Potential Expansion Research Articles

MOODv2: Masked Image Modeling for Out-of-Distribution Detection.

The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples. While previous methods predominantly leaned on recognition-based techniques for this purpose, they often resulted in shortcut learning, lacking comprehensive representations. In our study, we conducted a comprehensive analysis, exploring distinct pretraining tasks and employing various OOD score functions. The results highlight that the feature representations pre-trained through reconstruction yield a notable enhancement and narrow the performance gap among various score functions. This suggests that even simple score functions can rival complex ones when leveraging reconstruction-based pretext tasks. Reconstruction-based pretext tasks adapt well to various score functions. As such, it holds promising potential for further expansion. Our OOD detection framework, MOODv2, employs the masked image modeling pretext task. Without bells and whistles, MOODv2 impressively enhances 14.30% AUROC to 95.68% on ImageNet and achieves 99.98% on CIFAR-10.

Fetal Brain MRI Findings in Myotonic Dystrophy and Considerations for Prenatal Genetic Testing.

Congenital myotonic dystrophy type 1 (DM1) is a rare congenital neuromuscular disorder associated with high morbidity and potential early mortality requiring lifelong symptomatic management. Prenatal presentations of DM1 have been associated with nonspecific ultrasound findings such as clubbed foot, polyhydramnios, ventriculomegaly, and decreased fetal movement, but many cases of DM1 have no ultrasound anomalies. We sought to compare the clinical course and prenatal imaging findings in two cases of DM1 using retrospective chart review. This report demonstrates potential expansion of the prenatal phenotype of DM1 including fetal SVT and frontal bossing. Both cases shared unique prenatal imaging features of lateral ventricle dilation involving the anterior bodies and frontal horns on fetal MRI. Because congenital DM1 is most often maternally inherited, attention to maternal symptoms, physical examination, and family history can be helpful in recognizing cases. Molecular diagnosis of DM1 requires specialized testing of the 3' untranslated region of the DMPK gene, and DM1 will not be detected by current standard prenatal genetic testing with microarray, karyotype, or exome sequencing.

Deep-Eutectic-Solvent-Decorated Metal–Organic Framework for Food and Environmental Sample Preparation

Deep eutectic solvent (DES) is distinguished by its unique solvent properties, chemical stability, and eco-friendly nature, which are pivotal in a spectrum of chemical processes. It enhances the sample preparation process by increasing efficiency and minimizing the environmental impact. Metal–organic frameworks (MOFs), which are porous structures formed through coordination bonds between metal ions and organic ligands, are defined by their adjustable pore dimensions, extensive surface areas, and customizable architectures. The integration of DES within MOF to create DES@MOF capitalizes on the beneficial attributes of both materials, augmenting MOFs’ stability and versatility while providing a multifunctional carrier for DES. This composite material is both highly stable and readily tunable, establishing it as a leading contender for applications in sample preparation for food and environmental samples. This comprehensive review explores the application of DES-decorated MOF in food and environmental sample preparation and highlights the expansive potential of DES@MOF in diverse fields. We provide a detailed analysis of the characteristics of DES@MOF and its individual components, methods for decorating MOFs with DES, the advantages of these composite materials in sample pretreatment, and their specific applications in food safety and environmental monitoring. DESs are employed to modify MOFs, offering a multitude of benefits that can substantially improve the overall performance and applicability of MOFs. The review also discusses current challenges and future directions in this field, offering valuable insights for further research and development. The synergistic effects of DES and MOFs offer new opportunities for applications in food safety and other areas, leading to the development of more efficient, sensitive, and environmentally friendly analytical methods. This collaboration paves the way for sustainable technologies and innovative solutions to complex challenges.

Towards faster and robust solution for dynamic LR and QR factorization.

Dynamic LR and QR factorization are fundamental problems that exist widely in the control field. However, the existing solutions under noises are lack of convergence speed and anti-noise ability. To this end, this paper incorporates the advantages of Dynamic-Coefficient Type (DCT) and Integration-Enhance Type (IET) Zeroing Neural Dynamic (ZND), and proposes an Adaptive and Robust-Enhanced Neural Dynamic (AREND). On this basis, a Strategy of Integration-Coupling (SIC) is proposed to address multiple error function problems, improving model stability and application scenarios. This strategy is experimentally proven to be effective and has potential expansion capability. After that, the convergence and robustness of our AREND is theoretically analyzed. Furthermore, the proposed AREND is verified by numerical experiments of low-to-high dimensional factorization in comparison with existing solutions. Finally, the real-time 3-D Angle of Arrival (AoA) localization in multiple high-noise conditions, is validated to the accuracy of the proposed model. Code is available at https://github.com/Alana2a3/AREND-Code-Implementation .

