Comprehensive Research Research Articles

Treatment regimens and survival among patients with head and neck squamous cell carcinoma from Mizo tribal population in northeast India – a single centre, retrospective cohort study

Patients with early-stage head and neck squamous cell carcinoma (HNSCC) are treated using a single-modality approach that involves either surgery (S) or radiotherapy (RT). Conversely, those with advanced-stage disease are treated using a multi-modality approach incorporating a combination of chemotherapy (CT), RT and S. In addition to behavioural factors, such as alcohol and tobacco use, clinical parameters, such as leukocyte and neutrophil counts and T and N classification, have been linked to the survival of patients with head and neck cancer. This retrospective study was designed to provide insights into the types of treatment (induction chemotherapy [IC], concurrent chemoradiotherapy [CCRT], S and RT) administered to patients with HNSCC in Mizoram, analyse their 2-year outcome, and identify potential factors that may affect the response to treatment. A retrospective cohort study was conducted using patients diagnosed with HNSCC between 2017 and 2020 in Mizoram, northeast India. Data on clinical and demographic factors and treatments provided were collected from medical records from the Mizoram State Cancer Institute, Mizoram. Overall survival (OS) and progression free survival (PFS) were determined for each factor using the Kaplan-Meier method and compared using the log-rank test. Cox regression analysis was used to identify the factors that affected OS and PFS. Multicollinearity test was performed between the predictors using a variance inflation factor cut-off point of2. A retrospective study was performed on 210 patients with HNSCC who were followed up for a period of 2 years. The findings revealed that hypopharynx was the most affected site, followed by the nasopharynx, oral cavity, oropharynx, and larynx. Regarding treatment regimens, 85/210 (40.5%) of the patients received IC along with CCRT or RT in a sequential manner. Moreover, 86/210 (41.0%) underwent CCRT alone, 22/210 (10.5%) received RT alone and 17/210 (8.1%) underwent surgery followed by adjuvant CCRT or RT. Two-year OS and PFS estimated using the Kaplan-Meier analysis were 78.1% (95% CI=72.4%-84.2%) and 57.4% (95% CI=50.8%-64.8%), respectively. Log-rank test showed that leucocytosis (p=0.015) and neutrophilia (p=0.014) exerted effects on OS, whereas nodal involvement (p=0.005), neutrophilia (p=0.043) and IC (p=0.010) exerted effects on PFS. Multivariate analysis indicated that leucocytosis (p=0.010 [OS], 0.025 [PFS]), neutrophilia (p=0.029, 0.033), cancer site (laryngeal) (p=0.009, 0.028) and nodal involvement (N2) (p=0.020, 0.001) were predictors of poor OS and PFS. OS was better than PFS in HNSCC patients from Mizo population. Multi-modality approach offered survival advantages over single-modality approach. Leucocytosis, neutrophilia, nodal involvement, and cancer sites were associated with poor OS and PFS. More comprehensive research with a larger sample size is needed to confirm the findings from this study. There is no funding for this study.

Exploring the Dual Components and Mechanism of Panax notoginseng-Eucommia ulmoides for Managing Osteoporosis-Associated Lumbar Disc Herniation Using Network Pharmacology

