Weak Abilities Research Articles

Encapsulating Azolates Within Cationic Metal-Organic Frameworks for High-Energy-Density Materials.

Despite the synthesis of numerous cationic metal-organic frameworks (CMOFs), their counter anions have been primarily limited to inorganic Cl-, NO3 -, ClO4 -, BF4 -, and Cr2O7 2-, which have weak coordination abilities. In this study, a series of new CMOFs is synthesized using azolates with strong coordination abilities as counter anions, which are exclusively employed as ligands for coordinating with metals. Owing to the unique nitrogen-rich composition of azolates, the CMOFs demonstrate significant potential as high-energy-density materials. Notably, CMOF(CuTNPO) has an exceptionally high heat of detonation of 7375kJ kg-1, surpassing even that of the state-of-art CL-20 (6536kJ kg-1). To further validate the advantages of employing azolates as counter anions, analogues with azolates serving as ligands are also synthesized. The comparison study indicates that encapsulating azolates within the cationic frameworks confers both high energy and safety properties. X-ray data and quantum calculations indicate that their enhanced performance stems from stronger H─bonds and π-π interactions. This study introduces new roles for azolates in MOFs and expands possibilities for structural diversity and potential applications of framework materials.

Biomolecule‐responsive polymers and their bio‐applications

AbstractPrecise recognition and specific interactions between biomolecules are key prerequisites for ensuring the performance of all actives within living organisms. The convergence of biomolecular recognition systems into synthetic materials could endow the materials with high specificity and biological sensitivity; this, in turn, enables precise drug release, monitoring or detection of important biomolecules, and cell manipulation through targeted capture or release of specific biomolecules. Meanwhile, from the perspective of materials science, the application of conventional polymers in practical biological systems poses several challenges, such as low responsiveness and sensitivity, inadequate targetability, insufficient anti‐interference capacities, and unsatisfactory biocompatibility. These problems could be partly attributed to the polymers' weak discrimination abilities toward target biomolecules in the presence of interfering substances with high abundance. In particular, the proposition of “precision medicine” project raises higher demands for the design of biomaterials in terms of their precision and targetability. Therefore, there is an urgent demand for the development of new‐generation biomaterials with precise recognition and sensitive responsiveness comparable to biomacromolecules. This promotes a new research direction of biomolecule‐responsive polymers and their diverse applications. This review focuses on the origin and construction of biomolecule‐responsive polymers, as well as their attractive applications in drug delivery systems, bio‐detection, bio‐sensing, separation, and enrichment, as well as regulating cell adhesion.

Psychophysical correlates of musicality in musically untrained children: evidence for musical sleepers in children

BackgroundMusic learning induces significant neural changes, enhancing auditory, verbal, and visuospatial skills, IQ, and speech perception. Research studies reveal structural and functional brain plasticity due to music training in adults and children. While musical abilities are linked to formal training, the existence of “musical sleepers” with heightened speech perception without formal training is noteworthy in adults. This study addresses the gap in understanding such phenomena in children, exploring psychophysical abilities in musically adept children, and aiming to impact rehabilitation models.Materials and methodA pre-experimental study involved 164 typically developing children (mean age: 9.93) without musical training. Musical abilities were evaluated using the abbreviated version of Montreal Battery for Evaluation of Musical Abilities (MBEMA) test in DMDX software, covering melody, rhythm, and memory subtests. Psychophysical tests in MATLAB assessed temporal, frequency, intensity, timbre, and binaural resolution.ResultsUsing a median split based on MBEMA test scores (median: 42), 84 children exhibited poor musical abilities, while 80 showed good ones. Descriptive statistics for MBEMA scores and psychophysical tests were conducted. As data did not follow normal distribution, non-parametric inferential statistics were employed.Mann–Whitney U tests revealed significant differences favouring good musical abilities in all assessments: gap deduction (p = 0.01), pitch discrimination at 500 Hz and 4 kHz (p = 0.00), intensity discrimination at 500 Hz (p = 0.00) and 4 kHz (p = 0.01), profile analysis (p = 0.01), interaural level difference (p = 0.023), and interaural time difference (p = 0.038). Spearman correlation showed highest correlation with pitch discrimination at 500 Hz (r = − 0.538), 4 kHz (− 0.416), intensity discrimination at 4 kHz (r = − 0.367), and 500 Hz (r = − 0.311), profile analysis (r = − 0.313), interaural level difference (r = − 0.276), and gap deduction (r = − 0.235). All were significant (p < 0.01), except for the interaural time difference.ConclusionChildren excelling in music surpassed those with weaker abilities in psychophysical tasks. This echoes past research, underscoring auditory discrimination’s influence on musical skills in untrained children. These results potentially support the concept of musical sleepers in children. Key predictors include pitch discrimination, while interaural time difference exhibited minimal correlation. Utilising such assessments may predict musical training outcomes for children with auditory disorders, but additional robust statistical investigation is necessary.

