Robust Evaluation Research Articles

Breaking ground in ESG assessment: Integrated DEA and MCDM framework with spherical fuzzy sets for Vietnam's wire and cable sector

In corporate sustainability reporting, the credibility of environmental, social, and governance (ESG) information is paramount, especially for socially responsible investors relying on ESG metrics. This study proposes an integrated method combining Data Envelopment Analysis (DEA), Analytic Hierarchy Process (SF-AHP), and Weighted Aggregated Sum Product Assessment (SF-WASPAS) under the Spherical fuzzy environment to assess the ESG performance of Vietnam's wire and cable companies by integrating quantitative indicators from financial statements and qualitative factors from literature reviews and expert interviews. Firstly, the DEA-Malmquist model gauges the efficiency performance of the ten wire and cable enterprises through five critical financial indicators. Secondly, using expert opinions, the SF-AHP method determines the weights of 16 selected ESG criteria, offering a qualitative dimension to the evaluation. Subsequently, the SF-WASPAS approach ranks the ten businesses based on aggregated scores derived from the criteria, forming a robust foundation for ranking. Furthermore, a comparative analysis demonstrates the practicality and reliability of our proposed framework against the Technique for Order of Preference by Similarity to Ideal Solution (SF-TOPSIS) and Fermatean fuzzy WASPAS. While the Malmquist results highlight SAM Holding Corporation (A6) with the highest average efficiency score, a holistic assessment across all three ranking methods places Vietnam Electric Cable Corporation (A1) at the forefront, closely followed by Hai Phong Electronic Mechanical JSC (A2). By harmoniously blending quantitative and qualitative perspectives and employing robust evaluation techniques, this study extends to effective resource management, targeted investor attraction, and heightened stakeholder engagement, offering substantial benefits to the wire and cable industry's ESG endeavours.

In-Line Detection of Bed Fluidity in Gas-Solid Fluidized Beds Using Near-Infrared Spectroscopy.

A novel approach was developed to detect bed fluidity in gas-solid fluidized beds using diffuse reflectance near-infrared (NIR) spectroscopy. Because the flow dynamics of gas and solid phases are closely associated with the fluidization state, the fluidization quality can be evaluated through hydrodynamic characterization. In this study, the baseline level of NIR spectra was used to quantify the voidage of the fluidized bed. Two indicators derived from the NIR baseline fluctuation profiles were investigated to characterize bed fluidity, named bubble proportion and skewness. To establish a robust fluidity evaluation method, the relationships between the indicators and bed fluidity were investigated under different conditions firstly, including static bed height and average particle size. Then, a generalized threshold was identified to distinguish poor and good bed fluidity, ensuring that the probability of the α- and β-errors was less than 15% regardless of material conditions. The results show that both indicators were sensitive to changes in bed fluidity under the investigated conditions. The indicator of skewness was qualified to detect bed fluidity under varied conditions with a robust threshold of 1.20. Furthermore, the developed NIR method was successfully applied to monitor bed fluidity and for early warning of defluidization in a laboratory-scale fluidized bed granulation process.

A benchmarking methodology for top-of-rail products

Top-of-rail (TOR) products are designed to optimize adhesion, reduce wear and mitigate noise from the wheel-rail interface. Prior to testing these products in field conditions, a laboratory approach is suitable because it is not time-consuming and expensive. This study aims to design a benchmarking methodology to investigate the performance of TOR products using a standard tribometer in a ball-on-disc configuration. The resulting methodology is based on a multi-parameter approach enabling the assessment of TOR products from multiple perspectives. The core of the methodology is formed by traction tests and a robust evaluation phase resulting in a performance map dividing TOR products into four performance classes, Q1-Q4, based on which a suitable TOR product can be chosen for further testing.

(Invited) wearable Elastography Via Surface Mechano-Acoustic Sensing for Continuous Monitoring of Tissue Stiffness

