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This thesis explores the nature of holistic consciousness in Hermetic philosophy, drawing on the classical text of The Emerald Tablet and The Corpus Hermeticum attributed to Hermes Trismegistus. Central to this ancient philosophy from Hellenistic Alexandria is the “as above, so below” principle, highlighting the interconnectedness of all things. Hermetic philosophy views harmony as the balance and interconnection of all elements in the universe. Sound, tied to the divine word, is vital for this harmony and understanding of creation and divine wisdom. This thesis focuses on the relationship between human, cosmic, and divine consciousness. Through divinization, humans can align with cosmic consciousness, realizing their divine nature by transcending physical limitations. Furthermore, we will review Hermetic philosophy in light of contemporary challenges, such as materialism which has caused environmental, social and mental health crises. Hermetic philosophy’s holistic approach and emphasis on interconnectedness can counteract social atomization caused by digital technology. Hermetic philosophy having a scientific-rational basis, can be aligned with contemporary ideas like the holographic universe theory. Self-reflection, meditation, nature engagement, and inner dialogue are all proven methods for cultivating holistic consciousness, as advocated in Hermetic philosophy. We can better understand the universe by integrating Hermetic wisdom with science. 這篇論文探討赫爾墨斯哲學中整體意識的性質，參考了赫爾墨斯·特里斯梅吉斯圖斯所屬的古典文本《翡翠石板》和《赫爾墨斯文集》。這一源自希臘化亞歷山大的古老哲學的核心原則是「上如是下」，強調所有事物之間的相互連結。 赫爾墨斯哲學視和諧為宇宙中所有元素的平衡與相互關聯。聲音與神聖之語相連，對於這種和諧及對創造與神聖智慧的理解至關重要。這篇論文專注於人類、宇宙與神聖意識之間的關係。透過神化，人類能夠與宇宙意識對齊，超越身體的限制，實現其神聖本質。 此外，我們將審視赫爾墨斯哲學在當代挑戰下的意義，例如物質主義所導致的環境、社會和心理健康危機。赫爾墨斯哲學的整體方法和對相互連結的強調，可以對抗數位科技所造成的社會原子化現象。 赫爾墨斯哲學具科學理性基礎，能與當代觀念如全息宇宙理論相結合。自我反思、冥想、與自然的互動及內心對話，都是培養整體意識的有效方法，正如赫爾墨斯哲學所提倡的。我們可以通過將赫爾墨斯智慧與科學相結合，更深入地理解宇宙。

This board game is designed around China's major traditional festivals (such as the Spring Festival, Dragon Boat Festival, Mid-Autumn Festival, etc.) and their customs, with the aim of allowing players to experience and learn about China's rich cultural traditions through gameplay. The goal of the game is to earn points by completing festival tasks, answering questions about customs, and collecting festival items, with the highest scorer ultimately winning. The gameplay design includes elements like festival task cards, customs quizzes, and festival item collection to enhance interactivity and fun. Players will assume different roles in the game, using cards, maps, and festival-themed objects to gain a deeper understanding of the history and customs of each festival. Educational Value: This board game is not only entertaining but also helps players learn about traditional Chinese culture in a relaxed setting, enhancing cultural identity. It is suitable for family gatherings, friends' meetups, and educational settings, particularly for players interested in Chinese culture.

Caterpillar, one of the world's most recognised industrial brands, is a leading global manufacturer of construction and mining equipment, as well as diesel and natural gas engines and industrial gas turbines. Currently, Caterpillar operates in North America, Asia Pacific, EAME region and Latin America, with operations in North America accounting for approximately 47% of the company's net revenue in 2022. Caterpillar has been able to win customers' trust and achieve stable business growth through reliable products, advanced technology, user-friendly services and differentiated service solutions. Post-coronavirus, it can be seen that the industry in which Caterpillar operates is vulnerable to changes in the economic and social environment. In addition, the industry is becoming increasingly competitive, especially with the rise of emerging players, such as construction equipment manufacturers in China, who are entering the markets of developing countries with cost-effective products. In addition to that, such as the traditional machinery manufacturing companies in Europe and Japan, their products are not only highly intelligent, but also motorised equipment technology is quite reliable. It is a tough challenge to maintain a competitive edge. Understanding the impact of these changes on the industry and the company helps in assessing the intrinsic value of Caterpillar and its future positioning. Comparing the intrinsic value derived from the three valuation models to the company's market share price as of 31 December 2022 will provide valuable information to investors.