Substituent effects of Phenanthroline-Derived Phosphine-Oxide extractants on extraction and separation performance towards U(VI)/Th(IV)

Designing and selecting extractants with high selectivity between uranium and thorium, and developing the relevant separation process is one of the key points to realize efficient utilization of nuclear resources in the thorium-uranium nuclear fuel cycle. In this work, we used four phenanthroline-derived phosphine-oxide extractants with different substituents and studied their extraction ability and separation effect on U(VI)/Th(IV) under different conditions. The Et2-BPPhen and Ph2-BPPhen extractants show weak and strong extraction abilities for U(VI) and Th(IV), respectively. The Et-Ph-BPPhen(R,R) and Et-Ph-BPPhen(R,S) extractants could co-extract U(VI) and Th(IV) under high acidity (DU(VI) > 300, DTh(IV) > 40, SFU(VI)/ Th(IV) < 7, 4 M HNO3), and show high selectivity towards U(VI) over Th(IV) at low acidity (SFU(VI)/ Th(IV) > 200, 0.1 M HNO3). Most Th(IV) can be stripped by adjusting the acidity of the aqueous phase, while U(VI) still remains in the organic phase (STh(IV) ＞ 95 %, SU(VI) < 5 %), so as to achieve the step-by-step process for co-extraction and selective separation. Then, the slope analysis and NMR titration (1H and 31P) determined BPPhen ligands could form ML-type complex with U(VI), and can simultaneously form M/L = 1:1 and 1:2 complexes with Th(IV). The stability constants of the metal–ligand complexes were fitted by UV–Vis spectroscopic titration to estimate coordination abilities of four ligands. Finally, the single-crystals of complexes with the structures of [UO2(L)(NO3)2], [Th(L)(NO3)4] and [Th(L)(H2O)2]·4(ClO4), (L = Et2-BPPhen/Et-Ph-BPPhen(R,R)/Et-Ph-BPPhen(R,S)/Ph2-BPPhen) were produced, and their coordination and packing mode were analyzed. This work contributes to revealing the structure–activity relationship of phenanthroline phosphine-oxide extractants and providing a reference for designing new extractants with high U(VI)/Th(IV) separation efficiency.

Control of schistosomiasis by the selective competitive and predatory intervention of intermediate hosts: A mathematical modeling approach

Schistosomiasis, a freshwater-borne neglected tropical disease, disproportionately affects impoverished communities mainly in the tropical regions. Transmission involves humans and intermediate host (IH) snails. This manuscript introduces a mathematical model to probe schistosomiasis dynamics and the role of non-host snail competitors and predators as biological control agents for IH snails. The numerical analyses include investigations into steady-state conditions and reproduction numbers associated with uncontrolled scenarios, as well as scenarios involving non-host snail competitors and/or predators. Sensitivity analysis reveals that increasing snail mortality rates is a key to reducing the IH snail population and control of the transmission. Results show that specific snail competitors and/or predators with strong competition/predation abilities reduce IH snails and the subsequent infectious cercaria populations, reduce the transmission, and possibly eradicate the disease, while those with weaker abilities allow disease persistence. Hence our findings advocate for the effectiveness of snail competitors with suitable competitive pressures and/or predators with appropriate predatory abilities as nature-based solutions for combating schistosomiasis, all while preserving IH snail biodiversity. However, if these strategies are implemented at insignificant levels, IH snails can dominate, and disease persistence may pose challenges. Thus, experimental screening of potential (native) snail competitors and/or predators is crucial to assess the likely behavior of biological agents and determine the optimal biological control measures for IH snails.