Osteoporosis (OP) combined with lumbar intervertebral disc herniation (LDH) is a complex disease that exhibits limited efficacy in drug treatment, physical therapy, and surgical intervention, while also carrying potential side effects and risks. Traditional Chinese medicine treatment of OP combined with LDH primarily aims to address deficiencies and regulate the body. Several active ingredients found in Panax notoginseng and Eucommia ulmoides, such as quercetin and beta-sitosterol, have demonstrated the ability to enhance osteoblast proliferation and bone matrix synthesis. While historical literature contains mechanistic studies on individual herbs, there is a conspicuous dearth of comprehensive and systematic research on their mechanisms of action. By utilizing network pharmacology method, we identified 35 active ingredients present in Panax notoginseng and Eucommia ulmoides, along with 98 potential targets associated with the treatment of OP combined with LDH using Panax notoginseng-Eucommia ulmoides. Through protein-protein interaction network analysis, we identified 5 essential active ingredients (Quercetin, Beta-carotene, Kaempferol, Beta-sitosterol, and Notoginsenoside Fe) and 5 core targets (Nuclear factor kappa-B 3 [RELA], hypoxia-induciblefactor-1A [HIF-1A], Myelocytomatosis Viral Oncogene homolog [MYC], Transcription Factor AP-1 [JUN], and Estrogen Receptor 1 [ESR1]). The potential targets primarily involve IL-17, AGEs-RAGE, the TNF signaling pathway, and other pathways through which they exert therapeutic effects. The key active ingredient was docked with the core target molecule, resulting in all molecules exhibiting binding energies ≤-5 kcal/mol. This study provides preliminary evidence that treatment with Panax notoginseng-Eucommia ulmoides for OP combined with LDH may lead to improvements in inflammation, oxidative stress, cell proliferation, differentiation, and apoptosis, and cytokine regulation.

Diversifying Quranic revision methods using gamification-based teaching material for tahfiz education

This study discusses the development procedure of the global tahfiz game (GTG) as a teaching aid for the Quranic revision subject for tahfiz students based on the gamification concept. Comprehensive design and development research (DDR) was used as the research method. The analysis includes conceptual and material development, expert evaluation, testing, and the final product. The development of concepts and materials used in this gamification was based on the theoretical framework of mechanics, dynamics, and aesthetics (MDA), with the application of tahfiz education elements. To examine the ability of GTG prototype to tackle the users’ needs, we have conducted the usability analysis. The expert teacher's mean score for the trial run on this gamification was high at 4.85. The trial run score for student participants was also high (4.45). The result demonstrated that most of the constructs are perceived at a high level by respondents. Thus, this indicated the high usability of GTG to be used as a teaching aid for a Quranic revision subject. The gamification of Quranic tahfiz based on board games has been finalized through recommendations and improvements based on the evaluation. The study findings are expected to be useful for educational institutions or researchers in developing teaching materials using the MDA framework for gamification, particularly in the field of Islamic education.

AI Consciousness and Technological Advancement in Bangladesh's Higher Education: AI Awareness among the Learners

This comprehensive research article explores the transformative potential and multifaceted challenges of integrating Artificial Intelligence (AI) consciousness and technological advancements into the higher education system in Bangladesh. The study delves into various dimensions of this integration, including its implications for pedagogy, curriculum, institutional policy, and technological innovation. A particular emphasis is placed on addressing disparities in AI awareness among future students in Bangladesh's higher education system. The research adopts a balanced approach, juxtaposing the promise of technological advancements with critical concerns related to data privacy, educational equity, and cultural preservation. Through interdisciplinary analysis and a robust set of policy recommendations, the study posits that Bangladesh stands at a pivotal moment. It can either seize this technological epoch to elevate its higher education system to global standards while mitigating AI awareness disparities or risk exacerbating existing educational inequities.

Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data

ABSTRACT Geostationary satellites have the capability to offer AOD products with a higher frequency of observation within a given period, thus improving the geographical coverage of averaged AOD products compared to polar satellites. Moreover, the averaged AOD of geostationary satellites is more reflective of the average aerosol load as compared to AOD products derived from polar orbit satellites. Despite this, there is still an absence of comprehensive research on the comparative representativeness of AOD mean products from geostationary and polar orbit satellites, and most current research only focuses on retrieval accuracy. This paper compares the geographical coverage of averaged AOD products released by the Advanced Baseline Imager (ABI) sensor on the Geostationary Operational Environmental Satellite-R (GOES-R) (09:00 UTC-23:00 UTC) with AOD products released by the Moderate-resolution Imaging Spectroradiometer (MODIS) (16:00 UTC and 19:00 UTC) on hourly, daily, semi-monthly and monthly scales. Moreover, the Aerosol Robotic Network datasets were used to evaluate the accuracy of ABI AOD and to propose usage suggestions. In terms of daily AOD products, the AOD daily mean generated by the geostationary satellite GOES-R/ABI consistently outperforms the AOD daily mean generated by MODIS AOD in terms of spatial coverage. However, on monthly scales, the difference is no longer significant. With regard to accuracy, it is proved that when the time scale of averaging is gradually expanded, root mean square error (RMSE) and mean absolute error (MAE) gradually decrease. On the season scale, ABI AOD exhibits the highest level of accuracy during the autumn season (September, October, November); on the spatial scale, ABI AOD exhibits the best accuracy in North America. Therefore, if the daily, semi-monthly or monthly averaged ABI AOD datasets are used, the recommended time is autumn, and the study area is North America.