Abstract 4147632: Unusually Expeditious Expansion of an Atrial Myxoma

Description of the Case Report: A 44-year-old female with a past medical history of unspecified skin cancer presented with exertional dyspnea for 1 day before admission and syncope 7 months before admission. Physical exam was noted for soft low low-pitched diastolic murmur at the cardiac apex, not appreciated on prior exam. Echocardiography revealed a 3.3x4.7 cm pedunculated, homogenous mass in the left atrium obstructing the mitral valve. Serum BNP was measured as 326. An echocardiogram from less than 29 months prior was reviewed and showed no mass in the left atrium. The patient underwent successful surgical resection of the mass. The mass measured approximately 7x6x2 cm. Histologic analysis reported a proliferation of myxoid stroma with cytologically bland spindle cells positive for calretinin, compatible with cardiac myxoma. Following the surgery, the patient experienced resolution of symptoms. Discussion: Atrial myxomas are the most common primary cardiac tumors. They present as mitral valve obstruction, mitral valve regurgitation, arrhythmias, or systemic thromboembolism. They are exceedingly rare with an incidence of 0.5 to 1 case per million individuals annually and typically resected immediately. Therefore, instances with well-documented growth rates are uncommon. In this case, we present an unusually fast-growing myxoma in a woman with shortness of breath and syncope. The reported growth rate of atrial myxomas ranges from 1.3 to 6.9 mm a month. Our patient had a negative echocardiogram less than 2.5 years before her presentation, followed by the development of a 7 x 6 x 2 cm mass. Since the onset is unknown, the precise growth rate of our patient's tumor is unknown but was at least 24 mm/month. It is important to consider myxoma in the differential diagnosis of a woman with new onset murmur and syncopal episode. Furthermore, recognizing the potential for rapid expansion is crucial to assessing the risks versus benefits of myxoma section.

CTIM-06. OUTCOME OF PATIENTS WITH RECURRENT MEDULLOBLASTOMA AND EPENDYMOMA TREATED WITH PEMBROLIZUMAB, AN IMMUNE CHECKPOINT INHIBITOR: A PEDIATRIC BRAIN TUMOR CONSORTIUM STUDY (PBTC045)

Abstract BACKGROUND Children with recurrent medulloblastoma and ependymoma typically have a dismal prognosis. Immune checkpoint blockade has revolutionized treatment of some resistant cancers. PBTC045 is a safety and preliminary efficacy study of pembrolizumab, a PD-1 inhibitor, in children with recurrent/progressive brain tumors. Here we present the strata that included children with recurrent/progressive ependymoma and medulloblastoma. METHODS Eligibility included patients aged 1-21 years old with recurrent/progressive ependymoma or medulloblastoma and with functional scores ≥70, no greater than physiologic steroid usage and standard washout periods, among other eligibility criteria. Pembrolizumab was administered at 2mg/kg IV every three weeks. Tumor tissue (if available) and serum were collected for correlative studies. Primary endpoints included toxicity and response, and two stages were planned requiring objective responses for potential expansion. RESULTS Patients with progressive ependymoma (Stratum D, n=13) or medulloblastoma (Stratum E, n=14, one ineligible) were enrolled. The median age at diagnosis was 3.37 years (range, 1-12.7) and 7 years (range, 2.2-13.1), respectively. The median age at study entry was 8.5 years and 11.4 years, respectively. There were no first-stage objective responses in either stratum, and thus the expansion arms were not initiated. Grade 3 or greater treatment-related adverse events included rash (n=1), anemia (n=1) and neutropenia (n=2), with no dose-limiting toxicities. The median progression-free survival (PFS) for ependymoma and medulloblastoma were 1.38 months (range, 1.08-1.51) and 1.41 months (range, 1.22-3.45), respectively. Immune correlative analyses are ongoing. CONCLUSIONS Immune checkpoint inhibition holds potential for CNS cancer treatment, and as a single agent, is well tolerated. However, in the current study, checkpoint inhibition monotherapy does not appear to significantly prolong survival or induce radiographic responses in children with recurrent/progressive ependymoma or medulloblastoma. Potential incorporation of checkpoint inhibitors into treatment for these patients will likely require combinatorial approaches that may be informed by the correlative science.

Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.

Expansive conceptualisation of the impact of continuous learning programmes: An analysis of research-based workshops in higher vocational education and training

Finnish higher education institutions offer continuous learning opportunities, including professional specialisation education programmes for the health and social sector. This article focuses on the conceptualisation of the impact of these programmes, which was the topic of discussion by education and working-life actors in research-based workshops. Drawing on the cultural–historical activity theory (CHAT) approach, the study seeks to answer the following research questions: What are the main themes through which the participants conceptualise the impact of education? How do the themes expand the conceptualisation of the impact of education? A qualitative study analysed data from the multi-professional discussions facilitated by researcher-developers in online workshops during the COVID-19 pandemic. The findings describe the impact-related discussion themes and analyse their expansive potential. Participants’ discursive actions in research-based online workshops manifest expansive conceptuali-sation on multiple dimensions, but also reveal dilemmatic impact-related questions that reflect the underlying contradictions of continuous learning programmes in the health and social sector.

Hyperparameter Optimization for Atomic Cluster Expansion Potentials.

Machine learning-based interatomic potentials enable accurate materials simulations on extended time- and length scales. ML potentials based on the atomic cluster expansion (ACE) framework have recently shown promising performance for this purpose. Here, we describe a largely automated computational approach to optimizing hyperparameters for ACE potential models. We extend our openly available Python package, XPOT, to include an interface for ACE fitting, and discuss the optimization of the functional form and complexity of these models based on systematic sweeps across relevant hyperparameters. We showcase the usefulness of the approach for two example systems: the covalent network of silicon and the phase-change material Sb2Te3. More generally, our work emphasizes the importance of hyperparameter selection in the development of advanced ML potential models.

Introducing a prediction method for the photodegradation of p-cresol, a phenolic contaminant of emerging concern, in wastewater effluent

Despite extensive efforts to understand the photodegradation of phenolic contaminants of emerging concern (PhCECs) in aquatic systems, prediction methods, especially in waters containing effluent organic matter (EfOM), remain underdeveloped. This study introduces a prediction method for p-cresol, a representative PhCECs, based on correlations between EfOM optical parameters and p-cresol kinetic parameters. We examined p-cresol photodegradation in various EfOM samples, characterized by their optical properties, and used the reaction rate coefficient between EfOM and p-cresol, α3EfOM⁎, to quantify and predict p-cresol degradation in different wastewater effluent samples. Results showed significant correlations between p-cresol's photodegradation rate constant (0.144 to 0.441 h−1) and EfOM characteristics, with α3EfOM⁎ values ranging from 4 × 1011 to 10 × 1011 M−1 s−1. The method was validated with p-cresol at concentrations ranging from 25 to 100 μM and multiple EfOM samples. The method's applicability was further evaluated using propranolol, a pharmaceutical contaminant of emerging concern, demonstrating its versatility for predicting the degradation behavior of other contaminants in different wastewater samples. The method accurately predicted p-cresol and propranolol degradation across diverse wastewater samples, suggesting its potential for expansion to other classes of contaminants, aiding in water quality management, improving wastewater treatment processes, and enhancing environmental risk assessments.

Pangenome and pantranscriptome as the new reference for gene family characterisation – a case study of basic helix-loop-helix (bHLH) genes in barley