Vision-language joint representation learning for sketch less facial image retrieval

The traditional sketch-based facial image retrieval (SBFIR) framework assumes that a high-quality facial sketch has been prepared prior to the retrieval task. However, drawing such a sketch requires considerable skills and is time consuming, resulting in limited applicability. Sketch less facial image retrieval (SLFIR) framework aims to break these barriers through human–computer interaction during the sketching process. The primary challenges for the SLFIR problem can be noted that initial sketches (at early sketching) contain only local details and exhibit significant differences among users, resulting in poor performance at early stages and weak generalization abilities in practical testing. In this study, we developed a vision–language pretraining model to align the representation of facial images and their associated semantics. Based on this framework, we proposed a method for learning joint representations by fusing sketches with prior semantics, thereby enriching the information of initial sketches. Specifically, (1) we developed a series of well-designed operations to improve the quality of facial image–text pairs in the LAION-Face dataset; we trained a facial vision–language pretraining (FVLP) model to align the facial image and its semantics at the feature level. (2) subsequently, using FVLP as the backbone, we designed a convolutional attention module to fuse the multiscale features extracted from image encoder. This facilitated the learning of a multimodal representation crucial for the final retrieval process. In experiments, our proposed method achieved state-of-the-art performance at early stages on two public datasets; moreover, it exhibited good generalization capabilities during practical testing. Thus, our method significantly outperforms other baselines in terms of early retrieval performance. Codes are available at: https://github.com/ddw2AIGROUP2CQUPT/FVLP.

Melatonin Affects Leymus chinensis Aboveground Growth and Photosynthesis by Regulating Rhizome Growth

Leymus chinensis is a perennial rhizomatous clone plant. It exhibits strong rhizomatous tillering and clonal growth through asexual reproduction. The root system is interdependent with aboveground growth and root growth can regulate aboveground growth and photosynthesis. Melatonin has been shown to regulate root growth and promote photosynthesis. However, it remains unclear whether melatonin affects aboveground growth and photosynthesis by regulating rhizome growth. To address this gap, we studied nine Leymus chinensis from different geographical locations, all grown under the same conditions. We selected two materials with strong (LC19) and weak (LC2) rhizome growth abilities from nine materials and treated them with exogenous melatonin. We found there were significant positive correlations between stem length, plant height, leaf number and rhizome traits. Additionally, rhizome traits showed significant positive correlations with photosynthetic indices and chlorophyll content. Specifically, for LC2, treatment with 200 μmol/L melatonin significantly increased root length, the number of extravaginal ramets and rhizome clonal growth rate by 88.72%, 43.75% and 43.70%, respectively, resulting in significant increases in aboveground traits. Similarly, for LC19, 200 μmol/L melatonin treatment led to significant increases of 74.66%, 23.02%, 62.71% and 62.72% in four traits, respectively, along with aboveground trait improvements. Furthermore, around 300 μmol/L melatonin treatment promoted photosynthetic efficiency in LC2, while around 100 μmol/L melatonin treatment had the same effect in LC19. In conclusion, our study highlights the relationship between rhizome growth ability, aboveground growth and photosynthesis in Leymus chinensis. Additionally, it suggests that exogenous melatonin can enhance aboveground growth and photosynthesis by regulating rhizome growth.

Fungal co-culture enabled co-fermentation of food waste for production of endoglucanase enzyme

The primary scenarios of biochemical industries include diverse applications with high demand and the costly manufacturing of microbial cellulolytic enzymes. Using these enzymes to turn cellulosic waste into fuel is one of the prime applications of these enzymes to produce green fuel. Nevertheless, low production and weak functional abilities make these enzymes expensive and technically non-viable. This study presents multifactor bioprocess approaches to enhance cellulolytic enzyme production and efficiency using a low-cost strategy. Co-fermentation of pea pods (PPs) and potato peel (PP) wastes as food wastes has been conducted for solid-state fermentation (SSF) using fungal strain-based co-culture. To set the SSF, the nutrient medium and moisture balance of SSF have been maintained using an extract of PPs and PP waste, which are found to be rich in sugar and starch composition. Starch may function as a natural inducer in the SSF process to alleviate enzyme production in the presence of sugar composition. At 37 oC and pH 5.0, a maximum of 2.4 U/mL FPA (filter paper activity) has been produced at a substrate ratio of 4:04 (PPs:PP) using 50% of the moisture content on day 5 of the harvesting period. Additionally, 24.7 IU/mL of CMCase (carboxymethyl cellulase) in day 8 and 18.8 IU/mL of BGL (β-Glucosidase) in day 7 have been obtained. A temperature of 37 oC was found to be optimal, while a pH range of 5.0–9.0 was noticed to be optimal for maximum CMCase activity. Thus, the study may provide a sustainable approach to valorizing domestic solid food waste for the economic production of hydrolytic enzymes.