Time evolution of the galactic B - rho relation: The impact of the magnetic field morphology

One of the indicators most frequently used to characterize the magnetic field's influence on star formation is the relation between the magnetic field strength and the gas density (the $ B- rho $ relation), usually expressed as a power law of the form $ B kappa $. The value of kappa is an indication of the dynamical importance of the magnetic field during gas compression. In this work, we investigate the role of the global magnetic field morphology on a galaxy's $ B- rho $ relation, as well as the evolution of the relation over time. We developed magnetohydrodynamic simulations of Milky Way-like galaxies that include gravity, star formation, and supernova feedback. The models take into account nonequilibrium chemistry up to $ H_2$ formation, which is used to fuel star formation. We considered two different initial magnetic field morphologies: one completely ordered (toroidal) and the other completely random. Using these models, we studied the dynamical importance of the magnetic field through the plasma beta and the $ B- rho $ relation. For both magnetic morphologies, low-density regions are thermally supported, while high-density regions are magnetically dominated. Equipartition is reached earlier and at lower densities in the toroidal model. However, the $ B- relation varies, even within the same galaxy, as it consistently includes two different branches for a given density, with kappa ranging from about 0.2 to 0.8. The mean value of kappa for each model also varies significantly over time, which supersedes the differences between the two models. While our findings suggest that the magnetic field morphology does influence the galactic $ B- relation, its impact is transient in nature since time-averaged differences between the models fall within the large temporal scatter. The context and time-dependent nature of the $ B- relation underscore the need for comprehensive research and observations to understand the intricate role of magnetic fields in star formation processes across diverse galactic environments.

Biomass-Derived Catalytically Active Carbon Materials for the Air Electrode of Zn-air Batteries.

Given the growing environmental and energy problems, developing clean, renewable electrochemical energy storage devices is of great interest. Zn-air batteries (ZABs) have broad prospects in energy storage because of their high specific capacity and environmental friendliness. The unavailability of cheap air electrode materials and effective and stable oxygen electrocatalysts to catalyze air electrodes are main barriers to large-scale implementation of ZABs. Due to the abundant biomass resources, self-doped heteroatoms, and unique pore structure, biomass-derived catalytically active carbon materials (CACs) have great potential to prepare carbon-based catalysts and porous electrodes with excellent performance for ZABs. This paper reviews the research progress of biomass-derived CACs applied to ZABs air electrodes. Specifically, the principle of ZABs and the source and preparation method of biomass-derived CACs are introduced. To prepare efficient biomass-based oxygen electrocatalysts, heteroatom doping and metal modification were introduced to improve the efficiency and stability of carbon materials. Finally, the effects of electron transfer number and H2 O2 yield in ORR on the performance of ZABs were evaluated. This review aims to deepen the understanding of the advantages and challenges of biomass-derived CACs in the air electrodes of ZABs, promote more comprehensive research on biomass resources, and accelerate the commercial application of ZABs.

