the origins of these misconceptions and provide insights that might lead to collaborative and informed policymaking.

Revision of the Quaternary calcareous nannofossil biochronology of Arctic Ocean sediments

Despite extensive chronological studies, the relationship between the age and sub-seafloor depth of Arctic Ocean sediments remains ambiguous. This prevents confident identification of paleoceanographic changes in the Arctic during the Quaternary. Currently, age-depth models derived from uranium-series decay in Arctic sediments diverge by hundreds of thousands of years compared to those built on known evolutionary appearances and extinctions of calcareous nannoplankton, a group of globally valuable age-markers. Here we report on high-resolution biostratigraphic analysis of late Quaternary sediments in six cores from the central Arctic Ocean (CAO). We applied paired light microscope (LM) and scanning electron microscope (SEM) imaging to improve nannofossil diagnosis. We argue that low abundances and poor preservation have led to misidentification of the true stratigraphic depth of the critical Pleistocene nannofossil bio-events that have underpinned age models for many Arctic sedimentary records for decades. The revised calcareous nannofossil biochronology provides a radically different geochronological framework for CAO sediments – indicating that what had previously been identified as Marine Isotope Stage (MIS) 7 (191–243 ka) in many sedimentary records is older than MIS 12 (424–478 ka). Furthermore, it suggests that previously inferred sub-stages of MIS 5 could represent full interglacial periods rather than interstadials. The results help reconcile the different dating approaches and provide a transformative step towards resolving the disparity in Quaternary Arctic age-depth models, bringing us one step closer to accurate paleoceanographic reconstructions based on sediment cores.

Super-Airy beams.

We report on the numerical simulation results of the propagation of Fourier-transformed super-Gaussian beams with cubic phase modulation, which we refer to as super-Airy beams. We show the effect of the truncation profile on the propagation of the "Airy" intensity distribution and propose methods to improve the non-diffracting propagation length. We show that super-Gaussian beams with cubic phase modulation would form, what we believe to be, truncated Airy beams after being optically Fourier-transformed. The results show that Airy beams with smaller curvature would preserve their non-diffracting characteristics for longer propagation distances than Airy beams with larger curvature. Airy beam curvature is a controllable parameter by the phase transformation step. The presented results suggest that Fourier-transforming super-Gaussian beams with cubic phase modulation would form Airy beams with up to > 350% increase in their non-diffracting propagation distance compared to Fourier-transformed Gaussian beams.

Carbon and nitrogen transformation mechanism and functional microorganisms of sheep manure composting on the Qinghai-Tibet Plateau under different moisture content

In this study, metagenomics technology was used to explore the microbial mechanism and functional microorganisms of carbon and nitrogen transformation during sheep manure composting under 45% (S45) and 60% moisture content (S60) on the Qinghai-Tibet Plateau (QTP). Results showed that the organic carbon oxidation was the main step of carbon transformation during composting, and Corynebacterium, Nocardiopsis, Brachybacterium, Brevibacterium, Luteimonas, Pseudoxanthomonas, and Thermobifida were the main participants in this step. The encoding gene abundance of functional enzymes such as cellulase, glucosidase, and chitinase involved in organic carbon oxidation of S45 was higher than that of S60, which resulted in S45 having a higher lignocellulosic degradation rate. In nitrogen transformation, nitrate reduction was the most important nitrogen transformation step in the early stage. As composting proceeded, nitric oxide reduction, nitrite reduction, and nitrite ammonification gradually enhanced. The abundance of functional genes involved in nitrite reduction and nitric oxide reduction in S45 was lower than that in S60, which would result in lower nitrogen loss and higher alkali-hydrolyzed nitrogen content in S45. Nocardiopsis, Corynebacterium, Pseudomonas, Pseudorhizobium, Planifilum, Sphingobacterium, and Luteimonas were the main participants in denitrification. Cellulomonas, Nocardiopsis, Altererythrobacter, Cellulosimicrobium, Devosia, and Streptomyces were the main participants in nitrite ammonification.

Roxarsone biotransformation by a nitroreductase and an acetyltransferase in Pseudomonas chlororaphis, a bacterium isolated from soil

Roxarsone (3-nitro-4-hydroxyphenylarsonic acid, Rox), a widely used organoarsenical feed additive, can enter soils and be further biotransformed into various arsenic species that pose human health and ecological risks. However, the pathway and molecular mechanism of Rox biotransformation by soil microbes are not well studied. Therefore, in this study, we isolated a Rox-transforming bacterium from manure-fertilized soil and identified it as Pseudomonas chlororaphis through morphological analysis and 16S rRNA gene sequencing. Pseudomonas chlororaphis was able to biotransform Rox to 3-amino-4-hydroxyphenylarsonic acid (3-AHPAA), N-acetyl-4-hydroxy-m-arsanilic acid (N-AHPAA), arsenate [As(V)], arsenite [As(III)], and dimethylarsenate [DMAs(V)]. The complete genome of Pseudomonas chlororaphis was sequenced. PcmdaB, encoding a nitroreductase, and PcnhoA, encoding an acetyltransferase, were identified in the genome of Pseudomonas chlororaphis. Expression of PcmdaB and PcnhoA in E. coli Rosetta was shown to confer Rox(III) and 3-AHPAA(III) resistance through Rox nitroreduction and 3-AHPAA acetylation, respectively. The PcMdaB and PcNhoA enzymes were further purified and functionally characterized in vitro. The kinetic data of both PcMdaB and PcNhoA were well fit to the Michaelis-Menten equation, and nitroreduction catalyzed by PcMdaB is the rate-limiting step for Rox transformation. Our results provide new insights into the environmental risk assessment and bioremediation of Rox(V)-contaminated soils.

Exploring the Transformation Path of High Energy-consuming Industries in A Low-carbon Economy

In recent years, under the background of "double carbon", reducing carbon emission and realizing green transformation and green development have become the necessary way for high energy-consuming enterprises to achieve sustainable development. However, the green transformation of high energy-consuming enterprises faces the problems of high investment cost and slow transformation, which makes it difficult for many high energy-consuming enterprises to take the step of green transformation. Based on the above background, this paper analyzes the concepts and theories related to low carbon economy and proposes the transformation path of high energy-consuming industries under low carbon economy environment.