Evaluation of biological properties and phytochemical profile of Vitex trifolia L. extract and its infused oil as a value-added product for health promotion

Vitex trifolia L., a shrub plant, has long been used as a massage oil to treat a variety of ailments, particularly pain relief. Despite its long history of use, there has not been enough research done on its phytochemical profile and biological properties to validate its traditional use. Therefore, this current research aimed to evaluate its phytochemical profile and biological properties in two forms: Vitex trifolia L. ethanolic extract (VTE) and Vitex trifolia L. oil (VTO), which could be advantageous for clinical usage. Quality control of raw plant material, total phenolic, total flavonoid, and casticin of VTE and VTO were investigated. Furthermore, the antibacterial, antioxidant, and anti-inflammatory activities and cytotoxicity of VTE and VTO were evaluated. The study indicated that VTE contained more total phenolics (66.74±15.52 mg GAE/g), total flavonoids (29.68±2.05 mg CE/g extract), and casticin (2.45±0.02 mg/mL) than VTO. VTE consistently demonstrated stronger antibacterial, antioxidant, and anti-inflammatory properties than VTO. Taken together, VTE possessed remarkable antioxidant, anti-inflammatory, and antibacterial activities with no cytotoxic effects on cells. Even VTO had weak biological abilities. However, its stability testing revealed that the modestly accepted adjustments remained stable. Therefore, this study suggests that Vitex trifolia L. could be used as a natural product for health promotion and therapy.

Exploring the Effectiveness of Psychological Health Education for College Students under the Background of Course Ideology and Politics

Due to social progress and technological development, China is entering a period of diverse cultural development. However, this diverse cultural development has also had some negative impacts, such as the worship of materialism and hedonism. College students are young, have limited social experience, and have weak self-restraint abilities, all of which can lead to an imbalance in their mentality and loss of thought. To better promote their mental health growth and further improve the quality of talent cultivation, we should strengthen our concepts, improve teaching methods, and integrate ideological and political education into the curriculum in mental health. In this way, we can integrate and interact with both. This article aims to explore how universities can closely link mental health with college students, and how to improve their psychology through this approach, making it more targeted in practical work.

Academic Self-problems of Candidate Teachers of Native Language: “I am not sure what or how to learn.” But I must be sure of what I can teach.”

Academic self-concept is an individuals’ self-definition using their experiences in the educational process or their views on their own skills and interests. This study aims to determine the academic self-perception of teacher candidates of native language. This study was conducted with a qualitative approach in a phenomenological design. The study group consisted of 10 female and 6 male native language teacher candidates at a university located in the eastern part of Turkey. During the data collection process, semi-structured interviews were conducted with open-ended questions, the data collected with a voice recorder was converted into a written text. Participant views were subjected to content analysis. The first, last, and focus codings were completed for “coder reliability;” and an external controller was also used. In this study, the data source was diversified with reminder notes and observation reports for stability and transparency. The findings revealed that most participants had high verbal abilities and interests, and weak numerical abilities and interests. Additionally, the views that they were unable to make the right choices in line with their abilities and interests and therefore could not achieve their academic self-concept and goals are remarkable.

Effects of weather and air pollution on outpatient visits for insect-and-mite-caused dermatitis: an empirical and predictive analysis

BackgroundDermatitis caused by insects and mites, diagnosed as papular urticaria or scabies, is a common skin disease. However, there is still a lack of studies about the effects of weather and air pollution on outpatient visits for this disease. This study aims to explore the impacts of meteorological and environmental factors on daily visits of dermatitis outpatients.MethodsAnalyses are conducted on a total of 43,101 outpatient visiting records during the years 2015–2020 from the largest dermatology specialist hospital in Guangzhou, China. Hierarchical cluster models based on Pearson correlation between risk factors are utilized to select regression variables. Linear regression models are fitted to identify the statistically significant associations between the risk factors and daily visits, taking into account the short-term effects of temperatures. Permutation importance is adopted to evaluate the predictive ability of these factors.ResultsShort-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. In terms of total outpatients, the one-day lagged temperature not only has a significant impact on daily visits, but also has the highest median value of permutation importance. This conclusion is robust across most subgroups except for subgroups of summer and scabies, wherein the three-day lagged temperature has a negative effect. By contrast, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. Moreover, weekdays, holidays and trends have significant impacts on daily visits, but with weak predictive abilities.ConclusionsOur study suggests that short-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. Nevertheless, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.