Inner Voice in Silent Reading and Speaking

Current research reveals that individuals use two languages: external language and internal language. The external language is a loud, noisy language that everyone can hear. The internal language is a silent, mysterious, personal language that merely the individual hears. This phenomenon, called inner language, inner voice or inner speech, is a mental activity seen in most people. This activity plays important roles in language and cognitive processes, helps the processes such as reading, writing, understanding, questioning, learning, thinking, controlling emotions, motivation, and so on. The inner voice emerges during silent reading and contributes significantly to understanding words, sentences, and text. Expert readers read and understand the words by hearing the small inner sound of the words in their brain. Controversy, internal speech is when an individual talks silently to himself. Inner speech is referred when planning daily tasks, making decisions, solving problems, and leading social and emotional issues. Nonetheless, research on the inner voice and inner speech, which are effective in the development of the individual, is yet insufficient. There is a need for clearer and more comprehensive research on the role and function of inner speech in processes such as reading, speaking, understanding, and thinking. It is recommended that this phenomenon, which exists in every individual, be handled scientifically and used to develop language and cognitive skills.

A comprehensive research of deep learning approaches for High Definition map construction in autonomous driving

In the realm of autonomous driving, High Definition Maps (HD maps) are indispensable for safe and precise navigation. Traditional HD map construction methods involving point cloud capture and SLAM have proven effective but labor-intensive. This paper addresses the growing interest in leveraging deep learning techniques to streamline HD map creation. This paper presents a systematic exploration of deep learning methodologies for HD map construction. It categorizes these approaches into two core components: Feature Extraction and Feature Decoding. Feature Extraction involves the transformation of input data, comprising images and LiDAR point clouds, into Bird's Eye View (BEV) representations. Feature Decoding is dissected into rasterized map objectives and vector map objectives. Detailed analysis is conducted on prominent methodologies. The paper provides a nuanced evaluation of these deep learning techniques, highlighting their respective strengths and limitations. Factors such as precision, computational efficiency, and the preservation of fine-grained details are considered when selecting the most suitable method. This comprehensive review summarizes and prospects the research in related fields.

Exploring the molecular mechanisms network of breast cancer by multi-omics analysis.

Breast cancer (BC), the most prevalent malignancy in women globally, still lacks comprehensive research on its molecular targets and necessitates further investigation into the underlying molecular mechanisms driving its initiation and progression. The GSE20685 Series Matrix File downloaded from the Gene Expression Omnibus database was divided into a high-risk group (n=49) and a low-risk group (n=278) to construct the co-expression network. Four hub genes were identified based on the Weighted Gene Co-expression Network Analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed. Hub gene immune infiltration was investigated using the Tumor Immune Estimation Resource database, and CD4+ T cell expression levels were substantially correlated with hub gene expression. Based on the CancerRxGene database (Genomics of Drug Sensitivity in Cancer database), it was found that the hub genes were highly sensitive to common chemotherapy drugs such as AKT inhibitor VIII and Erlotinib. The expression of Secreted Frizzled-Related Protein 1, melanoma-inhibiting activity (MIA), and Keratin 14 was related to tumor mutation burden, and the expression of MIA also affected the microsatellite instability of the tumor. This study employs multi-omics analysis to investigate the molecular network associated with the prognosis of BC, highlighting its intricate connection with the immune microenvironment. These findings pinpoint four crucial genes in BC progression, offering targets for further research and therapy. Their connections to immune infiltration and chemotherapy sensitivity underscore complex interactions in the tumor microenvironment.

Immune landscape in APC and TP53 related tumor microenvironment in colon adenocarcinoma: A bioinformatic analysis.