<jats:p>PM10 emissions have been a significant concern in rock crushing and quarry operations (study site #1) and iron ore mining projects (study site #2) in certain regions of Malaysia, posing fears to the health and well-being of nearby communities with severe air pollution. To address this issue, it is crucial to develop effective mitigation strategies to reduce dust particle emissions like PM10 in the ambient air. The AERMOD model was applied to predict PM10 emissions during quarry operations and iron ore mining projects, both with and without control measures. The results indicated that PM10 emissions were reduced when control measures were implemented. The modeling result shows the mean PM10 concentration with and without control measures in study site #1 is 74.85 µg/m3 and 20,557.69 µg/m3, respectively. In comparison, the average PM10 concentration with and without control measures in study site #2 is 53.95 µg/m3 and 135.69 µg/m3. Therefore, the control measure has successfully reduced the PM10 concentrations by 99.90% and 60.24% in study sites #1 and #2, respectively, and ensures the air quality complies with the Malaysian Ambient Air Quality Guidelines (MAAQG) 24 h threshold limits at 100 µg/m3. In addition, the AERMOD modeling results showed that mitigation measures performed better in rock crushing and quarry operations than in iron ore mining projects in this case study.</jats:p>

It has been previously demonstrated that stochastic volatility emerges as the gauge field necessary to restore local symmetry under changes in stock prices in the Black–Scholes (BS) equation. When this occurs, a Merton–Garman-like equation emerges. From the perspective of manifolds, this means that the Black–Scholes and Merton–Garman (MG) equations can be considered locally equivalent. In this scenario, the MG Hamiltonian is a special case of a more general Hamiltonian, here referred to as the gauge Hamiltonian. We then show that the gauge character of volatility implies a specific functional relationship between stock prices and volatility. The connection between stock prices and volatility is a powerful tool for improving volatility estimations in the stock market, which is a key ingredient for investors to make good decisions. Finally, we define an extended version of the martingale condition, defined for the gauge Hamiltonian.

Nickel–cobalt-layered double hydroxide (NiCo-LDH) has received considerable attention in the field of supercapacitor; however, the low conductivity and structural instability limit its practical applications. In this paper, a facile strategy for the fabrication of NiCo-layered double hydroxide (NiCo-LDH) coating on nitrogen/oxygen co-doped carbon nanocages (NOCN) was proposed in this paper. This multicomponent nanocomposite, NiCo-LDH@NOCN, has a high specific capacitance of 1842 F g−1 at 1 A g−1, a remarkable rate capability of 68.1% increasing the current density to 30 A g−1, and excellent cycling stability of 72.1% after 1000 cycles. Furthermore, an asymmetric supercapacitor (ASC) composed of NiCo-LDH@NOCN as a positive electrode and NOCN as a negative electrode was fabricated. The ASC shows a superior energy density of 31.6 Wh kg−1 at a power density of 700 W kg−1, which suggests the great practical application potential of supercapacitor.

<jats:p>Electric vehicles (EVs) must be used as the primary mode of transportation as part of the gradual transition to more environmentally friendly clean energy technology and cleaner power sources. Vehicle-to-grid (V2G) technology has the potential to improve electricity demand, control load variability, and improve the sustainability of smart grids. The operation and principles of V2G and its varieties, the present classifications and types of EVs sold on the market, applicable policies for V2G and business strategy, implementation challenges, and current problem-solving techniques have not been thoroughly examined. This paper exposes the research gap in the V2G area and more accurately portrays the present difficulties and future potential in V2G deployment globally. The investigation starts by discussing the advantages of the V2G system and the necessary regulations and commercial representations implemented in the last decade, followed by a description of the V2G technology, charging communication standards, issues related to V2G and EV batteries, and potential solutions. A few major issues were brought to light by this investigation, including the lack of a transparent business model for V2G, the absence of stakeholder involvement and government subsidies, the excessive strain that V2G places on EV batteries, the lack of adequate bidirectional charging and standards, the introduction of harmonic voltage and current into the grid, and the potential for unethical and unscheduled V2G practices. The results of recent studies and publications from international organizations were altered to offer potential answers to these research constraints and, in some cases, to highlight the need for further investigation. V2G holds enormous potential, but the plan first needs a lot of financing, teamwork, and technological development.</jats:p>