Representing and discovering heterogeneous interactions for financial risk assessment of SMEs

Small and medium-sized enterprises (SMEs) generally have weak risk management abilities, yet their financial risk assessment faces unique challenges due to limited information about those firms. The emergence of network data constructed from the interactions between the SMEs and other stakeholders createsan opportunity to enhance the assessment of SMEs’ financial risks. While existing studies have extracted features from thenetwork data to utilize structural information, they overlook the heterogeneous interactions among the focal firms and other entities in the network. This research aims to fill the above gaps by proposing a represent-then-discover framework based on graph neural networks. The framework comprises an entity-importance based graph contrastive model (EIGC) that represents firms as the embeddings of their heterogeneous interactions and a novel subgraph-distillation based frequent interaction mining model (SDIM) that discovers heterogeneous interactions that signal firms with or without financial risks. The firm embeddings are first trained following an unsupervised paradigm and thenused to build models for predicting financial risks. Our empirical evaluation results not only demonstrate the superior performance of EIGC over state-of-the-art methods but also discover unique interaction patterns that distinguish normal SMEs compared to financial risk SMEs. This study contributes an effective framework for assessing and explaining SMEs’ financial risk in the fintech era. The proposed EIGC provides a promising avenue for constructing predictive features by leveraging network data and graph neural networks.

Research on the Construction of Information Literacy Education System in Universities under AI Environment

Traditional information literacy education in China focuses on using an information literacy course to cultivate students' theoretical learning such as information source collection. Students have a single learning channel and weak abilities in self-directed learning and innovation. The research on the cultivation system of information literacy in universities in China is still incomplete. Resulting in a low overall level of information literacy and insufficient information knowledge among college students, which greatly undermines their innovation and entrepreneurship abilities; There is a lack of information ethics education, and students often participate in situations such as online violence and attacks. This article studies the technical and application characteristics of AI smart education environment, explores the education system model of information literacy in universities, and improves the overall information literacy level of university students.

Rational design of triazine-based conjugated polymers with enhanced charge separation ability for photocatalytic hydrogen evolution

Triazine-based conjugated polymers (TCPs) are promising organic catalysts for green H2 production, since their photocatalytic performance can be easily regulated via appropriate molecular design. However, apart from weak absorption of visible light, weak charge separation and transport abilities also considerably restrict the photocatalytic performance of TCPs. Herein, we report two novel TCP photocatalysts with donor-acceptor (D-A) and donor-π-acceptor (D-π-A) structures using dibenzo[g,p]chrysene (Dc), thiophene (T), and 2,4,6-triphenyl-1,3,5-triazine (Tz) as the donor, π-spacer, and acceptor, respectively. Compared to Dc-Tz with a D-A structure, Dc-T-Tz exhibits a broader light absorption edge and more efficient charge separation and transmission due to its D-π-A structure and strong dipole effect. These properties enable Dc-T-Tz to display a prominent H2 production rate of 45.13 mmol h−1 g−1 under ultraviolet–visible (UV–Vis) light (λ > 300 nm). Therefore, Dc-T-Tz represents state-of-the-art TCP photocatalysts to date.

Effects of imagery training to improve the quality of athletes' swimming skills: a mixed research method

Purpose: This study aims to investigate the effect of implementing Imagery training towards the quality of swimming skills among athletes through mixed research method.&#x0D; Material and methods. This study adopted a quantitative and qualitative research (mixed research method). The participants involved in this study were beginner athletes from the Tirta Prima Medan Swimming Association (n=20) (Indonesia). Participants were divided into two groups, namely experimental group which consisted of 5 males and 5 females (age: 16.06±2.3 years, weight: 50.78±6.5 kg, height: 1.60±0.5 cm) and the control group which consisted of 5 males and 5 females (age: 16.57±0.9 years, weight: 51.65±5.8 kg, height: 1.61±0.3 cm). The quantitative instruments included tests for freestyle, backstroke, breaststroke and butterfly swimming style while the qualitative instruments included in-depth interviews. Quantitative statistical analysis was conducted using IBM SPSS, included: normality, mean and standard deviation. The differences of values in the experimental and control groups before and after the experiment was analyzed through the independent sample t-test and Paired sample t-test. While qualitative study used thematic analysis included recording, coding and categorized into three themes.&#x0D; Results The quantitative study results showed several findings. First, there was no difference in the scores of swimming skills before the experiment between the experimental and control groups (p≥0.05). Second, there was differences in the scores of swimming skills between the experimental and control groups after the experiment (p≤0.05). Third, the experimental group had a significant effect to improve the quality of swimming skills (p≤.05). On the contrary, the control group only had an effect on improving the quality of freestyle swimming skills (p≤0.05), but it had not affect in other styles (p≥0.05). Qualitative findings showed that participants considered that Imagery training has the advantage to recreate the training experience in the brain, however it would not be effective if athletes had weak memory abilities and it had a positive impact on improving the quality of swimming skills.&#x0D; Conclusions: This study concluded that Imagery training was effective to improve the quality of athletes' swimming skills.