APC and TP53 are the two most regularly mutated genes in colon adenocarcinoma (COAD), especially in progressive malignancies and antitumoral immune response. The current bioinformatics analysis investigates the APC and TP53 gene expression profile in colon adenocarcinoma as a prognostic characteristic for survival, particularly concentrating on the correlated immune microenvironment. Clinical and genetic data of colon cancer and normal tissue samples were obtainedfrom The Cancer Genome Atlas (TCGA)-COAD and Genotype-Tissue Expression (GTEx) online databases, respectively. The genetic differential expressions were analyzed in both groups via the one-way ANOVA test. Kaplan-Meier survival curves were applied to estimate the overall survival (OS). P < 0.05 was fixed as statistically significant. On Tumor Immune Estimation Resource and Gene Expression Profiling Interactive Analysis databases, the linkage between immune cell recruitment and APC and TP53 status was assessed through Spearman's correlation analysis. APC and TP53 were found mutated in 66.74% and 85.71% of the 454 and 7 TCGA-COAD patients in colon and rectosigmoid junction primary sites, respectively with a higher log2-transcriptome per million reads compared to the GTEx group (318 samples in sigmoid and 368 samples in transverse). Survival curves revealed a worse significant OS for the high-APC and TP53 profile colon. Spearman's analysis of immune cells demonstrated a strong positive correlation between the APC status and infiltration of Tcell CD4+, T cell CD8+, NK cell, and macrophages and also a positive correlation between status and infiltration of T cell CD4+, T cell CD8+. APC and TP53 gene mutations prevail in colon cancer and are extremely associated with poor prognosis and shortest survival. The infiltrating T cell CD4+, T cell CD8+, NK cell, and macrophages populate the colon microenvironment and regulate the mechanisms of tumor advancement, immune evasion, and sensitivity to standard chemotherapy. More comprehensive research is needed to demonstrate these results and turn them into new therapeutic outlooks.

Construction of China's food security evaluation index system and spatiotemporal evolution.

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.

The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of particular interest are predictors of pathological complete response (pCR) that can guide personalized treatment. There are currently no clinical biomarkers which can accurately predict neoadjuvant therapy (NAT) response but body composition (BC) measures present as an emerging contender. The primary aim of the study was to determine if artificial intelligence (AI) derived body composition variables can predict pCR in patients with LARC. LARC patients who underwent NAT followed by surgery from 2012 to 2023 were identified from the Australian Comprehensive Cancer Outcomes and Research Database registry (ACCORD). A validated in-house pre-trained 3D AI model was used to measure body composition via computed tomography images of the entire Lumbar-3 vertebral level to produce a volumetric measurement of visceral fat (VF), subcutaneous fat (SCF) and skeletal muscle (SM). Multivariate analysis between patient body composition and histological outcomes was performed. Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (P = 0.015) and age (P = 0.03) were positively associated with pCR in both male and female patients. SCF volume was associated with decreased likelihood of pCR (P = 0.059). This is the first study in the literature utilizing AI-measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.

Applications and Learning Outcomes of Game Based Learning in Education

Game-based learning (GBL) is considered the most engaging means of learning and brings several benefits as regards the learning outputs. Game-based learning is applied in all different educational stages from pre-school education to tertiary education, and workplace. This review study discusses and extends previous findings by organizing the application of GBL approaches in different educational stages and presenting the main learning outcomes. The review study collects and analyzes 104 scientific papers ranging from 2013-2021. The benefits and limitations of GBL are also discussed in the paper, while different types of games including augmented reality (AR) approaches are considered. According to the findings, in primary and secondary education, GBL demonstrates a positive impact on learning outcomes and engagement, enhancing students' understanding of complex concepts and fostering real-world application of learned knowledge. While GBL in tertiary education facilitates deeper understanding, critical thinking, and skill development, its implementation requires careful design and evaluation aligned with learning objectives. The study also explores various types of GBL games, including memory, simulation, interactive, quiz, puzzle, strategy, and reality-testing games, each tailored to different educational objectives and subjects. Furthermore, Augmented Reality (AR) in GBL shows promise across subjects but faces challenges like technical limitations, training needs, privacy concerns, potential distractions, and a lack of comprehensive research on its efficacy. Overall, while GBL demonstrates considerable potential in enhancing education, its successful implementation requires careful consideration of age-appropriateness, varied game types, and integration of emerging technologies like AR.