Recent progress in wearable devices provides new opportunities for precise, non-invasive, long-term recording of body mechanics. The soft device incorporating a single MEMS accelerometer captures subtle vibration of the skin with a resolution of 1×10-4 g/√Hz in the frequency range from 0 to 800 Hz. In this study, we introduce an on-body mechano-acoustic sensing technology based on a skin-mounted accelerometer array to assess the mechanical profiles of subdermal tissues in-vivo, similar to seismology. A system-level wearable device construction, optimized for a comfortable skin interface and high precision, incorporates a broadband dual-accelerometer sensor, an audio actuator, and a Bluetooth System-on-Chip and enables a wireless, automated operation. An automated algorithm, leveraging the spectral analysis of surface waves (SASW) methods, computes the depth-sensitive, elastic modulus information of the propagation media from the mechanical dispersion relationship. Comprehensive theoretical and experimental investigation on bi-layer phantom materials and biological tissues, with a storage moduli range of 19–1439 kPa, demonstrates the capability of rapid and robust evaluation of modulus in a depth range of 2–46 mm. The device identifies the softening of porcine tissues with increasing injected water content and the changes of modulus of muscle under different levels of tension. The results are in agreement with the in-parallel Ultrasound Elastography measurements. A quantitative assessment of the stiffness profile of the bicep brachii and rectus femoris muscle during gym exercises demonstrate the device operation in an ambulant environment for a non-invasive and continuous assessment of deep tissue stiffness with a temporal resolution of 0.5 s.

Colorectal cancer disparities in Latin America: Mortality trends 1990-2019 and a paradox association with human development.

Colorectal cancer mortality is growing in Latin America. It is known for a marked income disparity between its countries, and there is a consistent association with development. Our purpose was to describe trends in colorectal cancer mortality in Latin America between 1990 and 2019, identifying differences by human development categories. We extracted age-adjusted mortality rate from the Global Burden of Disease (GBD) Study from 22 Latin American countries, subregions, and country groups previously ranked by the GBD study due to Sociodemographic Index (SDI) between 1990 and 2019. We applied the segmented regression model to analyze the time trend. Also, we estimated the correlation between mortality rates and Human Development Index (HDI) categories for countries. Between 1990 and 2019, colorectal cancer adjusted mortality rate increased by 20.56% in Latin America (95% CI 19.75% - 21.25%). Between 1990 and 2004, the average annual percentage change (APC) was 0.11% per year (95% CI 0.10-0.12), and between 2004 and 2019 there was a deceleration (APC = 0.04% per year, 95% CI 0.03%- 0.05%). There is great heterogeneity among the countries of the region. Correlation between these two variables was 0.52 for 1990 and 2019. When separated into HDI groups, the correlation varied in the direction of the association and its magnitude, typifying an effect modification known as Simpson's Paradox. Human development factors may be important for assessing variation in cancer mortality on a global scale. Studies that assess the social and -economic contexts of countries are necessary for robust evaluation and provision of preventive, diagnostic and curative services to reduce cancer mortality in Latin America.

Potential biomarkers to predict return to fertility after discontinuation of female contraceptives-looking to the future.

Nowadays there are multiple types of contraceptive methods, from reversible to permanent, for those choosing to delay pregnancy. Misconceptions about contraception and infertility are a key factor for discontinuation or the uptake of family planning methods. Regaining fertility (the ability to conceive) after contraceptive discontinuation is therefore pivotal. Technical studies to date have evaluated return to fertility by assessing pregnancy as an outcome, with variable results, or return to ovulation as a surrogate measure by assessing hormone levels (such as progesterone, LH, FSH) with or without transvaginal ultrasound. In general, relying on time to pregnancy as an indicator of return to fertility following contraceptive method discontinuation can be problematic due to variable factors independent of contraceptive effects on fertility, hormone clearance, and fertility recovery. Since the ability to conceive after contraceptive method discontinuation is a critical factor influencing product uptake, it is important to have robust biomarkers that easily and accurately predict the timing of fertility return following contraception and isolate that recovery from extrinsic and circumstantial factors. The main aim of this review is to summarize the current approaches, existing knowledge, and gaps in methods of evaluating return-to-fertility as well as to provide insights into the potential of new biomarkers to more accurately predict fertility restoration after contraceptive discontinuation. Biomarker candidates proposed in this document include those associated with folliculogenesis, cumulus cell expansion, follicular rupture and ovulation, and endometrial transport and receptivity which have been selected and scored on predefined criteria meant to evaluate their probable viability for advancement. The review also describes limitations, regulatory requirements, and a potential path to clinically testing these selected biomarkers. It is important to understand fertility restoration after contraceptive method discontinuation to provide users and health providers with accurate evidence-based information. Predictive biomarkers, if easy and low-cost, have the potential to enable robust evaluation of RTF, and provide potential users the information they desire when selecting a contraceptive method. This could lead to expanded uptake and continuation of modern contraception and inform the development of new contraceptive methods to widen user's family planning choices.