ARTeFACTo is a transdisciplinary studies journal and conference centered around the concept of digital media and computer artifacts, which were the genesis of its name. It aims to give visibility to all digital media and technology-based artifacts, creators, or contributors from a transdisciplinary, humanistic, and holistic perspective of integration and composition. The main objective is constructing and disseminating knowledge through descriptions of artistic practice, authorship or co-authorship (including the narration and description of other works), and graphic data (images, diagrams, animations, drawings, sketches). The Journal is open to international contributions from all those studying digital media/computer artifacts, embracing their conceptualization, design, creative and research processes, computational implementation, exhibition and fruition, and role in today’s information and knowledge society. Also welcomed are articles fostering the establishment of the conceptual foundations of an artifact theory in the digital media world. Accepted articles shall be original (in compliance with the standards of conduct of the COPE – Code of Conduct and Good Practices) in Portuguese and English, with a mandatory abstract in English. The Conference has the same goals as the Journal but provides a specific floor for the concrete exhibition of artifacts along with their presentation and discussion. All submissions will be evaluated by peer review, including two advisors from the Scientific Council. All feedback will be sent to the authors, indicating their approval, refusal of publication, or requests for correction or recast. The authors will be notified of the publication once the entire selection process has been completed. ARTeFACTo is a joint project between Artech International and Universidade Aberta.

The information paradox suggests that the black hole loses information when it emits radiation. In this way, the spectrum of radiation corresponds to a mixed (non-pure) quantum state even if the internal state generating the black hole is expected to be pure in essence. In this paper we propose an argument solving this paradox by developing an understanding of the process by which spontaneous symmetry breaks when a black hole selects one of the many possible ground states and emits radiation as a consequence of it. Here, the particle operator number is the order parameter. This mechanism explains the connection between the density matrix, corresponding to the pure state describing the black hole state, and the density matrix describing the spectrum of radiation (mixed quantum state). From this perspective, we can recover black hole information from the superposition principle, applied to the different possible order parameters (particle number operators).

This study aims to explore the difficulties and challenges faced by regular primary school teachers in Macao when dealing with inclusive students in the classroom. As the concept of inclusive education deepens, the number of inclusive students in Macao has been increasing, but the growth of schools participating in inclusive education has been slow, posing more challenges for regular primary school teachers. This research adopts a mixed-method approach, combining classroom observations and in-depth interviews to comprehensively understand the issues teachers face. The study conducted structured observations of 15 lessons, covering 5 grade levels and 5 subjects, and carried out in-depth interviews with 7 teachers. The findings reveal: (1) Inclusive students generally experience difficulties in classroom participation, task completion, and independent learning; (2) Teachers generally lack sufficient professional knowledge and skills to effectively address the needs of inclusive students; (3) Schools lack resources and support, including adaptive curriculum arrangements and assessment methods for inclusive students; (4) There are contradictions between current educational policies and practical needs, such as the no-retention policy for grades 1-4, which may lead to missing the best intervention timing for inclusive students. Based on the research results, this paper proposes recommendations including strengthening teacher professional development, optimizing school resource allocation, improving educational policies, reinforcing early identification and intervention mechanisms, and promoting home-school cooperation. These findings and suggestions provide important references for policy-making and practical improvements in inclusive education in Macao. 本研究旨在探討澳門普通小學教師在課堂應對融合學生時所面臨的困難及挑戰。隨著融合教育理念的普及，澳門融合學生數量不斷增加，但參與融合教育的學校數量增長緩慢，使得普通小學教師面臨更多挑戰。本研究採用混合研究方法，結合課堂觀察和深度訪談，以全面了解教師面臨的問題。 研究對15節課進行了結構化觀察，涵蓋5個年級和5個學科，並對7位教師進行了深度訪談。研究發現：（一、）融合學生在課堂參與、任務完成和自主學習等方面普遍存在困難；（二、）教師普遍缺乏足夠的專業知識和技能來有效應對融合學生；（三、）學校資源和支援不足，缺乏針對融合學生的適應性課程安排和評核方式；（四、）現行教育政策與實際需求之間存在矛盾，如一至四年級不留級制度可能導致錯過融合學生最佳干預時機。 基於研究結果，本文提出了加強教師專業發展、優化學校資源配置、完善教育政策、強化早期識別和干預機制，以及促進家校合作等建議。這些發現和建議為澳門融合教育的政策制定和實踐改進提供了重要參考。