Identifying Chinese students’ perceptions of smart classrooms and their relationships with design features

ABSTRACT Taking classes in a smart classroom is a novel experience for most college students in China, and comprehensive research on how Chinese students perceive smart classroom environments is limited. This study explored students’ perceptions of smart classroom environments in plain language through a survey involving 358 students. The semantic difference and Kansei Engineering methods were employed to investigate students’ perceptions of learning and physical environments and their relationships with the design features of smart classrooms. Statistical analysis revealed six factors of subjective assessments with a total interpretation rate of 66.29% and five design features of smart classrooms with a total interpretation rate of 59.40%. No significant differences were found across all the factors based on gender, but there were significant differences in students’ technology self-efficacy according to each factor of subjective evaluation and design feature. All factors of the subjective assessments and design features exhibited significant correlations. Furthermore, regression models revealed a significant relationship between affective perception and other dimensions of subjective assessments and an important connection between the active learning environment and all design features. Thus, this study provides an empirical foundation for understanding students’ perceptions of smart classrooms and offers a user-based perspective for designing smart classrooms.

Are carbon emissions trading and green financial instruments synergistic? -Comprehensive quantitative research based on content analysis.

Coordinating policies is an essential guarantee for carbon emission reduction and sustainable development. Based on the theoretical framework of the policy paradigm, we quantitatively analyze 266 policy documents on promoting carbon emission trading and green financial policies from 2011 to 2022 using the content analysis research method. Based on the matching network of "policy objectives-policy tools," we analyze the synergistic characteristics of carbon emission trading policies and green financial policies in promoting carbon emission reduction targets and reveal the matching mode of "objectives-tools" of green financial policies by using social network analysis. It is found that, first, from the perspective of policy objectives, the main policy objectives of carbon emissions trading are to promote green innovation of enterprises, and the main policy objectives of green finance are to promote green development, which reflects the consistency and endogenous motivation of policy objectives. Secondly, command-control and market incentive policy tools are the main policy tools in the structure of policy tools. The proportion of public participation policy tools is small, and there is a structural asymmetry. Third, carbon emissions trading tools focus on supervision, adjustment, and platform construction. The green financial policy tools have the characteristics of guidance, public welfare, and externality. The two constitute a complementary, embedded, and integrated ' double synergy ' carbon emission reduction policy. Based on this, this paper puts forward some suggestions to promote policy coordination and provides a reference for China to achieve the dual carbon goal.

Unveiling Nanogranular Advancements in Nickel-Doped Tungsten Oxide for Superior Electrochromic Performance

Electrochromic materials allow for precise control of their optical properties by applying an electric field, which has led to recent developments in energy-saving and indoor temperature control systems like smart windows. The selective incorporation of metal dopants is an effective technique for generating highly advanced semiconducting metal oxides with precisely customized physicochemical characteristics. In this report, we employed a one-step electrodeposition process to fabricate nickel-doped tungsten oxide (W–Ni) thin films, systematically probing the impact of nickel (Ni) doping on the collective material characteristics. Comprehensive X-ray diffraction research revealed significant changes in diffraction patterns, suggesting slight modifications in the structure caused by Ni doping. The scanning electron microscopy showed complex differences in the microstructure of the film, such as a dense surface, porosity, and clustering of nanogranules. The WNi-3% thin film doped at 3 wt. % exhibited excellent electrochromic performance by efficiently handling lithium ions and displaying favorable electrochromic properties. The improved electrode, WNi-3%, showed a maximum optical modulation of 81.90%, exceptional reversibility of 99.4%, and a high coloration efficiency of 75.12 cm2/C. These findings underscore the efficacy of Ni-doping in tailoring the electrochromic properties of nickel-doped tungsten oxide thin films, thereby advancing the frontiers of high-performance electrochromic materials for energy-efficient applications.