Self-directed self-management interventions to prevent or address distress in young people with long-term physical conditions: A rapid review.

Comorbid distress in adolescents and young adults with physical long-term conditions (LTCs) is common but can be difficult to identify and manage. Self-directed self-management interventions to reduce distress and improve wellbeing may be beneficial. It is unknown, however, which intervention characteristics are successful in supporting young people. This rapid review aimed to identify characteristics of self-directed self-management interventions that aimed, in whole or part, to address distress, wellbeing or self-efficacy in this population. A systematic search was conducted for relevant controlled studies in six databases. Data on study settings, population, intervention characteristics, outcome measures, process measures and summary effects were extracted. The risk of bias was assessed using the Cochrane Risk of Bias tool v1, and the strength of evidence was rated (informed by Grading of Recommendations, Assessment, Development and Evaluations). Patient and public involvement members supported the review process, including interpretation of results. The rapid review was registered with PROSPERO (ID: CRD42021285867). Fourteen studies were included, all of which were randomised trials. Heterogeneity was identified in the health conditions targeted; type of intervention; outcome measures; duration of intervention and follow-up. Three had distress, wellbeing or self-efficacy as their primary outcome. Four modes of delivery were identified across interventions-websites, smartphone applications, text messages and workbooks; and within these, 38 individual components. Six interventions had a significant benefit in mental health, wellbeing or self-efficacy; however, intervention characteristics were similar for beneficial and non-beneficial interventions. There is a paucity of interventions directly targeting distress and wellbeing in young people with physical LTCs. In those identified, the heterogeneity of interventions and study design makes it difficult to identify which characteristics result in positive outcomes. We propose the need for high-quality, evidence-based self-management interventions for this population; including (1) more detailed reporting of intervention design, content and delivery; (2) robust process evaluation; (3) a core outcome set for measuring mental health and wellbeing for self-management interventionsand (4) consistency in follow up periods. Seven young people with anLTC were involved throughout the rapid review, from the development of the review protocol where they informed the focus and aims, with a central role in the interpretation of findings.

Evidence-based guideline: unexplained infertility†.

Evidence-based guideline: unexplained infertility†.

Recent Advances in Fabrication of Durable, Transparent, and Superhydrophobic Surfaces.

Transparent superhydrophobic coatings have been extensively investigated due to their ability to provide self-cleaning properties for outdoor applications. However, the widespread implementation of these coatings on a large scale is impeded by the challenges of poor durability and complex fabrication procedures. In this review, the fundamentals and theories governing the mutually exclusive properties of superhydrophobicity, optical transparency, and susceptibility to wear are introduced, followed by a discussion of representative examples of advanced surface design and processing optimizations. Also, robust evaluation protocols for assessing mechanical and chemical stabilities are briefed and potential research directions are presented. This review can offer the research community a better understanding of durable and transparent superhydrophobic surfaces, thereby facilitating their development for real-world applications.

Water, Sanitation, and Hygiene Policies and the Enhancement of Quality Education in Lower and Upper Basic Schools within Region One Education Directorate, The Gambia

This study focused on the importance of School Water, Sanitation, and Hygiene (WASH) policy in enhancing the quality of education in Lower and Upper Basic Schools within Region One Education Directorate of The Gambia. The study utilised a mixed-methods approach, combining qualitative analysis of policy documents with quantitative data obtained from questionnaires administered to head teachers. The Human Rights-Based Approach (HRBA) and Systems Thinking provide valuable theoretical lenses to analyse the lack of WASH policies. The HRBA emphasises the recognition of WASH as a fundamental human right, while the Systems Thinking approach considers the interconnectedness and complexity of the WASH system. The findings indicated that a significant number of schools in the region lack a WASH policy or rely on general directives, leading to inconsistent approaches and practices in addressing WASH issues. This inconsistency results in inadequate provision of WASH facilities, ineffective hygiene education, and a lack of clear learning objectives for students. The absence of WASH-related guidance in key school management tools and policy documents further exacerbates the challenges. The study highlighted the need for a comprehensive School WASH policy to oversee, regulate, and provide guidance for the implementation of WASH standards in educational institutions. Recommendations included developing and implementing a comprehensive School WASH policy, enhancing the curriculum framework to ensure the coverage of WASH topics, providing clear guidelines and resources, incorporating WASH into policy documents, conducting capacity-building and awareness programmes, and establishing a robust monitoring and evaluation system.