Ikea’s entry into the China market and how its become successful in the foreign country

As an important part of the global economy, family business have made a lot of contributions to the world economy. With the deepening of China's reform and opening up, the scale and volume of family business have increased dramatically in the national economy. In recent years, more and more family-owned enterprises have entered the list of “Top 500 Chinese Enterprises” and become an important part of the national economy. As a special enterprise organization, family business has certain characteristics. Most of the Chinese family business were founded in the early stage of China's reform and opening up, and now more and more family business are facing the problem that the founders need to leave the family business due to their advanced age. How to ensure the smooth succession of family business and avoid conflicts and contradictions in the process of succession is an important challenge faced by many family business. How to ensure that family business can continue to develop and maintain a high performance status is of great significance to the founders and their successors, as well as to the Chinese economy. Therefore, this research examines how the characteristics of successors affect enterprise performance based on the context of intergenerational succession in family business. This research is based on a multi-case study in which in-depth interviews were conducted with the founders and successors of five cases, which led to the conclusion that five successor characteristics have an impact on enterprise performance. They are inclusiveness, forward thinking, sociable and good communication, sense of social responsibility and sense of family mission. This finding has significant implications for the training of successors and the management of the succession process in Chinese family business. This study is conducive to the discovery of the mechanism of successor characteristics on enterprise performance as well as to make a realistic contribution to the research on the outcomes of family business in China.

<jats:p>Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This study presents a comprehensive evaluation of ten state-of-the-art deep learning models—VGG16, VGG19, ResNet50, ResNet101, DenseNet, GoogLeNet V1, MobileNet V1, EfficientNet V2, ShuffleNet V2, and RepVGG—on the task of facial expression recognition using the FER2013 dataset. Key performance metrics, including test accuracy, training time, and weight file size, were analyzed to assess the learning efficiency, generalization capabilities, and architectural innovations of each model. EfficientNet V2 and ResNet50 emerged as top performers, achieving high accuracy and stable convergence using compound scaling and residual connections, enabling them to capture complex emotional features with minimal overfitting. DenseNet, GoogLeNet V1, and RepVGG also demonstrated strong performance, leveraging dense connectivity, inception modules, and re-parameterization techniques, though they exhibited slower initial convergence. In contrast, lightweight models such as MobileNet V1 and ShuffleNet V2, while excelling in computational efficiency, faced limitations in accuracy, particularly in challenging emotion categories like “fear” and “disgust”. The results highlight the critical trade-offs between computational efficiency and predictive accuracy, emphasizing the importance of selecting appropriate architecture based on application-specific requirements. This research contributes to ongoing advancements in deep learning, particularly in domains such as facial expression recognition, where capturing subtle and complex patterns is essential for high-performance outcomes.</jats:p>