Genome-wide identification and comparative gene family analyses have been commonly performed to investigate species-specific evolution linked to various traits and molecular pathways. However, most previous studies were limited to gene screening in a single reference genome, failing to account for the gene presence/absence variations (gPAVs) in a species. Here, we propose an innovative pangenome-based approach of gene family analyses based on orthologous gene groups (OGGs). Using the basic helix-loop-helix (bHLH) transcription factor family in barley as an example, we identified 161 ∼ 176 bHLHs in 20 barley genomes, which could be classified into 201 OGGs. These 201 OGGs were further classified into 140 core, 12 soft-core, 29 shell, and 20 line-specific/cloud bHLHs, revealing a complete profile of bHLH in barley. Using a genome-scan approach, we overcome the genome annotation bias and identified on average 1.5 un-annotated core bHLHs per barley genome. We found that all core bHLHs belong to whole genome/segmental duplicates whilst dispensable bHLHs were more likely to result from small scale duplication events. Interestingly, we noticed that the dispensable bHLHs tended to enrich in specific subfamilies SF13, SF27, and SF28, implying the potential biased expansion of specific bHLHs in barley. We found that 50% of the bHLHs contain at least one intact transposon element within the 2kb upstream-to-downstream region. bHLHs with CNV have 1.48 TEs on average, significantly higher than 1.36 for core bHLH without CNV, supporting TEs’ potential role in bHLH expansion. Selection pressure analyses showed that dispensable bHLHs had experienced clear relaxed selection compared to core bHLHs, consistent with their conservation patterns. We further integrate pangenome with recently available barley pantranscriptome data in 5 tissues and discovered apparent transcriptional divergence within and across bHLH subfamilies. We conclude that pangenome-based gene family analyses can better describe the genuine evolution status of bHLHs untapped before and provided novel insights into bHLH evolution in barley. We expect this study will inspire similar analyses in many other gene families and species.

Experimental and Numerical Investigations of Low-Permeability Sandstone Under Water Injection–Induced Dilation in West Oilfield, South China Sea

With the development of offshore oil fields, the reservoirs of X oilfield in the west of the South China Sea are poor in physical property, serious in pollution, and increasingly prominent in interlayer contradictions. Water injection dilation technology has strongly affected the development of loose sandstone reservoirs. To explore whether this technology applies to the low-permeability sandstone of X oilfield in the west of the South China Sea and the dilation effect and radius of water injection dilation technology on the target reservoir, low confining pressure rock mechanics experiments and numerical simulation of water injection in this reservoir section are carried out. The triaxial shear experiment of low confining pressure shows that the target reservoir sandstone with low-permeability can have a shear strength of 45 MPa when the effective confining pressure is 0.5 MPa, and the target reservoir core can have dilatancy. When the axial strain is 2.5%, the core dilatancy is 1%, and the permeability changes by 1.17 times. It was found that the core volume dilation was obviously under low effective confining pressure, and the permeability is 2 orders higher than in the initial condition. The numerical simulation of the target reservoir shows that the bottom-hole pressure reaches 47.12 MPa at the end of water injection in typical wells. The reservoir was deformed to different degrees around the well, and the top layer was raised by 5.58 mm. This paper characterizes the rock expansion potential and expansion flow capacity of low-permeability sandstone reservoirs from multiple perspectives and establishes a three-dimensional, full-size wellbore formation crustal stress strict matching geological model for offshore expansion wells. We have provided theoretical guidance for on-site construction.

Exploration and innovative application of digital technology in pediatric dentistry

Pediatric dentistry treats patients aged 0-18 years, encompassing the characteristics of general dentistry. The clinical techniques are closely related to restorative dentistry, endodontics, orthodontics, and maxillofacial surgery, while also possessing unique aspects. It particularly focuses on the growth and development patterns of children's dentition and maxillofacial structures. Currently, digital technology has deeply integrated with various clinical disciplines of dentistry, and pediatric dentistry has made significant progress with pediatric-specific digital applications. On one hand, the introduction of mature digital technologies is enabling the digital transformation of pediatric dental treatment models; on the other hand, there is innovative development of new digital technologies tailored to the specific needs of pediatric dental care, filling gaps in the field. Based on years of research in digital technology for pediatric dentistry, our team details the current innovative applications and scientific explorations in this area from these two perspectives. Additionally, we anticipate the potential expansion of digital technologies not yet applied in pediatric dentistry, providing a reference for clinical scholars in the field to conduct exploratory research.

Uranium Mill Tailings Generation and Management Challenges

Uranium mining has been ongoing for the past 200 years, yet managing uranium waste remains a significant challenge. This difficulty arises due to strict regulations imposed on uranium-related industries, driven by the assumed health risks associated with radiation. As a result, much of the waste generated by nuclear industries is currently stored. In India, uranium mining and milling waste is stored that require large tracts of land and continuous monitoring. Given the high value of land in India and the potential expansion of uranium mining to meet net-zero emission targets, an effective solution for managing uranium mill tailings is urgently needed. Meanwhile, India's construction sector is rapidly growing that require a huge quantity of materials. This paper reviewed the history of uranium mining and mill tailings generation and propose that the construction industry could effectively utilize these tailings. This approach not only addresses resource scarcity but also promotes environmental sustainability by conserving natural resources, reducing ecological imbalances, and advancing a circular economy.