Fostering sustainability performance in the Malaysian manufacturing companies: the role of green technology innovation and innovation capabilities

PurposeThe purpose of this study is to examine the influence of green technology innovation on sustainability performance with the moderating effect of innovation capabilities in the Malaysian manufacturing listed companies.Design/methodology/approachThis was a quantitative study and carried out by applying a research survey. The questionnaire was used to collect the data from 204 Malaysian manufacturing companies of the “consumer products and services” sector listed at Bursa Malaysia, incorporating a five-point Likert scale. All the hypothesized relationships were tested by using the partial least square structural equation modeling (PLS-SEM).FindingsThe empirical results showed that the comprehensive adoption of green technology innovation significantly promotes sustainability performance including economic, environmental and social performance. In addition, innovation capabilities significantly and positively moderate the relationship between green technology innovation and sustainability performance.Research limitations/implicationsThe scope of this study is specifically confined to the Malaysian manufacturing listed companies, operating within the consumer products and services sector listed at Bursa Malaysia. Consequently, the findings of this study may not be generalized to manufacturing companies of the different geographical contexts.Practical implicationsThe findings of this study may help the top management and policymakers of the Malaysian manufacturing listed companies to scrutinize green technology innovation and innovation capabilities to achieve higher sustainability performance.Originality/valueThis study magnifies and provides new insights into the extant literature by developing a comprehensive research model that concurrently tests the direct and moderation effects between green technology innovation, innovation capabilities and sustainability performance. Additionally, this is the first study to examine the influence of green technology innovation on sustainability performance with the moderating effect of innovation capabilities in the Malaysian manufacturing listed companies. This distinct approach significantly bolsters the originality of this study.

Visual Gustation via Regulable Elastic Photonic Crystals.

The unique structural sensitivity of photonic crystals (PCs) endows them with stretchable or elastic tunability for light propagation and spontaneous emission modulation. Hydrogel PCs have been demonstrated to have biocompatibility and flexibility for potential human health detection and environmental security monitoring. However, current elastic PCs still possess a fixed elastic modulus and uncontrollable structural colors based on a tunable elastic modulus, posing considerable challenges for in situ detection, particularly in wearable or portable sensing devices. In this work, we introduced a novel chemo-mechanical transduction mechanism embedded within a photonic crystal nanomatrix, leading to the creation of structural colors and giving rise to a visual gustation sensing experience. By utilizing the captivating structural colors generated by the hydrogel PC, we employ abundant optical information to identify various analytes. The finite element analysis proved the electric field distribution in the PC matrix during stretch operations. The elastic-optical behaviors with various chemical cosolvents, including cations, anions, saccharides, or organic acids, were investigated. The mechanism of the Hofmeister effect regulating the elasticity of hydrogels was demonstrated with the network nanostructure of the hydrogels. The hydrogel PC matrix demonstrates remarkable capability in efficiently distinguishing a wide range of cations, anions, saccharides, and organic acids across various concentrations, mixtures, and even real food samples, such as tastes and soups. Through comprehensive research, a precise relationship between the structural colors and the elastic modulus of hydrogel PCs has been established, contributing to the biomatching elastic-optics platform for wearable devices, a dynamic environment, and clinical or health monitoring auxiliary.

A Study on Factors Influencing Designers’ Behavioral Intention in Using AI-Generated Content for Assisted Design: Perceived Anxiety, Perceived Risk, and UTAUT

This study aims to comprehensively understand the intention to use Artificial Intelligence Generated Assistance in Design Tools (AIGC) among design students and practitioners, along with its influencing factors. Utilizing Smart-PLS software and Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, we constructed a comprehensive research model. Based on 404 valid questionnaire responses, we systematically analyzed the underlying mechanisms of designers’ attitudes towards AIGC tools. The sample encompasses diverse schools and levels of professional experience, ensuring the wide applicability of research outcomes. In the data analysis process, professional statistical analysis methods, including path analysis and standardized path coefficients, were employed to ensure a profound exploration of research questions. The results indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly positively influence the willingness to use AIGC tools, while perceived anxiety and perceived risk exert negative impacts. This study, by integrating traditional and novel factors, provides crucial theoretical and practical guidance for the actual application of AIGC technology in the design field, offering profound insights for the future development and education of design technology.