Unveiling use of Information and Communication Technology (ICT) in Western Uttar Pradesh, India: The Farmers’ Perspective

This study examines the dynamics of information utilization in agricultural practices among 200 farmers in Western Uttar Pradesh, India. The survey covered a diverse range of participants across various age groups and income brackets. The findings of the study reveal a significant dependence on radio broadcasts, as 56.5% of the participants reported using this medium, especially among individuals with weak literacy abilities. The demographic cohort consisting of individuals aged 35-50 shown the highest level of engagement in utilising information, whilst younger age groups exhibited a preference for online resources. Surprisingly, the study revealed a minimal correlation between farming experience and the adoption of agriculture helplines. However, a clear association emerged between higher income levels and increased utilization of radio and online services in the agricultural sector. The research highlights the necessity for extension service workers to receive training in contemporary information and communication technology (ICT) technologies in order to enhance their ability to effectively support agricultural communities. Keywords: Radio broadcast, agriculture helpline number, Tv broadcast, internet services, income, education.

Screening Image Features of Collapsed Buildings for Operational and Rapid Remote Sensing Identification

The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.

Can Alexander Zinoviev Be Considered Nationally Oriented and Conservative? (Critical Notes on the Works by Alexander Zinoviev)

This paper examines the question of whether the Russian thinker Alexander Alexandrovich Zinoviev can be reckoned among nationalist conservatives, and provides a reasoned negative answer. One of the reasons for this is Alexander Zinoviev’s sharply negative and even nihilistic attitude towards the Russian Orthodox Church and historical, pre-revolutionary Russia, which was unthinkable for any real Russian conservative. Therefore, his views could be conditionally labeled as Soviet conservatism. But even here there are nuances that complicate matters greatly, which are discussed in this paper. It is also noted that despite the great hype around the name of Alexander Zinoviev, his creative heritage is still very poorly studied at the moment. The paper briefly considers his creative evolution, his propensity for positivism starting from his dissertation. The pros and cons of his concept of communism as the core of all Alexander Zinoviev’s ideas are analyzed in more detail. The poverty of its basic ideological scheme and its weak prognostic abilities are noted. At the end of the paper, three points are highlighted stating why Alexander Zinoviev cannot be considered a real conservative. Firstly, this is his sharply negative attitude towards historical Russia and Orthodoxy. Secondly, this is the ahistoricism of his project on logical sociology and the positivism associated with it. Thirdly, Alexander Zinoviev became an apologist of the Soviet social system only after it left the stage of history, that is post factum. During the active period of the Soviet social system, Alexander Zinoviev always criticized and satirised it sharply.

Reversed domain adaptation for nuclei segmentation-based pathological image classification

Despite the fact that digital pathology has provided a new paradigm for modern medicine, the insufficiency of annotations for training remains a significant challenge. Due to the weak generalization abilities of deep-learning models, their performance is notably constrained in domains without sufficient annotations. Our research aims to enhance the model’s generalization ability through domain adaptation, increasing the prediction ability for the target domain data while only using the source domain labels for training. To further enhance classification performance, we introduce nuclei segmentation to provide the classifier with more diagnostically valuable nuclei information. In contrast to the general domain adaptation that generates source-like results in the target domain, we propose a reversed domain adaptation strategy that generates target-like results in the source domain, enabling the classification model to be more robust to inaccurate segmentation results. The proposed reversed unsupervised domain adaptation can effectively reduce the disparities in nuclei segmentation between the source and target domains without any target domain labels, leading to improved image classification performance in the target domain. The whole framework is designed in a unified manner so that the segmentation and classification modules can be trained jointly. Extensive experiments demonstrate that the proposed method significantly improves the classification performance in the target domain and outperforms existing general domain adaptation methods.