Puzzlingly Low Utilization of Solar Irrigation Pumps by Smallholders in Nepal Undermines Cost-Effectiveness

Abstract Solar Powered Irrigation Pumps (SIPs) hold substantial potential for low carbon irrigation expansion, particularly where affordable electricity is limited. In contrast to diesel-based irrigation, which carries steep fuel costs, irrigation by SIPs requires zero marginal costs, but high initial investments. This makes their competitiveness with diesel pumps highly dependent on the temporal frequency of their usage. Using unique and detailed data on SIP usage by smallholders in Nepal, we show SIP usage frequency is low, making it financially competitive with Diesel for only a small fraction of farmers. We analyze characteristics of farmers who make low/high usage of the SIP, and explore potential explanations for the puzzling low level of SIP use.

An Industrial Economics Perspective on the Economic Valuation of Cornus officinalis: A Case Study from Xixia County, Henan Province

The dual nature of Cornus officinalis as both a medicinal and edible resource endows it with expansive market potential. This research develops an analytical framework to evaluate the economic value of Cornus officinalis through the lens of industrial economics. Utilizing theoretical exploration and a case study centered on Xixia County, Henan Province, the study draws several key conclusions: First, within the primary industry, Cornus officinalis contributes to the enhancement of farmers’ incomes and fosters the growth of local agriculture-related sectors through the cultivation of traditional Chinese medicinal herbs. Second, from the perspective of the secondary industry, Cornus officinalis generates substantial economic worth via food processing and pharmaceutical commerce. Finally, in the realm of the tertiary industry, the economic value of Cornus officinalis is predominantly realized through leisure and wellness services, as well as the tourism sector.

The Oriental hornet, Vespa orientalis Linnaeus, 1771 (Hymenoptera, Vespidae): diagnosis, potential distribution, and geometric morphometrics across its natural distribution range.

We present a short review of the biology, diagnostic characteristics, and invasiveness of the Oriental hornet, Vespa orientalis. We also performed an analysis of the shape of the forewings (geometric morphometrics) of different geographic groups along their native distribution and their potential geographical distribution using the MaxEnt entropy modeling. Our results show a wide potential expansion range of the species, including an increase in environmentally suitable areas in Europe, Asia, and Africa but more especially the Western Hemisphere, where the species was recently introduced. The geometric morphometric analysis of the forewings shows that there are three different morphogroups: one distributed along the Mediterranean coast of Europe and the Middle East (MEDI), another along the Arabian Peninsula and Western Asia but excluding the Mediterranean coast (MEAS), and one more in northern Africa north of the Sahara and south of the Mediterranean coast (AFRI), all of which show differences in their potential distribution as a result of the pressure from the different environments and which will also determine the capacity of the different morphogroups to successfully invade new habitats.

All-cause Years of life lost due to death at municipality level in Norway. Association to educational attainment and median income

Abstract Background Norway, with 5.5 million people spread across 356 municipalities, has pursued an active policy to reduce social and geographical health inequalities. Despite, studies find significant health disparities linked to socioeconomic factors at the individual-level, although evidence at larger geographical-levels has been mixed. We used a key burden of disease (BoD) metric, Years of Life Lost (YLL), to examine spatial health inequities among Norwegian municipalities and their association with socioeconomic factors. Methods We analysed data from all residents in Norwegian municipalities with over 1000 inhabitants as of January 1, 2019. Data was sourced from the National Population Register and Statistics Norway. All-cause YLL were calculated based on age-specific aspirational life expectancy obtained from GBD 2019. Deprivation was measured using educational attainment and median income, categorized into quartiles. Geographical maps illustrating municipality-level all-cause age-standardised YLL rates for males and females were generated. Negative binomial regression models assessed associations between socioeconomic factors and YLL rates, adjusting for age, sex, and an index of centralisation. Results High-deprivation municipalities exhibited a 9% higher YLL rate in terms of education (Incidence Rate Ratio [IRR] = 1.09, 95% Confidence Interval [CI] = 1.01-1.17) and a 10% higher YLL rate in terms of income (IRR = 1.10, 95% CI = 1.02-1.08) compared to the least deprived, translating to absolute differences of 1391 and 1466 YLL per 100,000 population, respectively. Additionally, we discuss the potential expansion of this study to incorporate cause-specific outcomes and other BoD metrics based on previous county-level analyses. Conclusions Our study demonstrates the feasibility of using BoD methods to assess geographical deprivation and premature mortality at the municipality level, providing valuable insights for public health policy targeted at small administrative areas.

