Transformation Model Research Articles

Modeling of Soil Cation Exchange Capacity Based on Chemometrics, Various Spectral Transformations, and Multivariate Approaches in Some Soils of Arid Zones

Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study’s requirements, with R2 > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R2 = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a “quick”, inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions.

Prevalence and Risk Factors of Intensive Care Unit-acquired Weakness in Patients With COVID-19: A Systematic Review and Meta-analysis.

Intensive care unit acquired weakness (ICUAW) is a common neuromuscular complication of critical illness, impacting patients' recovery and long-term outcomes. However, limited evidence is available on pooled prevalence and risk factors of ICUAW specifically in the COVID-19-infected population. We searched on PubMed, Embase, Cochrane Library, Web of Science, PEDro, and EBSCOhost/CINAHL up to January 31, 2024. Data synthesis was conducted using the Freeman-Tukey double-arcsine transformation model for the pooled prevalence rate and odds ratios with corresponding 95% confidence intervals was used to identify risk factors. The pooled prevalence of ICUAW in COVID-19 patients was 55% in eight studies on 868 patients. Risk factors for developing ICUAW in these patients were: old age (WMD 4.78, 95% CI, 1.06-8.49), pre-existing hypertension (OR = 1.63, 95% CI, 1.02-2.61), medical intervention of prone position (OR = 5.21, 95% CI, 2.72-9.98), use of neuromuscular blocking agents (NMBA) (OR = 12.04, 95% CI, 6.20-23.39), needed tracheostomy (OR = 18.07, 95% CI, 5.64-57.92) and renal replacement therapy (RRT) (OR = 5.24, 95% CI = 2.36-11.63). The prevalence of ICUAW in patients with COVID-19 was considered relatively high. Older age, pre-existing hypertension, medical intervention of prone position, NMBA use, needed tracheostomy and RRT were likely risk factors. In the future, interdisciplinary medical team should pay attention to high-risk groups for ICUAW prevention and early treatments.

LSTM and BERT based transformers models for cyber threat intelligence for intent identification of social media platforms exploitation from darknet forums

LSTM and BERT based transformers models for cyber threat intelligence for intent identification of social media platforms exploitation from darknet forums

Formation and degradation of textural pedofeatures in soils of ancient tropical coastal plains in southeastern Brazil

Textural features in soils are defined as having a distinct granulometric composition compared to the surrounding groundmass. Among these, clay coatings are found in various pedo-environments, covering a range of climatic conditions, from wetter regions to semiarid areas. This study aimed to understand how textural features in ancient coastal plain soils can reveal mechanisms of their pedogenetic evolution and how this relates to environmental transformations. Three soil profiles were studied at different points in a toposequence, representing the upper, middle, and lower thirds. The study area is located in the municipality of Seropédica, in the state of Rio de Janeiro, southeastern Brazil. Morphological, physical, chemical, mineralogical, and micromorphological analyses were conducted on the selected profiles. Acrisols, formed in free-draining conditions, were later transformed into Planosols due to base level oscillation and the percolation of brackish solutions through the soils. The pedogenetic model proposed in this study involves the following stages: i) Clay formation by in situ mineral weathering, mainly of phyllosilicates, with pseudomorphization of the structures. ii) Destruction of the pseudomorphized structures by pedoplasmation with proximal migration of clay and genesis of textural features of coating and filling. iii) Partial and episodic saturation of the base of the profile with solutions enriched in sodium of a fluvial-marine nature. iv) Dispersion of the clays, leading to the destruction of the textural features and soil micromass, accompanied by relative sand enrichment and E horizon formation. v) Local lowering of the surface and smoothing of the base of the slopes. This transformation model is unprecedented for the southeastern region of Brazil, increasing the evolutionary complexity of soil genesis in areas of ancient fluvial-marine plains.

The Impact of War on the Transformation of the Strategic Paradigm of Organizational Change Management in Conditions of Increased National Security Threats

In times of war or increased national security threats, the traditional paradigms of managing these transitions must adapt to the rapidly changing circumstances. War alters the economic, social, and political environments dramatically, necessitating that organizations quickly adapt to ensure survival and continue operations. This requires a reevaluation of strategic objectives and management practices to align with the broader context of national security. The purpose of the article is to characterize the features of the impact of war on the change of the strategic paradigm of organizational change management in conditions of increased national security threats. The object of the study is the system of organizational change management in conditions of increased national security threats. The methodology of the study involves the use of the IDEF0 method. As a result of the study, a modern IDEF0 model of transformation of the strategic paradigm of organizational change management in conditions of increased national security threats was presented.