Fish body mucus plays a protective role, especially in Halobatrachus didactylus, which inhabits intertidal zones vulnerable to anthropogenic contaminants. In silico predicted bioactive peptides were identified in its body mucus, namely, EDNSELGQETPTLR (HdKTLR), DPPNPKNL (HdKNL), PAPPPPPP (HdPPP), VYPFPGPLPN (HdVLPN), and PFPGPLPN (HdLPN). These peptides were studied in vitro for bioactivities and aggregation behavior under different ionic strengths and pH values. Size exclusion chromatography revealed significant peptide aggregation at 344 mM and 700 mM ionic strengths at pH 7.0, decreasing at pH 3.0 and pH 5.0. Although none exhibited antimicrobial properties, they inhibited Pseudomonas aeruginosa biofilm formation. Notably, HdVLPN demonstrated potential antioxidant activity (ORAC: 1.560 mu mol TE/mu mol of peptide; ABTS: 1.755 mu mol TE/mu mol of peptide) as well as HdLPN (ORAC: 0.195 mu mol TE/mu mol of peptide; ABTS: 0.128 mu mol TE/mu mol of peptide). Antioxidant activity decreased at pH 5.0 and pH 3.0. Interactions between the peptides and mucus synergistically enhanced antioxidant effects. HdVLPN and HdLPN were non-toxic to Caco-2 and HaCaT cells at 100 mu g of peptide/mL. HdPPP showed potential antihypertensive and antidiabetic effects, with IC50 values of 557 mu g of peptide/mL for ACE inhibition and 1700 mu g of peptide/mL for alpha-glucosidase inhibition. This study highlights the importance of validating peptide bioactivities in vitro, considering their native environment (mucus), and bioprospecting novel bioactive molecules while promoting species conservation.

<jats:p>Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focus on prediction and pattern recognition. In contrast, causal machine learning goes a step further by revealing causal relationships between different variables. We explore a novel concept called Double Machine Learning that embraces causal machine learning in this research. The core goal is to select independent variables from a gesture identification problem that are causally related to final gesture results. This selection allows us to classify and analyze gestures more efficiently, thereby improving models’ performance and interpretability. Compared to commonly used feature selection methods such as Variance Threshold, Select From Model, Principal Component Analysis, Least Absolute Shrinkage and Selection Operator, Artificial Neural Network, and TabNet, Double Machine Learning methods focus more on causal relationships between variables rather than correlations. Our research shows that variables selected using the Double Machine Learning method perform well under different classification models, with final results significantly better than those of traditional methods. This novel Double Machine Learning-based approach offers researchers a valuable perspective for feature selection and model construction. It enhances the model’s ability to uncover causal relationships within complex data. Variables with causal significance can be more informative than those with only correlative significance, thus improving overall prediction performance and reliability.</jats:p>

This study aims to become a conduit of a missing conversation wherein our front line help-givers can express their experiences. Through a series of semi-structured questions, and theoretical analyses, the themes regarding the Experience and Perception of Helping Relationships in Macau. Findings include difficulties and challenges that the helping profession in Macau encounters in different settings related to role ambiguity and public understanding of the profession. Stigma around a person considered to be a “problem” as well as stigma related to nomenclature of mental illnesses. Factors that enable and facilitate a helping relationship were identified. Trust, sincerity, listening and positive regard were mentioned. Due to Macau’s multi-cultural background, an enabling agent to facilitate a helping relationship was identified – language. As a qualitative enquiry medium for reflection and discovery, this study hopes to bring forward the unique experiences of eleven helping professionals with a spectrum of background to provide richness and newness to the current body of literature.

Purpose Research on battery electric vehicles (BEVs) has typically considered environmental concern a key determinant of behavioral intention that leads individuals to prefer electric vehicles. This paper challenges this assumption and argues that technology frameworks may require new variables to capture consumers' preferences. A UTAUT2-based study has been developed to assess the role of environmental concern in the BEVs context and put forward the technology show-off (TS) concept to explain the technology's acceptance. Design/methodology/approach A quantitative and cross-sectional look at behavioral intention is adopted. The study uses structural equation modeling to analyze a sample of 236 Macau residents to determine the relevance of the factors behind the choice to adopt BEVs. Findings The findings indicate that environmental concern and price may be relevant to explain behavioral intention to adopt the BEVs technology. Furthermore, the UTAUT2 framework seems to benefit from adding new variables, with TS playing a pertinent role in explaining technology acceptance. Social implications The findings show that environmental concern fails to build an argument for the shift to full electric mobility and promote the desired behavioral change toward adopting BEVs. Herein lies the necessity to consider new variables that can better describe the characteristics of modern society. Originality/value This paper proposes the TS construct, combining visibility and trialability as significant determinants of behavioral intention to use technology. The study also stresses the need to reconsider the role of environmental concerns' impact on consumer decision-making.