Evaluation of Managerial Economics Applications Strategy using the SPSS Method

Managerial Economics is an economy branch is, this economic theory and size using methods solve real-world managerial problems. It helps businesses and organizations make informed decisions by using economic analysis. Here are some common applications of managerial economics: Demand Analysis and Forecasting: Businesses use managerial economics to understand consumer behavior and forecast future demand for their products or services. This involves studying factors that affect demand, such as price, income levels, and consumer preferences. Production and Cost Analysis: Managerial economics helps businesses optimize production processes and minimize costs. It involves analyzing production functions, cost structures, and economies of scale to determine the most efficient way to produce goods or services. Pricing Strategies: A product or the right price for the service It is up to a company to decide Crucial to profitability. Managerial economics helps businesses set prices by considering factors like production costs, competition, and consumer willingness to pay. Market Structure Analysis: Understanding the type of market, a company operates in (e.g., perfect competition, monopoly, oligopoly) is essential for making strategic decisions. Managerial economics helps analyses market structures and determine appropriate strategies for each. Risk and Uncertainty Analysis: Businesses often face uncertainty in various aspects, such as demand fluctuations, production costs, and market conditions. Managerial economics provides tools to assess and manage risks, including techniques like decision tree analysis and expected value calculations. Capital Budgeting and Investment Analysis: Managerial economics assists in evaluating investment opportunities and capital projects. It involves methods Payback period analysis of investments. Regulatory and Government Policy Analysis: Managerial economics helps businesses understand and navigate government regulations and policies that affect their operations. This can include areas like taxation, environmental regulations, and trade policies. Strategic Planning: Using economic analysis, businesses can develop long-term strategies for growth and sustainability. This may involve assessing market trends, competitive dynamics, and potential expansion opportunities. Game Theory and Competitive Strategy: Managerial economics incorporates game theory to analyze strategic interactions between competitors. It helps businesses formulate optimal strategies in competitive environments. Resource Allocation: Efficient allocation of resources, including labor, capital, and technology, is crucial for a company's success. Managerial economics provides tools to distribute funds in a manner that optimisesoverall productivity and profitability. Demand Analysis and Forecasting, Production and Cost Analysis, Pricing Strategies, Market Structure Analysis, Risk and Uncertainty Analysis, Capital Budgeting and Investment Analysis and Regulatory and Government Policy Analysis. The Cranach’s Alpha Reliability result. The overall Cranach’s Alpha value for the model is .744 which indicates 74% reliability. From the literature review, the above 50% Cranach’s Alpha value model can be considered for analysis.

Identifying key monitoring areas for tree insect pest risks in China under climate change.

Climate change can exacerbate pest population growth, posing significant threats to ecosystem functions and services, social development, and food security. Risk assessment is a valuable tool for effective pest management that identifies potential pest expansion and ecosystem dispersal patterns. We applied a habitat suitability model coupled with priority protection planning software to determine key monitoring areas (KMA) for tree insect pest risks under climate change and used forest ecoregions and nature reserves to assess the ecological risk of insect pest invasion. Finally, we collated the prevention and control measures for reducing future pest invasions. The KMA for tree insect pests in our current and future climate is mainly concentrated in eastern and southern China. However, with climate change, the KMA gradually expands from southeastern to northeastern China. In the current and future climate scenarios, ecoregions requiring high monitoring levels were restricted to the eastern and southern coastal areas of China, and nature reserves requiring the highest monitoring levels were mainly distributed in southeastern China. Tree insect pest invasion assessment using ecoregions and nature reserves identified that future climates increase the risk of pest invasions in forest ecoregions and nature reserves, especially in northeastern China. The increased risk and severity of tree insect pest invasions require implementing monitoring and preventative measures in these areas. We effectively assessed the pest invasion risks using forest ecoregions and nature reserves under climate change. Our assessments suggest that monitoring and early prevention should focus on southeastern and northeastern China.