Integrating lifestyle medicine into cancer care: A new paradigm.

179 Background: Amid rising global cancer rates and obesity-related cancer risks, oncology must blend traditional cancer treatments with evidence-based lifestyle medicine (LM) to enhance patient outcomes. LM—evidence-based interventions in nutrition, exercise, sleep, avoiding substances, and social connection —emerges as a critical tool in the care spectrum. ASCO and ACS guidelines incorporating lifestyle interventions are limited, and existing ones have notable implementation gaps. This study advocates for integration of LM into cancer care. Such interventions, supported by growing research, offer significant promise in reducing cancer treatment side effects and improving outcomes, highlighting an urgent need for their incorporation into oncological protocols. Methods: The Department of Preventive Medicine at Cleveland Clinic Abu Dhabi (CCAD) launched an LM program at the Oncology Institute. Oncologists refer to LM physicians who assess patients using an LM-validated questionnaire. Cardiometabolic risk factors: body composition, waist circumference, lipid profile, hemoglobin A1c, high-sensitivity CRP, liver function, and liver elastography are tested at baseline and 3 months. Patients receive personalized LM treatment by certified physicians, dieticians, physiatrists, and psychologists, aimed at reducing treatment side effects and enhancing cancer outcomes while improving cardiometabolic health. Follow-ups at 6 weeks and 3 months post-intervention ensure progress tracking and reassessment. Results: Initiated in January 2021, it began as a pilot program for breast cancer patients and has since expanded to all cancer patients. To date, it enrolled 425 patients, evidencing sustainability and acceptance within the patient community. Referral rates from oncologists have shown a steady increase, reflecting the program's growing credibility and utility. Patient satisfaction levels are reported to be high. Preliminary outcomes indicate improvements in both physical and psychological health metrics, suggestive of the program's comprehensive impact. Conclusions: The pioneering oncology LM program at CCAD established a transformative model that seamlessly integrates LM into traditional oncology care and acts as a bridge to survivorship. By ensuring sustained access to lifestyle interventions from the point of diagnosis through post-treatment, the program aligns with the latest research and ASCO/ACS guidelines, pioneering a new standard in cancer treatment. It can effectively address cardiometabolic risks, including obesity, and reduce treatment-related adverse outcomes. This program demonstrates the feasibility and a replicable global model, one that merges evidence-based lifestyle practices with standard oncological care to create a unified approach that could be adopted by healthcare systems worldwide, marking a significant step in the evolution of patient-centered cancer care.

Digital Transformation and Business Model Innovation: Moderating Role of Digital Leadership

Purpose: The manufacturing sector in Ghana has become increasingly important to the country's economic growth, driven by digital transformation and innovation. This research aims to explore the complexities of digital transformation within Ghanaian manufacturing companies, with a specific focus on the moderating role of digital leadership. Design/Methodology/Approach: This conceptual paper presents a framework that integrates socio-technical systems theory and the upper echelon theory. This framework offers a detailed understanding of how digital transformation, digital leadership, organizational adaptation, and business model innovation interact within the Ghanaian manufacturing sector. Findings: The Ghanaian manufacturing sector, a significant contributor to the country's GDP, has seen substantial growth, largely due to small and medium-sized enterprises (SMEs). However, many industries within the sector face challenges in embracing the digital age, including inadequate technological infrastructure and limited access to funding. The Ghanaian government has responded with policies to enhance digital capabilities and innovation, positioning the country for digital readiness. Originality: This paper contributes to the field by providing practical strategies for leadership development and decision-making within manufacturing organizations. It also advances theoretical perspectives and offers empirical insights specific to the Ghanaian context. Overall, this paper serves as a valuable resource for manufacturing leaders, policymakers, and researchers navigating digital transformation, innovation, and leadership in Ghana's manufacturing sector, with broader implications for emerging economies globally.

An imaging-based model to predict the malignant potential of intraductal papillary mucinous neoplasm of the pancreas.