<i>Corrigendum to</i>: A holistic approach towards a generalizable machine learning predictor of cell penetrating peptides

The development of machine learning (ML) predictors does not necessarily require the employment of expansive classifiers and complex feature encoding schemes to achieve the highest accuracy scores. It rather requires data pre-processing, feature optimization, and robust evaluation to ensure consistent results and generalizability. Herein, we describe a multi-stage process to develop a reliable ML predictor of cell penetrating peptides (CPPs). We emphasize the challenges of: (i) the generation of representative datasets with all required pre-processing procedures; (ii) comprehensive and exclusive encoding of peptides using their amino acid composition; (iii) obtaining an optimized feature set using a simple classifier (support vector machine, SVM); (iv) ensuring consistent results; and (v) verifying generalizability at the highest achievable accuracy scores. Two peptide sub-spaces were used to generate the negative examples, which are required, along with the known CPPs, to train the classifier. These included: (i) randomly generated peptides with all amino acid types being equally represented and (ii) extracted peptides from receptor proteins. Results indicated that the randomly generated dataset performed perfectly well within its own peptide sub-space, while it poorly generalized to the other sub-space. Conversely, the dataset extracted from receptor proteins, while achieving lower accuracies, showed a perfect generalizability to the other peptide sub-space. We combined the qualities of these two datasets by utilizing the average of their predictions within our ultimate framework. This functional ML predictor, WLVCPP, and associated software and datasets can be downloaded from <a ext-link-type="uri" href="https://github.com/BahaaIsmail/WLVCPP">https://github.com/BahaaIsmail/WLVCPP</a>.

Intensive community care services for children and young people in psychiatric crisis: an expert opinion

BackgroundChildren and young people’s (CYP) mental health is worsening, and an increasing number are seeking psychiatric and mental health care. Whilst many CYPs with low-to-medium levels of psychiatric distress can be treated in outpatient services, CYPs in crisis often require inpatient hospital treatment. Although necessary in many cases, inpatient care can be distressing for CYPs and their families. Amongst other things, inpatient stays often isolate CYPs from their support networks and disrupt their education. In response to such limitations, and in order to effectively support CYPs with complex mental health needs, intensive community-based treatment models, which are known in this paper as intensive community care services (ICCS), have been developed. Although ICCS have been developed in a number of settings, there is, at present, little to no consensus of what ICCS entails.MethodsA group of child and adolescent mental health clinicians, researchers and academics convened in London in January 2023. They met to discuss and agree upon the minimum requirements of ICCS. The discussion was semi-structured and used the Dartmouth Assertive Community Treatment Fidelity Scale as a framework. Following the meeting, the agreed features of ICCS, as described in this paper, were written up.ResultsICCS was defined as a service which provides treatment primarily outside of hospital in community settings such as the school or home. Alongside this, ICCS should provide at least some out-of-hours support, and a minimum of 90% of CYPs should be supported at least twice per week. The maximum caseload should be approximately 5 clients per full time equivalent (FTE), and the minimum number of staff for an ICCS team should be 4 FTE. The group also confirmed the importance of supporting CYPs engagement with their communities and the need to remain flexible in treatment provision. Finally, the importance of robust evaluation utilising tools including the Children’s Global Assessment Scale were agreed.ConclusionsThis paper presents the agreed minimum requirements of intensive community-based psychiatric care. Using the parameters laid out herein, clinicians, academics, and related colleagues working in ICCS should seek to further develop the evidence base for this treatment model.

Paediatric Medicinal Formulation Development: Utilising Human Taste Panels and Incorporating Their Data into Machine Learning Training.

This review paper explores the role of human taste panels and artificial neural networks (ANNs) in taste-masking paediatric drug formulations. Given the ethical, practical, and regulatory challenges of employing children, young adults (18-40) can serve as suitable substitutes due to the similarity in their taste sensitivity. Taste panellists need not be experts in sensory evaluation so long as a reference product is used during evaluation; however, they should be screened for bitterness taste detection thresholds. For a more robust evaluation during the developmental phase, considerations of a scoring system and the calculation of an acceptance value may be beneficial in determining the likelihood of recommending a formulation for further development. On the technological front, artificial neural networks (ANNs) can be exploited in taste-masking optimisation of medicinal formulations as they can model complex relationships between variables and enable predictions not possible previously to optimise product profiles. Machine learning classifiers may therefore tackle the challenge of predicting the bitterness intensity of paediatric formulations. While advancements have been made, further work is needed to identify effective taste-masking techniques for specific drug molecules. Continuous refinement of machine learning algorithms, using human panellist acceptability scores, can aid in enhancing paediatric formulation development and overcoming taste-masking challenges.

Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm.

To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug-resistant epilepsy. This was a prospective, single-arm, interventional study (Idea, Development, Exploration, Assessment, and Long-Term Follow-Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG-defined seizure-onset zone (SOZ). Twenty patients (median age 12 years, range 4-18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. We demonstrate early-stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms. The focal cortical dysplasia detection algorithm collocated with the seizure-onset zone (SOZ) in 4 out of 19 patients. The algorithm changed the resection boundaries in 1 of 19 patients undergoing stereoelectroencephalography for drug-resistant epilepsy. The patient with an altered resection due to the algorithm was seizure-free 1 year after resective surgery. Overall, the algorithm did not increase the proportion of patients in whom SOZ was identified.

Urban Vulnerability Analysis Based on Micro-Geographic Unit with Multi-Source Data—Case Study in Urumqi, Xinjiang, China

This study introduces a novel approach to urban public safety analysis inspired by a streetscape analysis commonly applied in urban criminology, leveraging the concept of micro-geographical units to account for urban spatial heterogeneity. Recognizing the intrinsic uniformity within these smaller, distinct environments of a city, the methodology represents a shift from large-scale regional studies to a more localized and precise exploration of urban vulnerability. The research objectives focus on three key aspects: first, establishing a framework for identifying and dividing cities into micro-geographical units; second, determining the type and nature of data that effectively illustrate the potential vulnerability of these units; and third, developing a robust and reliable evaluation index system for urban vulnerability. We apply this innovative method to Urumqi’s Tianshan District in Xinjiang, China, resulting in the formation of 30 distinct micro-geographical units. Using WorldView-2 remote sensing imagery and the object-oriented classification method, we extract and evaluate features related to vehicles, roads, buildings, and vegetation for each unit. This information feeds into the construction of a comprehensive index, used to assess public security vulnerability at a granular level. The findings from our study reveal a wide spectrum of vulnerability levels across the 30 units. Notably, units X1 (Er Dao Bridge) and X7 (Gold Coin Mountain International Plaza) showed high vulnerability due to factors such as a lack of green spaces, poor urban planning, dense building development, and traffic issues. Our research validates the utility and effectiveness of the micro-geographical unit concept in assessing urban vulnerability, thereby introducing a new paradigm in urban safety studies. This micro-geographical approach, combined with a multi-source data strategy involving high-resolution remote sensing and field survey data, offers a robust and comprehensive tool for urban vulnerability assessment. Moreover, the urban vulnerability evaluation index developed through this study provides a promising model for future urban safety research across different cities.

Robust calibration and evaluation of a percolation-based effective‐medium approximation model for thermal conductivity of unsaturated soils

Thermal conductivity (λ) is a property characterizing heat transfer in porous media, such as soils and rocks, with broad applications to geothermal systems and aquifer characterizations. Numerous empirical and physically-based models have been developed for thermal conductivity in unsaturated soils. Recently, Ghanbarian and Daigle (G&D) proposed a theoretical model using the percolation-based effective-medium approximation. An explicit form of the G&D model relating λ to water content (θ) and equations to estimate the model parameters were also derived. In this study, we calibrated the G&D model and two widely applied empirical λ(θ) models using a robust calibration dataset of 41 soils. All three λ(θ) model performances were evaluated using a validation dataset of 58 soils. After calibration, the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) of the G&D model were 0.092 W−1 m−1 K−1, 0.067 W−1 m−1 K−1 and 0.97, respectively. For the two empirical models, RMSEs ranged from 0.086 to 0.096 W−1 m−1 K−1, MAEs from 0.063 to 0.071 W−1 m−1 K−1, and R2 values were about 0.97. All three metrics indicated that calibration improved the performance of the G&D model, and it had an accuracy similar to that of the two empirical λ(θ) models. Such a robust performance confirmed that the theoretically-based G&D model can be applied to study soil heat transfer and potentially other related fields.