To develop and validate imaging-based models for predicting the malignancy risk of intraductal papillary mucinous neoplasm (IPMN). We retrospectively analyzed data from 241 IPMN patients who underwent preoperative CT and MRI for model development. Cyst size, presence and size of the enhancing mural nodule (EMN), main pancreatic duct (MPD) diameter, thickened/enhancing cyst wall, abrupt MPD caliber change with distal atrophy, and lymphadenopathy were assessed. Multiple logistic regression models predicting malignancy risk were created using either continuous (Model C) or dichotomized variables (Model D) using these imaging features. Validation included internal (n = 55) and external (n = 43) datasets. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared with that of the international guideline-based model (Model F). Model C identified age, EMN size, MPD diameter, and lymphadenopathy as independent predictors on CT, and age and presence and size of EMN on MRI. Model D identified age ≥ 68, cyst size ≥ 31 mm, EMN ≥ 6 mm, MPD ≥ 7 mm, and lymphadenopathy as independent predictors on CT, and age ≥ 68, EMN ≥ 4.5 mm, and lymphadenopathy on MRI. Model C (AUC, 0.763-0.899) performed slightly better than Model D (AUC, 0.753-0.912) without statistical significance. No significant difference was observed between Models C and F (AUC, 0.729-0.952). Combining Model C with obstructive jaundice improved performance (AUC, 0.802-0.941) without statistical significance. Our imaging-based models effectively predicted the malignancy risk of IPMNs, comparable to international consensus guidelines. Imaging features are important for predicting the malignant potential of IPMNs. Our imaging-based model may help determine surgical candidacy for patients with IPMNs. Non-invasively determining the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) allows for appropriate treatment decision-making We identified multiple imaging features that are associated with malignant transformation and developed models for this prediction. Our model performs comparably with international consensus guidelines in predicting the malignant potential of IPMNs.

A study of online academic risk prediction based on neural network multivariate time series features

SummaryNeural networks are becoming increasingly widely used in various fields, especially for academic risk forecasts. Academic risk prediction is a hot topic in the field of big data in education that aims to identify and help students who experience great academic difficulties. In recent years, the use of machine learning algorithms and deep learning algorithms to achieve academic risk prediction has garnered increased attention and development. However, most of these studies use nontime series data as features for prediction, which are slightly insufficient in terms of timeliness. Therefore, this article focuses on time series data features that are more expressive of changes in students' learning status and uses multivariate time series data as predictive features. This article proposes a method based on multivariate time series features and a neural network to predict academic risk. The method includes three steps: first, the multivariate time series feature is extracted from the interaction records of the students' online learning platforms; second, the multivariate time series feature transformation model ROCKET is applied to convert the multivariate time series feature into a new feature; third, the new feature is converted into a final prediction result using a linear classification model. Comparative tests show that the proposed method has high effectiveness.

The complementary nature of transformational and transactional leadership styles in modern hospital and healthcare systems

With healthcare systems becoming increasingly complex, managers must practice leadership styles that best serve their organisations, staff and service users. Rainer WG Gruessner discusses the pros and cons of different leadership styles, suggesting that transformational and transactional models can and should be combined for modern healthcare management.

Book Review: Social work practice during times of disaster: A transformative green social work model for theory, education and practice in disaster interventions by Dominelli Lena

Book Review: <i>Social work practice during times of disaster: A transformative green social work model for theory, education and practice in disaster interventions</i> by Dominelli Lena

Managing Green and Sustainable Technologies: Climate-Informed Corrosion Prediction for Steel Structures

The unpredictability of atmospheric circumstances is one of the major elements that contribute to the capability to anticipate the corrosion growth in metal structures over time accurately. Climate shifts can potentially modify the long-term attributes of these factors throughout the operational life of metal structures, both those currently in existence and those newly developed. The impact of climate irregularity on the probabilistic nature of atmospheric variables, which significantly impact corrosion situations, can add intricacy to corrosion predictions in these constructions. This project presents an incorporated framework to quantify the impact of climate alteration on the corrosion rates of steel structures in Jordan. It considers the changes in environmental conditions, specifically temperature, relative humidity, and wind speed, and their impacts on atmospheric corrosion. Global Climate Models are employed to assess the long-term effects of climate transformation on these environmental circumstances. An analytical model for anticipating corrosion rate is integrated with climate transformation models to predict modifications in the corrosion rates of steel parts relative to historical situations. This project also examines the impact of climate transformation on the fluctuations of these climatic parameters and offers a contrast between historical data and projected conditions across the country. The findings indicate a significant increase in corrosion rates across Jordan, which calls for localized green building codes and standards to ensure that future infrastructure is sustainable and capable of withstanding the new climatic norms. This approach addresses the immediate challenges posed by climate change and contributes to the broader goals of sustainable urban development and managing green technology adoption in Jordan. Doi: 10.28991/CEJ-2024-010-08-016 Full Text: PDF

New insights into the function and mechanisms of piRNA PMLCPIR in promoting PM2.5-induced lung cancer

IntroductionExtensive studies have established the correlation between long-term PM2.5 exposure and lung cancer, yet the mechanisms underlying this association remain poorly understood. PIWI-interacting RNAs (piRNAs), a novel category of small non-coding RNAs, serve important roles in various diseases. However, their biological function and mechanism in PM2.5-induced lung cancer have not been thoroughly investigated. ObjectivesWe aimed to explore the oncogenic role of piRNA in lung cancer induced by PM2.5 exposure, as well as the underlying mechanisms. MethodsWe conducted a PM2.5-induced human lung epithelial cell malignant transformation model. Human samples were used to further verify the finding. In vitro proliferation, migration, and invasion assays were performed to study the function of piRNA. RNA-sequencing was used to elucidate the the mechanisms of how piRNA mediates cell functions. PiRNA pull-down and computational docking analysis were conducted to identify proteins that binding to piRNA. In vivo experiments were used to explore whether inhibition of PMLCPIR could have a therapeutic effect on lung cancer. ResultsWe identified a new up-regulated piRNA, termed PM2.5-induced lung cancer up-regulation piRNA (PMLCPIR), which promotes the proliferation of PM2.5-transformed cells and lung cancer cells. RNA sequencing revealed ITGB1 as a downstream target of PMLCPIR. Importantly, PMLCPIR binds to nucleolin (NCL) and increases the expression of its target gene, ITGB1, thereby activating PI3K/AKT signaling. The inhibition of PMLCPIR could promote apoptosis in lung cancer cells and enhance their chemosensitivity to anti-tumor drugs. ConclusionWe systematically identified the alterations of piRNA expression profiles in the PM2.5-induced malignant transformation model. Then, PMLCPIR was recognized as a novel oncogenic piRNA in PM2.5-induced lung cancer. Mechanically, PMLCPIR binds to NCL, enhancing ITGB1 expression and activating the ontogenetic PI3K/AKT signaling, potentially contributing to lung cancer progression. This study provides novel insights into the revelation of a new epigenetic regulator in PM2.5-induced lung cancer.

Low expression of Lnc-ENST00000535078 inhibits the migration, invasion, and enhances apoptosis of CTPE-induced malignantly transformed BEAS-2B cells.

Long non-coding RNA (LncRNA) plays an important role in malignant transformation of cells. This study aimed to explore the role of Lnc-ENST00000535078 in the malignant transformation of immortalized human bronchial epithelial cells (BEAS-2B) induced by coal tar pitch extract (CTPE). The malignant transformation model of BEAS-2B cells exposed to CTPE. Cell proliferation was examined by Cell counting kit-8 (CCK-8) assay. Colony formation assay was used to assess the colony of cells. Cell migration and invasion were detected by Transwell analysis. Cell cycle progression and apoptotic status were assessed by flow cytometry. Differentially expressed genes were screened by RNA sequencing. The results showed that Lnc-ENST00000535078 expression was highest in malignantly transformed BEAS-2B cells passaged to the 30th generation. Knockdown of Lnc-ENST00000535078 inhibited the migration, invasion and anti-apoptotic abilities of malignantly transformed BEAS-2B cells. Transcriptome analysis found 608 differential genes. CCND1 and FOS genes were screened out because of their levels were positive correlation with the expression of Lnc-ENST00000535078, which were consistent with the RNA sequencing results. In conclusion, Low expression of Lnc-ENST00000535078 inhibits the migration and invasion of malignant transformed BEAS-2B cells and promotes apoptosis in these cells. Lnc-ENST00000556926 might affect the malignant transformation of cells through the regulation of CCND1 and FOS. This study may provide a potential target for intervention in CTPE-induced lung cancer.