Post-frac evaluation of deep shale gas wells based on a new geology-engineering integrated workflow

The development of shale gas reservoir is now shifting focus from mid-shallow reservoirs to deep reservoirs in the southern Sichuan Basin. For such reservoirs with high temperature, pressure, and stress difference, along with multiple folds, faults, and complex fracture networks, improving the stimulation performance of shale gas wells is the ultimate challenge. This study presents a new geology-engineering integrated workflow coupling hydraulic fracture simulation and reservoir simulation together with artificial intelligence to perform post-frac evaluations. A two-step calibration workflow of fracturing and reservoir simulations generated an ensemble of representative models, and a confidence interval of estimated ultimate recovery forecast were obtained. Four deep shale gas wells with two different completion versions (version 1.0 and version 2.0) in Y101 study area were evaluated. Some of the key learnings include: (1) the ratio of stimulated rock volume and drained rock volume is quantified to 8%–25% for the four characterized deep shale gas wells; (2) the version 2.0 completion design is able to stimulate the near-well region more effectively than version 1.0; (3) this novel geology-engineering integrated workflow performed the first reliable, robust and quantifiable post-frac evaluation in the southern Sichuan Basin deep shale reservoir.

Teacher professional identities and their impacts on translanguaging pedagogies in a STEM EMI classroom context in China: a nexus analysis

While translanguaging has gained increasing recognition as a multiliteracy pedagogy in English-medium instruction (EMI) education, research exploring its implementation in STEM classroom contexts remains limited. Furthermore, the interplay of EMI teachers’ professional identities and their instructional strategies has received little attention. This qualitative study explores how STEM academics in an EMI programme in China implemented translanguaging pedagogy, developed their professional identities, and examined the impact of identity on their classroom instructional language use. Drawing upon nexus analysis, the study maps the intersecting discourses influencing two EMI lecturers’ divergent language ideologies and translanguaging strategies. The findings highlight the role of teacher identity and agency in navigating institutional and classroom discourses, facilitating planned and effective translanguaging pedagogy. The study reveals identity struggles within the examined institution, where academic staff faced a challenge in balancing their roles as effective EMI teachers and successful researchers due to a discourse of research meritocracy and were constrained in exploring translanguaging pedagogy due to a discourse of internationalism. These challenges undermined their motivation to invest in teaching identity and pedagogical skills. This study underscores the need for a balanced view of research and teaching, more robust teacher evaluation systems, and institutional support to foster effective translanguaging pedagogy in EMI by incorporating teacher identity construction into EMI teacher preparedness.

Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first present MobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we propose SemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the real-world effects for robust training and evaluation, we further introduce a novel adversarial training method SemMixed to harden the ESC against SemAdv attacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., our MobileSC can even be more robust than classical block-wise communication systems in the low SNR regime.

Integrating multi-influencing factor techniques and fuzzy methods to identify recommendation domains for out-scaling conservation agriculture in China

Climate-smart agriculture (CSA) is a global development strategy aimed to address the interlinked challenges of food security and climate change. Expanding the implementation of conservation agriculture (CA), a vital component of CSA, is essential for enhancing agricultural and food security resilience while sustainably managing arable land. However, the extensive heterogeneity of biophysical and socioeconomic conditions presents significant complexities in promoting CA adoption. Addressing these challenges, this study carried out a comprehensive theoretical investigation of biophysical and socioeconomic factors influencing CA adoption and performance, integrating stakeholder feedback to create a systematic and robust evaluation index system for assessing CA suitability. By integrating multi-influencing factor techniques and fuzzy logic methods, we spatially identified suitable areas for CA implementation in China, providing valuable insights for land use policy. The reliability of the models was verified through a sensitivity analysis using the map removal sensitivity analysis method and the extended Fourier amplitude sensitivity test. The results indicated that 29.78% of the cultivated land was unsuitable or marginally suitable for CA, while 29.30 and 40.92% were determined to be moderately suitable and suitable zones, respectively. Suitable cultivated land was primarily distributed in the northern arid and semi-arid regions, the Loess Plateau, the Huang-Huai-Hai Plain, and the Northeast China Plain. Conversely, unsuitable, and marginally suitable cultivated land was predominantly located in the Qinghai Tibet Plateau, Middle-lower Yangtze Plain, Sichuan Basin and surrounding areas, the Yunnan-Guizhou Plateau, and Southern China. The topographical index, annual mean precipitation, humidity index, and population density were identified as the most significant factors influencing CA suitability. The CA suitability maps generated in this study will guide the development and extension agents targeting CA to suitable locations with a high potential impact, thereby maximizing the likelihood of adoption and minimizing the risk of failure.