Development of a MSMEs Digitalization Transformation Model: Optimization of The Use of Crowdfunding as an Alternative to Information Technology Based Capital Solutions

Development of a MSMEs Digitalization Transformation Model: Optimization of The Use of Crowdfunding as an Alternative to Information Technology Based Capital Solutions

Synphytoindication models of the anthropogenic transformation of ecosystems

Human activity related to the exploitation or protection of environmental elements requires the creation of forecasts of the consequences involved. Such forecasts are not possible without the construction of models of ecosystems and the processes occurring in them. One of the critical elements in modern forecasting of the dynamics of ecosystems is the modelling of their anthropogenic block. Our research aims to develop methods for assessing the anthropogenic transformation of ecosystems necessary for modelling their state and dynamics. In accordance with the goal, we set ourselves the following tasks: to determine the limits of anthropotolerance of plant species; to evaluate the hemeroby of plant groups by the methods of synphytoindication; to create models of mutual dependence between anthropogenic transformation and natural dynamics. To implement the tasks, we used standard geobotanical methods (creating a geobotanical description, classification of plant communities, and synphytoindication). Hemeroby of plant communities can be used as an indicator of the anthropogenic transformation of ecosystems. To do this, we measure the boundaries of anthropotolerance of the plant species that make up these groupings. The range of anthropotolerance of individual plant species can be determined by the 12 most common types of human activity. The strength of their impact on ecosystems was determined by the induced changes in above-ground phytomass. The transition from anthropotolerance of individual species to hemeroby of plant communities was carried out according to the classical synphytoindication technique. We created an 18-point scale by assigning three points to each classic type of hemoroby. The use of the synphytoindication scale allows modelling of the interdependence between anthropogenic transformation and the measurable characteristics of the ecosystem. An inverse linear relationship with a probability of approximation of 0.2 and a correlation coefficient of 0.45 is an indicator of natural dynamics and the level of anthropogenic transformation. The correlation coefficient is 0.29 for ecosystems that are not suitable for economic use, and 0.85 for the rest.

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.

The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents. Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes. Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs. The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.

GOLD PRICE PREDICTION IN INDONESIA BASED ON INTEREST RATE USING DISTRIBUTED LAG ALMON TRANSFORMATION

Gold is valued for its safety and profitability, driven by steady price changes and influenced by interest rates. Accurately predicting gold prices is very important to make the right investment decisions. This study aims to build a gold price model in Indonesia using the Almon transformation lag distribution and see gold price predictions based on the model that has been built. We used the data on gold prices and interest rates from January 2016 to December 2023. Based on the results of the analysis, the best Almon transformation model used in this study is the Almon model with a maximum lag length of 16 and the second polynomial degree. The prediction results have a MAPE of 16.49%, which shows that the Almon model can predict gold prices well for one year. This study contributes to the understanding of gold price dynamics amid economic variations. However, limitations in the model assumptions should be considered.

Evident structural anisotropies arising from near-zero particle asphericity in granular spherocylinder packings.

With magnetic resonance imaging experiments, we study packings of granular spherocylinders with merely 2% asphericity. Evident structural anisotropies across all length scales are identified. Most interestingly, the global nematic order decreases with increasing packing fraction, while the local contact anisotropy shows an opposing trend. We attribute this counterintuitive phenomenon to a competition between gravity-driven ordering aided by frictional contacts and a geometric frustration effect at the marginally jammed state. It is also surprising to notice that such slight particle asphericity can trigger non-negligible correlations between contact-level and mesoscale structures, manifested in drastically different nonaffine structural rearrangements upon compaction from that of granular spheres. These observations can help improve statistical mechanical models for the orientational order transformation of nonspherical granular particle packings, which involves complex interplays between particle shape, frictional contacts, and external force field.

The effects of internet use on the cultivation of non-timber forest products

Cultivating Non-Timber Forest Products (NTFPs) serves as a crucial means to increase the income of impoverished households in remote areas, while also providing an important avenue towards achieving equitable economic development and environmental preservation. This paper explores the impact of the internet use on households' NTFPs cultivation practices. Based on the available dataset of 641 valid questionnaire responses collected through field research conducted by the research team, this study employs an in-depth analysis utilizing endogenous transformation models. Furthermore, the study recognizes the importance of considering heterogeneity among different user groups and purposes. The results indicate that households who use the internet with the proportion of NTFPs in their forest land being 8.863 % higher than others. Households who use the internet for sales having a proportion of NTFPs in their forest land that is 12.083 % higher than that of non-internet-using households, while households who use it for production only has a 6.971 % increase. Further analysis reveals that internet use increases households' social capital levels, thereby promoting NTFP cultivation, which is attributable to the rise of special e-commerce models such as WeChat business. Furthermore, the use of the internet to some extent reduces the supply of agricultural labor.